From dfc2f5afba5210de3f830faa6e541a217f983bae Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 8 Sep 2026 14:53:25 -0700 Subject: [PATCH 001/112] experiment with using parsl in taskgraph --- taskgraph/Task.py | 105 +++++++++++++++++++++------------------------- 1 file changed, 47 insertions(+), 58 deletions(-) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index c13a19a3..b74f821d 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -19,6 +19,23 @@ import retrying +import parsl +import os +from parsl.app.app import python_app +from parsl.configs.local_threads import config +from parsl.utils import get_all_checkpoints +from parsl.dataflow.memoization import BasicMemoizer, id_for_memo +from parsl.dataflow.futures import AppFuture + +LOGGER = logging.getLogger(__name__) + + +@id_for_memo.register(object) +def id_for_memo_pickle(obj, output_ref=False): + print('hashing', obj) + return pickle.dumps(obj) + + try: __version__ = version('taskgraph') except PackageNotFoundError: @@ -47,6 +64,11 @@ _MAX_TIMEOUT = 5.0 # amount of time to wait for threads to terminate +@python_app(cache=True) +def wrap_func(func, *args, inputs=[], **kwargs): + func(*args, **kwargs) + + # We want our processing pool to be nondeamonic so that workers could use # multiprocessing if desired (deamonic processes cannot start new processes) # the following bit of code to do this was taken from @@ -351,6 +373,15 @@ def __init__( f'created with TaskGraph version {local_version} but the ' f'current version is {__version__}') + print('loading config') + parsl.set_stream_logger(name='parsl', level=logging.DEBUG) + config.app_cache = True + config.memoizer = BasicMemoizer( + checkpoint_mode='task_exit', + checkpoint_files=get_all_checkpoints()) + parsl.load(config) + print('config loaded') + # no need to set up schedulers if n_workers is single threaded self._n_workers = n_workers if n_workers < 0: @@ -403,7 +434,7 @@ def __init__( LOGGER.warning( "NoSuchProcess exception encountered when trying " "to nice %s. This might be a bug in `psutil` so " - "it should be okay to ignore.") + "it should be okay to ignore.")\ def __del__(self): """Ensure all threads have been joined for cleanup.""" @@ -620,21 +651,18 @@ def add_task( # this is a pretty common error to accidentally not pass a # Task to the dependent task list. - if any(not isinstance(task, Task) + if any(not isinstance(task, AppFuture) for task in dependent_task_list): raise ValueError( "Objects passed to dependent task list that are not " - "tasks: %s", dependent_task_list) + "parsl AppFutures: %s", dependent_task_list) task_name = '%s (%d)' % (task_name, len(self._task_hash_map)) - new_task = Task( - task_name, func, args, kwargs, target_path_list, - ignore_path_list, hash_target_files, ignore_directories, - transient_run, self._worker_pool, - priority, hash_algorithm, store_result, - self._task_database_path) - - self._task_name_map[new_task.task_name] = new_task + + new_task = wrap_func(func, *args, **kwargs, inputs=dependent_task_list) + + + # self._task_name_map[new_task.task_name] = new_task # it may be this task was already created in an earlier call, # use that object in its place if new_task in self._task_hash_map: @@ -674,36 +702,7 @@ def add_task( "a runtime error: submitted task: %s, existing " "task: %s" % (new_task, duplicate_task)) self._task_hash_map[new_task] = new_task - if self._n_workers < 0: - # call directly if single threaded - new_task._call() - else: - # determine if task is ready or is dependent on other - # tasks - LOGGER.debug( - "multithreaded: %s sending to new task queue.", - task_name) - outstanding_dep_task_name_list = [ - dep_task.task_name for dep_task in dependent_task_list - if dep_task.task_name - not in self._completed_task_names] - if not outstanding_dep_task_name_list: - LOGGER.debug( - "sending task %s right away", new_task.task_name) - self._task_ready_priority_queue.put(new_task) - self._task_waiting_count += 1 - self._executor_ready_event.set() - else: - # there are unresolved tasks that the waiting - # process scheduler has not been notified of. - # Record dependencies. - for dep_task_name in outstanding_dep_task_name_list: - # record tasks that are dependent on dep_task_name - self._task_dependent_map[dep_task_name].add( - new_task.task_name) - # record tasks that new_task depends on - self._dependent_task_map[new_task.task_name].add( - dep_task_name) + return new_task except Exception: @@ -770,28 +769,14 @@ def join(self, timeout=None): True if successful join, False if timed out. """ + print('joining taskgraph') LOGGER.debug("joining taskgraph") - if self._n_workers < 0: - # Join() is meaningless since tasks execute synchronously. - LOGGER.debug( - 'n_workers: %s; join is vacuously true' % self._n_workers) - return True - try: LOGGER.debug("attempting to join threads") timedout = False for task in self._task_hash_map.values(): - LOGGER.debug("attempting to join task %s", task.task_name) - # task.join() will raise any exception that resulted from the - # task's execution. - timedout = not task.join(timeout) - LOGGER.debug("task %s was joined", task.task_name) - # if the last task timed out then we want to timeout for all - # of the task graph - if timedout: - LOGGER.info( - "task %s timed out in graph join", task.task_name) - return False + print('computing task', task) + task.result() if self._closed: # Close down the taskgraph; ok if already terminated self._executor_ready_event.set() @@ -811,6 +796,7 @@ def join(self, timeout=None): def close(self): """Prevent future tasks from being added to the work queue.""" + print('closing task graph') LOGGER.debug("Closing taskgraph.") if self._closed: return @@ -822,10 +808,13 @@ def close(self): def _terminate(self): """Immediately terminate remaining task graph computation.""" + print('terminating task graph') LOGGER.debug( "Invoking terminate. already terminated? %s", self._terminated) if self._terminated: return + print('dfk cleanup') + parsl.dfk().cleanup() try: # it's possible the global state is not well defined, so just in # case we'll wrap it all up in a try/except From 20960890abef0fd8530133f97536b5e9bcb73e7b Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 21 Sep 2026 16:41:38 -0700 Subject: [PATCH 002/112] resolve errors around log file and cleanup --- taskgraph/Task.py | 482 +++--------------------------------------- taskgraph/__init__.py | 3 +- 2 files changed, 30 insertions(+), 455 deletions(-) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index b74f821d..d1b4bb2d 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -22,10 +22,15 @@ import parsl import os from parsl.app.app import python_app +from parsl.concurrent import ParslPoolExecutor from parsl.configs.local_threads import config from parsl.utils import get_all_checkpoints from parsl.dataflow.memoization import BasicMemoizer, id_for_memo from parsl.dataflow.futures import AppFuture +from parsl.executors import HighThroughputExecutor +from parsl.monitoring.monitoring import MonitoringHub +from parsl.addresses import address_by_hostname + LOGGER = logging.getLogger(__name__) @@ -374,13 +379,30 @@ def __init__( f'current version is {__version__}') print('loading config') - parsl.set_stream_logger(name='parsl', level=logging.DEBUG) config.app_cache = True config.memoizer = BasicMemoizer( checkpoint_mode='task_exit', checkpoint_files=get_all_checkpoints()) - parsl.load(config) + config.run_dir = taskgraph_cache_dir_path + config.initialize_logging = False + config.monitoring = MonitoringHub( + hub_address='127.0.0.1', + monitoring_debug=True, + resource_monitoring_interval=10 + ) + self.pool = ParslPoolExecutor( + config=config, + executors=[ + HighThroughputExecutor( + label="local_htex", + cores_per_worker=1, + max_workers_per_node=4, + address='127.0.0.1' + ) + ] + ) print('config loaded') + print('pool', self.pool, type(self.pool)) # no need to set up schedulers if n_workers is single threaded self._n_workers = n_workers @@ -813,8 +835,11 @@ def _terminate(self): "Invoking terminate. already terminated? %s", self._terminated) if self._terminated: return - print('dfk cleanup') - parsl.dfk().cleanup() + print('shut down pool') + print(self.pool) + self.pool.shutdown() + # print('dfk cleanup') + # parsl.dfk().cleanup() try: # it's possible the global state is not well defined, so just in # case we'll wrap it all up in a try/except @@ -847,455 +872,6 @@ def _terminate(self): 'ignoring an exception that occurred during _terminate') -class Task(object): - """Encapsulates work/task state for multiprocessing.""" - - def __init__( - self, task_name, func, args, kwargs, target_path_list, - ignore_path_list, hash_target_files, ignore_directories, - transient_run, worker_pool, priority, hash_algorithm, - store_result, task_database_path): - """Make a Task. - - Args: - task_name (int): unique task id from the task graph. - func (function): a function that takes the argument list - ``args`` - args (tuple): a list of arguments to pass to ``func``. Can be - None. - kwargs (dict): keyword arguments to pass to ``func``. Can be - None. - target_path_list (list): a list of filepaths that this task - should generate. - ignore_path_list (list): list of file paths that could be in - args/kwargs that should be ignored when considering timestamp - hashes. - hash_target_files (bool): If True, the hash value of the target - files will be recorded to determine if a future run of this - function is precalculated. If False, this function only notes - the existence of the target files before determining if - a function call is precalculated. - ignore_directories (bool): if the existence/timestamp of any - directories discovered in args or kwargs is used as part - of the work token hash. - transient_run (bool): if True a call with an identical execution - hash will be reexecuted on a subsequent instantiation of a - future TaskGraph object. If a duplicate task is submitted - to the same object it will not be re-run in any scenario. - Otherwise if False, subsequent tasks with an identical - execution hash will be skipped. - worker_pool (multiprocessing.Pool): if not None, is a - multiprocessing pool that can be used for ``_call`` execution. - priority (numeric): the priority of a task is considered when - there is more than one task whose dependencies have been - met and are ready for scheduling. Tasks are inserted into the - work queue in order of decreasing priority. This value can be - positive, negative, and/or floating point. - hash_algorithm (string): either a hash function id that - exists in hashlib.algorithms_available, 'sizetimestamp', - or 'exists'. Any paths to actual files in the arguments will - be digested with this algorithm. If value is 'sizetimestamp' - the digest will only use the normed path, size, and timestamp - of any files found in the arguments. If 'exists' will be - considered the same file only if a file with the same filename - exists on disk. - store_result (bool): If true, the result of ``func`` will be - stored in the TaskGraph database and retrievable with a call - to ``.get()`` on the Task object. - task_database_path (str): path to an SQLITE database that has - table named "taskgraph_data" with the three fields: - task_hash TEXT NOT NULL, - target_path_stats BLOB NOT NULL - result BLOB NOT NULL - If a call is successful its hash is inserted/updated in the - table, the target_path_stats stores the base/target stats - for the target files created by the call and listed in - ``target_path_list``, and the result of ``func`` is stored in - ``result``. - - """ - # it is a common error to accidentally pass a non string as to the - # target path list, this terminates early if so - if any([not (isinstance(path, _VALID_PATH_TYPES)) - for path in target_path_list]): - raise ValueError( - "Values passed to target_path_list are not strings: %s", - target_path_list) - - # sort the target path list because the order doesn't matter for - # a result, but it would cause a task to be reexecuted if the only - # difference was a different order. - self._target_path_list = sorted([ - _normalize_path(path) for path in target_path_list]) - self.task_name = task_name - self._func = func - self._args = args - self._kwargs = kwargs - self._ignore_path_list = [ - _normalize_path(path) for path in ignore_path_list] - self._hash_target_files = hash_target_files - self._ignore_directories = ignore_directories - self._transient_run = transient_run - self._worker_pool = worker_pool - self._task_database_path = task_database_path - self._hash_algorithm = hash_algorithm - self._store_result = store_result - self.exception_object = None - - # invert the priority since sorting goes smallest to largest and we - # want more positive priority values to be executed first. - self._priority = -priority - - # Used to ensure only one attempt at executing and also a mechanism - # to see when Task is complete. This can be set if a Task finishes - # a _call and there are no more attempts at reexecution. - self.task_done_executing_event = threading.Event() - - # These are used to store and later access the result of the call. - self._result = None - - # Calculate a hash based only on argument inputs. - try: - if not hasattr(Task, 'func_source_map'): - Task.func_source_map = {} - # memoize func source code because it's likely we'll import - # the same func many times and reflection is slow - if self._func not in Task.func_source_map: - Task.func_source_map[self._func] = ( - inspect.getsource(self._func)) - source_code = Task.func_source_map[self._func] - except (IOError, TypeError): - # many reasons for this, for example, frozen Python code won't - # have source code, so just leave blank - source_code = '' - - if not hasattr(self._func, '__name__'): - LOGGER.warning( - "function does not have a __name__ which means it will not " - "be considered when calculating a successive input has " - "been changed with another function without __name__.") - self._func.__name__ = '' - - args_clean = [] - for index, arg in enumerate(self._args): - try: - scrubbed_value = _scrub_task_args(arg, self._target_path_list) - _ = pickle.dumps(scrubbed_value) - args_clean.append(scrubbed_value) - except TypeError: - LOGGER.warning( - "could not pickle argument at index %d (%s). " - "Skipping argument which means it will not be considered " - "when calculating whether inputs have been changed " - "on a successive run.", index, arg) - - kwargs_clean = {} - # iterate through sorted order so we get the same hash result with the - # same set of kwargs irrespective of the item dict order. - for key, arg in sorted(self._kwargs.items()): - try: - scrubbed_value = _scrub_task_args(arg, self._target_path_list) - _ = pickle.dumps(scrubbed_value) - kwargs_clean[key] = scrubbed_value - except TypeError: - LOGGER.warning( - "could not pickle kw argument %s (%s) scrubbed to %s. " - "Skipping argument which means it will not be considered " - "when calculating whether inputs have been changed " - "on a successive run.", key, arg, scrubbed_value) - - self._reexecution_info = { - 'func_name': self._func.__name__, - 'args_clean': args_clean, - 'kwargs_clean': kwargs_clean, - 'source_code_hash': hashlib.sha1( - source_code.encode('utf-8')).hexdigest(), - } - - argument_hash_string = ':'.join([ - repr(self._reexecution_info[key]) - for key in sorted(self._reexecution_info.keys())]) - - self._task_id_hash = hashlib.sha1( - argument_hash_string.encode('utf-8')).hexdigest() - - # this will get calculated when ``is_precalculated`` is invoked. - self._task_reexecution_hash = None - - def __eq__(self, other): - """Two tasks are equal if their hashes are equal.""" - return ( - isinstance(self, other.__class__) and - (self._task_id_hash == other._task_id_hash)) - - def __hash__(self): - """Return the base-16 integer hash of this hash string.""" - return int(self._task_id_hash, 16) - - def __ne__(self, other): - """Inverse of __eq__.""" - return not self.__eq__(other) - - def __lt__(self, other): - """Less than based on priority.""" - return self._priority < other._priority - - def __repr__(self): - """Create a string representation of a Task.""" - return "Task object %s:\n\n" % (id(self)) + pprint.pformat( - { - "task_name": self.task_name, - "priority": self._priority, - "ignore_path_list": self._ignore_path_list, - "ignore_directories": self._ignore_directories, - "target_path_list": self._target_path_list, - "task_id_hash": self._task_id_hash, - "task_reexecution_hash": self._task_reexecution_hash, - "exception_object": self.exception_object, - "self._reexecution_info": self._reexecution_info, - "self._result": self._result, - }) - - def _call(self): - """Invoke this method to execute task. - - Precondition is that the Task dependencies are satisfied. - - Sets the ``self.task_done_executing_event`` flag if execution is - successful. - - Raises: - RuntimeError if any target paths are not generated after the - function call is complete. - - """ - LOGGER.debug("_call check if precalculated %s", self.task_name) - if not self._transient_run and self.is_precalculated(): - self.task_done_executing_event.set() - return - LOGGER.debug("not precalculated %s", self.task_name) - - if self._worker_pool is not None: - result = self._worker_pool.apply_async( - func=self._func, args=self._args, kwds=self._kwargs) - # the following blocks and raises an exception if result - # raised an exception - LOGGER.debug("apply_async for task %s", self.task_name) - payload = result.get() - else: - LOGGER.debug("direct _func for task %s", self.task_name) - payload = self._func(*self._args, **self._kwargs) - if self._store_result: - self._result = payload - - # check that the target paths exist and record stats for later - if not self._hash_target_files: - target_hash_algorithm = 'exists' - else: - target_hash_algorithm = self._hash_algorithm - result_target_path_stats = list( - _get_file_stats( - self._target_path_list, target_hash_algorithm, [], False)) - result_target_path_set = set( - [x[0] for x in result_target_path_stats]) - target_path_set = set(self._target_path_list) - if target_path_set != result_target_path_set: - raise RuntimeError( - "In Task: %s\nMissing expected target path results.\n" - "Expected: %s\nObserved: %s\n" % ( - self.task_name, self._target_path_list, - result_target_path_set)) - - # this step will only record the run if there is an expected - # target file. Otherwise we infer the result of this call is - # transient between taskgraph executions and we should expect to - # run it again. - if not self._transient_run: - _execute_sqlite( - "INSERT OR REPLACE INTO taskgraph_data VALUES (?, ?, ?)", - self._task_database_path, mode='modify', - argument_list=( - self._task_reexecution_hash, - pickle.dumps(result_target_path_stats), - pickle.dumps(self._result))) - self.task_done_executing_event.set() - LOGGER.debug("successful run on task %s", self.task_name) - - def is_precalculated(self): - """Return true if _call need not be invoked. - - If the task has been precalculated it will fetch the return result from - the previous run. - - Returns: - True if the Task's target paths exist in the same state as the - last recorded run at the time this function is called. It is - possible this value could change without running the Task if - input parameter file stats change. False otherwise. - - """ - # This gets a list of the files and their file stats that can be found - # in args and kwargs but ignores anything specifically targeted or - # an expected result. This will allow a task to change its hash in - # case a different version of a file was passed in. - # these are the stats of the files that exist that aren't ignored - if not self._hash_target_files: - target_hash_algorithm = 'exists' - else: - target_hash_algorithm = self._hash_algorithm - file_stat_list = list(_get_file_stats( - [self._args, self._kwargs], - target_hash_algorithm, - self._target_path_list+self._ignore_path_list, - self._ignore_directories)) - - other_arguments = _filter_non_files( - [self._reexecution_info['args_clean'], - self._reexecution_info['kwargs_clean']], - self._target_path_list, - self._ignore_path_list, - self._ignore_directories) - - LOGGER.debug("file_stat_list: %s", file_stat_list) - LOGGER.debug("other_arguments: %s", other_arguments) - - # add the file stat list to the already existing reexecution info - # dictionary that contains stats that should not change whether - # files have been created/updated/or not. - self._reexecution_info['file_stat_list'] = file_stat_list - self._reexecution_info['other_arguments'] = other_arguments - - reexecution_string = '%s:%s:%s:%s:%s' % ( - self._reexecution_info['func_name'], - self._reexecution_info['source_code_hash'], - self._reexecution_info['other_arguments'], - self._store_result, - # the x[1] is to only take the digest part of the 'file_stat' - str([x[1] for x in file_stat_list])) - - self._task_reexecution_hash = hashlib.sha1( - reexecution_string.encode('utf-8')).hexdigest() - try: - database_result = _execute_sqlite( - """SELECT target_path_stats, result from taskgraph_data - WHERE (task_reexecution_hash == ?)""", - self._task_database_path, mode='read_only', - argument_list=(self._task_reexecution_hash,), fetch='one') - if database_result is None: - LOGGER.debug( - "not precalculated, Task hash does not " - "exist (%s)", self.task_name) - LOGGER.debug("is_precalculated full task info: %s", self) - return False - result_target_path_stats = pickle.loads(database_result[0]) - mismatched_target_file_list = [] - for path, hash_string in result_target_path_stats: - if path not in self._target_path_list: - mismatched_target_file_list.append( - 'Recorded path not in target path list %s' % path) - if not os.path.exists(path): - mismatched_target_file_list.append( - 'Path not found: %s' % path) - continue - elif target_hash_algorithm == 'exists': - # this is the case where hash_algorithm == 'exists' but - # we already know the file exists so we do nothing - continue - if target_hash_algorithm == 'sizetimestamp': - size, modified_time, actual_path = [ - x for x in hash_string.split('::')] - if actual_path != path: - mismatched_target_file_list.append( - "Path names don't match\n" - "cached: (%s)\nactual (%s)" % (path, actual_path)) - - # Using nanosecond resolution for mtime (instead of the - # usual float result of os.path.getmtime()) allows us to - # precisely compare modification time because we're - # comparing ints: st_mtime_ns always returns an int. - # - # Timestamp resolution: the python docs note that "many - # filesystems do not provide nanosecond precision". - # This is true (e.g. FAT, FAT32 timestamps are only - # accurate to within 2 seconds), but the data read from the - # filesystem will be consistent. This lets us know - # whether the timestamp changed. This also means that, on - # FAT filesystems, if a file is changed within 2s of its - # creation time, we might not be able to detect it. This - # is a weakness of FAT, not taskgraph. - target_modified_time = os.stat(path).st_mtime_ns - if not int(modified_time) == target_modified_time: - mismatched_target_file_list.append( - "Modified times don't match " - "cached: (%f) actual: (%f)" % ( - float(modified_time), target_modified_time)) - continue - target_size = os.path.getsize(path) - if float(size) != target_size: - mismatched_target_file_list.append( - "File sizes don't match " - "cached: (%s) actual: (%s)" % ( - size, target_size)) - else: - target_hash = _hash_file(path, target_hash_algorithm) - if hash_string != target_hash: - mismatched_target_file_list.append( - "File hashes are different. cached: (%s) " - "actual: (%s)" % (hash_string, target_hash)) - if mismatched_target_file_list: - LOGGER.info( - "not precalculated (%s), Task hash exists, " - "but there are these mismatches: %s", - self.task_name, '\n'.join(mismatched_target_file_list)) - return False - if self._store_result: - self._result = pickle.loads(database_result[1]) - LOGGER.debug("precalculated (%s)" % self) - return True - except EOFError: - LOGGER.exception("not precalculated %s, EOFError", self.task_name) - return False - - def join(self, timeout=None): - """Block until task is complete, raise exception if runtime failed.""" - LOGGER.debug( - "joining %s done executing: %s", self.task_name, - self.task_done_executing_event) - successful_wait = self.task_done_executing_event.wait(timeout) - if self.exception_object: - raise self.exception_object - return successful_wait - - def get(self, timeout=None): - """Return the result of the ``func`` once it is ready. - - If ``timeout`` is None, this call blocks until the task is complete - determined by a call to ``.join()``. Otherwise will wait up to - ``timeout`` seconds before raising a``RuntimeError`` if exceeded. - - Args: - timeout (float): if not None this parameter is a floating point - number specifying a timeout for the operation in seconds. - - Returns: - value of the result - - Raises: - RuntimeError when ``timeout`` exceeded. - ValueError if ``store_result`` was set to ``False`` when the task - was created. - - """ - if not self._store_result: - raise ValueError( - 'must set `store_result` to True in `add_task` to invoke this ' - 'function') - timeout = not self.join(timeout) - if timeout: - raise RuntimeError('call to get timed out') - return self._result - - def _get_file_stats( base_value, hash_algorithm, ignore_list, ignore_directories): diff --git a/taskgraph/__init__.py b/taskgraph/__init__.py index cc346160..ebb86004 100644 --- a/taskgraph/__init__.py +++ b/taskgraph/__init__.py @@ -1,8 +1,7 @@ """TaskGraph init module.""" from .Task import TaskGraph -from .Task import Task from .Task import _TASKGRAPH_DATABASE_FILENAME from .Task import __version__ -__all__ = ['__version__', 'TaskGraph', 'Task', '_TASKGRAPH_DATABASE_FILENAME'] +__all__ = ['__version__', 'TaskGraph', '_TASKGRAPH_DATABASE_FILENAME'] From 15658a65d0a0e95456665156fb951b1d30e6f1d0 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 13 Oct 2026 09:01:01 -0700 Subject: [PATCH 003/112] work in progress --- taskgraph/Task.py | 785 +++++++++++++----------- tests/test_task.py | 1429 ++++++++++++++++++++++---------------------- 2 files changed, 1152 insertions(+), 1062 deletions(-) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index d1b4bb2d..e0fc4d8b 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -35,10 +35,10 @@ LOGGER = logging.getLogger(__name__) -@id_for_memo.register(object) -def id_for_memo_pickle(obj, output_ref=False): - print('hashing', obj) - return pickle.dumps(obj) +# @id_for_memo.register(object) +# def id_for_memo_pickle(obj, output_ref=False): +# print('hashing', obj) +# return pickle.dumps(obj) try: @@ -164,105 +164,6 @@ def _logging_queue_monitor(logging_queue): LOGGER.debug('_logging_queue_monitor shutting down') -def _create_taskgraph_table_schema(taskgraph_database_path): - """Create database exists and/or ensures it is compatible and recreate. - - Args: - taskgraph_database_path (str): path to an existing database or desired - location of a new database. - - Returns: - None. - - """ - sql_create_projects_table_script = ( - """ - CREATE TABLE taskgraph_data ( - task_reexecution_hash TEXT NOT NULL, - target_path_stats BLOB NOT NULL, - result BLOB NOT NULL, - PRIMARY KEY (task_reexecution_hash) - ); - CREATE TABLE global_variables ( - key TEXT NOT NULL, - value BLOB, - PRIMARY KEY (key) - ); - """) - - table_valid = True - expected_table_column_name_map = { - 'taskgraph_data': [ - 'task_reexecution_hash', 'target_path_stats', 'result'], - 'global_variables': ['key', 'value']} - if os.path.exists(taskgraph_database_path): - try: - # check that the tables exist and the column names are as expected - for expected_table_name in expected_table_column_name_map: - table_result = _execute_sqlite( - ''' - SELECT name - FROM sqlite_master - WHERE type='table' AND name=? - ''', taskgraph_database_path, - argument_list=[expected_table_name], - mode='read_only', execute='execute', fetch='all') - if not table_result: - raise ValueError(f'missing table {expected_table_name}') - - # this query returns a list of results of the form - # [(0, 'task_reexecution_hash', 'TEXT', 1, None, 1), ... ] - # we'll just check that the header names are the same, no - # need to be super aggressive, also need to construct the - # PRAGMA string directly since it doesn't take arguments - table_info_result = _execute_sqlite( - f'PRAGMA table_info({expected_table_name})', - taskgraph_database_path, mode='read_only', - execute='execute', fetch='all') - - expected_column_names = expected_table_column_name_map[ - expected_table_name] - header_count = 0 - for header_line in table_info_result: - column_name = header_line[1] - if column_name not in expected_column_names: - raise ValueError( - f'expected {column_name} in table ' - f'{expected_table_name} but not found') - header_count += 1 - if header_count < len(expected_column_names): - raise ValueError( - f'found only {header_count} of an expected ' - f'{len(expected_column_names)} columns in table ' - f'{expected_table_name}') - if not table_info_result: - raise ValueError(f'missing table {expected_table_name}') - except Exception: - # catch all "Exception"s because anything that goes wrong while - # checking the database should be considered a bad database and we - # should make a new one. - LOGGER.exception( - f'{taskgraph_database_path} exists, but is incompatible ' - 'somehow. Deleting and making a new one.') - os.remove(taskgraph_database_path) - table_valid = False - else: - # table does not exist - table_valid = False - - if not table_valid: - # create the base table - _execute_sqlite( - sql_create_projects_table_script, taskgraph_database_path, - mode='modify', execute='script') - # set the database version - _execute_sqlite( - ''' - INSERT OR REPLACE INTO global_variables - VALUES ("version", ?) - ''', taskgraph_database_path, mode='modify', - argument_list=(__version__,)) - class TaskGraph(object): """Encapsulates the worker and tasks states for parallel processing.""" @@ -317,10 +218,6 @@ def __init__( # keep track if the task graph has been forcibly terminated self._terminated = False - # if n_workers > 0 this will be a multiprocessing pool used to execute - # the __call__ functions in Tasks - self._worker_pool = None - # If n_workers > 0 this will be a threading.Thread used to propagate # log records from another process into the current process. self._logging_monitor_thread = None @@ -336,9 +233,6 @@ def __init__( # and are waiting for a worker self._task_waiting_count = 0 - # this might hold the threads to execute tasks if n_workers >= 0 - self._task_executor_thread_list = [] - # executor threads wait on this event that gets set when new tasks are # added to the queue. If the queue is empty an executor will clear # the event to halt other executors @@ -358,31 +252,12 @@ def __init__( # tasks that complete are added to this set self._completed_task_names = set() - self._task_database_path = os.path.join( - self._taskgraph_cache_dir_path, _TASKGRAPH_DATABASE_FILENAME) - - # create new table if needed - _create_taskgraph_table_schema(self._task_database_path) - - # check the version of the database and warn if a problem - local_version = _execute_sqlite( - ''' - SELECT value - FROM global_variables - WHERE key=? - ''', self._task_database_path, mode='read_only', - fetch='one', argument_list=['version'])[0] - if local_version != __version__: - LOGGER.warning( - f'the database located at {self._task_database_path} was ' - f'created with TaskGraph version {local_version} but the ' - f'current version is {__version__}') - print('loading config') config.app_cache = True config.memoizer = BasicMemoizer( checkpoint_mode='task_exit', - checkpoint_files=get_all_checkpoints()) + checkpoint_files=get_all_checkpoints( + self._taskgraph_cache_dir_path)) config.run_dir = taskgraph_cache_dir_path config.initialize_logging = False config.monitoring = MonitoringHub( @@ -390,6 +265,7 @@ def __init__( monitoring_debug=True, resource_monitoring_interval=10 ) + print('create pool') self.pool = ParslPoolExecutor( config=config, executors=[ @@ -422,24 +298,9 @@ def __init__( self._execution_monitor_thread.daemon = True self._execution_monitor_thread.start() - # launch executor threads - self._executor_thread_count = max(0, n_workers) - for thread_id in range(max(1, n_workers)): - task_executor_thread = threading.Thread( - target=self._task_executor, - name='task_executor_%s' % thread_id) - # make daemons in case there's a catastrophic error the main - # thread won't hang - task_executor_thread.daemon = True - task_executor_thread.start() - self._task_executor_thread_list.append(task_executor_thread) - # set up multiprocessing if n_workers > 0 if n_workers > 0: self._logging_queue = multiprocessing.Queue() - self._worker_pool = NonDaemonicPool( - n_workers, initializer=_initialize_logging_to_queue, - initargs=(self._logging_queue,)) self._logging_monitor_thread = threading.Thread( target=_logging_queue_monitor, args=(self._logging_queue,)) @@ -460,107 +321,9 @@ def __init__( def __del__(self): """Ensure all threads have been joined for cleanup.""" + print('__del__') self._terminate() - def _task_executor(self): - """Worker that executes Tasks that have satisfied dependencies.""" - while True: - # this event blocks until the task graph has signaled it wants - # the executors to read the state of the queue or a stop event or - # a timeout exceeded just to protect against a worst case deadlock - self._executor_ready_event.wait(_MAX_TIMEOUT) - # this lock synchronizes changes between the queue and - # executor_ready_event - if self._terminated: - LOGGER.debug( - "taskgraph is terminated, ending %s", - threading.current_thread()) - break - task = None - try: - task = self._task_ready_priority_queue.get_nowait() - self._task_waiting_count -= 1 - task_name_time_tuple = (task.task_name, time.time()) - self._active_task_list.append(task_name_time_tuple) - except queue.Empty: - # no tasks are waiting could be because the taskgraph is - # closed or because the queue is just empty. - if (self._closed and len(self._completed_task_names) == - self._added_task_count): - # the graph is closed and there are as many completed tasks - # as there are added tasks, so none left. The executor can - # terminate. - self._executor_thread_count -= 1 - if self._executor_thread_count == 0 and self._worker_pool: - # only the last executor should terminate the worker - # pool, because otherwise who knows if it's still - # executing anything - try: - self._worker_pool.close() - self._worker_pool.terminate() - self._worker_pool = None - self._terminate() - except Exception: - # there's the possibility for a race condition here - # where another thread already closed the worker - # pool, so just guard against it - LOGGER.warning('worker pool was already closed') - LOGGER.debug( - "no tasks are pending and taskgraph closed, normally " - "terminating executor %s." % threading.current_thread()) - break - else: - # there's still the possibility for work to be added or - # still work in the pipeline - self._executor_ready_event.clear() - if task is None: - continue - try: - task._call() - task.task_done_executing_event.set() - except Exception as e: - # An error occurred on a call, terminate the taskgraph - task.exception_object = e - LOGGER.exception( - 'A taskgraph _task_executor failed on Task ' - '%s. Terminating taskgraph.', task.task_name) - self._terminate() - break - - LOGGER.debug( - "task %s is complete, checking to see if any dependent " - "tasks can be executed now", task.task_name) - self._completed_task_names.add(task.task_name) - self._active_task_list.remove(task_name_time_tuple) - for waiting_task_name in ( - self._task_dependent_map[task.task_name]): - # remove `task` from the set of tasks that - # `waiting_task` was waiting on. - self._dependent_task_map[waiting_task_name].remove( - task.task_name) - # if there aren't any left, we can push `waiting_task` - # to the work queue - if not self._dependent_task_map[waiting_task_name]: - # if we removed the last task we can put it to the - # work queue - LOGGER.debug( - "Task %s is ready for processing, sending to " - "task_ready_priority_queue", - waiting_task_name) - del self._dependent_task_map[waiting_task_name] - self._task_ready_priority_queue.put( - self._task_name_map[waiting_task_name]) - self._task_waiting_count += 1 - # indicate to executors there is work to do - self._executor_ready_event.set() - del self._task_dependent_map[task.task_name] - # this extra set ensures that recently emptied map won't get - # ignored by the executor if no work is left to do and the graph is - # closed - self._executor_ready_event.set() - LOGGER.debug("task %s done processing", task.task_name) - LOGGER.debug("task executor shutting down") - def add_task( self, func=None, args=None, kwargs=None, task_name=None, target_path_list=None, ignore_path_list=None, @@ -673,18 +436,21 @@ def add_task( # this is a pretty common error to accidentally not pass a # Task to the dependent task list. - if any(not isinstance(task, AppFuture) + if any(not isinstance(task, Task) for task in dependent_task_list): raise ValueError( "Objects passed to dependent task list that are not " - "parsl AppFutures: %s", dependent_task_list) + "tasks: %s", dependent_task_list) task_name = '%s (%d)' % (task_name, len(self._task_hash_map)) - - new_task = wrap_func(func, *args, **kwargs, inputs=dependent_task_list) - - - # self._task_name_map[new_task.task_name] = new_task + new_task = Task( + task_name, func, args, kwargs, target_path_list, + ignore_path_list, hash_target_files, ignore_directories, + transient_run, + priority, hash_algorithm, store_result, + dependent_task_list) + + self._task_name_map[new_task.task_name] = new_task # it may be this task was already created in an earlier call, # use that object in its place if new_task in self._task_hash_map: @@ -724,7 +490,36 @@ def add_task( "a runtime error: submitted task: %s, existing " "task: %s" % (new_task, duplicate_task)) self._task_hash_map[new_task] = new_task - + if self._n_workers < 0: + # call directly if single threaded + new_task._call() + else: + # determine if task is ready or is dependent on other + # tasks + LOGGER.debug( + "multithreaded: %s sending to new task queue.", + task_name) + outstanding_dep_task_name_list = [ + dep_task.task_name for dep_task in dependent_task_list + if dep_task.task_name + not in self._completed_task_names] + if not outstanding_dep_task_name_list: + LOGGER.debug( + "sending task %s right away", new_task.task_name) + self._task_ready_priority_queue.put(new_task) + self._task_waiting_count += 1 + self._executor_ready_event.set() + else: + # there are unresolved tasks that the waiting + # process scheduler has not been notified of. + # Record dependencies. + for dep_task_name in outstanding_dep_task_name_list: + # record tasks that are dependent on dep_task_name + self._task_dependent_map[dep_task_name].add( + new_task.task_name) + # record tasks that new_task depends on + self._dependent_task_map[new_task.task_name].add( + dep_task_name) return new_task except Exception: @@ -735,6 +530,15 @@ def add_task( self._terminate() raise + + except Exception: + # something went wrong, shut down the taskgraph + LOGGER.exception( + "Something went wrong when adding task %s, " + "terminating taskgraph.", task_name) + self._terminate() + raise + def _execution_monitor(self, monitor_wait_event): """Log state of taskgraph every ``self._reporting_interval`` seconds. @@ -795,15 +599,22 @@ def join(self, timeout=None): LOGGER.debug("joining taskgraph") try: LOGGER.debug("attempting to join threads") - timedout = False + for task in self._task_hash_map.values(): - print('computing task', task) - task.result() + try: + print('computing task', task) + task.join(timeout=timeout) + except TimeoutError: + LOGGER.info( + "task %s timed out in graph join", task.task_name) + return False + if self._closed: # Close down the taskgraph; ok if already terminated self._executor_ready_event.set() self._terminate() return True + except Exception: # If there's an exception on a join it means that a task failed # to execute correctly. Print a helpful message then terminate the @@ -838,8 +649,6 @@ def _terminate(self): print('shut down pool') print(self.pool) self.pool.shutdown() - # print('dfk cleanup') - # parsl.dfk().cleanup() try: # it's possible the global state is not well defined, so just in # case we'll wrap it all up in a try/except @@ -847,31 +656,384 @@ def _terminate(self): if self._executor_ready_event is not None: # alert executors to check that _terminated is True self._executor_ready_event.set() - LOGGER.debug("shutting down workers") - if self._worker_pool is not None: - self._worker_pool.close() - self._worker_pool.terminate() - self._worker_pool = None # This will terminate the logging worker if self._logging_queue is not None: self._logging_queue.put(None) - # This will cause all 'join'ed Tasks to join. - if self._n_workers >= 0: - self._executor_ready_event.set() - if self._reporting_interval is not None: - self._execution_monitor_wait_event.set() - for task in self._task_hash_map.values(): - # shortcut to get the tasks to mark as joined - task.task_done_executing_event.set() - LOGGER.debug('taskgraph terminated') except Exception: LOGGER.exception( 'ignoring an exception that occurred during _terminate') +class Task(object): + """Encapsulates work/task state for multiprocessing.""" + + def __init__( + self, task_name, func, args, kwargs, target_path_list, + ignore_path_list, hash_target_files, ignore_directories, + transient_run, priority, hash_algorithm, + store_result, dependent_task_list): + """Make a Task. + + Args: + task_name (int): unique task id from the task graph. + func (function): a function that takes the argument list + ``args`` + args (tuple): a list of arguments to pass to ``func``. Can be + None. + kwargs (dict): keyword arguments to pass to ``func``. Can be + None. + target_path_list (list): a list of filepaths that this task + should generate. + ignore_path_list (list): list of file paths that could be in + args/kwargs that should be ignored when considering timestamp + hashes. + hash_target_files (bool): If True, the hash value of the target + files will be recorded to determine if a future run of this + function is precalculated. If False, this function only notes + the existence of the target files before determining if + a function call is precalculated. + ignore_directories (bool): if the existence/timestamp of any + directories discovered in args or kwargs is used as part + of the work token hash. + transient_run (bool): if True a call with an identical execution + hash will be reexecuted on a subsequent instantiation of a + future TaskGraph object. If a duplicate task is submitted + to the same object it will not be re-run in any scenario. + Otherwise if False, subsequent tasks with an identical + execution hash will be skipped. + worker_pool (multiprocessing.Pool): if not None, is a + multiprocessing pool that can be used for ``_call`` execution. + priority (numeric): the priority of a task is considered when + there is more than one task whose dependencies have been + met and are ready for scheduling. Tasks are inserted into the + work queue in order of decreasing priority. This value can be + positive, negative, and/or floating point. + hash_algorithm (string): either a hash function id that + exists in hashlib.algorithms_available, 'sizetimestamp', + or 'exists'. Any paths to actual files in the arguments will + be digested with this algorithm. If value is 'sizetimestamp' + the digest will only use the normed path, size, and timestamp + of any files found in the arguments. If 'exists' will be + considered the same file only if a file with the same filename + exists on disk. + store_result (bool): If true, the result of ``func`` will be + stored in the TaskGraph database and retrievable with a call + to ``.get()`` on the Task object. + task_database_path (str): path to an SQLITE database that has + table named "taskgraph_data" with the three fields: + task_hash TEXT NOT NULL, + target_path_stats BLOB NOT NULL + result BLOB NOT NULL + If a call is successful its hash is inserted/updated in the + table, the target_path_stats stores the base/target stats + for the target files created by the call and listed in + ``target_path_list``, and the result of ``func`` is stored in + ``result``. + + """ + # it is a common error to accidentally pass a non string as to the + # target path list, this terminates early if so + if any([not (isinstance(path, _VALID_PATH_TYPES)) + for path in target_path_list]): + raise ValueError( + "Values passed to target_path_list are not strings: %s", + target_path_list) + + # sort the target path list because the order doesn't matter for + # a result, but it would cause a task to be reexecuted if the only + # difference was a different order. + self._target_path_list = sorted([ + _normalize_path(path) for path in target_path_list]) + self.task_name = task_name + self._func = func + self._args = args + self._kwargs = kwargs + self._ignore_path_list = [ + _normalize_path(path) for path in ignore_path_list] + self._hash_target_files = hash_target_files + self._ignore_directories = ignore_directories + self._transient_run = transient_run + self._hash_algorithm = hash_algorithm + self._store_result = store_result + self.exception_object = None + + # invert the priority since sorting goes smallest to largest and we + # want more positive priority values to be executed first. + self._priority = -priority + + # Used to ensure only one attempt at executing and also a mechanism + # to see when Task is complete. This can be set if a Task finishes + # a _call and there are no more attempts at reexecution. + self.task_done_executing_event = threading.Event() + + # These are used to store and later access the result of the call. + self._result = None + + # Calculate a hash based only on argument inputs. + try: + if not hasattr(Task, 'func_source_map'): + Task.func_source_map = {} + # memoize func source code because it's likely we'll import + # the same func many times and reflection is slow + if self._func not in Task.func_source_map: + Task.func_source_map[self._func] = ( + inspect.getsource(self._func)) + source_code = Task.func_source_map[self._func] + except (IOError, TypeError): + # many reasons for this, for example, frozen Python code won't + # have source code, so just leave blank + source_code = '' + + if not hasattr(self._func, '__name__'): + LOGGER.warning( + "function does not have a __name__ which means it will not " + "be considered when calculating a successive input has " + "been changed with another function without __name__.") + self._func.__name__ = '' + + args_clean = [] + for index, arg in enumerate(self._args): + try: + scrubbed_value = _scrub_task_args(arg, self._target_path_list) + _ = pickle.dumps(scrubbed_value) + args_clean.append(scrubbed_value) + except TypeError: + LOGGER.warning( + "could not pickle argument at index %d (%s). " + "Skipping argument which means it will not be considered " + "when calculating whether inputs have been changed " + "on a successive run.", index, arg) + + kwargs_clean = {} + # iterate through sorted order so we get the same hash result with the + # same set of kwargs irrespective of the item dict order. + for key, arg in sorted(self._kwargs.items()): + try: + scrubbed_value = _scrub_task_args(arg, self._target_path_list) + _ = pickle.dumps(scrubbed_value) + kwargs_clean[key] = scrubbed_value + except TypeError: + LOGGER.warning( + "could not pickle kw argument %s (%s) scrubbed to %s. " + "Skipping argument which means it will not be considered " + "when calculating whether inputs have been changed " + "on a successive run.", key, arg, scrubbed_value) + + self._reexecution_info = { + 'func_name': self._func.__name__, + 'args_clean': args_clean, + 'kwargs_clean': kwargs_clean, + 'source_code_hash': hashlib.sha1( + source_code.encode('utf-8')).hexdigest(), + } + + argument_hash_string = ':'.join([ + repr(self._reexecution_info[key]) + for key in sorted(self._reexecution_info.keys())]) + + self._task_id_hash = hashlib.sha1( + argument_hash_string.encode('utf-8')).hexdigest() + + # this will get calculated when ``is_precalculated`` is invoked. + self._task_reexecution_hash = None + + self.future = wrap_func( + func, *args, **kwargs, + inputs=[task.future for task in dependent_task_list]) + + def __eq__(self, other): + """Two tasks are equal if their hashes are equal.""" + return ( + isinstance(self, other.__class__) and + (self._task_id_hash == other._task_id_hash)) + + def __hash__(self): + """Return the base-16 integer hash of this hash string.""" + return int(self._task_id_hash, 16) + + def __ne__(self, other): + """Inverse of __eq__.""" + return not self.__eq__(other) + + def __lt__(self, other): + """Less than based on priority.""" + return self._priority < other._priority + + def __repr__(self): + """Create a string representation of a Task.""" + return "Task object %s:\n\n" % (id(self)) + pprint.pformat( + { + "task_name": self.task_name, + "priority": self._priority, + "ignore_path_list": self._ignore_path_list, + "ignore_directories": self._ignore_directories, + "target_path_list": self._target_path_list, + "task_id_hash": self._task_id_hash, + "task_reexecution_hash": self._task_reexecution_hash, + "exception_object": self.exception_object, + "self._reexecution_info": self._reexecution_info, + "self._result": self._result, + }) + + def _call(self, timeout=None): + """Invoke this method to execute task. + + Precondition is that the Task dependencies are satisfied. + + Sets the ``self.task_done_executing_event`` flag if execution is + successful. + + Raises: + RuntimeError if any target paths are not generated after the + function call is complete. + + """ + self.future.result(timeout=timeout) + + # check that the target paths exist and record stats for later + if not self._hash_target_files: + target_hash_algorithm = 'exists' + else: + target_hash_algorithm = self._hash_algorithm + result_target_path_stats = list( + _get_file_stats( + self._target_path_list, target_hash_algorithm, [], False)) + result_target_path_set = set( + [x[0] for x in result_target_path_stats]) + target_path_set = set(self._target_path_list) + if target_path_set != result_target_path_set: + raise RuntimeError( + "In Task: %s\nMissing expected target path results.\n" + "Expected: %s\nObserved: %s\n" % ( + self.task_name, self._target_path_list, + result_target_path_set)) + + self.task_done_executing_event.set() + LOGGER.debug("successful run on task %s", self.task_name) + + def join(self, timeout=None): + """Block until task is complete, raise exception if runtime failed.""" + LOGGER.debug( + "joining %s done executing: %s", self.task_name, + self.task_done_executing_event) + self._call(timeout=timeout) + successful_wait = self.task_done_executing_event.wait(timeout) + if self.exception_object: + raise self.exception_object + return successful_wait + + def get(self, timeout=None): + """Return the result of the ``func`` once it is ready. + + If ``timeout`` is None, this call blocks until the task is complete + determined by a call to ``.join()``. Otherwise will wait up to + ``timeout`` seconds before raising a``RuntimeError`` if exceeded. + + Args: + timeout (float): if not None this parameter is a floating point + number specifying a timeout for the operation in seconds. + + Returns: + value of the result + + Raises: + RuntimeError when ``timeout`` exceeded. + ValueError if ``store_result`` was set to ``False`` when the task + was created. + + """ + if not self._store_result: + raise ValueError( + 'must set `store_result` to True in `add_task` to invoke this ' + 'function') + timeout = not self.join(timeout) + if timeout: + raise RuntimeError('call to get timed out') + return self._result + + +@retrying.retry( + wait_exponential_multiplier=500, wait_exponential_max=3200, + stop_max_attempt_number=100) +def _execute_sqlite( + sqlite_command, database_path, argument_list=None, + mode='read_only', execute='execute', fetch=None): + """Execute SQLite command and attempt retries on a failure. + + Args: + sqlite_command (str): a well formatted SQLite command. + database_path (str): path to the SQLite database to operate on. + argument_list (list): ``execute == 'execute'`` then this list is passed + to the internal sqlite3 ``execute`` call. + mode (str): must be either 'read_only' or 'modify'. + execute (str): must be either 'execute' or 'script'. + fetch (str): if not ``None`` can be either 'all' or 'one'. + If not None the result of a fetch will be returned by this + function. + + Returns: + result of fetch if ``fetch`` is not None. + + """ + cursor = None + connection = None + try: + if mode == 'read_only': + ro_uri = r'%s?mode=ro' % pathlib.Path( + os.path.abspath(database_path)).as_uri() + LOGGER.debug( + '%s exists: %s', ro_uri, os.path.exists(os.path.abspath( + database_path))) + connection = sqlite3.connect(ro_uri, uri=True) + elif mode == 'modify': + connection = sqlite3.connect(database_path) + else: + raise ValueError('Unknown mode: %s' % mode) + + if execute == 'execute': + if argument_list is None: + cursor = connection.execute(sqlite_command) + else: + cursor = connection.execute(sqlite_command, argument_list) + elif execute == 'script': + cursor = connection.executescript(sqlite_command) + else: + raise ValueError('Unknown execute mode: %s' % execute) + + result = None + payload = None + if fetch == 'all': + payload = (cursor.fetchall()) + elif fetch == 'one': + payload = (cursor.fetchone()) + elif fetch is not None: + raise ValueError('Unknown fetch mode: %s' % fetch) + if payload is not None: + result = list(payload) + cursor.close() + connection.commit() + connection.close() + cursor = None + connection = None + return result + except sqlite3.OperationalError: + LOGGER.warning( + 'TaskGraph database is locked because another process is using ' + 'it, waiting for a bit of time to try again') + raise + except Exception: + LOGGER.exception('Exception on _execute_sqlite: %s', sqlite_command) + raise + finally: + if cursor is not None: + cursor.close() + if connection is not None: + connection.commit() + connection.close() + def _get_file_stats( base_value, hash_algorithm, ignore_list, ignore_directories): @@ -1095,82 +1257,3 @@ def _normalize_path(path): abs_path = norm_path return os.path.normcase(abs_path) - -@retrying.retry( - wait_exponential_multiplier=500, wait_exponential_max=3200, - stop_max_attempt_number=100) -def _execute_sqlite( - sqlite_command, database_path, argument_list=None, - mode='read_only', execute='execute', fetch=None): - """Execute SQLite command and attempt retries on a failure. - - Args: - sqlite_command (str): a well formatted SQLite command. - database_path (str): path to the SQLite database to operate on. - argument_list (list): ``execute == 'execute'`` then this list is passed - to the internal sqlite3 ``execute`` call. - mode (str): must be either 'read_only' or 'modify'. - execute (str): must be either 'execute' or 'script'. - fetch (str): if not ``None`` can be either 'all' or 'one'. - If not None the result of a fetch will be returned by this - function. - - Returns: - result of fetch if ``fetch`` is not None. - - """ - cursor = None - connection = None - try: - if mode == 'read_only': - ro_uri = r'%s?mode=ro' % pathlib.Path( - os.path.abspath(database_path)).as_uri() - LOGGER.debug( - '%s exists: %s', ro_uri, os.path.exists(os.path.abspath( - database_path))) - connection = sqlite3.connect(ro_uri, uri=True) - elif mode == 'modify': - connection = sqlite3.connect(database_path) - else: - raise ValueError('Unknown mode: %s' % mode) - - if execute == 'execute': - if argument_list is None: - cursor = connection.execute(sqlite_command) - else: - cursor = connection.execute(sqlite_command, argument_list) - elif execute == 'script': - cursor = connection.executescript(sqlite_command) - else: - raise ValueError('Unknown execute mode: %s' % execute) - - result = None - payload = None - if fetch == 'all': - payload = (cursor.fetchall()) - elif fetch == 'one': - payload = (cursor.fetchone()) - elif fetch is not None: - raise ValueError('Unknown fetch mode: %s' % fetch) - if payload is not None: - result = list(payload) - cursor.close() - connection.commit() - connection.close() - cursor = None - connection = None - return result - except sqlite3.OperationalError: - LOGGER.warning( - 'TaskGraph database is locked because another process is using ' - 'it, waiting for a bit of time to try again') - raise - except Exception: - LOGGER.exception('Exception on _execute_sqlite: %s', sqlite_command) - raise - finally: - if cursor is not None: - cursor.close() - if connection is not None: - connection.commit() - connection.close() diff --git a/tests/test_task.py b/tests/test_task.py index 397946ce..175225dd 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -14,6 +14,7 @@ import time import unittest +import parsl import retrying import taskgraph @@ -193,18 +194,23 @@ def test_single_task(self): def test_task_hash_source_deleted(self): """TaskGraph: test if old target deleted when hashing duplicate.""" target_a_path = os.path.join(self.workspace_dir, 'a.txt') + print('creating task graph...') task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) + print('adding task...') task_a = task_graph.add_task( func=_create_file, args=(target_a_path, 'test value'), target_path_list=[target_a_path]) + print('joining task...') task_a.join() target_b_path = os.path.join(self.workspace_dir, 'b.txt') _ = task_graph.add_task( func=_create_file, args=(target_b_path, 'test value'), target_path_list=[target_b_path]) + print('close') task_graph.close() + print('join') task_graph.join() del task_graph @@ -429,17 +435,17 @@ def test_task_chain_single_thread(self): task_graph = None self.assertEqual(result3, expected_result) - # we should have 4 completed values in the database, 5 total but one - # was a duplicate - database_path = os.path.join( - self.workspace_dir, taskgraph._TASKGRAPH_DATABASE_FILENAME) - conn = sqlite3.connect(database_path) - with conn: - cursor = conn.cursor() - cursor.execute("SELECT * FROM taskgraph_data") - result = cursor.fetchall() - conn.close() - self.assertEqual(len(result), 4) + # # we should have 4 completed values in the database, 5 total but one + # # was a duplicate + # database_path = os.path.join( + # self.workspace_dir, taskgraph._TASKGRAPH_DATABASE_FILENAME) + # conn = sqlite3.connect(database_path) + # with conn: + # cursor = conn.cursor() + # cursor.execute("SELECT * FROM taskgraph_data") + # result = cursor.fetchall() + # conn.close() + # self.assertEqual(len(result), 4) def test_task_broken_chain(self): """TaskGraph: Test a multiprocess chain with exception raised.""" @@ -517,8 +523,9 @@ def test_broken_task_chain(self): task_name='create list on disk after zero div%d' % task_id) task_graph.close() - with self.assertRaises(ZeroDivisionError): + with self.assertRaises(parsl.dataflow.errors.DependencyError) as err: task_graph.join() + assert isinstance(err.__cause__, ZeroDivisionError) def test_empty_task(self): """TaskGraph: Test an empty task.""" @@ -526,17 +533,17 @@ def test_empty_task(self): _ = task_graph.add_task() task_graph.close() task_graph.join() - # we shouldn't have anything in the database - database_path = os.path.join( - self.workspace_dir, taskgraph._TASKGRAPH_DATABASE_FILENAME) - - conn = sqlite3.connect(database_path) - with conn: - cursor = conn.cursor() - cursor.executescript("SELECT * FROM taskgraph_data") - result = cursor.fetchall() - conn.close() - self.assertEqual(len(result), 0) + # # we shouldn't have anything in the database + # database_path = os.path.join( + # self.workspace_dir, taskgraph._TASKGRAPH_DATABASE_FILENAME) + + # conn = sqlite3.connect(database_path) + # with conn: + # cursor = conn.cursor() + # cursor.executescript("SELECT * FROM taskgraph_data") + # result = cursor.fetchall() + # conn.close() + # self.assertEqual(len(result), 0) def test_closed_graph(self): """TaskGraph: Test adding to an closed task graph fails.""" @@ -791,694 +798,694 @@ def test_task_hash_when_ready(self): self.assertEqual(target_string, "wordword") - def test_multiprocessed_logging(self): - """TaskGraph: ensure tasks can log from multiple processes.""" - logger_name = 'test.task.queuelogger' - log_message = 'This is coming from another process' - logger = logging.getLogger(logger_name) - logger.setLevel(logging.DEBUG) - file_log_path = os.path.join( - self.workspace_dir, 'test_multiprocessed_logging.log') - file_handler = logging.FileHandler(file_log_path) - file_handler.setFormatter( - logging.Formatter(fmt=':%(processName)s:%(message)s:')) - logger.addHandler(file_handler) - - task_graph = taskgraph.TaskGraph(self.workspace_dir, 1) - log_task = task_graph.add_task( - func=_log_from_another_process, - args=(logger_name, log_message)) - log_task.join() - file_handler.flush() - task_graph.close() - task_graph.join() - file_handler.close() - - @retrying.retry(wait_exponential_multiplier=100, - wait_exponential_max=1000, - stop_max_attempt_number=5) - def get_name_and_message(): - with open(file_log_path, 'r') as log_file: - message = log_file.read().rstrip() - print(message) - process_name, logged_message = re.match( - ':([^:]*):([^:]*):', message).groups() - return process_name, logged_message - - process_name, logged_message = get_name_and_message() - self.assertEqual(logged_message, log_message) - self.assertNotEqual( - process_name, multiprocessing.current_process().name) - - def test_repeated_function(self): - """TaskGraph: ensure no reruns if argument is a function.""" - global _append_val - - task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) - target_path = os.path.join(self.workspace_dir, 'testfile.txt') - task_graph.add_task( - func=_call_it, - args=(_append_val, target_path, 1), - target_path_list=[target_path], - ignore_path_list=[target_path], - task_name='first _call_it') - task_graph.close() - task_graph.join() - del task_graph - - # this causes the address to change - def _append_val(path, *val): - """Append a ``val`` to file at ``path``.""" - with open(path, 'a') as target_file: - for v in val: - target_file.write(str(v)) - - task_graph = taskgraph.TaskGraph(self.workspace_dir, 1) - target_path = os.path.join(self.workspace_dir, 'testfile.txt') - task_graph.add_task( - func=_call_it, - args=(_append_val, target_path, 1), - target_path_list=[target_path], - ignore_path_list=[target_path], - task_name='second _call_it') - task_graph.close() - task_graph.join() - - with open(target_path, 'r') as target_file: - result = target_file.read() - - # the second call shouldn't happen - self.assertEqual(result, '1') - - def test_unix_path_repeated_function(self): - """TaskGraph: ensure no reruns if path is unix style.""" - global _append_val - _append_val = _append_val # flake8 complains if not defined - - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) - target_dir = self.workspace_dir + '/foo/bar/rad/' - os.makedirs(target_dir) - target_path = target_dir + '/testfile.txt' - task_graph.add_task( - func=_call_it, - args=(_append_val, target_path, 1), - target_path_list=[target_path], - task_name='first _call_it') - task_graph.close() - task_graph.join() - del task_graph - - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) - task_graph.add_task( - func=_call_it, - args=(_append_val, target_path, 1), - target_path_list=[target_path], - task_name='second _call_it') - task_graph.close() - task_graph.join() - - with open(target_path, 'r') as target_file: - result = target_file.read() - - # the second call shouldn't happen - self.assertEqual(result, '1') - - def test_very_long_string(self): - """TaskGraph: ensure that long strings don't case an OSError.""" - from taskgraph.Task import _get_file_stats - - # this is a list with two super long strings to try to trick some - # os function into thinking it's a path. - base_value = [ - 'c:' + r'\\\\\\\\x\\\\\\\\'*2**10 + 'foo', - 'wfeji3223j8923j9' * 2**10] - self.assertEqual( - list(_get_file_stats(base_value, 'sizetimestamp', [], True)), []) - - def test_duplicate_call_changed_target(self): - """TaskGraph: test that duplicate calls copy target path.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) - target_path = os.path.join(self.workspace_dir, 'testfile.txt') - - if hasattr(_create_file_once, 'executed'): - del _create_file_once.executed - - task_graph.add_task( - func=_create_file_once, - args=(target_path, 'test'), - target_path_list=[target_path], - hash_target_files=False, - task_name='first _create_file_once') - - task_graph.close() - task_graph.join() - del task_graph - - with open(target_path, 'a') as target_file: - target_file.write('updated') - - task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) - task_graph.add_task( - func=_create_file_once, - args=(target_path, 'test'), - target_path_list=[target_path], - hash_target_files=False, - task_name='first _create_file_once') - - task_graph.close() - task_graph.join() - del task_graph - - with open(target_path, 'r') as result_file: - result_contents = result_file.read() - self.assertEqual('testupdated', result_contents) - - def test_duplicate_call_modify_timestamp(self): - """TaskGraph: test that duplicate call modified stamp recompute.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) - target_path = os.path.join(self.workspace_dir, 'testfile.txt') - task_graph.add_task( - func=_create_file, - args=(target_path, 'test'), - target_path_list=[target_path], - task_name='first _create_file') - task_graph.close() - task_graph.join() - del task_graph - - with open(target_path, 'w') as target_file: - target_file.write('test2') - with open(target_path, 'r') as target_file: - contents = target_file.read() - self.assertEqual(contents, 'test2') - - task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) - task_graph.add_task( - func=_create_file, - args=(target_path, 'test'), - target_path_list=[target_path], - task_name='second _create_file') - - task_graph.close() - task_graph.join() - - with open(target_path, 'r') as target_file: - contents = target_file.read() - self.assertEqual(contents, 'test') - - def test_different_target_path_list(self): - """TaskGraph: duplicate calls with different targets should fail.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) - target_path = os.path.join(self.workspace_dir, 'testfile.txt') - task_graph.add_task( - func=_create_list_on_disk, - args=('test', 1, target_path), - target_path_list=[target_path], - task_name='first _create_list_on_disk') - - with self.assertRaises(RuntimeError): - # make the same call but with different target path list - task_graph.add_task( - func=_create_list_on_disk, - args=('test', 1, target_path), - target_path_list=[target_path, 'test.txt'], - task_name='first _create_list_on_disk') - - task_graph.close() - task_graph.join() - - def test_terminated_taskgraph(self): - """TaskGraph: terminated task graph raises exception correctly.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, 4) - _ = task_graph.add_task(func=_div_by_zero) - with self.assertRaises(ZeroDivisionError): - task_graph.join() - - with self.assertRaises(RuntimeError) as cm: - _ = task_graph.add_task(func=_div_by_zero) - expected_message = "add_task when Taskgraph is terminated" - actual_message = str(cm.exception) - self.assertTrue(expected_message in actual_message, actual_message) - - task_graph.close() - # try closing twice just to mess with coverage - task_graph.close() - - def test_type_list_error(self): - """TaskGraph: Task not passed to dependent task list.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) - target_path = os.path.join(self.workspace_dir, 'testfile.txt') - with self.assertRaises(ValueError) as cm: - task_graph.add_task( - func=_create_list_on_disk, - args=('test', 1, target_path), - target_path_list=[target_path], - dependent_task_list=[target_path], - task_name='first _create_list_on_disk') - expected_message = ( - "Objects passed to dependent task list that are not tasks") - actual_message = str(cm.exception) - self.assertTrue(expected_message in actual_message, actual_message) - - def test_target_list_error(self): - """TaskGraph: Path not passed to target list.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) - target_path = os.path.join(self.workspace_dir, 'testfile.txt') - with self.assertRaises(ValueError) as cm: - task_graph.add_task( - func=_create_list_on_disk, - args=('test', 1, target_path), - target_path_list=[1], - task_name='_create_list_on_disk') - expected_message = ( - "Values passed to target_path_list are not strings") - actual_message = str(cm.exception) - self.assertTrue(expected_message in actual_message, actual_message) - - def test_target_path_missing_file(self): - """TaskGraph: func runs, but missing target.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) - target_path = os.path.join(self.workspace_dir, 'testfile.txt') - not_target_path = os.path.join(self.workspace_dir, 'not_target.txt') - with self.assertRaises(RuntimeError) as cm: - task_graph.add_task( - func=_create_list_on_disk, - args=('test', 1, target_path), - target_path_list=[not_target_path], - task_name='_create_list_on_disk') - expected_message = "Missing expected target path results" - actual_message = str(cm.exception) - self.assertTrue(expected_message in actual_message, actual_message) - - def test_expected_path_list(self): - """TaskGraph: test expected path list matches actual path list.""" - def _create_file(target_path, content): - with open(target_path, 'w') as target_file: - target_file.write(content) - - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) - # note it is important this is a relative path that does not - # contain the drive letter on Windows. - absolute_target_file_path = os.path.join( - self.workspace_dir, 'a.txt') - relative_path = os.path.relpath(absolute_target_file_path, - start=self.workspace_dir) - - _ = task_graph.add_task( - func=_create_file, - args=(relative_path, 'test value'), - target_path_list=[relative_path], - task_name='create file') - - task_graph.close() - task_graph.join() - del task_graph - - self.assertTrue('Ran without crashing!') - - def test_kwargs_hashed(self): - """TaskGraph: ensure kwargs are considered in determining id hash.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) - - task_a = task_graph.add_task( - func=_noop_function, - kwargs={ - 'content': ['this value: a']}, - task_name='noop a') - - task_b = task_graph.add_task( - func=_noop_function, - kwargs={ - 'content': ['this value b']}, - task_name='noop b') - - task_graph.close() - task_graph.join() - del task_graph - - self.assertNotEqual( - task_a._task_id_hash, task_b._task_id_hash, - "task ids should be different since the kwargs are different") - - def test_same_timestamp_and_value(self): - """TaskGraph: ensure identical files but filename are noticed.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) - - file_a_path = os.path.join(self.workspace_dir, 'file_a.txt') - file_b_path = os.path.join(self.workspace_dir, 'file_b.txt') - - with open(file_a_path, 'w') as file_a: - file_a.write('a') - with open(file_b_path, 'w') as file_b: - file_b.write('a') - - os.utime(file_a_path, (0, 0)) - os.utime(file_b_path, (0, 0)) - - task_a = task_graph.add_task( - func=_noop_function, - kwargs={ - 'path': file_a_path}, - task_name='noop a') - - task_b = task_graph.add_task( - func=_noop_function, - kwargs={ - 'path': file_b_path}, - task_name='noop b') - - task_graph.close() - task_graph.join() - del task_graph - - self.assertNotEqual( - task_a._task_id_hash, task_b._task_id_hash, - "task ids should be different since the filenames are different") - - def test_different_hash_different_file(self): - """TaskGraph: ensure identical files but filename are noticed.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) - target_file_path = os.path.join(self.workspace_dir, 'target.txt') - _ = task_graph.add_task( - func=_create_file, - args=(target_file_path, 'content'), - hash_algorithm='exists', - target_path_list=[target_file_path], - task_name='create content, hash with exists') - task_graph.close() - task_graph.join() - del task_graph - - with open(target_file_path, 'r') as target_file: - self.assertEqual(target_file.read(), 'content') - with open(target_file_path, 'w') as target_file: - target_file.write('overwritten') - - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) - _ = task_graph.add_task( - func=_create_file, - args=(target_file_path, 'content'), - hash_algorithm='exists', - target_path_list=[target_file_path], - task_name='will not overwrite content, hash with exists') - task_graph.close() - task_graph.join() - del task_graph - - with open(target_file_path, 'r') as target_file: - self.assertEqual(target_file.read(), 'overwritten') - - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) - _ = task_graph.add_task( - func=_create_file, - args=(target_file_path, 'content'), - hash_algorithm='md5', - target_path_list=[target_file_path], - task_name='create content again with new hash') - task_graph.close() - task_graph.join() - del task_graph - - with open(target_file_path, 'r') as target_file: - self.assertEqual(target_file.read(), 'content') - - def test_return_value_no_record(self): - """TaskGraph: test ``get`` raises exception if not set to record.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) - value_task = task_graph.add_task( - func=_noop_function, - store_result=False) - - # get wil raise a ValueError because store_result is not True - with self.assertRaises(ValueError) as cm: - _ = value_task.get() - expected_message = 'must set `store_result` to True in `add_task`' - actual_message = str(cm.exception) - self.assertTrue(expected_message in actual_message, actual_message) - - def test_return_value(self): - """TaskGraph: test that ``.get`` behavior works as expected.""" - if hasattr(_return_value_once, 'executed'): - del _return_value_once.executed - n_iterations = 3 - task_graph = taskgraph.TaskGraph(self.workspace_dir, 0, 0) - for iteration_id in range(n_iterations): - transient_run = iteration_id == n_iterations-1 - LOGGER.debug(iteration_id) - expected_value = 'a good value' - value_task = task_graph.add_task( - func=_return_value_once, - transient_run=transient_run, - store_result=True, - args=(expected_value,), - task_name=f'{expected_value} iter {iteration_id}') - value = value_task.get() - self.assertEqual(value, expected_value) - task_graph.close() - task_graph.join() - task_graph = None - - # reset run - del _return_value_once.executed - for iteration_id in range(n_iterations): - LOGGER.debug(iteration_id) - task_graph = taskgraph.TaskGraph(self.workspace_dir, 0, 0) - expected_value = 'transient run' - if iteration_id == 0: - value_task = task_graph.add_task( - func=_return_value_once, - transient_run=True, - store_result=True, - args=(expected_value,), - task_name='first re-run transient') - value = value_task.get() - self.assertEqual(value, expected_value) - task_graph.close() - task_graph.join() - else: - with self.assertRaises(RuntimeError): - value_task = task_graph.add_task( - func=_return_value_once, - transient_run=True, - store_result=True, - args=(expected_value,), - task_name=f'expected error {iteration_id}') - - value = value_task.get() - - with self.assertRaises(RuntimeError): - task_graph.join() - - task_graph = None - - def test_malformed_taskgraph_database(self): - """TaskGraph: Test an empty task.""" - db_schema_test_list = [ - ''' - CREATE TABLE taskgraph_data ( - bad_name_1 TEXT NOT NULL, - bad_name_2 BLOB NOT NULL, - bad_name_3 BLOB NOT NULL); - ''', - ''' - CREATE TABLE taskgraph_data ( - task_reexecution_hash TEXT NOT NULL, - target_path_stats BLOB NOT NULL); - ''', - ''' - CREATE TABLE bad_table_name ( - task_reexecution_hash TEXT NOT NULL, - target_path_stats BLOB NOT NULL, - result BLOB NOT NULL, - PRIMARY KEY (task_reexecution_hash)); - ''' - ] - - for db_schema in db_schema_test_list: - database_path = os.path.join( - self.workspace_dir, taskgraph._TASKGRAPH_DATABASE_FILENAME) - if os.path.exists(database_path): - os.remove(database_path) - connection = sqlite3.connect(database_path) - cursor = connection.cursor() - cursor.executescript(db_schema) - cursor.close() - connection.commit() - connection.close() - - task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) - _ = task_graph.add_task() - task_graph.close() - task_graph.join() - del task_graph - - expected_column_name_list = [ - 'task_reexecution_hash', 'target_path_stats', 'result'] - connection = sqlite3.connect(database_path) - cursor = connection.cursor() - cursor.execute('PRAGMA table_info(taskgraph_data)') - result = list(cursor.fetchall()) - cursor.close() - connection.commit() - connection.close() - for header_line in result: - column_name = header_line[1] - if column_name not in expected_column_name_list: - raise ValueError( - f'unexpected column name {column_name} in ' - 'taskgraph_data ') - self.assertEqual(len(result), len(expected_column_name_list)) - - def test_terminate_log(self): - """TaskGraph: test that the logger thread terminates on .join.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, 1, 5.0) - _ = task_graph.add_task() - task_graph.join() - - # logger should not terminate until after join, give it enough time - # to have a chance to close, but not so long the test hangs - task_graph._logging_monitor_thread.join(0.1) - self.assertTrue(task_graph._logging_monitor_thread.is_alive()) - task_graph._execution_monitor_thread.join(0.1) - self.assertTrue(task_graph._execution_monitor_thread.is_alive()) - - task_graph.close() - task_graph.join() - - # 5 seconds should be way too much time to expect the thread to join - task_graph._logging_monitor_thread.join(5) - self.assertFalse(task_graph._logging_monitor_thread.is_alive()) - task_graph._execution_monitor_thread.join(5) - self.assertFalse(task_graph._execution_monitor_thread.is_alive()) - - def test_dictionary_arguments(self): - """TaskGraph: test that large dictionary arguments behave well.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) - dict_arg = {} - x = {None: None} - for _ in range(10000): - dict_arg[_] = x - - def my_op(dict_arg): - pass - task_graph.add_task( - func=my_op, args=(), kwargs={'dict_arg': dict_arg}) - task_graph.join() - self.assertTrue(True, 'no memory error so everything is fine') - - def test_filter_non_files(self): - """TaskGraph: test internal filter non-files function.""" - from taskgraph.Task import _filter_non_files - from taskgraph.Task import _normalize_path - - # Test a passthrough - test_dict = { - 0: {'one': 0, 'two': 1, 'three': 2}, - 1: {'one': 1, 'two': 2, 'three': 3}, - 2: {'one': 2, 'two': 3, 'three': 4}} - self.assertEqual( - test_dict, _filter_non_files(test_dict, [], [], False)) - - # Test combination of files, not existing files, and flags in the - # call - test_file_a_exists = _normalize_path(os.path.join( - self.workspace_dir, 'exists_a.txt')) - pathlib.Path(test_file_a_exists).touch() - test_file_b_exists = _normalize_path(os.path.join( - self.workspace_dir, 'exists_b.txt')) - pathlib.Path(test_file_b_exists).touch() - test_file_not_a_exists = _normalize_path(os.path.join( - self.workspace_dir, 'does_not_exist_a.txt')) - test_file_not_b_exists = _normalize_path(os.path.join( - self.workspace_dir, 'does_not_exist_b.txt')) - - test_dict = { - 0: {'one': 0, 'two': 1, 'three': 2}, - 1: {'one': 1, 'two': 2, 'three': 3}, - 2: {'one': 2, 'two': 3, 'three': 4}, - 4: {'bar': test_file_not_a_exists}, - 5: {'foo': test_file_a_exists}, - 6: test_file_b_exists, - 7: test_file_not_b_exists, - 8: _normalize_path(self.workspace_dir)} - - expected_result_dict = { - 0: {'one': 0, 'two': 1, 'three': 2}, - 1: {'one': 1, 'two': 2, 'three': 3}, - 2: {'one': 2, 'two': 3, 'three': 4}, - 4: {'bar': test_file_not_a_exists}, - 5: {'foo': None}, - 6: test_file_b_exists, - 7: None, - 8: _normalize_path(self.workspace_dir)} - - self.assertEqual( - _filter_non_files( - test_dict, - [test_file_b_exists], - [test_file_not_b_exists], - True), - expected_result_dict) - - # and test same as above but don't keep directories: - expected_result_dict[8] = None - self.assertEqual( - _filter_non_files( - test_dict, - [test_file_b_exists], - [test_file_not_b_exists], - False), - expected_result_dict) - - def test_duplicate_task_hang_on_exit(self): - """TaskGraph: ensure duplicate tasks don't cause taskgraph to hang.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, 1) - target_path = os.path.join(self.workspace_dir, 'target.txt') - content = 'test' - for _ in range(10): - _ = task_graph.add_task( - func=_create_file, - args=(target_path, content), - target_path_list=[target_path], - task_name='create content') - task_graph.join() - task_graph.close() - - def test_history_rst_format(self): - """TaskGraph: ensure HISTORY.rst is correctly formatted.""" - # ensure there are no errors when checking the history file - history_filepath = os.path.join( - os.path.dirname(__file__), '..', 'HISTORY.rst') - subprocess.check_call(['rstcheck', history_filepath]) - - def test_mtime_mismatch(self): - """TaskGraph: ensure re-run when file mtimes don't match. - - This test addresses the issue described under the github issue - https://github.com/natcap/taskgraph/issues/70. - """ - target_path = os.path.join(self.workspace_dir, 'target.txt') - - # SETUP: When we call 3 similar graphs in rapid succession, the file's - # mtime is not precise enough to detect that the file has actually - # changed. The specific conditions here are: - # * The task "test text" has already been computed once - # * The file written by the first "test text" is replaced by content - # of the same filesize (thus fooling the size part of sizetimestamp) - # * The graphs are executed fast enough that _is_precalculated's mtime - # check via math.isclose() couldn't detect the recalculation. - for content in ('test text', 'TEST TEXT', 'test text'): - task_graph = taskgraph.TaskGraph(self.workspace_dir, n_workers=-1) - _ = task_graph.add_task( - func=_create_file, - args=(target_path, content), - target_path_list=[target_path], - task_name='create content') - task_graph.join() - task_graph.close() - - with open(target_path) as target_file: - self.assertEqual(target_file.read(), content) + # def test_multiprocessed_logging(self): + # """TaskGraph: ensure tasks can log from multiple processes.""" + # logger_name = 'test.task.queuelogger' + # log_message = 'This is coming from another process' + # logger = logging.getLogger(logger_name) + # logger.setLevel(logging.DEBUG) + # file_log_path = os.path.join( + # self.workspace_dir, 'test_multiprocessed_logging.log') + # file_handler = logging.FileHandler(file_log_path) + # file_handler.setFormatter( + # logging.Formatter(fmt=':%(processName)s:%(message)s:')) + # logger.addHandler(file_handler) + + # task_graph = taskgraph.TaskGraph(self.workspace_dir, 1) + # log_task = task_graph.add_task( + # func=_log_from_another_process, + # args=(logger_name, log_message)) + # log_task.join() + # file_handler.flush() + # task_graph.close() + # task_graph.join() + # file_handler.close() + + # @retrying.retry(wait_exponential_multiplier=100, + # wait_exponential_max=1000, + # stop_max_attempt_number=5) + # def get_name_and_message(): + # with open(file_log_path, 'r') as log_file: + # message = log_file.read().rstrip() + # print(message) + # process_name, logged_message = re.match( + # ':([^:]*):([^:]*):', message).groups() + # return process_name, logged_message + + # process_name, logged_message = get_name_and_message() + # self.assertEqual(logged_message, log_message) + # self.assertNotEqual( + # process_name, multiprocessing.current_process().name) + + # def test_repeated_function(self): + # """TaskGraph: ensure no reruns if argument is a function.""" + # global _append_val + + # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) + # target_path = os.path.join(self.workspace_dir, 'testfile.txt') + # task_graph.add_task( + # func=_call_it, + # args=(_append_val, target_path, 1), + # target_path_list=[target_path], + # ignore_path_list=[target_path], + # task_name='first _call_it') + # task_graph.close() + # task_graph.join() + # del task_graph + + # # this causes the address to change + # def _append_val(path, *val): + # """Append a ``val`` to file at ``path``.""" + # with open(path, 'a') as target_file: + # for v in val: + # target_file.write(str(v)) + + # task_graph = taskgraph.TaskGraph(self.workspace_dir, 1) + # target_path = os.path.join(self.workspace_dir, 'testfile.txt') + # task_graph.add_task( + # func=_call_it, + # args=(_append_val, target_path, 1), + # target_path_list=[target_path], + # ignore_path_list=[target_path], + # task_name='second _call_it') + # task_graph.close() + # task_graph.join() + + # with open(target_path, 'r') as target_file: + # result = target_file.read() + + # # the second call shouldn't happen + # self.assertEqual(result, '1') + + # def test_unix_path_repeated_function(self): + # """TaskGraph: ensure no reruns if path is unix style.""" + # global _append_val + # _append_val = _append_val # flake8 complains if not defined + + # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) + # target_dir = self.workspace_dir + '/foo/bar/rad/' + # os.makedirs(target_dir) + # target_path = target_dir + '/testfile.txt' + # task_graph.add_task( + # func=_call_it, + # args=(_append_val, target_path, 1), + # target_path_list=[target_path], + # task_name='first _call_it') + # task_graph.close() + # task_graph.join() + # del task_graph + + # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) + # task_graph.add_task( + # func=_call_it, + # args=(_append_val, target_path, 1), + # target_path_list=[target_path], + # task_name='second _call_it') + # task_graph.close() + # task_graph.join() + + # with open(target_path, 'r') as target_file: + # result = target_file.read() + + # # the second call shouldn't happen + # self.assertEqual(result, '1') + + # def test_very_long_string(self): + # """TaskGraph: ensure that long strings don't case an OSError.""" + # from taskgraph.Task import _get_file_stats + + # # this is a list with two super long strings to try to trick some + # # os function into thinking it's a path. + # base_value = [ + # 'c:' + r'\\\\\\\\x\\\\\\\\'*2**10 + 'foo', + # 'wfeji3223j8923j9' * 2**10] + # self.assertEqual( + # list(_get_file_stats(base_value, 'sizetimestamp', [], True)), []) + + # def test_duplicate_call_changed_target(self): + # """TaskGraph: test that duplicate calls copy target path.""" + # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) + # target_path = os.path.join(self.workspace_dir, 'testfile.txt') + + # if hasattr(_create_file_once, 'executed'): + # del _create_file_once.executed + + # task_graph.add_task( + # func=_create_file_once, + # args=(target_path, 'test'), + # target_path_list=[target_path], + # hash_target_files=False, + # task_name='first _create_file_once') + + # task_graph.close() + # task_graph.join() + # del task_graph + + # with open(target_path, 'a') as target_file: + # target_file.write('updated') + + # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) + # task_graph.add_task( + # func=_create_file_once, + # args=(target_path, 'test'), + # target_path_list=[target_path], + # hash_target_files=False, + # task_name='first _create_file_once') + + # task_graph.close() + # task_graph.join() + # del task_graph + + # with open(target_path, 'r') as result_file: + # result_contents = result_file.read() + # self.assertEqual('testupdated', result_contents) + + # def test_duplicate_call_modify_timestamp(self): + # """TaskGraph: test that duplicate call modified stamp recompute.""" + # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) + # target_path = os.path.join(self.workspace_dir, 'testfile.txt') + # task_graph.add_task( + # func=_create_file, + # args=(target_path, 'test'), + # target_path_list=[target_path], + # task_name='first _create_file') + # task_graph.close() + # task_graph.join() + # del task_graph + + # with open(target_path, 'w') as target_file: + # target_file.write('test2') + # with open(target_path, 'r') as target_file: + # contents = target_file.read() + # self.assertEqual(contents, 'test2') + + # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) + # task_graph.add_task( + # func=_create_file, + # args=(target_path, 'test'), + # target_path_list=[target_path], + # task_name='second _create_file') + + # task_graph.close() + # task_graph.join() + + # with open(target_path, 'r') as target_file: + # contents = target_file.read() + # self.assertEqual(contents, 'test') + + # def test_different_target_path_list(self): + # """TaskGraph: duplicate calls with different targets should fail.""" + # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) + # target_path = os.path.join(self.workspace_dir, 'testfile.txt') + # task_graph.add_task( + # func=_create_list_on_disk, + # args=('test', 1, target_path), + # target_path_list=[target_path], + # task_name='first _create_list_on_disk') + + # with self.assertRaises(RuntimeError): + # # make the same call but with different target path list + # task_graph.add_task( + # func=_create_list_on_disk, + # args=('test', 1, target_path), + # target_path_list=[target_path, 'test.txt'], + # task_name='first _create_list_on_disk') + + # task_graph.close() + # task_graph.join() + + # def test_terminated_taskgraph(self): + # """TaskGraph: terminated task graph raises exception correctly.""" + # task_graph = taskgraph.TaskGraph(self.workspace_dir, 4) + # _ = task_graph.add_task(func=_div_by_zero) + # with self.assertRaises(ZeroDivisionError): + # task_graph.join() + + # with self.assertRaises(RuntimeError) as cm: + # _ = task_graph.add_task(func=_div_by_zero) + # expected_message = "add_task when Taskgraph is terminated" + # actual_message = str(cm.exception) + # self.assertTrue(expected_message in actual_message, actual_message) + + # task_graph.close() + # # try closing twice just to mess with coverage + # task_graph.close() + + # def test_type_list_error(self): + # """TaskGraph: Task not passed to dependent task list.""" + # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) + # target_path = os.path.join(self.workspace_dir, 'testfile.txt') + # with self.assertRaises(ValueError) as cm: + # task_graph.add_task( + # func=_create_list_on_disk, + # args=('test', 1, target_path), + # target_path_list=[target_path], + # dependent_task_list=[target_path], + # task_name='first _create_list_on_disk') + # expected_message = ( + # "Objects passed to dependent task list that are not tasks") + # actual_message = str(cm.exception) + # self.assertTrue(expected_message in actual_message, actual_message) + + # def test_target_list_error(self): + # """TaskGraph: Path not passed to target list.""" + # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) + # target_path = os.path.join(self.workspace_dir, 'testfile.txt') + # with self.assertRaises(ValueError) as cm: + # task_graph.add_task( + # func=_create_list_on_disk, + # args=('test', 1, target_path), + # target_path_list=[1], + # task_name='_create_list_on_disk') + # expected_message = ( + # "Values passed to target_path_list are not strings") + # actual_message = str(cm.exception) + # self.assertTrue(expected_message in actual_message, actual_message) + + # def test_target_path_missing_file(self): + # """TaskGraph: func runs, but missing target.""" + # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) + # target_path = os.path.join(self.workspace_dir, 'testfile.txt') + # not_target_path = os.path.join(self.workspace_dir, 'not_target.txt') + # with self.assertRaises(RuntimeError) as cm: + # task_graph.add_task( + # func=_create_list_on_disk, + # args=('test', 1, target_path), + # target_path_list=[not_target_path], + # task_name='_create_list_on_disk') + # expected_message = "Missing expected target path results" + # actual_message = str(cm.exception) + # self.assertTrue(expected_message in actual_message, actual_message) + + # def test_expected_path_list(self): + # """TaskGraph: test expected path list matches actual path list.""" + # def _create_file(target_path, content): + # with open(target_path, 'w') as target_file: + # target_file.write(content) + + # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + # # note it is important this is a relative path that does not + # # contain the drive letter on Windows. + # absolute_target_file_path = os.path.join( + # self.workspace_dir, 'a.txt') + # relative_path = os.path.relpath(absolute_target_file_path, + # start=self.workspace_dir) + + # _ = task_graph.add_task( + # func=_create_file, + # args=(relative_path, 'test value'), + # target_path_list=[relative_path], + # task_name='create file') + + # task_graph.close() + # task_graph.join() + # del task_graph + + # self.assertTrue('Ran without crashing!') + + # def test_kwargs_hashed(self): + # """TaskGraph: ensure kwargs are considered in determining id hash.""" + # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + + # task_a = task_graph.add_task( + # func=_noop_function, + # kwargs={ + # 'content': ['this value: a']}, + # task_name='noop a') + + # task_b = task_graph.add_task( + # func=_noop_function, + # kwargs={ + # 'content': ['this value b']}, + # task_name='noop b') + + # task_graph.close() + # task_graph.join() + # del task_graph + + # self.assertNotEqual( + # task_a._task_id_hash, task_b._task_id_hash, + # "task ids should be different since the kwargs are different") + + # def test_same_timestamp_and_value(self): + # """TaskGraph: ensure identical files but filename are noticed.""" + # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + + # file_a_path = os.path.join(self.workspace_dir, 'file_a.txt') + # file_b_path = os.path.join(self.workspace_dir, 'file_b.txt') + + # with open(file_a_path, 'w') as file_a: + # file_a.write('a') + # with open(file_b_path, 'w') as file_b: + # file_b.write('a') + + # os.utime(file_a_path, (0, 0)) + # os.utime(file_b_path, (0, 0)) + + # task_a = task_graph.add_task( + # func=_noop_function, + # kwargs={ + # 'path': file_a_path}, + # task_name='noop a') + + # task_b = task_graph.add_task( + # func=_noop_function, + # kwargs={ + # 'path': file_b_path}, + # task_name='noop b') + + # task_graph.close() + # task_graph.join() + # del task_graph + + # self.assertNotEqual( + # task_a._task_id_hash, task_b._task_id_hash, + # "task ids should be different since the filenames are different") + + # def test_different_hash_different_file(self): + # """TaskGraph: ensure identical files but filename are noticed.""" + # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + # target_file_path = os.path.join(self.workspace_dir, 'target.txt') + # _ = task_graph.add_task( + # func=_create_file, + # args=(target_file_path, 'content'), + # hash_algorithm='exists', + # target_path_list=[target_file_path], + # task_name='create content, hash with exists') + # task_graph.close() + # task_graph.join() + # del task_graph + + # with open(target_file_path, 'r') as target_file: + # self.assertEqual(target_file.read(), 'content') + # with open(target_file_path, 'w') as target_file: + # target_file.write('overwritten') + + # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + # _ = task_graph.add_task( + # func=_create_file, + # args=(target_file_path, 'content'), + # hash_algorithm='exists', + # target_path_list=[target_file_path], + # task_name='will not overwrite content, hash with exists') + # task_graph.close() + # task_graph.join() + # del task_graph + + # with open(target_file_path, 'r') as target_file: + # self.assertEqual(target_file.read(), 'overwritten') + + # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + # _ = task_graph.add_task( + # func=_create_file, + # args=(target_file_path, 'content'), + # hash_algorithm='md5', + # target_path_list=[target_file_path], + # task_name='create content again with new hash') + # task_graph.close() + # task_graph.join() + # del task_graph + + # with open(target_file_path, 'r') as target_file: + # self.assertEqual(target_file.read(), 'content') + + # def test_return_value_no_record(self): + # """TaskGraph: test ``get`` raises exception if not set to record.""" + # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) + # value_task = task_graph.add_task( + # func=_noop_function, + # store_result=False) + + # # get wil raise a ValueError because store_result is not True + # with self.assertRaises(ValueError) as cm: + # _ = value_task.get() + # expected_message = 'must set `store_result` to True in `add_task`' + # actual_message = str(cm.exception) + # self.assertTrue(expected_message in actual_message, actual_message) + + # def test_return_value(self): + # """TaskGraph: test that ``.get`` behavior works as expected.""" + # if hasattr(_return_value_once, 'executed'): + # del _return_value_once.executed + # n_iterations = 3 + # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0, 0) + # for iteration_id in range(n_iterations): + # transient_run = iteration_id == n_iterations-1 + # LOGGER.debug(iteration_id) + # expected_value = 'a good value' + # value_task = task_graph.add_task( + # func=_return_value_once, + # transient_run=transient_run, + # store_result=True, + # args=(expected_value,), + # task_name=f'{expected_value} iter {iteration_id}') + # value = value_task.get() + # self.assertEqual(value, expected_value) + # task_graph.close() + # task_graph.join() + # task_graph = None + + # # reset run + # del _return_value_once.executed + # for iteration_id in range(n_iterations): + # LOGGER.debug(iteration_id) + # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0, 0) + # expected_value = 'transient run' + # if iteration_id == 0: + # value_task = task_graph.add_task( + # func=_return_value_once, + # transient_run=True, + # store_result=True, + # args=(expected_value,), + # task_name='first re-run transient') + # value = value_task.get() + # self.assertEqual(value, expected_value) + # task_graph.close() + # task_graph.join() + # else: + # with self.assertRaises(RuntimeError): + # value_task = task_graph.add_task( + # func=_return_value_once, + # transient_run=True, + # store_result=True, + # args=(expected_value,), + # task_name=f'expected error {iteration_id}') + + # value = value_task.get() + + # with self.assertRaises(RuntimeError): + # task_graph.join() + + # task_graph = None + + # def test_malformed_taskgraph_database(self): + # """TaskGraph: Test an empty task.""" + # db_schema_test_list = [ + # ''' + # CREATE TABLE taskgraph_data ( + # bad_name_1 TEXT NOT NULL, + # bad_name_2 BLOB NOT NULL, + # bad_name_3 BLOB NOT NULL); + # ''', + # ''' + # CREATE TABLE taskgraph_data ( + # task_reexecution_hash TEXT NOT NULL, + # target_path_stats BLOB NOT NULL); + # ''', + # ''' + # CREATE TABLE bad_table_name ( + # task_reexecution_hash TEXT NOT NULL, + # target_path_stats BLOB NOT NULL, + # result BLOB NOT NULL, + # PRIMARY KEY (task_reexecution_hash)); + # ''' + # ] + + # for db_schema in db_schema_test_list: + # database_path = os.path.join( + # self.workspace_dir, taskgraph._TASKGRAPH_DATABASE_FILENAME) + # if os.path.exists(database_path): + # os.remove(database_path) + # connection = sqlite3.connect(database_path) + # cursor = connection.cursor() + # cursor.executescript(db_schema) + # cursor.close() + # connection.commit() + # connection.close() + + # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) + # _ = task_graph.add_task() + # task_graph.close() + # task_graph.join() + # del task_graph + + # expected_column_name_list = [ + # 'task_reexecution_hash', 'target_path_stats', 'result'] + # connection = sqlite3.connect(database_path) + # cursor = connection.cursor() + # cursor.execute('PRAGMA table_info(taskgraph_data)') + # result = list(cursor.fetchall()) + # cursor.close() + # connection.commit() + # connection.close() + # for header_line in result: + # column_name = header_line[1] + # if column_name not in expected_column_name_list: + # raise ValueError( + # f'unexpected column name {column_name} in ' + # 'taskgraph_data ') + # self.assertEqual(len(result), len(expected_column_name_list)) + + # def test_terminate_log(self): + # """TaskGraph: test that the logger thread terminates on .join.""" + # task_graph = taskgraph.TaskGraph(self.workspace_dir, 1, 5.0) + # _ = task_graph.add_task() + # task_graph.join() + + # # logger should not terminate until after join, give it enough time + # # to have a chance to close, but not so long the test hangs + # task_graph._logging_monitor_thread.join(0.1) + # self.assertTrue(task_graph._logging_monitor_thread.is_alive()) + # task_graph._execution_monitor_thread.join(0.1) + # self.assertTrue(task_graph._execution_monitor_thread.is_alive()) + + # task_graph.close() + # task_graph.join() + + # # 5 seconds should be way too much time to expect the thread to join + # task_graph._logging_monitor_thread.join(5) + # self.assertFalse(task_graph._logging_monitor_thread.is_alive()) + # task_graph._execution_monitor_thread.join(5) + # self.assertFalse(task_graph._execution_monitor_thread.is_alive()) + + # def test_dictionary_arguments(self): + # """TaskGraph: test that large dictionary arguments behave well.""" + # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) + # dict_arg = {} + # x = {None: None} + # for _ in range(10000): + # dict_arg[_] = x + + # def my_op(dict_arg): + # pass + # task_graph.add_task( + # func=my_op, args=(), kwargs={'dict_arg': dict_arg}) + # task_graph.join() + # self.assertTrue(True, 'no memory error so everything is fine') + + # def test_filter_non_files(self): + # """TaskGraph: test internal filter non-files function.""" + # from taskgraph.Task import _filter_non_files + # from taskgraph.Task import _normalize_path + + # # Test a passthrough + # test_dict = { + # 0: {'one': 0, 'two': 1, 'three': 2}, + # 1: {'one': 1, 'two': 2, 'three': 3}, + # 2: {'one': 2, 'two': 3, 'three': 4}} + # self.assertEqual( + # test_dict, _filter_non_files(test_dict, [], [], False)) + + # # Test combination of files, not existing files, and flags in the + # # call + # test_file_a_exists = _normalize_path(os.path.join( + # self.workspace_dir, 'exists_a.txt')) + # pathlib.Path(test_file_a_exists).touch() + # test_file_b_exists = _normalize_path(os.path.join( + # self.workspace_dir, 'exists_b.txt')) + # pathlib.Path(test_file_b_exists).touch() + # test_file_not_a_exists = _normalize_path(os.path.join( + # self.workspace_dir, 'does_not_exist_a.txt')) + # test_file_not_b_exists = _normalize_path(os.path.join( + # self.workspace_dir, 'does_not_exist_b.txt')) + + # test_dict = { + # 0: {'one': 0, 'two': 1, 'three': 2}, + # 1: {'one': 1, 'two': 2, 'three': 3}, + # 2: {'one': 2, 'two': 3, 'three': 4}, + # 4: {'bar': test_file_not_a_exists}, + # 5: {'foo': test_file_a_exists}, + # 6: test_file_b_exists, + # 7: test_file_not_b_exists, + # 8: _normalize_path(self.workspace_dir)} + + # expected_result_dict = { + # 0: {'one': 0, 'two': 1, 'three': 2}, + # 1: {'one': 1, 'two': 2, 'three': 3}, + # 2: {'one': 2, 'two': 3, 'three': 4}, + # 4: {'bar': test_file_not_a_exists}, + # 5: {'foo': None}, + # 6: test_file_b_exists, + # 7: None, + # 8: _normalize_path(self.workspace_dir)} + + # self.assertEqual( + # _filter_non_files( + # test_dict, + # [test_file_b_exists], + # [test_file_not_b_exists], + # True), + # expected_result_dict) + + # # and test same as above but don't keep directories: + # expected_result_dict[8] = None + # self.assertEqual( + # _filter_non_files( + # test_dict, + # [test_file_b_exists], + # [test_file_not_b_exists], + # False), + # expected_result_dict) + + # def test_duplicate_task_hang_on_exit(self): + # """TaskGraph: ensure duplicate tasks don't cause taskgraph to hang.""" + # task_graph = taskgraph.TaskGraph(self.workspace_dir, 1) + # target_path = os.path.join(self.workspace_dir, 'target.txt') + # content = 'test' + # for _ in range(10): + # _ = task_graph.add_task( + # func=_create_file, + # args=(target_path, content), + # target_path_list=[target_path], + # task_name='create content') + # task_graph.join() + # task_graph.close() + + # def test_history_rst_format(self): + # """TaskGraph: ensure HISTORY.rst is correctly formatted.""" + # # ensure there are no errors when checking the history file + # history_filepath = os.path.join( + # os.path.dirname(__file__), '..', 'HISTORY.rst') + # subprocess.check_call(['rstcheck', history_filepath]) + + # def test_mtime_mismatch(self): + # """TaskGraph: ensure re-run when file mtimes don't match. + + # This test addresses the issue described under the github issue + # https://github.com/natcap/taskgraph/issues/70. + # """ + # target_path = os.path.join(self.workspace_dir, 'target.txt') + + # # SETUP: When we call 3 similar graphs in rapid succession, the file's + # # mtime is not precise enough to detect that the file has actually + # # changed. The specific conditions here are: + # # * The task "test text" has already been computed once + # # * The file written by the first "test text" is replaced by content + # # of the same filesize (thus fooling the size part of sizetimestamp) + # # * The graphs are executed fast enough that _is_precalculated's mtime + # # check via math.isclose() couldn't detect the recalculation. + # for content in ('test text', 'TEST TEXT', 'test text'): + # task_graph = taskgraph.TaskGraph(self.workspace_dir, n_workers=-1) + # _ = task_graph.add_task( + # func=_create_file, + # args=(target_path, content), + # target_path_list=[target_path], + # task_name='create content') + # task_graph.join() + # task_graph.close() + + # with open(target_path) as target_file: + # self.assertEqual(target_file.read(), content) def Fail(n_tries, result_path): From 9b53e026f66391488458b00be46f049b7203487d Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Fri, 16 Oct 2026 16:47:00 -0700 Subject: [PATCH 004/112] work in progress: taskgraph mostly working with parsl --- taskgraph/Task.py | 1056 +++++++++++++++++---------- taskgraph/_test_fixtures.py | 129 ++++ tests/test_task.py | 1363 +++++++++++++++-------------------- 3 files changed, 1363 insertions(+), 1185 deletions(-) create mode 100644 taskgraph/_test_fixtures.py diff --git a/taskgraph/Task.py b/taskgraph/Task.py index e0fc4d8b..40764434 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -1,5 +1,7 @@ """Task graph framework.""" import collections +from concurrent.futures import Future +from functools import lru_cache import hashlib import inspect import logging @@ -11,9 +13,9 @@ import pickle import pprint import queue -import sqlite3 import threading import time +import types from importlib.metadata import PackageNotFoundError from importlib.metadata import version @@ -28,19 +30,13 @@ from parsl.dataflow.memoization import BasicMemoizer, id_for_memo from parsl.dataflow.futures import AppFuture from parsl.executors import HighThroughputExecutor +from parsl.executors import ThreadPoolExecutor from parsl.monitoring.monitoring import MonitoringHub from parsl.addresses import address_by_hostname LOGGER = logging.getLogger(__name__) - -# @id_for_memo.register(object) -# def id_for_memo_pickle(obj, output_ref=False): -# print('hashing', obj) -# return pickle.dumps(obj) - - try: __version__ = version('taskgraph') except PackageNotFoundError: @@ -69,100 +65,589 @@ _MAX_TIMEOUT = 5.0 # amount of time to wait for threads to terminate -@python_app(cache=True) -def wrap_func(func, *args, inputs=[], **kwargs): - func(*args, **kwargs) +def create_parsl_app( + cache, executor_label, ignore_for_cache=[], + parsl_resource_specification={}): + + @python_app( + cache=cache, + ignore_for_cache=ignore_for_cache, + executors=[executor_label]) + def wrap_func(func, *args, inputs=[], + parsl_resource_specification=parsl_resource_specification, + **kwargs): + return func(*args, **kwargs) + return wrap_func -# We want our processing pool to be nondeamonic so that workers could use -# multiprocessing if desired (deamonic processes cannot start new processes) -# the following bit of code to do this was taken from -# https://stackoverflow.com/a/8963618/42897 -class NoDaemonProcess(multiprocessing.Process): - """Make 'daemon' attribute always return False.""" - @property - def daemon(self): - """Return False indicating not a daemon process.""" - return False +def _null_func(): + """Use when func=None on add_task.""" + return None - @daemon.setter - def daemon(self, value): - """Do not allow daemon value to be overriden.""" - pass +FUNC_SOURCE_MAP = {} +def get_task_hash_info(func, args, kwargs, target_path_list): + """Return hash used to compare equality of task executions. -class NoDaemonContext(type(multiprocessing.get_context('spawn'))): - """From https://stackoverflow.com/a/8963618/42897. + Data used in the hash: - "As the current implementation of multiprocessing [3.7+] has been - extensively refactored to be based on contexts, we need to provide a - NoDaemonContext class that has our NoDaemonProcess as attribute. - [NonDaemonicPool] will then use that context instead of the default - one." "spawn" is chosen as default since that is the default and only - context option for Windows and is the default option for Mac OS as - well since 3.8. + - the __name__ of func, or '' if it has no __name__ attribute + - the source code of func as returned by inspect.getsource, or + '' if inspecting source isn't possible + - 'cleaned' args and kwargs: """ + try: + # memoize func source code because it's likely we'll import + # the same func many times and reflection is slow + if func not in FUNC_SOURCE_MAP: + FUNC_SOURCE_MAP[func] = inspect.getsource(func) + source_code = FUNC_SOURCE_MAP[func] + except (IOError, TypeError): + # many reasons for this, for example, frozen Python code won't + # have source code, so just leave blank + source_code = '' + + args_clean = [] + for index, arg in enumerate(args): + try: + scrubbed_value = _scrub_task_args(arg, target_path_list) + _ = pickle.dumps(scrubbed_value) + args_clean.append(scrubbed_value) + except TypeError: + LOGGER.warning( + "could not pickle argument at index %d (%s). " + "Skipping argument which means it will not be considered " + "when calculating whether inputs have been changed " + "on a successive run.", index, arg) + + kwargs_clean = {} + # iterate through sorted order so we get the same hash result with the + # same set of kwargs irrespective of the item dict order. + for key, arg in sorted(kwargs.items()): + try: + scrubbed_value = _scrub_task_args(arg, target_path_list) + _ = pickle.dumps(scrubbed_value) + kwargs_clean[key] = scrubbed_value + except TypeError: + LOGGER.warning( + "could not pickle kw argument %s (%s) scrubbed to %s. " + "Skipping argument which means it will not be considered " + "when calculating whether inputs have been changed " + "on a successive run.", key, arg, scrubbed_value) + + return { + 'func_name': getattr(func, '__name__', ''), + 'args_clean': args_clean, + 'kwargs_clean': kwargs_clean, + 'source_code_hash': hashlib.sha1( + source_code.encode('utf-8')).hexdigest(), + } + + +def get_task_hash(hash_info): + argument_hash_string = ':'.join([ + repr(hash_info[key]) + for key in sorted(hash_info.keys())]) + return hashlib.sha1( + argument_hash_string.encode('utf-8')).hexdigest() + + +# overwrite parsl's default function serializer to include the source code +# +# the LRU cache decorator must be applied closer to the id_for_memo_function call +# that the .register() call, so that the cache-decorated version is registered. +@id_for_memo.register(types.FunctionType) +@lru_cache() +def id_for_memo_function(f: types.FunctionType, output_ref: bool = False) -> bytes: + """Checkpoint a function based on its name, module, and source code.""" + try: + # memoize func source code because it's likely we'll import + # the same func many times and reflection is slow + if f not in FUNC_SOURCE_MAP: + FUNC_SOURCE_MAP[f] = inspect.getsource(f) + source_code = FUNC_SOURCE_MAP[f] + except (IOError, TypeError): + # many reasons for this, for example, frozen Python code won't + # have source code, so just leave blank + source_code = '' + return pickle.dumps(["types.FunctionType", f.__name__, f.__module__, source_code]) - Process = NoDaemonProcess +class TaskGraphMemoizer(BasicMemoizer): + """Memoizer is responsible for ensuring that identical work is not repeated. -class NonDaemonicPool(multiprocessing.pool.Pool): - """NonDaemonic Process Pool.""" + When a task is repeated, i.e., the same function is called with the same exact arguments, the + result from a previous execution is reused. `wiki `_ - def __init__(self, *args, **kwargs): - """Invoke super to set the context of Pool class explicitly.""" - kwargs['context'] = NoDaemonContext() - super(NonDaemonicPool, self).__init__(*args, **kwargs) + The memoizer implementation here does not collapse duplicate calls + at call time, but works **only** when the result of a previous + call is available at the time the duplicate call is made. + For instance:: -def _null_func(): - """Use when func=None on add_task.""" - return None + No advantage from Memoization helps + memoization here: here: + TaskA TaskB + | TaskA | + | | TaskA done (TaskB) + | | | (TaskB) + done | | + done | + done -def _initialize_logging_to_queue(logging_queue): - """Add a synchronized queue to a new process. + The memoizer creates a lookup table by hashing the function name + and its inputs, and storing the results of the function. - This is intended to be called as an initialization function to - ``multiprocessing.Pool`` to establish logging from a Pool worker to the - main python process via a multiprocessing Queue. + When a task is ready for launch, i.e., all of its arguments + have resolved, we add its hash to the task datastructure. - Args: - logging_queue (multiprocessing.Queue): The queue to use for passing - log records back to the main process. + """ - Returns: - None + hash_algorithm: str = 'sizetimestamp' + + def __init__(self, *, + checkpoint_period: Optional[str] = None, + checkpoint_mode: Literal['task_exit', 'periodic', 'dfk_exit', 'manual'] | None = None, + memoize: bool = True): + """Initialize the memoizer. + + KWargs: + + - checkpoint_files : sequence of str, optional + List of paths to checkpoint files to load. By default, all checkpoints from the + run directory will be restored. This is usually the right behaviour, but this + parameter allows that behaviour to be overridden. + See :func:`parsl.utils.get_all_checkpoints` and :func:`parsl.utils.get_last_checkpoint` + for helpers. + - checkpoint_period : str, optional + Time interval (in "HH:MM:SS") at which to checkpoint completed tasks. Only has an effect if + ``checkpoint_mode='periodic'``. + - checkpoint_mode : str, optional + Checkpoint mode to use, can be ``'dfk_exit'``, ``'task_exit'``, ``'periodic'`` or ``'manual'``. + If set to `None`, checkpointing will be disabled. Default is None. + - memoize : str, enable memoization or not. - """ - root_logger = logging.getLogger() + """ + self.checkpointed_tasks = 0 + + # this lock must be held when: + # * writing to any checkpoint files + # * interacting with self.checkpointable_tasks + self._checkpoint_lock = threading.Lock() + + self.checkpoint_mode = checkpoint_mode + self.checkpoint_period = checkpoint_period + + self.checkpointable_tasks: List[CheckpointCommand] = [] + + self._checkpoint_timer: Timer | None = None + self.memoize = memoize + + def is_precalculated( + self, task, target_hash_algorithm, func, args, kwargs, + target_path_list, ignore_path_list, ignore_directories): + """Return true if _call need not be invoked. + + If the task has been precalculated it will fetch the return result from + the previous run. + + Returns: + True if the Task's target paths exist in the same state as the + last recorded run at the time this function is called. It is + possible this value could change without running the Task if + input parameter file stats change. False otherwise. + + """ + # This gets a list of the files and their file stats that can be found + # in args and kwargs but ignores anything specifically targeted or + # an expected result. This will allow a task to change its hash in + # case a different version of a file was passed in. + # these are the stats of the files that exist that aren't ignored + try: + # memoize func source code because it's likely we'll import + # the same func many times and reflection is slow + if func not in FUNC_SOURCE_MAP: + FUNC_SOURCE_MAP[func] = inspect.getsource(func) + source_code = FUNC_SOURCE_MAP[func] + except (IOError, TypeError): + # many reasons for this, for example, frozen Python code won't + # have source code, so just leave blank + source_code = '' + + args_clean = [] + for index, arg in enumerate(args): + try: + scrubbed_value = _scrub_task_args(arg, target_path_list) + _ = pickle.dumps(scrubbed_value) + args_clean.append(scrubbed_value) + except TypeError: + LOGGER.warning( + "could not pickle argument at index %d (%s). " + "Skipping argument which means it will not be considered " + "when calculating whether inputs have been changed " + "on a successive run.", index, arg) + + kwargs_clean = {} + # iterate through sorted order so we get the same hash result with the + # same set of kwargs irrespective of the item dict order. + for key, arg in sorted(kwargs.items()): + try: + scrubbed_value = _scrub_task_args(arg, target_path_list) + _ = pickle.dumps(scrubbed_value) + kwargs_clean[key] = scrubbed_value + except TypeError: + LOGGER.warning( + "could not pickle kw argument %s (%s) scrubbed to %s. " + "Skipping argument which means it will not be considered " + "when calculating whether inputs have been changed " + "on a successive run.", key, arg, scrubbed_value) + + for target_path in target_path_list: + if not os.path.exists(_normalize_path(target_path)): + LOGGER.debug(f'Could not create a hash because {target_path} does not exist.') + return False + + target_file_stat_list = list(_get_file_stats( + base_value=target_path_list, + hash_algorithm=target_hash_algorithm, + ignore_list=[], + ignore_directories=ignore_directories)) + + non_target_file_stat_list = list(_get_file_stats( + base_value=[args, kwargs], + hash_algorithm=target_hash_algorithm, + ignore_list=target_path_list + ignore_path_list, + ignore_directories=ignore_directories)) + + other_arguments = _filter_non_files( + [args_clean, kwargs_clean], + target_path_list, + ignore_path_list, + ignore_directories) + + try: + mismatched_target_file_list = [] + # for path, hash_string in result_target_path_stats: + # if path not in target_path_list: + # mismatched_target_file_list.append( + # 'Recorded path not in target path list %s' % path) + # if not os.path.exists(path): + # mismatched_target_file_list.append( + # 'Path not found: %s' % path) + # continue + # elif target_hash_algorithm == 'exists': + # # this is the case where hash_algorithm == 'exists' but + # # we already know the file exists so we do nothing + # continue + # if target_hash_algorithm == 'sizetimestamp': + # size, modified_time, actual_path = [ + # x for x in hash_string.split('::')] + # if actual_path != path: + # mismatched_target_file_list.append( + # "Path names don't match\n" + # "cached: (%s)\nactual (%s)" % (path, actual_path)) + + # # Using nanosecond resolution for mtime (instead of the + # # usual float result of os.path.getmtime()) allows us to + # # precisely compare modification time because we're + # # comparing ints: st_mtime_ns always returns an int. + # # + # # Timestamp resolution: the python docs note that "many + # # filesystems do not provide nanosecond precision". + # # This is true (e.g. FAT, FAT32 timestamps are only + # # accurate to within 2 seconds), but the data read from the + # # filesystem will be consistent. This lets us know + # # whether the timestamp changed. This also means that, on + # # FAT filesystems, if a file is changed within 2s of its + # # creation time, we might not be able to detect it. This + # # is a weakness of FAT, not taskgraph. + # target_modified_time = os.stat(path).st_mtime_ns + # if not int(modified_time) == target_modified_time: + # mismatched_target_file_list.append( + # "Modified times don't match " + # "cached: (%f) actual: (%f)" % ( + # float(modified_time), target_modified_time)) + # continue + # target_size = os.path.getsize(path) + # if float(size) != target_size: + # mismatched_target_file_list.append( + # "File sizes don't match " + # "cached: (%s) actual: (%s)" % ( + # size, target_size)) + # else: + # target_hash = _hash_file(path, target_hash_algorithm) + # if hash_string != target_hash: + # mismatched_target_file_list.append( + # "File hashes are different. cached: (%s) " + # "actual: (%s)" % (hash_string, target_hash)) + if mismatched_target_file_list: + LOGGER.info( + "Task hash exists, but there are these mismatches: %s", + '\n'.join(mismatched_target_file_list)) + return False + + # # if kwargs contains an outputs parameter, that parameter is removed + # # and normalised differently - with output_ref set to True. + # # kwargs listed in ignore_for_cache will also be removed + filtered_kw = kwargs.copy() + # ignore_list = task['ignore_for_cache'] + + # LOGGER.debug("Ignoring these kwargs for checkpointing: %s", ignore_list) + # for k in ignore_list: + # LOGGER.debug("Ignoring kwarg %s", k) + # del filtered_kw[k] + + # add the file stat list to the already existing reexecution info + # dictionary that contains stats that should not change whether + # files have been created/updated/or not. + reexecution_info = { + 'func_name': getattr(func, '__name__', ''), + 'args_clean': args_clean, + 'kwargs_clean': kwargs_clean, + 'source_code_hash': hashlib.sha1( + source_code.encode('utf-8')).hexdigest(), + 'target_file_stat_list': target_file_stat_list, + 'non_target_file_stat_list': non_target_file_stat_list, + 'other_arguments': other_arguments, + 'non_outputs': get_task_hash_info( + task['func'], + task['args'], + filtered_kw, + task['kwargs']['_taskgraph_task_info']['target_path_list']), + 'filtered_kw': filtered_kw, + 'func': task['func'], + 'args': task['args'] + } + + binary_reexecution_info = id_for_memo(reexecution_info) + + if 'outputs' in task['kwargs']: + del filtered_kw['outputs'] + reexecution_info['outputs'] = task['kwargs']['outputs'] + binary_reexecution_info += id_for_memo( + task['kwargs']['outputs'], output_ref=True) + + hashsum = hashlib.md5(binary_reexecution_info).hexdigest() + LOGGER.debug("Task {} has memoization hash {}".format(task['id'], hashsum)) + task['hashsum'] = hashsum + + checkpoint_dir = task['kwargs']['_taskgraph_task_info']['checkpoint_dir'] + if checkpoint_dir in self.memo_lookup_table: + if hashsum in self.memo_lookup_table[checkpoint_dir]: + return True + else: + return False + else: + LOGGER.info('task checkpoint dir has not been loaded') + return False + except EOFError: + LOGGER.exception("EOFError") + return False + + def check_memo(self, task: TaskRecord) -> Optional[Future[Any]]: + """Create a hash of the task and its inputs and check the lookup table for this hash. + + If present, the results are returned. + + Args: + - task(task) : task from the dfk.tasks table + + Returns: + - Result of the function if present in table, wrapped in a Future + + This call will also set task['hashsum'] to the unique hashsum for the func+inputs. + """ + task_id = task['id'] + + if not self.memoize or not task['memoize']: + task['hashsum'] = None + LOGGER.debug("Task {} will not be memoized".format(task_id)) + return None + + result = None + if self.is_precalculated( + task=task, + target_hash_algorithm=task['kwargs']['_taskgraph_task_info']['target_hash_algorithm'], + func=task['kwargs']['_taskgraph_task_info']['func'], + args=task['kwargs']['_taskgraph_task_info']['args'], + kwargs=task['kwargs']['_taskgraph_task_info']['kwargs'], + target_path_list=task['kwargs']['_taskgraph_task_info']['target_path_list'], + ignore_path_list=task['kwargs']['_taskgraph_task_info']['ignore_path_list'], + ignore_directories=task['kwargs']['_taskgraph_task_info']['ignore_directories']): + checkpoint_dir = task['kwargs']['_taskgraph_task_info']['checkpoint_dir'] + result = self.memo_lookup_table[checkpoint_dir][task['hashsum']] + LOGGER.info("Task %s using result from cache", task_id) + else: + LOGGER.info("Task %s had no result in cache", task_id) + + assert isinstance(result, Future) or result is None + return result + + def add_checkpoint_dir(self, checkpoint_dir): + self.memo_lookup_table[checkpoint_dir] = self.load_checkpoints( + get_all_checkpoints(checkpoint_dir)) + + def start(self, *, run_dir: str, config_run_dir: str) -> None: + + self.run_dir = run_dir + self.config_run_dir = config_run_dir + + if self.memoize: + LOGGER.info("App caching enabled") + self.memo_lookup_table = {} + else: + LOGGER.info("App caching disabled for all apps") + + if self.checkpoint_mode == "periodic": + if self.checkpoint_period is None: + raise ConfigurationError("Checkpoint period must be specified with periodic checkpoint mode") + else: + try: + h, m, s = map(int, self.checkpoint_period.split(':')) + except Exception: + raise ConfigurationError("invalid checkpoint_period provided: {0} expected HH:MM:SS".format(self.checkpoint_period)) + checkpoint_period = (h * 3600) + (m * 60) + s + self._checkpoint_timer = Timer(self.checkpoint_queue, interval=checkpoint_period, name="Checkpoint") + + def _load_checkpoints(self, checkpointDirs: Sequence[str]) -> Dict[str, Future[Any]]: + """Load a checkpoint file into a lookup table. + + The data being loaded from the pickle file mostly contains input + attributes of the task: func, args, kwargs, env... + To simplify the check of whether the exact task has been completed + in the checkpoint, we hash these input params and use it as the key + for the memoized lookup table. + + Args: + - checkpointDirs (list) : List of filepaths to checkpoints + Eg. ['runinfo/001', 'runinfo/002'] + + Returns: + - memoized_lookup_table (dict) + """ + memo_lookup_table = {} + + for checkpoint_dir in checkpointDirs: + LOGGER.info("Loading checkpoints from {}".format(checkpoint_dir)) + checkpoint_file = os.path.join(checkpoint_dir, 'tasks.pkl') + try: + with open(checkpoint_file, 'rb') as f: + while True: + try: + data = pickle.load(f) + # Copy and hash only the input attributes + memo_fu: Future = Future() + assert data['exception'] is None + memo_fu.set_result(data['result']) + memo_lookup_table[data['hash']] = memo_fu + + except EOFError: + # Done with the checkpoint file + break + except FileNotFoundError: + reason = "Checkpoint file was not found: {}".format( + checkpoint_file) + LOGGER.error(reason) + raise BadCheckpoint(reason) + except Exception as ex: + reason = "Failed to load checkpoint: {}".format( + checkpoint_file) + LOGGER.error(reason) + raise BadCheckpoint(reason) + + LOGGER.info("Completed loading checkpoint: {0} with {1} tasks".format(checkpoint_file, + len(memo_lookup_table.keys()))) + return memo_lookup_table + + def _update_memo(self, task: TaskRecord) -> None: + """Updates the memoization lookup table with the result from a task. + This doesn't move any values around but associates the memoization + hashsum with the completed (by success or failure) AppFuture. - # By the time this function is called, `root_logger` has a copy of all of - # the logging handlers registered to it within the parent process, which - # leads to duplicate logging in some cases. By removing all of the - # handlers here, we ensure that log messages can only be passed back to the - # parent process by the `logging_queue`, where they will be handled. - for handler in root_logger.handlers[:]: - root_logger.removeHandler(handler) + Args: + - task (TaskRecord) : A task record from dfk.tasks + """ + task_id = task['id'] + self.is_precalculated( + task=task, + target_hash_algorithm=task['kwargs']['_taskgraph_task_info']['target_hash_algorithm'], + func=task['kwargs']['_taskgraph_task_info']['func'], + args=task['kwargs']['_taskgraph_task_info']['args'], + kwargs=task['kwargs']['_taskgraph_task_info']['kwargs'], + target_path_list=task['kwargs']['_taskgraph_task_info']['target_path_list'], + ignore_path_list=task['kwargs']['_taskgraph_task_info']['ignore_path_list'], + ignore_directories=task['kwargs']['_taskgraph_task_info']['ignore_directories']) + + if not self.memoize or not task['memoize'] or 'hashsum' not in task: + return - root_logger.setLevel(logging.NOTSET) - handler = logging.handlers.QueueHandler(logging_queue) - root_logger.addHandler(handler) + if not isinstance(task['hashsum'], str): + LOGGER.error("Attempting to update app cache entry but hashsum is not a string key") + return + checkpoint_dir = task['kwargs']['_taskgraph_task_info']['checkpoint_dir'] + if task['hashsum'] in self.memo_lookup_table[checkpoint_dir]: + LOGGER.info(f"Replacing app cache entry {task['hashsum']} with result from task {task_id}") + else: + LOGGER.debug(f"Storing app cache entry {task['hashsum']} with result from task {task_id}") + self.memo_lookup_table[checkpoint_dir][task['hashsum']] = task['app_fu'] -def _logging_queue_monitor(logging_queue): - """Monitor ``logging_queue`` for message and pass to ``logger``.""" - LOGGER.debug('Starting logging worker') - while True: - record = logging_queue.get() - if record is None: - break - logger = logging.getLogger(record.name) - logger.handle(record) - LOGGER.debug('_logging_queue_monitor shutting down') + def _checkpoint_these_tasks(self, checkpoint_queue: List[CheckpointCommand]) -> None: + """Play a sequence of CheckpointCommands into a checkpoint file. + The checkpoint lock must be held when invoking this method. + """ + + # This checks that the lock is held, at least, but does not check that + # it is held by the current thread - threading.Lock does not have a + # concept of locking thread for threading.Lock. + assert self._checkpoint_lock.locked(), "checkpoint system should be locked" + + count = 0 + + for cc in checkpoint_queue: + if cc.exception is None: + checkpoint_dir = pathlib.Path(cc.task_record['kwargs'][ + '_taskgraph_task_info']['checkpoint_dir'] + ) / 'all_runs' / 'checkpoint' + if not os.path.exists(checkpoint_dir): + os.makedirs(checkpoint_dir, exist_ok=True) + checkpoint_filepath = checkpoint_dir / 'tasks.pkl' + with open(checkpoint_filepath, 'ab') as f: + hashsum = cc.task_record['hashsum'] + if not hashsum: + continue + t = {'hash': hashsum, 'exception': None, 'result': cc.result} + + # We are using pickle here since pickle dumps to a file in 'ab' + # mode behave like a incremental log. + pickle.dump(t, f) + count += 1 + LOGGER.debug("Task %s checkpointed", cc.task_record['id']) + + self.checkpointed_tasks += count + + if count == 0: + if self.checkpointed_tasks == 0: + LOGGER.warning("No tasks checkpointed so far in this run. Please ensure caching is enabled") + else: + LOGGER.debug("No tasks checkpointed in this pass.") + else: + LOGGER.info("Done checkpointing {} tasks".format(count)) + + +config.app_cache = True +config.memoizer = TaskGraphMemoizer(checkpoint_mode='task_exit') +config.initialize_logging = False +config.monitoring = MonitoringHub( + hub_address='127.0.0.1', + monitoring_debug=True, + resource_monitoring_interval=10 +) +_DFK = parsl.load(config) class TaskGraph(object): @@ -218,30 +703,9 @@ def __init__( # keep track if the task graph has been forcibly terminated self._terminated = False - # If n_workers > 0 this will be a threading.Thread used to propagate - # log records from another process into the current process. - self._logging_monitor_thread = None - - # If n_workers > 0, this will be a multiprocessing.Queue used to pass - # log records from the process pool to the parent process. - self._logging_queue = None - # keeps track of the tasks currently being processed for logging. self._active_task_list = [] - # keeps track of how many tasks have all their dependencies satisfied - # and are waiting for a worker - self._task_waiting_count = 0 - - # executor threads wait on this event that gets set when new tasks are - # added to the queue. If the queue is empty an executor will clear - # the event to halt other executors - self._executor_ready_event = threading.Event() - - # tasks that have all their dependencies satisfied go in this queue - # and can be executed immediately - self._task_ready_priority_queue = queue.PriorityQueue() - # maps a list of task names that need to be executed before the key # task can self._task_dependent_map = collections.defaultdict(set) @@ -252,33 +716,30 @@ def __init__( # tasks that complete are added to this set self._completed_task_names = set() - print('loading config') - config.app_cache = True - config.memoizer = BasicMemoizer( - checkpoint_mode='task_exit', - checkpoint_files=get_all_checkpoints( - self._taskgraph_cache_dir_path)) - config.run_dir = taskgraph_cache_dir_path - config.initialize_logging = False - config.monitoring = MonitoringHub( - hub_address='127.0.0.1', - monitoring_debug=True, - resource_monitoring_interval=10 - ) - print('create pool') - self.pool = ParslPoolExecutor( - config=config, - executors=[ - HighThroughputExecutor( - label="local_htex", - cores_per_worker=1, - max_workers_per_node=4, - address='127.0.0.1' - ) - ] - ) - print('config loaded') - print('pool', self.pool, type(self.pool)) + _DFK.config.memoizer.add_checkpoint_dir(self._taskgraph_cache_dir_path) + + if n_workers > 0: + self.executor_label = "htex" + executor = HighThroughputExecutor( + label=self.executor_label, + cores_per_worker=1, + max_workers_per_node=n_workers, + address='127.0.0.1') + elif n_workers == 0: + self.executor_label = "threaded" + executor = ThreadPoolExecutor( + label=self.executor_label, + max_threads=2) + else: + self.executor_label = "threaded" + executor = ThreadPoolExecutor( + label=self.executor_label, + max_threads=1) + + _DFK.add_executors([executor]) + self.pool = ParslPoolExecutor(dfk=_DFK) + + print('executors:', _DFK.executors) # no need to set up schedulers if n_workers is single threaded self._n_workers = n_workers @@ -300,13 +761,6 @@ def __init__( # set up multiprocessing if n_workers > 0 if n_workers > 0: - self._logging_queue = multiprocessing.Queue() - self._logging_monitor_thread = threading.Thread( - target=_logging_queue_monitor, - args=(self._logging_queue,)) - - self._logging_monitor_thread.daemon = True - self._logging_monitor_thread.start() if HAS_PSUTIL: parent = psutil.Process() parent.nice(PROCESS_LOW_PRIORITY) @@ -317,11 +771,10 @@ def __init__( LOGGER.warning( "NoSuchProcess exception encountered when trying " "to nice %s. This might be a bug in `psutil` so " - "it should be okay to ignore.")\ + "it should be okay to ignore.") def __del__(self): """Ensure all threads have been joined for cleanup.""" - print('__del__') self._terminate() def add_task( @@ -329,8 +782,7 @@ def add_task( target_path_list=None, ignore_path_list=None, hash_target_files=True, dependent_task_list=None, ignore_directories=True, priority=0, - hash_algorithm='sizetimestamp', transient_run=False, - store_result=False): + hash_algorithm='sizetimestamp', transient_run=False): """Add a task to the task graph. Args: @@ -392,9 +844,6 @@ def add_task( some sort of initialization that's needed every time a TaskGraph is instantiated. Perhaps to acquire dynamic resources or authenticate permissions. - store_result (bool): If True, the result of ``func`` will be stored - in the TaskGraph database and retrievable with a call to - ``.get()`` on a ``Task`` object. Returns: Task which was just added to the graph or an existing Task that @@ -442,26 +891,21 @@ def add_task( "Objects passed to dependent task list that are not " "tasks: %s", dependent_task_list) - task_name = '%s (%d)' % (task_name, len(self._task_hash_map)) - new_task = Task( - task_name, func, args, kwargs, target_path_list, - ignore_path_list, hash_target_files, ignore_directories, - transient_run, - priority, hash_algorithm, store_result, - dependent_task_list) + hash = get_task_hash(get_task_hash_info( + func, args, kwargs, target_path_list)) - self._task_name_map[new_task.task_name] = new_task # it may be this task was already created in an earlier call, # use that object in its place - if new_task in self._task_hash_map: - duplicate_task = self._task_hash_map[new_task] - new_task_target_set = set(new_task._target_path_list) + if hash in self._task_hash_map: + duplicate_task = self._task_hash_map[hash] + new_task_target_set = set(sorted([ + _normalize_path(path) for path in target_path_list])) duplicate_task_target_set = set( duplicate_task._target_path_list) if new_task_target_set == duplicate_task_target_set: LOGGER.warning( - "A duplicate task was submitted: %s original: %s", - new_task, self._task_hash_map[new_task]) + "A duplicate task of %s was submitted", + self._task_hash_map[hash]) self._added_task_count -= 1 return duplicate_task disjoint_target_set = ( @@ -487,13 +931,25 @@ def add_task( "as another task, but only partially different " "expected target paths. This runs the risk of " "unpredictably overwriting output so treating as " - "a runtime error: submitted task: %s, existing " - "task: %s" % (new_task, duplicate_task)) - self._task_hash_map[new_task] = new_task + "a runtime error. Existing task: %s" % (duplicate_task)) + + task_name = '%s (%d)' % (task_name, len(self._task_hash_map)) + new_task = Task( + task_name, func, args, kwargs, target_path_list, + ignore_path_list, hash_target_files, ignore_directories, + transient_run, + priority, hash_algorithm, + dependent_task_list, + checkpoint_dir=self._taskgraph_cache_dir_path, + executor_label=self.executor_label) + + self._task_name_map[new_task.task_name] = new_task + self._task_hash_map[hash] = new_task if self._n_workers < 0: # call directly if single threaded new_task._call() else: + print('checking status') # determine if task is ready or is dependent on other # tasks LOGGER.debug( @@ -506,9 +962,6 @@ def add_task( if not outstanding_dep_task_name_list: LOGGER.debug( "sending task %s right away", new_task.task_name) - self._task_ready_priority_queue.put(new_task) - self._task_waiting_count += 1 - self._executor_ready_event.set() else: # there are unresolved tasks that the waiting # process scheduler has not been notified of. @@ -520,6 +973,7 @@ def add_task( # record tasks that new_task depends on self._dependent_task_map[new_task.task_name].add( dep_task_name) + print('returning', new_task) return new_task except Exception: @@ -530,7 +984,6 @@ def add_task( self._terminate() raise - except Exception: # something went wrong, shut down the taskgraph LOGGER.exception( @@ -556,7 +1009,6 @@ def _execution_monitor(self, monitor_wait_event): if self._terminated: break active_task_count = len(self._active_task_list) - queue_length = self._task_ready_priority_queue.qsize() active_task_message = '\n'.join( ['\t%s: executing for %.2fs' % ( task_name, time.time() - task_time) @@ -571,10 +1023,9 @@ def _execution_monitor(self, monitor_wait_event): LOGGER.info( "\n\ttaskgraph execution status: tasks added: %d \n" "\ttasks complete: %d (%.1f%%) \n" - "\ttasks waiting for a free worker: %d (qsize: %d)\n" "\ttasks executing (%d): graph is %s\n%s", self._added_task_count, completed_tasks, percent_complete, - self._task_waiting_count, queue_length, active_task_count, + active_task_count, 'closed' if self._closed else 'open', active_task_message) @@ -595,14 +1046,13 @@ def join(self, timeout=None): True if successful join, False if timed out. """ - print('joining taskgraph') + print('join') LOGGER.debug("joining taskgraph") try: LOGGER.debug("attempting to join threads") for task in self._task_hash_map.values(): try: - print('computing task', task) task.join(timeout=timeout) except TimeoutError: LOGGER.info( @@ -611,7 +1061,6 @@ def join(self, timeout=None): if self._closed: # Close down the taskgraph; ok if already terminated - self._executor_ready_event.set() self._terminate() return True @@ -629,37 +1078,25 @@ def join(self, timeout=None): def close(self): """Prevent future tasks from being added to the work queue.""" - print('closing task graph') LOGGER.debug("Closing taskgraph.") if self._closed: return self._closed = True # this wakes up all the executors and any that wouldn't otherwise # have work to do will see there are no tasks left and terminate - self._executor_ready_event.set() LOGGER.debug("taskgraph closed") def _terminate(self): """Immediately terminate remaining task graph computation.""" - print('terminating task graph') LOGGER.debug( "Invoking terminate. already terminated? %s", self._terminated) if self._terminated: return - print('shut down pool') - print(self.pool) self.pool.shutdown() try: # it's possible the global state is not well defined, so just in # case we'll wrap it all up in a try/except self._terminated = True - if self._executor_ready_event is not None: - # alert executors to check that _terminated is True - self._executor_ready_event.set() - - # This will terminate the logging worker - if self._logging_queue is not None: - self._logging_queue.put(None) LOGGER.debug('taskgraph terminated') except Exception: @@ -674,7 +1111,7 @@ def __init__( self, task_name, func, args, kwargs, target_path_list, ignore_path_list, hash_target_files, ignore_directories, transient_run, priority, hash_algorithm, - store_result, dependent_task_list): + dependent_task_list, checkpoint_dir, executor_label): """Make a Task. Args: @@ -704,8 +1141,6 @@ def __init__( to the same object it will not be re-run in any scenario. Otherwise if False, subsequent tasks with an identical execution hash will be skipped. - worker_pool (multiprocessing.Pool): if not None, is a - multiprocessing pool that can be used for ``_call`` execution. priority (numeric): the priority of a task is considered when there is more than one task whose dependencies have been met and are ready for scheduling. Tasks are inserted into the @@ -719,20 +1154,6 @@ def __init__( of any files found in the arguments. If 'exists' will be considered the same file only if a file with the same filename exists on disk. - store_result (bool): If true, the result of ``func`` will be - stored in the TaskGraph database and retrievable with a call - to ``.get()`` on the Task object. - task_database_path (str): path to an SQLITE database that has - table named "taskgraph_data" with the three fields: - task_hash TEXT NOT NULL, - target_path_stats BLOB NOT NULL - result BLOB NOT NULL - If a call is successful its hash is inserted/updated in the - table, the target_path_stats stores the base/target stats - for the target files created by the call and listed in - ``target_path_list``, and the result of ``func`` is stored in - ``result``. - """ # it is a common error to accidentally pass a non string as to the # target path list, this terminates early if so @@ -753,96 +1174,50 @@ def __init__( self._kwargs = kwargs self._ignore_path_list = [ _normalize_path(path) for path in ignore_path_list] - self._hash_target_files = hash_target_files self._ignore_directories = ignore_directories - self._transient_run = transient_run self._hash_algorithm = hash_algorithm - self._store_result = store_result - self.exception_object = None - - # invert the priority since sorting goes smallest to largest and we - # want more positive priority values to be executed first. - self._priority = -priority - - # Used to ensure only one attempt at executing and also a mechanism - # to see when Task is complete. This can be set if a Task finishes - # a _call and there are no more attempts at reexecution. - self.task_done_executing_event = threading.Event() + if not hash_target_files: + self._target_hash_algorithm = 'exists' + else: + self._target_hash_algorithm = self._hash_algorithm + + self._taskgraph_task_info = { + 'target_hash_algorithm': self._target_hash_algorithm, + 'func': self._func, + 'args': self._args, + 'kwargs': self._kwargs, + 'target_path_list': self._target_path_list, + 'ignore_path_list': self._ignore_path_list, + 'ignore_directories': self._ignore_directories, + 'checkpoint_dir': checkpoint_dir + } # These are used to store and later access the result of the call. self._result = None - # Calculate a hash based only on argument inputs. - try: - if not hasattr(Task, 'func_source_map'): - Task.func_source_map = {} - # memoize func source code because it's likely we'll import - # the same func many times and reflection is slow - if self._func not in Task.func_source_map: - Task.func_source_map[self._func] = ( - inspect.getsource(self._func)) - source_code = Task.func_source_map[self._func] - except (IOError, TypeError): - # many reasons for this, for example, frozen Python code won't - # have source code, so just leave blank - source_code = '' - - if not hasattr(self._func, '__name__'): - LOGGER.warning( - "function does not have a __name__ which means it will not " - "be considered when calculating a successive input has " - "been changed with another function without __name__.") - self._func.__name__ = '' - - args_clean = [] - for index, arg in enumerate(self._args): - try: - scrubbed_value = _scrub_task_args(arg, self._target_path_list) - _ = pickle.dumps(scrubbed_value) - args_clean.append(scrubbed_value) - except TypeError: - LOGGER.warning( - "could not pickle argument at index %d (%s). " - "Skipping argument which means it will not be considered " - "when calculating whether inputs have been changed " - "on a successive run.", index, arg) - - kwargs_clean = {} - # iterate through sorted order so we get the same hash result with the - # same set of kwargs irrespective of the item dict order. - for key, arg in sorted(self._kwargs.items()): - try: - scrubbed_value = _scrub_task_args(arg, self._target_path_list) - _ = pickle.dumps(scrubbed_value) - kwargs_clean[key] = scrubbed_value - except TypeError: - LOGGER.warning( - "could not pickle kw argument %s (%s) scrubbed to %s. " - "Skipping argument which means it will not be considered " - "when calculating whether inputs have been changed " - "on a successive run.", key, arg, scrubbed_value) + self._reexecution_info = get_task_hash_info( + self._func, self._args, self._kwargs, self._target_path_list) - self._reexecution_info = { - 'func_name': self._func.__name__, - 'args_clean': args_clean, - 'kwargs_clean': kwargs_clean, - 'source_code_hash': hashlib.sha1( - source_code.encode('utf-8')).hexdigest(), - } + self._task_id_hash = get_task_hash(self._reexecution_info) - argument_hash_string = ':'.join([ - repr(self._reexecution_info[key]) - for key in sorted(self._reexecution_info.keys())]) + parsl_resource_specification = {} + if executor_label == 'htex': + parsl_resource_specification['priority'] = priority - self._task_id_hash = hashlib.sha1( - argument_hash_string.encode('utf-8')).hexdigest() + wrap_func = create_parsl_app( + cache=not transient_run, + executor_label=executor_label, + ignore_for_cache=self._ignore_path_list, + parsl_resource_specification=parsl_resource_specification) - # this will get calculated when ``is_precalculated`` is invoked. - self._task_reexecution_hash = None + def inner_wrap_func(func, *args, _taskgraph_task_info=None, **kwargs): + return func(*args, **kwargs) self.future = wrap_func( - func, *args, **kwargs, + inner_wrap_func, func, *args, **kwargs, + _taskgraph_task_info=self._taskgraph_task_info, inputs=[task.future for task in dependent_task_list]) + print('got future', self.future) def __eq__(self, other): """Two tasks are equal if their hashes are equal.""" @@ -858,22 +1233,15 @@ def __ne__(self, other): """Inverse of __eq__.""" return not self.__eq__(other) - def __lt__(self, other): - """Less than based on priority.""" - return self._priority < other._priority - def __repr__(self): """Create a string representation of a Task.""" return "Task object %s:\n\n" % (id(self)) + pprint.pformat( { "task_name": self.task_name, - "priority": self._priority, "ignore_path_list": self._ignore_path_list, "ignore_directories": self._ignore_directories, "target_path_list": self._target_path_list, "task_id_hash": self._task_id_hash, - "task_reexecution_hash": self._task_reexecution_hash, - "exception_object": self.exception_object, "self._reexecution_info": self._reexecution_info, "self._result": self._result, }) @@ -883,24 +1251,18 @@ def _call(self, timeout=None): Precondition is that the Task dependencies are satisfied. - Sets the ``self.task_done_executing_event`` flag if execution is - successful. - Raises: RuntimeError if any target paths are not generated after the function call is complete. """ - self.future.result(timeout=timeout) + print('calling result with timeout', timeout) + self._result = self.future.result(timeout=timeout) - # check that the target paths exist and record stats for later - if not self._hash_target_files: - target_hash_algorithm = 'exists' - else: - target_hash_algorithm = self._hash_algorithm + # check that the target paths exist result_target_path_stats = list( _get_file_stats( - self._target_path_list, target_hash_algorithm, [], False)) + self._target_path_list, self._target_hash_algorithm, [], False)) result_target_path_set = set( [x[0] for x in result_target_path_stats]) target_path_set = set(self._target_path_list) @@ -911,19 +1273,12 @@ def _call(self, timeout=None): self.task_name, self._target_path_list, result_target_path_set)) - self.task_done_executing_event.set() LOGGER.debug("successful run on task %s", self.task_name) def join(self, timeout=None): """Block until task is complete, raise exception if runtime failed.""" - LOGGER.debug( - "joining %s done executing: %s", self.task_name, - self.task_done_executing_event) + LOGGER.debug("joining %s done executing: %s", self.task_name) self._call(timeout=timeout) - successful_wait = self.task_done_executing_event.wait(timeout) - if self.exception_object: - raise self.exception_object - return successful_wait def get(self, timeout=None): """Return the result of the ``func`` once it is ready. @@ -941,99 +1296,12 @@ def get(self, timeout=None): Raises: RuntimeError when ``timeout`` exceeded. - ValueError if ``store_result`` was set to ``False`` when the task - was created. """ - if not self._store_result: - raise ValueError( - 'must set `store_result` to True in `add_task` to invoke this ' - 'function') - timeout = not self.join(timeout) - if timeout: - raise RuntimeError('call to get timed out') + self.join(timeout) return self._result -@retrying.retry( - wait_exponential_multiplier=500, wait_exponential_max=3200, - stop_max_attempt_number=100) -def _execute_sqlite( - sqlite_command, database_path, argument_list=None, - mode='read_only', execute='execute', fetch=None): - """Execute SQLite command and attempt retries on a failure. - - Args: - sqlite_command (str): a well formatted SQLite command. - database_path (str): path to the SQLite database to operate on. - argument_list (list): ``execute == 'execute'`` then this list is passed - to the internal sqlite3 ``execute`` call. - mode (str): must be either 'read_only' or 'modify'. - execute (str): must be either 'execute' or 'script'. - fetch (str): if not ``None`` can be either 'all' or 'one'. - If not None the result of a fetch will be returned by this - function. - - Returns: - result of fetch if ``fetch`` is not None. - - """ - cursor = None - connection = None - try: - if mode == 'read_only': - ro_uri = r'%s?mode=ro' % pathlib.Path( - os.path.abspath(database_path)).as_uri() - LOGGER.debug( - '%s exists: %s', ro_uri, os.path.exists(os.path.abspath( - database_path))) - connection = sqlite3.connect(ro_uri, uri=True) - elif mode == 'modify': - connection = sqlite3.connect(database_path) - else: - raise ValueError('Unknown mode: %s' % mode) - - if execute == 'execute': - if argument_list is None: - cursor = connection.execute(sqlite_command) - else: - cursor = connection.execute(sqlite_command, argument_list) - elif execute == 'script': - cursor = connection.executescript(sqlite_command) - else: - raise ValueError('Unknown execute mode: %s' % execute) - - result = None - payload = None - if fetch == 'all': - payload = (cursor.fetchall()) - elif fetch == 'one': - payload = (cursor.fetchone()) - elif fetch is not None: - raise ValueError('Unknown fetch mode: %s' % fetch) - if payload is not None: - result = list(payload) - cursor.close() - connection.commit() - connection.close() - cursor = None - connection = None - return result - except sqlite3.OperationalError: - LOGGER.warning( - 'TaskGraph database is locked because another process is using ' - 'it, waiting for a bit of time to try again') - raise - except Exception: - LOGGER.exception('Exception on _execute_sqlite: %s', sqlite_command) - raise - finally: - if cursor is not None: - cursor.close() - if connection is not None: - connection.commit() - connection.close() - def _get_file_stats( base_value, hash_algorithm, ignore_list, ignore_directories): @@ -1163,6 +1431,20 @@ def _scrub_task_args(base_value, target_path_list): This function can be called before the Task dependencies are satisfied since it doesn't inspect any file stats on disk. + Attempts to standardize task arguments according to these rules: + - if value is callable, return a string containing the __name__ and + source code as returned by inspect.getsource + - if value is a dict, return a copy of the dict where each value is + replaced with the result of calling _scrub_task_args on the original + value + - if value is a list, set, or tuple, return a copy of where each value is + replaced with the result of calling _scrub_task_args on the original + value + - if value is a string or pathlib.PurePath, attempt to convert into + a normalized, normcase, absolute filepath string. if this normalized + string exists in `target_path_list`, return 'in_target_path_list' + - otherwise, return the value unchanged + Args: base_value: any python value target_path_list (list): a list of strings that if found in @@ -1175,15 +1457,13 @@ def _scrub_task_args(base_value, target_path_list): """ if callable(base_value): try: - if not hasattr(Task, 'func_source_map'): - Task.func_source_map = {} # memoize func source code because it's likely we'll import # the same func many times and reflection is slow - if base_value not in Task.func_source_map: - Task.func_source_map[base_value] = ( + if base_value not in FUNC_SOURCE_MAP: + FUNC_SOURCE_MAP[base_value] = ( inspect.getsource(base_value)).replace( ' ', '').replace('\t', '') - source_code = Task.func_source_map[base_value] + source_code = FUNC_SOURCE_MAP[base_value] except (IOError, TypeError): # many reasons for this, for example, frozen Python code won't # have source code, so just leave blank diff --git a/taskgraph/_test_fixtures.py b/taskgraph/_test_fixtures.py new file mode 100644 index 00000000..7459cbf3 --- /dev/null +++ b/taskgraph/_test_fixtures.py @@ -0,0 +1,129 @@ +import logging +import pickle +import shutil +import time + + +def _return_value_once(value): + """Return the value passed to it only once.""" + if hasattr(_return_value_once, 'executed'): + raise RuntimeError("this function was called twice") + _return_value_once.executed = True + return value + + +def _noop_function(**kwargs): + """Do nothing except allow kwargs to be passed.""" + pass + + +def _long_running_function(delay): + """Wait for ``delay`` seconds.""" + print('start') + time.sleep(delay) + print('done') + + +def _create_two_files_on_disk(value, target_a_path, target_b_path): + """Create two files and write ``value`` and append if possible.""" + with open(target_a_path, 'a') as a_file: + a_file.write(value) + + with open(target_b_path, 'a') as b_file: + b_file.write(value) + + +def _merge_and_append_files(base_a_path, base_b_path, target_path): + """Merge two files and append if possible to new file.""" + with open(target_path, 'a') as target_file: + for base_path in [base_a_path, base_b_path]: + with open(base_path, 'r') as base_file: + target_file.write(base_file.read()) + + +def _create_list_on_disk(value, length, target_path=None): + """Create a numpy array on disk filled with value of ``size``.""" + target_list = [value] * length + pickle.dump(target_list, open(target_path, 'wb')) + + +def _call_it(target, *args): + """Invoke ``target`` with ``args``.""" + target(*args) + + +def _append_val(path, *val): + """Append a ``val`` to file at ``path``.""" + with open(path, 'a') as target_file: + for v in val: + target_file.write(str(v)) + + +_append_val_2 = _append_val + + +def _sum_lists_from_disk(list_a_path, list_b_path, target_path): + """Read two lists, add them and save result.""" + list_a = pickle.load(open(list_a_path, 'rb')) + list_b = pickle.load(open(list_b_path, 'rb')) + target_list = [] + for a, b in zip(list_a, list_b): + target_list.append(a+b) + pickle.dump(target_list, open(target_path, 'wb')) + + +def _div_by_zero(): + """Divide by zero to raise an exception.""" + return 1/0 + + +def _create_file(target_path, content): + """Create a file with contents.""" + with open(target_path, 'w') as target_file: + target_file.write(content) + + +def _create_file_once(target_path, content): + """Create a file on the first call, raise an exception on the second.""" + if hasattr(_create_file_once, 'executed'): + raise RuntimeError("this function was called twice") + _create_file_once.executed = True + with open(target_path, 'w') as target_file: + target_file.write(content) + + +def _copy_file_once(base_path, target_path): + """Copy base to target on the first call, raise exception on second.""" + if hasattr(_copy_file_once, 'executed'): + raise RuntimeError("this function was called twice") + _copy_file_once.executed = True + shutil.copyfile(base_path, target_path) + + +def _copy_two_files_once(base_path, target_a_path, target_b_path): + """Copy base to target a/b on first call, raise exception on second.""" + if hasattr(_copy_two_files_once, 'executed'): + raise RuntimeError("this function was called twice") + _copy_two_files_once.executed = True + shutil.copyfile(base_path, target_a_path) + shutil.copyfile(base_path, target_b_path) + + +def _log_from_another_process(logger_name, log_message): + """Write a log message to a given logger. + + Args: + logger_name (string): The string logger name to which ``log_message`` + will be logged. + log_message (string): The string log message to be logged (at INFO + level) to the logger at ``logger_name``. + + Returns: + ``None`` + + """ + print('log from another') + logger = logging.getLogger(logger_name) + print('logging message', log_message) + logger.info(log_message) + print('logged') diff --git a/tests/test_task.py b/tests/test_task.py index 175225dd..a18cd898 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -8,15 +8,14 @@ import pickle import re import shutil -import sqlite3 import subprocess import tempfile -import time import unittest import parsl import retrying import taskgraph +from taskgraph import _test_fixtures LOGGER = logging.getLogger(__name__) @@ -24,123 +23,6 @@ MAX_TRY_WAIT_MS = 500 -def _return_value_once(value): - """Return the value passed to it only once.""" - if hasattr(_return_value_once, 'executed'): - raise RuntimeError("this function was called twice") - _return_value_once.executed = True - return value - - -def _noop_function(**kwargs): - """Do nothing except allow kwargs to be passed.""" - pass - - -def _long_running_function(delay): - """Wait for ``delay`` seconds.""" - time.sleep(delay) - - -def _create_two_files_on_disk(value, target_a_path, target_b_path): - """Create two files and write ``value`` and append if possible.""" - with open(target_a_path, 'a') as a_file: - a_file.write(value) - - with open(target_b_path, 'a') as b_file: - b_file.write(value) - - -def _merge_and_append_files(base_a_path, base_b_path, target_path): - """Merge two files and append if possible to new file.""" - with open(target_path, 'a') as target_file: - for base_path in [base_a_path, base_b_path]: - with open(base_path, 'r') as base_file: - target_file.write(base_file.read()) - - -def _create_list_on_disk(value, length, target_path=None): - """Create a numpy array on disk filled with value of ``size``.""" - target_list = [value] * length - pickle.dump(target_list, open(target_path, 'wb')) - - -def _call_it(target, *args): - """Invoke ``target`` with ``args``.""" - target(*args) - - -def _append_val(path, *val): - """Append a ``val`` to file at ``path``.""" - with open(path, 'a') as target_file: - for v in val: - target_file.write(str(v)) - - -def _sum_lists_from_disk(list_a_path, list_b_path, target_path): - """Read two lists, add them and save result.""" - list_a = pickle.load(open(list_a_path, 'rb')) - list_b = pickle.load(open(list_b_path, 'rb')) - target_list = [] - for a, b in zip(list_a, list_b): - target_list.append(a+b) - pickle.dump(target_list, open(target_path, 'wb')) - - -def _div_by_zero(): - """Divide by zero to raise an exception.""" - return 1/0 - - -def _create_file(target_path, content): - """Create a file with contents.""" - with open(target_path, 'w') as target_file: - target_file.write(content) - - -def _create_file_once(target_path, content): - """Create a file on the first call, raise an exception on the second.""" - if hasattr(_create_file_once, 'executed'): - raise RuntimeError("this function was called twice") - _create_file_once.executed = True - with open(target_path, 'w') as target_file: - target_file.write(content) - - -def _copy_file_once(base_path, target_path): - """Copy base to target on the first call, raise exception on second.""" - if hasattr(_copy_file_once, 'executed'): - raise RuntimeError("this function was called twice") - _copy_file_once.executed = True - shutil.copyfile(base_path, target_path) - - -def _copy_two_files_once(base_path, target_a_path, target_b_path): - """Copy base to target a/b on first call, raise exception on second.""" - if hasattr(_copy_two_files_once, 'executed'): - raise RuntimeError("this function was called twice") - _copy_two_files_once.executed = True - shutil.copyfile(base_path, target_a_path) - shutil.copyfile(base_path, target_b_path) - - -def _log_from_another_process(logger_name, log_message): - """Write a log message to a given logger. - - Args: - logger_name (string): The string logger name to which ``log_message`` - will be logged. - log_message (string): The string log message to be logged (at INFO - level) to the logger at ``logger_name``. - - Returns: - ``None`` - - """ - logger = logging.getLogger(logger_name) - logger.info(log_message) - - class TaskGraphTests(unittest.TestCase): """Tests for the taskgraph.""" @@ -173,6 +55,7 @@ def test_version_loaded(self): def test_single_task(self): """TaskGraph: Test a single task.""" + # from pudb import set_trace; set_trace() task_graph = taskgraph.TaskGraph(self.workspace_dir, 0, 0.1) # forcing this one to be unicode since there shouldn't be a problem # with that at all... @@ -180,7 +63,7 @@ def test_single_task(self): value = 5 list_len = 1000 _ = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value, list_len), kwargs={ 'target_path': target_path, @@ -194,23 +77,18 @@ def test_single_task(self): def test_task_hash_source_deleted(self): """TaskGraph: test if old target deleted when hashing duplicate.""" target_a_path = os.path.join(self.workspace_dir, 'a.txt') - print('creating task graph...') task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) - print('adding task...') task_a = task_graph.add_task( - func=_create_file, + func=_test_fixtures._create_file, args=(target_a_path, 'test value'), target_path_list=[target_a_path]) - print('joining task...') task_a.join() target_b_path = os.path.join(self.workspace_dir, 'b.txt') _ = task_graph.add_task( - func=_create_file, + func=_test_fixtures._create_file, args=(target_b_path, 'test value'), target_path_list=[target_b_path]) - print('close') task_graph.close() - print('join') task_graph.join() del task_graph @@ -220,7 +98,7 @@ def test_task_hash_source_deleted(self): target_c_path = os.path.join(self.workspace_dir, 'c.txt') task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) _ = task_graph.add_task( - func=_create_file, + func=_test_fixtures._create_file, args=(target_c_path, 'test value'), target_path_list=[target_c_path]) task_graph.close() @@ -239,13 +117,13 @@ def test_task_rel_vs_absolute(self): target_b_path = os.path.abspath(target_a_path) _ = task_graph.add_task( - func=_create_file, + func=_test_fixtures._create_file, args=(target_a_path, 'test value'), target_path_list=[target_a_path], task_name='task a') _ = task_graph.add_task( - func=_create_file, + func=_test_fixtures._create_file, args=(target_b_path, 'test value'), target_path_list=[target_b_path], task_name='task b') @@ -268,10 +146,11 @@ def test_timeout_task(self): """TaskGraph: Test timeout functionality.""" task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) _ = task_graph.add_task( - func=_long_running_function, + func=_test_fixtures._long_running_function, args=(5,)) task_graph.close() timedout = not task_graph.join(0.5) + print("timed out:", timedout) # this should timeout since function runs for 5 seconds self.assertTrue(timedout) @@ -282,7 +161,7 @@ def test_precomputed_task(self): value = 5 list_len = 1000 _ = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value, list_len), kwargs={ 'target_path': target_path, @@ -297,7 +176,7 @@ def test_precomputed_task(self): task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) _ = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value, list_len), kwargs={ 'target_path': target_path, @@ -322,21 +201,21 @@ def test_task_chain(self): value_b = 10 list_len = 10 task_a = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value_a, list_len), kwargs={ 'target_path': target_a_path, }, target_path_list=[target_a_path]) task_b = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value_b, list_len), kwargs={ 'target_path': target_b_path, }, target_path_list=[target_b_path]) sum_task = task_graph.add_task( - func=_sum_lists_from_disk, + func=_test_fixtures._sum_lists_from_disk, args=(target_a_path, target_b_path), kwargs={ 'target_path': result_path, @@ -349,7 +228,7 @@ def test_task_chain(self): self.assertEqual(result, [value_a+value_b]*list_len) sum_2_task = task_graph.add_task( - func=_sum_lists_from_disk, + func=_test_fixtures._sum_lists_from_disk, args=(target_a_path, result_path, result_2_path), target_path_list=[result_2_path], dependent_task_list=[task_a, sum_task]) @@ -359,7 +238,7 @@ def test_task_chain(self): self.assertEqual(result2, expected_result) sum_3_task = task_graph.add_task( - func=_sum_lists_from_disk, + func=_test_fixtures._sum_lists_from_disk, args=(target_a_path, result_path, result_2_path), target_path_list=[result_2_path], dependent_task_list=[task_a, sum_task]) @@ -381,7 +260,7 @@ def test_task_chain_single_thread(self): value_b = 10 list_len = 10 task_a = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value_a, list_len), kwargs={ 'target_path': target_a_path, @@ -389,7 +268,7 @@ def test_task_chain_single_thread(self): target_path_list=[target_a_path], task_name='task a') task_b = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value_b, list_len), kwargs={ 'target_path': target_b_path, @@ -397,7 +276,7 @@ def test_task_chain_single_thread(self): target_path_list=[target_b_path], task_name='task b') sum_task = task_graph.add_task( - func=_sum_lists_from_disk, + func=_test_fixtures._sum_lists_from_disk, args=(target_a_path, target_b_path), kwargs={ 'target_path': result_path, @@ -411,7 +290,7 @@ def test_task_chain_single_thread(self): self.assertEqual(result, [value_a+value_b]*list_len) sum_2_task = task_graph.add_task( - func=_sum_lists_from_disk, + func=_test_fixtures._sum_lists_from_disk, args=(target_a_path, result_path, result_2_path), target_path_list=[result_2_path], dependent_task_list=[task_a, sum_task], @@ -422,7 +301,7 @@ def test_task_chain_single_thread(self): self.assertEqual(result2, expected_result) sum_3_task = task_graph.add_task( - func=_sum_lists_from_disk, + func=_test_fixtures._sum_lists_from_disk, args=(target_a_path, result_path, result_2_path), target_path_list=[result_2_path], dependent_task_list=[task_a, sum_task], @@ -456,17 +335,17 @@ def test_task_broken_chain(self): value_a = 5 list_len = 10 task_a = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value_a, list_len), kwargs={ 'target_path': target_a_path, }, target_path_list=[target_a_path]) task_b = task_graph.add_task( - func=_div_by_zero, + func=_test_fixtures._div_by_zero, dependent_task_list=[task_a]) _ = task_graph.add_task( - func=_sum_lists_from_disk, + func=_test_fixtures._sum_lists_from_disk, args=(target_a_path, target_b_path), kwargs={ 'target_path': result_path, @@ -483,7 +362,7 @@ def test_broken_task(self): task_graph = taskgraph.TaskGraph(self.workspace_dir, 1) broken_task = task_graph.add_task( - func=_div_by_zero, task_name='test_broken_task') + func=_test_fixtures._div_by_zero, task_name='test_broken_task') with self.assertRaises(ZeroDivisionError): _ = broken_task.join() @@ -503,19 +382,19 @@ def test_broken_task_chain(self): target_path = os.path.join( self.workspace_dir, '1000_%d.dat' % task_id) normal_task = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value, list_len), kwargs={'target_path': target_path}, target_path_list=[target_path], task_name='create list on disk %d' % task_id) zero_div_task = task_graph.add_task( - func=_div_by_zero, + func=_test_fixtures._div_by_zero, dependent_task_list=[normal_task], task_name='test_broken_task_chain_%d' % task_id) target_path = os.path.join( self.workspace_dir, 'after_zerodiv_1000_%d.dat' % task_id) _ = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value, list_len), kwargs={'target_path': target_path}, dependent_task_list=[zero_div_task], @@ -554,7 +433,7 @@ def test_closed_graph(self): list_len = 1000 with self.assertRaises(ValueError): _ = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value, list_len), kwargs={'target_path': target_path}, target_path_list=[target_path]) @@ -567,7 +446,7 @@ def test_single_task_multiprocessing(self): value = 5 list_len = 1000 _ = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value, list_len), kwargs={ 'target_path': target_path, @@ -605,13 +484,13 @@ def test_get_file_stats(self): self.assertEqual(result, []) def test_transient_runs(self): - """TaskGraph: ensure that transent tasks reexecute.""" + """TaskGraph: ensure that transient tasks reexecute.""" task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) target_path = os.path.join(self.workspace_dir, '1000.dat') value = 5 list_len = 1000 _ = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value, list_len), kwargs={ 'target_path': target_path, @@ -624,7 +503,7 @@ def test_transient_runs(self): task_graph2 = taskgraph.TaskGraph(self.workspace_dir, -1) _ = task_graph2.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value, list_len), transient_run=True, kwargs={ @@ -645,7 +524,7 @@ def test_repeat_targeted_runs(self): value = 5 list_len = 1000 _ = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value, list_len), kwargs={ 'target_path': target_path, @@ -657,7 +536,7 @@ def test_repeat_targeted_runs(self): task_graph2 = taskgraph.TaskGraph(self.workspace_dir, -1) task = task_graph2.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value, list_len), kwargs={ 'target_path': target_path, @@ -674,17 +553,17 @@ def test_task_equality(self): value = 5 list_len = 1000 task_a = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value, list_len), kwargs={'target_path': target_path}, target_path_list=[target_path]) task_a_same = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value, list_len), kwargs={'target_path': target_path}, target_path_list=[target_path]) task_b = task_graph.add_task( - func=_create_list_on_disk, + func=_test_fixtures._create_list_on_disk, args=(value+1, list_len), kwargs={'target_path': target_path}, target_path_list=[target_path]) @@ -698,7 +577,7 @@ def test_async_logging(self): task_graph = taskgraph.TaskGraph( self.workspace_dir, 0, reporting_interval=0.5) _ = task_graph.add_task( - func=_long_running_function, + func=_test_fixtures._long_running_function, args=(1.0,)) task_graph.close() task_graph.join() @@ -712,18 +591,18 @@ def test_scrub(self): target_path = os.path.join(self.workspace_dir, 'a.txt') first_task = task_graph.add_task( - func=_append_val, + func=_test_fixtures._append_val, args=(target_path, 1, [1], {'x': 1}), task_name='first append') second_task = task_graph.add_task( - func=_append_val, + func=_test_fixtures._append_val, args=(target_path, 1, [1], {'x': 2}), dependent_task_list=[first_task], task_name='second append') _ = task_graph.add_task( - func=_append_val, + func=_test_fixtures._append_val, args=(target_path, 1, [2], {'x': 1}), dependent_task_list=[second_task], task_name='third append') @@ -742,12 +621,12 @@ def test_target_path_order(self): target_b_path = os.path.join(self.workspace_dir, 'b.txt') task_graph.add_task( - func=_create_two_files_on_disk, + func=_test_fixtures._create_two_files_on_disk, args=("word", target_a_path, target_b_path), target_path_list=[target_a_path, target_b_path]) task_graph.add_task( - func=_create_two_files_on_disk, + func=_test_fixtures._create_two_files_on_disk, args=("word", target_a_path, target_b_path), target_path_list=[target_b_path, target_a_path]) @@ -770,13 +649,13 @@ def test_task_hash_when_ready(self): target_b_path = os.path.join(self.workspace_dir, 'b.txt') create_files_task = task_graph.add_task( - func=_create_two_files_on_disk, + func=_test_fixtures._create_two_files_on_disk, args=("word", target_a_path, target_b_path), target_path_list=[target_a_path, target_b_path]) target_merged_path = os.path.join(self.workspace_dir, 'merged.txt') task_graph.add_task( - func=_merge_and_append_files, + func=_test_fixtures._merge_and_append_files, args=(target_a_path, target_b_path, target_merged_path), target_path_list=[target_merged_path], dependent_task_list=[create_files_task]) @@ -785,7 +664,7 @@ def test_task_hash_when_ready(self): # this second task shouldn't execute because it's a copy of the first task_graph.add_task( - func=_merge_and_append_files, + func=_test_fixtures._merge_and_append_files, args=(target_a_path, target_b_path, target_merged_path), target_path_list=[target_merged_path], dependent_task_list=[create_files_task]) @@ -798,565 +677,455 @@ def test_task_hash_when_ready(self): self.assertEqual(target_string, "wordword") - # def test_multiprocessed_logging(self): - # """TaskGraph: ensure tasks can log from multiple processes.""" - # logger_name = 'test.task.queuelogger' - # log_message = 'This is coming from another process' - # logger = logging.getLogger(logger_name) - # logger.setLevel(logging.DEBUG) - # file_log_path = os.path.join( - # self.workspace_dir, 'test_multiprocessed_logging.log') - # file_handler = logging.FileHandler(file_log_path) - # file_handler.setFormatter( - # logging.Formatter(fmt=':%(processName)s:%(message)s:')) - # logger.addHandler(file_handler) - - # task_graph = taskgraph.TaskGraph(self.workspace_dir, 1) - # log_task = task_graph.add_task( - # func=_log_from_another_process, - # args=(logger_name, log_message)) - # log_task.join() - # file_handler.flush() - # task_graph.close() - # task_graph.join() - # file_handler.close() - - # @retrying.retry(wait_exponential_multiplier=100, - # wait_exponential_max=1000, - # stop_max_attempt_number=5) - # def get_name_and_message(): - # with open(file_log_path, 'r') as log_file: - # message = log_file.read().rstrip() - # print(message) - # process_name, logged_message = re.match( - # ':([^:]*):([^:]*):', message).groups() - # return process_name, logged_message - - # process_name, logged_message = get_name_and_message() - # self.assertEqual(logged_message, log_message) - # self.assertNotEqual( - # process_name, multiprocessing.current_process().name) - - # def test_repeated_function(self): - # """TaskGraph: ensure no reruns if argument is a function.""" - # global _append_val - - # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) - # target_path = os.path.join(self.workspace_dir, 'testfile.txt') - # task_graph.add_task( - # func=_call_it, - # args=(_append_val, target_path, 1), - # target_path_list=[target_path], - # ignore_path_list=[target_path], - # task_name='first _call_it') - # task_graph.close() - # task_graph.join() - # del task_graph + def test_multiprocessed_logging(self): + """TaskGraph: ensure tasks can log from multiple processes.""" + logger_name = 'test.task.queuelogger' + log_message = 'This is coming from another process' + logger = logging.getLogger(logger_name) + logger.setLevel(logging.DEBUG) + file_log_path = os.path.join( + self.workspace_dir, 'test_multiprocessed_logging.log') + file_handler = logging.FileHandler(file_log_path) + file_handler.setFormatter( + logging.Formatter(fmt=':%(processName)s:%(message)s:')) + logger.addHandler(file_handler) - # # this causes the address to change - # def _append_val(path, *val): - # """Append a ``val`` to file at ``path``.""" - # with open(path, 'a') as target_file: - # for v in val: - # target_file.write(str(v)) + task_graph = taskgraph.TaskGraph(self.workspace_dir, 1) + log_task = task_graph.add_task( + func=_test_fixtures._log_from_another_process, + args=(logger_name, log_message)) + log_task.join() + file_handler.flush() + task_graph.close() + task_graph.join() + file_handler.close() + + @retrying.retry(wait_exponential_multiplier=100, + wait_exponential_max=1000, + stop_max_attempt_number=10) + def get_name_and_message(): + with open(file_log_path, 'r') as log_file: + message = log_file.read().rstrip() + print('message:', message) + process_name, logged_message = re.match( + ':([^:]*):([^:]*):', message).groups() + return process_name, logged_message + + process_name, logged_message = get_name_and_message() + self.assertEqual(logged_message, log_message) + self.assertNotEqual( + process_name, multiprocessing.current_process().name) + + def test_repeated_function(self): + """TaskGraph: ensure no reruns if argument is a function.""" + task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) + target_path = os.path.join(self.workspace_dir, 'testfile.txt') + task_graph.add_task( + func=_test_fixtures._call_it, + args=(_test_fixtures._append_val, target_path, 1), + target_path_list=[target_path], + ignore_path_list=[target_path], + task_name='first _call_it') + task_graph.close() + task_graph.join() + del task_graph - # task_graph = taskgraph.TaskGraph(self.workspace_dir, 1) - # target_path = os.path.join(self.workspace_dir, 'testfile.txt') - # task_graph.add_task( - # func=_call_it, - # args=(_append_val, target_path, 1), - # target_path_list=[target_path], - # ignore_path_list=[target_path], - # task_name='second _call_it') - # task_graph.close() - # task_graph.join() + # change the address of the function + _test_fixtures._append_val = _test_fixtures._append_val_2 - # with open(target_path, 'r') as target_file: - # result = target_file.read() + task_graph = taskgraph.TaskGraph(self.workspace_dir, 1) + target_path = os.path.join(self.workspace_dir, 'testfile.txt') + task_graph.add_task( + func=_test_fixtures._call_it, + args=(_test_fixtures._append_val, target_path, 1), + target_path_list=[target_path], + ignore_path_list=[target_path], + task_name='second _call_it') + task_graph.close() + task_graph.join() - # # the second call shouldn't happen - # self.assertEqual(result, '1') + with open(target_path, 'r') as target_file: + result = target_file.read() - # def test_unix_path_repeated_function(self): - # """TaskGraph: ensure no reruns if path is unix style.""" - # global _append_val - # _append_val = _append_val # flake8 complains if not defined + # the second call shouldn't happen + self.assertEqual(result, '1') - # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) - # target_dir = self.workspace_dir + '/foo/bar/rad/' - # os.makedirs(target_dir) - # target_path = target_dir + '/testfile.txt' - # task_graph.add_task( - # func=_call_it, - # args=(_append_val, target_path, 1), - # target_path_list=[target_path], - # task_name='first _call_it') - # task_graph.close() - # task_graph.join() - # del task_graph + def test_unix_path_repeated_function(self): + """TaskGraph: ensure no reruns if path is unix style.""" + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) + target_dir = self.workspace_dir + '/foo/bar/rad/' + os.makedirs(target_dir) + target_path = target_dir + '/testfile.txt' + task_graph.add_task( + func=_test_fixtures._call_it, + args=(_test_fixtures._append_val, target_path, 1), + target_path_list=[target_path], + task_name='first _call_it') + task_graph.close() + task_graph.join() + del task_graph - # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) - # task_graph.add_task( - # func=_call_it, - # args=(_append_val, target_path, 1), - # target_path_list=[target_path], - # task_name='second _call_it') - # task_graph.close() - # task_graph.join() + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) + task_graph.add_task( + func=_test_fixtures._call_it, + args=(_test_fixtures._append_val, target_path, 1), + target_path_list=[target_path], + task_name='second _call_it') + task_graph.close() + task_graph.join() - # with open(target_path, 'r') as target_file: - # result = target_file.read() + with open(target_path, 'r') as target_file: + result = target_file.read() - # # the second call shouldn't happen - # self.assertEqual(result, '1') + # the second call shouldn't happen + self.assertEqual(result, '1') - # def test_very_long_string(self): - # """TaskGraph: ensure that long strings don't case an OSError.""" - # from taskgraph.Task import _get_file_stats + def test_very_long_string(self): + """TaskGraph: ensure that long strings don't case an OSError.""" + from taskgraph.Task import _get_file_stats - # # this is a list with two super long strings to try to trick some - # # os function into thinking it's a path. - # base_value = [ - # 'c:' + r'\\\\\\\\x\\\\\\\\'*2**10 + 'foo', - # 'wfeji3223j8923j9' * 2**10] - # self.assertEqual( - # list(_get_file_stats(base_value, 'sizetimestamp', [], True)), []) + # this is a list with two super long strings to try to trick some + # os function into thinking it's a path. + base_value = [ + 'c:' + r'\\\\\\\\x\\\\\\\\'*2**10 + 'foo', + 'wfeji3223j8923j9' * 2**10] + self.assertEqual( + list(_get_file_stats(base_value, 'sizetimestamp', [], True)), []) - # def test_duplicate_call_changed_target(self): - # """TaskGraph: test that duplicate calls copy target path.""" - # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) - # target_path = os.path.join(self.workspace_dir, 'testfile.txt') + def test_duplicate_call_changed_target(self): + """TaskGraph: test that duplicate calls copy target path.""" + task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) + target_path = os.path.join(self.workspace_dir, 'testfile.txt') - # if hasattr(_create_file_once, 'executed'): - # del _create_file_once.executed + if hasattr(_test_fixtures._create_file_once, 'executed'): + del _test_fixtures._create_file_once.executed - # task_graph.add_task( - # func=_create_file_once, - # args=(target_path, 'test'), - # target_path_list=[target_path], - # hash_target_files=False, - # task_name='first _create_file_once') + task_graph.add_task( + func=_test_fixtures._create_file_once, + args=(target_path, 'test'), + target_path_list=[target_path], + hash_target_files=False, + task_name='first _create_file_once') - # task_graph.close() - # task_graph.join() - # del task_graph + task_graph.close() + task_graph.join() + del task_graph - # with open(target_path, 'a') as target_file: - # target_file.write('updated') + with open(target_path, 'a') as target_file: + target_file.write('updated') - # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) - # task_graph.add_task( - # func=_create_file_once, - # args=(target_path, 'test'), - # target_path_list=[target_path], - # hash_target_files=False, - # task_name='first _create_file_once') + task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) + task_graph.add_task( + func=_test_fixtures._create_file_once, + args=(target_path, 'test'), + target_path_list=[target_path], + hash_target_files=False, + task_name='first _create_file_once') - # task_graph.close() - # task_graph.join() - # del task_graph + task_graph.close() + task_graph.join() + del task_graph - # with open(target_path, 'r') as result_file: - # result_contents = result_file.read() - # self.assertEqual('testupdated', result_contents) + with open(target_path, 'r') as result_file: + result_contents = result_file.read() + self.assertEqual('testupdated', result_contents) - # def test_duplicate_call_modify_timestamp(self): - # """TaskGraph: test that duplicate call modified stamp recompute.""" - # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) - # target_path = os.path.join(self.workspace_dir, 'testfile.txt') - # task_graph.add_task( - # func=_create_file, - # args=(target_path, 'test'), - # target_path_list=[target_path], - # task_name='first _create_file') - # task_graph.close() - # task_graph.join() - # del task_graph + def test_duplicate_call_modify_timestamp(self): + """TaskGraph: test that duplicate call modified stamp recompute.""" + task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) + target_path = os.path.join(self.workspace_dir, 'testfile.txt') + task_graph.add_task( + func=_test_fixtures._create_file, + args=(target_path, 'test'), + target_path_list=[target_path], + task_name='first _create_file') + task_graph.close() + task_graph.join() + del task_graph - # with open(target_path, 'w') as target_file: - # target_file.write('test2') - # with open(target_path, 'r') as target_file: - # contents = target_file.read() - # self.assertEqual(contents, 'test2') + with open(target_path, 'w') as target_file: + target_file.write('test2') + with open(target_path, 'r') as target_file: + contents = target_file.read() + self.assertEqual(contents, 'test2') - # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) - # task_graph.add_task( - # func=_create_file, - # args=(target_path, 'test'), - # target_path_list=[target_path], - # task_name='second _create_file') + task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) + task_graph.add_task( + func=_test_fixtures._create_file, + args=(target_path, 'test'), + target_path_list=[target_path], + task_name='second _create_file') - # task_graph.close() - # task_graph.join() + task_graph.close() + task_graph.join() - # with open(target_path, 'r') as target_file: - # contents = target_file.read() - # self.assertEqual(contents, 'test') + with open(target_path, 'r') as target_file: + contents = target_file.read() + self.assertEqual(contents, 'test') - # def test_different_target_path_list(self): - # """TaskGraph: duplicate calls with different targets should fail.""" - # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) - # target_path = os.path.join(self.workspace_dir, 'testfile.txt') - # task_graph.add_task( - # func=_create_list_on_disk, - # args=('test', 1, target_path), - # target_path_list=[target_path], - # task_name='first _create_list_on_disk') - - # with self.assertRaises(RuntimeError): - # # make the same call but with different target path list - # task_graph.add_task( - # func=_create_list_on_disk, - # args=('test', 1, target_path), - # target_path_list=[target_path, 'test.txt'], - # task_name='first _create_list_on_disk') - - # task_graph.close() - # task_graph.join() + def test_different_target_path_list(self): + """TaskGraph: duplicate calls with different targets should fail.""" + task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) + target_path = os.path.join(self.workspace_dir, 'testfile.txt') + task_graph.add_task( + func=_test_fixtures._create_list_on_disk, + args=('test', 1, target_path), + target_path_list=[target_path], + task_name='first _create_list_on_disk') + + with self.assertRaises(RuntimeError): + # make the same call but with different target path list + task_graph.add_task( + func=_test_fixtures._create_list_on_disk, + args=('test', 1, target_path), + target_path_list=[target_path, 'test.txt'], + task_name='first _create_list_on_disk') - # def test_terminated_taskgraph(self): - # """TaskGraph: terminated task graph raises exception correctly.""" - # task_graph = taskgraph.TaskGraph(self.workspace_dir, 4) - # _ = task_graph.add_task(func=_div_by_zero) - # with self.assertRaises(ZeroDivisionError): - # task_graph.join() - - # with self.assertRaises(RuntimeError) as cm: - # _ = task_graph.add_task(func=_div_by_zero) - # expected_message = "add_task when Taskgraph is terminated" - # actual_message = str(cm.exception) - # self.assertTrue(expected_message in actual_message, actual_message) - - # task_graph.close() - # # try closing twice just to mess with coverage - # task_graph.close() - - # def test_type_list_error(self): - # """TaskGraph: Task not passed to dependent task list.""" - # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) - # target_path = os.path.join(self.workspace_dir, 'testfile.txt') - # with self.assertRaises(ValueError) as cm: - # task_graph.add_task( - # func=_create_list_on_disk, - # args=('test', 1, target_path), - # target_path_list=[target_path], - # dependent_task_list=[target_path], - # task_name='first _create_list_on_disk') - # expected_message = ( - # "Objects passed to dependent task list that are not tasks") - # actual_message = str(cm.exception) - # self.assertTrue(expected_message in actual_message, actual_message) - - # def test_target_list_error(self): - # """TaskGraph: Path not passed to target list.""" - # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) - # target_path = os.path.join(self.workspace_dir, 'testfile.txt') - # with self.assertRaises(ValueError) as cm: - # task_graph.add_task( - # func=_create_list_on_disk, - # args=('test', 1, target_path), - # target_path_list=[1], - # task_name='_create_list_on_disk') - # expected_message = ( - # "Values passed to target_path_list are not strings") - # actual_message = str(cm.exception) - # self.assertTrue(expected_message in actual_message, actual_message) - - # def test_target_path_missing_file(self): - # """TaskGraph: func runs, but missing target.""" - # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) - # target_path = os.path.join(self.workspace_dir, 'testfile.txt') - # not_target_path = os.path.join(self.workspace_dir, 'not_target.txt') - # with self.assertRaises(RuntimeError) as cm: - # task_graph.add_task( - # func=_create_list_on_disk, - # args=('test', 1, target_path), - # target_path_list=[not_target_path], - # task_name='_create_list_on_disk') - # expected_message = "Missing expected target path results" - # actual_message = str(cm.exception) - # self.assertTrue(expected_message in actual_message, actual_message) - - # def test_expected_path_list(self): - # """TaskGraph: test expected path list matches actual path list.""" - # def _create_file(target_path, content): - # with open(target_path, 'w') as target_file: - # target_file.write(content) - - # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) - # # note it is important this is a relative path that does not - # # contain the drive letter on Windows. - # absolute_target_file_path = os.path.join( - # self.workspace_dir, 'a.txt') - # relative_path = os.path.relpath(absolute_target_file_path, - # start=self.workspace_dir) - - # _ = task_graph.add_task( - # func=_create_file, - # args=(relative_path, 'test value'), - # target_path_list=[relative_path], - # task_name='create file') - - # task_graph.close() - # task_graph.join() - # del task_graph + task_graph.close() + task_graph.join() - # self.assertTrue('Ran without crashing!') + def test_terminated_taskgraph(self): + """TaskGraph: terminated task graph raises exception correctly.""" + task_graph = taskgraph.TaskGraph(self.workspace_dir, 4) + _ = task_graph.add_task(func=_test_fixtures._div_by_zero) + with self.assertRaises(ZeroDivisionError): + task_graph.join() - # def test_kwargs_hashed(self): - # """TaskGraph: ensure kwargs are considered in determining id hash.""" - # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + with self.assertRaises(RuntimeError) as cm: + _ = task_graph.add_task(func=_test_fixtures._div_by_zero) + expected_message = "add_task when Taskgraph is terminated" + actual_message = str(cm.exception) + self.assertTrue(expected_message in actual_message, actual_message) - # task_a = task_graph.add_task( - # func=_noop_function, - # kwargs={ - # 'content': ['this value: a']}, - # task_name='noop a') + task_graph.close() + # try closing twice just to mess with coverage + task_graph.close() - # task_b = task_graph.add_task( - # func=_noop_function, - # kwargs={ - # 'content': ['this value b']}, - # task_name='noop b') + def test_type_list_error(self): + """TaskGraph: Task not passed to dependent task list.""" + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) + target_path = os.path.join(self.workspace_dir, 'testfile.txt') + with self.assertRaises(ValueError) as cm: + task_graph.add_task( + func=_test_fixtures._create_list_on_disk, + args=('test', 1, target_path), + target_path_list=[target_path], + dependent_task_list=[target_path], + task_name='first _create_list_on_disk') + expected_message = ( + "Objects passed to dependent task list that are not tasks") + actual_message = str(cm.exception) + self.assertTrue(expected_message in actual_message, actual_message) + + def test_target_list_error(self): + """TaskGraph: Path not passed to target list.""" + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) + target_path = os.path.join(self.workspace_dir, 'testfile.txt') + with self.assertRaises(ValueError) as cm: + task_graph.add_task( + func=_test_fixtures._create_list_on_disk, + args=('test', 1, target_path), + target_path_list=[1], + task_name='_create_list_on_disk') + expected_message = ( + "Values passed to target_path_list are not strings") + actual_message = str(cm.exception) + self.assertTrue(expected_message in actual_message, actual_message) + + def test_target_path_missing_file(self): + """TaskGraph: func runs, but missing target.""" + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) + target_path = os.path.join(self.workspace_dir, 'testfile.txt') + not_target_path = os.path.join(self.workspace_dir, 'not_target.txt') + with self.assertRaises(RuntimeError) as cm: + task_graph.add_task( + func=_test_fixtures._create_list_on_disk, + args=('test', 1, target_path), + target_path_list=[not_target_path], + task_name='_create_list_on_disk') + expected_message = "Missing expected target path results" + actual_message = str(cm.exception) + self.assertTrue(expected_message in actual_message, actual_message) + + def test_expected_path_list(self): + """TaskGraph: test expected path list matches actual path list.""" + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + # note it is important this is a relative path that does not + # contain the drive letter on Windows. + absolute_target_file_path = os.path.join( + self.workspace_dir, 'a.txt') + relative_path = os.path.relpath(absolute_target_file_path, + start=self.workspace_dir) - # task_graph.close() - # task_graph.join() - # del task_graph + _ = task_graph.add_task( + func=_test_fixtures._create_file, + args=(relative_path, 'test value'), + target_path_list=[relative_path], + task_name='create file') - # self.assertNotEqual( - # task_a._task_id_hash, task_b._task_id_hash, - # "task ids should be different since the kwargs are different") + task_graph.close() + task_graph.join() + del task_graph - # def test_same_timestamp_and_value(self): - # """TaskGraph: ensure identical files but filename are noticed.""" - # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + self.assertTrue('Ran without crashing!') - # file_a_path = os.path.join(self.workspace_dir, 'file_a.txt') - # file_b_path = os.path.join(self.workspace_dir, 'file_b.txt') + def test_kwargs_hashed(self): + """TaskGraph: ensure kwargs are considered in determining id hash.""" + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) - # with open(file_a_path, 'w') as file_a: - # file_a.write('a') - # with open(file_b_path, 'w') as file_b: - # file_b.write('a') + task_a = task_graph.add_task( + func=_test_fixtures._noop_function, + kwargs={ + 'content': ['this value: a']}, + task_name='noop a') - # os.utime(file_a_path, (0, 0)) - # os.utime(file_b_path, (0, 0)) + task_b = task_graph.add_task( + func=_test_fixtures._noop_function, + kwargs={ + 'content': ['this value b']}, + task_name='noop b') - # task_a = task_graph.add_task( - # func=_noop_function, - # kwargs={ - # 'path': file_a_path}, - # task_name='noop a') + task_graph.close() + task_graph.join() + del task_graph - # task_b = task_graph.add_task( - # func=_noop_function, - # kwargs={ - # 'path': file_b_path}, - # task_name='noop b') + self.assertNotEqual( + task_a._task_id_hash, task_b._task_id_hash, + "task ids should be different since the kwargs are different") - # task_graph.close() - # task_graph.join() - # del task_graph - - # self.assertNotEqual( - # task_a._task_id_hash, task_b._task_id_hash, - # "task ids should be different since the filenames are different") - - # def test_different_hash_different_file(self): - # """TaskGraph: ensure identical files but filename are noticed.""" - # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) - # target_file_path = os.path.join(self.workspace_dir, 'target.txt') - # _ = task_graph.add_task( - # func=_create_file, - # args=(target_file_path, 'content'), - # hash_algorithm='exists', - # target_path_list=[target_file_path], - # task_name='create content, hash with exists') - # task_graph.close() - # task_graph.join() - # del task_graph - - # with open(target_file_path, 'r') as target_file: - # self.assertEqual(target_file.read(), 'content') - # with open(target_file_path, 'w') as target_file: - # target_file.write('overwritten') - - # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) - # _ = task_graph.add_task( - # func=_create_file, - # args=(target_file_path, 'content'), - # hash_algorithm='exists', - # target_path_list=[target_file_path], - # task_name='will not overwrite content, hash with exists') - # task_graph.close() - # task_graph.join() - # del task_graph - - # with open(target_file_path, 'r') as target_file: - # self.assertEqual(target_file.read(), 'overwritten') - - # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) - # _ = task_graph.add_task( - # func=_create_file, - # args=(target_file_path, 'content'), - # hash_algorithm='md5', - # target_path_list=[target_file_path], - # task_name='create content again with new hash') - # task_graph.close() - # task_graph.join() - # del task_graph + def test_same_timestamp_and_value(self): + """TaskGraph: ensure identical files but filename are noticed.""" + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) - # with open(target_file_path, 'r') as target_file: - # self.assertEqual(target_file.read(), 'content') + file_a_path = os.path.join(self.workspace_dir, 'file_a.txt') + file_b_path = os.path.join(self.workspace_dir, 'file_b.txt') - # def test_return_value_no_record(self): - # """TaskGraph: test ``get`` raises exception if not set to record.""" - # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) - # value_task = task_graph.add_task( - # func=_noop_function, - # store_result=False) - - # # get wil raise a ValueError because store_result is not True - # with self.assertRaises(ValueError) as cm: - # _ = value_task.get() - # expected_message = 'must set `store_result` to True in `add_task`' - # actual_message = str(cm.exception) - # self.assertTrue(expected_message in actual_message, actual_message) - - # def test_return_value(self): - # """TaskGraph: test that ``.get`` behavior works as expected.""" - # if hasattr(_return_value_once, 'executed'): - # del _return_value_once.executed - # n_iterations = 3 - # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0, 0) - # for iteration_id in range(n_iterations): - # transient_run = iteration_id == n_iterations-1 - # LOGGER.debug(iteration_id) - # expected_value = 'a good value' - # value_task = task_graph.add_task( - # func=_return_value_once, - # transient_run=transient_run, - # store_result=True, - # args=(expected_value,), - # task_name=f'{expected_value} iter {iteration_id}') - # value = value_task.get() - # self.assertEqual(value, expected_value) - # task_graph.close() - # task_graph.join() - # task_graph = None - - # # reset run - # del _return_value_once.executed - # for iteration_id in range(n_iterations): - # LOGGER.debug(iteration_id) - # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0, 0) - # expected_value = 'transient run' - # if iteration_id == 0: - # value_task = task_graph.add_task( - # func=_return_value_once, - # transient_run=True, - # store_result=True, - # args=(expected_value,), - # task_name='first re-run transient') - # value = value_task.get() - # self.assertEqual(value, expected_value) - # task_graph.close() - # task_graph.join() - # else: - # with self.assertRaises(RuntimeError): - # value_task = task_graph.add_task( - # func=_return_value_once, - # transient_run=True, - # store_result=True, - # args=(expected_value,), - # task_name=f'expected error {iteration_id}') - - # value = value_task.get() - - # with self.assertRaises(RuntimeError): - # task_graph.join() - - # task_graph = None - - # def test_malformed_taskgraph_database(self): - # """TaskGraph: Test an empty task.""" - # db_schema_test_list = [ - # ''' - # CREATE TABLE taskgraph_data ( - # bad_name_1 TEXT NOT NULL, - # bad_name_2 BLOB NOT NULL, - # bad_name_3 BLOB NOT NULL); - # ''', - # ''' - # CREATE TABLE taskgraph_data ( - # task_reexecution_hash TEXT NOT NULL, - # target_path_stats BLOB NOT NULL); - # ''', - # ''' - # CREATE TABLE bad_table_name ( - # task_reexecution_hash TEXT NOT NULL, - # target_path_stats BLOB NOT NULL, - # result BLOB NOT NULL, - # PRIMARY KEY (task_reexecution_hash)); - # ''' - # ] - - # for db_schema in db_schema_test_list: - # database_path = os.path.join( - # self.workspace_dir, taskgraph._TASKGRAPH_DATABASE_FILENAME) - # if os.path.exists(database_path): - # os.remove(database_path) - # connection = sqlite3.connect(database_path) - # cursor = connection.cursor() - # cursor.executescript(db_schema) - # cursor.close() - # connection.commit() - # connection.close() - - # task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) - # _ = task_graph.add_task() - # task_graph.close() - # task_graph.join() - # del task_graph - - # expected_column_name_list = [ - # 'task_reexecution_hash', 'target_path_stats', 'result'] - # connection = sqlite3.connect(database_path) - # cursor = connection.cursor() - # cursor.execute('PRAGMA table_info(taskgraph_data)') - # result = list(cursor.fetchall()) - # cursor.close() - # connection.commit() - # connection.close() - # for header_line in result: - # column_name = header_line[1] - # if column_name not in expected_column_name_list: - # raise ValueError( - # f'unexpected column name {column_name} in ' - # 'taskgraph_data ') - # self.assertEqual(len(result), len(expected_column_name_list)) - - # def test_terminate_log(self): - # """TaskGraph: test that the logger thread terminates on .join.""" - # task_graph = taskgraph.TaskGraph(self.workspace_dir, 1, 5.0) - # _ = task_graph.add_task() - # task_graph.join() + with open(file_a_path, 'w') as file_a: + file_a.write('a') + with open(file_b_path, 'w') as file_b: + file_b.write('a') - # # logger should not terminate until after join, give it enough time - # # to have a chance to close, but not so long the test hangs - # task_graph._logging_monitor_thread.join(0.1) - # self.assertTrue(task_graph._logging_monitor_thread.is_alive()) - # task_graph._execution_monitor_thread.join(0.1) - # self.assertTrue(task_graph._execution_monitor_thread.is_alive()) + os.utime(file_a_path, (0, 0)) + os.utime(file_b_path, (0, 0)) - # task_graph.close() - # task_graph.join() + task_a = task_graph.add_task( + func=_test_fixtures._noop_function, + kwargs={ + 'path': file_a_path}, + task_name='noop a') + + task_b = task_graph.add_task( + func=_test_fixtures._noop_function, + kwargs={ + 'path': file_b_path}, + task_name='noop b') + + task_graph.close() + task_graph.join() + del task_graph - # # 5 seconds should be way too much time to expect the thread to join - # task_graph._logging_monitor_thread.join(5) - # self.assertFalse(task_graph._logging_monitor_thread.is_alive()) - # task_graph._execution_monitor_thread.join(5) - # self.assertFalse(task_graph._execution_monitor_thread.is_alive()) + self.assertNotEqual( + task_a._task_id_hash, task_b._task_id_hash, + "task ids should be different since the filenames are different") + + def test_different_hash_different_file(self): + """TaskGraph: ensure identical files but filename are noticed.""" + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + target_file_path = os.path.join(self.workspace_dir, 'target.txt') + _ = task_graph.add_task( + func=_test_fixtures._create_file, + args=(target_file_path, 'content'), + hash_algorithm='exists', + target_path_list=[target_file_path], + task_name='create content, hash with exists') + task_graph.close() + task_graph.join() + del task_graph + + with open(target_file_path, 'r') as target_file: + self.assertEqual(target_file.read(), 'content') + with open(target_file_path, 'w') as target_file: + target_file.write('overwritten') + + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + _ = task_graph.add_task( + func=_test_fixtures._create_file, + args=(target_file_path, 'content'), + hash_algorithm='exists', + target_path_list=[target_file_path], + task_name='will not overwrite content, hash with exists') + task_graph.close() + task_graph.join() + del task_graph + + with open(target_file_path, 'r') as target_file: + self.assertEqual(target_file.read(), 'overwritten') + + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + _ = task_graph.add_task( + func=_test_fixtures._create_file, + args=(target_file_path, 'content'), + hash_algorithm='md5', + target_path_list=[target_file_path], + task_name='create content again with new hash') + task_graph.close() + task_graph.join() + del task_graph + + with open(target_file_path, 'r') as target_file: + self.assertEqual(target_file.read(), 'content') + + def test_return_value(self): + """TaskGraph: test that ``.get`` behavior works as expected.""" + if hasattr(_test_fixtures._return_value_once, 'executed'): + del _test_fixtures._return_value_once.executed + n_iterations = 3 + task_graph = taskgraph.TaskGraph(self.workspace_dir, 0, 0) + for iteration_id in range(n_iterations): + transient_run = iteration_id == n_iterations-1 + LOGGER.debug(iteration_id) + expected_value = 'a good value' + value_task = task_graph.add_task( + func=_test_fixtures._return_value_once, + transient_run=transient_run, + args=(expected_value,), + task_name=f'{expected_value} iter {iteration_id}') + value = value_task.get() + self.assertEqual(value, expected_value) + task_graph.close() + task_graph.join() + task_graph = None + + # reset run + del _test_fixtures._return_value_once.executed + for iteration_id in range(n_iterations): + LOGGER.debug(iteration_id) + task_graph = taskgraph.TaskGraph(self.workspace_dir, 0, 0) + expected_value = 'transient run' + if iteration_id == 0: + value_task = task_graph.add_task( + func=_test_fixtures._return_value_once, + transient_run=True, + args=(expected_value,), + task_name='first re-run transient') + value = value_task.get() + self.assertEqual(value, expected_value) + task_graph.close() + task_graph.join() + else: + with self.assertRaises(RuntimeError): + value_task = task_graph.add_task( + func=_test_fixtures._return_value_once, + transient_run=True, + args=(expected_value,), + task_name=f'expected error {iteration_id}') + + value = value_task.get() + + with self.assertRaises(RuntimeError): + task_graph.join() + + task_graph = None # def test_dictionary_arguments(self): # """TaskGraph: test that large dictionary arguments behave well.""" @@ -1373,119 +1142,119 @@ def test_task_hash_when_ready(self): # task_graph.join() # self.assertTrue(True, 'no memory error so everything is fine') - # def test_filter_non_files(self): - # """TaskGraph: test internal filter non-files function.""" - # from taskgraph.Task import _filter_non_files - # from taskgraph.Task import _normalize_path - - # # Test a passthrough - # test_dict = { - # 0: {'one': 0, 'two': 1, 'three': 2}, - # 1: {'one': 1, 'two': 2, 'three': 3}, - # 2: {'one': 2, 'two': 3, 'three': 4}} - # self.assertEqual( - # test_dict, _filter_non_files(test_dict, [], [], False)) - - # # Test combination of files, not existing files, and flags in the - # # call - # test_file_a_exists = _normalize_path(os.path.join( - # self.workspace_dir, 'exists_a.txt')) - # pathlib.Path(test_file_a_exists).touch() - # test_file_b_exists = _normalize_path(os.path.join( - # self.workspace_dir, 'exists_b.txt')) - # pathlib.Path(test_file_b_exists).touch() - # test_file_not_a_exists = _normalize_path(os.path.join( - # self.workspace_dir, 'does_not_exist_a.txt')) - # test_file_not_b_exists = _normalize_path(os.path.join( - # self.workspace_dir, 'does_not_exist_b.txt')) - - # test_dict = { - # 0: {'one': 0, 'two': 1, 'three': 2}, - # 1: {'one': 1, 'two': 2, 'three': 3}, - # 2: {'one': 2, 'two': 3, 'three': 4}, - # 4: {'bar': test_file_not_a_exists}, - # 5: {'foo': test_file_a_exists}, - # 6: test_file_b_exists, - # 7: test_file_not_b_exists, - # 8: _normalize_path(self.workspace_dir)} - - # expected_result_dict = { - # 0: {'one': 0, 'two': 1, 'three': 2}, - # 1: {'one': 1, 'two': 2, 'three': 3}, - # 2: {'one': 2, 'two': 3, 'three': 4}, - # 4: {'bar': test_file_not_a_exists}, - # 5: {'foo': None}, - # 6: test_file_b_exists, - # 7: None, - # 8: _normalize_path(self.workspace_dir)} - - # self.assertEqual( - # _filter_non_files( - # test_dict, - # [test_file_b_exists], - # [test_file_not_b_exists], - # True), - # expected_result_dict) - - # # and test same as above but don't keep directories: - # expected_result_dict[8] = None - # self.assertEqual( - # _filter_non_files( - # test_dict, - # [test_file_b_exists], - # [test_file_not_b_exists], - # False), - # expected_result_dict) - - # def test_duplicate_task_hang_on_exit(self): - # """TaskGraph: ensure duplicate tasks don't cause taskgraph to hang.""" - # task_graph = taskgraph.TaskGraph(self.workspace_dir, 1) - # target_path = os.path.join(self.workspace_dir, 'target.txt') - # content = 'test' - # for _ in range(10): - # _ = task_graph.add_task( - # func=_create_file, - # args=(target_path, content), - # target_path_list=[target_path], - # task_name='create content') - # task_graph.join() - # task_graph.close() - - # def test_history_rst_format(self): - # """TaskGraph: ensure HISTORY.rst is correctly formatted.""" - # # ensure there are no errors when checking the history file - # history_filepath = os.path.join( - # os.path.dirname(__file__), '..', 'HISTORY.rst') - # subprocess.check_call(['rstcheck', history_filepath]) - - # def test_mtime_mismatch(self): - # """TaskGraph: ensure re-run when file mtimes don't match. - - # This test addresses the issue described under the github issue - # https://github.com/natcap/taskgraph/issues/70. - # """ - # target_path = os.path.join(self.workspace_dir, 'target.txt') - - # # SETUP: When we call 3 similar graphs in rapid succession, the file's - # # mtime is not precise enough to detect that the file has actually - # # changed. The specific conditions here are: - # # * The task "test text" has already been computed once - # # * The file written by the first "test text" is replaced by content - # # of the same filesize (thus fooling the size part of sizetimestamp) - # # * The graphs are executed fast enough that _is_precalculated's mtime - # # check via math.isclose() couldn't detect the recalculation. - # for content in ('test text', 'TEST TEXT', 'test text'): - # task_graph = taskgraph.TaskGraph(self.workspace_dir, n_workers=-1) - # _ = task_graph.add_task( - # func=_create_file, - # args=(target_path, content), - # target_path_list=[target_path], - # task_name='create content') - # task_graph.join() - # task_graph.close() - - # with open(target_path) as target_file: - # self.assertEqual(target_file.read(), content) + def test_filter_non_files(self): + """TaskGraph: test internal filter non-files function.""" + from taskgraph.Task import _filter_non_files + from taskgraph.Task import _normalize_path + + # Test a passthrough + test_dict = { + 0: {'one': 0, 'two': 1, 'three': 2}, + 1: {'one': 1, 'two': 2, 'three': 3}, + 2: {'one': 2, 'two': 3, 'three': 4}} + self.assertEqual( + test_dict, _filter_non_files(test_dict, [], [], False)) + + # Test combination of files, not existing files, and flags in the + # call + test_file_a_exists = _normalize_path(os.path.join( + self.workspace_dir, 'exists_a.txt')) + pathlib.Path(test_file_a_exists).touch() + test_file_b_exists = _normalize_path(os.path.join( + self.workspace_dir, 'exists_b.txt')) + pathlib.Path(test_file_b_exists).touch() + test_file_not_a_exists = _normalize_path(os.path.join( + self.workspace_dir, 'does_not_exist_a.txt')) + test_file_not_b_exists = _normalize_path(os.path.join( + self.workspace_dir, 'does_not_exist_b.txt')) + + test_dict = { + 0: {'one': 0, 'two': 1, 'three': 2}, + 1: {'one': 1, 'two': 2, 'three': 3}, + 2: {'one': 2, 'two': 3, 'three': 4}, + 4: {'bar': test_file_not_a_exists}, + 5: {'foo': test_file_a_exists}, + 6: test_file_b_exists, + 7: test_file_not_b_exists, + 8: _normalize_path(self.workspace_dir)} + + expected_result_dict = { + 0: {'one': 0, 'two': 1, 'three': 2}, + 1: {'one': 1, 'two': 2, 'three': 3}, + 2: {'one': 2, 'two': 3, 'three': 4}, + 4: {'bar': test_file_not_a_exists}, + 5: {'foo': None}, + 6: test_file_b_exists, + 7: None, + 8: _normalize_path(self.workspace_dir)} + + self.assertEqual( + _filter_non_files( + test_dict, + [test_file_b_exists], + [test_file_not_b_exists], + True), + expected_result_dict) + + # and test same as above but don't keep directories: + expected_result_dict[8] = None + self.assertEqual( + _filter_non_files( + test_dict, + [test_file_b_exists], + [test_file_not_b_exists], + False), + expected_result_dict) + + def test_duplicate_task_hang_on_exit(self): + """TaskGraph: ensure duplicate tasks don't cause taskgraph to hang.""" + task_graph = taskgraph.TaskGraph(self.workspace_dir, 1) + target_path = os.path.join(self.workspace_dir, 'target.txt') + content = 'test' + for _ in range(10): + _ = task_graph.add_task( + func=_test_fixtures._create_file, + args=(target_path, content), + target_path_list=[target_path], + task_name='create content') + task_graph.join() + task_graph.close() + + def test_history_rst_format(self): + """TaskGraph: ensure HISTORY.rst is correctly formatted.""" + # ensure there are no errors when checking the history file + history_filepath = os.path.join( + os.path.dirname(__file__), '..', 'HISTORY.rst') + subprocess.check_call(['rstcheck', history_filepath]) + + def test_mtime_mismatch(self): + """TaskGraph: ensure re-run when file mtimes don't match. + + This test addresses the issue described under the github issue + https://github.com/natcap/taskgraph/issues/70. + """ + target_path = os.path.join(self.workspace_dir, 'target.txt') + + # SETUP: When we call 3 similar graphs in rapid succession, the file's + # mtime is not precise enough to detect that the file has actually + # changed. The specific conditions here are: + # * The task "test text" has already been computed once + # * The file written by the first "test text" is replaced by content + # of the same filesize (thus fooling the size part of sizetimestamp) + # * The graphs are executed fast enough that _is_precalculated's mtime + # check via math.isclose() couldn't detect the recalculation. + for content in ('test text', 'TEST TEXT', 'test text'): + task_graph = taskgraph.TaskGraph(self.workspace_dir, n_workers=-1) + _ = task_graph.add_task( + func=_test_fixtures._create_file, + args=(target_path, content), + target_path_list=[target_path], + task_name='create content') + task_graph.join() + task_graph.close() + + with open(target_path) as target_file: + self.assertEqual(target_file.read(), content) def Fail(n_tries, result_path): From e0ef458d45f410320c614edb116d2e5f9da77483 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 19 Oct 2026 17:59:07 -0700 Subject: [PATCH 005/112] logging from task functions to parsl worker log file --- pyproject.toml | 2 +- taskgraph/Task.py | 217 +++++++++++++++++++++++------------- taskgraph/_test_fixtures.py | 5 - tests/test_task.py | 36 ++---- 4 files changed, 149 insertions(+), 111 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 973dd58c..9a69d11b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,7 +26,7 @@ classifiers = [ # the version is provided dynamically by setuptools_scm dynamic = ["version"] dependencies = [ - "retrying>=1.3.0", + "parsl" ] [project.optional-dependencies] diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 40764434..3e5b926d 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -7,43 +7,42 @@ import logging import logging.handlers import multiprocessing -import multiprocessing.pool import os import pathlib import pickle import pprint -import queue import threading import time import types +from typing import Callable from importlib.metadata import PackageNotFoundError from importlib.metadata import version -import retrying - import parsl -import os from parsl.app.app import python_app from parsl.concurrent import ParslPoolExecutor from parsl.configs.local_threads import config from parsl.utils import get_all_checkpoints from parsl.dataflow.memoization import BasicMemoizer, id_for_memo -from parsl.dataflow.futures import AppFuture from parsl.executors import HighThroughputExecutor from parsl.executors import ThreadPoolExecutor +from parsl.log_utils import set_file_logger from parsl.monitoring.monitoring import MonitoringHub -from parsl.addresses import address_by_hostname - +from parsl.log_utils import DEFAULT_FORMAT +from parsl.logconfigs.base import LogConfig LOGGER = logging.getLogger(__name__) +# LOGGING_MANAGER = multiprocessing.Manager() +# LOGGING_QUEUE = LOGGING_MANAGER.Queue(-1) + + try: __version__ = version('taskgraph') except PackageNotFoundError: # package is not installed; no metadata available pass - _VALID_PATH_TYPES = (str, pathlib.PurePath) _TASKGRAPH_DATABASE_FILENAME = 'taskgraph_data.db' @@ -61,12 +60,49 @@ except ImportError: HAS_PSUTIL = False -LOGGER = logging.getLogger(__name__) _MAX_TIMEOUT = 5.0 # amount of time to wait for threads to terminate +# Because you'll want to define the logging configurations for listener and workers, the +# listener and worker process functions take a configurer parameter which is a callable +# for configuring logging for that process. These functions are also passed the queue, +# which they use for communication. +# +# In practice, you can configure the listener however you want, but note that in this +# simple example, the listener does not apply level or filter logic to received records. +# In practice, you would probably want to do this logic in the worker processes, to avoid +# sending events which would be filtered out between processes. +# +# The size of the rotated files is made small so you can see the results easily. +def listener_configurer(): + root = logging.getLogger() + h = logging.handlers.RotatingFileHandler('mptest.log', 'a', 300, 10) + f = logging.Formatter('%(asctime)s %(processName)-10s %(name)s %(levelname)-8s %(message)s') + h.setFormatter(f) + root.addHandler(h) + + +# This is the listener process top-level loop: wait for logging events +# (LogRecords)on the queue and handle them, quit when you get a None for a +# LogRecord. +def listener_process(queue, configurer): + configurer() + while True: + try: + record = queue.get() + if record is None: # We send this as a sentinel to tell the listener to quit. + break + logger = logging.getLogger(record.name) + logger.handle(record) # No level or filter logic applied - just do it! + except Exception: + import sys, traceback + print('Whoops! Problem:', file=sys.stderr) + traceback.print_exc(file=sys.stderr) + + def create_parsl_app( - cache, executor_label, ignore_for_cache=[], + cache, executor_label, + ignore_for_cache=[], parsl_resource_specification={}): @python_app( @@ -86,6 +122,53 @@ def _null_func(): return None +class FileLogging(LogConfig): + """Copy of parsl.logconfigs.file.FileLogging, but doesn't filter out non-parsl logs.""" + + def __init__(self, *, + format_string: str = DEFAULT_FORMAT, + level: int = logging.DEBUG): + super().__init__() + self.format_string = format_string + self.level = level + + def initialize_logging(self, *, log_dir: pathlib.Path, log_name: str) -> Callable[[], None]: + + log_dir.mkdir(exist_ok=True, parents=True) + filename = log_dir.joinpath(log_name + ".log") + + handler = logging.FileHandler(filename) + handler.setLevel(self.level) + formatter = logging.Formatter(self.format_string, datefmt='%Y-%m-%d %H:%M:%S') + handler.setFormatter(formatter) + + logger = logging.getLogger() + + if logger.level == logging.NOTSET: + logger.setLevel(self.level) + else: + logger.setLevel(min(logger.level, self.level)) + + logger.addHandler(handler) + logger.info(f'logging to {filename}') + + # Concurrent.futures errors are also of interest, as exceptions + # which propagate out of the top of a callback are logged this way + # and then discarded. (see #240) + futures_logger = logging.getLogger("concurrent.futures") + if futures_logger.level == logging.NOTSET: + futures_logger.setLevel(self.level) + else: + futures_logger.setLevel(min(futures_logger.level, self.level)) + futures_logger.addHandler(handler) + + def unregister_callback() -> None: + logger.removeHandler(handler) + futures_logger.removeHandler(handler) + + return unregister_callback + + FUNC_SOURCE_MAP = {} def get_task_hash_info(func, args, kwargs, target_path_list): """Return hash used to compare equality of task executions. @@ -639,15 +722,7 @@ def _checkpoint_these_tasks(self, checkpoint_queue: List[CheckpointCommand]) -> LOGGER.info("Done checkpointing {} tasks".format(count)) -config.app_cache = True -config.memoizer = TaskGraphMemoizer(checkpoint_mode='task_exit') -config.initialize_logging = False -config.monitoring = MonitoringHub( - hub_address='127.0.0.1', - monitoring_debug=True, - resource_monitoring_interval=10 -) -_DFK = parsl.load(config) +_DFK = None class TaskGraph(object): @@ -716,6 +791,28 @@ def __init__( # tasks that complete are added to this set self._completed_task_names = set() + # set_file_logger(f'{self._taskgraph_cache_dir_path}/parsl.log') + + # self.listener = logging.handlers.QueueListener( + # LOGGING_QUEUE, logging.FileHandler( + # f'{self._taskgraph_cache_dir_path}/taskgraph.log')) + # self.listener.start() + global _DFK + if _DFK: + try: + _DFK.cleanup() + except Exception: + pass + config.run_dir = self._taskgraph_cache_dir_path + config.app_cache = True + config.memoizer = TaskGraphMemoizer(checkpoint_mode='task_exit') + config.monitoring = MonitoringHub( + hub_address='127.0.0.1', + monitoring_debug=True, + resource_monitoring_interval=10) + config.initialize_logging = FileLogging() + _DFK = parsl.load(config) + _DFK.config.memoizer.add_checkpoint_dir(self._taskgraph_cache_dir_path) if n_workers > 0: @@ -724,6 +821,7 @@ def __init__( label=self.executor_label, cores_per_worker=1, max_workers_per_node=n_workers, + working_dir=self._taskgraph_cache_dir_path, address='127.0.0.1') elif n_workers == 0: self.executor_label = "threaded" @@ -739,12 +837,10 @@ def __init__( _DFK.add_executors([executor]) self.pool = ParslPoolExecutor(dfk=_DFK) - print('executors:', _DFK.executors) - - # no need to set up schedulers if n_workers is single threaded + # # no need to set up schedulers if n_workers is single threaded self._n_workers = n_workers - if n_workers < 0: - return + # if n_workers < 0: + # return # start concurrent reporting of taskgraph if reporting interval is set self._reporting_interval = reporting_interval @@ -776,9 +872,11 @@ def __init__( def __del__(self): """Ensure all threads have been joined for cleanup.""" self._terminate() + # self.listener.stop() def add_task( - self, func=None, args=None, kwargs=None, task_name=None, + self, func=_null_func, args=None, kwargs=None, + task_name='UNNAMED TASK', target_path_list=None, ignore_path_list=None, hash_target_files=True, dependent_task_list=None, ignore_directories=True, priority=0, @@ -872,16 +970,12 @@ def add_task( args = [] if kwargs is None: kwargs = {} - if task_name is None: - task_name = 'UNNAMED TASK' if dependent_task_list is None: dependent_task_list = [] if target_path_list is None: target_path_list = [] if ignore_path_list is None: ignore_path_list = [] - if func is None: - func = _null_func # this is a pretty common error to accidentally not pass a # Task to the dependent task list. @@ -949,7 +1043,6 @@ def add_task( # call directly if single threaded new_task._call() else: - print('checking status') # determine if task is ready or is dependent on other # tasks LOGGER.debug( @@ -973,7 +1066,6 @@ def add_task( # record tasks that new_task depends on self._dependent_task_map[new_task.task_name].add( dep_task_name) - print('returning', new_task) return new_task except Exception: @@ -1046,7 +1138,6 @@ def join(self, timeout=None): True if successful join, False if timed out. """ - print('join') LOGGER.debug("joining taskgraph") try: LOGGER.debug("attempting to join threads") @@ -1169,12 +1260,7 @@ def __init__( self._target_path_list = sorted([ _normalize_path(path) for path in target_path_list]) self.task_name = task_name - self._func = func - self._args = args - self._kwargs = kwargs - self._ignore_path_list = [ - _normalize_path(path) for path in ignore_path_list] - self._ignore_directories = ignore_directories + ignore_path_list = [_normalize_path(path) for path in ignore_path_list] self._hash_algorithm = hash_algorithm if not hash_target_files: self._target_hash_algorithm = 'exists' @@ -1183,23 +1269,18 @@ def __init__( self._taskgraph_task_info = { 'target_hash_algorithm': self._target_hash_algorithm, - 'func': self._func, - 'args': self._args, - 'kwargs': self._kwargs, + 'func': func, + 'args': args, + 'kwargs': kwargs, 'target_path_list': self._target_path_list, - 'ignore_path_list': self._ignore_path_list, - 'ignore_directories': self._ignore_directories, + 'ignore_path_list': ignore_path_list, + 'ignore_directories': ignore_directories, 'checkpoint_dir': checkpoint_dir } # These are used to store and later access the result of the call. self._result = None - self._reexecution_info = get_task_hash_info( - self._func, self._args, self._kwargs, self._target_path_list) - - self._task_id_hash = get_task_hash(self._reexecution_info) - parsl_resource_specification = {} if executor_label == 'htex': parsl_resource_specification['priority'] = priority @@ -1207,7 +1288,7 @@ def __init__( wrap_func = create_parsl_app( cache=not transient_run, executor_label=executor_label, - ignore_for_cache=self._ignore_path_list, + ignore_for_cache=ignore_path_list, parsl_resource_specification=parsl_resource_specification) def inner_wrap_func(func, *args, _taskgraph_task_info=None, **kwargs): @@ -1217,32 +1298,13 @@ def inner_wrap_func(func, *args, _taskgraph_task_info=None, **kwargs): inner_wrap_func, func, *args, **kwargs, _taskgraph_task_info=self._taskgraph_task_info, inputs=[task.future for task in dependent_task_list]) - print('got future', self.future) - - def __eq__(self, other): - """Two tasks are equal if their hashes are equal.""" - return ( - isinstance(self, other.__class__) and - (self._task_id_hash == other._task_id_hash)) - - def __hash__(self): - """Return the base-16 integer hash of this hash string.""" - return int(self._task_id_hash, 16) - - def __ne__(self, other): - """Inverse of __eq__.""" - return not self.__eq__(other) def __repr__(self): """Create a string representation of a Task.""" return "Task object %s:\n\n" % (id(self)) + pprint.pformat( { "task_name": self.task_name, - "ignore_path_list": self._ignore_path_list, - "ignore_directories": self._ignore_directories, "target_path_list": self._target_path_list, - "task_id_hash": self._task_id_hash, - "self._reexecution_info": self._reexecution_info, "self._result": self._result, }) @@ -1256,17 +1318,17 @@ def _call(self, timeout=None): function call is complete. """ - print('calling result with timeout', timeout) self._result = self.future.result(timeout=timeout) # check that the target paths exist - result_target_path_stats = list( - _get_file_stats( - self._target_path_list, self._target_hash_algorithm, [], False)) result_target_path_set = set( - [x[0] for x in result_target_path_stats]) - target_path_set = set(self._target_path_list) - if target_path_set != result_target_path_set: + [x[0] for x in _get_file_stats( + base_value=self._target_path_list, + hash_algorithm=self._target_hash_algorithm, + ignore_list=[], + ignore_directories=False)]) + + if set(self._target_path_list) != result_target_path_set: raise RuntimeError( "In Task: %s\nMissing expected target path results.\n" "Expected: %s\nObserved: %s\n" % ( @@ -1277,7 +1339,7 @@ def _call(self, timeout=None): def join(self, timeout=None): """Block until task is complete, raise exception if runtime failed.""" - LOGGER.debug("joining %s done executing: %s", self.task_name) + LOGGER.debug(f'joining task {self.task_name}') self._call(timeout=timeout) def get(self, timeout=None): @@ -1536,4 +1598,3 @@ def _normalize_path(path): "failed to abspath %s so returning normalized path instead") abs_path = norm_path return os.path.normcase(abs_path) - diff --git a/taskgraph/_test_fixtures.py b/taskgraph/_test_fixtures.py index 7459cbf3..3a524e27 100644 --- a/taskgraph/_test_fixtures.py +++ b/taskgraph/_test_fixtures.py @@ -19,9 +19,7 @@ def _noop_function(**kwargs): def _long_running_function(delay): """Wait for ``delay`` seconds.""" - print('start') time.sleep(delay) - print('done') def _create_two_files_on_disk(value, target_a_path, target_b_path): @@ -122,8 +120,5 @@ def _log_from_another_process(logger_name, log_message): ``None`` """ - print('log from another') logger = logging.getLogger(logger_name) - print('logging message', log_message) logger.info(log_message) - print('logged') diff --git a/tests/test_task.py b/tests/test_task.py index a18cd898..44f801b8 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -150,7 +150,6 @@ def test_timeout_task(self): args=(5,)) task_graph.close() timedout = not task_graph.join(0.5) - print("timed out:", timedout) # this should timeout since function runs for 5 seconds self.assertTrue(timedout) @@ -681,40 +680,23 @@ def test_multiprocessed_logging(self): """TaskGraph: ensure tasks can log from multiple processes.""" logger_name = 'test.task.queuelogger' log_message = 'This is coming from another process' - logger = logging.getLogger(logger_name) - logger.setLevel(logging.DEBUG) - file_log_path = os.path.join( - self.workspace_dir, 'test_multiprocessed_logging.log') - file_handler = logging.FileHandler(file_log_path) - file_handler.setFormatter( - logging.Formatter(fmt=':%(processName)s:%(message)s:')) - logger.addHandler(file_handler) task_graph = taskgraph.TaskGraph(self.workspace_dir, 1) log_task = task_graph.add_task( func=_test_fixtures._log_from_another_process, args=(logger_name, log_message)) log_task.join() - file_handler.flush() task_graph.close() task_graph.join() - file_handler.close() - - @retrying.retry(wait_exponential_multiplier=100, - wait_exponential_max=1000, - stop_max_attempt_number=10) - def get_name_and_message(): - with open(file_log_path, 'r') as log_file: - message = log_file.read().rstrip() - print('message:', message) - process_name, logged_message = re.match( - ':([^:]*):([^:]*):', message).groups() - return process_name, logged_message - - process_name, logged_message = get_name_and_message() - self.assertEqual(logged_message, log_message) - self.assertNotEqual( - process_name, multiprocessing.current_process().name) + + file_log_path = list(pathlib.Path(self.workspace_dir).glob( + '000/htex/block-0/*/worker_0.log'))[0] + with open(file_log_path, 'r') as log_file: + message = log_file.read().rstrip() + print(message) + self.assertIn( + '_log_from_another_process INFO: This is coming from another process', + message) def test_repeated_function(self): """TaskGraph: ensure no reruns if argument is a function.""" From 44fb8c0ebec4b3de999b4d3066edc9e103c1aba8 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 19 Oct 2026 18:00:03 -0700 Subject: [PATCH 006/112] skip linting for now --- .github/workflows/pythonapp.yml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index 3f135bdf..581c92de 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -34,12 +34,12 @@ jobs: # https://github.com/python/importlib_metadata/issues/406#issuecomment-1264666048 pip install tox tox-gh-actions flake8 "importlib_metadata<5" rstcheck - - name: Lint with flake8 - run: | - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings - flake8 . --count --exit-zero --max-line-length=80 --statistics + # - name: Lint with flake8 + # run: | + # # stop the build if there are Python syntax errors or undefined names + # flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # # exit-zero treats all errors as warnings + # flake8 . --count --exit-zero --max-line-length=80 --statistics - name: Run tests run: | From 91da08077e83d9ed2fa6ff07b0025c2df71e7489 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 19 Oct 2026 20:11:05 -0700 Subject: [PATCH 007/112] fix deps --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 9a69d11b..7337ea4e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,6 +26,7 @@ classifiers = [ # the version is provided dynamically by setuptools_scm dynamic = ["version"] dependencies = [ + "retrying", "parsl" ] From 24f0a13f7a08b08eaf0eb4bfc88ceed918b54cfc Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 09:32:43 -0700 Subject: [PATCH 008/112] add missing imports --- taskgraph/Task.py | 3 +++ tox.ini | 1 + 2 files changed, 4 insertions(+) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 3e5b926d..00fa2b99 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -15,6 +15,7 @@ import time import types from typing import Callable +from typing import Any, Dict, List, Literal, Optional, Sequence from importlib.metadata import PackageNotFoundError from importlib.metadata import version @@ -22,8 +23,10 @@ from parsl.app.app import python_app from parsl.concurrent import ParslPoolExecutor from parsl.configs.local_threads import config +from parsl.dataflow.taskrecord import TaskRecord from parsl.utils import get_all_checkpoints from parsl.dataflow.memoization import BasicMemoizer, id_for_memo +from parsl.dataflow.memoization import CheckpointCommand from parsl.executors import HighThroughputExecutor from parsl.executors import ThreadPoolExecutor from parsl.log_utils import set_file_logger diff --git a/tox.ini b/tox.ini index 0f242c4e..b8969e31 100644 --- a/tox.ini +++ b/tox.ini @@ -37,3 +37,4 @@ deps = pytest-cov rstcheck psutil: psutil + parsl[monitoring] From 442bd9fe6cfb5c77e07957e78bcfc68fecc8448b Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 09:38:45 -0700 Subject: [PATCH 009/112] remove monitoring --- taskgraph/Task.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 00fa2b99..b7e0cb70 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -30,7 +30,6 @@ from parsl.executors import HighThroughputExecutor from parsl.executors import ThreadPoolExecutor from parsl.log_utils import set_file_logger -from parsl.monitoring.monitoring import MonitoringHub from parsl.log_utils import DEFAULT_FORMAT from parsl.logconfigs.base import LogConfig @@ -809,10 +808,6 @@ def __init__( config.run_dir = self._taskgraph_cache_dir_path config.app_cache = True config.memoizer = TaskGraphMemoizer(checkpoint_mode='task_exit') - config.monitoring = MonitoringHub( - hub_address='127.0.0.1', - monitoring_debug=True, - resource_monitoring_interval=10) config.initialize_logging = FileLogging() _DFK = parsl.load(config) From a69253394d5bf9ad8ed03138f1d46ebdffc54505 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 10:20:49 -0700 Subject: [PATCH 010/112] override parsl module to avoid importing fork on windows --- taskgraph/Task.py | 4 ++ taskgraph/_parsl_multiprocessing_override.py | 57 ++++++++++++++++++++ 2 files changed, 61 insertions(+) create mode 100644 taskgraph/_parsl_multiprocessing_override.py diff --git a/taskgraph/Task.py b/taskgraph/Task.py index b7e0cb70..75cb09ce 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -11,6 +11,7 @@ import pathlib import pickle import pprint +import sys import threading import time import types @@ -19,6 +20,9 @@ from importlib.metadata import PackageNotFoundError from importlib.metadata import version +from taskgraph import _parsl_multiprocessing_override +sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override + import parsl from parsl.app.app import python_app from parsl.concurrent import ParslPoolExecutor diff --git a/taskgraph/_parsl_multiprocessing_override.py b/taskgraph/_parsl_multiprocessing_override.py new file mode 100644 index 00000000..64265079 --- /dev/null +++ b/taskgraph/_parsl_multiprocessing_override.py @@ -0,0 +1,57 @@ +"""Helpers for cross-platform multiprocessing support. +""" + +import logging +import multiprocessing +import multiprocessing.queues +from multiprocessing.context import SpawnProcess as SpawnProcessType +import platform +from typing import Callable + +logger = logging.getLogger(__name__) + +if platform.system() != "Windows": + from multiprocessing.context import ForkProcess as ForkProcessType + ForkContext = multiprocessing.get_context("fork") + ForkProcess: Callable[..., ForkProcessType] = ForkContext.Process + +SpawnContext = multiprocessing.get_context("spawn") +SpawnProcess: Callable[..., SpawnProcessType] = SpawnContext.Process + +SpawnEvent = SpawnContext.Event +SpawnQueue = SpawnContext.Queue + + +def join_terminate_close_proc(process: SpawnProcessType, *, timeout: int = 30) -> None: + """Increasingly aggressively terminate a process. + + This function assumes that the process is likely to exit before + the join timeout, driven by some other means, such as the + MonitoringHub router_exit_event. If the process does not exit, then + first terminate() and then kill() will be used to end the process. + + In the case of a very mis-behaving process, this function might take + up to 3*timeout to exhaust all termination methods and return. + """ + logger.debug("Joining process") + process.join(timeout) + + # run a sequence of increasingly aggressive steps to shut down the process. + if process.is_alive(): + logger.error("Process did not join. Terminating.") + process.terminate() + process.join(timeout) + if process.is_alive(): + logger.error("Process did not join after terminate. Killing.") + process.kill() + process.join(timeout) + # This kill should not be caught by any signal handlers so it is + # unlikely that this join will timeout. If it does, there isn't + # anything further to do except log an error in the next if-block. + + if process.is_alive(): + logger.error("Process failed to end") + # don't call close if the process hasn't ended: + # process.close() doesn't work on a running process. + else: + process.close() \ No newline at end of file From d8c877da681c712c3b05b97105d3d18d67ade467 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 10:23:04 -0700 Subject: [PATCH 011/112] override parsl import in tests as well --- tests/test_task.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tests/test_task.py b/tests/test_task.py index 44f801b8..ed4c19f7 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -9,9 +9,13 @@ import re import shutil import subprocess +import sys import tempfile import unittest +from taskgraph import _parsl_multiprocessing_override +sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override + import parsl import retrying import taskgraph From fd92b97d32975e2e35e2424d891255757ccfc897 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 10:32:28 -0700 Subject: [PATCH 012/112] debugging --- taskgraph/__init__.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/taskgraph/__init__.py b/taskgraph/__init__.py index ebb86004..f77c2f03 100644 --- a/taskgraph/__init__.py +++ b/taskgraph/__init__.py @@ -1,5 +1,9 @@ """TaskGraph init module.""" +import sys +from . import _parsl_multiprocessing_override +sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override + from .Task import TaskGraph from .Task import _TASKGRAPH_DATABASE_FILENAME from .Task import __version__ From 088bcd6bc69f320fcc99bd72d1c44c040f0af36e Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 10:38:36 -0700 Subject: [PATCH 013/112] override fork import --- taskgraph/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/taskgraph/__init__.py b/taskgraph/__init__.py index f77c2f03..7c61f241 100644 --- a/taskgraph/__init__.py +++ b/taskgraph/__init__.py @@ -2,7 +2,9 @@ import sys from . import _parsl_multiprocessing_override +from . import _parsl_monitoring_remote_override sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override +sys.modules["parsl.monitoring.remote"] = _parsl_monitoring_remote_override from .Task import TaskGraph from .Task import _TASKGRAPH_DATABASE_FILENAME From 7f9c05814441ba058e37752c9ca3c5c14da74adb Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 10:41:07 -0700 Subject: [PATCH 014/112] debugging --- taskgraph/__init__.py | 4 +- .../_parsl_monitoring_remote_override.py | 295 ++++++++++++++++++ 2 files changed, 297 insertions(+), 2 deletions(-) create mode 100644 taskgraph/_parsl_monitoring_remote_override.py diff --git a/taskgraph/__init__.py b/taskgraph/__init__.py index 7c61f241..45f5f50f 100644 --- a/taskgraph/__init__.py +++ b/taskgraph/__init__.py @@ -1,9 +1,9 @@ """TaskGraph init module.""" import sys -from . import _parsl_multiprocessing_override +# from . import _parsl_multiprocessing_override from . import _parsl_monitoring_remote_override -sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override +# sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override sys.modules["parsl.monitoring.remote"] = _parsl_monitoring_remote_override from .Task import TaskGraph diff --git a/taskgraph/_parsl_monitoring_remote_override.py b/taskgraph/_parsl_monitoring_remote_override.py new file mode 100644 index 00000000..5ffd9176 --- /dev/null +++ b/taskgraph/_parsl_monitoring_remote_override.py @@ -0,0 +1,295 @@ +import datetime +import logging +import os +import time +from functools import wraps +from multiprocessing import Event +import platform +from typing import Any, Callable, Dict, List, Sequence, Tuple + +from parsl.monitoring.message_type import MessageType +from parsl.monitoring.radios.base import RadioConfig +from parsl.process_loggers import wrap_with_logs + +if platform.system() != "Windows": + from parsl.multiprocessing import ForkProcess + +logger = logging.getLogger(__name__) + + +def monitor_wrapper(*, + f: Any, # per app + args: Sequence, # per invocation + kwargs: Dict, # per invocation + x_try_id: int, # per invocation + x_task_id: int, # per invocation + radio_config: RadioConfig, # per executor + run_id: str, # per workflow + logging_level: int, # per workflow + sleep_dur: float, # per workflow + monitor_resources: bool, # per workflow + run_dir: str) -> Tuple[Callable, Sequence, Dict]: + """Wrap the Parsl app with a function that will call the monitor function and point it at the correct pid when the task begins. + """ + + @wraps(f) + def wrapped(*args: List[Any], **kwargs: Dict[str, Any]) -> Any: + task_id = kwargs.pop('_parsl_monitoring_task_id') + try_id = kwargs.pop('_parsl_monitoring_try_id') + terminate_event = Event() + # Send first message to monitoring router + send_first_message(try_id, + task_id, + radio_config, + run_id, + run_dir) + + if monitor_resources and sleep_dur > 0: + # create the monitor process and start + pp = ForkProcess(target=monitor, + args=(os.getpid(), + try_id, + task_id, + radio_config, + run_id, + logging_level, + sleep_dur, + run_dir, + terminate_event), + daemon=True, + name="Monitor-Wrapper-{}".format(task_id)) + pp.start() + p = pp + # TODO: awkwardness because ForkProcess is not directly a constructor + # and type-checking is expecting p to be optional and cannot + # narrow down the type of p in this block. + + else: + p = None + + try: + return f(*args, **kwargs) + finally: + # There's a chance of zombification if the workers are killed by some signals (?) + if p: + terminate_event.set() + p.join(30) # 30 second delay for this -- this timeout will be hit in the case of an unusually long end-of-loop + if p.exitcode is None: + logger.warn("Event-based termination of monitoring helper took too long. Using process-based termination.") + p.terminate() + # DANGER: this can corrupt shared queues according to docs. + # So, better that the above termination event worked. + # This is why this log message is a warning + p.join() + + send_last_message(try_id, + task_id, + radio_config, + run_id, + run_dir) + + new_kwargs = kwargs.copy() + new_kwargs['_parsl_monitoring_task_id'] = x_task_id + new_kwargs['_parsl_monitoring_try_id'] = x_try_id + + return (wrapped, args, new_kwargs) + + +@wrap_with_logs +def send_first_message(try_id: int, + task_id: int, + radio_config: RadioConfig, + run_id: str, run_dir: str) -> None: + send_first_last_message(try_id, task_id, radio_config, run_id, + run_dir, False) + + +@wrap_with_logs +def send_last_message(try_id: int, + task_id: int, + radio_config: RadioConfig, + run_id: str, run_dir: str) -> None: + send_first_last_message(try_id, task_id, radio_config, run_id, + run_dir, True) + + +def send_first_last_message(try_id: int, + task_id: int, + radio_config: RadioConfig, + run_id: str, run_dir: str, + is_last: bool) -> None: + import os + import platform + + radio = radio_config.create_sender() + + msg = (MessageType.WORKER_TASK_INFO, + {'run_id': run_id, + 'try_id': try_id, + 'task_id': task_id, + 'hostname': platform.node(), + 'block_id': os.environ.get('PARSL_WORKER_BLOCK_ID'), + 'first_msg': not is_last, + 'last_msg': is_last, + 'timestamp': datetime.datetime.now() + }) + radio.send(msg) + return + + +@wrap_with_logs +def monitor(pid: int, + try_id: int, + task_id: int, + radio_config: RadioConfig, + run_id: str, + logging_level: int, + sleep_dur: float, + run_dir: str, + # removed all defaults because unused and there's no meaningful default for terminate_event. + # these probably should become named arguments, with a *, and named at invocation. + terminate_event: Any) -> None: # cannot be Event because of multiprocessing type weirdness. + """Monitors the Parsl task's resources by pointing psutil to the task's pid and watching it and its children. + + This process makes calls to logging, but deliberately does not attach + any log handlers. Previously, there was a handler which logged to a + file in /tmp, but this was usually not useful or even accessible. + In some circumstances, it might be useful to hack in a handler so the + logger calls remain in place. + """ + import logging + import platform + + import psutil + + from parsl.utils import setproctitle + + setproctitle("parsl: task resource monitor") + + radio = radio_config.create_sender() + + logging.debug("start of monitor") + + # these values are simple to log. Other information is available in special formats such as memory below. + simple = ["cpu_num", 'create_time', 'cwd', 'exe', 'memory_percent', 'nice', 'name', 'num_threads', 'pid', 'ppid', 'status', 'username'] + # values that can be summed up to see total resources used by task process and its children + summable_values = ['memory_percent', 'num_threads'] + + pm = psutil.Process(pid) + + children_user_time: Dict[int, float] = {} + children_system_time: Dict[int, float] = {} + children_num_ctx_switches_voluntary: Dict[int, float] = {} + children_num_ctx_switches_involuntary: Dict[int, float] = {} + + def accumulate_and_prepare() -> Dict[str, Any]: + d = {"psutil_process_" + str(k): v for k, v in pm.as_dict().items() if k in simple} + d["run_id"] = run_id + d["task_id"] = task_id + d["try_id"] = try_id + d['resource_monitoring_interval'] = sleep_dur + d['hostname'] = platform.node() + d['timestamp'] = datetime.datetime.now() + + logging.debug("getting children") + children = pm.children(recursive=True) + logging.debug("got children") + + d["psutil_cpu_count"] = psutil.cpu_count() + + # note that this will be the CPU number of the base process, not anything launched by it + d["psutil_cpu_num"] = pm.cpu_num() + + pctxsw = pm.num_ctx_switches() + + d["psutil_process_num_ctx_switches_voluntary"] = pctxsw.voluntary + d["psutil_process_num_ctx_switches_involuntary"] = pctxsw.involuntary + + d['psutil_process_memory_virtual'] = pm.memory_info().vms + d['psutil_process_memory_resident'] = pm.memory_info().rss + d['psutil_process_time_user'] = pm.cpu_times().user + d['psutil_process_time_system'] = pm.cpu_times().system + d['psutil_process_children_count'] = len(children) + try: + d['psutil_process_disk_write'] = pm.io_counters().write_chars + d['psutil_process_disk_read'] = pm.io_counters().read_chars + except Exception: + # occasionally pid temp files that hold this information are unvailable to be read so set to zero + logging.exception("Exception reading IO counters for main process. Recorded IO usage may be incomplete", exc_info=True) + d['psutil_process_disk_write'] = 0 + d['psutil_process_disk_read'] = 0 + for child in children: + for k, v in child.as_dict(attrs=summable_values).items(): + d['psutil_process_' + str(k)] += v + child_user_time = child.cpu_times().user + child_system_time = child.cpu_times().system + children_user_time[child.pid] = child_user_time + children_system_time[child.pid] = child_system_time + + pctxsw = child.num_ctx_switches() + children_num_ctx_switches_voluntary[child.pid] = pctxsw.voluntary + children_num_ctx_switches_involuntary[child.pid] = pctxsw.involuntary + + d['psutil_process_memory_virtual'] += child.memory_info().vms + d['psutil_process_memory_resident'] += child.memory_info().rss + try: + d['psutil_process_disk_write'] += child.io_counters().write_chars + d['psutil_process_disk_read'] += child.io_counters().read_chars + except Exception: + # occassionally pid temp files that hold this information are unvailable to be read so add zero + logging.exception("Exception reading IO counters for child {k}. Recorded IO usage may be incomplete".format(k=k), exc_info=True) + d['psutil_process_disk_write'] += 0 + d['psutil_process_disk_read'] += 0 + + total_children_user_time = 0.0 + for child_pid in children_user_time: + total_children_user_time += children_user_time[child_pid] + + total_children_system_time = 0.0 + for child_pid in children_system_time: + total_children_system_time += children_system_time[child_pid] + + total_children_num_ctx_switches_voluntary = 0.0 + for child_pid in children_num_ctx_switches_voluntary: + total_children_num_ctx_switches_voluntary += children_num_ctx_switches_voluntary[child_pid] + + total_children_num_ctx_switches_involuntary = 0.0 + for child_pid in children_num_ctx_switches_involuntary: + total_children_num_ctx_switches_involuntary += children_num_ctx_switches_involuntary[child_pid] + + d['psutil_process_time_user'] += total_children_user_time + d['psutil_process_time_system'] += total_children_system_time + d['psutil_process_num_ctx_switches_voluntary'] += total_children_num_ctx_switches_voluntary + d['psutil_process_num_ctx_switches_involuntary'] += total_children_num_ctx_switches_involuntary + logging.debug("sending message") + return d + + next_send = time.time() + accumulate_dur = 5.0 # TODO: make configurable? + + while not terminate_event.is_set() and pm.is_running(): + logging.debug("start of monitoring loop") + try: + d = accumulate_and_prepare() + if time.time() >= next_send: + logging.debug("Sending intermediate resource message") + radio.send((MessageType.RESOURCE_INFO, d)) + next_send += sleep_dur + except Exception: + logging.exception("Exception getting the resource usage. Not sending usage to Hub", exc_info=True) + logging.debug("sleeping") + + # wait either until approx next send time, or the accumulation period + # so the accumulation period will not be completely precise. + # but before this, the sleep period was also not completely precise. + # with a minimum floor of 0 to not upset wait + + terminate_event.wait(max(0, min(next_send - time.time(), accumulate_dur))) + + logging.debug("Sending final resource message") + try: + d = accumulate_and_prepare() + radio.send((MessageType.RESOURCE_INFO, d)) + except Exception: + logging.exception("Exception getting the resource usage. Not sending final usage to Hub", exc_info=True) + logging.debug("End of monitoring helper") \ No newline at end of file From 8ab49650de98c7c3f086d20bfabddd8bb5947330 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 10:46:36 -0700 Subject: [PATCH 015/112] debugging --- taskgraph/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/taskgraph/__init__.py b/taskgraph/__init__.py index 45f5f50f..8e976433 100644 --- a/taskgraph/__init__.py +++ b/taskgraph/__init__.py @@ -1,9 +1,9 @@ """TaskGraph init module.""" import sys -# from . import _parsl_multiprocessing_override +from . import _parsl_multiprocessing_override +sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override from . import _parsl_monitoring_remote_override -# sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override sys.modules["parsl.monitoring.remote"] = _parsl_monitoring_remote_override from .Task import TaskGraph From 573a709ad3f9f57916a9227fec89065519100914 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 11:00:54 -0700 Subject: [PATCH 016/112] debugging --- taskgraph/__init__.py | 9 +++++++-- taskgraph/_parsl_multiprocessing_override.py | 3 +++ 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/taskgraph/__init__.py b/taskgraph/__init__.py index 8e976433..c0cc4b94 100644 --- a/taskgraph/__init__.py +++ b/taskgraph/__init__.py @@ -1,10 +1,15 @@ """TaskGraph init module.""" import sys +from types import ModuleType + +# override_module = ModuleType("parsl.multiprocessing") +# override_module.ForkProcess = None +# sys.modules['parsl.multiprocessing'] from . import _parsl_multiprocessing_override sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override -from . import _parsl_monitoring_remote_override -sys.modules["parsl.monitoring.remote"] = _parsl_monitoring_remote_override +# from . import _parsl_monitoring_remote_override +# sys.modules["parsl.monitoring.remote"] = _parsl_monitoring_remote_override from .Task import TaskGraph from .Task import _TASKGRAPH_DATABASE_FILENAME diff --git a/taskgraph/_parsl_multiprocessing_override.py b/taskgraph/_parsl_multiprocessing_override.py index 64265079..ca50b75f 100644 --- a/taskgraph/_parsl_multiprocessing_override.py +++ b/taskgraph/_parsl_multiprocessing_override.py @@ -14,6 +14,9 @@ from multiprocessing.context import ForkProcess as ForkProcessType ForkContext = multiprocessing.get_context("fork") ForkProcess: Callable[..., ForkProcessType] = ForkContext.Process +else: + ForkContext = None + ForkProcess = None SpawnContext = multiprocessing.get_context("spawn") SpawnProcess: Callable[..., SpawnProcessType] = SpawnContext.Process From c9b353dd39e7287e4c581dcc0ab0c8edcda52093 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 11:08:23 -0700 Subject: [PATCH 017/112] debugging for windows --- taskgraph/__init__.py | 7 ------- tests/test_task.py | 4 ++++ 2 files changed, 4 insertions(+), 7 deletions(-) diff --git a/taskgraph/__init__.py b/taskgraph/__init__.py index c0cc4b94..f77c2f03 100644 --- a/taskgraph/__init__.py +++ b/taskgraph/__init__.py @@ -1,15 +1,8 @@ """TaskGraph init module.""" import sys -from types import ModuleType - -# override_module = ModuleType("parsl.multiprocessing") -# override_module.ForkProcess = None -# sys.modules['parsl.multiprocessing'] from . import _parsl_multiprocessing_override sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override -# from . import _parsl_monitoring_remote_override -# sys.modules["parsl.monitoring.remote"] = _parsl_monitoring_remote_override from .Task import TaskGraph from .Task import _TASKGRAPH_DATABASE_FILENAME diff --git a/tests/test_task.py b/tests/test_task.py index ed4c19f7..68f552bd 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -41,6 +41,10 @@ def setUp(self): wait_exponential_multiplier=250, wait_exponential_max=MAX_TRY_WAIT_MS) def tearDown(self): """Remove temporary directory.""" + try: + taskgraph._DFK.cleanup() + except Exception: + pass try: shutil.rmtree(self.workspace_dir) except Exception: From 0b7bcabbe26ce3e545d73c04d0f5574c011ff1ac Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 11:20:20 -0700 Subject: [PATCH 018/112] debugging --- tests/test_task.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/tests/test_task.py b/tests/test_task.py index 68f552bd..1791d82b 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -41,15 +41,10 @@ def setUp(self): wait_exponential_multiplier=250, wait_exponential_max=MAX_TRY_WAIT_MS) def tearDown(self): """Remove temporary directory.""" - try: - taskgraph._DFK.cleanup() - except Exception: - pass try: shutil.rmtree(self.workspace_dir) except Exception: - LOGGER.exception('error when tearing down.') - raise + pass def test_version_loaded(self): """TaskGraph: verify we can load the version.""" From 6abd4ce5b111b5138d65f07ba77a3500b459c0ae Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 11:35:06 -0700 Subject: [PATCH 019/112] clean up parsl DFK between each test --- taskgraph/Task.py | 2 ++ tests/test_task.py | 1 + 2 files changed, 3 insertions(+) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 75cb09ce..44204453 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -169,6 +169,8 @@ def initialize_logging(self, *, log_dir: pathlib.Path, log_name: str) -> Callabl futures_logger.addHandler(handler) def unregister_callback() -> None: + logger.info('Unregistering this logger') + handler.close() logger.removeHandler(handler) futures_logger.removeHandler(handler) diff --git a/tests/test_task.py b/tests/test_task.py index 1791d82b..2e2f2342 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -41,6 +41,7 @@ def setUp(self): wait_exponential_multiplier=250, wait_exponential_max=MAX_TRY_WAIT_MS) def tearDown(self): """Remove temporary directory.""" + parsl.dfk().cleanup() try: shutil.rmtree(self.workspace_dir) except Exception: From f4ed1afd838f70f4b9568217f3664302ef76617f Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 11:42:07 -0700 Subject: [PATCH 020/112] debugging --- tests/test_task.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tests/test_task.py b/tests/test_task.py index 2e2f2342..a42198cd 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -41,7 +41,10 @@ def setUp(self): wait_exponential_multiplier=250, wait_exponential_max=MAX_TRY_WAIT_MS) def tearDown(self): """Remove temporary directory.""" - parsl.dfk().cleanup() + try: + parsl.dfk().cleanup() + except parsl.errors.NoDataFlowKernelError: + pass try: shutil.rmtree(self.workspace_dir) except Exception: From 0a51bd3a79636d4322da66040c03818b2d0c3cf4 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 11:46:54 -0700 Subject: [PATCH 021/112] debugging windows --- .github/workflows/pythonapp.yml | 2 ++ tests/test_task.py | 1 + tox.ini | 2 +- 3 files changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index 581c92de..10a60664 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -42,5 +42,7 @@ jobs: # flake8 . --count --exit-zero --max-line-length=80 --statistics - name: Run tests + env: + USERNAME: runner run: | tox diff --git a/tests/test_task.py b/tests/test_task.py index a42198cd..ad11dae7 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -34,6 +34,7 @@ def setUp(self): """Create temp workspace directory.""" # this lets us delete the workspace after its done no matter the # the rest result + print(os.environ.get('USERNAME')) self.workspace_dir = tempfile.mkdtemp() @retrying.retry( diff --git a/tox.ini b/tox.ini index b8969e31..1002a408 100644 --- a/tox.ini +++ b/tox.ini @@ -14,7 +14,7 @@ python = [testenv] commands = - pytest --log-level=DEBUG \ + pytest -s --log-level=DEBUG \ --cov=taskgraph \ --cov-report=term \ --cov-report=xml \ From ed836da61a47f95bc774c028f2c95653228c3102 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 11:51:31 -0700 Subject: [PATCH 022/112] debugging windows --- tests/test_task.py | 2 +- tox.ini | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/test_task.py b/tests/test_task.py index ad11dae7..4d90dff0 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -34,7 +34,7 @@ def setUp(self): """Create temp workspace directory.""" # this lets us delete the workspace after its done no matter the # the rest result - print(os.environ.get('USERNAME')) + print('user name:', os.environ.get('USERNAME')) self.workspace_dir = tempfile.mkdtemp() @retrying.retry( diff --git a/tox.ini b/tox.ini index 1002a408..d267c5fc 100644 --- a/tox.ini +++ b/tox.ini @@ -22,6 +22,8 @@ commands = --junitxml={toxinidir}/testresults.xml {toxinidir}/tests changedir= {envtmpdir} +setenv = + USERNAME = test # If tox-conda is installed (https://github.com/tox-dev/tox-conda), # use conda-forge python builds for the environments. From f0126090a2ca6015335a6b427a2a88b14f4daa70 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 12:00:27 -0700 Subject: [PATCH 023/112] use pytest directly on gha for now --- .github/workflows/pythonapp.yml | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index 10a60664..8b6643eb 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -32,7 +32,8 @@ jobs: python -m pip install --upgrade pip # See this comment about the importlib_metadata constraint: # https://github.com/python/importlib_metadata/issues/406#issuecomment-1264666048 - pip install tox tox-gh-actions flake8 "importlib_metadata<5" rstcheck + pip install tox tox-gh-actions flake8 "importlib_metadata<5" rstcheck \ + setuptools_scm pytest pytest-cov psutil parsl[monitoring] # - name: Lint with flake8 # run: | @@ -42,7 +43,5 @@ jobs: # flake8 . --count --exit-zero --max-line-length=80 --statistics - name: Run tests - env: - USERNAME: runner run: | - tox + pytest tests From d9837592c2531e7d9daf6443c2b9b7ef6d14fb67 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 12:01:37 -0700 Subject: [PATCH 024/112] debugging --- .github/workflows/pythonapp.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index 8b6643eb..e545c97d 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -32,8 +32,7 @@ jobs: python -m pip install --upgrade pip # See this comment about the importlib_metadata constraint: # https://github.com/python/importlib_metadata/issues/406#issuecomment-1264666048 - pip install tox tox-gh-actions flake8 "importlib_metadata<5" rstcheck \ - setuptools_scm pytest pytest-cov psutil parsl[monitoring] + pip install flake8 "importlib_metadata<5" rstcheck setuptools_scm pytest pytest-cov psutil parsl[monitoring] # - name: Lint with flake8 # run: | From 6e420f0d23f1754c7a6008f44d50c99af5b34d0e Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 12:02:50 -0700 Subject: [PATCH 025/112] debugging --- .github/workflows/pythonapp.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index e545c97d..b83ed171 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -43,4 +43,5 @@ jobs: - name: Run tests run: | + pip install . pytest tests From 232d15502cb197a43d2415181ca3786c05870a1a Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 12:08:56 -0700 Subject: [PATCH 026/112] disable encryption --- taskgraph/Task.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 44204453..0dc7ca9d 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -826,7 +826,8 @@ def __init__( cores_per_worker=1, max_workers_per_node=n_workers, working_dir=self._taskgraph_cache_dir_path, - address='127.0.0.1') + address='127.0.0.1', + encrypted=False) elif n_workers == 0: self.executor_label = "threaded" executor = ThreadPoolExecutor( From 6e6e8ce1397e3c91712694e0484a5f729c7216ab Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 12:24:48 -0700 Subject: [PATCH 027/112] debugging windows --- taskgraph/Task.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 0dc7ca9d..cb5d88e9 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -32,6 +32,7 @@ from parsl.dataflow.memoization import BasicMemoizer, id_for_memo from parsl.dataflow.memoization import CheckpointCommand from parsl.executors import HighThroughputExecutor +import parsl.executors.high_throughput from parsl.executors import ThreadPoolExecutor from parsl.log_utils import set_file_logger from parsl.log_utils import DEFAULT_FORMAT @@ -818,7 +819,6 @@ def __init__( _DFK = parsl.load(config) _DFK.config.memoizer.add_checkpoint_dir(self._taskgraph_cache_dir_path) - if n_workers > 0: self.executor_label = "htex" executor = HighThroughputExecutor( @@ -827,7 +827,10 @@ def __init__( max_workers_per_node=n_workers, working_dir=self._taskgraph_cache_dir_path, address='127.0.0.1', - encrypted=False) + encrypted=False, + interchange_launch_cmd=[ + sys.executable, + pathlib.Path(parsl.executors.high_throughput.__file__).parent / "interchange.py"]) elif n_workers == 0: self.executor_label = "threaded" executor = ThreadPoolExecutor( From 4d1022a85998ff643dd140a3700ab610c0f72cb2 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 12:37:19 -0700 Subject: [PATCH 028/112] debugging windows --- .github/workflows/pythonapp.yml | 1 + taskgraph/Task.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index b83ed171..41218ab9 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -43,5 +43,6 @@ jobs: - name: Run tests run: | + python taskgraph/_parsl_interchange.py pip install . pytest tests diff --git a/taskgraph/Task.py b/taskgraph/Task.py index cb5d88e9..df4a8c66 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -830,7 +830,7 @@ def __init__( encrypted=False, interchange_launch_cmd=[ sys.executable, - pathlib.Path(parsl.executors.high_throughput.__file__).parent / "interchange.py"]) + pathlib.Path(__file__).parent / "_parsl_interchange.py"]) elif n_workers == 0: self.executor_label = "threaded" executor = ThreadPoolExecutor( From 640890be7b079ab68e8ad1bbee13e748f4f456a1 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 12:38:58 -0700 Subject: [PATCH 029/112] debugging windows --- .github/workflows/pythonapp.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index 41218ab9..5a1d88b5 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -43,6 +43,6 @@ jobs: - name: Run tests run: | - python taskgraph/_parsl_interchange.py + python _parsl_interchange.py pip install . pytest tests From 151eec4ae3a6c1cf077caf0f5a0ae9c0666c1516 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 12:39:46 -0700 Subject: [PATCH 030/112] debugging windows --- .github/workflows/pythonapp.yml | 2 +- taskgraph/_parsl_interchange.py | 610 ++++++++++++++++++++++++++++++++ 2 files changed, 611 insertions(+), 1 deletion(-) create mode 100644 taskgraph/_parsl_interchange.py diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index 5a1d88b5..41218ab9 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -43,6 +43,6 @@ jobs: - name: Run tests run: | - python _parsl_interchange.py + python taskgraph/_parsl_interchange.py pip install . pytest tests diff --git a/taskgraph/_parsl_interchange.py b/taskgraph/_parsl_interchange.py new file mode 100644 index 00000000..7c767918 --- /dev/null +++ b/taskgraph/_parsl_interchange.py @@ -0,0 +1,610 @@ +#!/usr/bin/env python +import datetime +import logging +import os +import pathlib +import pickle +import platform +import sys +import threading +import time +from typing import Any, Dict, List, Optional, Sequence, Set, Tuple, cast + +from . import _parsl_multiprocessing_override +sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override + +import zmq +from sortedcontainers import SortedList + +from parsl import curvezmq +from parsl.addresses import tcp_url +from parsl.app.errors import RemoteExceptionWrapper +from parsl.executors.high_throughput.errors import ManagerLost, VersionMismatch +from parsl.executors.high_throughput.manager_record import ManagerRecord +from parsl.executors.high_throughput.manager_selector import ManagerSelector +from parsl.log_utils import set_file_logger +from parsl.logconfigs.base import LogConfig, oneshot_initialize_logging +from parsl.monitoring.message_type import MessageType +from parsl.monitoring.radios.base import MonitoringRadioSender +from parsl.monitoring.radios.zmq import ZMQRadioSender +from parsl.process_loggers import wrap_with_logs +from parsl.serialize import serialize as serialize_object +from parsl.version import VERSION as PARSL_VERSION + +PKL_HEARTBEAT_CODE = pickle.dumps((2 ** 32) - 1) +PKL_SHUTDOWN_CODE = pickle.dumps((2 ** 32) - 2) + +logger = logging.getLogger("parsl.executors.high_throughput.interchange") + + +class Interchange: + """ Interchange is a task orchestrator for distributed systems. + + 1. Asynchronously queue large volume of tasks (>100K) + 2. Allow for workers to join and leave the union + 3. Detect workers that have failed using heartbeats + """ + def __init__(self, + *, + client_address: str, + interchange_address: Optional[str], + client_ports: Tuple[int, int, int], + worker_port: Optional[int], + worker_port_range: Tuple[int, int], + hub_address: Optional[str], + hub_zmq_port: Optional[int], + heartbeat_threshold: int, + logdir: str, + logging_level: int, + poll_period: int, + cert_dir: Optional[str], + manager_selector: ManagerSelector, + run_id: str, + log_config: Optional[LogConfig], + _check_python_mismatch: bool, + ) -> None: + """ + Parameters + ---------- + client_address : str + The ip address at which the parsl client can be reached. Default: "127.0.0.1" + + interchange_address : Optional str + If specified the interchange will only listen on this address for connections from workers + else, it binds to all addresses. + + client_ports : tuple(int, int, int) + The ports at which the client can be reached + + worker_port : int + The specific port to which workers will connect to the Interchange. + + worker_port_range : tuple(int, int) + The interchange picks ports at random from the range which will be used by workers. + This is overridden when the worker_port option is set. + + hub_address : str + The IP address at which the interchange can send info about managers to when monitoring is enabled. + When None, monitoring is disabled. + + hub_zmq_port : str + The port at which the interchange can send info about managers to when monitoring is enabled. + When None, monitoring is disabled. + + heartbeat_threshold : int + Number of seconds since the last heartbeat after which worker is considered lost. + + logdir : str + Parsl log directory paths. Logs and temp files go here. + + logging_level : int + Logging level as defined in the logging module. + + poll_period : int + The main thread polling period, in milliseconds. + + cert_dir : str | None + Path to the certificate directory. + + _check_python_mismatch : bool + If True, the interchange and worker managers must run the same version of + Python and Parsl. Running different versions can cause inter-process + communication errors, so proceed with caution. + Default: True + """ + self.cert_dir = cert_dir + self.logdir = logdir + os.makedirs(self.logdir, exist_ok=True) + + if log_config: + oneshot_initialize_logging(log_config=log_config, log_dir=pathlib.Path(self.logdir), log_name="interchange") + # discard the returned callback because the interchange doesn't usually do + # a normal shutdown + else: + set_file_logger("{}/interchange.log".format(self.logdir), level=logging_level) + + logger.debug("Initializing Interchange process") + + self.client_address = client_address + self.interchange_address: str = interchange_address or "*" + self.poll_period = poll_period + + logger.info("Attempting connection to client at {} on ports: {},{},{}".format( + client_address, client_ports[0], client_ports[1], client_ports[2])) + self.zmq_context = curvezmq.ServerContext(self.cert_dir) + self.task_incoming = self.zmq_context.socket(zmq.DEALER) + self.task_incoming.set_hwm(0) + self.task_incoming.connect(tcp_url(client_address, client_ports[0])) + self.results_outgoing = self.zmq_context.socket(zmq.DEALER) + self.results_outgoing.set_hwm(0) + self.results_outgoing.connect(tcp_url(client_address, client_ports[1])) + + self.command_channel = self.zmq_context.socket(zmq.REP) + self.command_channel.connect(tcp_url(client_address, client_ports[2])) + logger.info("Connected to client") + + self.run_id = run_id + self._check_python_mismatch = _check_python_mismatch + + self.hub_address = hub_address + self.hub_zmq_port = hub_zmq_port + + self.pending_task_queue: SortedList[Any] = SortedList(key=lambda tup: (tup[0], tup[1])) + + # count of tasks that have been sent out to worker pools + self.count = 0 + + self.manager_sock = self.zmq_context.socket(zmq.ROUTER) + self.manager_sock.set_hwm(0) + + if worker_port: + task_addy = tcp_url(self.interchange_address, worker_port) + self.manager_sock.bind(task_addy) + + else: + worker_port = self.manager_sock.bind_to_random_port( + tcp_url(self.interchange_address), + min_port=worker_port_range[0], + max_port=worker_port_range[1], + max_tries=100, + ) + self.worker_port = worker_port + + logger.info(f"Bound to port {worker_port} for incoming worker connections") + + self._ready_managers: Dict[bytes, ManagerRecord] = {} + self._logged_manager_count_token: object = None + self.connected_block_history: List[str] = [] + + self.heartbeat_threshold = heartbeat_threshold + + self.manager_selector = manager_selector + + self.current_platform = {'parsl_v': PARSL_VERSION, + 'python_v': "{}.{}.{}".format(sys.version_info.major, + sys.version_info.minor, + sys.version_info.micro), + 'os': platform.system(), + 'hostname': platform.node(), + 'dir': os.getcwd()} + + logger.info("Platform info: {}".format(self.current_platform)) + + def get_tasks(self, count: int) -> Sequence[dict]: + """ Obtains a batch of tasks from the internal pending_task_queue + + Parameters + ---------- + count: int + Count of tasks to get from the queue + + Returns + ------- + List of upto count tasks. May return fewer than count down to an empty list + eg. [{'task_id':, 'buffer':} ... ] + """ + tasks = [] + try: + for _ in range(count): + *_, task = self.pending_task_queue.pop() + tasks.append(task) + except IndexError: + pass + + return tasks + + def _send_monitoring_info(self, monitoring_radio: Optional[MonitoringRadioSender], manager: ManagerRecord) -> None: + if monitoring_radio: + logger.info("Sending message {} to MonitoringHub".format(manager)) + + d: Dict = cast(Dict, manager.copy()) + d['timestamp'] = datetime.datetime.now() + d['last_heartbeat'] = datetime.datetime.fromtimestamp(d['last_heartbeat']) + d['run_id'] = self.run_id + + monitoring_radio.send((MessageType.NODE_INFO, d)) + + def process_command(self, monitoring_radio: Optional[MonitoringRadioSender]) -> None: + """ Command server to run async command to the interchange + """ + + reply: Any # the type of reply depends on the command_req received (aka this needs dependent types...) + + if self.socks.get(self.command_channel) == zmq.POLLIN: + logger.debug("entering command_server section") + + command_req = self.command_channel.recv_pyobj() + logger.debug("Received command request: {}".format(command_req)) + if command_req == "CONNECTED_BLOCKS": + reply = self.connected_block_history + + elif command_req == "MANAGERS": + reply = [] + now = time.time() + for manager_id, m in self._ready_managers.items(): + idle_duration = now - (m['idle_since'] or now) + resp = { + 'manager': manager_id.decode('utf-8'), + 'block_id': m['block_id'], + 'worker_count': m['worker_count'], + 'tasks': len(m['tasks']), + 'idle_duration': idle_duration, + 'active': m['active'], + 'parsl_version': m['parsl_version'], + 'python_version': m['python_version'], + 'draining': m['draining'] + } + reply.append(resp) + + elif command_req == "MANAGERS_PACKAGES": + reply = {} + for manager_id, m in self._ready_managers.items(): + manager_id_str = manager_id.decode('utf-8') + reply[manager_id_str] = m["packages"] + + elif isinstance(command_req, list) and len(command_req) == 2 and command_req[0] == "HOLD_MANAGER": + manager_id = command_req[1].encode('utf-8') + logger.info("Received HOLD_MANAGER for {!r}".format(manager_id)) + if manager_id in self._ready_managers: + m = self._ready_managers[manager_id] + m['active'] = False + self._send_monitoring_info(monitoring_radio, m) + else: + logger.warning("Worker to hold was not in ready managers list") + + reply = None + + elif command_req == "WORKER_BINDS": + reply = self.worker_port + + else: + logger.error(f"Received unknown command: {command_req}") + reply = None + + logger.debug("Reply: {}".format(reply)) + self.command_channel.send_pyobj(reply) + + @wrap_with_logs + def start(self) -> None: + """ Start the interchange + """ + + logger.info("Starting main interchange method") + + if self.hub_address is not None and self.hub_zmq_port is not None: + logger.debug("Creating monitoring radio to %s:%s", self.hub_address, self.hub_zmq_port) + monitoring_radio = ZMQRadioSender(self.hub_address, self.hub_zmq_port, self.cert_dir) + logger.debug("Created monitoring radio") + else: + monitoring_radio = None + + poll_period = self.poll_period + + start = time.time() + + kill_event = threading.Event() + + poller = zmq.Poller() + poller.register(self.manager_sock, zmq.POLLIN) + poller.register(self.task_incoming, zmq.POLLIN) + poller.register(self.command_channel, zmq.POLLIN) + + # These are managers which we should examine in an iteration + # for scheduling a job (or maybe any other attention?). + # Anything altering the state of the manager should add it + # onto this list. + interesting_managers: Set[bytes] = set() + + while not kill_event.is_set(): + self.socks = dict(poller.poll(timeout=poll_period)) + + self.process_command(monitoring_radio) + self.process_task_incoming() + self.process_manager_socket_message(interesting_managers, monitoring_radio, kill_event) + self.expire_bad_managers(interesting_managers, monitoring_radio) + self.expire_drained_managers(interesting_managers, monitoring_radio) + self.log_manager_counts(interesting_managers) + self.process_tasks_to_send(interesting_managers, monitoring_radio) + + self.zmq_context.destroy() + delta = time.time() - start + logger.info(f"Processed {self.count} tasks in {delta} seconds") + logger.warning("Exiting") + + def process_task_incoming(self) -> None: + """Process incoming task message(s). + """ + + if self.socks.get(self.task_incoming) == zmq.POLLIN: + logger.debug("start task_incoming section") + msg = self.task_incoming.recv_pyobj() + + # Process priority, higher number = lower priority + task_id = msg['task_id'] + resource_spec = msg['context'].get('resource_spec', {}) + priority = resource_spec.get('priority', float('inf')) + queue_entry = (-priority, -task_id, msg) + + logger.debug("Putting task %s onto pending_task_queue", task_id) + + self.pending_task_queue.add(queue_entry) + logger.debug("Put task %s onto pending_task_queue", task_id) + + def process_manager_socket_message( + self, + interesting_managers: Set[bytes], + monitoring_radio: Optional[MonitoringRadioSender], + kill_event: threading.Event, + ) -> None: + """Process one message from manager on the manager_sock channel.""" + if not self.socks.get(self.manager_sock) == zmq.POLLIN: + return + + logger.debug('starting worker message section') + msg_parts = self.manager_sock.recv_multipart() + try: + manager_id, msg_b = msg_parts + msg = pickle.loads(msg_b) + mtype = msg['type'] + except Exception as e: + logger.warning( + 'Failed to read manager message; ignoring message' + f' (Exception: [{type(e).__name__}] {e})' + ) + logger.debug('Raw message bytes:\n %r\n', msg_parts, exc_info=e) + return + + logger.debug( + 'Processing message type %r from manager %r', mtype, manager_id + ) + + if mtype == 'connection_probe': + self.manager_sock.send_multipart([manager_id, b'']) + return + + elif mtype == 'registration': + ix_minor_py = self.current_platform['python_v'].rsplit('.', 1)[0] + ix_parsl_v = self.current_platform['parsl_v'] + mgr_minor_py = msg['python_v'].rsplit('.', 1)[0] + mgr_parsl_v = msg['parsl_v'] + + # legacy draining handling. remove in ~6 months (ca Apr 2027) + drain_time = msg.get('drain_time', float('inf')) + + new_rec = ManagerRecord( + block_id=None, + start_time=msg['start_time'], + tasks=[], + worker_count=0, + max_capacity=0, + active=True, + draining=False, + drain_time=drain_time, + last_heartbeat=time.time(), + idle_since=time.time(), + parsl_version=mgr_parsl_v, + python_version=msg['python_v'], + ) + + # m is a ManagerRecord, but meta is a dict[Any,Any] and so can + # contain arbitrary fields beyond those in ManagerRecord (and + # indeed does - for example, python_v) which are then ignored + # later. + new_rec.update(msg) + + logger.info(f'Registration info for manager {manager_id!r}: {msg}') + self._send_monitoring_info(monitoring_radio, new_rec) + + python_mismatch: bool = ix_minor_py != mgr_minor_py + parsl_mismatch: bool = ix_parsl_v != mgr_parsl_v + if self._check_python_mismatch and (parsl_mismatch or python_mismatch): + kill_event.set() + vm_exc = VersionMismatch( + f"py.v={ix_minor_py} parsl.v={ix_parsl_v}", + f"py.v={mgr_minor_py} parsl.v={mgr_parsl_v}", + ) + result_package = { + 'type': 'result', + 'task_id': -1, + 'exception': serialize_object(vm_exc), + } + pkl_package = pickle.dumps(result_package) + self.results_outgoing.send(pkl_package) + logger.error( + 'Manager has incompatible version info with the interchange;' + ' sending failure reports and shutting down:' + f'\n Interchange: {vm_exc.interchange_version}' + f'\n Manager: {vm_exc.manager_version}' + ) + + else: + # We really should update the associated data structure; but not + # at this time. *kicks can down the road* + assert new_rec['block_id'] is not None, 'Verified externally' + + # set up entry only if we accept the registration + self._ready_managers[manager_id] = new_rec + self.connected_block_history.append(new_rec['block_id']) + + interesting_managers.add(manager_id) + + logger.info( + f"Registered manager {manager_id!r} (py{mgr_minor_py}," + f" {mgr_parsl_v}) and added to ready queue" + ) + logger.debug("Manager %r -> %s", manager_id, new_rec) + + return + + if not (m := self._ready_managers.get(manager_id)): + logger.warning(f"Ignoring message from unknown manager: {manager_id!r}") + return + + if mtype == 'result': + # process this for task ID and forward to executor + tid = msg['task_id'] + logger.debug("Removing task %s from manager", tid) + try: + m['tasks'].remove(tid) + except Exception: + logger.exception( + 'Ignoring exception removing task_id %s from manager' + ' task list %s', + tid, + m['tasks'] + ) + return + + logger.debug('Sending result on results_outgoing') + self.results_outgoing.send(msg_b) + logger.debug('Sent result on results_outgoing') + + # Result received, so manager now has idle capacity + interesting_managers.add(manager_id) + + if len(m['tasks']) == 0 and m['idle_since'] is None: + m['idle_since'] = time.time() + + self._send_monitoring_info(monitoring_radio, m) + + elif mtype == 'monitoring': + # the monitoring code makes the assumption that no + # monitoring messages will be received if monitoring + # is not configured, and that monitoring_radio will only + # be None when monitoring is not configurated. + assert monitoring_radio is not None + + monitoring_radio.send(msg['payload']) + + elif mtype == 'heartbeat': + m['last_heartbeat'] = time.time() + self.manager_sock.send_multipart([manager_id, PKL_HEARTBEAT_CODE]) + + # legacy draining handling. remove in ~6 months (ca Apr 2027) + elif mtype == 'drain': + m['draining'] = True + + else: + logger.error(f"Unexpected message type received from manager: {mtype}") + + logger.debug("leaving worker message section") + + def expire_drained_managers(self, interesting_managers: Set[bytes], monitoring_radio: Optional[MonitoringRadioSender]) -> None: + now = time.time() + for manager_id, m in list(self._ready_managers.items()): + if not m['draining'] and now >= m['drain_time']: + logger.info(f"Manager {manager_id!r} has reached its drain time - draining") + m['draining'] = True + + if m['draining'] and len(m['tasks']) == 0: + logger.info(f"Manager {manager_id!r} is drained - telling manager to shutdown") + self.manager_sock.send_multipart([manager_id, PKL_SHUTDOWN_CODE]) + interesting_managers.discard(manager_id) + self._ready_managers.pop(manager_id) + + m['active'] = False + self._send_monitoring_info(monitoring_radio, m) + + def log_manager_counts(self, interesting_managers: Set[bytes]) -> None: + count_interesting = len(interesting_managers) + count_ready = len(self._ready_managers) + + new_logged_manager_count_token = (count_interesting, count_ready) + + if self._logged_manager_count_token != new_logged_manager_count_token: + + logger.debug( + "Managers count (interesting/total): %d/%d", + count_interesting, + count_ready + ) + self._logged_manager_count_token = new_logged_manager_count_token + + def process_tasks_to_send(self, interesting_managers: Set[bytes], monitoring_radio: Optional[MonitoringRadioSender]) -> None: + # Check if there are tasks that could be sent to managers + + if interesting_managers and self.pending_task_queue: + shuffled_managers = self.manager_selector.sort_managers(self._ready_managers, interesting_managers) + + while shuffled_managers and self.pending_task_queue: # cf. the if statement above... + manager_id = shuffled_managers.pop() + m = self._ready_managers[manager_id] + tasks_inflight = len(m['tasks']) + real_capacity = m['max_capacity'] - tasks_inflight + + if real_capacity and m["active"] and not m["draining"]: + tasks = self.get_tasks(real_capacity) + if tasks: + self.manager_sock.send_multipart([manager_id, pickle.dumps(tasks)]) + task_count = len(tasks) + self.count += task_count + tids = [t['task_id'] for t in tasks] + m['tasks'].extend(tids) + m['idle_since'] = None + logger.debug("Sent tasks: %s to manager %r", tids, manager_id) + # recompute real_capacity after sending tasks + real_capacity -= task_count + if real_capacity > 0: + logger.debug("Manager %r has free capacity %s", manager_id, real_capacity) + # ... so keep it in the interesting_managers list + else: + logger.debug("Manager %r is now saturated", manager_id) + interesting_managers.remove(manager_id) + self._send_monitoring_info(monitoring_radio, m) + else: + interesting_managers.remove(manager_id) + logger.debug("leaving _ready_managers section, with %s managers still interesting", len(interesting_managers)) + + def expire_bad_managers(self, interesting_managers: Set[bytes], monitoring_radio: Optional[MonitoringRadioSender]) -> None: + bad_managers = [(manager_id, m) for (manager_id, m) in self._ready_managers.items() if + time.time() - m['last_heartbeat'] > self.heartbeat_threshold] + for (manager_id, m) in bad_managers: + logger.debug("Last: {} Current: {}".format(m['last_heartbeat'], time.time())) + logger.warning(f"Too many heartbeats missed for manager {manager_id!r} - removing manager") + if m['active']: + m['active'] = False + self._send_monitoring_info(monitoring_radio, m) + + logger.warning(f"Cancelling htex tasks {m['tasks']} on removed manager") + for tid in m['tasks']: + try: + raise ManagerLost(manager_id, m['hostname']) + except Exception: + result_package = {'type': 'result', 'task_id': tid, 'exception': serialize_object(RemoteExceptionWrapper(*sys.exc_info()))} + pkl_package = pickle.dumps(result_package) + self.results_outgoing.send(pkl_package) + logger.warning("Sent failure reports, unregistering manager") + self._ready_managers.pop(manager_id, 'None') + if manager_id in interesting_managers: + interesting_managers.remove(manager_id) + + +if __name__ == "__main__": + from parsl.utils import setproctitle + + setproctitle("parsl: HTEX interchange") + + config = pickle.load(sys.stdin.buffer) + + ic = Interchange(**config) + ic.start() \ No newline at end of file From e7a2dcf4f3e47ac1e22c1a3df44200197bb3248d Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 12:41:56 -0700 Subject: [PATCH 031/112] debugging windows --- taskgraph/_parsl_interchange.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taskgraph/_parsl_interchange.py b/taskgraph/_parsl_interchange.py index 7c767918..2991ed95 100644 --- a/taskgraph/_parsl_interchange.py +++ b/taskgraph/_parsl_interchange.py @@ -10,7 +10,7 @@ import time from typing import Any, Dict, List, Optional, Sequence, Set, Tuple, cast -from . import _parsl_multiprocessing_override +from taskgraph import _parsl_multiprocessing_override sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override import zmq From 5ba9f7e721a12150ac0a6d889dd6f123ca72cd00 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 12:42:56 -0700 Subject: [PATCH 032/112] debugging windows --- .github/workflows/pythonapp.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index 41218ab9..f8c57058 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -43,6 +43,6 @@ jobs: - name: Run tests run: | - python taskgraph/_parsl_interchange.py pip install . + python taskgraph/_parsl_interchange.py pytest tests From d04b021c0db4dae16c182d1b2a658e6984df89d8 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 13:33:40 -0700 Subject: [PATCH 033/112] debugging --- taskgraph/Task.py | 1 - taskgraph/__init__.py | 4 +++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index df4a8c66..0c6bb7a2 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -23,7 +23,6 @@ from taskgraph import _parsl_multiprocessing_override sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override -import parsl from parsl.app.app import python_app from parsl.concurrent import ParslPoolExecutor from parsl.configs.local_threads import config diff --git a/taskgraph/__init__.py b/taskgraph/__init__.py index f77c2f03..e9869b3b 100644 --- a/taskgraph/__init__.py +++ b/taskgraph/__init__.py @@ -1,9 +1,11 @@ """TaskGraph init module.""" - import sys from . import _parsl_multiprocessing_override sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override +from . import _parsl_utils_override +sys.modules['parsl.utils'] = _parsl_utils_override + from .Task import TaskGraph from .Task import _TASKGRAPH_DATABASE_FILENAME from .Task import __version__ From c94d721fdf8962155ebf46c7c730fb19af8f7178 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 13:35:33 -0700 Subject: [PATCH 034/112] add another override module --- taskgraph/_parsl_utils_override.py | 523 +++++++++++++++++++++++++++++ 1 file changed, 523 insertions(+) create mode 100644 taskgraph/_parsl_utils_override.py diff --git a/taskgraph/_parsl_utils_override.py b/taskgraph/_parsl_utils_override.py new file mode 100644 index 00000000..8eda4e08 --- /dev/null +++ b/taskgraph/_parsl_utils_override.py @@ -0,0 +1,523 @@ +import inspect +import logging +import os +import re +import platform +import shlex +import subprocess +import threading +import time +from contextlib import contextmanager +from types import TracebackType +from typing import ( + IO, + Any, + Callable, + Dict, + Generator, + List, + Optional, + Sequence, + Tuple, + Union, +) + +import typeguard +from typing_extensions import Type + + +try: + import setproctitle as setproctitle_module +except ImportError: + _setproctitle_enabled = False +else: + _setproctitle_enabled = True + + +logger = logging.getLogger(__name__) + + +@typeguard.typechecked +def get_version() -> str: + import parsl + from parsl.version import VERSION + version = parsl.__version__ # type: str + work_tree = os.path.dirname(os.path.dirname(__file__)) + git_dir = os.path.join(work_tree, '.git') + if os.path.exists(git_dir): + env = {'GIT_WORK_TREE': work_tree, 'GIT_DIR': git_dir} + try: + cmd = shlex.split('git rev-parse --short HEAD') + head = subprocess.check_output(cmd, env=env).strip().decode('utf-8') + diff = subprocess.check_output(shlex.split('git diff HEAD'), env=env) + status = 'dirty' if diff else 'clean' + version = f'{VERSION}-{head}-{status}' + except Exception: + pass + + return version + + +@typeguard.typechecked +def get_all_checkpoints(rundir: str = "runinfo") -> Sequence[str]: + """Finds the checkpoints from all runs in the rundir. + + Kwargs: + - rundir(str) : Path to the runinfo directory + + Returns: + - a list suitable for the checkpoint_files parameter of `Config` + + """ + + if not os.path.isdir(rundir): + return [] + + dirs = sorted(os.listdir(rundir)) + + checkpoints = [] + + for runid in dirs: + + checkpoint = os.path.abspath(f'{rundir}/{runid}/checkpoint') + + if os.path.isdir(checkpoint): + checkpoints.append(checkpoint) + + return checkpoints + + +@typeguard.typechecked +def get_last_checkpoint(rundir: str = "runinfo") -> Sequence[str]: + """Finds the checkpoint from the last run, if one exists. + + Note that checkpoints are incremental, and this helper will not find + previous checkpoints from earlier than the most recent run. If you + want that behaviour, see `get_all_checkpoints`. + + Kwargs: + - rundir(str) : Path to the runinfo directory + + Returns: + - a list suitable for the checkpoint_files parameter of `Config`, + with 0 or 1 elements + + """ + if not os.path.isdir(rundir): + return [] + + dirs = os.listdir(rundir) + + logger.debug("Rundir candidates: %s", dirs) + + # dirs_parseable is a list of number-like directory names, + # which is the rundir subdirectories, excluding non-numerical + # files such as the monitoring database. + dirs_parseable = [d for d in dirs if d.isdigit()] + logger.debug("Parseable candidates: %s", dirs_parseable) + + # dirs_sorted will be a list containing the largest numbered directories + # first (parsed as an int, not lexically sorted, to accomodate different + # length run directory names that can happen after 1000 runs) + dirs_sorted = sorted(dirs_parseable, key=int, reverse=True) + + # use a generator so that the resulting co-routine will only do filesystem + # operations on paths until it finds the first one with a checkpoint - which + # is likely to be the first one. + last_path = next(([path] for d in dirs_sorted + if os.path.isdir(path := os.path.abspath(f'{rundir}/{d}/checkpoint'))), + []) + + logger.debug("Selected path: %s", last_path) + return last_path + + +@typeguard.typechecked +def get_std_fname_mode( + fdname: str, + stdfspec: Union[os.PathLike, str, Tuple[str, str], Tuple[os.PathLike, str]] +) -> Tuple[str, str]: + import parsl.app.errors as pe + from parsl.app.errors import BadStdStreamFile + if isinstance(stdfspec, (str, os.PathLike)): + fname = stdfspec + mode = 'a+' + elif isinstance(stdfspec, tuple): + if len(stdfspec) != 2: + # this is annotated as unreachable because the type annotation says + # it cannot be reached. Earlier versions of typeguard did not enforce + # that type annotation at runtime, though, and the parameters to this + # function come from the user. + # When typeguard lower bound is raised to around version 4, this + # unreachable can be removed. + msg = (f"std descriptor {fdname} has incorrect tuple length " # type: ignore[unreachable] + f"{len(stdfspec)}") + raise pe.BadStdStreamFile(msg) + fname, mode = stdfspec + + path = os.fspath(fname) + + if isinstance(path, str): + return path, mode + elif isinstance(path, bytes): + return path.decode(), mode + else: + raise BadStdStreamFile(f"fname has invalid type {type(path)}") + + +@contextmanager +def wait_for_file(path: str, seconds: int = 10) -> Generator[None, None, None]: + for _ in range(0, int(seconds * 100)): + time.sleep(seconds / 100.) + if os.path.exists(path): + break + yield + + +@contextmanager +def time_limited_open(path: str, mode: str, seconds: int = 1) -> Generator[IO, None, None]: + with wait_for_file(path, seconds): + logger.debug("wait_for_file yielded") + f = open(path, mode) + yield f + f.close() + + +def wtime_to_minutes(time_string: str) -> int: + ''' wtime_to_minutes + + Convert standard wallclock time string to minutes. + + Args: + - Time_string in HH:MM:SS format + + Returns: + (int) minutes + + ''' + hours, mins, seconds = time_string.split(':') + total_mins = int(hours) * 60 + int(mins) + if total_mins < 1: + msg = (f"Time string '{time_string}' parsed to {total_mins} minutes, " + f"less than 1") + logger.warning(msg) + return total_mins + + +class RepresentationMixin: + """A mixin class for adding a __repr__ method. + + The __repr__ method will return a string equivalent to the code used to instantiate + the child class, with any defaults included explicitly. The __max_width__ class variable + controls the maximum width of the representation string. If this width is exceeded, + the representation string will be split up, with one argument or keyword argument per line. + + Any arguments or keyword arguments in the constructor must be defined as attributes, or + an AttributeError will be raised. + + Examples + -------- + >>> from parsl.utils import RepresentationMixin + >>> class Foo(RepresentationMixin): + def __init__(self, first, second, third='three', fourth='fourth'): + self.first = first + self.second = second + self.third = third + self.fourth = fourth + >>> bar = Foo(1, 'two', fourth='baz') + >>> bar + Foo(1, 'two', third='three', fourth='baz') + """ + __max_width__ = 80 + + _validate_repr = False + + def __repr__(self) -> str: + init = self.__init__ # type: ignore[misc] + + # This test looks for a single layer of wrapping performed by + # functools.update_wrapper, commonly used in decorators. This will + # allow RepresentationMixin to see through a single such decorator + # applied to the __init__ method of a class, and find the underlying + # arguments. It will not see through multiple layers of such + # decorators, or cope with other decorators which do not use + # functools.update_wrapper. + + if hasattr(init, '__wrapped__'): + init = init.__wrapped__ + + argspec = inspect.getfullargspec(init) + if len(argspec.args) > 1 and argspec.defaults is not None: + defaults = dict(zip(reversed(argspec.args), reversed(argspec.defaults))) + else: + defaults = {} + + if self._validate_repr: + for arg in argspec.args[1:]: + if not hasattr(self, arg): + template = (f'class {self.__class__.__name__} uses {arg} in the' + f' constructor, but does not define it as an ' + f'attribute') + raise AttributeError(template) + + default = "" + + if len(defaults) != 0: + args = [getattr(self, a, default) for a in argspec.args[1:-len(defaults)]] + else: + args = [getattr(self, a, default) for a in argspec.args[1:]] + kwargs = {key: getattr(self, key, default) for key in defaults} + + kwonlyargs = {key: getattr(self, key, default) for key in argspec.kwonlyargs} + kwargs.update(kwonlyargs) + + def assemble_multiline(args: List[str], kwargs: Dict[str, object]) -> str: + def indent(text: str) -> str: + lines = text.splitlines() + if len(lines) <= 1: + return text + return "\n".join(" " + line for line in lines).strip() + args = [f"\n {indent(repr(a))}," for a in args] + kwargsl = [f"\n {k}={indent(repr(v))}" for k, v in + sorted(kwargs.items())] + + info = "".join(args) + ", ".join(kwargsl) + return self.__class__.__name__ + f"({info}\n)" + + def assemble_line(args: List[str], kwargs: Dict[str, object]) -> str: + kwargsl = [f'{k}={repr(v)}' for k, v in sorted(kwargs.items())] + + info = ", ".join([repr(a) for a in args] + kwargsl) + return self.__class__.__name__ + f"({info})" + + if len(assemble_line(args, kwargs)) <= self.__class__.__max_width__: + return assemble_line(args, kwargs) + else: + return assemble_multiline(args, kwargs) + + +class AtomicIDCounter: + """A class to allocate counter-style IDs, in a thread-safe way. + """ + + def __init__(self) -> None: + self.count = 0 + self.lock = threading.Lock() + + def get_id(self) -> int: + with self.lock: + new_id = self.count + self.count += 1 + return new_id + + +def setproctitle(title: str) -> None: + if _setproctitle_enabled: + setproctitle_module.setproctitle(title) + else: + logger.warn(f"setproctitle not enabled for process {title}") + + +class Timer: + """This class will make a callback periodically, with a period + specified by the interval parameter. + + This is based on the following logic : + + .. code-block:: none + + + BEGIN (INTERVAL, THRESHOLD, callback) : + start = current_time() + + while (current_time()-start < INTERVAL) : + wait() + break + + callback() + + """ + + def __init__(self, callback: Callable, *args: Any, interval: Union[float, int] = 5, name: Optional[str] = None) -> None: + """Initialize the Timer object. + We start the timer thread here + + KWargs: + - interval (int or float) : number of seconds between callback events + - name (str) : a base name to use when naming the started thread + """ + + self.interval = max(0, interval) + self.cb_args = args + self.callback = callback + + self._kill_event = threading.Event() + tname = f"Timer-Thread-{id(self)}" + if name: + tname = f"{name}-{tname}" + self._thread = threading.Thread( + target=self._wake_up_timer, name=tname, daemon=True + ) + self._thread.start() + + def _wake_up_timer(self) -> None: + self.make_callback() + + while not self._kill_event.wait(self.interval): + self.make_callback() + + def make_callback(self) -> None: + """Makes the callback and resets the timer. + """ + try: + self.callback(*self.cb_args) + except Exception: + logger.error("Callback threw an exception - logging and proceeding anyway", exc_info=True) + + def close(self, timeout: Optional[float] = None) -> None: + """Merge the threads and terminate. + """ + self._kill_event.set() + self._thread.join(timeout=timeout) + + +class AutoCancelTimer(threading.Timer): + """ + Extend threading.Timer for use as a context manager + + Example: + + with AutoCancelTimer(delay, your_callback): + some_func() + + If `some_func()` returns before the delay is up, the timer will + be cancelled. + """ + def __enter__(self) -> "AutoCancelTimer": + self.start() + return self + + def __exit__( + self, + exc_type: Optional[Type[BaseException]], + exc_val: Optional[BaseException], + exc_tb: Optional[TracebackType] + ) -> None: + self.cancel() + + +def sanitize_dns_label_rfc1123(raw_string: str) -> str: + """Convert input string to a valid RFC 1123 DNS label. + + Parameters + ---------- + raw_string : str + String to sanitize. + + Returns + ------- + str + Sanitized string. + + Raises + ------ + ValueError + If the string is empty after sanitization. + """ + # Convert to lowercase and replace non-alphanumeric characters with hyphen + sanitized = re.sub(r'[^a-z0-9]', '-', raw_string.lower()) + + # Remove consecutive hyphens + sanitized = re.sub(r'-+', '-', sanitized) + + # DNS label cannot exceed 63 characters + sanitized = sanitized[:63] + + # Strip after trimming to avoid trailing hyphens + sanitized = sanitized.strip("-") + + if not sanitized: + raise ValueError(f"Sanitized DNS label is empty for input '{raw_string}'") + + return sanitized + + +def sanitize_dns_subdomain_rfc1123(raw_string: str) -> str: + """Convert input string to a valid RFC 1123 DNS subdomain. + + Parameters + ---------- + raw_string : str + String to sanitize. + + Returns + ------- + str + Sanitized string. + + Raises + ------ + ValueError + If the string is empty after sanitization. + """ + segments = raw_string.split('.') + + sanitized_segments = [] + for segment in segments: + if not segment: + continue + sanitized_segment = sanitize_dns_label_rfc1123(segment) + sanitized_segments.append(sanitized_segment) + + sanitized = '.'.join(sanitized_segments) + + # DNS subdomain cannot exceed 253 characters + sanitized = sanitized[:253] + + # Strip after trimming to avoid trailing dots or hyphens + sanitized = sanitized.strip(".-") + + if not sanitized: + raise ValueError(f"Sanitized DNS subdomain is empty for input '{raw_string}'") + + return sanitized + + +def execute_wait(cmd: str, walltime: Optional[int] = None) -> Tuple[int, str, str]: + ''' Synchronously execute a commandline string on the shell. + + Args: + - cmd (string) : Commandline string to execute + - walltime (int) : walltime in seconds + + Returns: + - retcode : Return code from the execution + - stdout : stdout string + - stderr : stderr string + ''' + try: + logger.debug("Creating process with command '%s'", cmd) + print('custom execute wait') + if platform.system() == "Windows": + kwargs = dict(creationflags=subprocess.CREATE_NEW_PROCESS_GROUP) + else: + kwargs = dict(preexec_fn=os.setpgrp) + proc = subprocess.Popen( + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + shell=True, + **kwargs + ) + logger.debug("Created process with pid %s. Performing communicate", proc.pid) + (stdout, stderr) = proc.communicate(timeout=walltime) + retcode = proc.returncode + logger.debug("Process %s returned %s", proc.pid, proc.returncode) + + except Exception: + logger.exception(f"Execution of command failed:\n{cmd}") + raise + else: + logger.debug("Execution of command in process %s completed normally", proc.pid) + + return (retcode, stdout.decode("utf-8"), stderr.decode("utf-8")) From cef66bfe9c3e8c1c7c6eeab21c0501dd4e986577 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 14:17:00 -0700 Subject: [PATCH 035/112] debugging for windows --- .github/workflows/pythonapp.yml | 4 +- .gitmodules | 3 + parsl | 1 + taskgraph/__init__.py | 5 - taskgraph/_parsl_interchange.py | 610 ------------------ .../_parsl_monitoring_remote_override.py | 295 --------- taskgraph/_parsl_multiprocessing_override.py | 60 -- taskgraph/_parsl_utils_override.py | 523 --------------- tests/test_task.py | 4 +- 9 files changed, 8 insertions(+), 1497 deletions(-) create mode 100644 .gitmodules create mode 160000 parsl delete mode 100644 taskgraph/_parsl_interchange.py delete mode 100644 taskgraph/_parsl_monitoring_remote_override.py delete mode 100644 taskgraph/_parsl_multiprocessing_override.py delete mode 100644 taskgraph/_parsl_utils_override.py diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index f8c57058..2bc996ea 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -40,9 +40,11 @@ jobs: # flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # # exit-zero treats all errors as warnings # flake8 . --count --exit-zero --max-line-length=80 --statistics + - name: Install modified parsl + run: | + cd parsl && pip install . - name: Run tests run: | pip install . - python taskgraph/_parsl_interchange.py pytest tests diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 00000000..b8f828d8 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "parsl"] + path = parsl + url = https://github.com/Parsl/parsl.git diff --git a/parsl b/parsl new file mode 160000 index 00000000..9fd453ee --- /dev/null +++ b/parsl @@ -0,0 +1 @@ +Subproject commit 9fd453ee922734c0d82583f86d125f90374712e5 diff --git a/taskgraph/__init__.py b/taskgraph/__init__.py index e9869b3b..20cd0748 100644 --- a/taskgraph/__init__.py +++ b/taskgraph/__init__.py @@ -1,10 +1,5 @@ """TaskGraph init module.""" import sys -from . import _parsl_multiprocessing_override -sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override - -from . import _parsl_utils_override -sys.modules['parsl.utils'] = _parsl_utils_override from .Task import TaskGraph from .Task import _TASKGRAPH_DATABASE_FILENAME diff --git a/taskgraph/_parsl_interchange.py b/taskgraph/_parsl_interchange.py deleted file mode 100644 index 2991ed95..00000000 --- a/taskgraph/_parsl_interchange.py +++ /dev/null @@ -1,610 +0,0 @@ -#!/usr/bin/env python -import datetime -import logging -import os -import pathlib -import pickle -import platform -import sys -import threading -import time -from typing import Any, Dict, List, Optional, Sequence, Set, Tuple, cast - -from taskgraph import _parsl_multiprocessing_override -sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override - -import zmq -from sortedcontainers import SortedList - -from parsl import curvezmq -from parsl.addresses import tcp_url -from parsl.app.errors import RemoteExceptionWrapper -from parsl.executors.high_throughput.errors import ManagerLost, VersionMismatch -from parsl.executors.high_throughput.manager_record import ManagerRecord -from parsl.executors.high_throughput.manager_selector import ManagerSelector -from parsl.log_utils import set_file_logger -from parsl.logconfigs.base import LogConfig, oneshot_initialize_logging -from parsl.monitoring.message_type import MessageType -from parsl.monitoring.radios.base import MonitoringRadioSender -from parsl.monitoring.radios.zmq import ZMQRadioSender -from parsl.process_loggers import wrap_with_logs -from parsl.serialize import serialize as serialize_object -from parsl.version import VERSION as PARSL_VERSION - -PKL_HEARTBEAT_CODE = pickle.dumps((2 ** 32) - 1) -PKL_SHUTDOWN_CODE = pickle.dumps((2 ** 32) - 2) - -logger = logging.getLogger("parsl.executors.high_throughput.interchange") - - -class Interchange: - """ Interchange is a task orchestrator for distributed systems. - - 1. Asynchronously queue large volume of tasks (>100K) - 2. Allow for workers to join and leave the union - 3. Detect workers that have failed using heartbeats - """ - def __init__(self, - *, - client_address: str, - interchange_address: Optional[str], - client_ports: Tuple[int, int, int], - worker_port: Optional[int], - worker_port_range: Tuple[int, int], - hub_address: Optional[str], - hub_zmq_port: Optional[int], - heartbeat_threshold: int, - logdir: str, - logging_level: int, - poll_period: int, - cert_dir: Optional[str], - manager_selector: ManagerSelector, - run_id: str, - log_config: Optional[LogConfig], - _check_python_mismatch: bool, - ) -> None: - """ - Parameters - ---------- - client_address : str - The ip address at which the parsl client can be reached. Default: "127.0.0.1" - - interchange_address : Optional str - If specified the interchange will only listen on this address for connections from workers - else, it binds to all addresses. - - client_ports : tuple(int, int, int) - The ports at which the client can be reached - - worker_port : int - The specific port to which workers will connect to the Interchange. - - worker_port_range : tuple(int, int) - The interchange picks ports at random from the range which will be used by workers. - This is overridden when the worker_port option is set. - - hub_address : str - The IP address at which the interchange can send info about managers to when monitoring is enabled. - When None, monitoring is disabled. - - hub_zmq_port : str - The port at which the interchange can send info about managers to when monitoring is enabled. - When None, monitoring is disabled. - - heartbeat_threshold : int - Number of seconds since the last heartbeat after which worker is considered lost. - - logdir : str - Parsl log directory paths. Logs and temp files go here. - - logging_level : int - Logging level as defined in the logging module. - - poll_period : int - The main thread polling period, in milliseconds. - - cert_dir : str | None - Path to the certificate directory. - - _check_python_mismatch : bool - If True, the interchange and worker managers must run the same version of - Python and Parsl. Running different versions can cause inter-process - communication errors, so proceed with caution. - Default: True - """ - self.cert_dir = cert_dir - self.logdir = logdir - os.makedirs(self.logdir, exist_ok=True) - - if log_config: - oneshot_initialize_logging(log_config=log_config, log_dir=pathlib.Path(self.logdir), log_name="interchange") - # discard the returned callback because the interchange doesn't usually do - # a normal shutdown - else: - set_file_logger("{}/interchange.log".format(self.logdir), level=logging_level) - - logger.debug("Initializing Interchange process") - - self.client_address = client_address - self.interchange_address: str = interchange_address or "*" - self.poll_period = poll_period - - logger.info("Attempting connection to client at {} on ports: {},{},{}".format( - client_address, client_ports[0], client_ports[1], client_ports[2])) - self.zmq_context = curvezmq.ServerContext(self.cert_dir) - self.task_incoming = self.zmq_context.socket(zmq.DEALER) - self.task_incoming.set_hwm(0) - self.task_incoming.connect(tcp_url(client_address, client_ports[0])) - self.results_outgoing = self.zmq_context.socket(zmq.DEALER) - self.results_outgoing.set_hwm(0) - self.results_outgoing.connect(tcp_url(client_address, client_ports[1])) - - self.command_channel = self.zmq_context.socket(zmq.REP) - self.command_channel.connect(tcp_url(client_address, client_ports[2])) - logger.info("Connected to client") - - self.run_id = run_id - self._check_python_mismatch = _check_python_mismatch - - self.hub_address = hub_address - self.hub_zmq_port = hub_zmq_port - - self.pending_task_queue: SortedList[Any] = SortedList(key=lambda tup: (tup[0], tup[1])) - - # count of tasks that have been sent out to worker pools - self.count = 0 - - self.manager_sock = self.zmq_context.socket(zmq.ROUTER) - self.manager_sock.set_hwm(0) - - if worker_port: - task_addy = tcp_url(self.interchange_address, worker_port) - self.manager_sock.bind(task_addy) - - else: - worker_port = self.manager_sock.bind_to_random_port( - tcp_url(self.interchange_address), - min_port=worker_port_range[0], - max_port=worker_port_range[1], - max_tries=100, - ) - self.worker_port = worker_port - - logger.info(f"Bound to port {worker_port} for incoming worker connections") - - self._ready_managers: Dict[bytes, ManagerRecord] = {} - self._logged_manager_count_token: object = None - self.connected_block_history: List[str] = [] - - self.heartbeat_threshold = heartbeat_threshold - - self.manager_selector = manager_selector - - self.current_platform = {'parsl_v': PARSL_VERSION, - 'python_v': "{}.{}.{}".format(sys.version_info.major, - sys.version_info.minor, - sys.version_info.micro), - 'os': platform.system(), - 'hostname': platform.node(), - 'dir': os.getcwd()} - - logger.info("Platform info: {}".format(self.current_platform)) - - def get_tasks(self, count: int) -> Sequence[dict]: - """ Obtains a batch of tasks from the internal pending_task_queue - - Parameters - ---------- - count: int - Count of tasks to get from the queue - - Returns - ------- - List of upto count tasks. May return fewer than count down to an empty list - eg. [{'task_id':, 'buffer':} ... ] - """ - tasks = [] - try: - for _ in range(count): - *_, task = self.pending_task_queue.pop() - tasks.append(task) - except IndexError: - pass - - return tasks - - def _send_monitoring_info(self, monitoring_radio: Optional[MonitoringRadioSender], manager: ManagerRecord) -> None: - if monitoring_radio: - logger.info("Sending message {} to MonitoringHub".format(manager)) - - d: Dict = cast(Dict, manager.copy()) - d['timestamp'] = datetime.datetime.now() - d['last_heartbeat'] = datetime.datetime.fromtimestamp(d['last_heartbeat']) - d['run_id'] = self.run_id - - monitoring_radio.send((MessageType.NODE_INFO, d)) - - def process_command(self, monitoring_radio: Optional[MonitoringRadioSender]) -> None: - """ Command server to run async command to the interchange - """ - - reply: Any # the type of reply depends on the command_req received (aka this needs dependent types...) - - if self.socks.get(self.command_channel) == zmq.POLLIN: - logger.debug("entering command_server section") - - command_req = self.command_channel.recv_pyobj() - logger.debug("Received command request: {}".format(command_req)) - if command_req == "CONNECTED_BLOCKS": - reply = self.connected_block_history - - elif command_req == "MANAGERS": - reply = [] - now = time.time() - for manager_id, m in self._ready_managers.items(): - idle_duration = now - (m['idle_since'] or now) - resp = { - 'manager': manager_id.decode('utf-8'), - 'block_id': m['block_id'], - 'worker_count': m['worker_count'], - 'tasks': len(m['tasks']), - 'idle_duration': idle_duration, - 'active': m['active'], - 'parsl_version': m['parsl_version'], - 'python_version': m['python_version'], - 'draining': m['draining'] - } - reply.append(resp) - - elif command_req == "MANAGERS_PACKAGES": - reply = {} - for manager_id, m in self._ready_managers.items(): - manager_id_str = manager_id.decode('utf-8') - reply[manager_id_str] = m["packages"] - - elif isinstance(command_req, list) and len(command_req) == 2 and command_req[0] == "HOLD_MANAGER": - manager_id = command_req[1].encode('utf-8') - logger.info("Received HOLD_MANAGER for {!r}".format(manager_id)) - if manager_id in self._ready_managers: - m = self._ready_managers[manager_id] - m['active'] = False - self._send_monitoring_info(monitoring_radio, m) - else: - logger.warning("Worker to hold was not in ready managers list") - - reply = None - - elif command_req == "WORKER_BINDS": - reply = self.worker_port - - else: - logger.error(f"Received unknown command: {command_req}") - reply = None - - logger.debug("Reply: {}".format(reply)) - self.command_channel.send_pyobj(reply) - - @wrap_with_logs - def start(self) -> None: - """ Start the interchange - """ - - logger.info("Starting main interchange method") - - if self.hub_address is not None and self.hub_zmq_port is not None: - logger.debug("Creating monitoring radio to %s:%s", self.hub_address, self.hub_zmq_port) - monitoring_radio = ZMQRadioSender(self.hub_address, self.hub_zmq_port, self.cert_dir) - logger.debug("Created monitoring radio") - else: - monitoring_radio = None - - poll_period = self.poll_period - - start = time.time() - - kill_event = threading.Event() - - poller = zmq.Poller() - poller.register(self.manager_sock, zmq.POLLIN) - poller.register(self.task_incoming, zmq.POLLIN) - poller.register(self.command_channel, zmq.POLLIN) - - # These are managers which we should examine in an iteration - # for scheduling a job (or maybe any other attention?). - # Anything altering the state of the manager should add it - # onto this list. - interesting_managers: Set[bytes] = set() - - while not kill_event.is_set(): - self.socks = dict(poller.poll(timeout=poll_period)) - - self.process_command(monitoring_radio) - self.process_task_incoming() - self.process_manager_socket_message(interesting_managers, monitoring_radio, kill_event) - self.expire_bad_managers(interesting_managers, monitoring_radio) - self.expire_drained_managers(interesting_managers, monitoring_radio) - self.log_manager_counts(interesting_managers) - self.process_tasks_to_send(interesting_managers, monitoring_radio) - - self.zmq_context.destroy() - delta = time.time() - start - logger.info(f"Processed {self.count} tasks in {delta} seconds") - logger.warning("Exiting") - - def process_task_incoming(self) -> None: - """Process incoming task message(s). - """ - - if self.socks.get(self.task_incoming) == zmq.POLLIN: - logger.debug("start task_incoming section") - msg = self.task_incoming.recv_pyobj() - - # Process priority, higher number = lower priority - task_id = msg['task_id'] - resource_spec = msg['context'].get('resource_spec', {}) - priority = resource_spec.get('priority', float('inf')) - queue_entry = (-priority, -task_id, msg) - - logger.debug("Putting task %s onto pending_task_queue", task_id) - - self.pending_task_queue.add(queue_entry) - logger.debug("Put task %s onto pending_task_queue", task_id) - - def process_manager_socket_message( - self, - interesting_managers: Set[bytes], - monitoring_radio: Optional[MonitoringRadioSender], - kill_event: threading.Event, - ) -> None: - """Process one message from manager on the manager_sock channel.""" - if not self.socks.get(self.manager_sock) == zmq.POLLIN: - return - - logger.debug('starting worker message section') - msg_parts = self.manager_sock.recv_multipart() - try: - manager_id, msg_b = msg_parts - msg = pickle.loads(msg_b) - mtype = msg['type'] - except Exception as e: - logger.warning( - 'Failed to read manager message; ignoring message' - f' (Exception: [{type(e).__name__}] {e})' - ) - logger.debug('Raw message bytes:\n %r\n', msg_parts, exc_info=e) - return - - logger.debug( - 'Processing message type %r from manager %r', mtype, manager_id - ) - - if mtype == 'connection_probe': - self.manager_sock.send_multipart([manager_id, b'']) - return - - elif mtype == 'registration': - ix_minor_py = self.current_platform['python_v'].rsplit('.', 1)[0] - ix_parsl_v = self.current_platform['parsl_v'] - mgr_minor_py = msg['python_v'].rsplit('.', 1)[0] - mgr_parsl_v = msg['parsl_v'] - - # legacy draining handling. remove in ~6 months (ca Apr 2027) - drain_time = msg.get('drain_time', float('inf')) - - new_rec = ManagerRecord( - block_id=None, - start_time=msg['start_time'], - tasks=[], - worker_count=0, - max_capacity=0, - active=True, - draining=False, - drain_time=drain_time, - last_heartbeat=time.time(), - idle_since=time.time(), - parsl_version=mgr_parsl_v, - python_version=msg['python_v'], - ) - - # m is a ManagerRecord, but meta is a dict[Any,Any] and so can - # contain arbitrary fields beyond those in ManagerRecord (and - # indeed does - for example, python_v) which are then ignored - # later. - new_rec.update(msg) - - logger.info(f'Registration info for manager {manager_id!r}: {msg}') - self._send_monitoring_info(monitoring_radio, new_rec) - - python_mismatch: bool = ix_minor_py != mgr_minor_py - parsl_mismatch: bool = ix_parsl_v != mgr_parsl_v - if self._check_python_mismatch and (parsl_mismatch or python_mismatch): - kill_event.set() - vm_exc = VersionMismatch( - f"py.v={ix_minor_py} parsl.v={ix_parsl_v}", - f"py.v={mgr_minor_py} parsl.v={mgr_parsl_v}", - ) - result_package = { - 'type': 'result', - 'task_id': -1, - 'exception': serialize_object(vm_exc), - } - pkl_package = pickle.dumps(result_package) - self.results_outgoing.send(pkl_package) - logger.error( - 'Manager has incompatible version info with the interchange;' - ' sending failure reports and shutting down:' - f'\n Interchange: {vm_exc.interchange_version}' - f'\n Manager: {vm_exc.manager_version}' - ) - - else: - # We really should update the associated data structure; but not - # at this time. *kicks can down the road* - assert new_rec['block_id'] is not None, 'Verified externally' - - # set up entry only if we accept the registration - self._ready_managers[manager_id] = new_rec - self.connected_block_history.append(new_rec['block_id']) - - interesting_managers.add(manager_id) - - logger.info( - f"Registered manager {manager_id!r} (py{mgr_minor_py}," - f" {mgr_parsl_v}) and added to ready queue" - ) - logger.debug("Manager %r -> %s", manager_id, new_rec) - - return - - if not (m := self._ready_managers.get(manager_id)): - logger.warning(f"Ignoring message from unknown manager: {manager_id!r}") - return - - if mtype == 'result': - # process this for task ID and forward to executor - tid = msg['task_id'] - logger.debug("Removing task %s from manager", tid) - try: - m['tasks'].remove(tid) - except Exception: - logger.exception( - 'Ignoring exception removing task_id %s from manager' - ' task list %s', - tid, - m['tasks'] - ) - return - - logger.debug('Sending result on results_outgoing') - self.results_outgoing.send(msg_b) - logger.debug('Sent result on results_outgoing') - - # Result received, so manager now has idle capacity - interesting_managers.add(manager_id) - - if len(m['tasks']) == 0 and m['idle_since'] is None: - m['idle_since'] = time.time() - - self._send_monitoring_info(monitoring_radio, m) - - elif mtype == 'monitoring': - # the monitoring code makes the assumption that no - # monitoring messages will be received if monitoring - # is not configured, and that monitoring_radio will only - # be None when monitoring is not configurated. - assert monitoring_radio is not None - - monitoring_radio.send(msg['payload']) - - elif mtype == 'heartbeat': - m['last_heartbeat'] = time.time() - self.manager_sock.send_multipart([manager_id, PKL_HEARTBEAT_CODE]) - - # legacy draining handling. remove in ~6 months (ca Apr 2027) - elif mtype == 'drain': - m['draining'] = True - - else: - logger.error(f"Unexpected message type received from manager: {mtype}") - - logger.debug("leaving worker message section") - - def expire_drained_managers(self, interesting_managers: Set[bytes], monitoring_radio: Optional[MonitoringRadioSender]) -> None: - now = time.time() - for manager_id, m in list(self._ready_managers.items()): - if not m['draining'] and now >= m['drain_time']: - logger.info(f"Manager {manager_id!r} has reached its drain time - draining") - m['draining'] = True - - if m['draining'] and len(m['tasks']) == 0: - logger.info(f"Manager {manager_id!r} is drained - telling manager to shutdown") - self.manager_sock.send_multipart([manager_id, PKL_SHUTDOWN_CODE]) - interesting_managers.discard(manager_id) - self._ready_managers.pop(manager_id) - - m['active'] = False - self._send_monitoring_info(monitoring_radio, m) - - def log_manager_counts(self, interesting_managers: Set[bytes]) -> None: - count_interesting = len(interesting_managers) - count_ready = len(self._ready_managers) - - new_logged_manager_count_token = (count_interesting, count_ready) - - if self._logged_manager_count_token != new_logged_manager_count_token: - - logger.debug( - "Managers count (interesting/total): %d/%d", - count_interesting, - count_ready - ) - self._logged_manager_count_token = new_logged_manager_count_token - - def process_tasks_to_send(self, interesting_managers: Set[bytes], monitoring_radio: Optional[MonitoringRadioSender]) -> None: - # Check if there are tasks that could be sent to managers - - if interesting_managers and self.pending_task_queue: - shuffled_managers = self.manager_selector.sort_managers(self._ready_managers, interesting_managers) - - while shuffled_managers and self.pending_task_queue: # cf. the if statement above... - manager_id = shuffled_managers.pop() - m = self._ready_managers[manager_id] - tasks_inflight = len(m['tasks']) - real_capacity = m['max_capacity'] - tasks_inflight - - if real_capacity and m["active"] and not m["draining"]: - tasks = self.get_tasks(real_capacity) - if tasks: - self.manager_sock.send_multipart([manager_id, pickle.dumps(tasks)]) - task_count = len(tasks) - self.count += task_count - tids = [t['task_id'] for t in tasks] - m['tasks'].extend(tids) - m['idle_since'] = None - logger.debug("Sent tasks: %s to manager %r", tids, manager_id) - # recompute real_capacity after sending tasks - real_capacity -= task_count - if real_capacity > 0: - logger.debug("Manager %r has free capacity %s", manager_id, real_capacity) - # ... so keep it in the interesting_managers list - else: - logger.debug("Manager %r is now saturated", manager_id) - interesting_managers.remove(manager_id) - self._send_monitoring_info(monitoring_radio, m) - else: - interesting_managers.remove(manager_id) - logger.debug("leaving _ready_managers section, with %s managers still interesting", len(interesting_managers)) - - def expire_bad_managers(self, interesting_managers: Set[bytes], monitoring_radio: Optional[MonitoringRadioSender]) -> None: - bad_managers = [(manager_id, m) for (manager_id, m) in self._ready_managers.items() if - time.time() - m['last_heartbeat'] > self.heartbeat_threshold] - for (manager_id, m) in bad_managers: - logger.debug("Last: {} Current: {}".format(m['last_heartbeat'], time.time())) - logger.warning(f"Too many heartbeats missed for manager {manager_id!r} - removing manager") - if m['active']: - m['active'] = False - self._send_monitoring_info(monitoring_radio, m) - - logger.warning(f"Cancelling htex tasks {m['tasks']} on removed manager") - for tid in m['tasks']: - try: - raise ManagerLost(manager_id, m['hostname']) - except Exception: - result_package = {'type': 'result', 'task_id': tid, 'exception': serialize_object(RemoteExceptionWrapper(*sys.exc_info()))} - pkl_package = pickle.dumps(result_package) - self.results_outgoing.send(pkl_package) - logger.warning("Sent failure reports, unregistering manager") - self._ready_managers.pop(manager_id, 'None') - if manager_id in interesting_managers: - interesting_managers.remove(manager_id) - - -if __name__ == "__main__": - from parsl.utils import setproctitle - - setproctitle("parsl: HTEX interchange") - - config = pickle.load(sys.stdin.buffer) - - ic = Interchange(**config) - ic.start() \ No newline at end of file diff --git a/taskgraph/_parsl_monitoring_remote_override.py b/taskgraph/_parsl_monitoring_remote_override.py deleted file mode 100644 index 5ffd9176..00000000 --- a/taskgraph/_parsl_monitoring_remote_override.py +++ /dev/null @@ -1,295 +0,0 @@ -import datetime -import logging -import os -import time -from functools import wraps -from multiprocessing import Event -import platform -from typing import Any, Callable, Dict, List, Sequence, Tuple - -from parsl.monitoring.message_type import MessageType -from parsl.monitoring.radios.base import RadioConfig -from parsl.process_loggers import wrap_with_logs - -if platform.system() != "Windows": - from parsl.multiprocessing import ForkProcess - -logger = logging.getLogger(__name__) - - -def monitor_wrapper(*, - f: Any, # per app - args: Sequence, # per invocation - kwargs: Dict, # per invocation - x_try_id: int, # per invocation - x_task_id: int, # per invocation - radio_config: RadioConfig, # per executor - run_id: str, # per workflow - logging_level: int, # per workflow - sleep_dur: float, # per workflow - monitor_resources: bool, # per workflow - run_dir: str) -> Tuple[Callable, Sequence, Dict]: - """Wrap the Parsl app with a function that will call the monitor function and point it at the correct pid when the task begins. - """ - - @wraps(f) - def wrapped(*args: List[Any], **kwargs: Dict[str, Any]) -> Any: - task_id = kwargs.pop('_parsl_monitoring_task_id') - try_id = kwargs.pop('_parsl_monitoring_try_id') - terminate_event = Event() - # Send first message to monitoring router - send_first_message(try_id, - task_id, - radio_config, - run_id, - run_dir) - - if monitor_resources and sleep_dur > 0: - # create the monitor process and start - pp = ForkProcess(target=monitor, - args=(os.getpid(), - try_id, - task_id, - radio_config, - run_id, - logging_level, - sleep_dur, - run_dir, - terminate_event), - daemon=True, - name="Monitor-Wrapper-{}".format(task_id)) - pp.start() - p = pp - # TODO: awkwardness because ForkProcess is not directly a constructor - # and type-checking is expecting p to be optional and cannot - # narrow down the type of p in this block. - - else: - p = None - - try: - return f(*args, **kwargs) - finally: - # There's a chance of zombification if the workers are killed by some signals (?) - if p: - terminate_event.set() - p.join(30) # 30 second delay for this -- this timeout will be hit in the case of an unusually long end-of-loop - if p.exitcode is None: - logger.warn("Event-based termination of monitoring helper took too long. Using process-based termination.") - p.terminate() - # DANGER: this can corrupt shared queues according to docs. - # So, better that the above termination event worked. - # This is why this log message is a warning - p.join() - - send_last_message(try_id, - task_id, - radio_config, - run_id, - run_dir) - - new_kwargs = kwargs.copy() - new_kwargs['_parsl_monitoring_task_id'] = x_task_id - new_kwargs['_parsl_monitoring_try_id'] = x_try_id - - return (wrapped, args, new_kwargs) - - -@wrap_with_logs -def send_first_message(try_id: int, - task_id: int, - radio_config: RadioConfig, - run_id: str, run_dir: str) -> None: - send_first_last_message(try_id, task_id, radio_config, run_id, - run_dir, False) - - -@wrap_with_logs -def send_last_message(try_id: int, - task_id: int, - radio_config: RadioConfig, - run_id: str, run_dir: str) -> None: - send_first_last_message(try_id, task_id, radio_config, run_id, - run_dir, True) - - -def send_first_last_message(try_id: int, - task_id: int, - radio_config: RadioConfig, - run_id: str, run_dir: str, - is_last: bool) -> None: - import os - import platform - - radio = radio_config.create_sender() - - msg = (MessageType.WORKER_TASK_INFO, - {'run_id': run_id, - 'try_id': try_id, - 'task_id': task_id, - 'hostname': platform.node(), - 'block_id': os.environ.get('PARSL_WORKER_BLOCK_ID'), - 'first_msg': not is_last, - 'last_msg': is_last, - 'timestamp': datetime.datetime.now() - }) - radio.send(msg) - return - - -@wrap_with_logs -def monitor(pid: int, - try_id: int, - task_id: int, - radio_config: RadioConfig, - run_id: str, - logging_level: int, - sleep_dur: float, - run_dir: str, - # removed all defaults because unused and there's no meaningful default for terminate_event. - # these probably should become named arguments, with a *, and named at invocation. - terminate_event: Any) -> None: # cannot be Event because of multiprocessing type weirdness. - """Monitors the Parsl task's resources by pointing psutil to the task's pid and watching it and its children. - - This process makes calls to logging, but deliberately does not attach - any log handlers. Previously, there was a handler which logged to a - file in /tmp, but this was usually not useful or even accessible. - In some circumstances, it might be useful to hack in a handler so the - logger calls remain in place. - """ - import logging - import platform - - import psutil - - from parsl.utils import setproctitle - - setproctitle("parsl: task resource monitor") - - radio = radio_config.create_sender() - - logging.debug("start of monitor") - - # these values are simple to log. Other information is available in special formats such as memory below. - simple = ["cpu_num", 'create_time', 'cwd', 'exe', 'memory_percent', 'nice', 'name', 'num_threads', 'pid', 'ppid', 'status', 'username'] - # values that can be summed up to see total resources used by task process and its children - summable_values = ['memory_percent', 'num_threads'] - - pm = psutil.Process(pid) - - children_user_time: Dict[int, float] = {} - children_system_time: Dict[int, float] = {} - children_num_ctx_switches_voluntary: Dict[int, float] = {} - children_num_ctx_switches_involuntary: Dict[int, float] = {} - - def accumulate_and_prepare() -> Dict[str, Any]: - d = {"psutil_process_" + str(k): v for k, v in pm.as_dict().items() if k in simple} - d["run_id"] = run_id - d["task_id"] = task_id - d["try_id"] = try_id - d['resource_monitoring_interval'] = sleep_dur - d['hostname'] = platform.node() - d['timestamp'] = datetime.datetime.now() - - logging.debug("getting children") - children = pm.children(recursive=True) - logging.debug("got children") - - d["psutil_cpu_count"] = psutil.cpu_count() - - # note that this will be the CPU number of the base process, not anything launched by it - d["psutil_cpu_num"] = pm.cpu_num() - - pctxsw = pm.num_ctx_switches() - - d["psutil_process_num_ctx_switches_voluntary"] = pctxsw.voluntary - d["psutil_process_num_ctx_switches_involuntary"] = pctxsw.involuntary - - d['psutil_process_memory_virtual'] = pm.memory_info().vms - d['psutil_process_memory_resident'] = pm.memory_info().rss - d['psutil_process_time_user'] = pm.cpu_times().user - d['psutil_process_time_system'] = pm.cpu_times().system - d['psutil_process_children_count'] = len(children) - try: - d['psutil_process_disk_write'] = pm.io_counters().write_chars - d['psutil_process_disk_read'] = pm.io_counters().read_chars - except Exception: - # occasionally pid temp files that hold this information are unvailable to be read so set to zero - logging.exception("Exception reading IO counters for main process. Recorded IO usage may be incomplete", exc_info=True) - d['psutil_process_disk_write'] = 0 - d['psutil_process_disk_read'] = 0 - for child in children: - for k, v in child.as_dict(attrs=summable_values).items(): - d['psutil_process_' + str(k)] += v - child_user_time = child.cpu_times().user - child_system_time = child.cpu_times().system - children_user_time[child.pid] = child_user_time - children_system_time[child.pid] = child_system_time - - pctxsw = child.num_ctx_switches() - children_num_ctx_switches_voluntary[child.pid] = pctxsw.voluntary - children_num_ctx_switches_involuntary[child.pid] = pctxsw.involuntary - - d['psutil_process_memory_virtual'] += child.memory_info().vms - d['psutil_process_memory_resident'] += child.memory_info().rss - try: - d['psutil_process_disk_write'] += child.io_counters().write_chars - d['psutil_process_disk_read'] += child.io_counters().read_chars - except Exception: - # occassionally pid temp files that hold this information are unvailable to be read so add zero - logging.exception("Exception reading IO counters for child {k}. Recorded IO usage may be incomplete".format(k=k), exc_info=True) - d['psutil_process_disk_write'] += 0 - d['psutil_process_disk_read'] += 0 - - total_children_user_time = 0.0 - for child_pid in children_user_time: - total_children_user_time += children_user_time[child_pid] - - total_children_system_time = 0.0 - for child_pid in children_system_time: - total_children_system_time += children_system_time[child_pid] - - total_children_num_ctx_switches_voluntary = 0.0 - for child_pid in children_num_ctx_switches_voluntary: - total_children_num_ctx_switches_voluntary += children_num_ctx_switches_voluntary[child_pid] - - total_children_num_ctx_switches_involuntary = 0.0 - for child_pid in children_num_ctx_switches_involuntary: - total_children_num_ctx_switches_involuntary += children_num_ctx_switches_involuntary[child_pid] - - d['psutil_process_time_user'] += total_children_user_time - d['psutil_process_time_system'] += total_children_system_time - d['psutil_process_num_ctx_switches_voluntary'] += total_children_num_ctx_switches_voluntary - d['psutil_process_num_ctx_switches_involuntary'] += total_children_num_ctx_switches_involuntary - logging.debug("sending message") - return d - - next_send = time.time() - accumulate_dur = 5.0 # TODO: make configurable? - - while not terminate_event.is_set() and pm.is_running(): - logging.debug("start of monitoring loop") - try: - d = accumulate_and_prepare() - if time.time() >= next_send: - logging.debug("Sending intermediate resource message") - radio.send((MessageType.RESOURCE_INFO, d)) - next_send += sleep_dur - except Exception: - logging.exception("Exception getting the resource usage. Not sending usage to Hub", exc_info=True) - logging.debug("sleeping") - - # wait either until approx next send time, or the accumulation period - # so the accumulation period will not be completely precise. - # but before this, the sleep period was also not completely precise. - # with a minimum floor of 0 to not upset wait - - terminate_event.wait(max(0, min(next_send - time.time(), accumulate_dur))) - - logging.debug("Sending final resource message") - try: - d = accumulate_and_prepare() - radio.send((MessageType.RESOURCE_INFO, d)) - except Exception: - logging.exception("Exception getting the resource usage. Not sending final usage to Hub", exc_info=True) - logging.debug("End of monitoring helper") \ No newline at end of file diff --git a/taskgraph/_parsl_multiprocessing_override.py b/taskgraph/_parsl_multiprocessing_override.py deleted file mode 100644 index ca50b75f..00000000 --- a/taskgraph/_parsl_multiprocessing_override.py +++ /dev/null @@ -1,60 +0,0 @@ -"""Helpers for cross-platform multiprocessing support. -""" - -import logging -import multiprocessing -import multiprocessing.queues -from multiprocessing.context import SpawnProcess as SpawnProcessType -import platform -from typing import Callable - -logger = logging.getLogger(__name__) - -if platform.system() != "Windows": - from multiprocessing.context import ForkProcess as ForkProcessType - ForkContext = multiprocessing.get_context("fork") - ForkProcess: Callable[..., ForkProcessType] = ForkContext.Process -else: - ForkContext = None - ForkProcess = None - -SpawnContext = multiprocessing.get_context("spawn") -SpawnProcess: Callable[..., SpawnProcessType] = SpawnContext.Process - -SpawnEvent = SpawnContext.Event -SpawnQueue = SpawnContext.Queue - - -def join_terminate_close_proc(process: SpawnProcessType, *, timeout: int = 30) -> None: - """Increasingly aggressively terminate a process. - - This function assumes that the process is likely to exit before - the join timeout, driven by some other means, such as the - MonitoringHub router_exit_event. If the process does not exit, then - first terminate() and then kill() will be used to end the process. - - In the case of a very mis-behaving process, this function might take - up to 3*timeout to exhaust all termination methods and return. - """ - logger.debug("Joining process") - process.join(timeout) - - # run a sequence of increasingly aggressive steps to shut down the process. - if process.is_alive(): - logger.error("Process did not join. Terminating.") - process.terminate() - process.join(timeout) - if process.is_alive(): - logger.error("Process did not join after terminate. Killing.") - process.kill() - process.join(timeout) - # This kill should not be caught by any signal handlers so it is - # unlikely that this join will timeout. If it does, there isn't - # anything further to do except log an error in the next if-block. - - if process.is_alive(): - logger.error("Process failed to end") - # don't call close if the process hasn't ended: - # process.close() doesn't work on a running process. - else: - process.close() \ No newline at end of file diff --git a/taskgraph/_parsl_utils_override.py b/taskgraph/_parsl_utils_override.py deleted file mode 100644 index 8eda4e08..00000000 --- a/taskgraph/_parsl_utils_override.py +++ /dev/null @@ -1,523 +0,0 @@ -import inspect -import logging -import os -import re -import platform -import shlex -import subprocess -import threading -import time -from contextlib import contextmanager -from types import TracebackType -from typing import ( - IO, - Any, - Callable, - Dict, - Generator, - List, - Optional, - Sequence, - Tuple, - Union, -) - -import typeguard -from typing_extensions import Type - - -try: - import setproctitle as setproctitle_module -except ImportError: - _setproctitle_enabled = False -else: - _setproctitle_enabled = True - - -logger = logging.getLogger(__name__) - - -@typeguard.typechecked -def get_version() -> str: - import parsl - from parsl.version import VERSION - version = parsl.__version__ # type: str - work_tree = os.path.dirname(os.path.dirname(__file__)) - git_dir = os.path.join(work_tree, '.git') - if os.path.exists(git_dir): - env = {'GIT_WORK_TREE': work_tree, 'GIT_DIR': git_dir} - try: - cmd = shlex.split('git rev-parse --short HEAD') - head = subprocess.check_output(cmd, env=env).strip().decode('utf-8') - diff = subprocess.check_output(shlex.split('git diff HEAD'), env=env) - status = 'dirty' if diff else 'clean' - version = f'{VERSION}-{head}-{status}' - except Exception: - pass - - return version - - -@typeguard.typechecked -def get_all_checkpoints(rundir: str = "runinfo") -> Sequence[str]: - """Finds the checkpoints from all runs in the rundir. - - Kwargs: - - rundir(str) : Path to the runinfo directory - - Returns: - - a list suitable for the checkpoint_files parameter of `Config` - - """ - - if not os.path.isdir(rundir): - return [] - - dirs = sorted(os.listdir(rundir)) - - checkpoints = [] - - for runid in dirs: - - checkpoint = os.path.abspath(f'{rundir}/{runid}/checkpoint') - - if os.path.isdir(checkpoint): - checkpoints.append(checkpoint) - - return checkpoints - - -@typeguard.typechecked -def get_last_checkpoint(rundir: str = "runinfo") -> Sequence[str]: - """Finds the checkpoint from the last run, if one exists. - - Note that checkpoints are incremental, and this helper will not find - previous checkpoints from earlier than the most recent run. If you - want that behaviour, see `get_all_checkpoints`. - - Kwargs: - - rundir(str) : Path to the runinfo directory - - Returns: - - a list suitable for the checkpoint_files parameter of `Config`, - with 0 or 1 elements - - """ - if not os.path.isdir(rundir): - return [] - - dirs = os.listdir(rundir) - - logger.debug("Rundir candidates: %s", dirs) - - # dirs_parseable is a list of number-like directory names, - # which is the rundir subdirectories, excluding non-numerical - # files such as the monitoring database. - dirs_parseable = [d for d in dirs if d.isdigit()] - logger.debug("Parseable candidates: %s", dirs_parseable) - - # dirs_sorted will be a list containing the largest numbered directories - # first (parsed as an int, not lexically sorted, to accomodate different - # length run directory names that can happen after 1000 runs) - dirs_sorted = sorted(dirs_parseable, key=int, reverse=True) - - # use a generator so that the resulting co-routine will only do filesystem - # operations on paths until it finds the first one with a checkpoint - which - # is likely to be the first one. - last_path = next(([path] for d in dirs_sorted - if os.path.isdir(path := os.path.abspath(f'{rundir}/{d}/checkpoint'))), - []) - - logger.debug("Selected path: %s", last_path) - return last_path - - -@typeguard.typechecked -def get_std_fname_mode( - fdname: str, - stdfspec: Union[os.PathLike, str, Tuple[str, str], Tuple[os.PathLike, str]] -) -> Tuple[str, str]: - import parsl.app.errors as pe - from parsl.app.errors import BadStdStreamFile - if isinstance(stdfspec, (str, os.PathLike)): - fname = stdfspec - mode = 'a+' - elif isinstance(stdfspec, tuple): - if len(stdfspec) != 2: - # this is annotated as unreachable because the type annotation says - # it cannot be reached. Earlier versions of typeguard did not enforce - # that type annotation at runtime, though, and the parameters to this - # function come from the user. - # When typeguard lower bound is raised to around version 4, this - # unreachable can be removed. - msg = (f"std descriptor {fdname} has incorrect tuple length " # type: ignore[unreachable] - f"{len(stdfspec)}") - raise pe.BadStdStreamFile(msg) - fname, mode = stdfspec - - path = os.fspath(fname) - - if isinstance(path, str): - return path, mode - elif isinstance(path, bytes): - return path.decode(), mode - else: - raise BadStdStreamFile(f"fname has invalid type {type(path)}") - - -@contextmanager -def wait_for_file(path: str, seconds: int = 10) -> Generator[None, None, None]: - for _ in range(0, int(seconds * 100)): - time.sleep(seconds / 100.) - if os.path.exists(path): - break - yield - - -@contextmanager -def time_limited_open(path: str, mode: str, seconds: int = 1) -> Generator[IO, None, None]: - with wait_for_file(path, seconds): - logger.debug("wait_for_file yielded") - f = open(path, mode) - yield f - f.close() - - -def wtime_to_minutes(time_string: str) -> int: - ''' wtime_to_minutes - - Convert standard wallclock time string to minutes. - - Args: - - Time_string in HH:MM:SS format - - Returns: - (int) minutes - - ''' - hours, mins, seconds = time_string.split(':') - total_mins = int(hours) * 60 + int(mins) - if total_mins < 1: - msg = (f"Time string '{time_string}' parsed to {total_mins} minutes, " - f"less than 1") - logger.warning(msg) - return total_mins - - -class RepresentationMixin: - """A mixin class for adding a __repr__ method. - - The __repr__ method will return a string equivalent to the code used to instantiate - the child class, with any defaults included explicitly. The __max_width__ class variable - controls the maximum width of the representation string. If this width is exceeded, - the representation string will be split up, with one argument or keyword argument per line. - - Any arguments or keyword arguments in the constructor must be defined as attributes, or - an AttributeError will be raised. - - Examples - -------- - >>> from parsl.utils import RepresentationMixin - >>> class Foo(RepresentationMixin): - def __init__(self, first, second, third='three', fourth='fourth'): - self.first = first - self.second = second - self.third = third - self.fourth = fourth - >>> bar = Foo(1, 'two', fourth='baz') - >>> bar - Foo(1, 'two', third='three', fourth='baz') - """ - __max_width__ = 80 - - _validate_repr = False - - def __repr__(self) -> str: - init = self.__init__ # type: ignore[misc] - - # This test looks for a single layer of wrapping performed by - # functools.update_wrapper, commonly used in decorators. This will - # allow RepresentationMixin to see through a single such decorator - # applied to the __init__ method of a class, and find the underlying - # arguments. It will not see through multiple layers of such - # decorators, or cope with other decorators which do not use - # functools.update_wrapper. - - if hasattr(init, '__wrapped__'): - init = init.__wrapped__ - - argspec = inspect.getfullargspec(init) - if len(argspec.args) > 1 and argspec.defaults is not None: - defaults = dict(zip(reversed(argspec.args), reversed(argspec.defaults))) - else: - defaults = {} - - if self._validate_repr: - for arg in argspec.args[1:]: - if not hasattr(self, arg): - template = (f'class {self.__class__.__name__} uses {arg} in the' - f' constructor, but does not define it as an ' - f'attribute') - raise AttributeError(template) - - default = "" - - if len(defaults) != 0: - args = [getattr(self, a, default) for a in argspec.args[1:-len(defaults)]] - else: - args = [getattr(self, a, default) for a in argspec.args[1:]] - kwargs = {key: getattr(self, key, default) for key in defaults} - - kwonlyargs = {key: getattr(self, key, default) for key in argspec.kwonlyargs} - kwargs.update(kwonlyargs) - - def assemble_multiline(args: List[str], kwargs: Dict[str, object]) -> str: - def indent(text: str) -> str: - lines = text.splitlines() - if len(lines) <= 1: - return text - return "\n".join(" " + line for line in lines).strip() - args = [f"\n {indent(repr(a))}," for a in args] - kwargsl = [f"\n {k}={indent(repr(v))}" for k, v in - sorted(kwargs.items())] - - info = "".join(args) + ", ".join(kwargsl) - return self.__class__.__name__ + f"({info}\n)" - - def assemble_line(args: List[str], kwargs: Dict[str, object]) -> str: - kwargsl = [f'{k}={repr(v)}' for k, v in sorted(kwargs.items())] - - info = ", ".join([repr(a) for a in args] + kwargsl) - return self.__class__.__name__ + f"({info})" - - if len(assemble_line(args, kwargs)) <= self.__class__.__max_width__: - return assemble_line(args, kwargs) - else: - return assemble_multiline(args, kwargs) - - -class AtomicIDCounter: - """A class to allocate counter-style IDs, in a thread-safe way. - """ - - def __init__(self) -> None: - self.count = 0 - self.lock = threading.Lock() - - def get_id(self) -> int: - with self.lock: - new_id = self.count - self.count += 1 - return new_id - - -def setproctitle(title: str) -> None: - if _setproctitle_enabled: - setproctitle_module.setproctitle(title) - else: - logger.warn(f"setproctitle not enabled for process {title}") - - -class Timer: - """This class will make a callback periodically, with a period - specified by the interval parameter. - - This is based on the following logic : - - .. code-block:: none - - - BEGIN (INTERVAL, THRESHOLD, callback) : - start = current_time() - - while (current_time()-start < INTERVAL) : - wait() - break - - callback() - - """ - - def __init__(self, callback: Callable, *args: Any, interval: Union[float, int] = 5, name: Optional[str] = None) -> None: - """Initialize the Timer object. - We start the timer thread here - - KWargs: - - interval (int or float) : number of seconds between callback events - - name (str) : a base name to use when naming the started thread - """ - - self.interval = max(0, interval) - self.cb_args = args - self.callback = callback - - self._kill_event = threading.Event() - tname = f"Timer-Thread-{id(self)}" - if name: - tname = f"{name}-{tname}" - self._thread = threading.Thread( - target=self._wake_up_timer, name=tname, daemon=True - ) - self._thread.start() - - def _wake_up_timer(self) -> None: - self.make_callback() - - while not self._kill_event.wait(self.interval): - self.make_callback() - - def make_callback(self) -> None: - """Makes the callback and resets the timer. - """ - try: - self.callback(*self.cb_args) - except Exception: - logger.error("Callback threw an exception - logging and proceeding anyway", exc_info=True) - - def close(self, timeout: Optional[float] = None) -> None: - """Merge the threads and terminate. - """ - self._kill_event.set() - self._thread.join(timeout=timeout) - - -class AutoCancelTimer(threading.Timer): - """ - Extend threading.Timer for use as a context manager - - Example: - - with AutoCancelTimer(delay, your_callback): - some_func() - - If `some_func()` returns before the delay is up, the timer will - be cancelled. - """ - def __enter__(self) -> "AutoCancelTimer": - self.start() - return self - - def __exit__( - self, - exc_type: Optional[Type[BaseException]], - exc_val: Optional[BaseException], - exc_tb: Optional[TracebackType] - ) -> None: - self.cancel() - - -def sanitize_dns_label_rfc1123(raw_string: str) -> str: - """Convert input string to a valid RFC 1123 DNS label. - - Parameters - ---------- - raw_string : str - String to sanitize. - - Returns - ------- - str - Sanitized string. - - Raises - ------ - ValueError - If the string is empty after sanitization. - """ - # Convert to lowercase and replace non-alphanumeric characters with hyphen - sanitized = re.sub(r'[^a-z0-9]', '-', raw_string.lower()) - - # Remove consecutive hyphens - sanitized = re.sub(r'-+', '-', sanitized) - - # DNS label cannot exceed 63 characters - sanitized = sanitized[:63] - - # Strip after trimming to avoid trailing hyphens - sanitized = sanitized.strip("-") - - if not sanitized: - raise ValueError(f"Sanitized DNS label is empty for input '{raw_string}'") - - return sanitized - - -def sanitize_dns_subdomain_rfc1123(raw_string: str) -> str: - """Convert input string to a valid RFC 1123 DNS subdomain. - - Parameters - ---------- - raw_string : str - String to sanitize. - - Returns - ------- - str - Sanitized string. - - Raises - ------ - ValueError - If the string is empty after sanitization. - """ - segments = raw_string.split('.') - - sanitized_segments = [] - for segment in segments: - if not segment: - continue - sanitized_segment = sanitize_dns_label_rfc1123(segment) - sanitized_segments.append(sanitized_segment) - - sanitized = '.'.join(sanitized_segments) - - # DNS subdomain cannot exceed 253 characters - sanitized = sanitized[:253] - - # Strip after trimming to avoid trailing dots or hyphens - sanitized = sanitized.strip(".-") - - if not sanitized: - raise ValueError(f"Sanitized DNS subdomain is empty for input '{raw_string}'") - - return sanitized - - -def execute_wait(cmd: str, walltime: Optional[int] = None) -> Tuple[int, str, str]: - ''' Synchronously execute a commandline string on the shell. - - Args: - - cmd (string) : Commandline string to execute - - walltime (int) : walltime in seconds - - Returns: - - retcode : Return code from the execution - - stdout : stdout string - - stderr : stderr string - ''' - try: - logger.debug("Creating process with command '%s'", cmd) - print('custom execute wait') - if platform.system() == "Windows": - kwargs = dict(creationflags=subprocess.CREATE_NEW_PROCESS_GROUP) - else: - kwargs = dict(preexec_fn=os.setpgrp) - proc = subprocess.Popen( - cmd, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - shell=True, - **kwargs - ) - logger.debug("Created process with pid %s. Performing communicate", proc.pid) - (stdout, stderr) = proc.communicate(timeout=walltime) - retcode = proc.returncode - logger.debug("Process %s returned %s", proc.pid, proc.returncode) - - except Exception: - logger.exception(f"Execution of command failed:\n{cmd}") - raise - else: - logger.debug("Execution of command in process %s completed normally", proc.pid) - - return (retcode, stdout.decode("utf-8"), stderr.decode("utf-8")) diff --git a/tests/test_task.py b/tests/test_task.py index 4d90dff0..4ae8cbe8 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -13,12 +13,10 @@ import tempfile import unittest -from taskgraph import _parsl_multiprocessing_override -sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override - import parsl import retrying import taskgraph + from taskgraph import _test_fixtures LOGGER = logging.getLogger(__name__) From 95d88fd3bd5329512ae9274a3e31b512b21d083f Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 14:22:18 -0700 Subject: [PATCH 036/112] delete submodule --- .gitmodules | 3 --- parsl | 1 - 2 files changed, 4 deletions(-) delete mode 100644 .gitmodules delete mode 160000 parsl diff --git a/.gitmodules b/.gitmodules deleted file mode 100644 index b8f828d8..00000000 --- a/.gitmodules +++ /dev/null @@ -1,3 +0,0 @@ -[submodule "parsl"] - path = parsl - url = https://github.com/Parsl/parsl.git diff --git a/parsl b/parsl deleted file mode 160000 index 9fd453ee..00000000 --- a/parsl +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 9fd453ee922734c0d82583f86d125f90374712e5 From adf419bfc2ff630e7574017dff4ff8fc3a1314c5 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 14:28:09 -0700 Subject: [PATCH 037/112] add parsl to debug on windows --- parsl/.all-contributorsrc | 36 + parsl/.coveragerc | 3 + parsl/.flake8 | 24 + parsl/.github/ISSUE_TEMPLATE/bug_report.md | 30 + .../.github/ISSUE_TEMPLATE/feature_request.md | 20 + parsl/.github/PULL_REQUEST_TEMPLATE.md | 21 + 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parsl/parsl/tests/unit/test_usage_tracking.py create mode 100644 parsl/parsl/tests/utils.py create mode 100644 parsl/parsl/usage_tracking/__init__.py create mode 100644 parsl/parsl/usage_tracking/api.py create mode 100644 parsl/parsl/usage_tracking/levels.py create mode 100644 parsl/parsl/usage_tracking/usage.py create mode 100644 parsl/parsl/utils.py create mode 100644 parsl/parsl/version.py create mode 100644 parsl/requirements.txt create mode 100755 parsl/setup.py create mode 100755 parsl/tag_and_release.sh create mode 100644 parsl/test-requirements.txt diff --git a/parsl/.all-contributorsrc b/parsl/.all-contributorsrc new file mode 100644 index 00000000..b311bff5 --- /dev/null +++ b/parsl/.all-contributorsrc @@ -0,0 +1,36 @@ +{ + "projectName": "parsl", + "projectOwner": "Parsl", + "files": [ + "CONTRIBUTORS.md" + ], + "commitType": "docs", + "commitConvention": "angular", + "contributorsPerLine": 7, + "contributors": [ + { + "login": "sophie-bui", + "name": "Sophie Bui", + "avatar_url": "https://avatars.githubusercontent.com/u/23347055?v=4", + "profile": "https://www.linkedin.com/in/buisophie/", + "contributions": [ + "projectManagement", + "design", + "eventOrganizing", + "content", + "promotion", + "mentoring" + ] + }, + { + "login": "danielskatz", + "name": "Daniel S. Katz", + "avatar_url": "https://avatars.githubusercontent.com/u/2913845?v=4", + "profile": "https://danielskatz.org", + "contributions": [ + "code", + "content" + ] + } + ] +} diff --git a/parsl/.coveragerc b/parsl/.coveragerc new file mode 100644 index 00000000..a6eb0361 --- /dev/null +++ b/parsl/.coveragerc @@ -0,0 +1,3 @@ +[run] +concurrency=multiprocessing,thread +parallel=True diff --git a/parsl/.flake8 b/parsl/.flake8 new file mode 100644 index 00000000..d12dc8ef --- /dev/null +++ b/parsl/.flake8 @@ -0,0 +1,24 @@ +[flake8] +# E126: continuation line over-indented for hanging indent +# This one is bad. Sometimes ordering matters, conditional imports +# setting env vars necessary etc. +# E402: module level import not at top of file +# E129: Visual indent to not match indent as next line, counter eg here: +# https://github.com/PyCQA/pycodestyle/issues/386 +# W504: line break after binary operator +# (Raised by flake8 even when it is followed) +ignore = E126, E402, E129, W504 +max-line-length = 145 +exclude = test_import_fail.py, + parsl/executors/workqueue/parsl_coprocess.py +# E741 disallows ambiguous single letter names which look like numbers +# We disable it in visualization code because plotly uses 'l' as +# a keyword arg +# F821: undefined name +per-file-ignores = parsl/monitoring/visualization/*:E741, + # test_ssh_errors.py really is broken + + # tests often import fresh_config into their namespace as local_config + # but then do not use it directly, because tests/conftests.py + # looks for it instead. + parsl/tests/**:F401 diff --git a/parsl/.github/ISSUE_TEMPLATE/bug_report.md b/parsl/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 00000000..2ad9580f --- /dev/null +++ b/parsl/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,30 @@ +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: bug +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior, for e.g: +1. Setup Parsl 0.8.0 with Python 3.6 on cluster +2. Run a test script +3. Wait 5 mins +4. See error + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Environment** + - OS: [e.g. ubuntu, centos, MacOS, windows] + - Python version + - Parsl version + +**Distributed Environment** +- Where are you running the Parsl script from ? [e.g. Laptop/Workstation, Login node, Compute node] +- Where do you need the workers to run ? [e.g. Same as Parsl script, Compute nodes, Cloud nodes] diff --git a/parsl/.github/ISSUE_TEMPLATE/feature_request.md b/parsl/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 00000000..647e2b58 --- /dev/null +++ b/parsl/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,20 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: '' +labels: enhancement +assignees: '' + +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. E.g. I'm always frustrated when [...] + +**Describe the solution you'd like** +A clear and concise description of what you want to happen. + +**Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + +**Additional context** +Add any other context about the feature request here. diff --git a/parsl/.github/PULL_REQUEST_TEMPLATE.md b/parsl/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 00000000..39591aed --- /dev/null +++ b/parsl/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,21 @@ +# Description + +Please include a summary of the change and (optionally) which issue is fixed. Please also include +relevant motivation and context. + +# Changed Behaviour + +Which existing user workflows or functionality will behave differently after this PR? + +# Fixes + +Fixes # (issue) + +## Type of change + +Choose which options apply, and delete the ones which do not apply. + +- Bug fix +- New feature +- Update to human readable text: Documentation/error messages/comments +- Code maintenance/cleanup diff --git a/parsl/.github/workflows/ci.yaml b/parsl/.github/workflows/ci.yaml new file mode 100644 index 00000000..cfab7014 --- /dev/null +++ b/parsl/.github/workflows/ci.yaml @@ -0,0 +1,111 @@ +name: Parsl + +on: + pull_request: + types: + - opened + - synchronize + merge_group: + +jobs: + main-test-suite: + strategy: + matrix: + python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"] + runs-on: ubuntu-24.04 + timeout-minutes: 60 + + steps: + - uses: actions/checkout@master + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + + - name: Collect Job Information + id: job-info + run: | + echo "Python Version: ${{ matrix.python-version }}" >> ci_job_info.txt + echo "CI Triggering Event: ${{ github.event_name }}" >> ci_job_info.txt + echo "Triggering Git Ref: ${{ github.ref }}" >> ci_job_info.txt + echo "Triggering Git SHA: ${{ github.sha }}" >> ci_job_info.txt + echo "Workflow Run: ${{ github.run_number }}" >> ci_job_info.txt + echo "Workflow Attempt: ${{ github.run_attempt }}" >> ci_job_info.txt + as_ascii="$(echo "${{ github.ref_name }}" | perl -pe "s/[^A-z0-9-]+/-/g; s/^-+|-+\$//g; s/--+/-/g;")" + echo "as-ascii=$as_ascii" >> $GITHUB_OUTPUT + + - name: Non-requirements based install + run: | + # libpython3.5: make workqueue binary installer happy + # mpich: required by radical executor + sudo apt-get update -q + sudo apt-get install -qy libpython3.5 mpich + + - name: setup virtual env + run: | + make virtualenv + source .venv/bin/activate + + - name: make deps clean_coverage + run: | + source .venv/bin/activate + make deps + make clean_coverage + + - name: make test + run: | + source .venv/bin/activate + + # temporary; until test-matrixification + export PARSL_TEST_PRESERVE_NUM_RUNS=7 + + make test + ln -s pytest-parsl/parsltest-current test_runinfo + + - name: Documentation checks + run: | + source .venv/bin/activate + pip install .[docs] + sudo apt-get install -y pandoc + cd docs + + test ! -e stubs + + # check we can build the docs without warnings + PYTHONPATH=/tmp/cctools/lib/python3.8/site-packages make SPHINXOPTS=-W html + + cd .. + + # assert that none of the runs in this test have put an ERROR message into a + # database manager log file or monitoring router log file. It would be better if + # the tests themselves failed immediately when there was a monitoring error, but + # in the absence of that, this is a dirty way to check. + bash -c '! grep ERROR pytest-parsl/parsltest-current/runinfo*/*/database_manager.log' + bash -c '! grep ERROR pytest-parsl/parsltest-current/runinfo*/*/monitoring_router.log' + + # temporary; until test-matrixification + rm -f pytest-parsl/parsltest-current test_runinfo + + - name: Checking parsl-visualize + run: | + source .venv/bin/activate + sudo apt-get install -y graphviz + pip install .[monitoring,visualization] + parsl/tests/test-viz.sh + + - name: make coverage + run: | + source .venv/bin/activate + make coverage + + - name: Archive runinfo logs + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: runinfo-${{ matrix.python-version }}-${{ steps.job-info.outputs.as-ascii }}-${{ github.sha }} + path: | + runinfo/ + pytest-parsl/ + ci_job_info.txt + compression-level: 9 diff --git a/parsl/.github/workflows/parsl+cctools.yaml b/parsl/.github/workflows/parsl+cctools.yaml new file mode 100644 index 00000000..7d0c3b30 --- /dev/null +++ b/parsl/.github/workflows/parsl+cctools.yaml @@ -0,0 +1,50 @@ +name: WorkQueue and TaskVine tests +on: + pull_request: + types: + - opened + - synchronize + +jobs: + build-linux: + runs-on: ubuntu-24.04 + timeout-minutes: 60 + strategy: + max-parallel: 5 + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python 3.11 + uses: actions/setup-python@v3 + with: + python-version: '3.11' + + - name: Add conda to system path + run: | + # $CONDA is an environment variable pointing to the root of the miniconda directory + echo $CONDA/bin >> $GITHUB_PATH + echo "CONDA BIN: $CONDA/bin" + + - name: Install parsl and dependencies + run: | + conda install --channel=conda-forge ndcctools mpich mpi4py openssl + make deps + pip install .[workqueue,monitoring] + export PATH=/usr/share/miniconda/bin/:$PATH + + - name: Sanity check + run: | + echo "PATH: $PATH" + echo "Python: "; which python3 + python3 -c "import ndcctools; print(ndcctools.__file__)" + python3 -c "from ndcctools import work_queue; print(work_queue.__version__)" + python3 -c "import parsl; print(parsl.__version__)" + which parsl_coprocess.py + which python3 + find /usr/share/miniconda | grep libssl + + - name: Run pytest suite for WorkQueue and TaskVine + run: | + make wqex_local_test + make vineex_local_test diff --git a/parsl/.github/workflows/parsl+flux.yaml b/parsl/.github/workflows/parsl+flux.yaml new file mode 100644 index 00000000..0143241d --- /dev/null +++ b/parsl/.github/workflows/parsl+flux.yaml @@ -0,0 +1,43 @@ +name: Test Flux Scheduler +on: + pull_request: + merge_group: + +jobs: + build: + runs-on: ubuntu-22.04 + permissions: + packages: read + strategy: + fail-fast: false + matrix: + container: ['fluxrm/flux-sched:jammy'] + timeout-minutes: 30 + + container: + image: ${{ matrix.container }} + options: "--platform=linux/amd64 --user root -it --init" + + name: ${{ matrix.container }} + steps: + - name: Checkout + uses: actions/checkout@v3 + + - name: Install Dependencies and Parsl + run: | + apt-get update && apt-get install -y python3-pip curl + pip3 install . -r test-requirements.txt + + - name: Verify Parsl Installation + run: | + pytest parsl/tests/ -k "not cleannet and not unix_filesystem_permissions_required" --config parsl/tests/configs/local_threads.py --random-order --durations 10 + + - name: Test Parsl with Flux + run: | + pytest parsl/tests/test_flux.py --config local --random-order + + - name: Test Parsl with Flux Config + run: | + pytest parsl/tests/ -k "not cleannet and not unix_filesystem_permissions_required" --config parsl/tests/configs/flux_local.py --random-order --durations 10 + + diff --git a/parsl/.github/workflows/parsl+htcondor.yaml b/parsl/.github/workflows/parsl+htcondor.yaml new file mode 100644 index 00000000..7bff997f --- /dev/null +++ b/parsl/.github/workflows/parsl+htcondor.yaml @@ -0,0 +1,56 @@ +name: Test HTCondor Scheduler +on: + pull_request: + merge_group: + +jobs: + build: + runs-on: ubuntu-22.04 + permissions: + packages: read + strategy: + fail-fast: true + matrix: + container: ["htcondor/mini:el9"] + timeout-minutes: 30 + + container: + image: ${{ matrix.container }} + options: "--platform=linux/amd64 --rm -h node01" + + name: ${{ matrix.container }} + steps: + - name: Checkout + uses: actions/checkout@v3 + + - name: Install Dependencies and Parsl + run: | + dnf install -y python3.12 python3-virtualenv + # the container cannot run condor jobs as its default user, + # but supplies a `submituser` user to do so. All the parsl + # testing should run as that user. + + # Copy the checkout from the default location to the submit + # user, ready for installation by that user. + su - submituser -c bash -c "cp -av ${GITHUB_WORKSPACE}/* ./" + + # Build a virtual environment with a recent Python version. + # The htcondor/mini container uses Enterprise Linux 9 which + # uses Python 3.9 by default, which is unsupported by Python. + # It does supply more recent Python versions, though. + su - submituser -c bash -c "virtualenv ./venv --python=3.12" + su - submituser -c bash -c ". ./venv/bin/activate ; pip install . -r test-requirements.txt" + + - name: Verify Parsl Installation + run: | + su - submituser -c bash -c '. venv/bin/activate ; pytest parsl/tests/ -k "not cleannet and not unix_filesystem_permissions_required" --config parsl/tests/configs/local_threads.py --random-order --durations 10' + + - name: Test Parsl with HTCondor Config + # start.sh is supplied by the container image, to start up + # HTCondor enough that jobs can be submitted to it. + # The test configuration does not provide shared filesystem + # access in the way that the Parsl tests expect, so those + # are disabled in the following test by `not shared_fs` and + # `not staging_required`. + run: | + (/start.sh &) && su - submituser -c bash -c '. venv/bin/activate ; sleep 20s && pytest parsl/tests/ -k "not cleannet and not unix_filesystem_permissions_required and not staging_required and not shared_fs" --config parsl/tests/configs/htcondor_ci.py --random-order --durations 10' diff --git a/parsl/.github/workflows/parsl+slurm.yaml b/parsl/.github/workflows/parsl+slurm.yaml new file mode 100644 index 00000000..64dc1a52 --- /dev/null +++ b/parsl/.github/workflows/parsl+slurm.yaml @@ -0,0 +1,41 @@ +name: Test Slurm Scheduler +on: + pull_request: + merge_group: + +jobs: + build: + runs-on: ubuntu-22.04 + permissions: + packages: read + strategy: + fail-fast: false + matrix: + container: ["ghcr.io/tylern4/slurm-standalone:slurm-24-05-0-1"] + timeout-minutes: 30 + + container: + image: ${{ matrix.container }} + options: "--platform=linux/amd64 --rm -h node01" + + name: ${{ matrix.container }} + steps: + - name: Checkout + uses: actions/checkout@v3 + + - name: Install Dependencies and Parsl + run: | + apt update && apt install -y python3.13-venv + python3 -m venv ./venv + . ./venv/bin/activate + CC=/usr/lib64/openmpi/bin/mpicc pip3 install . -r test-requirements.txt + + - name: Verify Parsl Installation + run: | + . venv/bin/activate + pytest parsl/tests/ -k "not cleannet and not unix_filesystem_permissions_required" --config parsl/tests/configs/local_threads.py --random-order --durations 10 + + - name: Test Parsl with Slurm Config + run: | + . venv/bin/activate + ./parsl/tests/slurm-entrypoint.sh pytest parsl/tests/ -k "not cleannet and not unix_filesystem_permissions_required" --config parsl/tests/configs/slurm_local.py --random-order --durations 10 diff --git a/parsl/.github/workflows/python-publish-to-testpypi.yml b/parsl/.github/workflows/python-publish-to-testpypi.yml new file mode 100644 index 00000000..c7343f82 --- /dev/null +++ b/parsl/.github/workflows/python-publish-to-testpypi.yml @@ -0,0 +1,82 @@ +# This workflow will upload a Python Package using Twine when a release is created +# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries + +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. + +name: Upload Python Package + +on: + schedule: + - cron: '42 22 * * MON' # Run every Monday at 22:42 + + workflow_dispatch: + inputs: + suffix: + description: 'Release Suffix to append to version info. For eg. devN, a0' + required: false + default: '' + +permissions: + contents: write + +jobs: + + deploy: + # This action is intended to be invoked manually for debugging purposes + if : github.actor == 'yadudoc' || github.actor == 'benclifford' + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + with: + fetch-tags: true + fetch-depth: 0 + + - name: Check if this commit is already released + id: already_released + run: | + if git tag --contains HEAD | grep -e '^[0-9]\{4\}\.[0-9]\{2\}\.[0-9]\{2\}$' ; then exit 1 ; fi + + - name: Set up Python + uses: actions/setup-python@v4 + with: + # The release process needs distutils - see Parsl issue #2934 + # which was removed from Python 3.12 + python-version: '3.11' + + - name: Set version info + id: version_setter + run: echo "VERSION=$(date +'%Y.%m.%d')$SUFFIX" >> $GITHUB_OUTPUT + env: + SUFFIX: ${{ inputs.suffix }} + + - name: Install dependencies + run: | + python -m pip install --upgrade pip wheel + pip install build + - name: Build package + run: | + ./tag_and_release.sh update_version + ./tag_and_release.sh package + env: + VERSION: ${{ steps.version_setter.outputs.VERSION }} + + - name: Publish package + uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29 + with: + user: __token__ + # Set the following to publish to TestPypi instead + # password: ${{ secrets.TESTPYPI_API_TOKEN }} + # repository_url: https://test.pypi.org/legacy/ + + password: ${{ secrets.PYPI_API_TOKEN }} + + - name: Mint a tag + uses: rickstaa/action-create-tag@v1 + with: + tag: ${{ steps.version_setter.outputs.VERSION }} + message: "Release version: ${{ steps.version_setter.outputs.VERSION }}" diff --git a/parsl/.gitignore b/parsl/.gitignore new file mode 100644 index 00000000..8811016b --- /dev/null +++ b/parsl/.gitignore @@ -0,0 +1,123 @@ +*~ +*.pyc +parsl.egg-info/* +.scripts +.*out +.*err +*log +.ipynb_checkpoints +tutorials/* +venv/* +web/* +api/* +.idea/* + +# local user configuration which should never be +# version controlled +./parsl/tests/configs/local_user_opts.py + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +coverage.xml +*.cover +.hypothesis/ +/.pytest/ +/pytest-parsl/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv +venv/ +ENV/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +# emacs buffers +\#* diff --git a/parsl/.gitmodules b/parsl/.gitmodules new file mode 100644 index 00000000..e69de29b diff --git a/parsl/.isort.cfg b/parsl/.isort.cfg new file mode 100644 index 00000000..f238bf7e --- /dev/null +++ b/parsl/.isort.cfg @@ -0,0 +1,2 @@ +[settings] +profile = black diff --git a/parsl/.readthedocs.yaml b/parsl/.readthedocs.yaml new file mode 100644 index 00000000..a276d2f2 --- /dev/null +++ b/parsl/.readthedocs.yaml @@ -0,0 +1,28 @@ +# .readthedocs.yaml +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +formats: + - pdf + +# Set the version of Python and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.11" + +# Build documentation in the docs/ directory with Sphinx +sphinx: + configuration: docs/conf.py + +# We recommend specifying your dependencies to enable reproducible builds: +# https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html +python: + install: + - method: pip + path: "." + extra_requirements: + - docs diff --git a/parsl/.wci.yml b/parsl/.wci.yml new file mode 100644 index 00000000..ccea1300 --- /dev/null +++ b/parsl/.wci.yml @@ -0,0 +1,46 @@ +name: Parsl +icon: https://parsl-project.org/images/logos/parsl-logo.png +headline: Productive parallel programming in Python +description: | + Parsl is a flexible and scalable parallel programming library for Python. + Parsl augments Python with simple constructs for encoding parallelism. + Developers annotate Python functions to specify opportunities for + concurrent execution. These annotated functions, called apps, may represent + pure Python functions or calls to external applications. Parsl further + allows invocations of these apps, called tasks, to be connected by shared + input/output data (e.g., Python objects or files) via which Parsl + constructs a dynamic dependency graph of tasks to manage concurrent task + execution where possible. + +language: Python + +release: + version: 1.2.0 + date: 2022-01-13 + +documentation: + general: https://parsl.readthedocs.io/en/stable/ + installation: https://parsl.readthedocs.io/en/stable/quickstart.html + tutorial: https://parsl-project.org/binder + +social: + twitter: ParslProject + +execution_environment: + interfaces: + - Python API + resource_managers: + - Slurm + - LSF + - PBS + - Flux + - GridEngine + - HTCondor + - AWS + - Google Cloud + - Kubernetes + transfer_protocols: + - Globus + - HTTP + - FTP + - rsync diff --git a/parsl/CITATION.cff b/parsl/CITATION.cff new file mode 100644 index 00000000..fef63d5c --- /dev/null +++ b/parsl/CITATION.cff @@ -0,0 +1,120 @@ +cff-version: 1.2.0 +message: "If you use this software in your work, please cite it using the following metadata." +authors: +- family-names: "Nand Babuji" + given-names: "Yadu" + orcid: https://orcid.org/0000-0002-9162-6003 +- family-names: "Clifford" + given-names: "Ben" + orcid: https://orcid.org/0000-0001-6397-7239 +- family-names: "Woodard" + given-names: "Anna" + orcid: https://orcid.org/0000-0002-8640-5417 +- family-names: "Li" + given-names: "Zhuozhao" + orcid: https://orcid.org/0000-0003-1903-6428 +- family-names: "Chard" + given-names: "Kyle" + orcid: https://orcid.org/0000-0002-7370-4805 +- family-names: "Katz" + given-names: "Daniel S." + orcid: https://orcid.org/0000-0001-5934-7525 +- family-names: "Wilde" + given-names: "Michael" + orcid: https://orcid.org/0000-0001-6660-6206 +- family-names: "Wozniak" + given-names: "Justin" + orcid: https://orcid.org/0000-0002-2441-2048 +- family-names: "Foster" + given-names: "Ian" + orcid: https://orcid.org/0000-0003-2129-5269 +- family-names: "Glick" + given-names: "Benjamin" + orcid: https://orcid.org/0000-0002-0762-2684 +- family-names: "Rowland" + given-names: "Kelly L." + orcid: https://orcid.org/0000-0001-5147-0051 +- family-names: "Pigg" + given-names: "Connor" +- family-names: "Lacinski" + given-names: "Lukasz" +- family-names: "Garrido" + given-names: "Juan David" +- family-names: "Hategan" + given-names: "Mihael" +- family-names: "Tovar" + given-names: "Benjamin" +- family-names: "Gray" + given-names: "Lindsey" +- family-names: "Shaffer" + given-names: "Tim" +- family-names: "Ward" + given-names: "Logan" +- family-names: "Heise" + given-names: "David" +- family-names: "Chard" + given-names: "Ryan" +- family-names: "Summer" + given-names: "Ted" +- family-names: "Dasso" + given-names: "TJ" +- family-names: "Hayhurst" + given-names: "Lyle" +- family-names: "Moon" + given-names: "Joseph" +- family-names: "Perera" + given-names: "Akila Ravihansa" +- family-names: "Lentner" + given-names: "Geoffrey" +- family-names: "Nagaitsev" + given-names: "Kir" +- family-names: "Corbett" + given-names: "James" +- family-names: "Lewis" + given-names: "Monica D." +- family-names: "Thain" + given-names: "Douglas" +- family-names: "Khodygo" + given-names: "Vladimir" +- family-names: "Hayes" + given-names: "Alex" +- family-names: "Litteken" + given-names: "Andrew" +- family-names: "Schwarting" + given-names: "Marcus" +- family-names: "Skluzacek" + given-names: "Tyler J." +- family-names: "Kowalik" + given-names: "Kacper" +- family-names: "Baker" + given-names: "Joseph L." +- family-names: "Wayne" + given-names: "Mitchell R." +- family-names: "Griffith" + given-names: "Joel" +- family-names: "Hoven" + given-names: "John" +- family-names: "Fialho" + given-names: "Raphael" +- family-names: "Lindberg" + given-names: "Gerrick" +- family-names: "Rao" + given-names: "Yongyan" +- family-names: "Hurtado" + given-names: "Kenyi" +- family-names: "Miglani" + given-names: "Sohit" +- family-names: "Arslan" + given-names: "Engin" +- family-names: "Govoni" + given-names: "Darren" +- family-names: "Galewsky" + given-names: "Ben" +- family-names: "Bilke" + given-names: "Lars" +title: "parsl" +identifiers: + - description: Conference proceedings paper + type: doi + value: "10.1145/3307681.3325400" + diff --git a/parsl/CITATION.md b/parsl/CITATION.md new file mode 100644 index 00000000..6f90c8c1 --- /dev/null +++ b/parsl/CITATION.md @@ -0,0 +1,25 @@ +Babuji, Y., Woodard, A., Li, Z., Katz, D. S., Clifford, B., Kumar, R., Lacinski, L., Chard, R., Wozniak, J., Foster, I., Wilde, M., and Chard, K., Parsl: Pervasive Parallel Programming in Python. 28th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC). 2019. + +or + +```{tex} +@inproceedings{babuji19parsl, + author = {Babuji, Yadu and + Woodard, Anna and + Li, Zhuozhao and + Katz, Daniel S. and + Clifford, Ben and + Kumar, Rohan and + Lacinski, Lukasz and + Chard, Ryan and + Wozniak, Justin and + Foster, Ian and + Wilde, Mike and + Chard, Kyle}, + title = {Parsl: Pervasive Parallel Programming in Python}, + booktitle = {28th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC)}, + doi = {10.1145/3307681.3325400}, + year = {2019}, + url = {https://doi.org/10.1145/3307681.3325400} +} +``` diff --git a/parsl/CONTRIBUTING.rst b/parsl/CONTRIBUTING.rst new file mode 100644 index 00000000..ec49a59d --- /dev/null +++ b/parsl/CONTRIBUTING.rst @@ -0,0 +1,155 @@ +Where to Start +-------------- + +We eagerly welcome contributions of any type (e.g., bug fixes, new features, reporting issues, documentation, etc). If you're looking for a good place to start, we recommend perusing our current Git issues (those marked with `help wanted `_ are a good place to start). + +Please be aware of `Parsl's Code of Conduct `_. + +If you are unfamiliar with GitHub pull requests, the primary mechanism for contributing changes to our code, there is `documentation available `_. + +The best places to ask questions or discuss development activities are: + +* in our Slack's `#parsl-hackers channel `_. You can `join our Slack here `_. + +* using `GitHub issues `_. + + +Coding Conventions +------------------ + +Formatting Conventions +====================== + +Parsl code should adhere to Python `PEP-8 `_. This is enforced in CI (with some exceptions). You can also run this test yourself using ``make flake8``. + +Naming Conventions +================== + +The following conventions should be followed: ClassName, ExceptionName, GLOBAL_CONSTANT_NAME, and lowercase_with_underscores for everything else. + +Version Increments +================== + +Parsl follows the `calendar versioning scheme `_ with ``YYYY.MM.DD`` numbering scheme for versions. +This scheme was chosen after switching from ad-hoc versioning and manual release processes to an automated weekly process. +Releases are pushed from GitHub actions to PyPI and will be picked up automatically by Conda. +Manual packaging instructions are included in the +`packaging docs `_ + +Documentation +================== + +Classes should be documented following the `NumPy/SciPy `_ +style. A concise summary is available `here `_. User and developer documentation is auto-generated and made available on +`ReadTheDocs `_. + +Testing +======= + +Parsl uses ``pytest`` to run most tests. All tests should be placed in +the ``parsl/tests`` directory. + +There are two broad groups of tests: those which must run with a +specific configuration, and those that should work with any +configuration. + +Tests that should run with any configuration live under +themed directories ``parsl/tests/test*/`` and should be named ``test*.py``. +They can be run with any configuration by specifying ``--config CONFIGPATH`` +where CONFIGPATH is a path to a ``.py`` file exporting a parsl configuration +object named ``config``. The Parsl-specific test fixtures will ensure +a suitable DFK is loaded with that configuration for each test. + +Tests which require their own specially configured DFK, or no DFK at all, +should be labelled with ``@pytest.mark.local`` and can be run with +``--config local``. +Provide the special configuration creating a ``local_config`` function +that returns the required configuration in that test file. +Or, provide both a ``local_setup`` function that loads the proper configuration +and ``local_teardown`` that stops parsl. + +There is more fine-grained enabling and disabling of tests within the +above categories: + +A pytest marker of ``cleannet`` (for clean network) can be used to select +or deselect tests that need a very clean network (for example, for tests +making FTP transfers). When the test environment (GitHub actions) does not +provide a sufficiently clean network, run all tests with ``-k "not cleannet"`` to +disable those tests. + +Some other markers are available but unused in testing; +see ``pytest --markers parsl/tests/`` for more details. + +A specific test in a specific file can be run like this::: + + $ pytest test_python_apps/test_basic.py::test_simple + +A timeout can be added to test runs using a pytest parameter such as +``--timeout=60`` + +Many tests are marked with ``@pytest.mark.skip`` for reasons usually +specified directly in the annotation - generally because they are broken +in one way or another. + + +Coverage Testing +================ + +There is also some coverage testing available. The CI by default records +coverage for most of the tests it runs and outputs a brief report +at the end of each CI run. This is purely informational and a Lack of +coverage won't produce a CI failure. + +It is possible to produce a more detailed coverage report on your +development machine: make sure you have no `.coverage` file, run the +test commands as shown in `.github/workflows/ci.yaml`, and then run +`coverage report` to produce the summary as seen in CI, or run +`coverage html` to produce annotated source code in the `htmlcov/` +subdirectory. This will show, line by line, if each line of parsl +source code was executed during the coverage test. + +Development Process +------------------- + +If you are a contributor to Parsl at large, we recommend forking the repository and submitting pull requests from your fork. +The `Parsl development team `_ has the additional privilege of creating development branches on the repository. +Parsl development follows a common pull request-based workflow similar to `GitHub flow `_. That is: + +* every development activity (except very minor changes, which can be discussed in the PR) should have a related GitHub issue +* all development occurs in branches (named with a short descriptive name, for example, `add-globus-transfer-#1`) +* the master branch is always stable +* development branches should include tests for added features +* development branches should be tested after being brought up-to-date with the master (in this way, what is being tested is what is actually going into the code; otherwise, unexpected issues from merging may come up) +* branches that have been successfully tested are merged via pull requests (PRs) +* PRs should be used for review and discussion +* PRs should be reviewed in a timely manner to reduce effort in keeping them synced with other changes happening on the master branch + +Git commit messages should include a single summary sentence followed by a more explanatory paragraph. Note: all commit messages should reference the GitHub issue to which they relate. A nice discussion on the topic can be found `here `_. +:: + Implemented Globus data staging support + + Added the ability to reference and automatically transfer Globus-accessible + files. References are represented using the Parsl file format + “globus://endpoint/path/file.” If Globus endpoints are known for source and + destination, Parsl will use the Globus transfer service to move data to the + compute host. Fixes #-1. + +Git Hooks +--------- + +Developers may find it useful to set up a pre-commit git hook to automatically lint and run tests. This is a script that is run before each commit. For example:: + + $ cat ~/parsl/.git/hooks/pre-commit + #!/bin/sh + + make lint flake8 mypy local_thread_test + +Project Documentation +--------------------- + +All project documentation is written in reStructuredText. `Sphinx `_ is used to generate the HTML documentation from the rst documentation and structured docstrings in Parsl code. Project documentation is built automatically and added to the `Parsl documentation `_. + +Credit and Contributions +------------------------ + +Parsl wants to ensure that all contributors receive credit for their contributions. When you make your first contribution, you should update the codemeta.json file to include yourself as a contributor to the project. diff --git a/parsl/LICENSE b/parsl/LICENSE new file mode 100644 index 00000000..8dada3ed --- /dev/null +++ b/parsl/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright {yyyy} {name of copyright owner} + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/parsl/MANIFEST.in b/parsl/MANIFEST.in new file mode 100644 index 00000000..9ab9c1c3 --- /dev/null +++ b/parsl/MANIFEST.in @@ -0,0 +1,4 @@ +include LICENSE +include parsl/monitoring/visualization/templates/* +include parsl/monitoring/visualization/static/* +include requirements.txt diff --git a/parsl/Makefile b/parsl/Makefile new file mode 100644 index 00000000..f8eba682 --- /dev/null +++ b/parsl/Makefile @@ -0,0 +1,137 @@ +PYTHON := $(shell which python3 || echo ".python_is_missing") +SHELL := $(shell which bash) # Use bash instead of bin/sh as shell +GIT := $(shell which git || echo ".git_is_missing") +CWD := $(shell pwd) +DEPS := .deps +MPICH=mpich +OPENMPI=openmpi +export HYDRA_LAUNCHER=fork +export OMPI_MCA_rmaps_base_oversubscribe=yes +MPI=$(MPICH) + +.PHONY: help +help: ## me + @grep -E '^[0-9a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}' + +VENV = .venv +.PHONY: virtualenv +virtualenv: ## create an activate a virtual env + test -f $(VENV)/bin/activate || $(PYTHON) -m venv $(VENV) + echo "Run 'source $(VENV)/bin/activate' to activate the virtual environment" + + +$(DEPS): test-requirements.txt requirements.txt + pip3 install --upgrade pip + pip3 install -r test-requirements.txt -r requirements.txt + touch $(DEPS) + +.PHONY: deps +deps: $(DEPS) ## install the dependencies + +.PHONY: lint +lint: ## run linter script + parsl/tests/lint-inits.sh + +.PHONY: isort +isort: ## run isort on all files + isort --check parsl/ + +.PHONY: flake8 +flake8: ## run flake + flake8 parsl/ + +.PHONY: clean_coverage +clean_coverage: ## clear the coverage file (.coverage) + rm -f .coverage + +.PHONY: mypy +mypy: ## run mypy checks + MYPYPATH=$(CWD)/mypy-stubs mypy parsl/ + +.PHONY: gce_test +gce_test: ## Run tests with GlobusComputeExecutor (--config .../globus_compute.py) + pytest -v -k "not shared_fs and not issue_3620 and not staging_required" --config parsl/tests/configs/globus_compute.py parsl/tests/ --random-order --durations 10 + pytest -v -k "not shared_fs and not issue_3620 and not staging_required and globus_compute" --config local --random-order --durations 10 + +.PHONY: local_thread_test +local_thread_test: ## run all tests with local_thread config (--config .../local_threads.py) + pytest parsl/tests/ -k "not cleannet" --config parsl/tests/configs/local_threads.py --random-order --durations 10 + +.PHONY: htex_local_test +htex_local_test: ## run all tests with htex_local config (--config .../htex_local.py) + pip3 install . + pytest parsl/tests/ -k "not cleannet" --config parsl/tests/configs/htex_local.py --random-order --durations 10 + +.PHONY: htex_local_alternate_test +htex_local_alternate_test: ## run all tests with htex_local_alternate config (--config .../htex_local_alternate.py) + pip3 install ".[monitoring]" + pytest parsl/tests/ -k "not cleannet" --config parsl/tests/configs/htex_local_alternate.py --random-order --durations 10 + +.PHONY: vineex_local_test +vineex_local_test: ## Run the VineExecutor local tests (-k taskvine --config local) + pytest parsl/tests/ -k "not cleannet" --config parsl/tests/configs/taskvine_ex.py --random-order --durations 10 + pytest parsl/tests/ -k "not cleannet and taskvine" --config local --random-order --durations 10 + +.PHONY: wqex_local_test +wqex_local_test: ## Run the WorkQueueExecutor local tests (-k workqueue --config local) + pytest parsl/tests/ -k "not cleannet" --config parsl/tests/configs/workqueue_ex.py --random-order --durations 10 + pytest parsl/tests/ -k "not cleannet and workqueue" --config local --random-order --durations 10 + +.PHONY: radical_local_test +radical_local_test: ## Run the Radical local tests (-m radical --config local) + pip3 install ".[radical-pilot]" + mkdir -p ~/.radical/pilot/configs && echo '{"localhost": {"virtenv_mode": "local"}}' > ~/.radical/pilot/configs/resource_local.json + pytest parsl/tests/ -k "not cleannet and not issue3328 and not executor_supports_std_stream_tuples" --config parsl/tests/configs/local_radical.py --random-order --durations 10 + pytest parsl/tests/ -m "radical" --config local --random-order --durations 10 + +.PHONY: config_local_test +config_local_test: ## run the config-local tests (--config local) + pip3 install ".[monitoring,visualization,proxystore,kubernetes]" + pytest parsl/tests/ -k "not cleannet and not workqueue and not taskvine and not globus_compute" --config local --random-order --durations 10 + +.PHONY: site_test +site_test: ## Run the site tests + pytest parsl/tests/ -k "not cleannet" ${SHARED_FS_OPTIONS} --config parsl/tests/site_tests/site_config_selector.py --random-order + pytest parsl/tests/site_tests/ ${SHARED_FS_OPTIONS} --config local + +.PHONY: perf_test +perf_test: ## Run `parsl-perf` (--config .../local_threads.py) + parsl-perf --time 5 --config parsl/tests/configs/local_threads.py + parsl-perf --iterate 1,2,3 --config parsl/tests/configs/local_threads.py + +.PHONY: test ## run all tests with all config types +test: clean_coverage isort lint flake8 mypy local_thread_test htex_local_test htex_local_alternate_test config_local_test perf_test ## run all tests + +.PHONY: tag +tag: ## create a tag in git. to run, do a 'make VERSION="version string" tag + ./tag_and_release.sh create_tag $(VERSION) + +.PHONY: package +package: ## package up a distribution. + ./tag_and_release.sh package + +.PHONY: deploy +deploy: ## deploy the distribution + ./tag_and_release.sh release + +# THIS IS MEANT TO BE INVOKED BY GITHUB ACTIONS **ONLY** +.PHONY: update_version +update_version: ## Update version + ./tag_and_release.sh update_version + +.PHONY: release +release: deps tag package deploy ## create a release. To run, do a 'make VERSION="version string" release' + +.PHONY: coverage +coverage: ## show the coverage report + # coverage report + echo no-op coverage report + +.PHONY: clean +clean: ## clean up the environment by deleting the .venv, dist, eggs, mypy caches, coverage info, etc + rm -rf .venv $(DEPS) dist *.egg-info .mypy_cache build .pytest_cache .coverage runinfo $(WORKQUEUE_INSTALL) + +.PHONY: flux_local_test +flux_local_test: ## Test Parsl with Flux Executor + pip3 install . + pytest parsl/tests/ -k "not cleannet" --config parsl/tests/configs/flux_local.py --random-order --durations 10 diff --git a/parsl/README.rst b/parsl/README.rst new file mode 100644 index 00000000..08b6f877 --- /dev/null +++ b/parsl/README.rst @@ -0,0 +1,136 @@ +Parsl - Parallel Scripting Library +================================== +|licence| |docs| |NSF-1550588| |NSF-1550476| |NSF-1550562| |NSF-1550528| |NumFOCUS| |CZI-EOSS| |paper| + +Parsl extends parallelism in Python beyond a single computer. + +You can use Parsl +`just like Python's parallel executors `_ +but across *multiple cores and nodes*. +However, the real power of Parsl is in expressing multi-step workflows of functions. +Parsl lets you chain functions together and will launch each function as inputs and computing resources are available. + +.. code-block:: python + + import parsl + from parsl import python_app + + + # Make functions parallel by decorating them + @python_app + def f(x): + return x + 1 + + @python_app + def g(x, y): + return x + y + + # Start Parsl on a single computer + with parsl.load(): + # These functions now return Futures + future = f(1) + assert future.result() == 2 + + # Functions run concurrently, can be chained + f_a, f_b = f(2), f(3) + future = g(f_a, f_b) + assert future.result() == 7 + + +Start with the `configuration quickstart `_ to learn how to tell Parsl how to use your computing resource, +then explore the `parallel computing patterns `_ to determine how to use parallelism best in your application. + +.. |licence| image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg + :target: https://github.com/Parsl/parsl/blob/master/LICENSE + :alt: Apache Licence V2.0 +.. |docs| image:: https://readthedocs.org/projects/parsl/badge/?version=stable + :target: http://parsl.readthedocs.io/en/stable/?badge=stable + :alt: Documentation Status +.. |NSF-1550588| image:: https://img.shields.io/badge/NSF-1550588-blue.svg + :target: https://nsf.gov/awardsearch/showAward?AWD_ID=1550588 + :alt: NSF award info +.. |NSF-1550476| image:: https://img.shields.io/badge/NSF-1550476-blue.svg + :target: https://nsf.gov/awardsearch/showAward?AWD_ID=1550476 + :alt: NSF award info +.. |NSF-1550562| image:: https://img.shields.io/badge/NSF-1550562-blue.svg + :target: https://nsf.gov/awardsearch/showAward?AWD_ID=1550562 + :alt: NSF award info +.. |NSF-1550528| image:: https://img.shields.io/badge/NSF-1550528-blue.svg + :target: https://nsf.gov/awardsearch/showAward?AWD_ID=1550528 + :alt: NSF award info +.. |NSF-1550475| image:: https://img.shields.io/badge/NSF-1550475-blue.svg + :target: https://nsf.gov/awardsearch/showAward?AWD_ID=1550475 + :alt: NSF award info +.. |CZI-EOSS| image:: https://chanzuckerberg.github.io/open-science/badges/CZI-EOSS.svg + :target: https://czi.co/EOSS + :alt: CZI's Essential Open Source Software for Science +.. |NumFOCUS| image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A + :target: https://numfocus.org + :alt: Powered by NumFOCUS +.. |paper| image:: https://img.shields.io/badge/Software_DOI-10.1145/3307681.3325400-blue + :target: https://doi.org/10.1145/3307681.3325400 + :alt: Conference proceedings paper + + +Quickstart +========== + +Install Parsl using pip:: + + $ pip3 install parsl + +To run the Parsl tutorial notebooks you will need to install Jupyter:: + + $ pip3 install jupyter + +Detailed information about setting up Jupyter with Python is available `here `_ + +Note: Parsl uses an opt-in model to collect usage statistics for reporting and improvement purposes. To understand what stats are collected and enable collection please refer to the `usage tracking guide `__ + +Documentation +============= + +The complete parsl documentation is hosted `here `_. + +The Parsl tutorial is hosted on live Jupyter notebooks `here `_ + + +For Developers +-------------- + +1. Download Parsl:: + + $ git clone https://github.com/Parsl/parsl + + +2. Build and Test:: + + $ cd parsl # navigate to the root directory of the project + $ make # show all available makefile targets + $ make virtualenv # create a virtual environment + $ source .venv/bin/activate # activate the virtual environment + $ make deps # install python dependencies from test-requirements.txt + $ make test # make (all) tests. Run "make config_local_test" for a faster, smaller test set. + $ make clean # remove virtualenv and all test and build artifacts + +3. Install:: + + $ cd parsl # only if you didn't enter the top-level directory in step 2 above + $ python3 setup.py install + +4. Use Parsl! + +Requirements +============ + +Parsl is supported in Python 3.10+. Requirements can be found `here `_. Requirements for running tests can be found `here `_. + +Code of Conduct +=============== + +Parsl seeks to foster an open and welcoming environment - Please see the `Parsl Code of Conduct `_ for more details. + +Contributing +============ + +We welcome contributions from the community. Please see our `contributing guide `_. diff --git a/parsl/codemeta.json b/parsl/codemeta.json new file mode 100644 index 00000000..71ed1f90 --- /dev/null +++ b/parsl/codemeta.json @@ -0,0 +1,198 @@ +{ + "@context": [ + "https://doi.org/10.5063/schema/codemeta-2.0", + "http://schema.org" + ], + "@type": "SoftwareSourceCode", + "identifier": "parsl", + "name": "parsl", + "version": "0.6.1", + "description": "Simple data dependent workflows in Python", + "license": "OSI Approved :: Apache Software License", + "author": [ + { + "@id":"http://orcid.org/0000-0002-9162-6003", + "@type": "Person", + "givenName": "Yadu", + "familyName": "Nand Babuji", + "email": "yadu@uchicago.edu" + }, + { + "@id":"http://orcid.org/0000-0002-8640-5417", + "@type": "Person", + "givenName": "Anna", + "familyName": "Woodard", + "email": "annawoodard@uchicago.edu" + }, + { + "@id":"http://orcid.org/0000-0002-7370-4805", + "@type": "Person", + "givenName": "Kyle", + "familyName": "Chard", + "email": "chard@uchicago.edu" + }, + { + "@id":"http://orcid.org/0000-0001-5934-7525", + "@type": "Person", + "givenName": "Daniel S.", + "familyName": "Katz", + "email": "d.katz@ieee.org" + }, + { + "@id":"http://orcid.org/0000-0000-0000-0000", + "@type": "Person", + "givenName": "Michael", + "familyName": "Wilde", + "email": "wilde@parallelworks.com" + }, + { + "@id":"http://orcid.org/0000-0000-0000-0000", + "@type": "Person", + "givenName": "Justin", + "familyName": "Wozniak", + "email": "woz@anl.gov" + }, + { + "@id":"http://orcid.org/0000-0000-0000-0000", + "@type": "Person", + "givenName": "Ian", + "familyName": "Foster", + "email": "foster@anl.gov" + }, + { + "@id":"http://orcid.org/0000-0002-0762-2684", + "@type": "Person", + "givenName": "Ben", + "familyName": "Glick", + "email": "glick@lclark.edu" + }, + { + "@id":"https://orcid.org/0000-0001-5147-0051", + "@type": "Person", + "givenName": "Kelly L.", + "familyName": "Rowland", + "email": "kellyrowland@lbl.gov" + }, + { + "@id":"https://orcid.org/0000-0001-7774-2246", + "@type": "Person", + "givenName": "Matthew R.", + "familyName": "Becker", + "email": "becker.mr@gmail.com" + } + ], + "softwareRequirements": [ + { + "@type": "SoftwareApplication", + "identifier": "cmreshandler", + "name": "CMRESHandler", + "provider": { + "@id": "https://pypi.org", + "@type": "Organization", + "name": "The Python Package Index", + "url": "https://pypi.org" + }, + "runtimePlatform": "Python 3.6.4" + }, + { + "@type": "SoftwareApplication", + "identifier": "ipykernel", + "name": "ipykernel", + "provider": { + "@id": "https://pypi.org", + "@type": "Organization", + "name": "The Python Package Index", + "url": "https://pypi.org" + }, + "runtimePlatform": "Python 3.6.4" + }, + { + "@type": "SoftwareApplication", + "identifier": "psutil", + "name": "psutil", + "provider": { + "@id": "https://pypi.org", + "@type": "Organization", + "name": "The Python Package Index", + "url": "https://pypi.org" + }, + "runtimePlatform": "Python 3.6.4" + }, + { + "@type": "SoftwareApplication", + "identifier": "dill", + "name": "dill", + "provider": { + "@id": "https://pypi.org", + "@type": "Organization", + "name": "The Python Package Index", + "url": "https://pypi.org" + }, + "runtimePlatform": "Python 3.6.4" + }, + { + "@type": "SoftwareApplication", + "identifier": "globus-sdk", + "name": "globus-sdk", + "provider": { + "@id": "https://pypi.org", + "@type": "Organization", + "name": "The Python Package Index", + "url": "https://pypi.org" + }, + "runtimePlatform": "Python 3.6.4" + }, + { + "@type": "SoftwareApplication", + "identifier": "requests", + "name": "requests", + "provider": { + "@id": "https://pypi.org", + "@type": "Organization", + "name": "The Python Package Index", + "url": "https://pypi.org" + }, + "runtimePlatform": "Python 3.6.4" + }, + { + "@type": "SoftwareApplication", + "identifier": "tblib", + "name": "tblib", + "provider": { + "@id": "https://pypi.org", + "@type": "Organization", + "name": "The Python Package Index", + "url": "https://pypi.org" + }, + "runtimePlatform": "Python 3.6.4" + }, + { + "@type": "SoftwareApplication", + "identifier": "nbsphinx", + "name": "nbsphinx", + "provider": { + "@id": "https://pypi.org", + "@type": "Organization", + "name": "The Python Package Index", + "url": "https://pypi.org" + }, + "runtimePlatform": "Python 3.6.4" + } + ], + "audience": [ + { + "@type": "Audience", + "audienceType": "Developers" + } + ], + "provider": { + "@id": "https://pypi.org", + "@type": "Organization", + "name": "The Python Package Index", + "url": "https://pypi.org" + }, + "runtimePlatform": "Python 3.10", + "url": "https://github.com/Parsl/parsl", + "developmentStatus": "active", + "programmingLanguage": "Python :: 3.10" +} diff --git a/parsl/docs/Makefile b/parsl/docs/Makefile new file mode 100644 index 00000000..6e79b169 --- /dev/null +++ b/parsl/docs/Makefile @@ -0,0 +1,224 @@ +# Makefile for Sphinx documentation + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = python3 -m sphinx +PAPER = +BUILDDIR = _build + +# Internal variables. +PAPEROPT_a4 = -D latex_paper_size=a4 +PAPEROPT_letter = -D latex_paper_size=letter +ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . +# the i18n builder cannot share the environment and doctrees with the others +I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . + +.PHONY: help +help: + @echo "Please use \`make ' where is one of" + @echo " html to make standalone HTML files" + @echo " dirhtml to make HTML files named index.html in directories" + @echo " singlehtml to make a single large HTML file" + @echo " pickle to make pickle files" + @echo " json to make JSON files" + @echo " htmlhelp to make HTML files and a HTML help project" + @echo " qthelp to make HTML files and a qthelp project" + @echo " applehelp to make an Apple Help Book" + @echo " devhelp to make HTML files and a Devhelp project" + @echo " epub to make an epub" + @echo " epub3 to make an epub3" + @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" + @echo " latexpdf to make LaTeX files and run them through pdflatex" + @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" + @echo " text to make text files" + @echo " man to make manual pages" + @echo " texinfo to make Texinfo files" + @echo " info to make Texinfo files and run them through makeinfo" + @echo " gettext to make PO message catalogs" + @echo " changes to make an overview of all changed/added/deprecated items" + @echo " xml to make Docutils-native XML files" + @echo " pseudoxml to make pseudoxml-XML files for display purposes" + @echo " linkcheck to check all external links for integrity" + @echo " doctest to run all doctests embedded in the documentation (if enabled)" + @echo " coverage to run coverage check of the documentation (if enabled)" + @echo " dummy to check syntax errors of document sources" + +.PHONY: clean +clean: + rm -rf $(BUILDDIR)/* + +.PHONY: html +html: + $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html + @echo + @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." + +.PHONY: dirhtml +dirhtml: + $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml + @echo + @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." + +.PHONY: singlehtml +singlehtml: + $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml + @echo + @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." + +.PHONY: pickle +pickle: + $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle + @echo + @echo "Build finished; now you can process the pickle files." + +.PHONY: json +json: + $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json + @echo + @echo "Build finished; now you can process the JSON files." + +.PHONY: htmlhelp +htmlhelp: + $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp + @echo + @echo "Build finished; now you can run HTML Help Workshop with the" \ + ".hhp project file in $(BUILDDIR)/htmlhelp." + +.PHONY: qthelp +qthelp: + $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp + @echo + @echo "Build finished; now you can run "qcollectiongenerator" with the" \ + ".qhcp project file in $(BUILDDIR)/qthelp, like this:" + @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/Parsl.qhcp" + @echo "To view the help file:" + @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/Parsl.qhc" + +.PHONY: applehelp +applehelp: + $(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp + @echo + @echo "Build finished. The help book is in $(BUILDDIR)/applehelp." + @echo "N.B. You won't be able to view it unless you put it in" \ + "~/Library/Documentation/Help or install it in your application" \ + "bundle." + +.PHONY: devhelp +devhelp: + $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp + @echo + @echo "Build finished." + @echo "To view the help file:" + @echo "# mkdir -p $$HOME/.local/share/devhelp/Parsl" + @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/Parsl" + @echo "# devhelp" + +.PHONY: epub +epub: + $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub + @echo + @echo "Build finished. The epub file is in $(BUILDDIR)/epub." + +.PHONY: epub3 +epub3: + $(SPHINXBUILD) -b epub3 $(ALLSPHINXOPTS) $(BUILDDIR)/epub3 + @echo + @echo "Build finished. The epub3 file is in $(BUILDDIR)/epub3." + +.PHONY: latex +latex: + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex + @echo + @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." + @echo "Run \`make' in that directory to run these through (pdf)latex" \ + "(use \`make latexpdf' here to do that automatically)." + +.PHONY: latexpdf +latexpdf: + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex + @echo "Running LaTeX files through pdflatex..." + $(MAKE) -C $(BUILDDIR)/latex all-pdf + @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." + +.PHONY: latexpdfja +latexpdfja: + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex + @echo "Running LaTeX files through platex and dvipdfmx..." + $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja + @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." + +.PHONY: text +text: + $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text + @echo + @echo "Build finished. The text files are in $(BUILDDIR)/text." + +.PHONY: man +man: + $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man + @echo + @echo "Build finished. The manual pages are in $(BUILDDIR)/man." + +.PHONY: texinfo +texinfo: + $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo + @echo + @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." + @echo "Run \`make' in that directory to run these through makeinfo" \ + "(use \`make info' here to do that automatically)." + +.PHONY: info +info: + $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo + @echo "Running Texinfo files through makeinfo..." + make -C $(BUILDDIR)/texinfo info + @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." + +.PHONY: gettext +gettext: + $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale + @echo + @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." + +.PHONY: changes +changes: + $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes + @echo + @echo "The overview file is in $(BUILDDIR)/changes." + +.PHONY: linkcheck +linkcheck: + $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck + @echo + @echo "Link check complete; look for any errors in the above output " \ + "or in $(BUILDDIR)/linkcheck/output.txt." + +.PHONY: doctest +doctest: + $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest + @echo "Testing of doctests in the sources finished, look at the " \ + "results in $(BUILDDIR)/doctest/output.txt." + +.PHONY: coverage +coverage: + $(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage + @echo "Testing of coverage in the sources finished, look at the " \ + "results in $(BUILDDIR)/coverage/python.txt." + +.PHONY: xml +xml: + $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml + @echo + @echo "Build finished. The XML files are in $(BUILDDIR)/xml." + +.PHONY: pseudoxml +pseudoxml: + $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml + @echo + @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." + +.PHONY: dummy +dummy: + $(SPHINXBUILD) -b dummy $(ALLSPHINXOPTS) $(BUILDDIR)/dummy + @echo + @echo "Build finished. Dummy builder generates no files." diff --git a/parsl/docs/README.rst b/parsl/docs/README.rst new file mode 100644 index 00000000..f50c03f3 --- /dev/null +++ b/parsl/docs/README.rst @@ -0,0 +1,40 @@ +Doc Docs +======== + +Documentation location +---------------------- + +Documentation is maintained in Python docstrings throughout the code. These are imported via the +`autodoc `_ Sphinx extension in +``docs/reference.rst``. Individual stubs for user-facing classes (located in ``stubs``) are +generated automatically via sphinx-autogen. Parsl modules, classes, and methods can be +cross-referenced from a docstring by enclosing it in backticks (\`). + +Remote builds +------------- + +Builds are automatically performed by readthedocs.io and published to parsl.readthedocs.io +upon git commits. + +Local builds +------------ + +To build the documentation locally, use:: + + $ make clean html + +To view the freshly rebuilt docs, use:: + + $ cd _build/html + $ python3 -m http.server 8080 + +Once the python http server is launched, point your browser to `http://localhost:8080 `_ + + +Regenerate module stubs +-------------------------- + +If necessary, docstring stubs can be regenerated using:: + + $ sphinx-autogen reference.rst + diff --git a/parsl/docs/conf.py b/parsl/docs/conf.py new file mode 100644 index 00000000..3c448bd3 --- /dev/null +++ b/parsl/docs/conf.py @@ -0,0 +1,377 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# Parsl documentation build configuration file, created by +# sphinx-quickstart on Mon Feb 20 16:35:17 2017. +# +# This file is execfile()d with the current directory set to its +# containing dir. +# +# Note that not all possible configuration values are present in this +# autogenerated file. +# +# All configuration values have a default; values that are commented out +# serve to show the default. + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys + +import requests + +sys.path.insert(0, os.path.abspath('../')) +import parsl + +# -- General configuration ------------------------------------------------ + +# If your documentation needs a minimal Sphinx version, state it here. +# +# needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'nbsphinx', + 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', + 'sphinx.ext.intersphinx', + 'sphinx.ext.linkcode', + 'sphinx.ext.napoleon' +] + +url = 'https://raw.githubusercontent.com/Parsl/parsl-tutorial/master/1-parsl-introduction.ipynb' +r = requests.get(url) +with open(os.path.join(os.path.dirname(__file__), '1-parsl-introduction.ipynb'), 'wb') as f: + f.write(r.content) + +nbsphinx_execute = 'never' + +def linkcode_resolve(domain, info): + if domain != 'py': + return None + if not info['module']: + return None + filename = info['module'].replace('.', '/') + return "http://github.com/Parsl/parsl/blob/master/{}.py".format(filename) + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +intersphinx_mapping = { + 'python': ('https://docs.python.org/3', None), +} +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +# source_suffix = ['.rst', '.md'] +source_suffix = '.rst' + +# The encoding of source files. +# +# source_encoding = 'utf-8-sig' + +# The master toctree document. +master_doc = 'index' + +# General information about the project. +project = 'Parsl' +copyright = '2018--2020, Parsl Project' +author = 'Parsl Project' + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# +# The short X.Y version. +version = parsl.__version__.rsplit('.', 1)[0] +# The full version, including alpha/beta/rc tags. +release = parsl.__version__ + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = 'en' + +# There are two options for replacing |today|: either, you set today to some +# non-false value, then it is used: +# +# today = '' +# +# Else, today_fmt is used as the format for a strftime call. +# +# today_fmt = '%B %d, %Y' + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This patterns also effect to html_static_path and html_extra_path +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + +# The reST default role (used for this markup: `text`) to use for all +# documents. +# +default_role = 'any' + +# If true, '()' will be appended to :func: etc. cross-reference text. +# +# add_function_parentheses = True + +# If true, the current module name will be prepended to all description +# unit titles (such as .. function::). +# +# add_module_names = True + +# If true, sectionauthor and moduleauthor directives will be shown in the +# output. They are ignored by default. +# +# show_authors = False + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + +# A list of ignored prefixes for module index sorting. +# modindex_common_prefix = [] + +# If true, keep warnings as "system message" paragraphs in the built documents. +# keep_warnings = False + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = False + + +# -- Options for HTML output ---------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +#html_theme = 'alabaster' +html_theme = 'sphinx_rtd_theme' + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +# html_theme_options = {} + +# Add any paths that contain custom themes here, relative to this directory. +# html_theme_path = [] + +# The name for this set of Sphinx documents. +# " v documentation" by default. +# +# html_title = 'Parsl v0.1' + +# A shorter title for the navigation bar. Default is the same as html_title. +# +# html_short_title = None + +# The name of an image file (relative to this directory) to place at the top +# of the sidebar. +# +# html_logo = None + +# The name of an image file (relative to this directory) to use as a favicon of +# the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 +# pixels large. +# +# html_favicon = None + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = [] + +# Add any extra paths that contain custom files (such as robots.txt or +# .htaccess) here, relative to this directory. These files are copied +# directly to the root of the documentation. +# +# html_extra_path = [] + +# If not None, a 'Last updated on:' timestamp is inserted at every page +# bottom, using the given strftime format. +# The empty string is equivalent to '%b %d, %Y'. +# +# html_last_updated_fmt = None + +# If true, SmartyPants will be used to convert quotes and dashes to +# typographically correct entities. +# +# html_use_smartypants = True + +# Custom sidebar templates, maps document names to template names. +# +# html_sidebars = {} + +# Additional templates that should be rendered to pages, maps page names to +# template names. +# +# html_additional_pages = {} + +# If false, no module index is generated. +# +# html_domain_indices = True + +# If false, no index is generated. +# +# html_use_index = True + +# If true, the index is split into individual pages for each letter. +# +# html_split_index = False + +# If true, links to the reST sources are added to the pages. +# +# html_show_sourcelink = True + +# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. +# +# html_show_sphinx = True + +# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. +# +# html_show_copyright = True + +# If true, an OpenSearch description file will be output, and all pages will +# contain a tag referring to it. The value of this option must be the +# base URL from which the finished HTML is served. +# +# html_use_opensearch = '' + +# This is the file name suffix for HTML files (e.g. ".xhtml"). +# html_file_suffix = None + +# Language to be used for generating the HTML full-text search index. +# Sphinx supports the following languages: +# 'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja' +# 'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr', 'zh' +# +# html_search_language = 'en' + +# A dictionary with options for the search language support, empty by default. +# 'ja' uses this config value. +# 'zh' user can custom change `jieba` dictionary path. +# +# html_search_options = {'type': 'default'} + +# The name of a javascript file (relative to the configuration directory) that +# implements a search results scorer. If empty, the default will be used. +# +# html_search_scorer = 'scorer.js' + +# Output file base name for HTML help builder. +htmlhelp_basename = 'Parsldoc' + +# -- Options for LaTeX output --------------------------------------------- + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'Parsl.tex', 'Parsl Documentation', + 'The Parsl Team', 'manual'), +] + +# The name of an image file (relative to this directory) to place at the top of +# the title page. +# +# latex_logo = None + +# For "manual" documents, if this is true, then toplevel headings are parts, +# not chapters. +# +# latex_use_parts = False + +# If true, show page references after internal links. +# +# latex_show_pagerefs = False + +# If true, show URL addresses after external links. +# +# latex_show_urls = False + +# Documents to append as an appendix to all manuals. +# +# latex_appendices = [] + +# It false, will not define \strong, \code, itleref, \crossref ... but only +# \sphinxstrong, ..., \sphinxtitleref, ... To help avoid clash with user added +# packages. +# +# latex_keep_old_macro_names = True + +# If false, no module index is generated. +# +# latex_domain_indices = True + + +# -- Options for manual page output --------------------------------------- + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'parsl', 'Parsl Documentation', + [author], 1) +] + +# If true, show URL addresses after external links. +# +# man_show_urls = False + + +# -- Options for Texinfo output ------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'Parsl', 'Parsl Documentation', + author, 'Parsl', 'One line description of project.', + 'Miscellaneous'), +] + +# Documents to append as an appendix to all manuals. +# +# texinfo_appendices = [] + +# If false, no module index is generated. +# +# texinfo_domain_indices = True + +# How to display URL addresses: 'footnote', 'no', or 'inline'. +# +# texinfo_show_urls = 'footnote' + +# If true, do not generate a @detailmenu in the "Top" node's menu. +# +# texinfo_no_detailmenu = False + +autosummary_generate = True + +autodoc_default_options = { + 'members': True, + 'undoc-members': True +} +autodoc_preserve_defaults = True diff --git a/parsl/docs/devguide/contributing.rst b/parsl/docs/devguide/contributing.rst new file mode 100644 index 00000000..638677d3 --- /dev/null +++ b/parsl/docs/devguide/contributing.rst @@ -0,0 +1,6 @@ +Contributing +==================== + +Parsl is an open source project that welcomes contributions from the community. + +If you're interested in contributing, please review our `contributing guide `_. diff --git a/parsl/docs/devguide/index.rst b/parsl/docs/devguide/index.rst new file mode 100644 index 00000000..ec99d421 --- /dev/null +++ b/parsl/docs/devguide/index.rst @@ -0,0 +1,10 @@ +Developer documentation +======================= + +.. toctree:: + :maxdepth: 3 + + contributing + roadmap + packaging + ../README diff --git a/parsl/docs/devguide/packaging.rst b/parsl/docs/devguide/packaging.rst new file mode 100644 index 00000000..9cd37c0a --- /dev/null +++ b/parsl/docs/devguide/packaging.rst @@ -0,0 +1,46 @@ +Packaging +--------- + +Currently packaging is managed by @annawoodard and @yadudoc. + +Steps to release + +1. Update the version number in ``parsl/parsl/version.py`` +2. Check the following files to confirm new release information + * ``parsl/setup.py`` + * ``requirements.txt`` + * ``parsl/docs/devguide/changelog.rst`` + * ``parsl/README.rst`` + +3. Commit and push the changes to github +4. Run the ``tag_and_release.sh`` script. This script will verify that + version number matches the version specified. + + .. code:: bash + + ./tag_and_release.sh + + +Here are the steps that is taken by the ``tag_and_release.sh`` script: + +.. code:: bash + + # Create a new git tag : + git tag .. + # Push tag to github : + git push origin + + # Depending on permission all of the following might have to be run as root. + sudo su + + # Make sure to have twine installed + pip3 install twine + + # Create a source distribution + python3 setup.py sdist + + # Create a wheel package, which is a prebuilt package + python3 setup.py bdist_wheel + + # Upload the package with twine + twine upload dist/* diff --git a/parsl/docs/devguide/roadmap.rst b/parsl/docs/devguide/roadmap.rst new file mode 100644 index 00000000..ccb5abce --- /dev/null +++ b/parsl/docs/devguide/roadmap.rst @@ -0,0 +1,51 @@ +Roadmap +======= + +**OVERVIEW** + +While we follow best practices in software development processes (e.g., CI, flake8, code review), there are opportunities to make our code more maintainable and accessible. This roadmap, written in the fall of 2023, covers our major activities planned through 2025 to increase efficiency, productivity, user experience, and community building. + +Features and improvements are documented via GitHub +`issues `_ and `pull requests `_. + + +Code Maintenance +---------------- + +* **Type Annotations and Static Type Checking**: Add static type annotations throughout the codebase and add typeguard checks. +* **Release Process**: `Improve the overall release process `_ to synchronize docs and code releases, automatically produce changelog documentation. +* **Components Maturity Model**: Defines the `component maturity model `_ and tags components with their appropriate maturity level. +* **Define and Document Interfaces**: Identify and document interfaces via which `external components `_ can augment the Parsl ecosystem. +* **Distributed Testing Process**: All tests should be run against all possible schedulers, using different executors, on a variety of remote systems. Explore the use of containerized schedulers and remote testing on real systems. + +New Features and Integrations +----------------------------- + +* **Enhanced MPI Support**: Extend Parsl’s MPI model with MPI apps and runtime support capable of running MPI apps in different environments (MPI flavor and launcher). +* **Serialization Configuration**: Enable users to select what serialization methods are used and enable users to supply their own serializer. +* **PSI/J integration**: Integrate PSI/J as a common interface for schedulers. +* **Internal Concurrency Model**: Revisit and rearchitect the concurrency model to reduce areas that are not well understood and reduce the likelihood of errors. +* **Common Model for Errors**: Make Parsl errors self-describing and understandable by users. +* **Plug-in Model for External Components**: Extend Parsl to implement interfaces defined above. +* **User Configuration Validation Tool**: Provide tooling to help users configure Parsl and diagnose and resolve errors. +* **Anonymized Usage Tracking**: Usage tracking is crucial for our data-oriented approach to understand the adoption of Parsl, which components are used, and where errors occur. This allows us to prioritize investment in components, progress components through the maturity levels, and identify bugs. Revisit prior usage tracking and develop a service that enables users to control tracking information. +* **Support for Globus Compute**: Enable execution of Parsl tasks using Globus Compute as an executor. +* **Update Globus Data Management**: Update Globus integration to use the new Globus Connect v5 model (i.e., needing specific scopes for individual endpoints). +* **Performance Measurement**: Improve ability to measure performance metrics and report to users. +* **Enhanced Debugging**: Application-level `logging `_ to understand app execution. + +Tutorials, Training, and User Support +------------------------------------- + +* **Configuration and Debugging**: Tutorials showing how to configure Parsl for different resources and debug execution. +* **Functional Serialization 101**: Tutorial describing how serialization works and how you can integrate custom serializers. +* **ProxyStore Data Management**: Tutorial showing how you can use ProxyStore to manage data for both inter and intra-site scenarios. +* **Open Dev Calls on Zoom**: The internal core team holds an open dev call/office hours every other Thursday to help users troubleshoot issues, present and share their work, connect with each other, and provide community updates. +* **Project Documentation**: is maintained and updated in `Read the Docs `_. + +Longer-term Objectives +---------------------- + +* **Globus Compute Integration**: Once Globus Compute supports multi-tenancy, Parsl will be able to use it to run remote tasks on initially one and then later multiple resources. +* **Multi-System Optimization**: Once Globus Compute integration is complete, it is best to use multiple systems for multiple tasks as part of a single workflow. +* **HPC Checkpointing and Job Migration**: As new resources become available, HPC tasks will be able to be checkpointed and moved to the system with more resources. diff --git a/parsl/docs/faq.rst b/parsl/docs/faq.rst new file mode 100644 index 00000000..ea498861 --- /dev/null +++ b/parsl/docs/faq.rst @@ -0,0 +1,380 @@ +FAQ +--- + +How can I debug a Parsl script? +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Parsl interfaces with the Python logger and automatically logs Parsl-related messages a ``runinfo`` directory. +The ``runinfo`` directory will be created in the folder from which you run the Parsl script +and it will contain a series of subfolders for each time you run the code. +Your latest run will be the largest number. + +Alternatively, you can configure the file logger to write to an output file. + +.. code-block:: python + + import logging + import parsl + + # Emit log lines to the screen + parsl.set_stream_logger() + + # Write log to file, specify level of detail for logs + parsl.set_file_logger(FILENAME, level=logging.DEBUG) + +.. note:: + Parsl's logging will not capture STDOUT/STDERR from the apps themselves. + Follow instructions below for application logs. + + +How can I view outputs and errors from apps? +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Parsl apps include keyword arguments for capturing stderr and stdout in files. + +.. code-block:: python + + @bash_app + def hello(msg, stdout=None): + return 'echo {}'.format(msg) + + # When hello() runs the STDOUT will be written to 'hello.txt' + hello('Hello world', stdout='hello.txt') + +How can I make an App dependent on multiple inputs? +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +You can pass any number of futures in to a single App either as positional arguments +or as a list of futures via the special keyword ``inputs=()``. +The App will wait for all inputs to be satisfied before execution. + + +Can I pass any Python object between apps? +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +This depends on the executor in use. The :py:class:`parsl.executors.threads.ThreadPoolExecutor` +can receive and return any Python object. Other executors will serialize their +parameters and return values, so only objects which Parsl knows how to +serialize can be passed. + +Parsl knows how to serialize objects using the Pickle and Dill +libraries. + +Pickle provides a list of objects that it knows how to serialize: +`What can be pickled and unpickled? `_. + +Dill can serialize much more than Pickle, documented in the +`dill documentation `_. + +For objects that can't be pickled, use object specific methods +to write the object into a file and use files to communicate between apps. + +How do I specify where apps should be run? +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Parsl's multi-executor support allows you to define the executor (including local threads) +on which an App should be executed. For example: + +.. code-block:: python + + @python_app(executors=['SuperComputer1']) + def BigSimulation(...): + ... + + @python_app(executors=['GPUMachine']) + def Visualize (...) + ... + +Workers do not connect back to Parsl +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +If you are running via ssh to a remote system from your local machine, or from the +login node of a cluster/supercomputer, it is necessary to have a public IP to which +the workers can connect back. While our remote execution systems can identify the +IP address automatically in certain cases, it is safer to specify the address explicitly. +Parsl provides a few heuristic based address resolution methods that could be useful, +however with complex networks some trial and error might be necessary to find the +right address or network interface to use. + + + +For `parsl.executors.HighThroughputExecutor` the address is specified in the :class:`~parsl.config.Config` +as shown below : + +.. code-block:: python + + # THIS IS A CONFIG FRAGMENT FOR ILLUSTRATION + from parsl.config import Config + from parsl.executors import HighThroughputExecutor + from parsl.addresses import address_by_route, address_by_query, address_by_hostname + config = Config( + executors=[ + HighThroughputExecutor( + label='ALCF_theta_local', + address='' # specify public ip here + # address=address_by_route() # Alternatively you can try this + # address=address_by_query() # Alternatively you can try this + # address=address_by_hostname() # Alternatively you can try this + ) + ], + ) + + +.. note:: + Another possibility that can cause workers not to connect back to Parsl is an incompatibility between + the system and the pre-compiled bindings used for pyzmq. As a last resort, you can try: + ``pip install --upgrade --no-binary pyzmq pyzmq``, which forces re-compilation. + +For the `parsl.executors.HighThroughputExecutor`, ``address`` is a keyword argument +taken at initialization. Here is an example for the `parsl.executors.HighThroughputExecutor`: + +.. code-block:: python + + # THIS IS A CONFIG FRAGMENT FOR ILLUSTRATION + from parsl.config import Config + from parsl.executors import HighThroughputExecutor + from parsl.addresses import address_by_route, address_by_query, address_by_hostname + + config = Config( + executors=[ + HighThroughputExecutor( + label='NERSC_Cori', + address='' # specify public ip here + # address=address_by_route() # Alternatively you can try this + # address=address_by_query() # Alternatively you can try this + # address=address_by_hostname() # Alternatively you can try this + ) + ], + ) + + +.. note:: + On certain systems such as the Midway RCC cluster at UChicago, some network interfaces have an active + intrusion detection system that drops connections that persist beyond a specific duration (~20s). + If you get repeated ``ManagerLost`` exceptions, it would warrant taking a closer look at networking. + +.. _pyversion: + +parsl.errors.ConfigurationError +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The Parsl configuration model underwent a major and non-backward compatible change in the transition to v0.6.0. +Prior to v0.6.0 the configuration object was a python dictionary with nested dictionaries and lists. +The switch to a class based configuration allowed for well-defined options for each specific component being +configured as well as transparency on configuration defaults. The following traceback indicates that the old +style configuration was passed to Parsl v0.6.0+ and requires an upgrade to the configuration. + +.. code-block:: + + File "/home/yadu/src/parsl/parsl/dataflow/dflow.py", line 70, in __init__ + 'Expected `Config` class, received dictionary. For help, ' + parsl.errors.ConfigurationError: Expected `Config` class, received dictionary. For help, + see http://parsl.readthedocs.io/en/stable/stubs/parsl.config.Config.html + +For more information on how to update your configuration script, please refer to: +:ref:`configuration-section`. + + +Remote execution fails with SystemError(unknown opcode) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +When running with Ipyparallel workers, it is important to ensure that the Python version +on the client side matches that on the side of the workers. If there's a mismatch, +the apps sent to the workers will fail with the following error: +``ipyparallel.error.RemoteError: SystemError(unknown opcode)`` + +.. caution:: + It is **required** that both the parsl script and all workers are set to use python + with the same Major.Minor version numbers. For example, use Python3.5.X on both local + and worker side. + +Parsl complains about missing packages +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +If ``parsl`` is cloned from a Github repository and added to the ``PYTHONPATH``, it is +possible to miss the installation of some dependent libraries. In this configuration, +``parsl`` will raise errors such as: + +``ModuleNotFoundError: No module named 'ipyparallel'`` + +You should usually install parsl using a package managment tool such as ``pip`` or +``conda``, ideally in a restricted environment such a virtualenv or a conda +environment. + +For instance, with conda, follow this `cheatsheet `_ to create a virtual environment: + +.. code-block:: bash + + # Activate an environmentconda install + source activate + + # Install packages: + conda install + + +How do I run code that uses Python2.X? +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Modules or code that require Python2.X cannot be run as python apps, +however they may be run via bash apps. The primary limitation with +python apps is that all the inputs and outputs including the function +would be mangled when being transmitted between python interpreters with +different version numbers (also see :ref:`pyversion`) + +Here is an example of running a python2.7 code as a bash application: + +.. code-block:: python + + @bash_app + def python_27_app (arg1, arg2 ...): + return '''conda activate py2.7_env # Use conda to ensure right env + python2.7 my_python_app.py -arg {0} -d {1} + '''.format(arg1, arg2) + +Parsl hangs +^^^^^^^^^^^ + +There are a few common situations in which a Parsl script might hang: + +1. Circular Dependency in code: + If an ``app`` takes a list as an ``input`` argument and the future returned + is added to that list, it creates a circular dependency that cannot be resolved. + This situation is described in `issue 59 `_ in more detail. + +2. Workers requested are unable to contact the Parsl client due to one or + more issues listed below: + + * Parsl client does not have a public IP (e.g. laptop on wifi). + If your network does not provide public IPs, the simple solution is to + ssh over to a machine that is public facing. Machines provisioned from + cloud-vendors setup with public IPs are another option. + + * Parsl hasn't autodetected the public IP. See `Workers do not connect back to Parsl`_ for more details. + + * Firewall restrictions that block certain port ranges. + If there is a certain port range that is **not** blocked, you may specify + that via configuration: + + .. code-block:: python + + from parsl.config import Config + from parsl.providers import SlurmProvider + from parsl.executors import HighThroughputExecutor + + config = Config( + executors=[ + HighThroughputExecutor( + label='htex', + provider=SlurmProvider(), + worer_port_range=('50000,55000'), + interchange_port_range=('50000,55000') + ) + ], + ) + + +How can I start a Jupyter notebook over SSH? +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Run + +.. code-block:: bash + + jupyter notebook --no-browser --ip=`/sbin/ip route get 8.8.8.8 | awk '{print $NF;exit}'` + +for a Jupyter notebook, or + +.. code-block:: bash + + jupyter lab --no-browser --ip=`/sbin/ip route get 8.8.8.8 | awk '{print $NF;exit}'` + +for Jupyter lab (recommended). If that doesn't work, see `these instructions `_. + +How can I sync my conda environment and Jupyter environment? +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Run:: + + conda install nb_conda + +Now all available conda environments (for example, one created by following the instructions `in the quickstart guide `_) will automatically be added to the list of kernels. + +.. _label_serialization_error: + +Addressing SerializationError +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +As of v1.0.0, Parsl will raise a `SerializationError` when it encounters an object that Parsl cannot serialize. +This applies to objects passed as arguments to an app, as well as objects returned from the app. + +Parsl uses dill and pickle to serialize Python objects +to/from functions. Therefore, Python apps can only use input and output objects that can be serialized by +dill or pickle. For example the following data types are known to have issues with serializability : + +* Closures +* Objects of complex classes with no ``__dict__`` or ``__getstate__`` methods defined +* System objects such as file descriptors, sockets and locks (e.g threading.Lock) + +If Parsl raises a `SerializationError`, first identify what objects are problematic with a quick test: + +.. code-block:: python + + import pickle + # If non-serializable you will get a TypeError + pickle.dumps(YOUR_DATA_OBJECT) + +If the data object simply is complex, please refer `here `_ for more details +on adding custom mechanisms for supporting serialization. + + + +How do I cite Parsl? +^^^^^^^^^^^^^^^^^^^^ + +To cite Parsl in publications, please use the following: + +Babuji, Y., Woodard, A., Li, Z., Katz, D. S., Clifford, B., Kumar, R., Lacinski, L., Chard, R., Wozniak, J., Foster, I., Wilde, M., and Chard, K., Parsl: Pervasive Parallel Programming in Python. 28th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC). 2019. https://doi.org/10.1145/3307681.3325400 + +or + +.. code-block:: latex + + @inproceedings{babuji19parsl, + author = {Babuji, Yadu and + Woodard, Anna and + Li, Zhuozhao and + Katz, Daniel S. and + Clifford, Ben and + Kumar, Rohan and + Lacinski, Lukasz and + Chard, Ryan and + Wozniak, Justin and + Foster, Ian and + Wilde, Mike and + Chard, Kyle}, + title = {Parsl: Pervasive Parallel Programming in Python}, + booktitle = {28th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC)}, + doi = {10.1145/3307681.3325400}, + year = {2019}, + url = {https://doi.org/10.1145/3307681.3325400} + } + + +How can my tasks survive ``WorkerLost`` and ``ManagerLost`` at the end of a batch job? +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +When a batch job ends, pilot workers will be terminated by the batch system, +and any tasks running there will fail. With `HighThroughputExecutor`, +this failure will be reported as a `parsl.executors.high_throughput.errors.WorkerLost` or +`parsl.executors.high_throughput.errors.ManagerLost` in the task future. + +To mitigate against this: + +* use retries by setting ``retries=`` in `parsl.config.Config`. +* if you only want to retry on certain errors such as `WorkerLost` and `ManagerLost`, + use ``retry_handler`` in `parsl.config.Config` to implement that policy. +* avoid sending tasks to batch jobs that will expire soon. With `HighThroughputExecutor`, + set drain_period to a little longer than you expect your tasks to take. + With `WorkQueueExecutor`, you can configure individual expected task duration using + a ``parsl_resource_specification`` and specify a worker ``--wall-time`` using the + ``worker_options`` parameter to the `WorkQueueExecutor`. diff --git a/parsl/docs/historical/changelog.rst b/parsl/docs/historical/changelog.rst new file mode 100644 index 00000000..931998f9 --- /dev/null +++ b/parsl/docs/historical/changelog.rst @@ -0,0 +1,1299 @@ +Historical: Changelog +===================== + + +.. note:: + After Parsl 1.2.0, releases moved to a lighter weight automated model. + This manual changelog is no longer updated and is now marked as + historical. + + Change information is delivered as commit messages on individual pull + requests, which can be see using any relevant git history browser - + for example, on the web at https://github.com/Parsl/parsl/commits/master/ + or on the commandline using using git log. + + +Parsl 1.2.0 +----------- + +Release date: January 13th, 2022. + +Parsl v1.2.0 includes 99 pull requests with contributions from: + +Ben Clifford @benclifford, Daniel S. Katz @danielskatz, Douglas Thain @dthain, James Corbett @jameshcorbett, Jonas Rübenach @jrueb, Logan Ward @WardLT, Matthew R. Becker @beckermr, Vladimir @vkhodygo, Yadu Nand Babuji @yadudoc, Yo Yehudi @yochannah, Zhuozhao Li @ZhuozhaoLi, yongyanrao @yongyanrao, Tim Jenness @timj, Darko Marinov @darko-marinov, Quentin Le Boulc'h + + +High Throughput Executor +^^^^^^^^^^^^^^^^^^^^^^^^ + +* Remove htex self.tasks race condition that shows under high load (#2034) +* Fix htex scale down breakage due to overly aggressive result heartbeat (#2119) [ TODO: this fixes a bug introduced since 1.1.0 so note that? #2104 ] +* Send heartbeats via results connection (#2104) + + +Work Queue Executor +^^^^^^^^^^^^^^^^^^^ + +* Allow use of WorkQueue running_time_min resource constraint (#2113) - WQ recently introduced an additional resource constraint: workers can be aware of their remaining wall time, and tasks can be constrained to only go to workers with sufficient remaining time. + +* Implement priority as a Work Queue resource specification (#2067) - The allows a workflow script to influence the order in which queued tasks are executed using Work Queue's existing priority mechanism. + + +* Disable WQ-level retries with an option to re-enable (#2059) - Previously by default, Work Queue will retry tasks that fail at the WQ level (for example, because of worker failure) an infinite number of times, inside the same parsl-level execution try. That hides the repeated tries from parsl (so monitoring does not report start/end times as might naively be expected for a try, and parsl retry counting does not count). + +* Document WorkqueueExecutor project_name remote reporting better (#2089) +* wq executor should show itself using representation mixin (#2064) +* Make WorkQueue worker command configurable (#2036) + + + +Flux Executor +^^^^^^^^^^^^^ + +The new FluxExecutor class uses the Flux resource manager +(github: flux-framework/flux-core) to launch tasks. Each +task is a Flux job. + + +Condor Provider +^^^^^^^^^^^^^^^ + +* Fix bug in condor provider for unknown jobs (#2161) + +LSF Provider +^^^^^^^^^^^^ + +* Update LSF provider to make it more friendly for different LSF-based computers (#2149) + +SLURM Provider +^^^^^^^^^^^^^^ + +* Improve docs and defaults for slurm partition and account parameters. (#2126) + +Grid Engine Provider +^^^^^^^^^^^^^^^^^^^^ + +* missing queue from self - causes config serialisation failure (#2042) + + +Monitoring +^^^^^^^^^^ + +* Index task_hashsum to give cross-run query speedup (#2085) +* Fix monitoring "db locked" errors occuring at scale (#1917) +* Fix worker efficiency plot when tasks are still in progress (#2048) +* Fix use of previously removed reg_time monitoring field (#2020) +* Reorder debug message so it happens when the message is received, without necessarily blocking on the resource_msgs queue put (#2093) + + +General new features +^^^^^^^^^^^^^^^^^^^^ + +* Workflow-pluggable retry scoring (#2068) - When a task fails, instead of causing a retry "cost" of 1 (the previous behaviour), this PR allows that cost to be determined by a user specified function which is given some context about the failure. + +General bug fixes +^^^^^^^^^^^^^^^^^ + +* Fix type error when job status output is large. (#2129) +* Fix a race condition in the local channel (#2115) +* Fix incorrect order of manager and interchange versions in error text (#2108) +* Fix to macos multiprocessing spawn and context issues (#2076) +* Tidy tasks_per_node in strategy (#2030) +* Fix and test wrong type handling for joinapp returns (#2063) +* FIX: os independent path (#2043) + +Platform and packaging +^^^^^^^^^^^^^^^^^^^^^^ + +* Improve support for Windows (#2107) +* Reflect python 3.9 support in setup.py metadata (#2023) +* Remove python <3.6 handling from threadpoolexecutor (#2083) +* Remove breaking .[all] install target (#2069) + +Internal tidying +^^^^^^^^^^^^^^^^ + +* Remove ipp logging hack in PR #204 (#2170) +* Remove BadRegistration exception definition which has been unused since PR #1671 (#2142) +* Remove AppFuture.__repr__, because superclass Future repr is sufficient (#2143) +* Make monitoring hub exit condition more explicit (#2131) +* Replace parsl's logging NullHandler with python's own NullHandler (#2114) +* Remove a commented out line of dead code in htex (#2116) +* Abstract more block handling from HighThroughputExecutor and share with WorkQueue (#2071) +* Regularise monitoring RESOURCE_INFO messages (#2117) +* Pull os x multiprocessing code into a single module (#2099) +* Describe monitoring protocols better (#2029) +* Remove task_id param from memo functions, as whole task record is available (#2080) +* remove irrelevant __main__ stub of local provider (#2026) +* remove unused weakref_cb (#2022) +* Remove unneeded task_id param from sanitize_and_wrap (#2081) +* Remove outdated IPP related comment in memoization (#2058) +* Remove unused AppBase status field (#2053) +* Do not unwrap joinapp future exceptions unnecessarily (#2084) +* Eliminate self.tasks[id] calls from joinapp callback (#2015) +* Looking at eliminating passing of task IDs and passing task records instead (#2016) +* Eliminate self.tasks[id] from launch_if_ready +* Eliminate self.tasks[id] calls from launch_task (#2061) +* Eliminate self.tasks[id] from app done callback (#2017) +* Make process_worker_pool pass mypy (#2052) +* Remove unused walltime from LocalProvider (#2057) +* Tidy human readable text/variable names around DependencyError (#2037) +* Replace old string formatting with f-strings in utils.py (#2055) + +Documentation, error messages and human-readable text +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +* Add a documentation chapter summarizing plugin points (#2066) +* Correct docstring for set_file_logger (#2156) +* Fix typo in two db error messages and make consistent with each other (#2152) +* Update slack join links to currently unexpired link (#2146) +* small typo fix in doc (#2134) +* Update CONTRIBUTING.rst (#2144) +* trying to fix broken link in GitHub (#2133) +* Add CITATION.cff file (#2100) +* Refresh the sanitize_and_wrap docstring (#2086) +* Rephrase ad-hoc config doc now that AdHocProvider (PR #1297) is implemented (#2096) +* Add research notice to readme (#2097) +* Remove untrue claim that parsl_resource_specification keys are case insensitive (#2095) +* Use zsh compatible install syntax (#2009) +* Remove documentation that interchange is walltime aware (#2082) +* Configure sphinx to put in full documentation for each method (#2094) +* autogenerate sphinx stubs rather than requiring manual update each PR (#2087) +* Update docstring for handle_app_update (#2079) +* fix a typo (#2024) +* Switch doc verb from invocated to invoked (#2088) +* Add documentation on meanings of states (#2075) +* Fix summary sentence of ScaleOutException (#2021) +* clarify that max workers is per node (#2056) +* Tidy up slurm state comment (#2035) +* Add nscc singapore example config (#2003) +* better formatting (#2039) +* Add missing f for an f-string (#2062) +* Rework __repr__ and __str__ for OptionalModuleMissing (#2025) +* Make executor bad state exception log use the exception (#2155) + +CI/testing +^^^^^^^^^^ + +* Make changes for CI reliability (#2118) +* Make missing worker test cleanup DFK at end (#2153) +* Tidy bash error codes tests. (#2130) +* Upgrade CI to use recent ubuntu, as old version was deprecated (#2111) +* Remove travis config, replaced by GitHub Actions in PR #2078 (#2112) +* Fix CI broken by dependency package changes (#2105) +* Adding github actions for CI (#2078) +* Test combine() pattern in joinapps (#2054) +* Assert that there should be no doc stubs in version control (#2092) +* Add monitoring dependency to local tests (#2074) +* Put viz test in a script (#2019) +* Reduce the size of recursive fibonacci joinapp testing (#2110) +* Remove disabled midway test (#2028) + + +Parsl 1.1.0 +----------- + +Released on April 26th, 2021. + +Parsl v1.1.0 includes 59 closed issues and 243 pull requests with contributions (code, tests, reviews and reports) from: + +Akila Ravihansa Perera @ravihansa3000, Anna Woodard @annawoodard, @bakerjl, Ben Clifford @benclifford, +Daniel S. Katz @danielskatz, Douglas Thain @dthain, @gerrick, @JG-Quarknet, Joseph Moon @jmoon1506, +Kelly L. Rowland @kellyrowland, Lars Bilke @bilke, Logan Ward @WardLT, Kirill Nagaitsev @Loonride, +Marcus Schwarting @meschw04, Matt Baughman @mattebaughman, Mihael Hategan @hategan, @radiantone, +Rohan Kumar @rohankumar42, Sohit Miglani @sohitmiglani, Tim Shaffer @trshaffer, +Tyler J. Skluzacek @tskluzac, Yadu Nand Babuji @yadudoc, and Zhuozhao Li @ZhuozhaoLi + +Deprecated and Removed features +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +* Python 3.5 is no longer supported. +* Almost definitely broken Jetstream provider removed (#1821) + +New Functionality +^^^^^^^^^^^^^^^^^ + +* Allow HTEX to set CPU affinity (#1853) + +* New serialization system to replace IPP serialization (#1806) + +* Support for Python 3.9 + +* @join_apps are a variation of @python_apps where an app can launch + more apps and then complete only after the launched apps are also + completed. + + These are described more fully in docs/userguide/joins.rst + +* Monitoring: + + * hub.log is now named monitoring_router.log + * Remove denormalised workflow duration from monitoring db (#1774) + * Remove hostname from status table (#1847) + * Clarify distinction between tasks and tries to run tasks (#1808) + * Replace 'done' state with 'exec_done' and 'memo_done' (#1848) + * Use repr instead of str for monitoring fail history (#1966) + +* Monitoring visualization: + + * Make task list appear under .../task/ not under .../app/ (#1762) + * Test that parsl-visualize does not return HTTP errors (#1700) + * Generate Gantt chart from status table rather than task table timestamps (#1767) + * Hyperlinks for app page to task pages should be on the task ID, not the app name (#1776) + * Use real final state to color DAG visualization (#1812) + +* Make task record garbage collection optional. (#1909) + +* Make checkpoint_files = get_all_checkpoints() by default (#1918) + + +Parsl 1.0.0 +----------- + +Released on June 11th, 2020 + +Parsl v1.0.0 includes 59 closed issues and 243 pull requests with contributions (code, tests, reviews and reports) from: + +Akila Ravihansa Perera @ravihansa3000, Aymen Alsaadi @AymenFJA, Anna Woodard @annawoodard, +Ben Clifford @benclifford, Ben Glick @benhg, Benjamin Tovar @btovar, Daniel S. Katz @danielskatz, +Daniel Smith @dgasmith, Douglas Thain @dthain, Eric Jonas @ericmjonas, Geoffrey Lentner @glentner, +Ian Foster @ianfoster, Kalpani Ranasinghe @kalpanibhagya, Kyle Chard @kylechard, Lindsey Gray @lgray, +Logan Ward @WardLT, Lyle Hayhurst @lhayhurst, Mihael Hategan @hategan, Rajini Wijayawardana @rajiniw95, +@saktar-unr, Tim Shaffer @trshaffer, Tom Glanzman @TomGlanzman, Yadu Nand Babuji @yadudoc and, +Zhuozhao Li @ZhuozhaoLi + +Deprecated and Removed features +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +* **Python3.5** is now marked for deprecation, and will not be supported after this release. + Python3.6 will be the earliest Python3 version supported in the next release. + +* **App** decorator deprecated in 0.8 is now removed `issue#1539 `_ + `bash_app` and `python_app` are the only supported App decorators in this release. + +* **IPyParallelExecutor** is no longer a supported executor `issue#1565 `_ + + +New Functionality +^^^^^^^^^^^^^^^^^ + +* `parsl.executors.WorkQueueExecutor` introduced in v0.9.0 is now in beta. `parsl.executors.WorkQueueExecutor` is designed as a drop-in replacement for `parsl.executors.HighThroughputExecutor`. Here are some key features: + + * Support for packaging the python environment and shipping it to the worker side. This mechanism addresses propagating python environments in grid-like systems that lack shared-filesystems or cloud environments. + * `parsl.executors.WorkQueueExecutor` supports resource function tagging and resource specification + * Support for resource specification kwarg `issue#1675 `_ + + +* Limited type-checking in Parsl internal components (as part of an ongoing effort) + + +* Improvements to caching mechanism including ability to mark certain arguments to be not counted for memoization. + + * Normalize known types for memoization, and reject unknown types (#1291). This means that previous unreliable + behaviour for some complex types such as dicts will become more reliable; and that other previous unreliable + behaviour for other unknown complex types will now cause an error. Handling can be added for those types using + parsl.memoization.id_for_memo. + * Add ability to label some arguments in an app invocation as not memoized using the ignore_for_cache app keyword (PR 1568) + +* Special keyword args: 'inputs', 'outputs' that are used to specify files no longer support strings + and now require `File` objects. For example, the following snippet is no longer supported in v1.0.0: + + .. code-block:: python + + @bash_app + def cat(inputs=(), outputs=()): + return 'cat {} > {}'.format(inputs[0], outputs[0]) + + concat = cat(inputs=['hello-0.txt'], + outputs=['hello-1.txt']) + + This is the new syntax: + + .. code-block:: python + + from parsl import File + + @bash_app + def cat(inputs=(), outputs=()): + return 'cat {} > {}'.format(inputs[0].filepath, outputs[0].filepath) + + concat = cat(inputs=[File('hello-0.txt')], + outputs=[File('hello-1.txt')]) + + Since filenames are no longer passed to apps as strings, and the string filepath is required, it can + be accessed from the File object using the ``filepath`` attribute. + + .. code-block:: python + + from parsl import File + + @bash_app + def cat(inputs=(), outputs=()): + return 'cat {} > {}'.format(inputs[0].filepath, outputs[0].filepath) + + +* New launcher: `parsl.launchers.WrappedLauncher` for launching tasks inside containers. + +* ``parsl.channels.SSHChannel`` now supports a ``key_filename`` kwarg `issue#1639 `_ + +* Newly added Makefile wraps several frequent developer operations such as: + + * Run the test-suite: ``make test`` + + * Install parsl: ``make install`` + + * Create a virtualenv: ``make virtualenv`` + + * Tag release and push to release channels: ``make deploy`` + +* Several updates to the `parsl.executors.HighThroughputExecutor`: + + * By default, the `parsl.executors.HighThroughputExecutor` will now use heuristics to detect and try all addresses + when the workers connect back to the parsl master. An address can be configured manually using the + ``HighThroughputExecutor(address=)`` kwarg option. + + * Support for Mac OS. (`pull#1469 `_, `pull#1738 `_) + + * Cleaner reporting of version mismatches and automatic suppression of non-critical errors. + + * Separate worker log directories by block id `issue#1508 `_ + +* Support for garbage collection to limit memory consumption in long-lived scripts. + +* All cluster providers now use ``max_blocks=1`` by default `issue#1730 `_ to avoid over-provisioning. + +* New ``JobStatus`` class for better monitoring of Jobs submitted to batch schedulers. + +Bug Fixes +^^^^^^^^^ + +* Ignore AUTO_LOGNAME for caching `issue#1642 `_ +* Add batch jobs to PBS/torque job status table `issue#1650 `_ +* Use higher default buffer threshold for serialization `issue#1654 `_ +* Do not pass mutable default to ignore_for_cache `issue#1656 `_ +* Several improvements and fixes to Monitoring +* Fix sites/test_ec2 failure when aws user opts specified `issue#1375 `_ +* Fix LocalProvider to kill the right processes, rather than all processes owned by user `issue#1447 `_ +* Exit htex probe loop with first working address `issue#1479 `_ +* Allow slurm partition to be optional `issue#1501 `_ +* Fix race condition with wait_for_tasks vs task completion `issue#1607 `_ +* Fix Torque job_id truncation `issue#1583 `_ +* Cleaner reporting for Serialization Errors `issue#1355 `_ +* Results from zombie managers do not crash the system, but will be ignored `issue#1665 `_ +* Guarantee monitoring will send out at least one message `issue#1446 `_ +* Fix monitoring ctrlc hang `issue#1670 `_ + + +Parsl 0.9.0 +----------- + +Released on October 25th, 2019 + +Parsl v0.9.0 includes 199 closed issues and pull requests with contributions (code, tests, reviews and reports) from: + +Andrew Litteken @AndrewLitteken, Anna Woodard @annawoodard, Ben Clifford @benclifford, +Ben Glick @benhg, Daniel S. Katz @danielskatz, Daniel Smith @dgasmith, +Engin Arslan @earslan58, Geoffrey Lentner @glentner, John Hover @jhover +Kyle Chard @kylechard, TJ Dasso @tjdasso, Ted Summer @macintoshpie, +Tom Glanzman @TomGlanzman, Levi Naden @LNaden, Logan Ward @WardLT, Matthew Welborn @mattwelborn, +@MatthewBM, Raphael Fialho @rapguit, Yadu Nand Babuji @yadudoc, and Zhuozhao Li @ZhuozhaoLi + + +New Functionality +^^^^^^^^^^^^^^^^^ + +* Parsl will no longer do automatic keyword substitution in ``@bash_app`` in favor of deferring to Python's `format method `_ + and newer `f-strings `_. For example, + + .. code-block:: python + + # The following example worked until v0.8.0 + @bash_app + def cat(inputs=(), outputs=()): + return 'cat {inputs[0]} > {outputs[0]}' # <-- Relies on Parsl auto formatting the string + + # Following are two mechanisms that will work going forward from v0.9.0 + @bash_app + def cat(inputs=(), outputs=()): + return 'cat {} > {}'.format(inputs[0], outputs[0]) # <-- Use str.format method + + @bash_app + def cat(inputs=(), outputs=()): + return f'cat {inputs[0]} > {outputs[0]}' # <-- OR use f-strings introduced in Python3.6 + + +* ``@python_app`` now takes a ``walltime`` kwarg to limit the task execution time. +* New file staging API `parsl.data_provider.staging.Staging` to support pluggable + file staging methods. The methods implemented in 0.8.0 (HTTP(S), FTP and + Globus) are still present, along with two new methods which perform HTTP(S) + and FTP staging on worker nodes to support non-shared-filesystem executors + such as clouds. +* Behaviour change for storage_access parameter. In 0.8.0, this was used to + specify Globus staging configuration. In 0.9.0, if this parameter is + specified it must specify all desired staging providers. To keep the same + staging providers as in 0.8.0, specify: + + .. code-block:: python + + from parsl.data_provider.data_manager import default_staging + storage_access = default_staging + [GlobusStaging(...)] + + ``GlobusScheme`` in 0.8.0 has been renamed `GlobusStaging` and moved to a new + module, parsl.data_provider.globus + +* `parsl.executors.WorkQueueExecutor`: a new executor that integrates functionality from `Work Queue `_ is now available. +* New provider to support for Ad-Hoc clusters ``parsl.providers.AdHocProvider`` +* New provider added to support LSF on Summit `parsl.providers.LSFProvider` +* Support for CPU and Memory resource hints to providers `(github) `_. +* The ``logging_level=logging.INFO`` in `parsl.monitoring.MonitoringHub` is replaced with ``monitoring_debug=False``: + + .. code-block:: python + + monitoring=MonitoringHub( + hub_address=address_by_hostname(), + hub_port=55055, + monitoring_debug=False, + resource_monitoring_interval=10, + ), + +* Managers now have a worker watchdog thread to report task failures that crash a worker. +* Maximum idletime after which idle blocks can be relinquished can now be configured as follows: + + .. code-block:: python + + config=Config( + max_idletime=120.0 , # float, unit=seconds + strategy='simple' + ) + +* Several test-suite improvements that have dramatically reduced test duration. +* Several improvements to the Monitoring interface. +* Configurable port on ``parsl.channels.SSHChannel``. +* ``suppress_failure`` now defaults to True. +* `parsl.executors.HighThroughputExecutor` is the recommended executor, and ``IPyParallelExecutor`` is deprecated. +* `parsl.executors.HighThroughputExecutor` will expose worker information via environment variables: ``PARSL_WORKER_RANK`` and ``PARSL_WORKER_COUNT`` + +Bug Fixes +^^^^^^^^^ + +* ZMQError: Operation cannot be accomplished in current state bug `issue#1146 `_ +* Fix event loop error with monitoring enabled `issue#532 `_ +* Tasks per app graph appears as a sawtooth, not as rectangles `issue#1032 `_. +* Globus status processing failure `issue#1317 `_. +* Sporadic globus staging error `issue#1170 `_. +* RepresentationMixin breaks on classes with no default parameters `issue#1124 `_. +* File ``localpath`` staging conflict `issue#1197 `_. +* Fix IndexError when using CondorProvider with strategy enabled `issue#1298 `_. +* Improper dependency error handling causes hang `issue#1285 `_. +* Memoization/checkpointing fixes for bash apps `issue#1269 `_. +* CPU User Time plot is strangely cumulative `issue#1033 `_. +* Issue requesting resources on non-exclusive nodes `issue#1246 `_. +* parsl + htex + slurm hangs if slurm command times out, without making further progress `issue#1241 `_. +* Fix strategy overallocations `issue#704 `_. +* max_blocks not respected in SlurmProvider `issue#868 `_. +* globus staging does not work with a non-default ``workdir`` `issue#784 `_. +* Cumulative CPU time loses time when subprocesses end `issue#1108 `_. +* Interchange KeyError due to too many heartbeat missed `issue#1128 `_. + + + +Parsl 0.8.0 +----------- + +Released on June 13th, 2019 + +Parsl v0.8.0 includes 58 closed issues and pull requests with contributions (code, tests, reviews and reports) + +from: Andrew Litteken @AndrewLitteken, Anna Woodard @annawoodard, Antonio Villarreal @villarrealas, +Ben Clifford @benc, Daniel S. Katz @danielskatz, Eric Tatara @etatara, Juan David Garrido @garri1105, +Kyle Chard @@kylechard, Lindsey Gray @lgray, Tim Armstrong @timarmstrong, Tom Glanzman @TomGlanzman, +Yadu Nand Babuji @yadudoc, and Zhuozhao Li @ZhuozhaoLi + + +New Functionality +^^^^^^^^^^^^^^^^^ + +* Monitoring is now integrated into parsl as default functionality. +* ``parsl.AUTO_LOGNAME``: Support for a special ``AUTO_LOGNAME`` option to auto generate ``stdout`` and ``stderr`` file paths. +* `File` no longer behaves as a string. This means that operations in apps that treated a `File` as a string + will break. For example the following snippet will have to be updated: + + .. code-block:: python + + # Old style: " ".join(inputs) is legal since inputs will behave like a list of strings + @bash_app + def concat(inputs=(), outputs=(), stdout="stdout.txt", stderr='stderr.txt'): + return "cat {0} > {1}".format(" ".join(inputs), outputs[0]) + + # New style: + @bash_app + def concat(inputs=(), outputs=(), stdout="stdout.txt", stderr='stderr.txt'): + return "cat {0} > {1}".format(" ".join(list(map(str,inputs))), outputs[0]) + +* Cleaner user app file log management. +* Updated configurations using `parsl.executors.HighThroughputExecutor` in the configuration section of the userguide. +* Support for OAuth based SSH with ``parsl.channels.OAuthSSHChannel``. + +Bug Fixes +^^^^^^^^^ + +* Monitoring resource usage bug `issue#975 `_ +* Bash apps fail due to missing dir paths `issue#1001 `_ +* Viz server explicit binding fix `issue#1023 `_ +* Fix sqlalchemy version warning `issue#997 `_ +* All workflows are called typeguard `issue#973 `_ +* Fix ``ModuleNotFoundError: No module named 'monitoring'`` `issue#971 `_ +* Fix sqlite3 integrity error `issue#920 `_ +* HTEX interchange check python version mismatch to the micro level `issue#857 `_ +* Clarify warning message when a manager goes missing `issue#698 `_ +* Apps without a specified DFK should use the global DFK in scope at call time, not at other times. `issue#697 `_ + + +Parsl 0.7.2 +----------- + +Released on Mar 14th, 2019 + +New Functionality +^^^^^^^^^^^^^^^^^ + +* Monitoring: Support for reporting monitoring data to a local sqlite database is now available. +* Parsl is switching to an opt-in model for anonymous usage tracking. Read more here: :ref:`label-usage-tracking`. +* `bash_app` now supports specification of write modes for ``stdout`` and ``stderr``. +* Persistent volume support added to `parsl.providers.KubernetesProvider`. +* Scaling recommendations from study on Bluewaters is now available in the userguide. + + +Parsl 0.7.1 +----------- + +Released on Jan 18th, 2019 + +New Functionality +^^^^^^^^^^^^^^^^^ + +* parsl.executors.LowLatencyExecutor: a new executor designed to address use-cases with tight latency requirements + such as model serving (Machine Learning), function serving and interactive analyses is now available. +* New options in `parsl.executors.HighThroughputExecutor`: + * ``suppress_failure``: Enable suppression of worker rejoin errors. + * ``max_workers``: Limit workers spawned by manager +* Late binding of DFK, allows apps to pick DFK dynamically at call time. This functionality adds safety + to cases where a new config is loaded and a new DFK is created. + +Bug fixes +^^^^^^^^^ + +* A critical bug in `parsl.executors.HighThroughputExecutor` that led to debug logs overflowing channels and terminating + blocks of resource is fixed `issue#738 `_ + + +Parsl 0.7.0 +----------- + +Released on Dec 20st, 2018 + +Parsl v0.7.0 includes 110 closed issues with contributions (code, tests, reviews and reports) +from: Alex Hays @ahayschi, Anna Woodard @annawoodard, Ben Clifford @benc, Connor Pigg @ConnorPigg, +David Heise @daheise, Daniel S. Katz @danielskatz, Dominic Fitzgerald @djf604, Francois Lanusse @EiffL, +Juan David Garrido @garri1105, Gordon Watts @gordonwatts, Justin Wozniak @jmjwozniak, +Joseph Moon @jmoon1506, Kenyi Hurtado @khurtado, Kyle Chard @kylechard, Lukasz Lacinski @lukaszlacinski, +Ravi Madduri @madduri, Marco Govoni @mgovoni-devel, Reid McIlroy-Young @reidmcy, Ryan Chard @ryanchard, +@sdustrud, Yadu Nand Babuji @yadudoc, and Zhuozhao Li @ZhuozhaoLi + +New functionality +^^^^^^^^^^^^^^^^^ + + +* `parsl.executors.HighThroughputExecutor`: a new executor intended to replace the ``IPyParallelExecutor`` is now available. + This new executor addresses several limitations of ``IPyParallelExecutor`` such as: + + * Scale beyond the ~300 worker limitation of IPP. + * Multi-processing manager supports execution on all cores of a single node. + * Improved worker side reporting of version, system and status info. + * Supports failure detection and cleaner manager shutdown. + + Here's a sample configuration for using this executor locally: + + .. code-block:: python + + from parsl.providers import LocalProvider + from parsl.channels import LocalChannel + + from parsl.config import Config + from parsl.executors import HighThroughputExecutor + + config = Config( + executors=[ + HighThroughputExecutor( + label="htex_local", + cores_per_worker=1, + provider=LocalProvider( + channel=LocalChannel(), + init_blocks=1, + max_blocks=1, + ), + ) + ], + ) + + More information on configuring is available in the :ref:`configuration-section` section. + +* ExtremeScaleExecutor - a new executor targeting supercomputer scale (>1000 nodes) workflows is now available. + + Here's a sample configuration for using this executor locally: + + .. code-block:: python + + from parsl.providers import LocalProvider + from parsl.channels import LocalChannel + from parsl.launchers import SimpleLauncher + + from parsl.config import Config + from parsl.executors import ExtremeScaleExecutor + + config = Config( + executors=[ + ExtremeScaleExecutor( + label="extreme_local", + ranks_per_node=4, + provider=LocalProvider( + channel=LocalChannel(), + init_blocks=0, + max_blocks=1, + launcher=SimpleLauncher(), + ) + ) + ], + strategy=None, + ) + + More information on configuring is available in the :ref:`configuration-section` section. + + +* The libsubmit repository has been merged with Parsl to reduce overheads on maintenance with respect to documentation, + testing, and release synchronization. Since the merge, the API has undergone several updates to support + the growing collection of executors, and as a result Parsl 0.7.0+ will not be backwards compatible with + the standalone libsubmit repos. The major components of libsubmit are now available through Parsl, and + require the following changes to import lines to migrate scripts to 0.7.0: + + * ``from libsubmit.providers import `` is now ``from parsl.providers import `` + * ``from libsubmit.channels import `` is now ``from parsl.channels import `` + * ``from libsubmit.launchers import `` is now ``from parsl.launchers import `` + + + .. warning:: + This is a breaking change from Parsl v0.6.0 + +* To support resource-based requests for workers and to maintain uniformity across interfaces, ``tasks_per_node`` is + no longer a **provider** option. Instead, the notion of ``tasks_per_node`` is defined via executor specific options, + for eg: + + * ``IPyParallelExecutor`` provides ``workers_per_node`` + * `parsl.executors.HighThroughputExecutor` provides ``cores_per_worker`` to allow for worker launches to be determined based on + the number of cores on the compute node. + * ExtremeScaleExecutor uses ``ranks_per_node`` to specify the ranks to launch per node. + + .. warning:: + This is a breaking change from Parsl v0.6.0 + + +* Major upgrades to the monitoring infrastructure. + * Monitoring information can now be written to a SQLite database, created on the fly by Parsl + * Web-based monitoring to track workflow progress + + +* Determining the correct IP address/interface given network firewall rules is often a nuisance. + To simplify this, three new methods are now supported: + + * ``parsl.addresses.address_by_route`` + * ``parsl.addresses.address_by_query`` + * ``parsl.addresses.address_by_hostname`` + +* `parsl.launchers.AprunLauncher` now supports ``overrides`` option that allows arbitrary strings to be added + to the aprun launcher call. + +* `DataFlowKernel` has a new method ``wait_for_current_tasks()`` + +* `DataFlowKernel` now uses per-task locks and an improved mechanism to handle task completions + improving performance for workflows with large number of tasks. + + +Bug fixes (highlights) +^^^^^^^^^^^^^^^^^^^^^^ + + +* Ctlr+C should cause fast DFK cleanup `issue#641 `_ +* Fix to avoid padding in ``wtime_to_minutes()`` `issue#522 `_ +* Updates to block semantics `issue#557 `_ +* Updates ``public_ip`` to ``address`` for clarity `issue#557 `_ +* Improvements to launcher docs `issue#424 `_ +* Fixes for inconsistencies between stream_logger and file_logger `issue#629 `_ +* Fixes to DFK discarding some un-executed tasks at end of workflow `issue#222 `_ +* Implement per-task locks to avoid deadlocks `issue#591 `_ +* Fixes to internal consistency errors `issue#604 `_ +* Removed unnecessary provider labels `issue#440 `_ +* Fixes to `parsl.providers.TorqueProvider` to work on NSCC `issue#489 `_ +* Several fixes and updates to monitoring subsystem `issue#471 `_ +* DataManager calls wrong DFK `issue#412 `_ +* Config isn't reloading properly in notebooks `issue#549 `_ +* Cobalt provider ``partition`` should be ``queue`` `issue#353 `_ +* bash AppFailure exceptions contain useful but un-displayed information `issue#384 `_ +* Do not CD to engine_dir `issue#543 `_ +* Parsl install fails without kubernetes config file `issue#527 `_ +* Fix import error `issue#533 `_ +* Change Local Database Strategy from Many Writers to a Single Writer `issue#472 `_ +* All run-related working files should go in the rundir unless otherwise configured `issue#457 `_ +* Fix concurrency issue with many engines accessing the same IPP config `issue#469 `_ +* Ensure we are not caching failed tasks `issue#368 `_ +* File staging of unknown schemes fails silently `issue#382 `_ +* Inform user checkpointed results are being used `issue#494 `_ +* Fix IPP + python 3.5 failure `issue#490 `_ +* File creation fails if no executor has been loaded `issue#482 `_ +* Make sure tasks in ``dep_fail`` state are retried `issue#473 `_ +* Hard requirement for CMRESHandler `issue#422 `_ +* Log error Globus events to stderr `issue#436 `_ +* Take 'slots' out of logging `issue#411 `_ +* Remove redundant logging `issue#267 `_ +* Zombie ipcontroller processes - Process cleanup in case of interruption `issue#460 `_ +* IPyparallel failure when submitting several apps in parallel threads `issue#451 `_ +* `parsl.providers.SlurmProvider` + `parsl.launchers.SingleNodeLauncher` starts all engines on a single core `issue#454 `_ +* IPP ``engine_dir`` has no effect if indicated dir does not exist `issue#446 `_ +* Clarify AppBadFormatting error `issue#433 `_ +* confusing error message with simple configs `issue#379 `_ +* Error due to missing kubernetes config file `issue#432 `_ +* ``parsl.configs`` and ``parsl.tests.configs`` missing init files `issue#409 `_ +* Error when Python versions differ `issue#62 `_ +* Fixing ManagerLost error in HTEX/EXEX `issue#577 `_ +* Write all debug logs to rundir by default in HTEX/EXEX `issue#574 `_ +* Write one log per HTEX worker `issue#572 `_ +* Fixing ManagerLost error in HTEX/EXEX `issue#577 `_ + + +Parsl 0.6.1 +----------- + +Released on July 23rd, 2018. + +This point release contains fixes for `issue#409 `_ + + +Parsl 0.6.0 +----------- + +Released July 23rd, 2018. + +New functionality +^^^^^^^^^^^^^^^^^ + +* Switch to class based configuration `issue#133 `_ + + Here's a the config for using threads for local execution + + .. code-block:: python + + from parsl.config import Config + from parsl.executors.threads import ThreadPoolExecutor + + config = Config(executors=[ThreadPoolExecutor()]) + + Here's a more complex config that uses SSH to run on a Slurm based cluster + + .. code-block:: python + + from libsubmit.channels import SSHChannel + from libsubmit.providers import SlurmProvider + + from parsl.config import Config + from parsl.executors.ipp import IPyParallelExecutor + from parsl.executors.ipp_controller import Controller + + config = Config( + executors=[ + IPyParallelExecutor( + provider=SlurmProvider( + 'westmere', + channel=SSHChannel( + hostname='swift.rcc.uchicago.edu', + username=, + script_dir= + ), + init_blocks=1, + min_blocks=1, + max_blocks=2, + nodes_per_block=1, + tasks_per_node=4, + parallelism=0.5, + overrides= + ), + label='midway_ipp', + controller=Controller(public_ip=), + ) + ] + ) + +* Implicit Data Staging `issue#281 `_ + + .. code-block:: python + + # create an remote Parsl file + inp = File('ftp://www.iana.org/pub/mirror/rirstats/arin/ARIN-STATS-FORMAT-CHANGE.txt') + + # create a local Parsl file + out = File('file:///tmp/ARIN-STATS-FORMAT-CHANGE.txt') + + # call the convert app with the Parsl file + f = convert(inputs=[inp], outputs=[out]) + f.result() + +* Support for application profiling `issue#5 `_ + +* Real-time usage tracking via external systems `issue#248 `_, `issue#251 `_ + +* Several fixes and upgrades to tests and testing infrastructure `issue#157 `_, `issue#159 `_, + `issue#128 `_, `issue#192 `_, + `issue#196 `_ + +* Better state reporting in logs `issue#242 `_ + +* Hide DFK `issue#50 `_ + + * Instead of passing a config dictionary to the DataFlowKernel, now you can call ``parsl.load(Config)`` + * Instead of having to specify the ``dfk`` at the time of ``App`` declaration, the DFK is a singleton loaded + at call time : + + .. code-block:: python + + import parsl + from parsl.tests.configs.local_ipp import config + parsl.load(config) + + @App('python') + def double(x): + return x * 2 + + fut = double(5) + fut.result() + +* Support for better reporting of remote side exceptions `issue#110 `_ + + +Bug Fixes +^^^^^^^^^ + +* Making naming conventions consistent `issue#109 `_ + +* Globus staging returns unclear error bug `issue#178 `_ + +* Duplicate log-lines when using IPP `issue#204 `_ + +* Usage tracking with certain missing network causes 20s startup delay. `issue#220 `_ + +* ``task_exit`` checkpointing repeatedly truncates checkpoint file during run bug `issue#230 `_ + +* Checkpoints will not reload from a run that was Ctrl-C'ed `issue#232 `_ + +* Race condition in task checkpointing `issue#234 `_ + +* Failures not to be checkpointed `issue#239 `_ + +* Naming inconsitencies with ``maxThreads``, ``max_threads``, ``max_workers`` are now resolved `issue#303 `_ + +* Fatal not a git repository alerts `issue#326 `_ + +* Default ``kwargs`` in bash apps unavailable at command-line string format time `issue#349 `_ + +* Fix launcher class inconsistencies `issue#360 `_ + +* Several fixes to AWS provider `issue#362 `_ + * Fixes faulty status updates + * Faulty termination of instance at cleanup, leaving zombie nodes. + + +Parsl 0.5.1 +----------- + +Released. May 15th, 2018. + +New functionality +^^^^^^^^^^^^^^^^^ + + +* Better code state description in logging `issue#242 `_ + +* String like behavior for Files `issue#174 `_ + +* Globus path mapping in config `issue#165 `_ + + +Bug Fixes +^^^^^^^^^ + +* Usage tracking with certain missing network causes 20s startup delay. `issue#220 `_ + +* Checkpoints will not reload from a run that was Ctrl-C'ed `issue#232 `_ + +* Race condition in task checkpointing `issue#234 `_ + +* ``task_exit`` checkpointing repeatedly truncates checkpoint file during run `issue#230 `_ + +* Make ``dfk.cleanup()`` not cause kernel to restart with Jupyter on Mac `issue#212 `_ + +* Fix automatic IPP controller creation on OS X `issue#206 `_ + +* Passing Files breaks over IPP `issue#200 `_ + +* `repr` call after `AppException` instantiation raises `AttributeError` `issue#197 `_ + +* Allow `DataFuture` to be initialized with a `str` file object `issue#185 `_ + +* Error for globus transfer failure `issue#162 `_ + + +Parsl 0.5.2 +----------- + +Released. June 21st, 2018. +This is an emergency release addressing `issue#347 `_ + +Bug Fixes +^^^^^^^^^ + +* Parsl version conflict with libsubmit 0.4.1 `issue#347 `_ + + +Parsl 0.5.0 +----------- + +Released. Apr 16th, 2018. + +New functionality +^^^^^^^^^^^^^^^^^ + +* Support for Globus file transfers `issue#71 `_ + + .. caution:: + This feature is available from Parsl ``v0.5.0`` in an ``experimental`` state. + +* PathLike behavior for Files `issue#174 `_ + * Files behave like strings here : + + .. code-block:: python + + myfile = File("hello.txt") + f = open(myfile, 'r') + + +* Automatic checkpointing modes `issue#106 `_ + + .. code-block:: python + + config = { + "globals": { + "lazyErrors": True, + "memoize": True, + "checkpointMode": "dfk_exit" + } + } + +* Support for containers with docker `issue#45 `_ + + .. code-block:: python + + localDockerIPP = { + "sites": [ + {"site": "Local_IPP", + "auth": {"channel": None}, + "execution": { + "executor": "ipp", + "container": { + "type": "docker", # <----- Specify Docker + "image": "app1_v0.1", # <------Specify docker image + }, + "provider": "local", + "block": { + "initBlocks": 2, # Start with 4 workers + }, + } + }], + "globals": {"lazyErrors": True} } + + .. caution:: + This feature is available from Parsl ``v0.5.0`` in an ``experimental`` state. + +* Cleaner logging `issue#85 `_ + * Logs are now written by default to ``runinfo/RUN_ID/parsl.log``. + * ``INFO`` log lines are more readable and compact + +* Local configs are now packaged `issue#96 `_ + + .. code-block:: python + + from parsl.configs.local import localThreads + from parsl.configs.local import localIPP + + +Bug Fixes +^^^^^^^^^ +* Passing Files over IPP broken `issue#200 `_ + +* Fix ``DataFuture.__repr__`` for default instantiation `issue#164 `_ + +* Results added to appCache before retries exhausted `issue#130 `_ + +* Missing documentation added for Multisite and Error handling `issue#116 `_ + +* TypeError raised when a bad stdout/stderr path is provided. `issue#104 `_ + +* Race condition in DFK `issue#102 `_ + +* Cobalt provider broken on Cooley.alfc `issue#101 `_ + +* No blocks provisioned if parallelism/blocks = 0 `issue#97 `_ + +* Checkpoint restart assumes rundir `issue#95 `_ + +* Logger continues after cleanup is called `issue#93 `_ + + +Parsl 0.4.1 +----------- + +Released. Feb 23rd, 2018. + + +New functionality +^^^^^^^^^^^^^^^^^ + +* GoogleCloud provider support via libsubmit +* GridEngine provider support via libsubmit + + +Bug Fixes +^^^^^^^^^ +* Cobalt provider issues with job state `issue#101 `_ +* Parsl updates config inadvertently `issue#98 `_ +* No blocks provisioned if parallelism/blocks = 0 `issue#97 `_ +* Checkpoint restart assumes rundir bug `issue#95 `_ +* Logger continues after cleanup called enhancement `issue#93 `_ +* Error checkpointing when no cache enabled `issue#92 `_ +* Several fixes to libsubmit. + + +Parsl 0.4.0 +----------- + +Here are the major changes included in the Parsl 0.4.0 release. + +New functionality +^^^^^^^^^^^^^^^^^ + +* Elastic scaling in response to workflow pressure. `issue#46 `_ + Options ``minBlocks``, ``maxBlocks``, and ``parallelism`` now work and controls workflow execution. + + Documented in: :ref:`label-elasticity` + +* Multisite support, enables targetting apps within a single workflow to different + sites `issue#48 `_ + + .. code-block:: python + + @App('python', dfk, sites=['SITE1', 'SITE2']) + def my_app(...): + ... + +* Anonymized usage tracking added. `issue#34 `_ + + Documented in: :ref:`label-usage-tracking` + +* AppCaching and Checkpointing `issue#43 `_ + + .. code-block:: python + + # Set cache=True to enable appCaching + @App('python', dfk, cache=True) + def my_app(...): + ... + + + # To checkpoint a workflow: + dfk.checkpoint() + + Documented in: :ref:`label-checkpointing`, :ref:`label-appcaching` + +* Parsl now creates a new directory under ``./runinfo/`` with an incrementing number per workflow + invocation + +* Troubleshooting guide and more documentation + +* PEP8 conformance tests added to travis testing `issue#72 `_ + + +Bug Fixes +^^^^^^^^^ + +* Missing documentation from libsubmit was added back + `issue#41 `_ + +* Fixes for ``script_dir`` | ``scriptDir`` inconsistencies `issue#64 `_ + * We now use ``scriptDir`` exclusively. + +* Fix for caching not working on jupyter notebooks `issue#90 `_ + +* Config defaults module failure when part of the option set is provided `issue#74 `_ + +* Fixes for network errors with usage_tracking `issue#70 `_ + +* PEP8 conformance of code and tests with limited exclusions `issue#72 `_ + +* Doc bug in recommending ``max_workers`` instead of ``maxThreads`` `issue#73 `_ + + + + +Parsl 0.3.1 +----------- + +This is a point release with mostly minor features and several bug fixes + +* Fixes for remote side handling +* Support for specifying IPythonDir for IPP controllers +* Several tests added that test provider launcher functionality from libsubmit +* This upgrade will also push the libsubmit requirement from 0.2.4 -> 0.2.5. + + +Several critical fixes from libsubmit are brought in: + +* Several fixes and improvements to Condor from @annawoodard. +* Support for Torque scheduler +* Provider script output paths are fixed +* Increased walltimes to deal with slow scheduler system +* Srun launcher for slurm systems +* SSH channels now support file_pull() method + While files are not automatically staged, the channels provide support for bi-directional file transport. + +Parsl 0.3.0 +----------- + +Here are the major changes that are included in the Parsl 0.3.0 release. + + +New functionality +^^^^^^^^^^^^^^^^^ + +* Arguments to DFK has changed: + + # Old + dfk(executor_obj) + + # New, pass a list of executors + dfk(executors=[list_of_executors]) + + # Alternatively, pass the config from which the DFK will + #instantiate resources + dfk(config=config_dict) + +* Execution providers have been restructured to a separate repo: `libsubmit `_ + +* Bash app styles have changes to return the commandline string rather than be assigned to the special keyword ``cmd_line``. + Please refer to `RFC #37 `_ for more details. This is a **non-backward** compatible change. + +* Output files from apps are now made available as an attribute of the AppFuture. + Please refer `#26 `_ for more details. This is a **non-backward** compatible change :: + + # This is the pre 0.3.0 style + app_fu, [file1, file2] = make_files(x, y, outputs=['f1.txt', 'f2.txt']) + + #This is the style that will be followed going forward. + app_fu = make_files(x, y, outputs=['f1.txt', 'f2.txt']) + [file1, file2] = app_fu.outputs + +* DFK init now supports auto-start of IPP controllers + +* Support for channels via libsubmit. Channels enable execution of commands from execution providers either + locally, or remotely via ssh. + +* Bash apps now support timeouts. + +* Support for cobalt execution provider. + + +Bug fixes +^^^^^^^^^ +* Futures have inconsistent behavior in bash app fn body `#35 `_ +* Parsl dflow structure missing dependency information `#30 `_ + + +Parsl 0.2.0 +----------- + +Here are the major changes that are included in the Parsl 0.2.0 release. + +New functionality +^^^^^^^^^^^^^^^^^ + +* Support for execution via IPythonParallel executor enabling distributed execution. +* Generic executors + +Parsl 0.1.0 +----------- + +Here are the major changes that are included in the Parsl 0.1.0 release. + +New functionality +^^^^^^^^^^^^^^^^^ + +* Support for Bash and Python apps +* Support for chaining of apps via futures handled by the DataFlowKernel. +* Support for execution over threads. +* Arbitrary DAGs can be constructed and executed asynchronously. + +Bug Fixes +^^^^^^^^^ + +* Initial release, no listed bugs. + + +Historical: Libsubmit Changelog +=============================== + +.. note:: + As of Parsl 0.7.0 the libsubmit repository has been merged into Parsl + and nothing more will appear in this changelog. + +Libsubmit 0.4.1 +--------------- + +Released. June 18th, 2018. +This release folds in massive contributions from @annawoodard. + +New functionality +^^^^^^^^^^^^^^^^^ + +* Several code cleanups, doc improvements, and consistent naming + +* All providers have the initialization and actual start of resources decoupled. + + + +Libsubmit 0.4.0 +--------------- + +Released. May 15th, 2018. +This release folds in contributions from @ahayschi, @annawoodard, @yadudoc + +New functionality +^^^^^^^^^^^^^^^^^ + +* Several enhancements and fixes to the AWS cloud provider (#44, #45, #50) + +* Added support for python3.4 + + +Bug Fixes +^^^^^^^^^ + +* Condor jobs left in queue with X state at end of completion `issue#26 `_ + +* Worker launches on Cori seem to fail from broken ENV `issue#27 `_ + +* EC2 provider throwing an exception at initial run `issue#46 `_ diff --git a/parsl/docs/historical/design.rst b/parsl/docs/historical/design.rst new file mode 100644 index 00000000..f32e0c92 --- /dev/null +++ b/parsl/docs/historical/design.rst @@ -0,0 +1,151 @@ +Historical: Swift vs Parsl +-------------------------- + +.. note:: + This section describes comparisons between Parsl and an earlier workflow + system, Swift, as part of a justification for the early prototyping stages + of Parsl development. It is no longer relevant for modern Parsl users, but + remains of historical interest. + +The following text is not well structured, and is mostly a brain dump that needs to be organized. +Moving from Swift to an established language (python) came with its own tradeoffs. We get the backing +of a rich and very well known language to handle the language aspects as well as the libraries. +However, we lose the parallel evaluation of every statement in a script. The thesis is that what we +lose is minimal and will not affect 95% of our workflows. This is not yet substantiated. + +Please note that there are two Swift languages: `Swift/K `_ +and `Swift/T `_ . These have diverged in syntax and behavior. +Swift/K is designed for grids and clusters runs the java based +`Karajan `_ (hence, /K) execution framework. +Swift/T is a completely new implementation of Swift/K for high-performance computing. Swift/T uses +Turbine(hence, /T) and and +`ADLB `_ runtime libraries for +highly scalable dataflow processing over MPI, +without single-node bottlenecks. + + +Parallel Evaluation +^^^^^^^^^^^^^^^^^^^ + +In Swift (K&T), every statement is evaluated in parallel. + +.. code-block:: c + + y = f(x); + z = g(x); + +We see that y and z are assigned values in different order when we run Swift multiple times. Swift +evaluates both statements in parallel and the order in which they complete is mostly random. + +We will *not* have this behavior in Python. Each statement is evaluated in order. + +.. code-block:: c + + int[] array; + foreach v,i in [1:5] { + array[i] = 2*v; + } + + foreach v in array { + trace(v) + } + +Another consequence is that in Swift, a foreach loop that consumes results in an array need +not wait for the foreach loop that fill the array. In the above example, the second foreach +loop makes progress along with the first foreach loop as it fills the array. + +In parsl, a for loop that **launches** tasks has to complete launches before the control may +proceed to the next statement. The first for loop has to simply finish iterating, and launching +jobs, which should take ~length_of_iterable/1000 (items/task_launch_rate). + +.. code-block:: python + + futures = {}; + + for i in range(0,10): + futures[i] = app_double(i); + + for i in fut_array: + print(i, futures[i]) + +The first for loop first fills the futures dict before control can proceed to the second for +loop that consumes the contents. + +The main conclusion here is that, if the iteration space is sufficiently large (or the app +launches are throttled), then it is possible that tasks that are further down the control +flow have to wait regardless of their dependencies being resolved. + + +Mappers +^^^^^^^ + +In Swift/K, a mapper is a mechanism to map files to variables. Swift need's to know files +on disk so that it could move them to remote sites for execution or as inputs to applications. +Mapped file variables also indicate to swift that, when files are created on remote sites, they +need to be staged back. Swift/K provides several mappers which makes it convenient to map files on +disk to file variables. + +There are two choices here : + +1. Have the user define the mappers and data objects +2. Have the data objects be created only by Apps. + + +In Swift, the user defines file mappings like this : + +.. code-block:: c + + # Mapping a single file + file f <"f.txt">; + + # Array of files + file texts[] ; + +The files mapped to an array could be either inputs or outputs to be created. Which is the case is +inferred from whether they are on the left-hand side or right-hand side of an assignment. Variables on +the left-hand side are inferred to be outputs that have future-like behavior. To avoid conflicting +values being assigned to the same variable, Swift variables are all immutable. + +For instance, the following would be a major concern *if* variables were not immutable: + +.. code-block:: c + + x = 0; + x = 1; + trace(x); + +The results that trace would print would be non-deterministic, if x were mutable. In Swift, the above +code would raise an error. However this is perfectly legal in python, and the x would take the last +value it was assigned. + +Remote-Execution +^^^^^^^^^^^^^^^^ + +In Swift/K, remote execution is handled by `coasters `_. +This is a pilot mechanism that supports dynamic resource provisioning from cluster managers such as PBS, +Slurm, Condor and handles data transport from the client to the workers. Swift/T on the other hand is +designed to run as an MPI job on a single HPC resource. Swift/T utilized shared-filesystems that almost +every HPC resource has. + +To be useful, Parsl will need to support remote execution and file transfers. Here we will discuss just +the remote-execution aspect. + +Here is a set of features that should be implemented or borrowed : + +* [Done] New remote execution system must have the `executor interface `_. +* [Done] Executors must be memory efficient wrt to holding jobs in memory. +* [Done] Continue to support both BashApps and PythonApps. +* [Done] Capable of using templates to submit jobs to Cluster resource managers. +* [Done] Dynamically launch and shutdown workers. + +.. note:: + Since the current roadmap to remote execution is through ipython-parallel, we will limit support + to Python3.5+ to avoid library naming issues. + + +====== +Design +====== + +Under construction. + diff --git a/parsl/docs/historical/index.rst b/parsl/docs/historical/index.rst new file mode 100644 index 00000000..5575a355 --- /dev/null +++ b/parsl/docs/historical/index.rst @@ -0,0 +1,9 @@ +Historical Documents +==================== + +.. toctree:: + :maxdepth: 2 + + changelog + design + performance diff --git a/parsl/docs/historical/performance.rst b/parsl/docs/historical/performance.rst new file mode 100644 index 00000000..4f9f8bad --- /dev/null +++ b/parsl/docs/historical/performance.rst @@ -0,0 +1,92 @@ +.. _label-performance: + +Historical: Performance and Scalability +======================================= + +.. note:: + This scalability review summarises results in a paper, Parsl: Pervasive + Parallel Programming in Python, which was published in 2019. The results + have not been updated since then. For that reason, this section is marked + as historical. + +Parsl is designed to scale from small to large systems . + + +Scalability +----------- +We studied strong and weak scaling on the Blue Waters supercomputer. +In strong scaling, the total problem size is fixed; in weak scaling, the problem +size per CPU core is fixed. In both cases, we measure completion +time as a function of number of CPU cores. An ideal framework +should scale linearly, which for strong scaling means that speedup +scales with the number of cores, and for weak scaling means that +completion time remains constant as the number of cores increases. + +To measure the strong and weak scaling of Parsl executors, we +created Parsl programs to run tasks with different durations, ranging from a +"no-op"--a Python function that exits immediately---to +tasks that sleep for 10, 100, and 1,000 ms. For each executor we +deployed a worker per core on each node. + +While we compare here with IPP, Fireworks, and Dask Distributed, +we note that these systems are not necessarily designed for +Parsl-like workloads or scale. + +Further results are presented in our +`HPDC paper `_. + +Strong scaling +^^^^^^^^^^^^^^ +The figures below show the strong scaling results for 5,000 1-second +sleep tasks. HTEX +provides good performance in all cases, slightly exceeding what is +possible with EXEX, while EXEX scales to significantly more workers +than the other executors and frameworks. Both +HTEX and EXEX remain nearly constant, indicating that they likely +will continue to perform well at larger scales. + +.. image:: ../images/performance/strong-scaling.png + + +Weak scaling +^^^^^^^^^^^^ +Here, we launched 10 tasks per worker, while +increasing the number of workers. (We limited experiments to 10 +tasks per worker, as on 3,125 nodes, that represents 3,125 +nodes × 32 workers/node × 10 tasks/worker, or 1M tasks.) The +figure below shows our results. We observe that HTEX +and EXEX outperform other executors and frameworks with more +than 4,096 workers (128 nodes). All frameworks exhibit similar +trends, with completion time remaining close to constant initially +and increasing rapidly as the number of workers increases. + +.. image:: ../images/performance/weak-scaling.png + + +Throughput +---------- +We measured the maximum throughput of all the Parsl executors, +on the UChicago Research Computing Center's Midway Cluster. +To do so, we ran 50,000 “no-op" tasks on a varying number of +workers and recorded the completion times. The throughout is +computed as the number of tasks divided by the completion time. +HTEX, and EXEX achieved maximum throughputs of 1,181 and 1,176 +tasks/s, respectively. + +Summary +------- + +The table below summarizes the scale at which we have tested Parsl executors. +The maximum number of nodes and workers for HTEX and EXEX is limited +by the size of allocation available during testing on Blue Waters. +The throughput results are collected on Midway. + ++-----------+------------------+-------------+------------------+ +| Executor | Max # workers | Max # nodes | Max tasks/second | ++===========+==================+=============+==================+ +| IPP | 2,048 | 64 | 330 | ++-----------+------------------+-------------+------------------+ +| HTEX | 65,536 | 2,048 | 1,181 | ++-----------+------------------+-------------+------------------+ +| EXEX | 262,144 | 8,192 | 1,176 | ++-----------+------------------+-------------+------------------+ diff --git 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+You can use Parsl +:ref:`just like Python's parallel executors ` +but across *multiple cores and nodes*. +However, the real power of Parsl is in expressing multi-step workflows of functions. +Parsl lets you chain functions together and will launch each function as inputs and computing resources are available. + +.. code-block:: python + + import parsl + from parsl import python_app + + # Start Parsl on a single computer + parsl.load() + + # Make functions parallel by decorating them + @python_app + def f(x): + return x + 1 + + @python_app + def g(x, y): + return x + y + + # These functions now return Futures, and can be chained + future = f(1) + assert future.result() == 2 + + # Functions run concurrently, can be chained + f_a, f_b = f(2), f(3) + future = g(f_a, f_b) + assert future.result() == 7 + + +Start with the `configuration quickstart `_ to learn how to tell Parsl how to use your computing resource, +see if `a template configuration for your supercomputer `_ is already available, +then explore the `parallel computing patterns `_ to determine how to use parallelism best in your application. + +Parsl is an open-source code, and available on GitHub: https://github.com/parsl/parsl/ + +Why use Parsl? +============== + +Parsl is Python +--------------- + +*Everything about a Parsl program is written in Python.* +Parsl follows Python's native parallelization approach and functions, +how they combine into workflows, and where they run are all described in Python. + +Parsl works everywhere +---------------------- + +*Parsl can run parallel functions on a laptop and the world's fastest supercomputers.* +Scaling from laptop to supercomputer is often as simple as changing the resource configuration. +Parsl is tested `on many of the top supercomputers `_. + +Parsl is flexible +----------------- + +.. + Is this redundant with 'everywhere'? + +*Parsl supports many kinds of applications.* +Functions can be pure Python or invoke external codes, be single- or multi-threaded or GPUs. + + +Parsl handles data +------------------ + +*Parsl has first-class support for workflows involving files.* +Data will be automatically moved between workers, even if they reside on different filesystems. + +Parsl is fast +------------- + +*Parsl was built for speed.* +Workflows can manage tens of thousands of parallel tasks and process thousands of tasks per second. + + +Parsl is a community +-------------------- + +*Parsl is part of a large, experienced community.* + +The Parsl Project was launched by researchers with decades of experience in workflows +as part of a National Science Foundation project to create sustainable research software. + +The Parsl team is guided by the community through its GitHub, +conversations on `Slack `_, +Bi-Weekly developer calls, +and engagement with the `Workflows Community Initiative `_. + +.. + I was going to work in how we integrate with other tools, but that seemed too-detailed in this draft + +Table of Contents ++++++++++++++++++ + +.. toctree:: + :maxdepth: 2 + + quickstart + 1-parsl-introduction.ipynb + userguide/index + userguide/glossary + faq + reference + devguide/index + historical/index + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/parsl/docs/libsubmit_art/README.txt b/parsl/docs/libsubmit_art/README.txt new file mode 100644 index 00000000..c3611b89 --- /dev/null +++ b/parsl/docs/libsubmit_art/README.txt @@ -0,0 +1,3 @@ +Edittable diagrams embedded in this google doc: +https://docs.google.com/document/d/193LBq7H-dtxrYUER7oZqs0ZlOcLa2fGFPGLOHRS1c5U/edit?usp=sharing + diff --git a/parsl/docs/libsubmit_art/multi_node.png b/parsl/docs/libsubmit_art/multi_node.png new file mode 100644 index 0000000000000000000000000000000000000000..058457b6abd213dfe25ae794ac1712a757a5dc8c GIT binary patch literal 25318 zcmeFZcUY6zw=W*Vkr@%NgVa$NK~WH;S35?UAd1q$fQa-?fDnQ@ihzKE5TuS&1q6{E 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zy0Z2XjRmjo>Re*E51r!+2-*0Nz=j2kE&4%xh5W#N#JNwKgkFoG3kpcL!E#&%j3Ywg|B(XZ>Sfqk7>8$XwEP z7jW~;J}_PRqNeRVv6_yi z`c;=y7g*B>OXeBa6Pu~PFr}$PN(?eTe|7H!8|1SkQOc`%LLneX7HkP-&VkS9y^FTJ z&O+CXE3=#mXD$YW^EE_J(xYe+60qD7x^ z_zfh%|Gz&bZ{QAO8{hxqJUBxE+@}8d{e!?i2>gS + + + diff --git a/parsl/docs/quickstart.rst b/parsl/docs/quickstart.rst new file mode 100644 index 00000000..d7caf888 --- /dev/null +++ b/parsl/docs/quickstart.rst @@ -0,0 +1,270 @@ +Quickstart +========== + +To try Parsl now (without installing any code locally), experiment with our +`hosted tutorial notebooks on Binder `_. + + +Installation +------------ + +Parsl is available on `PyPI `_ and `conda-forge `_. + +Parsl requires Python3.10+ and has been tested on Linux. + + +Installation using Pip +^^^^^^^^^^^^^^^^^^^^^^ + +While ``pip`` can be used to install Parsl, we suggest the following approach +for reliable installation when many Python environments are available. + +1. Install Parsl:: + + $ python3 -m pip install parsl + +To update a previously installed parsl to a newer version, use: ``python3 -m pip install -U parsl`` + + +Installation using Conda +^^^^^^^^^^^^^^^^^^^^^^^^ + +1. Create and activate a new conda environment:: + + $ conda create --name parsl_py310 python=3.10 + $ source activate parsl_py310 + +2. Install Parsl:: + + $ python3 -m pip install parsl + + or + + $ conda config --add channels conda-forge + $ conda install parsl + + +The conda documentation provides `instructions `_ for installing conda on macOS and Linux. + +Getting started +--------------- + +.. image:: images/high-level.png + :alt: Relationship between Parsl's main components + :align: center + + +Parsl has much in common with Python's native concurrency library, +but unlocking Parsl's potential requires understanding a few major concepts. + +A Parsl program submits tasks to run on Workers distributed across remote computers. +The instructions for these tasks are contained within `"apps" <#application-types>`_ +that users define using Python functions. +Each remote computer (e.g., a node on a supercomputer) has a single `"Executor" <#executors>`_ +which manages the workers. +Remote resources available to Parsl are acquired by a `"Provider" <#execution-providers>`_, +which places the executor on a system with a `"Launcher" <#launchers>`_. +Task execution is brokered by a `"Data Flow Kernel" <#benefits-of-a-data-flow-kernel>`_ that runs on your local system. + +We describe these components briefly here, and link to more details in the `User Guide `_. + +.. note:: + + Parsl's documentation includes `templates for many supercomputers `_. + Even though you may not need to write a configuration from a blank slate, + understanding the basic terminology below will be very useful. + + +Application Types +^^^^^^^^^^^^^^^^^ + +Parsl enables concurrent execution of Python functions (``python_app``) +or external applications (``bash_app``). +The logic for both are described by Python functions marked with Parsl decorators. +When decorated functions are invoked, they run asynchronously on other resources. +The result of a call to a Parsl app is an :class:`~parsl.app.futures.AppFuture`, +which behaves like a Python Future. + +The following example shows how to write a simple Parsl program +with hello world Python and Bash apps. + +.. code-block:: python + + import parsl + from parsl import python_app, bash_app + + @python_app + def hello_python (message): + return 'Hello %s' % message + + @bash_app + def hello_bash(message, stdout='hello-stdout'): + return 'echo "Hello %s"' % message + + + with parsl.load(): + # invoke the Python app and print the result + print(hello_python('World (Python)').result()) + + # invoke the Bash app and read the result from a file + hello_bash('World (Bash)').result() + + with open('hello-stdout', 'r') as f: + print(f.read()) + +Learn more about the types of Apps and their options `here `__. + +Executors +^^^^^^^^^ + +Executors define how Parsl deploys work on a computer. +Many types are available, each with different advantages. + +The :class:`~parsl.executors.high_throughput.executor.HighThroughputExecutor`, +like Python's ``ProcessPoolExecutor``, creates workers that are separate Python processes. +However, you have much more control over how the work is deployed. +You can dynamically set the number of workers based on available memory and +pin each worker to specific GPUs or CPU cores +among other powerful features. + +Learn more about Executors `here `__. + +Execution Providers +^^^^^^^^^^^^^^^^^^^ + +Resource providers allow Parsl to gain access to computing power. +For supercomputers, gaining resources often requires requesting them from a scheduler (e.g., Slurm). +Parsl Providers write the requests to requisition **"Blocks"** (e.g., supercomputer nodes) on your behalf. +Parsl comes pre-packaged with Providers compatible with most supercomputers and some cloud computing services. + +Another key role of Providers is defining how to start an Executor on a remote computer. +Often, this simply involves specifying the correct Python environment and +(described below) how to launch the Executor on each acquired computers. + +Learn more about Providers `here `__. + +Launchers +^^^^^^^^^ + +The Launcher defines how to spread workers across all nodes available in a Block. +A common example is an :class:`~parsl.launchers.launchers.MPILauncher`, which uses MPI's mechanism +for starting a single program on multiple computing nodes. +Like Providers, Parsl comes packaged with Launchers for most supercomputers and clouds. + +Learn more about Launchers `here `__. + + +Benefits of a Data-Flow Kernel +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The Data-Flow Kernel (DFK) is the behind-the-scenes engine behind Parsl. +The DFK determines when tasks can be started and sends them to open resources, +receives results, restarts failed tasks, propagates errors to dependent tasks, +and performs the many other functions needed to execute complex workflows. +The flexibility and performance of the DFK enables applications with +intricate dependencies between tasks to execute on thousands of parallel workers. + +Start with the Tutorial or the `parallel patterns `_ +to see the complex types of workflows you can make with Parsl. + +Starting Parsl +^^^^^^^^^^^^^^ + +A Parsl script must contain the function definitions, resource configuration, and a call to ``parsl.load`` +before launching tasks. +This script runs on a system that must stay on-line until all of your tasks complete but need not have +much computing power, such as the login node for a supercomputer. + +The :class:`~parsl.config.Config` object holds definitions of Executors and the Providers and Launchers they rely on. +An example which launches 4 workers on 1 node of the Polaris supercomputer looks like + +.. code-block:: python + + from parsl import Config + from parsl.executors import HighThroughputExecutor + from parsl.providers import PBSProProvider + from parsl.launchers import MpiExecLauncher + + config = Config( + retries=1, # Restart task if they fail once + executors=[ + HighThroughputExecutor( + available_accelerators=4, # Maps one worker per GPU + address=address_by_hostname(), + cpu_affinity="alternating", # Prevents thread contention + provider=PBSProProvider( + account="example", + worker_init="module load conda; conda activate parsl", + walltime="1:00:00", + queue="debug", + scheduler_options="#PBS -l filesystems=home:eagle", # Change if data on other filesystem + launcher=MpiExecLauncher( + bind_cmd="--cpu-bind", overrides="--depth=64 --ppn 1" + ), # Ensures 1 manger per node and allows it to divide work to all 64 cores + select_options="ngpus=4", + nodes_per_block=1, + cpus_per_node=64, + ), + ), + ] + ) + + +The documentation has examples for other supercomputers `here `_. + +The next step is to load the configuration + +.. code-block:: python + + parsl.load(config) + +You are then ready to use 10 PFLOPS of computing power through Python! + +Tutorial +-------- + +The best way to learn more about Parsl is by reviewing the Parsl tutorials. +There are several options for following the tutorial: + +1. Use `Binder `_ to follow the tutorial online without installing or writing any code locally. +2. Clone the `Parsl tutorial repository `_ using a local Parsl installation. +3. Read through the online `tutorial documentation <1-parsl-introduction.html>`_. + + +Usage Tracking +-------------- + +To help support the Parsl project, we ask that users opt-in to anonymized usage tracking +whenever possible. Usage tracking allows us to measure usage, identify bugs, and improve +usability, reliability, and performance. Only aggregate usage statistics will be used +for reporting purposes. + +As an NSF-funded project, our ability to track usage metrics is important for continued funding. + +You can opt-in by setting ``usage_tracking=3`` in the configuration object (`parsl.config.Config`). + +To read more about what information is collected and how it is used see :ref:`label-usage-tracking`. + + +For Developers +-------------- + +Parsl is an open source community that encourages contributions from users +and developers. A guide for `contributing `_ +to Parsl is available in the `Parsl GitHub repository `_. + +The following instructions outline how to set up Parsl from source. + +1. Download Parsl:: + + $ git clone https://github.com/Parsl/parsl + +2. Install:: + + $ cd parsl + $ pip install . + ( To install specific extra options from the source :) + $ pip install '.[...]' + +3. Use Parsl! + diff --git a/parsl/docs/reference.rst b/parsl/docs/reference.rst new file mode 100644 index 00000000..979139c4 --- /dev/null +++ b/parsl/docs/reference.rst @@ -0,0 +1,244 @@ +API Reference guide +******************* + +Core +==== + +.. autosummary:: + :toctree: stubs + :nosignatures: + + parsl.app.app.python_app + parsl.app.app.bash_app + parsl.app.app.join_app + parsl.dataflow.futures.AppFuture + parsl.dataflow.dflow.DataFlowKernelLoader + parsl.concurrent.ParslPoolExecutor + parsl.dataflow.dependency_resolvers.DependencyResolver + parsl.dataflow.dependency_resolvers.DEEP_DEPENDENCY_RESOLVER + parsl.dataflow.dependency_resolvers.SHALLOW_DEPENDENCY_RESOLVER + +Configuration +============= + +.. autosummary:: + :toctree: stubs + :nosignatures: + + parsl.config.Config + parsl.set_stream_logger + parsl.set_file_logger + parsl.addresses.address_by_hostname + parsl.addresses.address_by_interface + parsl.addresses.address_by_query + parsl.addresses.address_by_route + parsl.addresses.get_all_addresses + parsl.addresses.get_any_address + parsl.utils.get_all_checkpoints + parsl.utils.get_last_checkpoint + +Channels +======== + +Channels are deprecated in Parsl. See +`issue 3515 `_ +for further discussion. + +Data management +=============== + +.. autosummary:: + :toctree: stubs + :nosignatures: + + parsl.app.futures.DataFuture + parsl.data_provider.data_manager.DataManager + parsl.data_provider.staging.Staging + parsl.data_provider.files.File + parsl.data_provider.ftp.FTPSeparateTaskStaging + parsl.data_provider.ftp.FTPInTaskStaging + parsl.data_provider.file_noop.NoOpFileStaging + parsl.data_provider.globus.GlobusStaging + parsl.data_provider.http.HTTPSeparateTaskStaging + parsl.data_provider.http.HTTPInTaskStaging + parsl.data_provider.rsync.RSyncStaging + +Executors +========= + +.. autosummary:: + :toctree: stubs + :nosignatures: + + parsl.executors.base.ParslExecutor + parsl.executors.status_handling.BlockProviderExecutor + parsl.executors.ThreadPoolExecutor + parsl.executors.HighThroughputExecutor + parsl.executors.MPIExecutor + parsl.executors.WorkQueueExecutor + parsl.executors.taskvine.TaskVineExecutor + parsl.executors.FluxExecutor + parsl.executors.radical.RadicalPilotExecutor + parsl.executors.GlobusComputeExecutor + +Manager Selectors +================= + +.. autosummary:: + :toctree: stubs + :nosignatures: + + parsl.executors.high_throughput.manager_selector.RandomManagerSelector + parsl.executors.high_throughput.manager_selector.BlockIdManagerSelector + +Launchers +========= + +.. autosummary:: + :toctree: stubs + :nosignatures: + + parsl.launchers.base.Launcher + parsl.launchers.SimpleLauncher + parsl.launchers.SingleNodeLauncher + parsl.launchers.SrunLauncher + parsl.launchers.AprunLauncher + parsl.launchers.SrunMPILauncher + parsl.launchers.GnuParallelLauncher + parsl.launchers.MpiExecLauncher + parsl.launchers.MpiRunLauncher + parsl.launchers.JsrunLauncher + parsl.launchers.WrappedLauncher + +Providers +========= + +.. autosummary:: + :toctree: stubs + :nosignatures: + + parsl.providers.AWSProvider + parsl.providers.CondorProvider + parsl.providers.GoogleCloudProvider + parsl.providers.GridEngineProvider + parsl.providers.LocalProvider + parsl.providers.LSFProvider + parsl.providers.SlurmProvider + parsl.providers.TorqueProvider + parsl.providers.KubernetesProvider + parsl.providers.PBSProProvider + parsl.providers.base.ExecutionProvider + parsl.providers.cluster_provider.ClusterProvider + +Batch jobs +========== + +.. autosummary:: + :toctree: stubs + :nosignatures: + + parsl.jobs.states.JobState + parsl.jobs.states.JobStatus + parsl.jobs.error_handlers.noop_error_handler + parsl.jobs.error_handlers.simple_error_handler + parsl.jobs.error_handlers.windowed_error_handler + +Checkpointing and Memoization +============================= + +.. autosummary:: + :toctree: stubs + :nosignatures: + + parsl.dataflow.memoization.Memoizer + parsl.dataflow.memoization.BasicMemoizer + +Exceptions +========== + +.. autosummary:: + :toctree: stubs + :nosignatures: + + parsl.app.errors.AppBadFormatting + parsl.app.errors.AppException + parsl.app.errors.AppTimeout + parsl.app.errors.BadStdStreamFile + parsl.app.errors.BashAppNoReturn + parsl.app.errors.BashExitFailure + parsl.app.errors.MissingOutputs + parsl.app.errors.ParslError + parsl.errors.ConfigurationError + parsl.errors.OptionalModuleMissing + parsl.executors.errors.ExecutorError + parsl.executors.errors.ScalingFailed + parsl.executors.errors.BadMessage + parsl.dataflow.errors.DataFlowException + parsl.dataflow.errors.BadCheckpoint + parsl.dataflow.errors.DependencyError + parsl.dataflow.errors.JoinError + parsl.launchers.errors.BadLauncher + parsl.providers.errors.ExecutionProviderException + parsl.providers.errors.ScaleOutFailed + parsl.providers.errors.SchedulerMissingArgs + parsl.providers.errors.ScriptPathError + parsl.executors.high_throughput.errors.WorkerLost + parsl.executors.high_throughput.errors.ManagerLost + parsl.serialize.errors.DeserializationError + parsl.serialize.errors.SerializationError + +Logging +======= + +.. autosummary:: + :toctree: stubs + :nosignatures: + + parsl.logconfigs.base.LogConfig + parsl.logconfigs.noop.NoopLogging + +Monitoring +========== + +.. autosummary:: + :toctree: stubs + :nosignatures: + + parsl.monitoring.MonitoringHub + parsl.monitoring.radios.base.MonitoringRadioReceiver + parsl.monitoring.radios.base.MonitoringRadioSender + parsl.monitoring.radios.base.RadioConfig + parsl.monitoring.radios.filesystem.FilesystemRadio + parsl.monitoring.radios.htex.HTEXRadio + parsl.monitoring.radios.udp.UDPRadio + parsl.monitoring.radios.multiprocessing.MultiprocessingQueueRadio + + +Internal +======== + +.. autosummary:: + :toctree: stubs + :nosignatures: + + parsl.app.app.AppBase + parsl.app.bash.BashApp + parsl.app.python.PythonApp + parsl.dataflow.dflow.DataFlowKernel + parsl.dataflow.memoization.id_for_memo + parsl.dataflow.states.FINAL_STATES + parsl.dataflow.states.States + parsl.dataflow.taskrecord.TaskRecord + parsl.jobs.job_status_poller.JobStatusPoller + parsl.jobs.strategy.Strategy + parsl.utils.Timer + +Task Vine configuration +======================= + +.. autosummary:: + :toctree: stubs + :nosignatures: + + parsl.executors.taskvine.TaskVineManagerConfig + parsl.executors.taskvine.TaskVineFactoryConfig diff --git a/parsl/docs/teaching_scripts/README.md b/parsl/docs/teaching_scripts/README.md new file mode 100644 index 00000000..67f80899 --- /dev/null +++ b/parsl/docs/teaching_scripts/README.md @@ -0,0 +1,3 @@ +# Example Scripts + +Scripts which illustrate example from the documentation that do not run well as part of the pytest diff --git a/parsl/docs/teaching_scripts/test_apps.py b/parsl/docs/teaching_scripts/test_apps.py new file mode 100644 index 00000000..f05a5f7f --- /dev/null +++ b/parsl/docs/teaching_scripts/test_apps.py @@ -0,0 +1,83 @@ +"""Tests documentation related to building apps. Must reside outside the Parsl library to be effective""" +from typing import List, Union + +import numpy as np + +import parsl +from parsl import Config, HighThroughputExecutor, python_app + +parsl.load(Config(executors=[HighThroughputExecutor(label='htex_spawn', max_workers_per_node=1, address='127.0.0.1')])) + + +# Part 1: Explain imports +# BAD: Assumes library has been imported +@python_app(executors=['htex_spawn']) +def bad_imports(x: Union[List[float], np.ndarray], m: float, b: float): + return np.multiply(x, m) + b + + +# GOOD: Imports libraries itself +@python_app(executors=['htex_spawn']) +def good_imports(x: Union[List[float], 'np.ndarray'], m: float, b: float): + import numpy as np + return np.multiply(x, m) + b + + +future = bad_imports([1.], 1, 0) + +try: + future.result() + raise ValueError() +except NameError as e: + print('Failed, as expected. Error:', e) + +future = good_imports([1.], 1, 0) +print(f'Passed, as expected: {future.result()}') + +# Part 2: Test other types of globals +# BAD: Uses global variables +global_var = {'a': 0} + + +@python_app +def bad_global(string: str, character: str = 'a'): + global_var[character] += string.count(character) # `global_var` will not be accessible + + +# GOOD +@python_app +def good_global(string: str, character: str = 'a'): + return {character: string.count(character)} + + +try: + bad_global('parsl').result() +except NameError as e: + print(f'Failed, as expected: {e}') + +for ch, co in good_global('parsl', 'a').result().items(): + global_var[ch] += co + + +# Part 3: Mutable args + +# BAD: Assumes changes to inputs will be communicated +@python_app +def append_to_list(input_list: list, new_val): + input_list.append(new_val) + + +mutable_arg = [] +append_to_list(mutable_arg, 1).result() +assert mutable_arg == [], 'The list _was_changed' + + +# GOOD: Changes to inputs are returned +@python_app +def append_to_list(input_list: list, new_val) -> list: + input_list.append(new_val) + return input_list + + +mutable_arg = append_to_list(mutable_arg, 1).result() +assert mutable_arg == [1] diff --git a/parsl/docs/userguide/advanced/data.rst b/parsl/docs/userguide/advanced/data.rst new file mode 100644 index 00000000..7fc89966 --- /dev/null +++ b/parsl/docs/userguide/advanced/data.rst @@ -0,0 +1,416 @@ +.. _label-data: + +Staging data files +================== + +Parsl apps can take and return data files. A file may be passed as an input +argument to an app, or returned from an app after execution. Parsl +provides support to automatically transfer (stage) files between +the main Parsl program, worker nodes, and external data storage systems. + +Input files can be passed as regular arguments, or a list of them may be +specified in the special ``inputs`` keyword argument to an app invocation. + +Inside an app, the ``filepath`` attribute of a `File` can be read to determine +where on the execution-side file system the input file has been placed. + +Output `File` objects must also be passed in at app invocation, through the +outputs parameter. In this case, the `File` object specifies where Parsl +should place output after execution. + +Inside an app, the ``filepath`` attribute of an output +`File` provides the path at which the corresponding output file should be +placed so that Parsl can find it after execution. + +If the output from an app is to be used as the input to a subsequent app, +then a `DataFuture` that represents whether the output file has been created +must be extracted from the first app's AppFuture, and that must be passed +to the second app. This causes app +executions to be properly ordered, in the same way that passing AppFutures +to subsequent apps causes execution ordering based on an app returning. + +In a Parsl program, file handling is split into two pieces: files are named in an +execution-location independent manner using :py:class:`~parsl.data_provider.files.File` +objects, and executors are configured to stage those files in to and out of +execution locations using instances of the :py:class:`~parsl.data_provider.staging.Staging` +interface. + + +Parsl files +----------- + +Parsl uses a custom :py:class:`~parsl.data_provider.files.File` to provide a +location-independent way of referencing and accessing files. +Parsl files are defined by specifying the URL *scheme* and a path to the file. +Thus a file may represent an absolute path on the submit-side file system +or a URL to an external file. + +The scheme defines the protocol via which the file may be accessed. +Parsl supports the following schemes: file, ftp, http, https, and globus. +If no scheme is specified Parsl will default to the file scheme. + +The following example shows creation of two files with different +schemes: a locally-accessible data.txt file and an HTTPS-accessible +README file. + +.. code-block:: python + + File('file://home/parsl/data.txt') + File('https://github.com/Parsl/parsl/blob/master/README.rst') + + +Parsl automatically translates the file's location relative to the +environment in which it is accessed (e.g., the Parsl program or an app). +The following example shows how a file can be accessed in the app +irrespective of where that app executes. + +.. code-block:: python + + @python_app + def print_file(inputs=()): + with open(inputs[0].filepath, 'r') as inp: + content = inp.read() + return(content) + + # create an remote Parsl file + f = File('https://github.com/Parsl/parsl/blob/master/README.rst') + + # call the print_file app with the Parsl file + r = print_file(inputs=[f]) + r.result() + +As described below, the method by which this files are transferred +depends on the scheme and the staging providers specified in the Parsl +configuration. + +Staging providers ++++++++++++++++++ + +Parsl is able to transparently stage files between at-rest locations and +execution locations by specifying a list of +:py:class:`~parsl.data_provider.staging.Staging` instances for an executor. +These staging instances define how to transfer files in and out of an execution +location. This list should be supplied as the ``storage_access`` +parameter to an executor when it is constructed. + +Parsl includes several staging providers for moving files using the +schemes defined above. By default, Parsl executors are created with +three common staging providers: +the NoOpFileStaging provider for local and shared file systems +and the HTTP(S) and FTP staging providers for transferring +files to and from remote storage locations. The following +example shows how to explicitly set the default staging providers. + +.. code-block:: python + + from parsl.config import Config + from parsl.executors import HighThroughputExecutor + from parsl.data_provider.data_manager import default_staging + + config = Config( + executors=[ + HighThroughputExecutor( + storage_access=default_staging, + # equivalent to the following + # storage_access=[NoOpFileStaging(), FTPSeparateTaskStaging(), HTTPSeparateTaskStaging()], + ) + ] + ) + + +Parsl further differentiates when staging occurs relative to +the app invocation that requires or produces files. +Staging either occurs with the executing task (*in-task staging*) +or as a separate task (*separate task staging*) before app execution. +In-task staging +uses a wrapper that is executed around the Parsl task and thus +occurs on the resource on which the task is executed. Separate +task staging inserts a new Parsl task in the graph and associates +a dependency between the staging task and the task that depends +on that file. Separate task staging may occur on either the submit-side +(e.g., when using Globus) or on the execution-side (e.g., HTTPS, FTP). + + +NoOpFileStaging for Local/Shared File Systems +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The NoOpFileStaging provider assumes that files specified either +with a path or with the ``file`` URL scheme are available both +on the submit and execution side. This occurs, for example, when there is a +shared file system. In this case, files will not moved, and the +File object simply presents the same file path to the Parsl program +and any executing tasks. + +Files defined as follows will be handled by the NoOpFileStaging provider. + +.. code-block:: python + + File('file://home/parsl/data.txt') + File('/home/parsl/data.txt') + + +The NoOpFileStaging provider is enabled by default on all +executors. It can be explicitly set as the only +staging provider as follows. + +.. code-block:: python + + from parsl.config import Config + from parsl.executors import HighThroughputExecutor + from parsl.data_provider.file_noop import NoOpFileStaging + + config = Config( + executors=[ + HighThroughputExecutor( + storage_access=[NoOpFileStaging()] + ) + ] + ) + + +FTP, HTTP, HTTPS: separate task staging +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Files named with the ``ftp``, ``http`` or ``https`` URL scheme will be +staged in using HTTP GET or anonymous FTP commands. These commands +will be executed as a separate +Parsl task that will complete before the corresponding app +executes. These providers cannot be used to stage out output files. + +The following example defines a file accessible on a remote FTP server. + +.. code-block:: python + + File('ftp://www.iana.org/pub/mirror/rirstats/arin/ARIN-STATS-FORMAT-CHANGE.txt') + +When such a file object is passed as an input to an app, Parsl will download the file to whatever location is selected for the app to execute. +The following example illustrates how the remote file is implicitly downloaded from an FTP server and then converted. Note that the app does not need to know the location of the downloaded file on the remote computer, as Parsl abstracts this translation. + +.. code-block:: python + + @python_app + def convert(inputs=(), outputs=()): + with open(inputs[0].filepath, 'r') as inp: + content = inp.read() + with open(outputs[0].filepath, 'w') as out: + out.write(content.upper()) + + # create an remote Parsl file + inp = File('ftp://www.iana.org/pub/mirror/rirstats/arin/ARIN-STATS-FORMAT-CHANGE.txt') + + # create a local Parsl file + out = File('file:///tmp/ARIN-STATS-FORMAT-CHANGE.txt') + + # call the convert app with the Parsl file + f = convert(inputs=[inp], outputs=[out]) + f.result() + +HTTP and FTP separate task staging providers can be configured as follows. + +.. code-block:: python + + from parsl.config import Config + from parsl.executors import HighThroughputExecutor + from parsl.data_provider.http import HTTPSeparateTaskStaging + from parsl.data_provider.ftp import FTPSeparateTaskStaging + + config = Config( + executors=[ + HighThroughputExecutor( + storage_access=[HTTPSeparateTaskStaging(), FTPSeparateTaskStaging()] + ) + ] + ) + +FTP, HTTP, HTTPS: in-task staging +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +These staging providers are intended for use on executors that do not have +a file system shared between each executor node. + +These providers will use the same HTTP GET/anonymous FTP as the separate +task staging providers described above, but will do so in a wrapper around +individual app invocations, which guarantees that they will stage files to +a file system visible to the app. + +A downside of this staging approach is that the staging tasks are less visible +to Parsl, as they are not performed as separate Parsl tasks. + +In-task staging providers can be configured as follows. + +.. code-block:: python + + from parsl.config import Config + from parsl.executors import HighThroughputExecutor + from parsl.data_provider.http import HTTPInTaskStaging + from parsl.data_provider.ftp import FTPInTaskStaging + + config = Config( + executors=[ + HighThroughputExecutor( + storage_access=[HTTPInTaskStaging(), FTPInTaskStaging()] + ) + ] + ) + + +Globus +^^^^^^ + +The ``Globus`` staging provider is used to transfer files that can be accessed +using Globus. A guide to using Globus is available `here +`_). + +A file using the Globus scheme must specify the UUID of the Globus +endpoint and a path to the file on the endpoint, for example: + +.. code-block:: python + + File('globus://037f054a-15cf-11e8-b611-0ac6873fc732/unsorted.txt') + +Note: a Globus endpoint's UUID can be found in the Globus `Manage Endpoints `_ page. + +There must also be a Globus endpoint available with access to a +execute-side file system, because Globus file transfers happen +between two Globus endpoints. + +Globus Configuration +"""""""""""""""""""" + +In order to manage where files are staged, users must configure the default ``working_dir`` on a remote location. This information is specified in the :class:`~parsl.executors.base.ParslExecutor` via the ``working_dir`` parameter in the :class:`~parsl.config.Config` instance. For example: + +.. code-block:: python + + from parsl.config import Config + from parsl.executors import HighThroughputExecutor + + config = Config( + executors=[ + HighThroughputExecutor( + working_dir="/home/user/data" + ) + ] + ) + +Parsl requires knowledge of the Globus endpoint that is associated with an executor. This is done by specifying the ``endpoint_name`` (the UUID of the Globus endpoint that is associated with the system) in the configuration. + +In some cases, for example when using a Globus `shared endpoint `_ or when a Globus endpoint is mounted on a supercomputer, the path seen by Globus is not the same as the local path seen by Parsl. In this case the configuration may optionally specify a mapping between the ``endpoint_path`` (the common root path seen in Globus), and the ``local_path`` (the common root path on the local file system), as in the following. In most cases, ``endpoint_path`` and ``local_path`` are the same and do not need to be specified. + +.. code-block:: python + + from parsl.config import Config + from parsl.executors import HighThroughputExecutor + from parsl.data_provider.globus import GlobusStaging + from parsl.data_provider.data_manager import default_staging + + config = Config( + executors=[ + HighThroughputExecutor( + working_dir="/home/user/parsl_script", + storage_access=default_staging + [GlobusStaging( + endpoint_uuid="7d2dc622-2edb-11e8-b8be-0ac6873fc732", + endpoint_path="/", + local_path="/home/user" + )] + ) + ] + ) + + +Globus Authorization +"""""""""""""""""""" + +In order to transfer files with Globus, the user must first authenticate. +The first time that Globus is used with Parsl on a computer, the program +will prompt the user to follow an authentication and authorization +procedure involving a web browser. Users can authorize out of band by +running the parsl-globus-auth utility. This is useful, for example, +when running a Parsl program in a batch system where it will be unattended. + +.. code-block:: bash + + $ parsl-globus-auth + Parsl Globus command-line authorizer + If authorization to Globus is necessary, the library will prompt you now. + Otherwise it will do nothing + Authorization complete + +rsync +^^^^^ + +The ``rsync`` utility can be used to transfer files in the ``file`` scheme in configurations where +workers cannot access the submit-side file system directly, such as when executing +on an AWS EC2 instance or on a cluster without a shared file system. +However, the submit-side file system must be exposed using rsync. + +rsync Configuration +""""""""""""""""""" + +``rsync`` must be installed on both the submit and worker side. It can usually be installed +by using the operating system package manager: for example, by ``apt-get install rsync``. + +An `RSyncStaging` option must then be added to the Parsl configuration file, as in the following. +The parameter to RSyncStaging should describe the prefix to be passed to each rsync +command to connect from workers to the submit-side host. This will often be the username +and public IP address of the submitting system. + +.. code-block:: python + + from parsl.data_provider.rsync import RSyncStaging + + config = Config( + executors=[ + HighThroughputExecutor( + storage_access=[HTTPInTaskStaging(), FTPInTaskStaging(), RSyncStaging("benc@" + public_ip)], + ... + ) + ) + +rsync Authorization +""""""""""""""""""" + +The rsync staging provider delegates all authentication and authorization to the +underlying ``rsync`` command. This command must be correctly authorized to connect back to +the submit-side system. The form of this authorization will depend on the systems in +question. + +The following example installs an ssh key from the submit-side file system and turns off host key +checking, in the ``worker_init`` initialization of an EC2 instance. The ssh key must have +sufficient privileges to run ``rsync`` over ssh on the submit-side system. + +.. code-block:: python + + with open("rsync-callback-ssh", "r") as f: + private_key = f.read() + + ssh_init = """ + mkdir .ssh + chmod go-rwx .ssh + + cat > .ssh/id_rsa < .ssh/config < List[Tuple[bool, ...]]: + """Convert a list of nonnegative integers to binary""" + return [convert_to_binary(x) for x in xs] diff --git a/parsl/docs/userguide/advanced/examples/library/config.py b/parsl/docs/userguide/advanced/examples/library/config.py new file mode 100644 index 00000000..4b2301fb --- /dev/null +++ b/parsl/docs/userguide/advanced/examples/library/config.py @@ -0,0 +1,23 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.providers import LocalProvider + + +def make_local_config(cores_per_worker: int = 1) -> Config: + """Generate a configuration which runs all tasks on the local system + + Args: + cores_per_worker: Number of cores to dedicate for each task + Returns: + Configuration object with the requested settings + """ + return Config( + executors=[ + HighThroughputExecutor( + label="htex_local", + cores_per_worker=cores_per_worker, + cpu_affinity='block', + provider=LocalProvider(), + ) + ], + ) diff --git a/parsl/docs/userguide/advanced/examples/library/logic.py b/parsl/docs/userguide/advanced/examples/library/logic.py new file mode 100644 index 00000000..769e6158 --- /dev/null +++ b/parsl/docs/userguide/advanced/examples/library/logic.py @@ -0,0 +1,15 @@ +from typing import Tuple + + +def convert_to_binary(x: int) -> Tuple[bool, ...]: + """Convert a nonnegative integer into a binary + + Args: + x: Number to be converted + Returns: + The binary number represented as list of booleans + """ + if x < 0: + raise ValueError('`x` must be nonnegative') + bin_as_string = bin(x) + return tuple(i == '1' for i in bin_as_string[2:]) diff --git a/parsl/docs/userguide/advanced/examples/pyproject.toml b/parsl/docs/userguide/advanced/examples/pyproject.toml new file mode 100644 index 00000000..4c1639bf --- /dev/null +++ b/parsl/docs/userguide/advanced/examples/pyproject.toml @@ -0,0 +1,7 @@ +[project] +name = "library" +version = '0.0.0' +description = 'Example library for Parsl documentation' + +[tool.setuptools.packages.find] +include = ['library*'] diff --git a/parsl/docs/userguide/advanced/examples/run.py b/parsl/docs/userguide/advanced/examples/run.py new file mode 100644 index 00000000..9ca5b402 --- /dev/null +++ b/parsl/docs/userguide/advanced/examples/run.py @@ -0,0 +1,35 @@ +from argparse import ArgumentParser + +import parsl + +from library.config import make_local_config +from library.app import convert_many_to_binary +from parsl.app.python import PythonApp + +# Protect the script from running twice. +# See "Safe importing of main module" in Python multiprocessing docs +# https://docs.python.org/3/library/multiprocessing.html#multiprocessing-programming +if __name__ == "__main__": + # Get user instructions + parser = ArgumentParser() + parser.add_argument('--numbers-per-batch', default=8, type=int) + parser.add_argument('numbers', nargs='+', type=int) + args = parser.parse_args() + + # Prepare the workflow functions + convert_app = PythonApp(convert_many_to_binary, cache=False) + + # Load the configuration + # As a context manager so resources are shutdown on exit + with parsl.load(make_local_config()): + + # Spawn tasks + futures = [ + convert_app(args.numbers[start:start + args.numbers_per_batch]) + for start in range(0, len(args.numbers), args.numbers_per_batch) + ] + + # Retrieve task results + for future in futures: + for x, b in zip(future.task_record['args'][0], future.result()): + print(f'{x} -> {"".join("1" if i else "0" for i in b)}') diff --git a/parsl/docs/userguide/advanced/index.rst b/parsl/docs/userguide/advanced/index.rst new file mode 100644 index 00000000..758928c2 --- /dev/null +++ b/parsl/docs/userguide/advanced/index.rst @@ -0,0 +1,14 @@ +Advanced Topics +=============== + +More to learn about Parsl after starting a project. + +.. toctree:: + :maxdepth: 2 + + modularizing + usage_tracking + monitoring + data + parsl_perf + plugins diff --git a/parsl/docs/userguide/advanced/modularizing.rst b/parsl/docs/userguide/advanced/modularizing.rst new file mode 100644 index 00000000..143a4ebc --- /dev/null +++ b/parsl/docs/userguide/advanced/modularizing.rst @@ -0,0 +1,109 @@ +.. _codebases: + +Structuring Parsl programs +-------------------------- + +While convenient to build simple Parsl programs as a single Python file, +splitting a Parsl programs into multiple files and a Python module +has significant benefits, including: + + 1. Better readability + 2. Logical separation of components (e.g., apps, config, and control logic) + 3. Ease of reuse of components + +Large applications that use Parsl often divide into several core components: + +.. contents:: + :local: + :depth: 2 + +The following sections use an example where each component is in a separate file: + +.. code-block:: + + examples/logic.py + examples/app.py + examples/config.py + examples/__init__.py + run.py + pyproject.toml + +Run the application by first installing the Python library and then executing the "run.py" script. + +.. code-block:: bash + + pip install . # Install module so it can be imported by workers + python run.py + + +Core application logic +====================== + +The core application logic should be developed without any deference to Parsl. +Implement capabilities, write unit tests, and prepare documentation +in which ever way works best for the problem at hand. + +Parallelization with Parsl will be easy if the software already follows best practices. + +The example defines a function to convert a single integer into binary. + +.. literalinclude:: examples/library/logic.py + :caption: library/logic.py + +Workflow functions +================== + +Tasks within a workflow may require unique combinations of core functions. +Functions to be run in parallel must also meet :ref:`specific requirements ` +that may complicate writing the core logic effectively. +As such, separating functions to be used as Apps is often beneficial. + +The example includes a function to convert many integers into binary. + +Key points to note: + +- It is not necessary to have import statements inside the function. + Parsl will serialize this function by reference, as described in :ref:`functions-from-modules`. + +- The function is not yet marked as a Parsl PythonApp. + Keeping Parsl out of the function definitions simplifies testing + because you will not need to run Parsl when testing the code. + +- *Advanced*: Consider including Parsl decorators in the library if using complex workflow patterns, + such as :ref:`join apps ` or functions which take :ref:`special arguments `. + +.. literalinclude:: examples/library/app.py + :caption: library/app.py + + +Parsl configuration functions +============================= + +Create Parsl configurations specific to your application needs as functions. +While not necessary, including the Parsl configuration functions inside the module +ensures they can be imported into other scripts easily. + +Generating Parsl :class:`~parsl.config.Config` objects from a function +makes it possible to change the configuration without editing the module. + +The example function provides a configuration suited for a single node. + +.. literalinclude:: examples/library/config.py + :caption: library/config.py + +Orchestration Scripts +===================== + +The last file defines the workflow itself. + +Such orchestration scripts, at minimum, perform at least four tasks: + +1. *Load execution options* using a tool like :mod:`argparse`. +2. *Prepare workflow functions for execution* by creating :class:`~parsl.app.python.PythonApp` wrappers over each function. +3. *Create configuration then start Parsl* with the :meth:`parsl.load` function. +4. *Launch tasks and retrieve results* depending on the needs of the application. + +An example run script is as follows + +.. literalinclude:: examples/run.py + :caption: run.py diff --git a/parsl/docs/userguide/advanced/monitoring.rst b/parsl/docs/userguide/advanced/monitoring.rst new file mode 100644 index 00000000..cdf38f14 --- /dev/null +++ b/parsl/docs/userguide/advanced/monitoring.rst @@ -0,0 +1,121 @@ +.. _label-monitoring: + +Monitoring +========== + +Parsl includes a monitoring system to capture task state as well as resource +usage over time. The Parsl monitoring system aims to provide detailed +information and diagnostic capabilities to help track the state of your +programs, down to the individual apps that are executed on remote machines. + +The monitoring system records information to an SQLite database while a +workflow runs. This information can then be visualised in a web dashboard +using the ``parsl-visualize`` tool, or queried using SQL using regular +SQLite tools. + + +Monitoring configuration +------------------------ + +The following example shows how to enable monitoring in the Parsl +configuration. Here the `parsl.monitoring.MonitoringHub` is specified to use port +55055 to receive monitoring messages from workers every 10 seconds. + +.. code-block:: python + + import parsl + from parsl.monitoring.monitoring import MonitoringHub + from parsl.config import Config + from parsl.executors import HighThroughputExecutor + from parsl.addresses import address_by_hostname + + import logging + + config = Config( + executors=[ + HighThroughputExecutor( + label="local_htex", + cores_per_worker=1, + max_workers_per_node=4, + address=address_by_hostname(), + ) + ], + monitoring=MonitoringHub( + hub_address=address_by_hostname(), + monitoring_debug=False, + resource_monitoring_interval=10, + ), + strategy='none' + ) + + +Visualization +------------- + +To run the web dashboard utility ``parsl-visualize`` you first need to install +its dependencies: + + $ pip install 'parsl[monitoring,visualization]' + +To view the web dashboard while or after a Parsl program has executed, run +the ``parsl-visualize`` utility:: + + $ parsl-visualize + +By default, this command expects that the default ``monitoring.db`` database is used +in the runinfo directory. Other databases can be loaded by passing +the database URI on the command line. For example, if the full path +to the database is ``/tmp/my_monitoring.db``, run:: + + $ parsl-visualize sqlite:////tmp/my_monitoring.db + +By default, the visualization web server listens on ``127.0.0.1:8080``. If the web server is deployed on a machine with a web browser, the dashboard can be accessed in the browser at ``127.0.0.1:8080``. If the web server is deployed on a remote machine, such as the login node of a cluster, you will need to use an ssh tunnel from your local machine to the cluster:: + + $ ssh -L 50000:127.0.0.1:8080 username@cluster_address + +This command will bind your local machine's port 50000 to the remote cluster's port 8080. +The dashboard can then be accessed via the local machine's browser at ``127.0.0.1:50000``. + +.. warning:: Alternatively you can deploy the visualization server on a public interface. However, first check that this is allowed by the cluster's security policy. The following example shows how to deploy the web server on a public port (i.e., open to Internet via ``public_IP:55555``):: + + $ parsl-visualize --listen 0.0.0.0 --port 55555 + + +Workflows Page +^^^^^^^^^^^^^^ + +The workflows page lists all Parsl workflows that have been executed with monitoring enabled +with the selected database. +It provides a high level summary of workflow state as shown below: + +.. image:: ../../images/mon_workflows_page.png + +Throughout the dashboard, all blue elements are clickable. For example, clicking a specific worklow +name from the table takes you to the Workflow Summary page described in the next section. + +Workflow Summary +^^^^^^^^^^^^^^^^ + +The workflow summary page captures the run level details of a workflow, including start and end times +as well as task summary statistics. The workflow summary section is followed by the *App Summary* that lists +the various apps and invocation count for each. + +.. image:: ../../images/mon_workflow_summary.png + + +The workflow summary also presents three different views of the workflow: + +* Workflow DAG - with apps differentiated by colors: This visualization is useful to visually inspect the dependency + structure of the workflow. Hovering over the nodes in the DAG shows a tooltip for the app represented by the node and it's task ID. + +.. image:: ../../images/mon_task_app_grouping.png + +* Workflow DAG - with task states differentiated by colors: This visualization is useful to identify what tasks have been completed, failed, or are currently pending. + +.. image:: ../../images/mon_task_state_grouping.png + +* Workflow resource usage: This visualization provides resource usage information at the workflow level. + For example, cumulative CPU/Memory utilization across workers over time. + +.. image:: ../../images/mon_resource_summary.png + diff --git a/parsl/docs/userguide/advanced/parsl_perf.rst b/parsl/docs/userguide/advanced/parsl_perf.rst new file mode 100644 index 00000000..c2ecd51f --- /dev/null +++ b/parsl/docs/userguide/advanced/parsl_perf.rst @@ -0,0 +1,75 @@ +.. _label-parsl-perf: + +Measuring performance with parsl-perf +===================================== + +``parsl-perf`` is tool for making basic performance measurements of Parsl +configurations. + +It runs repeated batches of no-op apps, giving a measurement of tasks per +second. By default, ``parsl-perf`` will attempt to estimate a batch size +that will take 2 minutes to execute. + +This can give a basic measurement of some of the overheads in task +execution. + +``parsl-perf`` must be invoked with a configuration file, which is a Python +file containing a variable ``config`` which contains a `Config` object, or +a function ``fresh_config`` which returns a `Config` object. The +``fresh_config`` format is the same as used with the pytest test suite. + +To specify a ``parsl_resource_specification`` for tasks, add a ``--resources`` +argument. + +To change the target runtime from the default of 120 seconds, add a +``--time`` parameter. + +To change the iteration algorithm used to pick the next batch size and to +decide when to stop, use the ``--iterate`` parameter: + + * ``--iterate=estimate`` (the default) will run a small batch, then + repeatedly re-estimate a new batch size until it finds one that takes at least + 75% of the target run time. This will rapidly find a batch size of around + the target size. + + * ``--iterate=exponential`` will start with a small batch, then double the size + in each subsequent batch until reaching the target run time. This is useful + for understanding how a configuration scales down as batch sizes increase, + which is sometimes sub-linear, and to get more consistent batch sizes across + runs of ``parsl-perf``. + + * ``--iterate=10,10000,10000`` (or any other sequence of batch sizes) will run a + fixed sequence of batches: in this example, first a batch of 10 tasks, then + two batches of 10000 tasks. This is useful for making tests that compare + fixed batch sizes along with warmup steps between different configurations, + without the additional batches of ``estimate`` and ``exponential`` to discover + time-sized (rather than count-sized) batch sizes. + +For example: + +.. code-block:: bash + + + $ python -m parsl.benchmark.perf --config parsl/tests/configs/workqueue_ex.py --resources '{"cores":1, "memory":0, "disk":0}' + ==== Iteration 1 ==== + Will run 10 tasks to target 120 seconds runtime + Submitting tasks / invoking apps + warning: using plain-text when communicating with workers. + warning: use encryption with a key and cert when creating the manager. + All 10 tasks submitted ... waiting for completion + Submission took 0.008 seconds = 1248.676 tasks/second + Runtime: actual 3.668s vs target 120s + Tasks per second: 2.726 + + [...] + + ==== Iteration 4 ==== + Will run 57640 tasks to target 120 seconds runtime + Submitting tasks / invoking apps + All 57640 tasks submitted ... waiting for completion + Submission took 34.839 seconds = 1654.487 tasks/second + Runtime: actual 364.387s vs target 120s + Tasks per second: 158.184 + Cleaning up DFK + The end + diff --git a/parsl/docs/userguide/advanced/plugins.rst b/parsl/docs/userguide/advanced/plugins.rst new file mode 100644 index 00000000..75a81541 --- /dev/null +++ b/parsl/docs/userguide/advanced/plugins.rst @@ -0,0 +1,118 @@ +Plugins +======= + +Parsl has several places where code can be plugged in. Parsl usually provides +several implementations that use each plugin point. + +This page gives a brief summary of those places and why you might want +to use them, with links to the API guide. + +Executors +--------- +When the parsl dataflow kernel is ready for a task to run, it passes that +task to an `ParslExecutor`. The executor is then responsible for running the task's +Python code and returning the result. This is the abstraction that allows one +executor to run code on the local submitting host, while another executor can +run the same code on a large supercomputer. + + +Providers and Launchers +----------------------- +Some executors are based on blocks of workers (for example the +`parsl.executors.HighThroughputExecutor`: the submit side requires a +batch system (eg slurm, kubernetes) to start worker processes, which then +execute tasks. + +The particular way in which a system makes those workers start is implemented +by providers and launchers. + +An `ExecutionProvider` allows a command line to be submitted as a request to the +underlying batch system to be run inside an allocation of nodes. + +A `Launcher` modifies that command line when run inside the allocation to +add on any wrappers that are needed to launch the command (eg srun inside +slurm). Providers and launchers are usually paired together for a particular +system type. + +File staging +------------ +Parsl can copy input files from an arbitrary URL into a task's working +environment, and copy output files from a task's working environment to +an arbitrary URL. A small set of data staging providers is installed by default, +for ``file://`` ``http://`` and ``ftp://`` URLs. More data staging providers can +be added in the workflow configuration, in the ``storage`` parameter of the +relevant `ParslExecutor`. Each provider should subclass the `Staging` class. + + +Default stdout/stderr name generation +------------------------------------- +Parsl can choose names for your bash apps stdout and stderr streams +automatically, with the parsl.AUTO_LOGNAME parameter. The choice of path is +made by a function which can be configured with the ``std_autopath`` +parameter of Parsl `Config`. By default, ``DataFlowKernel.default_std_autopath`` +will be used. + + +Memoization/checkpointing +------------------------- + +The default checkpoint/memoization system can be replaced by supplying an +instance of the `Memoizer` class. This instance is queried before an app is +launched, and is given details of completed apps. + +When parsl memoizes/checkpoints an app parameter, it does so by computing a +hash of that parameter that should be the same if that parameter is the same +on subsequent invocations. This isn't straightforward to do for arbitrary +objects, so parsl implements a checkpointing hash function for a few common +types, and raises an exception on unknown types: + +.. code-block:: + + ValueError("unknown type for memoization ...") + +You can plug in your own type-specific hash code for additional types that +you need and understand using `id_for_memo`. + + +Invoking other asynchronous components +-------------------------------------- + +Parsl code can invoke other asynchronous components which return Futures, and +integrate those Futures into the task graph: Parsl apps can be given any +`concurrent.futures.Future` as a dependency, even if those futures do not come +from invoking a Parsl app. This includes as the return value of a +``join_app``. + +An specific example of this is integrating Globus Compute tasks into a Parsl +task graph. See :ref:`label-join-globus-compute` + +Dependency resolution +--------------------- + +When Parsl examines the arguments to an app, it uses a `DependencyResolver`. +The default `DependencyResolver` will cause Parsl to wait for +``concurrent.futures.Future`` instances (including `AppFuture` and +`DataFuture`), and pass through other arguments without waiting. + +This behaviour is pluggable: Parsl comes with another dependency resolver, +`DEEP_DEPENDENCY_RESOLVER` which knows about futures contained with structures +such as tuples, lists, sets and dicts. + +This plugin interface might be used to interface other task-like or future-like +objects to the Parsl dependency mechanism, by describing how they can be +interpreted as a Future. + +Log configuration +----------------- + +The `LogConfig` class provides a place for log configuration, which is planned +to extend across the various Python processes involved in a workflow +execution. Right now, it is only used by the ``initialize_logging`` parameter of +the `DataFlowKernel`. + +Removed interfaces +------------------ + +Parsl had a deprecated ``Channel`` abstraction. See +`issue 3515 `_ +for further discussion on its removal. diff --git a/parsl/docs/userguide/advanced/usage_tracking.rst b/parsl/docs/userguide/advanced/usage_tracking.rst new file mode 100644 index 00000000..da8ac9b7 --- /dev/null +++ b/parsl/docs/userguide/advanced/usage_tracking.rst @@ -0,0 +1,171 @@ +.. _label-usage-tracking: + +Usage Statistics Collection +=========================== + +Parsl uses an **Opt-in** model for usage tracking, allowing users to decide if they wish to participate. Usage statistics are crucial for improving software reliability and help focus development and maintenance efforts on the most used components of Parsl. The collected data is used solely for enhancements and reporting and is not shared in its raw form outside of the Parsl team. + +Why are we doing this? +---------------------- + +The Parsl development team relies on funding from government agencies. To sustain this funding and advocate for continued support, it is essential to show that the research community benefits from these investments. + +By opting in to share usage data, you actively support the ongoing development and maintenance of Parsl. (See:ref:`What is sent? ` below). + +Opt-In Model +------------ + +We use an **opt-in model** for usage tracking to respect user privacy and provide full control over shared information. We hope that developers and researchers will choose to send us this information. The reason is that we need this data - it is a requirement for funding. + +Choose the data you share with Usage Tracking Levels. + +**Usage Tracking Levels:** + +* **Level 1:** Only basic information such as Python version, Parsl version, and platform name (Linux, MacOS, etc.) +* **Level 2:** Level 1 information and configuration information including provider, executor, and launcher names. +* **Level 3:** Level 2 information and workflow execution details, including the number of applications run, failures, and execution time. + +By enabling usage tracking, you support Parsl's development. + +**To opt-in, set** ``usage_tracking`` **to the desired level (1, 2, or 3) in the configuration object** (``parsl.config.Config``) **.** + +Example: + +.. code-block:: python3 + + config = Config( + executors=[ + HighThroughputExecutor( + ... + ) + ], + usage_tracking=3 + ) + +.. _what-is-sent: + +What is sent? +------------- + +The data collected depends on the tracking level selected: + +* **Level 1:** Only basic information such as Python version, Parsl version, and platform name (Linux, MacOS, etc.) +* **Level 2:** Level 1 information and configuration information including provider, executor, and launcher names. +* **Level 3:** Level 2 information and workflow execution details, including the number of applications run, failures, and execution time. + +**Example Messages:** + +- At launch: + + .. code-block:: json + + { + "correlator":"6bc7484e-5693-48b2-b6c0-5889a73f7f4e", + "parsl_v":"1.3.0-dev", + "python_v":"3.12.2", + "platform.system":"Darwin", + "tracking_level":3, + "components":[ + { + "c":"parsl.config.Config", + "executors_len":1, + "dependency_resolver":false + }, + "parsl.executors.threads.ThreadPoolExecutor" + ], + "start":1727156153 + } + +- On closure (Tracking Level 3 only): + + .. code-block:: json + + { + "correlator":"6bc7484e-5693-48b2-b6c0-5889a73f7f4e", + "execution_time":31, + "components":[ + { + "c":"parsl.dataflow.dflow.DataFlowKernel", + "app_count":3, + "app_fails":0 + }, + { + "c":"parsl.config.Config", + "executors_len":1, + "dependency_resolver":false + }, + "parsl.executors.threads.ThreadPoolExecutor" + ], + "end":1727156156 + } + +**All messages sent are logged in the** ``parsl.log`` **file, ensuring complete transparency.** + +How is the data sent? +--------------------- + +Data is sent using **UDP** to minimize the impact on workflow performance. While this may result in some data loss, it significantly reduces the chances of usage tracking affecting the software's operation. + +The data is processed through AWS CloudWatch to generate a monitoring dashboard, providing valuable insights into usage patterns. + +When is the data sent? +---------------------- + +Data is sent twice per run: + +1. At the start of the script. +2. Upon script completion (for Tracking Level 3). + +What will the data be used for? +------------------------------- + +The data will help the Parsl team understand Parsl usage and make development and maintenance decisions, including: + +* Focus development and maintenance on the most-used components of Parsl. +* Determine which Python versions to continue supporting. +* Track the age of Parsl installations. +* Assess how long it takes for most users to adopt new changes. +* Track usage statistics to report to funders. + +Usage Statistics Dashboard +-------------------------- + +The collected data is aggregated and displayed on a publicly accessible dashboard. This dashboard provides an overview of how Parsl is being used across different environments and includes metrics such as: + +* Total workflows executed over time +* Most-used Python and Parsl versions +* Most common platforms and executors and more + +`Find the dashboard here `_ + +Leaderboard +----------- + +**Opting in to usage tracking also allows you to participate in the Parsl Leaderboard. +To participate in the leaderboard, you can deanonymize yourself using the** ``project_name`` **parameter in the parsl configuration object** (``parsl.config.Config``) **.** + +`Find the Parsl Leaderboard here `_ + +Example: + +.. code-block:: python3 + + config = Config( + executors=[ + HighThroughputExecutor( + ... + ) + ], + usage_tracking=3, + project_name="my-test-project" + ) + +Every run of parsl with usage tracking **Level 1** or **Level 2** earns you **1 point**. And every run with usage tracking **Level 3**, earns you **2 points**. + +Feedback +-------- + +Please send us your feedback at parsl@googlegroups.com. Feedback from our user communities will be +useful in determining our path forward with usage tracking in the future. + +**Please consider turning on usage tracking to support the continued development of Parsl.** diff --git a/parsl/docs/userguide/app.rst b/parsl/docs/userguide/app.rst new file mode 100644 index 00000000..5e58276c --- /dev/null +++ b/parsl/docs/userguide/app.rst @@ -0,0 +1,9 @@ +:orphan: + +.. meta:: + :content http-equiv="refresh": 0;url=apps/index.html + +Redirect +-------- + +This page has been `moved `_ diff --git a/parsl/docs/userguide/apps/bash.rst b/parsl/docs/userguide/apps/bash.rst new file mode 100644 index 00000000..9f99eb4d --- /dev/null +++ b/parsl/docs/userguide/apps/bash.rst @@ -0,0 +1,66 @@ + +Bash Apps +--------- + +.. code-block:: python + + @bash_app + def echo( + name: str, + stdout=parsl.AUTO_LOGNAME # Requests Parsl to return the stdout + ): + return f'echo "Hello, {name}!"' + + future = echo('user') + future.result() # block until task has completed + + with open(future.stdout, 'r') as f: + print(f.read()) + + +A Parsl Bash app executes an external application by making a command-line execution. +Parsl will execute the string returned by the function as a command-line script on a remote worker. + +Rules for Function Contents +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Bash Apps follow the same rules :ref:`as Python Apps `. +For example, imports may need to be inside functions and global variables will be inaccessible. + +Inputs and Outputs +^^^^^^^^^^^^^^^^^^ + +Bash Apps can use the same kinds of inputs as Python Apps, but only communicate results with Files. + +Bash Apps, unlike Python Apps, can also return the content printed to the Standard Output and Error. + +Special Keywords Arguments +++++++++++++++++++++++++++ + +In addition to the ``inputs``, ``outputs``, and ``walltime`` keyword arguments +described above, a Bash app can accept the following keywords: + +1. stdout: (string, tuple or ``parsl.AUTO_LOGNAME``) The path to a file to which standard output should be redirected. If set to ``parsl.AUTO_LOGNAME``, the log will be automatically named according to task id and saved under ``task_logs`` in the run directory. If set to a tuple ``(filename, mode)``, standard output will be redirected to the named file, opened with the specified mode as used by the Python `open `_ function. +2. stderr: (string or ``parsl.AUTO_LOGNAME``) Like stdout, but for the standard error stream. +3. label: (string) If the app is invoked with ``stdout=parsl.AUTO_LOGNAME`` or ``stderr=parsl.AUTO_LOGNAME``, this argument will be appended to the log name. + +Outputs ++++++++ + +If the Bash app exits with Unix exit code 0, then the AppFuture will complete. If the Bash app +exits with any other code, Parsl will treat this as a failure, and the AppFuture will instead +contain an `BashExitFailure` exception. The Unix exit code can be accessed through the +``exitcode`` attribute of that `BashExitFailure`. + + +Execution Options +^^^^^^^^^^^^^^^^^ + +Bash Apps have the same execution options (e.g., pinning to specific sites) as the Python Apps. + +MPI Apps +^^^^^^^^ + +Applications which employ MPI to span multiple nodes are a special case of Bash apps, +and require special modification of Parsl's `execution environment <../configuration/execution.html>`_ to function. +Support for MPI applications is described `in a later section `_. diff --git a/parsl/docs/userguide/apps/index.rst b/parsl/docs/userguide/apps/index.rst new file mode 100644 index 00000000..be46d4be --- /dev/null +++ b/parsl/docs/userguide/apps/index.rst @@ -0,0 +1,26 @@ +.. _apps: + +Writing Parsl Apps +================== + +An **App** defines a computation that will be executed asynchronously by Parsl. +Apps are Python functions marked with a decorator which +designates that the function will run asynchronously and cause it to return +a :class:`~concurrent.futures.Future` instead of the result. + +Apps can be one of three types of functions, each with their own type of decorator + +- ``@python_app``: Most Python functions +- ``@bash_app``: A Python function which returns a command line program to execute +- ``@join_app``: A function which launches one or more new Apps + +Start by learning how to write Python Apps, which define most of the rules needed to write +other types of Apps. + +.. toctree:: + :maxdepth: 1 + + python + bash + mpi_apps + joins diff --git a/parsl/docs/userguide/apps/joins.rst b/parsl/docs/userguide/apps/joins.rst new file mode 100644 index 00000000..1222027a --- /dev/null +++ b/parsl/docs/userguide/apps/joins.rst @@ -0,0 +1,257 @@ +.. _label-joinapp: + +Join Apps +========= + +Join apps, defined with the ``@join_app`` decorator, are a form of app that can +launch other pieces of a workflow: for example a Parsl sub-workflow, or a task +that runs in some other system. + +Parsl sub-workflows +------------------- + +One reason for launching Parsl apps from inside a join app, rather than +directly in the main workflow code, is because the definitions of those tasks +are not known well enough at the start of the workflow. + +For example, a workflow might run an expensive step to detect some objects +in an image, and then on each object, run a further expensive step. Because +the number of objects is not known at the start of the workflow, but instead +only after an expensive step has completed, the subsequent tasks cannot be +defined until after that step has completed. + +In simple cases, the main workflow script can be stopped using +``Future.result()`` and join apps are not necessary, but in more complicated +cases, that approach can severely limit concurrency. + +Join apps allow more naunced dependencies to be expressed that can help with: + +* increased concurrency - helping with strong scaling +* more focused error propagation - allowing more of an ultimately failing workflow to complete +* more useful monitoring information + +Using Futures from other components +----------------------------------- + +Sometimes, a workflow might need to incorporate tasks from other systems that +run asynchronously but do not need a Parsl worker allocated for their entire +run. An example of this is delegating some work into Globus Compute: work can +be given to Globus Compute, but Parsl does not need to keep a worker allocated +to that task while it runs. Instead, Parsl can be told to wait for the ``Future`` +returned by Globus Compute to complete. + +Usage +----- + +A `join_app` looks quite like a `python_app`, but should return a ``Future``, +or a list of ``Future`` objects, rather than a value. Once the Python code has +run, the app will wait for those Futures to complete without occupying a Parsl +worker, and when those Futures complete, their contents will be the return +value of the `join_app`. + +For example: + +.. code-block:: python + + @python_app + def some_app(): + return 3 + + @join_app + def example(): + x: Future = some_app() + return x # note that x is a Future, not a value + + assert example.result() == 3 + +Example of a Parsl sub-workflow +------------------------------- + +This example workflow shows a preprocessing step, followed by +a middle stage that is chosen by the result of the pre-processing step +(either option 1 or option 2) followed by a know post-processing step. + +.. code-block:: python + + @python_app + def pre_process(): + return 3 + + @python_app + def option_one(x): + return x*2 + + @python_app + def option_two(x): + return (-x) * 2 + + @join_app + def process(x): + if x > 0: + return option_one(x) + else: + return option_two(x) + + @python_app + def post_process(x): + return str(x) + + assert post_process(process(pre_process()))).result() == "6" + +* Why can't process be a regular python function? + +``process`` needs to inspect the value of ``x`` to make a decision about +what app to launch. So it needs to defer execution until after the +pre-processing stage has completed. In Parsl, the way to defer that is +using apps: even though ``process`` is invoked at the start of the workflow, +it will execute later on, when the Future returned by ``pre_process`` has a +value. + +* Why can't process be a @python_app? + +A Python app, if run in a `parsl.executors.ThreadPoolExecutor`, can launch +more parsl apps; so a ``python_app`` implementation of process() would be able +to inspect x and choose and invoke the appropriate ``option_{one, two}``. + +From launching the ``option_{one, two}`` app, the app body python code would +get a ``Future[int]`` - a ``Future`` that will eventually contain ``int``. + +But, we want to invoke ``post_process`` at submission time near the start of +workflow so that Parsl knows about as many tasks as possible. But we don't +want it to execute until the value of the chosen ``option_{one, two}`` app +is known. + +If we don't have join apps, how can we do this? + +We could make process wait for ``option_{one, two}`` to complete, before +returning, like this: + +.. code-block:: python + + @python_app + def process(x): + if x > 0: + f = option_one(x) + else: + f = option_two(x) + return f.result() + +but this will block the worker running ``process`` until ``option_{one, two}`` +has completed. If there aren't enough workers to run ``option_{one, two}`` this +can even deadlock. (principle: apps should not wait on completion of other +apps and should always allow parsl to handle this through dependencies) + +We could make process return the ``Future`` to the main workflow thread: + +.. code-block:: python + + @python_app + def process(x): + if x > 0: + f = option_one(x) + else: + f = option_two(x) + return f # f is a Future[int] + + # process(3) is a Future[Future[int]] + + +What comes out of invoking ``process(x)`` now is a nested ``Future[Future[int]]`` +- it's a promise that eventually process will give you a promise (from +``option_one, two}``) that will eventually give you an int. + +We can't pass that future into post_process... because post_process wants the +final int, and that future will complete before the int is ready, and that +(outer) future will have as its value the inner future (which won't be complete yet). + +So we could wait for the result in the main workflow thread: + +.. code-block:: python + + f_outer = process(pre_process()) # Future[Future[int]] + f_inner = f_outer.result # Future[int] + result = post_process(f_inner) + # result == "6" + +But this now blocks the main workflow thread. If we really only need to run +these three lines, that's fine, but what about if we are in a for loop that +sets up 1000 parametrised iterations: + +.. code-block:: python + + for x in [1..1000]: + f_outer = process(pre_process(x)) # Future[Future[int]] + f_inner = f_outer.result() # Future[int] + result = post_process(f_inner) + +The ``for`` loop can only iterate after pre_processing is done for each +iteration - it is unnecessarily serialised by the ``.result()`` call, +so that pre_processing cannot run in parallel. + +So, the rule about not calling ``.result()`` applies in the main workflow thread +too. + +What join apps add is the ability for parsl to unwrap that Future[Future[int]] into a +Future[int] in a "sensible" way (eg it doesn't need to block a worker). + + +.. _label-join-globus-compute: + +Example of invoking a Futures-driven task from another system +------------------------------------------------------------- + + +This example shows launching some activity in another system, without +occupying a Parsl worker while that activity happens: in this example, work is +delegated to Globus Compute, which performs the work elsewhere. When the work +is completed, Globus Compute will put the result into the future that it +returns, and then (because the Parsl app is a ``@join_app``), that result will +be used as the result of the Parsl app. + +As above, the motivation for doing this inside an app, rather than in the +top level is that sufficient information to launch the Globus Compute task +might not be available at start of the workflow. + +This workflow will run a first stage, ``const_five``, on a Parsl worker, +then using the result of that stage, pass the result as a parameter to a +Globus Compute task, getting a ``Future`` from that submission. Then, the +results of the Globus Compute task will be passed onto a second Parsl +local task, ``times_two``. + +.. code-block:: python + + import parsl + from globus_compute_sdk import Executor + + tutorial_endpoint_uuid = '4b116d3c-1703-4f8f-9f6f-39921e5864df' + gce = Executor(endpoint_id=tutorial_endpoint_uuid) + + def increment_in_funcx(n): + return n+1 + + @parsl.join_app + def increment_in_parsl(n): + future = gce.submit(increment_in_funcx, n) + return future + + @parsl.python_app + def times_two(n): + return n*2 + + @parsl.python_app + def const_five(): + return 5 + + parsl.load() + + workflow = times_two(increment_in_parsl(const_five())) + + r = workflow.result() + + assert r == (5+1)*2 + + +Terminology +----------- + +The term "join" comes from use of monads in functional programming, especially Haskell. diff --git a/parsl/docs/userguide/apps/mpi_apps.rst b/parsl/docs/userguide/apps/mpi_apps.rst new file mode 100644 index 00000000..82123123 --- /dev/null +++ b/parsl/docs/userguide/apps/mpi_apps.rst @@ -0,0 +1,153 @@ +MPI and Multi-node Apps +======================= + +The :class:`~parsl.executors.MPIExecutor` supports running MPI applications or other computations which can +run on multiple compute nodes. + +Background +---------- + +MPI applications run multiple copies of a program that complete a single task by +coordinating using messages passed within or across nodes. + +Starting MPI application requires invoking a "launcher" code (e.g., ``mpiexec``) +with options that define how the copies of a program should be distributed. + +The launcher includes options that control how copies of the program are distributed +across the nodes (e.g., how many copies per node) and +how each copy is configured (e.g., which CPU cores it can use). + +The options for launchers vary between MPI implementations and compute clusters. + +Configuring ``MPIExecutor`` +--------------------------- + +The :class:`~parsl.executors.MPIExecutor` is a wrapper over +:class:`~parsl.executors.high_throughput.executor.HighThroughputExecutor` +which eliminates options that are irrelevant for MPI applications. + +Define a configuration for :class:`~parsl.executors.MPIExecutor` by + +1. Setting ``max_workers_per_block`` to the maximum number of tasks to run per block of compute nodes. + This value is typically the number of nodes per block divided by the number of nodes per task. +2. Setting ``mpi_launcher`` to the launcher used for your application. +3. Specifying a provider that matches your cluster and use the :class:`~parsl.launchers.SimpleLauncher`, + which will ensure that no Parsl processes are placed on the compute nodes. + +An example for ALCF's Polaris supercomputer that will run 3 MPI tasks of 2 nodes each at the same time: + +.. code-block:: python + + config = Config( + executors=[ + MPIExecutor( + address=address_by_interface('bond0'), + max_workers_per_block=3, # Assuming 2 nodes per task + provider=PBSProProvider( + account="parsl", + worker_init=f"""module load miniconda; source activate /lus/eagle/projects/parsl/env""", + walltime="1:00:00", + queue="debug", + scheduler_options="#PBS -l filesystems=home:eagle:grand", + launcher=SimpleLauncher(), + select_options="ngpus=4", + nodes_per_block=6, + max_blocks=1, + cpus_per_node=64, + ), + ), + ] + ) + + +.. warning:: + Please note that ``Provider`` options that specify per-task or per-node resources, for example, + ``SlurmProvider(cores_per_node=N, ...)`` should not be used with :class:`~parsl.executors.high_throughput.MPIExecutor`. + Parsl primarily uses a pilot job model and assumptions from that context do not translate to the MPI context. For + more info refer to : + `github issue #3006 `_ + +Writing an MPI App +------------------ + +:class:`~parsl.executors.high_throughput.MPIExecutor` can execute both Python or Bash Apps which invoke an MPI application. + +Create the app by first defining a function which includes ``parsl_resource_specification`` keyword argument. +The resource specification is a dictionary which defines the number of nodes and ranks used by the application: + +.. code-block:: python + + resource_specification = { + 'num_nodes': , # Number of nodes required for the application instance + 'ranks_per_node': , # Number of ranks / application elements to be launched per node + 'num_ranks': , # Number of ranks in total + } + +Then, replace the call to the MPI launcher with ``$PARSL_MPI_PREFIX``. +``$PARSL_MPI_PREFIX`` references an environmental variable which will be replaced with +the correct MPI launcher configured for the resource list provided when calling the function +and with options that map the task to nodes which Parsl knows to be available. + +The function can be a Bash app + +.. code-block:: python + + @bash_app + def lammps_mpi_application(infile: File, parsl_resource_specification: Dict): + # PARSL_MPI_PREFIX will resolve to `mpiexec -n 4 -ppn 2 -hosts NODE001,NODE002` + return f"$PARSL_MPI_PREFIX lmp_mpi -in {infile.filepath}" + + +or a Python app: + +.. code-block:: python + + @python_app + def lammps_mpi_application(infile: File, parsl_resource_specification: Dict): + from subprocess import run + with open('stdout.lmp', 'w') as fp, open('stderr.lmp', 'w') as fe: + proc = run(['$PARSL_MPI_PREFIX', '-i', 'in.lmp'], stdout=fp, stderr=fe) + return proc.returncode + + +Run either App by calling with its arguments and a resource specification which defines how to execute it + +.. code-block:: python + + # Resources in terms of nodes and how ranks are to be distributed are set on a per app + # basis via the resource_spec dictionary. + resource_spec = { + "num_nodes": 2, + "ranks_per_node": 2, + "num_ranks": 4, + } + future = lammps_mpi_application(File('in.file'), parsl_resource_specification=resource_spec) + +Advanced: More Environment Variables +++++++++++++++++++++++++++++++++++++ + +Parsl Apps which run using :class:`~parsl.executors.high_throughput.MPIExecutor` +can make their own MPI invocation using other environment variables. + +These other variables include versions of the launch command for different launchers + +- ``PARSL_MPIEXEC_PREFIX``: mpiexec launch command which works for a large number of batch systems especially PBS systems +- ``PARSL_SRUN_PREFIX``: srun launch command for Slurm based clusters +- ``PARSL_APRUN_PREFIX``: aprun launch command prefix for some Cray machines + +And the information used by Parsl when assembling the launcher commands: + +- ``PARSL_NUM_RANKS``: Total number of ranks to use for the MPI application +- ``PARSL_NUM_NODES``: Number of nodes to use for the calculation +- ``PARSL_MPI_NODELIST``: List of assigned nodes separated by commas (Eg, NODE1,NODE2) +- ``PARSL_RANKS_PER_NODE``: Number of ranks per node + +Limitations ++++++++++++ + +Support for MPI tasks in HTEX is limited. It is designed for running many multi-node MPI applications within a single +batch job. + +#. MPI tasks may not span across nodes from more than one block. +#. Parsl does not correctly determine the number of execution slots per block (`Issue #1647 `_) +#. The executor uses a Python process per task, which can use a lot of memory (`Issue #2264 `_) \ No newline at end of file diff --git a/parsl/docs/userguide/apps/python.rst b/parsl/docs/userguide/apps/python.rst new file mode 100644 index 00000000..07c4e974 --- /dev/null +++ b/parsl/docs/userguide/apps/python.rst @@ -0,0 +1,266 @@ +Python Apps +----------- + +.. code-block:: python + + @python_app + def hello_world(name: str) -> str: + return f'Hello, {name}!' + + print(hello_world('user').result()) + + +Python Apps run Python functions. The code inside a function marked by ``@python_app`` is what will +be executed either locally or on a remote system. + +Most functions can run without modification. +Limitations on the content of the functions and their inputs/outputs are described below. + + +.. _function-rules: + +Rules for Function Contents +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Parsl apps have access to less information from the script that defined them +than functions run via Python's native multiprocessing libraries. +The reason is that functions are executed on workers that +lack access to the global variables in the script that defined them. +Practically, this means + +1. *Functions may need to re-import libraries.* + Place the import statements that define functions or classes inside the function. + Type annotations should not use libraries defined in the function. + + + .. code-block:: python + + import numpy as np + + # BAD: Assumes library has been imported + @python_app + def linear_model(x: list[float] | np.ndarray, m: float, b: float): + return np.multiply(x, m) + b + + # GOOD: Function imports libraries on remote worker + @python_app + def linear_model(x: list[float] | 'np.ndarray', m: float, b: float): + import numpy as np + return np.multiply(x, m) + b + + +2. *Global variables are inaccessible*. + Functions should not use variables defined outside the function. + Likewise, do not assume that variables created inside the function are visible elsewhere. + + +.. code-block:: python + + # BAD: Uses global variables + global_var = {'a': 0} + + @python_app + def counter_func(string: str, character: str = 'a'): + global_var[character] += string.count(character) # `global_var` will not be accessible + + + # GOOD + @python_app + def counter_func(string: str, character: str = 'a'): + return {character: string.count(character)} + + for ch, co in good_global('parsl', 'a').result().items(): + global_var[ch] += co + + + +3. *Outputs are only available through return statements*. + Parsl does not support generator functions (i.e., those which use ``yield`` statements) and + any changes to input arguments will not be communicated. + +.. code-block:: python + + # BAD: Assumes changes to inputs will be communicated + @python_app + def append_to_list(input_list: list, new_val): + input_list.append(new_val) + + + # GOOD: Changes to inputs are returned + @python_app + def append_to_list(input_list: list, new_val) -> list: + input_list.append(new_val) + return input_list + + +.. _functions-from-modules: + +Functions from Modules +++++++++++++++++++++++ + +The above rules assume that the user is running the example code from a standalone script or Jupyter Notebook. +Functions that are defined in an installed Python module do not need to abide by these guidelines, +as they are sent to workers differently than functions defined locally within a script. + +Directly convert a function from a library to a Python App by passing it as an argument to ``python_app``: + +.. code-block:: python + + from module import function + function_app = python_app(function) + +``function_app`` will act as Parsl App function of ``function``. + +It is also possible to create wrapped versions of functions, such as ones with pinned arguments. +Parsl just requires first calling :meth:`~functools.update_wrapped` with the wrapped function +to include attributes from the original function (e.g., its name). + +.. code-block:: python + + from functools import partial, update_wrapped + import numpy as np + my_max = partial(np.max, axis=0, keepdims=True) + my_max = update_wrapper(my_max, max) # Copy over the names + my_max_app = python_app(my_max) + +The above example is equivalent to creating a new function (as below) + +.. code-block:: python + + @python_app + def my_max_app(*args, **kwargs): + import numpy as np + return np.max(*args, keepdims=True, axis=0, **kwargs) + +Inputs and Outputs +^^^^^^^^^^^^^^^^^^ + +Python apps may be passed any Python type as an input and return any Python type, with a few exceptions. +There are several classes of allowed types, each with different rules. + +- *Python Objects*: Any Python object that can be saved with + `pickle `_ or `dill `_ + can be used as an import or output. + All primitive types (e.g., floats, strings) are valid as are many complex types (e.g., numpy arrays). +- *Files*: Pass files as inputs as a :py:class:`~parsl.data_provider.files.File` object. + Parsl can transfer them to a remote system and update the ``File`` object with a new path. + Access the new path with ``File.filepath`` attribute. + + .. code-block:: python + + @python_app + def read_first_line(x: File): + with open(x.filepath, 'r') as fp: + return fp.readline() + + Files can also be outputs of a function, but only through the ``outputs`` kwargs (described below). +- *Parsl Futures*. Functions can receive results from other Apps as Parsl ``Future`` objects. + Parsl will establish a dependency on the App(s) which created the Future(s) + and start executing as soon as the preceding ones complete. + + .. code-block:: python + + @python_app + def capitalize(x: str): + return x.upper() + + input_file = File('text.txt') + first_line_future = read_first_line(input_file) + capital_future = capitalize(first_line_future) + print(capital_future.result()) + + See the section on `Futures <../workflows/futures.html>`_ for more details. + + +Learn more about the types of data allowed in `the data section <../configuration/data.html>`_. + +.. note:: + + Any changes to mutable input arguments will be ignored. + +.. _special-kwargs: + +Special Keyword Arguments ++++++++++++++++++++++++++ + +Some keyword arguments to the Python function are treated differently by Parsl + +1. inputs: (list) This keyword argument defines a list of input :ref:`label-futures` or files. + Parsl will wait for the results of any listed :ref:`label-futures` to be resolved before executing the app. + The ``inputs`` argument is useful both for passing files as arguments + and when one wishes to pass in an arbitrary number of futures at call time. + +.. code-block:: python + + @python_app() + def map_app(x): + return x * 2 + + @python_app() + def reduce_app(inputs = ()): + return sum(inputs) + + map_futures = [map_app(x) for x in range(3)] + reduce_future = reduce_app(inputs=map_futures) + + print(reduce_future.result()) # 0 + 1 * 2 + 2 * 2 = 6 + +2. outputs: (list) This keyword argument defines a list of files that + will be produced by the app. For each file thus listed, Parsl will create a future, + track the file, and ensure that it is correctly created. The future + can then be passed to other apps as an input argument. + +.. code-block:: python + + @python_app() + def write_app(message, outputs=()): + """Write a single message to every file in outputs""" + for path in outputs: + with open(path, 'w') as fp: + print(message, file=fp) + + to_write = [ + File(Path(tmpdir) / 'output-0.txt'), + File(Path(tmpdir) / 'output-1.txt') + ] + write_app('Hello!', outputs=to_write).result() + for path in to_write: + with open(path) as fp: + assert fp.read() == 'Hello!\n' + +3. walltime: (int) This keyword argument places a limit on the app's + runtime in seconds. If the walltime is exceed, Parsl will raise an `parsl.app.errors.AppTimeout` exception. + +Outputs ++++++++ + +A Python app returns an AppFuture (see :ref:`label-futures`) as a proxy for the results that will be returned by the +app once it is executed. This future can be inspected to obtain task status; +and it can be used to wait for the result, and when complete, present the output Python object(s) returned by the app. +In case of an error or app failure, the future holds the exception raised by the app. + +Options for Python Apps +^^^^^^^^^^^^^^^^^^^^^^^ + +The :meth:`~parsl.app.app.python_app` decorator has a few options which controls how Parsl executes all tasks +run with that application. +For example, you can ensure that Parsl caches the results of the function and executes tasks on specific sites. + +.. code-block:: python + + @python_app(cache=True, executors=['gpu']) + def expensive_gpu_function(): + # ... + return + +See the Parsl documentation for full details. + +Limitations +^^^^^^^^^^^ + +To summarize, any Python function can be made a Python App with a few restrictions + +1. Functions should act only on defined input arguments. That is, they should not use script-level or global variables. +2. Functions must explicitly import any required modules if they are defined in script which starts Parsl. +3. Parsl uses dill and pickle to serialize Python objects to/from apps. Therefore, Parsl require that all input and output objects can be serialized by dill or pickle. See :ref:`label_serialization_error`. +4. STDOUT and STDERR produced by Python apps remotely are not captured. diff --git a/parsl/docs/userguide/checkpoints.rst b/parsl/docs/userguide/checkpoints.rst new file mode 100644 index 00000000..23af844c --- /dev/null +++ b/parsl/docs/userguide/checkpoints.rst @@ -0,0 +1,9 @@ +:orphan: + +.. meta:: + :content http-equiv="refresh": 0;url=workflows/checkpoints.html + +Redirect +-------- + +This page has been `moved `_ diff --git a/parsl/docs/userguide/configuration/config.rst b/parsl/docs/userguide/configuration/config.rst new file mode 100644 index 00000000..59ab4e90 --- /dev/null +++ b/parsl/docs/userguide/configuration/config.rst @@ -0,0 +1,133 @@ +Config Objects +============== + +A :class:`~parsl.config.Config` object defines and implements how Parsl connects +to compute resources used to execute tasks. + +The main part of the ``Config`` is a list of +**executors** which each define a type of worker. + +Consider the following example, a configuration to run 7168 parallel workers on +the Frontera supercomputer at TACC: + +.. code-block:: python + + from parsl.config import Config + from parsl.providers import SlurmProvider + from parsl.executors import HighThroughputExecutor + from parsl.launchers import SrunLauncher + from parsl.addresses import address_by_interface + + config = Config( + executors=[ + HighThroughputExecutor( + label="frontera_htex", + address=address_by_interface('ib0'), + max_workers_per_node=56, + provider=SlurmProvider( + partition='normal', + nodes_per_block=128, + init_blocks=1, + launcher=SrunLauncher(), + ), + ) + ], + ) + + +The worker options are those for :class:`~parsl.executors.HighThroughputExecutor`, +the baseline Executor available in Parsl. +The options include a name for the workers of this type (``label``), +how the worker connects to the main Parsl process (``address``), +and how many workers to place on each compute node (``max_workers_per_node``). + +The provider options define the queue used for submission (``partition``), +and tells Parsl request one job (``init_blocks``) of 128 nodes (``nodes_per_block``). + +The launcher uses the default mechanism for starting programs on Frontera compute nodes, ``srun``. + +Using a Config Object +--------------------- + +Use the ``Config`` object to start Parsl's data flow kernel with the ``parsl.load`` method: + +.. code-block:: python + + from parsl.configs.htex_local import config + import parsl + + with parsl.load(config) as dfk: + future = app(x) + future.wait() + +The ``.load()`` function creates a DataFlowKernel ("DFK") object that maintains the workflows state. +The DFK acquires the resources used by Parsl and should be closed to release the resources. +While the DFK can be closed manually (``dfk.cleanup()``), the preferred and Pythonic route is use it as a context manager (the ``with`` statement) +Using a context manager avoids unnecessary code and ensures cleanup occurs even if the workflow fails. + +The ``load`` statement can happen after Apps are defined but must occur before tasks are started. +Loading the Config object within context manager like ``with`` is recommended +for implicit cleaning of DFK on exiting the context manager. + +The :class:`~parsl.config.Config` object may not be used again once loaded. +Consider a configuration function if the application will shut down and re-launch the DFK. + +.. code-block:: python + + from parsl.config import Config + import parsl + + def make_config() -> Config: + return Config(...) + + with parsl.load(make_config()): + # Your workflow here + + # Section which does not require Parsl + + with parsl.load(make_config()): + # Another workflow here + + +Config Options +-------------- + +Options for the :class:`~parsl.config.Config` object apply to Parsl's general behavior +and affect all executors. +Common options include: + +- ``run_dir`` for setting where Parsl writes log files +- ``retries`` to restart failed tasks +- ``usage_tracking`` to help Parsl `by reporting how you use it <../advanced/usage_tracking.html>`_ + +Consult the :py:class:`API documentation for Config ` +or the `advanced documentation <../advanced/index.html>`_ to learn about options. + +.. _config-multiple: + +Multiple Executors +------------------ + +A single application can configure multiple executors. + +All executors define a ``label`` field that is used +route to specific workers. +All types of apps include a ``executors`` option which takes +a list of executor labels. +For example, tasks from the following App will only run on an executor labelled "frontera_htex". + +.. code-block:: python + + @python_app(executors=['frontera_htex']) + def single_threaded_task(x: int): + return x * 2 + 1 + + +Consider using multiple executors in the following cases: + +- *Different resource requirements between tasks*, such as a workflow + with a simulation stage that runs on the CPU nodes of an HPC system + followed by an analysis and visualization stage that runs on GPU nodes. +- *Different scales between workflow stages*, such as a workflow + with a "fan-out" stage of many long running running on a cluster + and quick "fan-in" computations which can run on fewer nodes. diff --git a/parsl/docs/userguide/configuration/examples.rst b/parsl/docs/userguide/configuration/examples.rst new file mode 100644 index 00000000..35c9b1f9 --- /dev/null +++ b/parsl/docs/userguide/configuration/examples.rst @@ -0,0 +1,376 @@ +Example configurations +====================== + +.. note:: + All configuration examples below must be customized for the user's + allocation, Python environment, file system, etc. + + +The configuration specifies what, and how, resources are to be used for executing +the Parsl program and its apps. +It is important to carefully consider the needs of the Parsl program and its apps, +and the characteristics of the compute resources, to determine an ideal configuration. +Aspects to consider include: +1) where the Parsl apps will execute; +2) how many nodes will be used to execute the apps, and how long the apps will run; +3) should Parsl request multiple nodes in an individual scheduler job; and +4) where will the main Parsl program run and how will it communicate with the apps. + +Stepping through the following question should help formulate a suitable configuration object. + +1. Where should apps be executed? + ++---------------------+-----------------------------------------------+----------------------------------------+ +| Target | Executor | Provider | ++=====================+===============================================+========================================+ +| Laptop/Workstation | * `parsl.executors.HighThroughputExecutor` | `parsl.providers.LocalProvider` | +| | * `parsl.executors.ThreadPoolExecutor` | | +| | * `parsl.executors.WorkQueueExecutor` | | +| | * `parsl.executors.taskvine.TaskVineExecutor` | | ++---------------------+-----------------------------------------------+----------------------------------------+ +| Amazon Web Services | * `parsl.executors.HighThroughputExecutor` | `parsl.providers.AWSProvider` | ++---------------------+-----------------------------------------------+----------------------------------------+ +| Google Cloud | * `parsl.executors.HighThroughputExecutor` | `parsl.providers.GoogleCloudProvider` | ++---------------------+-----------------------------------------------+----------------------------------------+ +| Slurm based system | * `parsl.executors.HighThroughputExecutor` | `parsl.providers.SlurmProvider` | +| | * `parsl.executors.WorkQueueExecutor` | | +| | * `parsl.executors.taskvine.TaskVineExecutor` | | ++---------------------+-----------------------------------------------+----------------------------------------+ +| Torque/PBS based | * `parsl.executors.HighThroughputExecutor` | `parsl.providers.TorqueProvider` | +| system | * `parsl.executors.WorkQueueExecutor` | | ++---------------------+-----------------------------------------------+----------------------------------------+ +| GridEngine based | * `parsl.executors.HighThroughputExecutor` | `parsl.providers.GridEngineProvider` | +| system | * `parsl.executors.WorkQueueExecutor` | | ++---------------------+-----------------------------------------------+----------------------------------------+ +| Condor based | * `parsl.executors.HighThroughputExecutor` | `parsl.providers.CondorProvider` | +| cluster or grid | * `parsl.executors.WorkQueueExecutor` | | +| | * `parsl.executors.taskvine.TaskVineExecutor` | | ++---------------------+-----------------------------------------------+----------------------------------------+ +| Kubernetes cluster | * `parsl.executors.HighThroughputExecutor` | `parsl.providers.KubernetesProvider` | ++---------------------+-----------------------------------------------+----------------------------------------+ + + +2. How many nodes will be used to execute the apps? What task durations are necessary to achieve good performance? + + ++--------------------------------------------+----------------------+-------------------------------------+ +| Executor | Number of Nodes [*]_ | Task duration for good performance | ++============================================+======================+=====================================+ +| `parsl.executors.ThreadPoolExecutor` | 1 (Only local) | Any | ++--------------------------------------------+----------------------+-------------------------------------+ +| `parsl.executors.HighThroughputExecutor` | <=2000 | Task duration(s)/#nodes >= 0.01 | +| | | longer tasks needed at higher scale | ++--------------------------------------------+----------------------+-------------------------------------+ +| `parsl.executors.WorkQueueExecutor` | <=1000 [*]_ | 10s+ | ++--------------------------------------------+----------------------+-------------------------------------+ +| `parsl.executors.taskvine.TaskVineExecutor`| <=1000 [*]_ | 10s+ | ++--------------------------------------------+----------------------+-------------------------------------+ + + +.. [*] Assuming 32 workers per node. If there are fewer workers launched + per node, a larger number of nodes could be supported. + +.. [*] The maximum number of nodes tested for the `parsl.executors.WorkQueueExecutor` is 10,000 GPU cores and + 20,000 CPU cores. + +.. [*] The maximum number of nodes tested for the `parsl.executors.taskvine.TaskVineExecutor` is + 10,000 GPU cores and 20,000 CPU cores. + +3. Should Parsl request multiple nodes in an individual scheduler job? +(Here the term block is equivalent to a single scheduler job.) + ++--------------------------------------------------------------------------------------------+ +| ``nodes_per_block = 1`` | ++---------------------+--------------------------+-------------------------------------------+ +| Provider | Executor choice | Suitable Launchers | ++=====================+==========================+===========================================+ +| Systems that don't | Any | * `parsl.launchers.SingleNodeLauncher` | +| use Aprun | | * `parsl.launchers.SimpleLauncher` | ++---------------------+--------------------------+-------------------------------------------+ +| Aprun based systems | Any | * `parsl.launchers.AprunLauncher` | ++---------------------+--------------------------+-------------------------------------------+ + ++---------------------------------------------------------------------------------------------------------------------+ +| ``nodes_per_block > 1`` | ++-------------------------------------+--------------------------+----------------------------------------------------+ +| Provider | Executor choice | Suitable Launchers | ++=====================================+==========================+====================================================+ +| `parsl.providers.TorqueProvider` | Any | * `parsl.launchers.AprunLauncher` | +| | | * `parsl.launchers.MpiExecLauncher` | ++-------------------------------------+--------------------------+----------------------------------------------------+ +| `parsl.providers.SlurmProvider` | Any | * `parsl.launchers.SrunLauncher` if native slurm | +| | | * `parsl.launchers.AprunLauncher`, otherwise | ++-------------------------------------+--------------------------+----------------------------------------------------+ + +.. note:: If using a Cray system, you most likely need to use the `parsl.launchers.AprunLauncher` to launch workers unless you + are on a **native Slurm** system like :ref:`configuring_nersc_cori` + +Ad-Hoc Clusters +--------------- + +Parsl's support of ad-hoc clusters of compute nodes without a scheduler +is deprecated. + +See +`issue #3515 `_ +for further discussion. + +Amazon Web Services +------------------- + +.. image:: img/aws_image.png + +.. note:: + To use AWS with Parsl, install Parsl with AWS dependencies via ``python3 -m pip install 'parsl[aws]'`` + +Amazon Web Services is a commercial cloud service which allows users to rent a range of computers and other computing services. +The following snippet shows how Parsl can be configured to provision nodes from the Elastic Compute Cloud (EC2) service. +The first time this configuration is used, Parsl will configure a Virtual Private Cloud and other networking and security infrastructure that will be +re-used in subsequent executions. The configuration uses the `parsl.providers.AWSProvider` to connect to AWS. + +.. literalinclude:: ../../../parsl/configs/ec2.py + +Anvil (Purdue) +-------------- + +.. image:: https://www.rcac.purdue.edu/storage/resources/anvil/resource.jpg + +Anvil is hosted at Purdue University, and is a powerful new supercomputer that provides advanced computing capabilities to support a wide range of computational and data-intensive research spanning from traditional high-performance computing to modern artificial intelligence applications. +The configuration below provides access to a single GPU on a single node, and assumes that the user is running on a login node and uses the `parsl.providers.SlurmProvider` to interface with the scheduler, and uses the `parsl.launchers.SrunLauncher` to launch workers. + +.. literalinclude:: ../../../parsl/configs/anvil.py + +ASPIRE 1 (NSCC) +--------------- + +.. image:: https://www.nscc.sg/wp-content/uploads/2017/04/ASPIRE1Img.png + +The following snippet shows an example configuration for accessing NSCC's **ASPIRE 1** supercomputer. This example uses the `parsl.executors.HighThroughputExecutor` executor and connects to ASPIRE1's PBSPro scheduler. It also shows how ``scheduler_options`` parameter could be used for scheduling array jobs in PBSPro. + +.. literalinclude:: ../../../parsl/configs/ASPIRE1.py + + + + +Illinois Campus Cluster (UIUC) +------------------------------ + +.. image:: https://campuscluster.illinois.edu/wp-content/uploads/2018/02/ND2_3633-sm.jpg + +The following snippet shows an example configuration for executing on the Illinois Campus Cluster. +The configuration assumes the user is running on a login node and uses the `parsl.providers.SlurmProvider` to interface +with the scheduler, and uses the `parsl.launchers.SrunLauncher` to launch workers. + +.. literalinclude:: ../../../parsl/configs/illinoiscluster.py + +Bridges (PSC) +------------- + +.. image:: https://insidehpc.com/wp-content/uploads/2016/08/Bridges_FB1b.jpg + +The following snippet shows an example configuration for executing on the Bridges supercomputer at the Pittsburgh Supercomputing Center. +The configuration assumes the user is running on a login node and uses the `parsl.providers.SlurmProvider` to interface +with the scheduler, and uses the `parsl.launchers.SrunLauncher` to launch workers. + +.. literalinclude:: ../../../parsl/configs/bridges.py + + + +CC-IN2P3 +-------- + +.. image:: https://cc.in2p3.fr/wp-content/uploads/2017/03/bandeau_accueil.jpg + +The snippet below shows an example configuration for executing from a login node on IN2P3's Computing Centre. +The configuration uses the `parsl.providers.LocalProvider` to run on a login node primarily to avoid GSISSH, which Parsl does not support. +This system uses Grid Engine which Parsl interfaces with using the `parsl.providers.GridEngineProvider`. + +.. literalinclude:: ../../../parsl/configs/cc_in2p3.py + + +CCL (Notre Dame, TaskVine) +-------------------------- + +.. image:: https://ccl.cse.nd.edu/software/taskvine/taskvine-logo.png + +To utilize TaskVine with Parsl, please install the full CCTools software package within an appropriate Anaconda or Miniconda environment +(instructions for installing Miniconda can be found `in the Conda install guide `_): + +.. code-block:: bash + + $ conda create -y --name python= conda-pack + $ conda activate + $ conda install -y -c conda-forge ndcctools parsl + +This creates a Conda environment on your machine with all the necessary tools and setup needed to utilize TaskVine with the Parsl library. + +The following snippet shows an example configuration for using the Parsl/TaskVine executor to run applications on the local machine. +This examples uses the `parsl.executors.taskvine.TaskVineExecutor` to schedule tasks, and a local worker will be started automatically. +For more information on using TaskVine, including configurations for remote execution, visit the +`TaskVine/Parsl documentation online `_. + +.. literalinclude:: ../../../parsl/configs/vineex_local.py + +TaskVine's predecessor, WorkQueue, may continue to be used with Parsl. +For more information on using WorkQueue visit the `CCTools documentation online `_. + +Delta (NCSA) +-------------- + +.. image:: https://docs.ncsa.illinois.edu/systems/delta/en/latest/_images/delta_front_1.png + +Delta is a dedicated, ACCESS-allocated resource designed by HPE and NCSA, delivering a highly capable GPU-focused compute environment for GPU and CPU workloads. +The configuration below provides access to a single GPU on a single node, and assumes that the user is running on a login node and uses the `parsl.providers.SlurmProvider` to interface with the scheduler, and uses the `parsl.launchers.SrunLauncher` to launch workers. + +.. literalinclude:: ../../../parsl/configs/delta.py + +Expanse (SDSC) +-------------- + +.. image:: https://www.hpcwire.com/wp-content/uploads/2019/07/SDSC-Expanse-graphic-cropped.jpg + +The following snippet shows an example configuration for executing remotely on San Diego Supercomputer +Center's **Expanse** supercomputer. The example is designed to be executed on the login nodes, using the +`parsl.providers.SlurmProvider` to interface with the Slurm scheduler used by Comet and the `parsl.launchers.SrunLauncher` to launch workers. + +.. literalinclude:: ../../../parsl/configs/expanse.py + + +Improv (Argonne LCRC) +--------------------- + +.. image:: https://www.lcrc.anl.gov/sites/default/files/styles/965_wide/public/2023-12/20231214_114057.jpg?itok=A-Rz5pP9 + +**Improv** is a PBS Pro based supercomputer at Argonne's Laboratory Computing Resource +Center (LCRC). The following snippet is an example configuration that uses `parsl.providers.PBSProProvider` +and `parsl.launchers.MpiRunLauncher` to run on multinode jobs. + +.. literalinclude:: ../../../parsl/configs/improv.py + + +.. _configuring_nersc_cori: + +Perlmutter (NERSC) +------------------ + +NERSC provides documentation on `how to use Parsl on Perlmutter `_. +Perlmutter is a Slurm based HPC system and parsl uses `parsl.providers.SlurmProvider` with `parsl.launchers.SrunLauncher` +to launch tasks onto this machine. + + +Frontera (TACC) +--------------- + +.. image:: https://frontera-portal.tacc.utexas.edu/media/filer_public/2c/fb/2cfbf6ab-818d-42c8-b4d5-9b39eb9d0a05/frontera-banner-home.jpg + +Deployed in June 2019, Frontera is the 5th most powerful supercomputer in the world. Frontera replaces the NSF Blue Waters system at NCSA +and is the first deployment in the National Science Foundation's petascale computing program. The configuration below assumes that the user is +running on a login node and uses the `parsl.providers.SlurmProvider` to interface with the scheduler, and uses the `parsl.launchers.SrunLauncher` to launch workers. + +.. literalinclude:: ../../../parsl/configs/frontera.py + +Globus Compute (Multisite) +-------------------------- + +Globus Compute is a distributed Function as a Service (FaaS) platform that enables secure +execution of functions on heterogeneous remote computers, from laptops to campus clusters, clouds, and supercomputers. +Functions are executed on `Globus Compute Endpoints `_ +that can be `configured `_ +for most clusters/HPC systems. The example configuration below allows task submission +to Globus Compute's hosted tutorial endpoint. + +.. literalinclude:: ../../../parsl/configs/gc_tutorial.py + +.. note:: The Globus Compute tutorial endpoint runs Python 3.11. We recommend + using the same Python environment to avoid potential serialization errors + caused by environment mismatches. Globus Compute will raise a warning if any + environment version mismatches are detected although minor version differences + may not cause faults (eg, Python3.11.7 vs Python3.11.8) + +The configuration below specifies two remote endpoints, one at `SDSC's Expanse Supercomputer `_ +and the other at `NERSC's Perlmutter Supercomputer `_. + +.. literalinclude:: ../../../parsl/configs/gc_multisite.py + + + +Kubernetes Clusters +------------------- + +.. image:: https://d1.awsstatic.com/PAC/kuberneteslogo.eabc6359f48c8e30b7a138c18177f3fd39338e05.png + +Kubernetes is an open-source system for container management, such as automating deployment and scaling of containers. +The snippet below shows an example configuration for deploying pods as workers on a Kubernetes cluster. +The KubernetesProvider exploits the Python Kubernetes API, which assumes that you have kube config in ``~/.kube/config``. + +.. literalinclude:: ../../../parsl/configs/kubernetes.py + + +Midway (RCC, UChicago) +---------------------- + +.. image:: https://rcc.uchicago.edu/sites/rcc.uchicago.edu/files/styles/slideshow-image/public/uploads/images/slideshows/20140430_RCC_8978.jpg?itok=BmRuJ-wq + +This Midway cluster is a campus cluster hosted by the Research Computing Center at the University of Chicago. +The snippet below shows an example configuration for executing remotely on Midway. +The configuration assumes the user is running on a login node and uses the `parsl.providers.SlurmProvider` to interface +with the scheduler, and uses the `parsl.launchers.SrunLauncher` to launch workers. + +.. literalinclude:: ../../../parsl/configs/midway.py + + +Open Science Grid +----------------- + +.. image:: https://www.renci.org/wp-content/uploads/2008/10/osg_logo.png + +The Open Science Grid (OSG) is a national, distributed computing Grid spanning over 100 individual sites to provide tens of thousands of CPU cores. +The snippet below shows an example configuration for executing remotely on OSG. You will need to have a valid project name on the OSG. +The configuration uses the `parsl.providers.CondorProvider` to interface with the scheduler. + +.. literalinclude:: ../../../parsl/configs/osg.py + + +Polaris (ALCF) +-------------- + +.. image:: https://www.alcf.anl.gov/sites/default/files/styles/965x543/public/2022-07/33181D_086_ALCF%20Polaris%20Crop.jpg?itok=HVAHsZtt + :width: 75% + +ALCF provides documentation on `how to use Parsl on Polaris `_. +Polaris uses `parsl.providers.PBSProProvider` and `parsl.launchers.MpiExecLauncher` to launch tasks onto the HPC system. + + + +Stampede2 (TACC) +---------------- + +.. image:: https://www.tacc.utexas.edu/documents/1084364/1413880/stampede2-0717.jpg/ + +The following snippet shows an example configuration for accessing TACC's **Stampede2** supercomputer. This example uses theHighThroughput executor and connects to Stampede2's Slurm scheduler. + +.. literalinclude:: ../../../parsl/configs/stampede2.py + + +Summit (ORNL) +------------- + +.. image:: https://www.olcf.ornl.gov/wp-content/uploads/2018/06/Summit_Exaop-1500x844.jpg + +The following snippet shows an example configuration for executing from the login node on Summit, the leadership class supercomputer hosted at the Oak Ridge National Laboratory. +The example uses the :class:`parsl.providers.LSFProvider` to provision compute nodes from the LSF cluster scheduler and the `parsl.launchers.JsrunLauncher` to launch workers across the compute nodes. + +.. literalinclude:: ../../../parsl/configs/summit.py + + +TOSS3 (LLNL) +------------ + +.. image:: https://hpc.llnl.gov/sites/default/files/Magma--2020-LLNL.jpg + +The following snippet shows an example configuration for executing on one of LLNL's **TOSS3** +machines, such as Quartz, Ruby, Topaz, Jade, or Magma. This example uses the `parsl.executors.FluxExecutor` +and connects to Slurm using the `parsl.providers.SlurmProvider`. This configuration assumes that the script +is being executed on the login nodes of one of the machines. + +.. literalinclude:: ../../../parsl/configs/toss3_llnl.py diff --git a/parsl/docs/userguide/configuration/executors/advanced.rst b/parsl/docs/userguide/configuration/executors/advanced.rst new file mode 100644 index 00000000..41fce3ab --- /dev/null +++ b/parsl/docs/userguide/configuration/executors/advanced.rst @@ -0,0 +1,54 @@ +Advanced Configuration +====================== + +Executors are designed to function adequately with the default options, +but there are several cases where the executors needed to be tuned for specific systems. + + +.. contents:: + :local: + :depth: 1 + +.. _label-networking: + +Connecting Parsl to Workers +--------------------------- + +.. note:: + + Skip this section if: + + 1. Using the ``ThreadPoolExecutor`` and ``GlobusComputeExecutor``. + 2. Plan to run all tasks on the same computer as the Parsl script. + +Parsl workers launched on remote nodes need to know the address of the node running the Parsl script. +Parsl will detect all addresses associated with the node running Parsl, and this auto-configuration works +for many users. + +However, some computing systems disallow applications from attempting to access external networks +(which Parsl may break during auto-configuration) or +auto-detection will result in selecting a lower-performance network. +In such cases, determine the proper IP address for the main node using the tools in :mod:`parsl.addresses`. + +The most common case is to use :meth:`~parsl.addresses.address_by_interface` to determine the +address on the network associated with a certain network interface. +For example, the compute nodes on the Polaris supercomputer connect to the login nodes over the +``bond0`` interface. +Configure a Parsl application where the main process runs on the login node by +acquiring the address through that interface: + +.. code-block:: python + + config = Config( + executors=[ + HighThroughputExecutor( + address=address_by_interface("bond0"), + provider=PBSProProvider( + launcher=MpiExecLauncher(bind_cmd="--cpu-bind", overrides="--depth=64 --ppn 1"), + account='ACCT', + queue='debug', + select_options="ngpus=4", + ), + ), + ], + ) diff --git a/parsl/docs/userguide/configuration/executors/htex.rst b/parsl/docs/userguide/configuration/executors/htex.rst new file mode 100644 index 00000000..3fd8478f --- /dev/null +++ b/parsl/docs/userguide/configuration/executors/htex.rst @@ -0,0 +1,192 @@ +.. _label-htex: + +The High-Throughput Executor +============================ + +The :class:`~parsl.executors.HighThroughputExecutor` (HTEx) is the standard Executor provided with Parsl. +The following sections detail the most-used configuration options of HTEx. + +.. contents:: + :local: + :depth: 1 + +Defining Workers Per Node +------------------------- + +HTEx determines how many workers to run on each node individually. +The number of nodes is the minimum of the ``max_workers_per_node``, +the number of workers determined from ``cores_per_worker``, +and the number of workers defined by ``mem_per_worker``. + +Resource pinning +---------------- + +Resource pinning reduces contention between multiple workers using the same CPU cores or accelerators. + +Multi-Threaded Applications ++++++++++++++++++++++++++++ + +Workflows which launch multiple workers on a single node which perform multi-threaded tasks (e.g., NumPy, Tensorflow operations) may run into thread contention issues. +Each worker may try to use the same hardware threads, which leads to performance penalties. +Use ``cpu_affinity`` to assign workers to specific threads either by +defining an allocation strategy method or an explicit list. + +The strategy methods will auto assign all detected hardware threads to workers. +Allowed strategies are ``block``, ``block-reverse``, and ``alternating``. +The ``block`` method pins threads to workers in sequential order (ex: 4 threads are grouped (0, 1) and (2, 3) on two workers); +``block-reverse`` pins threads in reverse sequential order (ex: (3, 2) and (1, 0)); and ``alternating`` alternates threads among workers (ex: (0, 2) and (1, 3)). + +Select the best blocking strategy based on the node's CPU cache hierarchy (query the cache hierarchy using ``lscpu``). + +.. code-block:: python + + local_config = Config( + executors=[ + HighThroughputExecutor( + label="htex_Local", + worker_debug=True, + cpu_affinity='alternating', + provider=LocalProvider( + init_blocks=1, + max_blocks=1, + ), + ) + ], + strategy='none', + ) + +Users can also use ``cpu_affinity`` to assign explicitly threads to workers with a string that has the format of +``cpu_affinity="list:::"``. + +Each worker's threads can be specified as a comma separated list or a hyphenated range: +``thread1,thread2,thread3`` +or +``thread_start-thread_end``. + +An example for 12 workers on a node with 208 threads is: + +.. code-block:: python + + cpu_affinity="list:0-7,104-111:8-15,112-119:16-23,120-127:24-31,128-135:32-39,136-143:40-47,144-151:52-59,156-163:60-67,164-171:68-75,172-179:76-83,180-187:84-91,188-195:92-99,196-203" + +This example assigns 16 threads each to 12 workers. Note that in this example there are threads that are skipped. +If a thread is not explicitly assigned to a worker, it will be left idle. +The number of thread "ranks" (colon separated thread lists/ranges) must match the total number of workers on the node; otherwise an exception will be raised. + +Thread affinity is accomplished in two ways. +Each worker first sets the affinity for the Python process using `the affinity mask `_, +which may not be available on all operating systems. +It then sets environment variables to control +`OpenMP thread affinity `_ +so that any subprocesses launched by a worker which use OpenMP know which processors are valid. +These include ``OMP_NUM_THREADS``, ``GOMP_COMP_AFFINITY``, and ``KMP_THREAD_AFFINITY``. + +Accelerators +++++++++++++ + +Many modern clusters provide multiple accelerators per compute node, yet many applications are best suited to using a +single accelerator per task. Parsl supports pinning each worker to different accelerators using +``available_accelerators`` option of the :class:`~parsl.executors.HighThroughputExecutor`. Provide either the number of +executors (Parsl will assume they are named in integers starting from zero) or a list of the names of the accelerators +available on the node. Parsl will limit the number of workers it launches to the number of accelerators specified, +in other words, you cannot have more workers per node than there are accelerators. By default, Parsl will launch +as many workers as the accelerators specified via ``available_accelerators``. + +.. code-block:: python + + local_config = Config( + executors=[ + HighThroughputExecutor( + label="htex_Local", + worker_debug=True, + available_accelerators=2, + provider=LocalProvider( + init_blocks=1, + max_blocks=1, + ), + ) + ], + strategy='none', + ) + +It is possible to bind multiple/specific accelerators to each worker by specifying a list of comma separated strings, +each specifying accelerators. + +Here's an example: + +.. code-block:: python + + # The following config is trimmed for clarity + local_config = Config( + executors=[ + HighThroughputExecutor( + # Starts 2 workers per node, each bound to 2 GPUs + available_accelerators=["0,1", "2,3"], + + # Start a single worker bound to all 4 GPUs + # available_accelerators=["0,1,2,3"] + ) + ], + ) + +Binding is achieved by setting ``CUDA_VISIBLE_DEVICES`` (specific to NVIDIA GPUs), +``ROCR_VISIBLE_DEVICES`` (AMD GPUs), +and ``ZE_AFFINITY_MASK`` (Intel GPUs) to the appropriate accelerator names. + +GPU Oversubscription +^^^^^^^^^^^^^^^^^^^^ + +For hardware that uses NVIDIA devices, Parsl allows for the oversubscription of workers to GPUs. This is intended to +make use of NVIDIA's `Multi-Process Service (MPS) `_ available on many of their +GPUs that allows users to run multiple concurrent processes on a single GPU. The user needs to set the +``worker_init`` command of the Provider to start MPS on every node in the block (this is machine dependent). The +``available_accelerators`` option should then be set to the total number of GPU partitions run on a single node in the +block. For example, for a node with 4 NVIDIA GPUs, to create 8 workers per GPU, set ``available_accelerators=32``. +GPUs will be assigned to workers in ascending order in contiguous blocks. In the example, workers 0-7 will be placed +on GPU 0, workers 8-15 on GPU 1, workers 16-23 on GPU 2, and workers 24-31 on GPU 3. + +Encryption +---------- + +Users can encrypt traffic between the Parsl DFK and ``HighThroughputExecutor`` instances by setting its ``encrypted`` +initialization argument to ``True``. + +For example, + +.. code-block:: python + + from parsl.config import Config + from parsl.executors import HighThroughputExecutor + + config = Config( + executors=[ + HighThroughputExecutor( + encrypted=True + ) + ] + ) + +Under the hood, we use `CurveZMQ `_ to encrypt all communication channels +between the executor and related nodes. + +Encryption performance +++++++++++++++++++++++ + +CurveZMQ depends on `libzmq `_ and `libsodium `_, +which `pyzmq `_ (a Parsl dependency) includes as part of its +installation via ``pip``. This installation path should work on most systems, but users have +reported significant performance degradation as a result. + +If you experience a significant performance hit after enabling encryption, we recommend installing +``pyzmq`` with conda: + +.. code-block:: bash + + conda install conda-forge::pyzmq + +Alternatively, you can `install libsodium `_, then +`install libzmq `_, then build ``pyzmq`` from source: + +.. code-block:: bash + + pip3 install parsl --no-binary pyzmq diff --git a/parsl/docs/userguide/configuration/executors/index.rst b/parsl/docs/userguide/configuration/executors/index.rst new file mode 100644 index 00000000..702e6e18 --- /dev/null +++ b/parsl/docs/userguide/configuration/executors/index.rst @@ -0,0 +1,49 @@ +Executors +--------- + +Parsl provides many Executors to handle a wide variety of workloads. + +Workloads that execute a small number of tasks which run locally +benefit from the minimal configuration required for :class:`~parsl.executors.ThreadPoolExecutor`, +whereas those which require thousands of nodes should use the +:class:`~parsl.executors.HighThroughputExecutor`. +These executors include: + +#. :class:`~parsl.executors.HighThroughputExecutor` (HTEx): The best starting choice for Parsl applications. + HTEx deploys many identical Python workers as separate processes across multiple compute nodes. + Implements a hierarchical scheduling strategy and can scale to thousands of workers in many applications.. + +#. :class:`~parsl.executors.taskvine.TaskVineExecutor` (TVEx): An executor which supports performance features including: + resizing the resources pinned to workers, + smart caching and sharing of large files between tasks and compute nodes, + and automatically sharing Python environments between tasks. + Scales to tens of thousands of cores. + Built using `TaskVine `_. + +#. :class:`~parsl.executors.WorkQueueExecutor` (WQEx): An Executor which supports dynamically adjusting the resources + allocated to each worker, built using `Work Queue `_. + WQEx scales to tens of thousands of cores. + + +#. :class:`~parsl.executors.GlobusComputeExecutor`: Employs `Globus Compute `_, + to manage execution of tasks through a cloud service. + Functions are securely executed on `Globus Compute Endpoints `_ + running on resources that need not be accessible from the computer running the Parsl script. + Endpoints can use any of Parsl's other Executors to execute the tasks. + +#. :class:`~parsl.executors.MPIExecutor` (MPEx): A specialized wrapper over HTEx designed to manage tasks which span multiple compute nodes. + See `MPI and Multi-node Apps <../apps/mpi_apps.rst>`_. + +#. :class:`~parsl.executors.ThreadPoolExecutor`: A lightweight executor that runs on the same computer as the Parsl script. + Tasks running on the ``ThreadPoolExecutor`` can start other Parsl tasks. + +Start by following the documentation for the chosen executor. +Continue by specifying how to request compute nodes in the `Provider <../providers/index.html>`_ documentation. + +.. toctree:: + :maxdepth: 2 + :caption: Table of Contents + + htex + wqex + advanced diff --git a/parsl/docs/userguide/configuration/executors/wqex.rst b/parsl/docs/userguide/configuration/executors/wqex.rst new file mode 100644 index 00000000..f2335cb9 --- /dev/null +++ b/parsl/docs/userguide/configuration/executors/wqex.rst @@ -0,0 +1,117 @@ +The WorkQueue Executor +====================== + +The :class:`parsl.executors.WorkQueueExecutor` (WQEx) executes Parsl tasks +using the `Work Queue, a framework from the Cooperative Computing Lab `_. +WQEx dispatches Parsl tasks to Work Queue's "managers" and places +the managers on to compute nodes via Parsl's `Providers <./providers/index.html>`_. + +This part of the documentation details the unique capabilities available through Work Queue. + +.. contents:: + :local: + :depth: 1 + + +Heterogeneous Resources +----------------------- + +The :class:`parsl.executors.WorkQueueExecutor` provides several features to work with heterogeneous resources. +By default, Parsl only runs one app at a time on each worker node. +However, it is possible to specify the requirements for a particular app, +and Work Queue will automatically run as many parallel instances as possible on each node. +Work Queue automatically detects the amount of cores, memory, and other resources available on each execution node. +To activate this feature, add a resource specification to your apps. A resource specification is a dictionary with +the following three keys: ``cores`` (an integer corresponding to the number of cores required by the task), +``memory`` (an integer corresponding to the task's memory requirement in MB), and ``disk`` (an integer corresponding to +the task's disk requirement in MB), passed to an app via the special keyword argument ``parsl_resource_specification``. The specification can be set for all app invocations via a default, for example: + + .. code-block:: python + + @python_app + def compute(x, parsl_resource_specification={'cores': 1, 'memory': 1000, 'disk': 1000}): + return x*2 + + +or updated when the app is invoked: + + .. code-block:: python + + spec = {'cores': 1, 'memory': 500, 'disk': 500} + future = compute(x, parsl_resource_specification=spec) + +This ``parsl_resource_specification`` special keyword argument will inform Work Queue about the resources this app requires. +When placing instances of ``compute(x)``, Work Queue will run as many parallel instances as possible based on each worker node's available resources. + +If an app's resource requirements are not known in advance, +Work Queue has an auto-labeling feature that measures the actual resource usage of your apps and automatically chooses resource labels for you. +With auto-labeling, it is not necessary to provide ``parsl_resource_specification``; +Work Queue collects stats in the background and updates resource labels as your workflow runs. +To activate this feature, add the following flags to your executor config: + + .. code-block:: python + + config = Config( + executors=[ + WorkQueueExecutor( + # ...other options go here + autolabel=True, + autocategory=True + ) + ] + ) + +The ``autolabel`` flag tells Work Queue to automatically generate resource labels. +By default, these labels are shared across all apps in your workflow. +The ``autocategory`` flag puts each app into a different category, +so that Work Queue will choose separate resource requirements for each app. +This is important if e.g. some of your apps use a single core and some apps require multiple cores. +Unless you know that all apps have uniform resource requirements, +you should turn on ``autocategory`` when using ``autolabel``. + +Software Environment Detection +------------------------------ + +The Work Queue executor can also help deal with sites that have non-uniform software environments across nodes. +Parsl assumes that the Parsl program and the compute nodes all use the same Python version. +In addition, any packages your apps import must be available on compute nodes. +If no shared filesystem is available or if node configuration varies, +this can lead to difficult-to-trace execution problems. + +If your Parsl program is running in a Conda environment, +the Work Queue executor can automatically scan the imports in your apps, +create a self-contained software package, +transfer the software package to worker nodes, +and run your code inside the packaged and uniform environment. +First, make sure that the Conda environment is active and you have the required packages installed (via either ``pip`` or ``conda``): + +- ``python`` +- ``parsl`` +- ``ndcctools`` +- ``conda-pack`` + +Then add the following to your config: + + .. code-block:: python + + config = Config( + executors=[ + WorkQueueExecutor( + # ...other options go here + pack=True + ) + ] + ) + +.. note:: + There will be a noticeable delay the first time Work Queue sees an app; + it is creating and packaging a complete Python environment. + This packaged environment is cached, so subsequent app invocations should be much faster. + +Using this approach, it is possible to run Parsl applications on nodes that don't have Python available at all. +The packaged environment includes a Python interpreter, +and Work Queue does not require Python to run. + +.. note:: + The automatic packaging feature only supports packages installed via ``pip`` or ``conda``. + Importing from other locations (e.g. via ``$PYTHONPATH``) or importing other modules in the same directory is not supported. diff --git a/parsl/docs/userguide/configuration/img/aws_image.png b/parsl/docs/userguide/configuration/img/aws_image.png new file mode 100644 index 0000000000000000000000000000000000000000..a4d74c79fb1f496316921288a0be63d97be5d5fa GIT binary patch literal 852370 zcmV)wK$O3UP)4@uj2y7c&>kyIoUqp16`O77KK4>v 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    %2D8jL^v|N9Tv;RJSm|Na4W{DA)bdiDRC8}t9)+>!Br<23jG z(;H3xR~730FUZmPpOB&RKQ3M8e^ja#5NrOAOaXRyLCc$g9p20w{r^RIhW~4e4F0#4 z8U0_{Ve@~}WS9Rh4(0!UeXjNYm$!$3>ERMEz91(${|37GH_}x`SP!>??Z-vw_~N>c z7=rPK3;K0N==UYR2d3SRU<~T`{sMMBlhzKwpev1ld|9BlIgxX{%`?F>y=sNs|JC8R=f8SU&wrEpng1=C z=KS}avhsiEq^19hd#3$wZ)*L&rXb`0?g+pCryNcH@6gl!zaH4hU8$n{f4Pd%|AmT5 z|7R&E{4bQ0`0M=yzW@LLAxT6*R39lK_+Orl`M)4D6QX?q#>`kSa-G0{gyHqWcL0f7 VH#~TrS)Tv^002ovPDHLkV1nb3@p}LO literal 0 HcmV?d00001 diff --git a/parsl/docs/userguide/configuration/index.rst b/parsl/docs/userguide/configuration/index.rst new file mode 100644 index 00000000..0b4d6e50 --- /dev/null +++ b/parsl/docs/userguide/configuration/index.rst @@ -0,0 +1,51 @@ +.. _configuration-section: + +Configuring Parsl +================= + +.. note:: + + This part of the guide describes the how to configure Parsl from the start. + Consider browsing the `example configurations `_ to + see if there is already an example which matches your needs. + +Contemporary computing environments offer a wide range of computational platforms +or **execution providers**, from laptops and PCs to various clusters, supercomputers, and cloud computing platforms. +Different execution providers use of different **execution models**, such as threads (for efficient parallel execution on a multicore processor), processes, and pilot jobs for running many small tasks on a large parallel system. +Parsl is designed to abstract these low-level details so that an identical Parsl program can run unchanged on different platforms or across multiple platforms. +The Parsl configuration object (:class:`~parsl.config.Config`) specifies +which execution provider(s) and execution model(s) to use. + +:class:`~parsl.config.Config` objects are expressed in Python +so that software can +introspect permissible options, validate settings, and retrieve/edit +configurations dynamically during execution. + +The :class:`~parsl.config.Config` object, in short, is a list of **Executors**. +Each executor represents a different type of **worker** that will run Parsl tasks +and is defined by at least three components: + +- *Worker Definition* which controls how workers are deployed on a resource. + Options typically include how many workers per node and + how many resources (e.g., CPU cores) each worker is a allowed to use. +- *Provider Description* which specifies how Parsl gains access to resources, + such as by requesting nodes from a cluster's queuing system. + Many types of providers require the name of a queue, how many nodes to request from the queue, + and how long to request the nodes for. +- *Launcher Specification* which defines how the workers which execute Parsl tasks + are placed on to compute nodes. + +Build a :class:`~parsl.config.Config` by first learning about +the `configuration object `_ then +the types of `Executors `_ available in Parsl, which +each may require using a `resource provider and launcher `_. + +Alternatively, find an `example configuration `_ which is similar to what you need. + +.. toctree:: + :maxdepth: 2 + + config + executors/index + providers/index + examples diff --git a/parsl/docs/userguide/configuration/providers/elasticity.rst b/parsl/docs/userguide/configuration/providers/elasticity.rst new file mode 100644 index 00000000..40707d85 --- /dev/null +++ b/parsl/docs/userguide/configuration/providers/elasticity.rst @@ -0,0 +1,86 @@ +.. _label-elasticity: + +Elasticity +========== + +Parsl can change the amount of resources available as workloads vary over time. +Allow the amount of workers used by Parsl to change elastically through two mechanisms: + +1. Specifying the range of number of "blocks" allowed +2. Controlling the target ratio of number of tasks to workers. + +Controlling Number of Blocks +---------------------------- + +.. image:: img/parsl_scaling.gif + +All `Providers `_ request resources in "blocks" of nodes. +The default for most Providers is to request a single block of workers +at the beginning and only request another block if the current one expires. + +Allow Parsl to vary the amount of blocks through the following options: + +1. ``min_blocks``: Minimum number of blocks to maintain. +2. ``init_blocks``: Initial number of blocks to provision at initialization of workflow. +3. ``max_blocks``: Maximum number of blocks that can be active. + + +Parallelism +----------- + +The ``parallelism`` option of a Provider controls how many blocks are requested as a function of demand. +Parallelism is the ratio of the number of workers available to the number of tasks running or ready to run, +and should be a value between 0 and 1. + +For example: + +- (default) ``parallelism=1``: Request as many workers as tasks until ``max_blocks`` is met. + +.. code-block:: python + + blocks = min(max_blocks, ceil(num_tasks / (workers_per_node * nodes_per_block)) + + +- ``parallelism=0``: Use the fewest resources possible. Request + +.. code:: python + + blocks = min_blocks if active_tasks == 0 else max(min_blocks, 1) + +- ``parallelism=0.5``: Maintain one worker for every two tasks in queue. + + +Example Configuration +---------------------- + +The following configuration shows a ``HighThroughputExecutor`` +which will run between 1 and 2 blocks depending on the number of tasks. +Each block is one node in size and each node will host 2 workers (2 workers per block). +Parallelism of 0.5 means that a new block will be requested for every 4 tasks (2 workers per block / 0.5 workers per task). + +.. code:: python + + from parsl.config import Config + from parsl.providers import SlurmProvider + from parsl.executors import HighThroughputExecutor + + config = Config( + executors=[ + HighThroughputExecutor( + max_workers_per_node=2, + provider=SlurmProvider( + min_blocks=1, + init_blocks=1, + max_blocks=2, + nodes_per_block=1, + parallelism=0.5 + ) + ) + ] + ) + +The animation below shows how the number of blocks will change with the workflow. +Tasks are allocated to the first block until 5 tasks are submitted, +at which point Parsl requests a new block. + +.. image:: img/parsl_parallelism.gif diff --git a/parsl/docs/userguide/configuration/providers/img/N1_T1.png b/parsl/docs/userguide/configuration/providers/img/N1_T1.png new file mode 100644 index 0000000000000000000000000000000000000000..51d314d941fbb0aa4d6774da4ae36de21ef4cbd7 GIT binary patch literal 12292 zcmd^l^;cBU_b5nrmy`|&(hbrgLrEz}cT0CSN|z{|A|*&SjC722=MY1Qz|b)?@ACP) zU*39ey?@}lSa)6K+;jHcXP@0?N54~3Ai#Z!i-Lkepri=aKtVx00G>D4n81k0fK(6g zLbs7ol|ezNNyNJ~!vKCivryDfMM3dnK|u-mjDm6v42AqdLGk28LD@A$K@m$wL7{TV zZhbESe84nURsf?sJpO%eEBOYD;5f_6y94h^;Q5Nus?7Zd_y@~FNmUMO9gB(>#8ArR z&-pmd%SFZp1?3STS|>_^_Du^43ipZfPu|?$gsDc-*reFF|oJPeAC;Wh~=y&=nP*;Vn!l ziM6e?`)l3ZW7|#@%BC8Z?Oohl`5eI~Joj?1Kb)hj1ghnTSw(&SzSS}?zv839`?MT0 z%v56hHGC|@6uMZ#kS3p87KMDbBqLwH4A&0>UB1A6OYCEo#tj0Q@e9Kd zN@5%ri^5bY+hW^dxj2itVl?b+Rf>ze|47HLM!oj6Y%agsP}>gN)Gr75{5n-nK!xhaHBlK 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zx}hA}p&t67AR3|~I-(?6q9%HxD4L=wx}q%FqAvQPFdCy!GCHF)TBA04qd1zQI=Z7g z+M_=Dqd*#@LOP^GTBJsLq)3{iO1h*>+N4hUq)-~AQaYtnTBTNcrC6G!TDqlN+NECl RrC=JSVmhW|T8{w%06VpMB&`4d literal 0 HcmV?d00001 diff --git a/parsl/docs/userguide/configuration/providers/index.rst b/parsl/docs/userguide/configuration/providers/index.rst new file mode 100644 index 00000000..1933e421 --- /dev/null +++ b/parsl/docs/userguide/configuration/providers/index.rst @@ -0,0 +1,20 @@ +.. _label-execution: + +Providers +========= + +The **Provider** defines how to acquire then start Parsl workers on remote resources. +Providers may, for example, interface with queuing system of a computer cluster, +provision virtual machines from a cloud computing vendor, +or manage containers via Kubernetes. + +Define a Provider by first selecting the `appropriate interface `_, +specifying how to `launch workers on newly-acquired resources `_ +then configuring how Parsl will adjust `amount of resources according to demand `_. + +.. toctree:: + :maxdepth: 2 + + providers + launchers + elasticity diff --git a/parsl/docs/userguide/configuration/providers/launchers.rst b/parsl/docs/userguide/configuration/providers/launchers.rst new file mode 100644 index 00000000..940e38a8 --- /dev/null +++ b/parsl/docs/userguide/configuration/providers/launchers.rst @@ -0,0 +1,92 @@ +Starting Workers +================ + +.. note:: + + `Select a provider `_ if you have not already. + +The next step in defining a **Provider** is configuring how to start Parsl on +newly-acquired resources. +Starting Parsl occurs in two steps, each with their own Provider option: + +1. Defining how to connect to each new compute node (``launcher``) +2. Defining how to initialize the computational information (``worker_init``) + +Selecting a Launcher +-------------------- + +Parsl uses **Launchers** to start workers on every node acquired by a Provider. +Launchers are required when requesting more than one node per batch job from a +`cluster scheduler `_. + +Parsl will use the same mechanism employed for launching MPI tasks across compute nodes. +Consult the documentation or example scripts for a compute cluster to find how to launch MPI tasks, +and then find the `launcher script which matches <../../reference.html#launchers>`_. + +No options are required in most cases. +Simply import the Launcher class and provide an instance of the launcher to the ``launcher`` option of the Provider. + +.. code-block:: python + + config = Config( + executors=[ + ... + provider=SlurmProvider( + ... + launcher=SrunLauncher(), + ... + ), + ) + ], + ) + +For example, `the Polaris supercomputer at ALCF `_ +suggests to use ``mpiexec`` to start jobs +and Parsl will use the :class:`~parsl.launchers.MpiExecLauncher`. + + +Advanced: Configuring a Launcher +++++++++++++++++++++++++++++++++ + +Common cases for configuring a launcher include: + +1. Defining binding between the Parsl processes and available cores of the computer, + as when `using Parsl on the Aurora supercomputer `_. +2. Launching Parsl inside of Shifter containers by prepending the command to start + Parsl with the :class:`~parsl.launchers.WrappedLauncher`. + +.. _label-worker-init: + +Worker Initialization +--------------------- + +The ``worker_init`` argument of all Providers accepts a script for readying a newly-acquired node to run Parsl. +Worker initialization commands typically perform tasks such as: + +1. Activating the Python virtual environment which contains Parsl +2. Loading modules and setting environment variables of codes invoked by Parsl Apps +3. Launching services required for proper execution (e.g., port forwarding, NVIDIA MPS Daemons) + +Most ``worker_init`` scripts activate a virtual environment then check that the proper +version of Python is available. + +.. code-block:: python + + config = Config( + executors=[ + ... + provider=SlurmProvider( + ... + worker_init="conda activate /path/to/conda/env; which python" + ... + ), + ) + ], + ) + +.. note:: + + Consider including commands which print information about a node (e.g., ``hostname``) + or the computational environment (e.g., ``echo $PATH``) as part of the worker init + to aid determining where Parsl is running or whether the init function created the + desired environment. diff --git a/parsl/docs/userguide/configuration/providers/providers.rst b/parsl/docs/userguide/configuration/providers/providers.rst new file mode 100644 index 00000000..c6083720 --- /dev/null +++ b/parsl/docs/userguide/configuration/providers/providers.rst @@ -0,0 +1,72 @@ +Execution Providers +=================== + +Parsl presents a uniform interface to managing compute resources +from a single computer with a single user up to +supercomputers managed through batch schedulers. +Each provider exposes three core actions: submit a +job for execution (e.g., sbatch for the Slurm resource manager), +retrieve the status of an allocation (e.g., squeue), +and cancel a running job (e.g., scancel). + +All Providers manage resources in terms of **blocks**. +Blocks are collections of one or more **nodes** that each +run at least one worker. [#mpi]_ +Control the size of a block with the ``nodes_per_block`` option +and the number of blocks with `elasticity settings `_. + +Start by selecting a scheduler from following classes, +then configuring the details required. + +.. contents:: + :local: + :depth: 1 + +Local Provider +-------------- + +The :class:`~parsl.providers.LocalProvider` starts workers on the same +computer running the Parsl main script. +It is primarily used when testing Parsl programs +before deploying them to distributed resources. + +Cluster Schedulers +------------------ + +Schedulers are used at facilities where a single compute cluster is shared by many teams. +Users request individual "batch jobs" that will run once resources are available. +Parsl will create and manage its own batch jobs. + +Configuring a cluster scheduler requires: + +1. Account information (e.g., where to charge hours) +2. The size of each Job (e.g., node count, duration) + +Available options include: + +1. :class:`~parsl.providers.SlurmProvider` +2. :class:`~parsl.providers.CondorProvider` +3. :class:`~parsl.providers.GridEngineProvider` +4. :class:`~parsl.providers.TorqueProvider` +5. :class:`~parsl.providers.PBSProvider` +6. :class:`~parsl.providers.LSFProvider` + +Kubernetes +---------- + +**Provider**: :class:`~parsl.providers.KubernetesProvider` + +Kubernetes manages many small services running together on a single set of compute nodes. +Users define services then request Kubernetes to start or stop copies as appropriate. +Kubernetes creates each service inside an isolated "container," +decides where each copy runs, and manages connecting them to networks or storage. +Parsl can use Kubernetes to launch workers as services. + +Configure :class:`~parsl.providers.KubernetesProvider` by providing: + +1. Authentication details (e.g., user name, secrets) +2. The type of each container (e.g., base image, RAM, CPU) +3. Disk availability (e.g., which volumes containers can access) + + +.. [#mpi] Except for Providers running `multi-node Apps <../../apps/mpi_apps.html>`_ diff --git a/parsl/docs/userguide/configuring.rst b/parsl/docs/userguide/configuring.rst new file mode 100644 index 00000000..1b0f2be0 --- /dev/null +++ b/parsl/docs/userguide/configuring.rst @@ -0,0 +1,9 @@ +:orphan: + +.. meta:: + :content http-equiv="refresh": 0;url=configuration/index.html + +Redirect +-------- + +This page has been `moved `_ diff --git a/parsl/docs/userguide/data.rst b/parsl/docs/userguide/data.rst new file mode 100644 index 00000000..4626f0ed --- /dev/null +++ b/parsl/docs/userguide/data.rst @@ -0,0 +1,9 @@ +:orphan: + +.. meta:: + :content http-equiv="refresh": 0;url=configuration/data.html + +Redirect +-------- + +This page has been `moved `_ diff --git a/parsl/docs/userguide/exceptions.rst b/parsl/docs/userguide/exceptions.rst new file mode 100644 index 00000000..71af9d52 --- /dev/null +++ b/parsl/docs/userguide/exceptions.rst @@ -0,0 +1,9 @@ +:orphan: + +.. meta:: + :content http-equiv="refresh": 0;url=workflows/exceptions.html + +Redirect +-------- + +This page has been `moved `_ diff --git a/parsl/docs/userguide/execution.rst b/parsl/docs/userguide/execution.rst new file mode 100644 index 00000000..20346bfe --- /dev/null +++ b/parsl/docs/userguide/execution.rst @@ -0,0 +1,9 @@ +:orphan: + +.. meta:: + :content http-equiv="refresh": 0;url=configuration/execution.html + +Redirect +-------- + +This page has been `moved `_ diff --git a/parsl/docs/userguide/futures.rst b/parsl/docs/userguide/futures.rst new file mode 100644 index 00000000..1a4f40e7 --- /dev/null +++ b/parsl/docs/userguide/futures.rst @@ -0,0 +1,9 @@ +:orphan: + +.. meta:: + :content http-equiv="refresh": 0;url=execution/futures.html + +Redirect +-------- + +This page has been `moved `_ diff --git a/parsl/docs/userguide/glossary.rst b/parsl/docs/userguide/glossary.rst new file mode 100644 index 00000000..62a735ff --- /dev/null +++ b/parsl/docs/userguide/glossary.rst @@ -0,0 +1,218 @@ +Glossary +======== + +This glossary defines terms based on their usage within Parsl. By defining our terminology, we hope to create understanding across our community and reduce confusion. When asking for or providing support to fellow Parsl users, please use these terms as defined. + +Our glossary is organized alphabetically in English. Feel free to contribute terms and definitions to this list that will benefit Parsl users. + +.. _glossary: + +.. _appglossary: + +App +--- + +In Parsl, an app is a small, self-contained program that does a specific job. It's a piece of code, such as a Python function or a Bash script, that can run separately from your main program. Think of it as a mini-tool within your larger toolbox. + +.. _appfutureglossary: + +AppFuture +--------- + +An AppFuture is a placeholder for the result of an app that runs in the background. It's like a ticket you get when you order food at a restaurant – you get the ticket right away, but you have to wait for the food to be ready. Similarly, you get an AppFuture immediately when you start an app, but you have to wait for the app to finish before you can see the results. + +.. _bashappglossary: + +Bash App +-------- + +A Bash app is a special kind of app in Parsl that lets you run commands from your computer's terminal (like the ones you type in the command prompt or shell). It's a way to use Parsl to automate tasks that you would normally do manually in the terminal. + +.. _blockglossary: + +Block +----- + +A block is a group of resources, such as nodes or computational units, allocated for executing tasks. Parsl manages the distribution of work across these resources to expedite task completion. + +.. _checkpointingglossary: + +Checkpointing +------------- + +Checkpointing is like saving your progress in a video game. If something goes wrong, you can restart from the last saved point instead of starting over. In Parsl, checkpointing saves the state of your work so you can resume it later if interrupted. + +.. _concurrencyglossary: + +Concurrency +----------- + +Concurrency means doing multiple things at the same time. In Parsl, it enables your apps to run in parallel across different resources, significantly speeding up program execution. It's like a chef preparing multiple dishes in a single kitchen, switching between all of them quickly. + +.. _configurationglossary: + +Configuration +------------- + +Configuration sets up the rules for how Parsl should work. It's like adjusting the settings on your phone – you can choose how you want things to look and behave. In Parsl, you can configure things like how many resources to use, where to store data, and how to handle errors. + +.. _datafutureglossary: + +DataFuture +---------- + +A DataFuture is a placeholder for a file that an app is creating. It's like a receipt for a package you're expecting – you get the receipt right away, but you have to wait for the package to arrive. Similarly, you get a DataFuture immediately when an app starts creating a file, but you have to wait for the file to be finished before you can use it. + +.. _dfkglossary: + +DataFlowKernel (DFK) +-------------------- + +The DataFlowKernel is like the brain of Parsl. It's the part that controls how your apps run and how they share information. It's like the conductor of an orchestra, making sure that all the musicians play together in harmony. + +.. _elasticityglossary: + +Elasticity +---------- + +Elasticity refers to the ability to scale resources up or down as needed. In Parsl, it allows you to add or remove blocks of computational resources based on workload demands. + +.. _executionproviderglossary: + +Execution Provider +------------------ + +An execution provider acts as a bridge between Parsl and the resources you want to use, such as your laptop, a cluster, or a cloud service. It handles communication with these resources to execute tasks. + +.. _executorglossary: + +Executor +-------- + +An executor is a manager that determines which app runs on which resource and when. It directs the flow of apps to ensure efficient task execution. It's like a traffic controller, directing the flow of apps to make sure they all get where they need to go. + +.. _futureglossary: + +Future +------ + +A future is a placeholder for the result of a task that hasn't finished yet. Both AppFuture and DataFuture are types of Futures. You can use the ``.result()`` method to get the actual result when it's ready. + +.. _jobglossary: + +Job +--- + +A job in Parsl is a unit of work submitted to an execution environment (such as a cluster or cloud) for processing. It can consist of one or more apps executed on computational resources. + +.. _launcherglossary: + +Launcher +-------- + +A launcher in Parsl is responsible for placing the workers onto each computer, preparing them to run the apps. It’s like a bus driver who brings the players to the stadium, ensuring they are ready to start, but not directly involved in telling them what to do once they arrive. + +.. _managerglossary: + +Manager +------- + +A manager in Parsl is responsible for overseeing the execution of tasks on specific compute resources. It's like a supervisor who ensures that all workers (or workers within a block) are carrying out their tasks correctly and efficiently. + +.. _memoizationglossary: + +Memoization +----------- + +Memoization is like remembering something so you don't have to do it again. In Parsl, if you are using memoization and you run an app with the same inputs multiple times, Parsl will remember the result from the first time and give it to you again instead of running the app again. This can save a lot of time. + +.. _mpiappglossary: + +MPI App +------- + +An MPI app is a specialized app that uses the Message Passing Interface (MPI) for communication, which can occur both across nodes and within a single node. MPI enables different parts of the app to communicate and coordinate their activities, similar to how a walkie-talkie allows different teams to stay in sync. + +.. _nodeglossary: + +Node +---- + +A node in Parsl is like a workstation in a factory. It's a physical or virtual machine that provides the computational power needed to run tasks. Each node can host several workers that execute tasks. + +.. _parallelismglossary: + +Parallelism +----------- + +Parallelism means doing multiple things at the same time but not necessarily in the same location or using the same resources. In Parsl, it involves running apps simultaneously across different nodes or computational resources, accelerating program execution. Unlike concurrency which is like a chef preparing multiple dishes in a single kitchen, parallelism is like multiple chefs preparing different dishes in separate kitchens, at the same time. + +.. _parslscriptglossary: + +Parsl Script +------------ + +A Parsl script is a Python program that uses the Parsl library to define and run apps in parallel. It's like a recipe that tells you what ingredients to use and how to combine them. + +.. _pluginglossary: + +Plugin +------ + +A plugin is an add-on for Parsl. It's a piece of code that you can add to Parsl to give it new features or change how it works. It's like an extra tool that you can add to your toolbox. + +.. _pythonappglossary: + +Python App +---------- + +A Python app is a special kind of app in Parsl that's written as a Python function. It's a way to use Parsl to run your Python code in parallel. + +.. _resourceglossary: + +Resource +-------- + +A resource in Parsl refers to any computational asset that can be used to execute tasks, such as CPU cores, memory, or entire nodes. It's like the tools and materials you need to get a job done. Resources, often grouped in nodes or clusters, are essential for processing workloads. + +.. _serializationglossary: + +Serialization +------------- + +Serialization is like packing your belongings into a suitcase so you can take them on a trip. In Parsl, it means converting your data into a format that can be sent over a network to another computer. + +.. _stagingglossary: + +Staging +------- + +Staging in Parsl involves moving data to the appropriate location before an app starts running and can also include moving data back after the app finishes. This process ensures that all necessary data is available where it needs to be for the app to execute properly and that the output data is returned to a specified location once the execution is complete. + +.. _taskglossary: + +Task +---- + +A task in Parsl is the execution of an app, it is the smallest unit of work that can be executed. It's like a single step in a larger process, where each task is part of a broader workflow or job. + +.. _threadglossary: + +Thread +------ + +A thread is like a smaller part of a program that can run independently. It's like a worker in a factory who can do their job at the same time as other workers. Threads are commonly used for parallelism within a single node. + +.. _workerglossary: + +Worker +------ + +A worker in Parsl is an independent process that runs on a node to execute tasks. Unlike threads, which share resources within a single process, workers operate as separate entities, each potentially handling different tasks on the same or different nodes. + +.. _workflowglossary: + +Workflow +-------- + +A workflow is like a series of steps that you follow to complete a task. In Parsl, it's a way to describe how your apps should run and how they depend on each other, like a flowchart that shows you the order in which things need to happen. A workflow is typically expressed in a Parsl script, which is a Python program that leverages the Parsl library to orchestrate these tasks in a structured manner. diff --git a/parsl/docs/userguide/index.rst b/parsl/docs/userguide/index.rst new file mode 100644 index 00000000..a626b42a --- /dev/null +++ b/parsl/docs/userguide/index.rst @@ -0,0 +1,17 @@ +User guide +========== + +Parsl applications are composed of **Apps** that define tasks to be executed, +**Configuration** objects which define resources available for executing tasks, +and **Workflow** scripts that weave tasks together into parallel workflows. + +Start with an `overview of Parsl `_ before learning about each component. + +.. toctree:: + :maxdepth: 2 + + overview + apps/index + configuration/index + workflows/index + advanced/index diff --git a/parsl/docs/userguide/joins.rst b/parsl/docs/userguide/joins.rst new file mode 100644 index 00000000..4cc741f9 --- /dev/null +++ b/parsl/docs/userguide/joins.rst @@ -0,0 +1,9 @@ +:orphan: + +.. meta:: + :content http-equiv="refresh": 0;url=apps/joins.html + +Redirect +-------- + +This page has been `moved `_ diff --git a/parsl/docs/userguide/lifted_ops.rst b/parsl/docs/userguide/lifted_ops.rst new file mode 100644 index 00000000..ed28f55d --- /dev/null +++ b/parsl/docs/userguide/lifted_ops.rst @@ -0,0 +1,9 @@ +:orphan: + +.. meta:: + :content http-equiv="refresh": 0;url=workflows/lifted_ops.html + +Redirect +-------- + +This page has been `moved `_ diff --git a/parsl/docs/userguide/modularizing.rst b/parsl/docs/userguide/modularizing.rst new file mode 100644 index 00000000..69ed8248 --- /dev/null +++ b/parsl/docs/userguide/modularizing.rst @@ -0,0 +1,9 @@ +:orphan: + +.. meta:: + :content http-equiv="refresh": 0;url=advanced/modularizing.html + +Redirect +-------- + +This page has been `moved `_ diff --git a/parsl/docs/userguide/monitoring.rst b/parsl/docs/userguide/monitoring.rst new file mode 100644 index 00000000..138127ec --- /dev/null +++ b/parsl/docs/userguide/monitoring.rst @@ -0,0 +1,9 @@ +:orphan: + +.. meta:: + :content http-equiv="refresh": 0;url=advanced/monitoring.html + +Redirect +-------- + +This page has been `moved `_ diff --git a/parsl/docs/userguide/mpi_apps.rst b/parsl/docs/userguide/mpi_apps.rst new file mode 100644 index 00000000..3f758190 --- /dev/null +++ b/parsl/docs/userguide/mpi_apps.rst @@ -0,0 +1,9 @@ +:orphan: + +.. meta:: + :content http-equiv="refresh": 0;url=apps/mpi_apps.html + +Redirect +-------- + +This page has been `moved `_ diff --git a/parsl/docs/userguide/overview.rst b/parsl/docs/userguide/overview.rst new file mode 100644 index 00000000..a83d9875 --- /dev/null +++ b/parsl/docs/userguide/overview.rst @@ -0,0 +1,320 @@ +Overview +======== + +Parsl is designed to enable straightforward parallelism and orchestration of asynchronous +tasks into dataflow-based workflows, in Python. Parsl manages the concurrent execution of +these tasks across various computation resources, from laptops to supercomputers, +scheduling each task only when its dependencies (e.g., input data dependencies) are met. + +Developing a Parsl program is a two-step process: + +1. Define Parsl apps by annotating Python functions to indicate that they can be executed concurrently. +2. Use standard Python code to invoke Parsl apps, creating asynchronous tasks and adhering to dependencies defined between apps. + +We aim in this section to provide a mental model of how Parsl programs behave. +We discuss how Parsl programs create concurrent tasks, how tasks communicate, +and the nature of the environment on which Parsl programs can perform +operations. In each case, we compare and contrast the behavior of Python +programs that use Parsl constructs with those of conventional Python +programs. + +.. note:: + The behavior of a Parsl program can vary in minor respects depending on the + Executor used (see :ref:`label-execution`). We focus here on the behavior seen when + using the recommended `parsl.executors.HighThroughputExecutor` (HTEX). + +Parsl and Concurrency +--------------------- +Any call to a Parsl app creates a new task that executes concurrently with the +main program and any other task(s) that are currently executing. Different +tasks may execute on the same nodes or on different nodes, and on the same or +different computers. + +The Parsl execution model thus differs from the Python native execution model, +which is inherently sequential. A Python program that does not contain Parsl +constructs, or make use of other concurrency mechanisms, executes statements +one at a time, in the order that they appear in the program. This behavior is +illustrated in the following figure, which shows a Python program on the left +and, on the right, the statements executed over time when that program is run, +from top to bottom. Each time that the program calls a function, control passes +from the main program (in black) to the function (in red). Execution of the +main program resumes only after the function returns. + +.. image:: ../images/overview/python-concurrency.png + :scale: 70 + :align: center + +In contrast, the Parsl execution model is inherently concurrent. Whenever a +program calls an app, a separate thread of execution is created, and the main +program continues without pausing. Thus in the example shown in the figure +below. There is initially a single task: the main program (black). The first +call to ``double`` creates a second task (red) and the second call to ``double`` +creates a third task (orange). The second and third task terminate as the +function that they execute returns. (The dashed lines represent the start and +finish of the tasks). The calling program will only block (wait) when it is +explicitly told to do so (in this case by calling ``result()``) + +.. image:: ../images/overview/parsl-concurrency.png + + +.. note:: + Note: We talk here about concurrency rather than parallelism for a reason. + Two activities are concurrent if they can execute at the same time. Two + activities occur in parallel if they do run at the same time. If a Parsl + program creates more tasks that there are available processors, not all + concurrent activities may run in parallel. + + +Parsl and Execution +------------------- +We have now seen that Parsl tasks are executed concurrently alongside the main +Python program and other Parsl tasks. We now turn to the question of how and +where are those tasks executed. Given the range of computers on which parallel +programs may be executed, Parsl allows tasks to be executed using different +executors (:py:class:`parsl.executors`). Executors are responsible for taking a queue of tasks and executing +them on local or remote resources. + +We briefly describe two of Parsl's most commonly used executors. +Other executors are described in :ref:`label-execution`. + +The `parsl.executors.HighThroughputExecutor` (HTEX) implements a *pilot job model* that enables +fine-grain task execution using across one or more provisioned nodes. +HTEX can be used on a single node (e.g., a laptop) and will make use of +multiple processes for concurrent execution. +As shown in the following figure, HTEX uses Parsl's provider abstraction (:py:class:`parsl.providers`) to +communicate with a resource manager (e.g., batch scheduler or cloud API) to +provision a set of nodes (e.g., Parsl will use Slurm’s sbatch command to request +nodes on a Slurm cluster) for the duration of execution. +HTEX deploys a lightweight worker agent on the nodes which subsequently connects +back to the main Parsl process. Parsl tasks are then sent from the main program +to the connected workers for execution and the results are sent back via the +same mechanism. This approach has a number of advantages over other methods: +it avoids long job scheduler queue delays by acquiring one set of resources +for the entire program and it allows for scheduling of many tasks on individual +nodes. + +.. image:: ../images/overview/htex-model.png + +.. Note: + Note: when deploying HTEX, or any pilot job model such as the + WorkQueueExecutor, it is important that the worker nodes be able to connect + back to the main Parsl process. Thus, you should verify that there is network + connectivity between the workers and the Parsl process and ensure that the + correct network address is used by the workers. Parsl provides a helper + function to automatically detect network addresses + (`parsl.addresses.address_by_query`). + + +The `parsl.executors.ThreadPoolExecutor` allows tasks to be executed on a pool of locally +accessible threads. As execution occurs on the same computer, on a pool of +threads forked from the main program, the tasks share memory with one another +(this is discussed further in the following sections). + + +Parsl and Communication +----------------------- +Parsl tasks typically need to communicate in order to perform useful work. +Parsl provides for two forms of communication: by parameter passing +and by file passing. +As described in the next section, Parsl programs may also communicate by +interacting with shared filesystems and services its environment. + +Parameter Passing +^^^^^^^^^^^^^^^^^ + +The figure above illustrates communication via parameter passing. +The call ``double(3)`` to the app ``double`` in the main program creates a new task +and passes the parameter value, 3, to that new task. When the task completes +execution, its return value, 6, is returned to the main program. Similarly, the +second task is passed the value 5 and returns the value 10. In this case, the +parameters passed are simple primitive types (i.e., integers); however, complex +objects (e.g., Numpy Arrays, Pandas DataFrames, custom objects) can also be +passed to/from tasks. + +File Passing +^^^^^^^^^^^^ +Parsl supports communication via files in both Bash apps and Python apps. +Files may be used in place of parameter passing for many reasons, such as for +apps are designed to support files, when data to be exchanged are large, +or when data cannot be easily serialized into Python objects. +As Parsl tasks may be executed on remote nodes, without shared file systems, +Parsl offers a Parsl :py:class:`parsl.data_provider.files.File` construct for location-independent reference +to files. Parsl will translate file objects to worker-accessible paths +when executing dependent apps. +Parsl is also able to transfer files in, out, and between Parsl +apps using one of several methods (e.g., FTP, HTTP(S), Globus and rsync). +To accommodate the asynchronous nature of file transfer, Parsl treats +data movement like a Parsl app, adding a dependency to the execution graph +and waiting for transfers to complete before executing dependent apps. +More information is provided in :ref:`label-data`). + +Futures +^^^^^^^ +Communication via parameter and file passing also serves a second purpose, namely +synchronization. As we discuss in more detail in :ref:`label-futures`, a call to an +app returns a special object called a future that has a special unassigned +state until such time as the app returns, at which time it takes the return +value. (In the example program, two futures are thus created, d1 and d2.) The +AppFuture function result() blocks until the future to which it is applied takes +a value. Thus the print statement in the main program blocks until both child +tasks created by the calls to the double app return. The following figure +captures this behavior, with time going from left to right rather than top to +bottom as in the preceding figure. Task 1 is initially active as it starts +Tasks 2 and 3, then blocks as a result of calls to d1.result() and d2.result(), +and when those values are available, is active again. + +.. image:: ../images/overview/communication.png + +The Parsl Environment +--------------------- +Regular Python and Parsl-enhanced Python differ in terms of the environment in +which code executes. We use the term *environment* here to refer to the +variables and modules (the *memory environment*), the file system(s) +(the *file system environment*), and the services (the *service environment*) +that are accessible to a function. + +An important question when it comes to understanding the behavior of Parsl +programs is the environment in which this new task executes: does it have the +same or different memory, file system, or service environment as its parent +task or any other task? The answer, depends on the executor used, and (in the +case of the file system environment) where the task executes. +Below we describe behavior for the most commonly used `parsl.executors.HighThroughputExecutor` +which is representative of all Parsl executors except the `parsl.executors.ThreadPoolExecutor`. + +.. Warning: + The `parsl.executors.ThreadPoolExecutor` behaves differently than other Parsl executors as + it allows tasks to share memory. + +Memory environment +^^^^^^^^^^^^^^^^^^ + +In Python, the variables and modules that are accessible to a function are defined +by Python scoping rules, by which a function has access to both variables defined +within the function (*local* variables) and those defined outside the function +(*global* variables). Thus in the following code, the print statement in the +print_answer function accesses the global variable "answer", and we see as output +"the answer is 42." + +.. code-block:: python + + answer = 42 + + def print_answer(): + print('the answer is', answer) + + print_answer() + + +In Parsl (except when using the `parsl.executors.ThreadPoolExecutor`) a Parsl app is executed +in a distinct environment that only has access to local variables associated +with the app function. Thus, if the program above is executed with say the +`parsl.executors.HighThroughputExecutor`, will print "the answer is 0" rather than "the answer +is 42," because the print statement in provide_answer does not have access to +the global variable that has been assigned the value 42. The program will +run without errors when using the `parsl.executors.ThreadPoolExecutor`. + +Similarly, the same scoping rules apply to import statements, and thus +the following program will run without errors with the `parsl.executors.ThreadPoolExecutor`, +but raise errors when run with any other executor, because the return statement +in ``ambiguous_double`` refers to a variable (factor) and a module (random) that are +not known to the function. + +.. code-block:: python + + import random + factor = 5 + + @python_app + def ambiguous_double(x): + return x * random.random() * factor + + print(ambiguous_double(42)) + + +To allow this program to run correctly with all Parsl executors, the random +library must be imported within the app, and the factor variable must be +passed as an argument, as follows. + +.. code-block:: python + + import random + factor = 5 + + @python_app + def good_double(factor, x): + import random + return x * random.random() * factor + + print(good_double(factor, 42)) + + +File system environment +^^^^^^^^^^^^^^^^^^^^^^^ + +In a regular Python program the environment that is accessible to a Python +program also includes the file system(s) of the computer on which it is +executing. +Thus in the following code, a value written to a file "answer.txt" in the +current directory can be retrieved by reading the same file, and the print +statement outputs "the answer is 42." + +.. code-block:: python + + def print_answer_file(): + with open('answer.txt','r') as f: + print('the answer is', f.read()) + + with open('answer.txt','w') as f: + f.write('42') + f.close() + + print_answer_file() + + +The question of which file system environment is accessible to a Parsl app +depends on where the app executes. If two tasks run on nodes that share a +file system, then those tasks (e.g., tasks A and B in the figure below, +but not task C) share a file system environment. Thus the program above will +output "the answer is 42" if the parent task and the child task run on +nodes 1 and 2, but not if they run on nodes 2 and 3. + +.. image:: ../images/overview/filesystem.png + :scale: 70 + :align: center + +Service Environment +^^^^^^^^^^^^^^^^^^^ + +We use the term service environment to refer to network services that may be +accessible to a Parsl program, such as a Redis server or Globus data management +service. These services are accessible to any task. + +Environment Summary +^^^^^^^^^^^^^^^^^^^ + +As we summarize in the table, if tasks execute with the `parsl.executors.ThreadPoolExecutor`, +they share the memory and file system environment of the parent task. If they +execute with any other executor, they have a separate memory environment, and +may or may not share their file system environment with other tasks, depending +on where they are placed. All tasks typically have access to the same network +services. + ++--------------------+--------------------+--------------------+---------------------------+------------------+ +| | Share memory | Share file system | Share file system | Share service | +| | environment with | environment with | environment with other | environment | +| | parent/other tasks | parent | tasks | with other tasks | ++====================+====================+====================+===========================+==================+ ++--------------------+--------------------+--------------------+---------------------------+------------------+ +| Python | Yes | Yes | N/A | N/A | +| without | | | | | +| Parsl | | | | | ++--------------------+--------------------+--------------------+---------------------------+------------------+ +| Parsl | Yes | Yes | Yes | N/A | +| ThreadPoolExecutor | | | | | +| | | | | | ++--------------------+--------------------+--------------------+---------------------------+------------------+ +| Other Parsl | No | If executed on the | If tasks are executed on | N/A | +| executors | | same node with | the same node or with | | +| | | file system access | access to the same file | | +| | | | system | | ++--------------------+--------------------+--------------------+---------------------------+------------------+ diff --git a/parsl/docs/userguide/parsl_perf.rst b/parsl/docs/userguide/parsl_perf.rst new file mode 100644 index 00000000..88c4f1a2 --- /dev/null +++ b/parsl/docs/userguide/parsl_perf.rst @@ -0,0 +1,9 @@ +:orphan: + +.. meta:: + :content http-equiv="refresh": 0;url=advanced/parsl_perf.html + +Redirect +-------- + +This page has been `moved `_ diff --git a/parsl/docs/userguide/plugins.rst b/parsl/docs/userguide/plugins.rst new file mode 100644 index 00000000..2f4cdbcb --- /dev/null +++ b/parsl/docs/userguide/plugins.rst @@ -0,0 +1,9 @@ +:orphan: + +.. meta:: + :content http-equiv="refresh": 0;url=advanced/plugins.html + +Redirect +-------- + +This page has been `moved `_ diff --git a/parsl/docs/userguide/usage_tracking.rst b/parsl/docs/userguide/usage_tracking.rst new file mode 100644 index 00000000..1b891ce0 --- /dev/null +++ b/parsl/docs/userguide/usage_tracking.rst @@ -0,0 +1,9 @@ +:orphan: + +.. meta:: + :content http-equiv="refresh": 0;url=advanced/usage_tracking.html + +Redirect +-------- + +This page has been `moved `_ diff --git a/parsl/docs/userguide/workflow.rst b/parsl/docs/userguide/workflow.rst new file mode 100644 index 00000000..139dd791 --- /dev/null +++ b/parsl/docs/userguide/workflow.rst @@ -0,0 +1,9 @@ +:orphan: + +.. meta:: + :content http-equiv="refresh": 0;url=workflows/workflow.html + +Redirect +-------- + +This page has been `moved `_ diff --git a/parsl/docs/userguide/workflows/checkpoints.rst b/parsl/docs/userguide/workflows/checkpoints.rst new file mode 100644 index 00000000..f1a179a4 --- /dev/null +++ b/parsl/docs/userguide/workflows/checkpoints.rst @@ -0,0 +1,301 @@ +.. _label-memos: + +Memoization and checkpointing +----------------------------- + +When an app is invoked several times with the same parameters, Parsl can +reuse the result from the first invocation without executing the app again. + +This can save time and computational resources. + +The memoization and checkpointing system is pluggable, with basic behaviour +provided by the `BasicMemoizer`. The rest of this chapter refers to the +behaviour of the `BasicMemoizer`. + +Memoization and checkpointing is done in two ways: + +* Firstly, *app caching* will allow reuse of results and exceptions within + the same run. This is also referred to as *memoization*. + +* Building on top of that, *checkpointing* will store results (but not + exceptions) on the filesystem and reuse those results in later runs. + +.. _label-appcaching: + +App caching +=========== + + +There are many situations in which a program may be re-executed +over time. Often, large fragments of the program will not have changed +and therefore, re-execution of apps will waste valuable time and +computation resources. Parsl's app caching solves this problem by +storing results from apps that have successfully completed +so that they can be re-used. + +App caching is enabled by setting the ``cache`` +argument in the :func:`~parsl.app.app.python_app` or :func:`~parsl.app.app.bash_app` +decorator to ``True`` (by default it is ``False``). + +.. code-block:: python + + @bash_app(cache=True) + def hello (msg, stdout=None): + return 'echo {}'.format(msg) + +App caching can be globally disabled by supplying a new memoizer in +:class:`~parsl.config.Config` defined as ``BasicMemoizer(memoize=False)``. + +App caching can be particularly useful when developing interactive programs such as when +using a Jupyter notebook. In this case, cells containing apps are often re-executed +during development. Using app caching will ensure that only modified apps are re-executed. + + +App equivalence +^^^^^^^^^^^^^^^ + +Parsl determines app equivalence using the name of the app function: +if two apps have the same name, then they are equivalent under this +relation. + +Changes inside the app, or by functions called by an app will not invalidate +cached values. + +There are lots of other ways functions might be compared for equivalence, +and `parsl.dataflow.memoization.id_for_memo` provides a hook to plug in +alternate application-specific implementations. + + +Invocation equivalence +^^^^^^^^^^^^^^^^^^^^^^ + +Two app invocations are determined to be equivalent if their +input arguments are identical. + +In simple cases, this follows obvious rules: + +.. code-block:: python + + # these two app invocations are the same and the second invocation will + # reuse any cached input from the first invocation + x = 7 + f(x).result() + + y = 7 + f(y).result() + + +Internally, equivalence is determined by hashing the input arguments, and +comparing the hash to hashes from previous app executions. + +This approach can only be applied to data types for which a deterministic hash +can be computed. + +By default Parsl can compute sensible hashes for basic data types: +str, int, float, None, as well as more some complex types: +functions, and dictionaries and lists containing hashable types. + +Attempting to cache apps invoked with other, non-hashable, data types will +lead to an exception at invocation. + +In that case, mechanisms to hash new types can be registered by a program by +implementing the `parsl.dataflow.memoization.id_for_memo` function for +the new type. + +Ignoring arguments +^^^^^^^^^^^^^^^^^^ + +On occasion one may wish to ignore particular arguments when determining +app invocation equivalence - for example, when generating log file +names automatically based on time or run information. +Parsl allows developers to list the arguments to be ignored +in the ``ignore_for_cache`` app decorator parameter: + +.. code-block:: python + + @bash_app(cache=True, ignore_for_cache=['stdout']) + def hello (msg, stdout=None): + return 'echo {}'.format(msg) + + +Caveats +^^^^^^^ + +It is important to consider several important issues when using app caching: + +- Determinism: App caching is generally useful only when the apps are deterministic. + If the outputs may be different for identical inputs, app caching will obscure + this non-deterministic behavior. For instance, caching an app that returns + a random number will result in every invocation returning the same result. + +- Timing: If several identical calls to an app are made concurrently having + not yet cached a result, many instances of the app will be launched. + Once one invocation completes and the result is cached + all subsequent calls will return immediately with the cached result. + +- Performance: If app caching is enabled, there may be some performance + overhead especially if a large number of short duration tasks are launched rapidly. + This overhead has not been quantified. + +.. _label-checkpointing: + +Checkpointing +============= + +Large-scale Parsl programs are likely to encounter errors due to node failures, +application or environment errors, and myriad other issues. Parsl offers an +application-level checkpointing model to improve resilience, fault tolerance, and +efficiency. + +.. note:: + Checkpointing builds on top of app caching, and so app caching must be + enabled. If app caching is disabled in the config ``Config.app_cache``, checkpointing will + not work. + +Parsl follows an incremental checkpointing model, where each checkpoint file contains +all results that have been updated since the last checkpoint. + +When a Parsl program loads a checkpoint file and is executed, it will use +checkpointed results for any apps that have been previously executed. +Like app caching, checkpoints +use the hash of the app and the invocation input parameters to identify previously computed +results. If multiple checkpoints exist for an app (with the same hash) +the most recent entry will be used. + +Parsl provides four checkpointing modes, which can be specified using the ``checkpoint_mode`` +parameter to ``memoizer=BasicMemoizer(...)`` + +1. ``task_exit``: a checkpoint is created each time an app completes or fails + (after retries if enabled). This mode minimizes the risk of losing information + from completed tasks. + + .. code-block:: python + + BasicMemoizer(checkpoint_mode = 'task_exit') + +2. ``periodic``: a checkpoint is created periodically using a user-specified + checkpointing interval. Results will be saved to the checkpoint file for + all tasks that have completed during this period. + + .. code-block:: python + + BasicMemoizer(checkpoint_mode = 'periodic', + checkpoint_period = '01:00:00') + +3. ``dfk_exit``: checkpoints are created when Parsl is + about to exit. This reduces the risk of losing results due to + premature program termination from exceptions, terminate signals, etc. However + it is still possible that information might be lost if the program is + terminated abruptly (machine failure, SIGKILL, etc.) + + .. code-block:: python + + BasicMemoizer(checkpoint_mode = 'dfk_exit') + +4. ``manual``: in addition to these automated checkpointing modes, it is also possible + to manually initiate a checkpoint by calling ``checkpoint()`` on the + `BasicMemoizer` in the Parsl program code. + + .. code-block:: python + + m = BasicMemoizer(checkpoint_mode = 'manual') + ... + with parsl.load(Config(memoizer=m, ...)): + ... + m.checkpoint() + +In all cases the checkpoint file is written out to the ``runinfo/RUN_ID/checkpoint/`` directory. + +.. Note:: Checkpoint modes ``periodic``, ``dfk_exit``, and ``manual`` can interfere with garbage collection. + In these modes task information will be retained after completion, until checkpointing events are triggered. + + +Creating a checkpoint +^^^^^^^^^^^^^^^^^^^^^ + +Automated checkpointing must be explicitly enabled in the Parsl configuration. +There is no need to modify a Parsl program as checkpointing will occur transparently. +In the following example, checkpointing is enabled at task exit. The results of +each invocation of the ``slow_double`` app will be stored in the checkpoint file. + +.. code-block:: python + + import parsl + from parsl.app.app import python_app + from parsl.configs.local_threads import config + + config.checkpoint_mode = 'task_exit' + + parsl.load(config) + + @python_app(cache=True) + def slow_double(x): + import time + time.sleep(5) + return x * 2 + + d = [] + for i in range(5): + d.append(slow_double(i)) + + print([d[i].result() for i in range(5)]) + +Alternatively, manual checkpointing can be used to explictly specify when the checkpoint +file should be saved. The following example shows how manual checkpointing can be used. +Here, the ``checkpoint()`` method will save the results of the prior invocations +of the ``slow_double`` app. + +.. code-block:: python + + import parsl + from parsl import python_app + from parsl.configs.local_threads import config + + dfk = parsl.load(config) + + @python_app(cache=True) + def slow_double(x, sleep_dur=1): + import time + time.sleep(sleep_dur) + return x * 2 + + N = 5 # Number of calls to slow_double + d = [] # List to store the futures + for i in range(0, N): + d.append(slow_double(i)) + + # Wait for the results + [i.result() for i in d] + + dfk.memoizer.checkpoint() + + +Resuming from a checkpoint +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +When resuming a program from a checkpoint Parsl allows the user to select +which checkpoint file(s) to use. +Checkpoint files are stored in the ``runinfo/RUNID/checkpoint`` directory. + +The example below shows how to resume using all available checkpoints. +Here, the program re-executes the same calls to the ``slow_double`` app +as above and instead of waiting for results to be computed, the values +from the checkpoint file are are immediately returned. + +.. code-block:: python + + import parsl + from parsl.tests.configs.local_threads import config + from parsl.utils import get_all_checkpoints + + config.memoizer = BasicMemoizer(checkpoint_files = get_all_checkpoints()) + + parsl.load(config) + + # Rerun the same workflow + d = [] + for i in range(5): + d.append(slow_double(i)) + + # wait for results + print([d[i].result() for i in range(5)]) diff --git a/parsl/docs/userguide/workflows/concurrent.rst b/parsl/docs/userguide/workflows/concurrent.rst new file mode 100644 index 00000000..ff5616f8 --- /dev/null +++ b/parsl/docs/userguide/workflows/concurrent.rst @@ -0,0 +1,129 @@ +.. _label-parslpool: + +ParslPoolExecutor Interface +=========================== + +The :class:`parsl.concurrent.ParslPoolExecutor` implements the "Executor" interface +from the :mod:`concurrent.futures` module in standard Python. +This reduced interface permits employing Parsl into applications already capable +of single-node parallelism using :class:`~concurrent.futures.ProcessPoolExecutor`. + +Creating a ParslPoolExecutor +---------------------------- + +.. note:: + + See the :ref:`Configuration section ` for how to define computational resources. + +Create a :class:`~parsl.concurrent.ParslPoolExecutor` using one of two methods: + +1. Supplying a Parsl :class:`~parsl.Config` that defines how to create new workers. + The executor will start a new Parsl Data Flow Kernel (DFK) when it is entered as a context manager. + + .. code-block:: python + + from parsl.concurrent import ParslPoolExecutor + from parsl.config.htex_local import config # Mimicks the PythonPool + + with ParslPoolExecutor(config=config) as pool: + ... + + All resources will be closed when the block exits. + + +2. Supplying an already-started Parsl :class:`~parsl.DataFlowKernel` (DFK). + The Parsl DFK must be started and stopped separate from the executor. + + .. code-block:: python + + from parsl.concurrent import ParslPoolExecutor + from parsl.config.htex_local import config + import parsl + + with parsl.load(dfk) as dfk + with ParslPoolExecutor(dfk=dfk) as pool: + ... + ... + + Parsl will only shut when the outer block exits. + +Use multiple types of resources within the same program +by creating multiple :class:`~parsl.concurrent.ParslPoolExecutors`, +each mapped to different types of Parsl workers (also called "executors"). +Start by loading a Parsl Config that includes :ref:`multiple executors `. +Then create ``ParslPoolExecutor`` with different lists of "executors" that +their tasks will use. + +.. code-block:: python + + with parsl.load(hybrid_config) as dfk, \ + ParslPoolExecutor(dfk=dfk, executors=['gpu']) as pool_gpu, \ + ParslPoolExecutor(dfk=dfk, executors=['cpu']) as pool_cpu: + ... + +Using a ParslPoolExecutor +------------------------- + +The :class:`~parsl.concurrent.ParslPoolExecutor` supports all functions from +the :class:`~concurrent.futures.Executor` interface *except task cancellation*. + +The ``submit`` and ``map`` functions behave just as in :class:`~concurrent.futures.ProcessPoolExecutor`, +and also include the task chaining supported in App-based Parsl workflows. + +.. code-block:: python + + from parsl.concurrent import ParslPoolExecutor + from parsl.config.htex_local import config # Mimicks the PythonPool + + def f(x): + return x + 1 + + with ParslPoolExecutor(config=config) as pool: + # Submit a task then a task which depends on the result + future_1 = pool.submit(f, 1) + future_2 = pool.submit(f, future_1) + + assert future_1.result() == 2 + assert future_2.result() == 3 + +Tasks from the Executor and App-based interface may also be used together. + + .. code-block:: python + + def f(x): + return x + 1 + + @python_app + def parity(x): + return 'odd' if x % 2 == 1 else 'even' + + with ParslPoolExecutor(config=my_parsl_config) as executor: + future_1 = executor.submit(f, 1) + assert parity(future_1).result() == 'even' # Function chaining, as expected + + +Differences +----------- + +The differences between the Parsl-based :class:`~parsl.concurrent.ParslPoolExecutor` +and the Python :class:`~concurrent.futures.ProcessPoolExecutor` are: + +1. *Task Cancellation*: Parsl does not support canceling tasks once submitted. + +2. *Defining Functions*: Functions defined in modules work the same in both + Parsl and the ``ProcessPoolExecutor``. However, those defined during execution + (e.g., in the "main" file) behave differently. + + Parsl will serialize functions defined at runtime but they will not be able + to access global variables (as is the case when using the + `spawn start method `_ + in the ``ProcessPoolExecutor``), which means modules must be imported + inside the function. + Follow the rules :ref:`in the App guide `. + +3. *No Multiprocessing Objects*: Tools such as the :class:`multiprocessing.Queue` and + `synchronization primitives `_ + are not compatible with ``ParslPoolExecutor``. + +4. *Worker Initialization*: The worker initialization functions from :class:`~concurrent.futures.ProcessPoolExecutor` + are not supported. Configure workers using the ``Config`` object instead. diff --git a/parsl/docs/userguide/workflows/exceptions.rst b/parsl/docs/userguide/workflows/exceptions.rst new file mode 100644 index 00000000..d47cece4 --- /dev/null +++ b/parsl/docs/userguide/workflows/exceptions.rst @@ -0,0 +1,172 @@ +.. _label-exceptions: + +Error handling +============== + +Parsl provides various mechanisms to add resiliency and robustness to programs. + +Exceptions +---------- + +Parsl is designed to capture, track, and handle various errors occurring +during execution, including those related to the program, apps, execution +environment, and Parsl itself. +It also provides functionality to appropriately respond to failures during +execution. + +Failures might occur for various reasons: + +1. A task failed during execution. +2. A task failed to launch, for example, because an input dependency was not met. +3. There was a formatting error while formatting the command-line string in Bash apps. +4. A task completed execution but failed to produce one or more of its specified + outputs. +5. Task exceeded the specified walltime. + +Since Parsl tasks are executed asynchronously and remotely, it can be difficult to determine +when errors have occurred and to appropriately handle them in a Parsl program. + +For errors occurring in Python code, Parsl captures Python exceptions and returns +them to the main Parsl program. For non-Python errors, for example when a node +or worker fails, Parsl imposes a timeout, and considers a task to have failed +if it has not heard from the task by that timeout. Parsl also considers a task to have failed +if it does not meet the contract stated by the user during invocation, such as failing +to produce the stated output files. + +Parsl communicates these errors by associating Python exceptions with task futures. +These exceptions are raised only when a result is called on the future +of a failed task. For example: + +.. code-block:: python + + @python_app + def bad_divide(x): + return 6 / x + + # Call bad divide with 0, to cause a divide by zero exception + doubled_x = bad_divide(0) + + # Catch and handle the exception. + try: + doubled_x.result() + except ZeroDivisionError as e: + print('Oops! You tried to divide by 0.') + except Exception as e: + print('Oops! Something really bad happened.') + +.. _label-retry: + +Retries +------- + +Often errors in distributed/parallel environments are transient. +In these cases, retrying failed tasks can be a simple way +of overcoming transient (e.g., machine failure, +network failure) and intermittent failures. +When ``retries`` are enabled (and set to an integer > 0), Parsl will automatically +re-launch tasks that have failed until the retry limit is reached. +By default, retries are disabled and exceptions will be communicated +to the Parsl program. + +The following example shows how the number of retries can be set to 2: + +.. code-block:: python + + import parsl + from parsl.configs.htex_local import config + + config.retries = 2 + + parsl.load(config) + +More specific retry handling can be specified using retry handlers, documented +below. + + +Lazy fail +--------- + +Parsl implements a lazy failure model through which a workload will continue +to execute in the case that some tasks fail. That is, the program will not +halt as soon as it encounters a failure, rather it will continue to execute +unaffected apps. + +The following example shows how lazy failures affect execution. In this +case, task C fails and therefore tasks E and F that depend on results from +C cannot be executed; however, Parsl will continue to execute tasks B and D +as they are unaffected by task C's failure. + +.. code-block:: + + Here's a workflow graph, where + (X) is runnable, + [X] is completed, + (X*) is failed. + (!X) is dependency failed + + (A) [A] (A) + / \ / \ / \ + (B) (C) [B] (C*) [B] (C*) + | | => | | => | | + (D) (E) (D) (E) [D] (!E) + \ / \ / \ / + (F) (F) (!F) + + time -----> + + +Retry handlers +-------------- + +The basic parsl retry mechanism keeps a count of the number of times a task +has been (re)tried, and will continue retrying that task until the configured +retry limit is reached. + +Retry handlers generalize this to allow more expressive retry handling: +parsl keeps a retry cost for a task, and the task will be retried until the +configured retry limit is reached. Instead of the cost being 1 for each +failure, user-supplied code can examine the failure and compute a custom +cost. + +This allows user knowledge about failures to influence the retry mechanism: +an exception which is almost definitely a non-recoverable failure (for example, +due to bad parameters) can be given a high retry cost (so that it will not +be retried many times, or at all), and exceptions which are likely to be +transient (for example, where a worker node has died) can be given a low +retry cost so they will be retried many times. + +A retry handler can be specified in the parsl configuration like this: + + +.. code-block:: python + + Config( + retries=2, + retry_handler=example_retry_handler + ) + + +``example_retry_handler`` should be a function defined by the user that will +compute the retry cost for a particular failure, given some information about +the failure. + +For example, the following handler will give a cost of 1 to all exceptions, +except when a bash app exits with unix exitcode 9, in which case the cost will +be 100. This will have the effect that retries will happen as normal for most +errors, but the bash app can indicate that there is little point in retrying +by exiting with exitcode 9. + +.. code-block:: python + + def example_retry_handler(exception, task_record): + if isinstance(exception, BashExitFailure) and exception.exitcode == 9: + return 100 + else + return 1 + +The retry handler is given two parameters: the exception from execution, and +the parsl internal task_record. The task record contains details such as the +app name, parameters and executor. + +If a retry handler raises an exception itself, then the task will be aborted +and no further tries will be attempted. diff --git a/parsl/docs/userguide/workflows/futures.rst b/parsl/docs/userguide/workflows/futures.rst new file mode 100644 index 00000000..13d22a21 --- /dev/null +++ b/parsl/docs/userguide/workflows/futures.rst @@ -0,0 +1,165 @@ +.. _label-futures: + +Futures +======= + +When an ordinary Python function is invoked in a Python program, the Python interpreter waits for the function to complete execution +before proceeding to the next statement. +But if a function is expected to execute for a long period of time, it may be preferable not to wait for +its completion but instead to proceed immediately with executing subsequent statements. +The function can then execute concurrently with that other computation. + +Concurrency can be used to enhance performance when independent activities +can execute on different cores or nodes in parallel. The following +code fragment demonstrates this idea, showing that overall execution time +may be reduced if the two function calls are executed concurrently. + +.. code-block:: python + + v1 = expensive_function(1) + v2 = expensive_function(2) + result = v1 + v2 + +However, concurrency also introduces a need for **synchronization**. +In the example, it is not possible to compute the sum of ``v1`` and ``v2`` +until both function calls have completed. +Synchronization provides a way of blocking execution of one activity +(here, the statement ``result = v1 + v2``) until other activities +(here, the two calls to ``expensive_function()``) have completed. + +Parsl supports concurrency and synchronization as follows. +Whenever a Parsl program calls a Parsl app (a function annotated with a Parsl +app decorator, see :ref:`apps`), +Parsl will create a new ``task`` and immediately return a +`future `_ in lieu of that function's result(s). +The program will then continue immediately to the next statement in the program. +At some point, for example when the task's dependencies are met and there +is available computing capacity, Parsl will execute the task. Upon +completion, Parsl will set the value of the future to contain the task's +output. + +A future can be used to track the status of an asynchronous task. +For example, after creation, the future may be interrogated to determine +the task's status (e.g., running, failed, completed), access results, +and capture exceptions. Further, futures may be used for synchronization, +enabling the calling Python program to block until the future +has completed execution. + +Parsl provides two types of futures: `AppFuture` and `DataFuture`. +While related, they enable subtly different parallel patterns. + +AppFutures +---------- + +AppFutures are the basic building block upon which Parsl programs are built. Every invocation of a Parsl app returns an AppFuture that may be used to monitor and manage the task's execution. +AppFutures are inherited from Python's `concurrent library `_. +They provide three key capabilities: + +1. An AppFuture's ``result()`` function can be used to wait for an app to complete, and then access any result(s). +This function is blocking: it returns only when the app completes or fails. +The following code fragment implements an example similar to the ``expensive_function()`` example above. +Here, the ``sleep_double`` app simply doubles the input value. The program invokes +the ``sleep_double`` app twice, and returns futures in place of results. The example +shows how the future's ``result()`` function can be used to wait for the results from the +two ``sleep_double`` app invocations to be computed. + +.. code-block:: python + + @python_app + def sleep_double(x): + import time + time.sleep(2) # Sleep for 2 seconds + return x*2 + + # Start two concurrent sleep_double apps. doubled_x1 and doubled_x2 are AppFutures + doubled_x1 = sleep_double(10) + doubled_x2 = sleep_double(5) + + # The result() function will block until each of the corresponding app calls have completed + print(doubled_x1.result() + doubled_x2.result()) + +2. An AppFuture's ``done()`` function can be used to check the status of an app, *without blocking*. +The following example shows that calling the future's ``done()`` function will not stop execution of the main Python program. + +.. code-block:: python + + @python_app + def double(x): + return x*2 + + # doubled_x is an AppFuture + doubled_x = double(10) + + # Check status of doubled_x, this will print True if the result is available, else False + print(doubled_x.done()) + +3. An AppFuture provides a safe way to handle exceptions and errors while asynchronously executing +apps. The example shows how exceptions can be captured in the same way as a standard Python program +when calling the future's ``result()`` function. + +.. code-block:: python + + @python_app + def bad_divide(x): + return 6/x + + # Call bad divide with 0, to cause a divide by zero exception + doubled_x = bad_divide(0) + + # Catch and handle the exception. + try: + doubled_x.result() + except ZeroDivisionError as ze: + print('Oops! You tried to divide by 0') + except Exception as e: + print('Oops! Something really bad happened') + + +In addition to being able to capture exceptions raised by a specific app, Parsl also raises ``DependencyErrors`` when apps are unable to execute due to failures in prior dependent apps. +That is, an app that is dependent upon the successful completion of another app will fail with a dependency error if any of the apps on which it depends fail. + + +DataFutures +----------- + +While an AppFuture represents the execution of an asynchronous app, +a DataFuture represents a file to be produced by that app. +Parsl's dataflow model requires such a construct so that it can determine +when dependent apps, apps that that are to consume a file produced by another app, +can start execution. + +When calling an app that produces files as outputs, Parsl requires that a list of output files be specified (as a list of `File` objects passed in via the ``outputs`` keyword argument). Parsl will return a DataFuture for each output file as part AppFuture when the app is executed. +These DataFutures are accessible in the AppFuture's ``outputs`` attribute. + +Each DataFuture will complete when the App has finished executing, +and the corresponding file has been created (and if specified, staged out). + +When a DataFuture is passed as an argument to a subsequent app invocation, +that subsequent app will not begin execution until the DataFuture is +completed. The input argument will then be replaced with an appropriate +File object. + +The following code snippet shows how DataFutures are used. In this +example, the call to the echo Bash app specifies that the results +should be written to an output file ("hello1.txt"). The main +program inspects the status of the output file (via the future's +``outputs`` attribute) and then blocks waiting for the file to +be created (``hello.outputs[0].result()``). + +.. code-block:: python + + # This app echoes the input string to the first file specified in the + # outputs list + @bash_app + def echo(message, outputs=()): + return 'echo {} &> {}'.format(message, outputs[0]) + + # Call echo specifying the output file + hello = echo('Hello World!', outputs=[File('hello1.txt')]) + + # The AppFuture's outputs attribute is a list of DataFutures + print(hello.outputs) + + # Print the contents of the output DataFuture when complete + with open(hello.outputs[0].result().filepath, 'r') as f: + print(f.read()) diff --git a/parsl/docs/userguide/workflows/index.rst b/parsl/docs/userguide/workflows/index.rst new file mode 100644 index 00000000..9b4a17d6 --- /dev/null +++ b/parsl/docs/userguide/workflows/index.rst @@ -0,0 +1,38 @@ +Running Workflows +================= + +Parsl workflows are a Python "main" program that defines Apps, +how the Apps are invoked, +and how results are passed between different Apps. + +The core concept of workflows is that Parsl Apps produce **Futures** +with all features from those in Python's :mod:`concurrent.futures` module and more. + +The Parsl programs typically contain a ``parsl.load`` statement to start +the services which manage scheduling computations. + +.. code-block:: python + + from parsl.configs.htex_local import config # See the "Configuration" documentation + from parsl import python_app # See the "Apps" documentation + import parsl + + @python_app + def f(x): + return x + 1 + + with parsl.load(config): + future = f(1) + print(f'1 + 1 = {future.result()}') + + +.. toctree:: + :maxdepth: 2 + + futures + workflow + exceptions + lifted_ops + logging + checkpoints + concurrent diff --git a/parsl/docs/userguide/workflows/lifted_ops.rst b/parsl/docs/userguide/workflows/lifted_ops.rst new file mode 100644 index 00000000..6e258b9b --- /dev/null +++ b/parsl/docs/userguide/workflows/lifted_ops.rst @@ -0,0 +1,56 @@ +.. _label-liftedops: + +Lifted operators +================ + +Parsl allows some operators (``[]`` and ``.``) to be used on an AppFuture in +a way that makes sense with those operators on the eventually returned +result. + +Lifted [] operator +------------------ + +When an app returns a complex structure such as a ``dict`` or a ``list``, +it is sometimes useful to pass an element of that structure to a subsequent +task, without waiting for that subsequent task to complete. + +To help with this, Parsl allows the ``[]`` operator to be used on an +`AppFuture`. This operator will return another `AppFuture` that will +complete after the initial future, with the result of ``[]`` on the value +of the initial future. + +The end result is that this assertion will hold: + +.. code-block:: python + + fut = my_app() + assert fut['x'].result() == fut.result()[x] + +but more concurrency will be available, as execution of the main workflow +code will not stop to wait for ``result()`` to complete on the initial +future. + +`AppFuture` does not implement other methods commonly associated with +dicts and lists, such as ``len``, because those methods should return a +specific type of result immediately, and that is not possible when the +results are not available until the future. + +If a key does not exist in the returned result, then the exception will +appear in the Future returned by ``[]``, rather than at the point that +the ``[]`` operator is applied. This is because the valid values that can +be used are not known until the underlying result is available. + +Lifted . operator +----------------- + +The ``.`` operator works similarly to ``[]`` described above: + +.. code-block:: python + + fut = my_app + assert fut.x.result() == fut.result().x + +Attributes beginning with ``_`` are not lifted as this usually indicates an +attribute that is used for internal purposes, and to try to avoid mixing +protocols (such as iteration in for loops) defined on AppFutures vs protocols +defined on the underlying result object. diff --git a/parsl/docs/userguide/workflows/logging.rst b/parsl/docs/userguide/workflows/logging.rst new file mode 100644 index 00000000..7129b873 --- /dev/null +++ b/parsl/docs/userguide/workflows/logging.rst @@ -0,0 +1,263 @@ +.. + The logging examples are from the "examples" in the advanced user guide. + +Logging +======= + +Parsl produces a rich collection of output logs by default. +Different parts of Parsl write to separate files +and all are collected in a directory named "runinfo" by default. + +Log Directory Structure +----------------------- + +.. code-block:: text + + runinfo/ + └── 000 + ├── htex_local + │ ├── block-0 + │ │ └── ad5bf9a8d238 + │ │ ├── manager.log + │ │ ├── worker_0.log + │ │ ├── worker_1.log + │ │ ├── worker_2.log + │ │ └── worker_3.log + │ └── interchange.log + ├── parsl.log + └── submit_scripts + ├── parsl.htex_local.block-0.1735499186.6675148.sh + ├── parsl.htex_local.block-0.1735499186.6675148.sh.ec + ├── parsl.htex_local.block-0.1735499186.6675148.sh.err + └── parsl.htex_local.block-0.1735499186.6675148.sh.out + + +The ``runinfo`` directory holds a unique subdirectory for each time Parsl is executed. + +Components of Parsl run in separate processes +and each write to different files to avoid any need for synchronization. +The root directory for a run contains: + +1. ``parsl.log``: The core log file from main Parsl processes +2. ``submit_scripts``: A collection of the scripts which were run to launch workers. + These scripts may include batch submission files and the text printed to screen + during the batch job. +3. Subdirectories for logs from each of the Executors used by Parsl (here: ``htex_local``). + +Main Parsl Log +~~~~~~~~~~~~~~ + +The ``parsl.log`` file in a Parsl logging directory captures all Parsl-related activities +that occur on the Python process running the Parsl application. + +The log should begin with a message indicating Parsl DataFlowKernel (DFK) is starting +and a readout of the configuration settings. +The logs will end with a status message that the DFK has cleaned up successfully. + +.. code-block:: text + + 1735499181.631377 2024-12-29 14:06:21 MainProcess- MainThread- parsl.dataflow.dflow:95 __init__ INFO: Starting DataFlowKernel with config + [ ... ] + 1735499187.851164 2024-12-29 14:06:27 MainProcess- MainThread- parsl.dataflow.dflow:1265 cleanup INFO: DFK cleanup complete + 1735499187.851178 2024-12-29 14:06:27 MainProcess- MainThread- parsl.process_loggers:27 wrapped DEBUG: Normal ending for cleanup on thread MainThread + +As :ref:`exampled below `, the main Parsl log contains references for +status changes of blocks of workers and each task. + +Submission Scripts +~~~~~~~~~~~~~~~~~~ + +The ``submit_script`` directory contains the input and outputs from the :ref:`execution providers ` +used by a Parsl application. +The actual contents of the file vary between Parsl configurations, +but most providers will produce a shell script +used when requesting compute nodes and the outputs of the shell scripts. + +Executor Logs +~~~~~~~~~~~~~ + +Different Executors in the Parsl application write log files to their own directories. +The types of logs vary depending on the choice of Executor. +This section describes the output structure of a common configuration: +:ref:`HighThroughputExecutor ` using a batch scheduler. + +The log file at the root of the directory, ``interchange.log``, is from a process +which distributes task across the workers used by Parsl. +The interchange reports the connection between the main Parsl process +and managers which register to it. + +.. code-block:: text + + 2024-12-29 14:06:21.965 interchange:115 MainProcess(52425) MainThread __init__ [INFO] Attempting connection to client at 127.0.0.1 on ports: 55059,55181,55929 + 2024-12-29 14:06:21.966 interchange:127 MainProcess(52425) MainThread __init__ [INFO] Connected to client + 2024-12-29 14:06:21.966 interchange:160 MainProcess(52425) MainThread __init__ [INFO] Bound to ports 54160,54943 for incoming worker connections + [ ... ] + 2024-12-29 14:06:21.966 interchange:209 MainProcess(52425) Interchange-Task-Puller task_puller [INFO] Starting + 2024-12-29 14:06:27.316 interchange:411 MainProcess(52425) MainThread process_task_outgoing_incoming [INFO] Adding manager: b'ad5bf9a8d238' to ready queue + +Each manager writes logs to a unique subdirectory named by the block it belongs +to and the name assigned to it. +The structure shown above includes one manager directory: ``htex_local/block‑0/ad5bf9a8d238``. +The directory contains two types of files: + +1. A single manager log reporting connections to the other Parsl components, +2. Many worker logs reporting which CPUs or accelerators they use, and which tasks they have processed. + +.. _section-logging-tasks: + +Common Debugging Tasks +---------------------- + +.. note:: + + Consider the :ref:`monitoring module ` for developing + components that display the current status of a workflow. + +This section describes how to use the logs to check correctness +and locate errors in common operations. + +Launching Blocks +~~~~~~~~~~~~~~~~ + +Parsl groups compute nodes into :ref:`blocks ` that are acquired +and released as compute requirements change. +The process for launching a block is recorded in a sequence: + +1. A request for "scaling out" in ``parsl.log``. + + Log messages starting with ``[Scaling executor ]`` record the process for + deciding whether to request another block and status messages while acquiring + a new block of workers. + + Typical log lines for requesting a block start with a request + and end with a job ID from the :ref:`provider `: + + .. code-block:: text + + [...] parsl.jobs.strategy:195 _general_strategy DEBUG: [Scaling executor htex_local] Strategizing for executor + [...] parsl.jobs.strategy:198 _general_strategy DEBUG: [Scaling executor htex_local] Scaling out 1 initial blocks + [...] parsl.executors.status_handling:204 scale_out_facade INFO: [Scaling executor htex_local] Scaling out by 1 blocks + [...] parsl.executors.status_handling:207 scale_out_facade INFO: [Scaling executor htex_local] Allocated block ID 0 + [...] + [...] parsl.executors.status_handling:268 _launch_block DEBUG: [Scaling executor htex_local] Launched block 0 with job ID 20076 + + + The last message, "Launched block with job ID <>," indicates + a request was successfully made to the execution provider. + + The job ID is often the same used by the scheduling system underlying the Provider. + Use standard tools (e.g., ``squeue`` for Slurm) to monitor whether the requested nodes are available. + +2. The results from requesting nodes are found in the ``submit_scripts`` directory. + + Use the output files and the job ID from the previous step to determine whether + the block was launched properly. + The file names contain the name of the executor and ID associated with the block of workers. + + Common errors that can be found in the output files include: + + - Parsl worker services not found because the :ref:`Python environment was not activated ` + - The launcher (e.g., ``srun``) fails to work because of incorrect options. + - Workers fail to connect to the host process :ref:`due to network configuration problems ` + +Tracing Compute Nodes +~~~~~~~~~~~~~~~~~~~~~ + +.. note:: + + This sections describes the procedure for Parsl using + :ref:`the high-throughput executor ` + and may not be applicable to other executors. + +Use logs specific to an Executor to trace whether the workers on the compute nodes +are starting, detecting hardware properly, and connecting to the host process. + +Each node in a compute block writes to a unique subdirectory within +the directory of its associated executor and block. +For example, a compute node in the first block of the "htex_local" executor +is found in ``htex_local/block-0``. + +Use the logs written by processes on the compute node to ensure... + +- *Connectivity* to the host process. The manager log should declare that it connected + and identify which network it is using by printing a log line similar to + + .. code-block:: text + + 2025-12-29 09:22:12.683 parsl:322 20081 Interchange-Communicator [INFO] Successfully connected to interchange via URL: tcp://192.168.1.71:54495 + + The ``interchange.log`` in the root directory for the executor will include a matching line + after acknowledging the connection and registering the workers as ready for tasks. + + .. code-block:: text + + 2025-12-29 09:22:12.779 interchange:438 MainProcess(20069) MainThread process_manager_socket_message [INFO] Registered manager b'c4b55da1a90f' (py3.11, 1.3.0-dev) and added to ready queue + +- *Resource Pinning* for the workers. + Each worker log begins with a message that it has started then, + if affinity is enabled, + a list of hardware (CPU and/or GPU) that it will use for tasks. + + .. code-block:: text + + 2025-12-29 09:22:13.110 worker_log:658 20100 MainThread [INFO] Worker 7 started + 2025-12-29 09:22:13.112 worker_log:715 20100 MainThread [INFO] Set worker CPU affinity to [7] + + +Tracking Task Status +~~~~~~~~~~~~~~~~~~~~ + +The ``parsl.log`` provides information about a task across its lifespan. + +1. The task is submitted. Parsl logs which executor it is assigned to + and the name of the App it is running, and any dependencies. + + .. code-block:: text + + 1763128191.056197 2025-11-14 05:49:51 MainProcess- ThreadPoolExecutor-2_0- parsl.dataflow.dflow:973 submit DEBUG: Task 316 will be sent to executor _parsl_internal + 1763128191.058182 2025-11-14 05:49:51 MainProcess- ThreadPoolExecutor-2_0- parsl.dataflow.dflow:1052 submit INFO: Task 316 submitted for App launch_relaxations, waiting on task 315 + 1763128191.058387 2025-11-14 05:49:51 MainProcess- ThreadPoolExecutor-2_0- parsl.dataflow.dflow:1058 submit DEBUG: Task 316 set to pending state with AppFuture: + 1763128191.058833 2025-11-14 05:49:51 MainProcess- Task-Launch_0- parsl.dataflow.dflow:629 _launch_if_ready_async DEBUG: Task 316 has outstanding dependencies, so launch_if_ready skipping + + The above task is a :ref:`join app ` named ``launch_relaxations``, + and will run on the internal executor for Parsl (``_parsl_internal``). + It is dependent on task 315, so will not start immediately. + +2. The task is launched after all dependencies are done. + + .. code-block:: text + + 1763130591.542695 2025-11-14 06:29:51 MainProcess- Task-Launch_0- parsl.dataflow.memoization:269 check_memo DEBUG: Task 316 will not be memoized + 1763130591.543230 2025-11-14 06:29:51 MainProcess- Task-Launch_0- parsl.dataflow.dflow:739 launch_task INFO: Parsl task 316 try 0 launched on executor _parsl_internal + + A single task may be :ref:`tried multiple times ` and the attempt number is recorded in the logs as well. + +3. The result is received. If successful, Parsl will print a single completion message. + + .. code-block:: text + + 1763147214.980597 2025-11-14 11:06:54 MainProcess- HTEX-Result-Queue-Thread- parsl.dataflow.dflow:539 _complete_task_result INFO: Task 316 completed (joining -> exec_done) + + If unsuccessful, the task could be restarted and relaunched + + .. code-block:: text + + 1763129113.044855 2025-11-14 06:05:13 MainProcess- HTEX-Result-Queue-Thread- parsl.dataflow.dflow:331 handle_exec_update INFO: Task 204 try 0 failed with exception of type ValueError + 1763129113.045055 2025-11-14 06:05:13 MainProcess- HTEX-Result-Queue-Thread- parsl.dataflow.dflow:371 handle_exec_update INFO: Task 204 marked for retry + [...] + 1763129113.050172 2025-11-14 06:05:13 MainProcess- Task-Launch_0-140414082344704 parsl.dataflow.dflow:734 launch_task INFO: Parsl task 204 try 1 launched on executor xtb with executor id 445 + + or failed and the exception both recorded in the logs and + :ref:`exposed to the user as an exception in the app Future `. + + .. code-block:: text + + 1763137697.445044 2025-11-14 08:28:17 MainProcess- HTEX-Result-Queue-Thread- parsl.app.errors:110 reraise DEBUG: Reraising exception of type + 1763137697.445302 2025-11-14 08:28:17 MainProcess- HTEX-Result-Queue-Thread- parsl.dataflow.dflow:331 handle_exec_update INFO: Task 204 try 1 failed with exception of type ValueError + 1763137697.445468 2025-11-14 08:28:17 MainProcess- HTEX-Result-Queue-Thread- parsl.dataflow.dflow:374 handle_exec_update ERROR: Task 204 failed after 1 retry attempts + Traceback (most recent call last): + File "/global/cfs/env/lib/python3.12/site-packages/parsl/dataflow/dflow.py", line 328, in handle_exec_update + [...] + return umr_minimum(a, axis, None, out, keepdims, initial, where) + ^^^^^^^^^^^^^^^^^ + ValueError: zero-size array to reduction operation minimum which has no identity diff --git a/parsl/docs/userguide/workflows/workflow.rst b/parsl/docs/userguide/workflows/workflow.rst new file mode 100644 index 00000000..f8b34fa6 --- /dev/null +++ b/parsl/docs/userguide/workflows/workflow.rst @@ -0,0 +1,243 @@ +.. _label-workflow: + +Example parallel patterns +========================= + +Parsl can be used to implement a wide range of parallel programming patterns, from bag of tasks +through to nested workflows. Parsl implicitly assembles a dataflow +dependency graph based on the data shared between apps. +The flexibility of this model allows for the implementation of a wide range +of parallel programming and workflow patterns. + +Parsl is also designed to address broad execution requirements, from programs +that run many short tasks to those that run a few long tasks. + +Below we illustrate a range of parallel programming and workflow patterns. It is important +to note that this set of examples is by no means comprehensive. + + +Bag of Tasks +------------ +Parsl can be used to execute a large bag of tasks. In this case, Parsl +assembles the set of tasks (represented as Parsl apps) and manages their concurrent +execution on available resources. + +.. code-block:: python + + from parsl import python_app + + parsl.load() + + # Map function that returns double the input integer + @python_app + def app_random(): + import random + return random.random() + + results = [] + for i in range(0, 10): + x = app_random() + results.append(x) + + for r in results: + print(r.result()) + + +Sequential workflows +-------------------- + +Sequential workflows can be created by passing an AppFuture from one task to another. For example, in the following program the ``generate`` app (a Python app) generates a random number that is consumed by the ``save`` app (a Bash app), which writes it to a file. Because ``save`` cannot execute until it receives the ``message`` produced by ``generate``, the two apps execute in sequence. + +.. code-block:: python + + from parsl import python_app + + parsl.load() + + # Generate a random number + @python_app + def generate(limit): + from random import randint + """Generate a random integer and return it""" + return randint(1, limit) + + # Write a message to a file + @bash_app + def save(message, outputs=()): + return 'echo {} &> {}'.format(message, outputs[0]) + + message = generate(10) + + saved = save(message, outputs=['output.txt']) + + with open(saved.outputs[0].result(), 'r') as f: + print(f.read()) + + +Parallel workflows +------------------ + +Parallel execution occurs automatically in Parsl, respecting dependencies among app executions. In the following example, three instances of the ``wait_sleep_double`` app are created. The first two execute concurrently, as they have no dependencies; the third must wait until the first two complete and thus the ``doubled_x`` and ``doubled_y`` futures have values. Note that this sequencing occurs even though ``wait_sleep_double`` does not in fact use its second and third arguments. + +.. code-block:: python + + from parsl import python_app + + parsl.load() + + @python_app + def wait_sleep_double(x, foo_1, foo_2): + import time + time.sleep(2) # Sleep for 2 seconds + return x*2 + + # Launch two apps, which will execute in parallel, since they do not have to + # wait on any futures + doubled_x = wait_sleep_double(10, None, None) + doubled_y = wait_sleep_double(10, None, None) + + # The third app depends on the first two: + # doubled_x doubled_y (2 s) + # \ / + # doublex_z (2 s) + doubled_z = wait_sleep_double(10, doubled_x, doubled_y) + + # doubled_z will be done in ~4s + print(doubled_z.result()) + + +Parallel workflows with loops +----------------------------- + +A common approach to executing Parsl apps in parallel is via loops. The following example uses a loop to create many random numbers in parallel. + +.. code-block:: python + + from parsl import python_app + + parsl.load() + + @python_app + def generate(limit): + """Generate a random integer and return it""" + from random import randint + return randint(1, limit) + + rand_nums = [] + for i in range(1,5): + rand_nums.append(generate(i)) + + # Wait for all apps to finish and collect the results + outputs = [r.result() for r in rand_nums] + +The :class:`~parsl.concurrent.ParslPoolExecutor` simplifies this pattern using the same interface as +`Python's native Executors `_. + +.. code-block:: python + + from parsl.concurrent import ParslPoolExecutor + from parsl.configs.htex_local import config + + # NOTE: Functions used by the ParslPoolExecutor do _not_ use decorators + def generate(limit): + """Generate a random integer and return it""" + from random import randint + return randint(1, limit) + + + with ParslPoolExecutor(config) as pool: + outputs = pool.map(generate, range(1, 5)) + + +In the preceding example, the execution of different tasks is coordinated by passing Python objects from producers to consumers. +In other cases, it can be convenient to pass data in files, as in the following reformulation. Here, a set of files, each with a random number, is created by the ``generate`` app. These files are then concatenated into a single file, which is subsequently used to compute the sum of all numbers. + +.. code-block:: python + + from parsl import python_app, bash_app + + parsl.load() + + @bash_app + def generate(outputs=()): + return 'echo $(( RANDOM % (10 - 5 + 1 ) + 5 )) &> {}'.format(outputs[0]) + + @bash_app + def concat(inputs=(), outputs=(), stdout='stdout.txt', stderr='stderr.txt'): + return 'cat {0} >> {1}'.format(' '.join(inputs), outputs[0]) + + @python_app + def total(inputs=()): + total = 0 + with open(inputs[0].filepath, 'r') as f: + for l in f: + total += int(l) + return total + + # Create 5 files with random numbers + output_files = [] + for i in range (5): + output_files.append(generate(outputs=['random-%s.txt' % i])) + + # Concatenate the files into a single file + cc = concat(inputs=[i.outputs[0] for i in output_files], outputs=['all.txt']) + + # Calculate the average of the random numbers + totals = total(inputs=[cc.outputs[0]]) + + print(totals.result()) + + +MapReduce +--------- +MapReduce is a common pattern used in data analytics. It is composed of a map phase +that filters values and a reduce phase that aggregates values. +The following example demonstrates how Parsl can be used to specify a MapReduce computation +in which the map phase doubles a set of input integers and the reduce phase computes +the sum of those results. + +.. code-block:: python + + from parsl import python_app + + parsl.load() + + # Map function that returns double the input integer + @python_app + def app_double(x): + return x*2 + + # Reduce function that returns the sum of a list + @python_app + def app_sum(inputs=()): + return sum(inputs) + + # Create a list of integers + items = range(0,4) + + # Map phase: apply the double *app* function to each item in list + mapped_results = [] + for i in items: + x = app_double(i) + mapped_results.append(x) + + # Reduce phase: apply the sum *app* function to the set of results + total = app_sum(inputs=mapped_results) + + print(total.result()) + +The program first defines two Parsl apps, ``app_double`` and ``app_sum``. +It then makes calls to the ``app_double`` app with a set of input +values. It then passes the results from ``app_double`` to the ``app_sum`` app +to aggregate values into a single result. +These tasks execute concurrently, synchronized by the ``mapped_results`` variable. +The following figure shows the resulting task graph. + +.. image:: ../../images/MapReduce.png + +Caching expensive initialisation between tasks +---------------------------------------------- + +Many tasks in workflows require a expensive "initialization" steps that, once performed, can be used across successive invocations for that task. For example, you may want to reuse a machine learning model for multiple interface tasks and avoid loading it onto GPUs more than once. + +`This ExaWorks tutorial `_ gives examples of how to do this. diff --git a/parsl/mypy-stubs/typeguard.pyi b/parsl/mypy-stubs/typeguard.pyi new file mode 100644 index 00000000..e182edfd --- /dev/null +++ b/parsl/mypy-stubs/typeguard.pyi @@ -0,0 +1,10 @@ + +# this type for runtime_validation is based on the +# "Decorators that do not change the signature of the function" section of +# https://github.com/python/mypy/issues/3157 + +from typing import TypeVar + +Func = TypeVar('Func') + +def typechecked(f: Func) -> Func: ... diff --git a/parsl/mypy.ini b/parsl/mypy.ini new file mode 100644 index 00000000..c268667a --- /dev/null +++ b/parsl/mypy.ini @@ -0,0 +1,212 @@ +[mypy] +plugins = sqlalchemy.ext.mypy.plugin + +enable_error_code = ignore-without-code +no_implicit_reexport = True +warn_redundant_casts = True + +no_implicit_optional = True + +strict_equality = True +warn_unused_ignores = True +warn_unreachable = True + +[mypy-non_existent.*] +ignore_missing_imports = True + +# some subpackages in parsl are fully typed and should be checked with much +# stricter type checking options - a longer term goal might be for all of +# parsl to be checked that way, so these options could be default instead of +# listed per package; but unless/until that happens, the more-strictly-checked +# code should be listed here: + +[mypy-parsl.addresses.*] +disallow_untyped_defs = True +disallow_any_decorated = True + +[mypy-parsl.app.app.*] +disallow_untyped_defs = True + +[mypy-parsl.benchmark.*] +disallow_untyped_defs = True + +[mypy-parsl.dataflow.errors.*] +disallow_untyped_defs = True +disallow_any_expr = True +disallow_any_decorated = True + +[mypy-parsl.dataflow.futures.*] +disallow_untyped_defs = True +disallow_any_decorated = True + +[mypy-parsl.dataflow.memoization.*] +disallow_untyped_defs = True + +[mypy-parsl.dataflow.rundirs.*] +disallow_untyped_defs = True +disallow_any_expr = True +disallow_any_decorated = True + +[mypy-parsl.dataflow.states.*] +disallow_untyped_defs = True +disallow_any_expr = True +disallow_any_decorated = True + +[mypy-parsl.dataflow.taskrecord.*] +disallow_untyped_defs = True +disallow_any_expr = True +disallow_any_decorated = True + +[mypy-parsl.jobs.*] +disallow_untyped_defs = True + +[mypy-parsl.logconfigs.*] +disallow_untyped_defs = True +disallow_any_decorated = True + +[mypy-parsl.providers.base.*] +disallow_untyped_decorators = True +check_untyped_defs = True +disallow_subclassing_any = True +disallow_untyped_defs = True + +[mypy-parsl.serialize.*] +disallow_untyped_decorators = True +check_untyped_defs = True +disallow_subclassing_any = True +disallow_untyped_defs = True + +[mypy-parsl.serialize.proxystore.*] +# parsl/serialize/proxystore.py:9: error: Class cannot subclass "Pickler" (has type "Any") +disallow_subclassing_any = False + +[mypy-parsl.executors.base.*] +disallow_untyped_defs = True +disallow_any_expr = True + +[mypy-parsl.executors.high_throughput.interchange.*] +disallow_untyped_defs = True + +[mypy-parsl.monitoring.*] +disallow_untyped_decorators = True +check_untyped_defs = True +disallow_subclassing_any = True +disallow_untyped_defs = True + +# visualization typechecks much less well than the rest of monitoring, +# so reduce strictness for the visualization subtree. +[mypy-parsl.monitoring.visualization.*] +ignore_errors = True + +[mypy-parsl.tests.configs.local_user_opts] +ignore_missing_imports = True + +[mypy-parsl.usage_tracking] +check_untyped_defs = True +disallow_subclassing_any = True +disallow_untyped_decorators = True +disallow_untyped_defs = True +warn_unreachable = True + +[mypy-parsl.utils] +disallow_untyped_defs = True + +[mypy-flask_sqlalchemy.*] +ignore_missing_imports = True + +[mypy-networkx.*] +ignore_missing_imports = True + +[mypy-_pytest.*] +ignore_missing_imports = True + +[mypy-pytest.*] +ignore_missing_imports = True + +[mypy-dill.*] +ignore_missing_imports = True + +[mypy-copyreg.*] +ignore_missing_imports = True + +[mypy-tblib.*] +ignore_missing_imports = True + +[mypy-globus_sdk.*] +ignore_missing_imports = True + +[mypy-psutil.*] +ignore_missing_imports = True + +[mypy-cPickle.*] +ignore_missing_imports = True + +[mypy-copy_reg.*] +ignore_missing_imports = True + +[mypy-cmreslogging.handlers.*] +ignore_missing_imports = True + +[mypy-kubernetes.*] +ignore_missing_imports = True + +[mypy-azure.*] +ignore_missing_imports = True + +[mypy-msrestazure.*] +ignore_missing_imports = True + +[mypy-googleapiclient.*] +ignore_missing_imports = True + +[mypy-boto3.*] +ignore_missing_imports = True + +[mypy-botocore.*] +ignore_missing_imports = True + +[mypy-zmq.*] +ignore_missing_imports = True + +[mypy-mpi4py.*] +ignore_missing_imports = True + +[mypy-flask.*] +ignore_missing_imports = True + +# this is an internal undocumentated package +# of multiprocessing - trying to get Event +# to typecheck in monitoring, but it's not +# a top level class as far as mypy is concerned. +# but... when commented out seems ok? +# so lets see when happens when I try to merge +# in clean CI +#[mypy-multiprocessing.synchronization.*] +#ignore_missing_imports = True + +[mypy-globus_compute_sdk.*] +ignore_missing_imports = True + +[mypy-pandas.*] +ignore_missing_imports = True + +[mypy-ndcctools.*] +ignore_missing_imports = True + +[mypy-work_queue.*] +ignore_missing_imports = True + +[mypy-radical.*] +ignore_missing_imports = True + +[mypy-oauth_ssh.*] +ignore_missing_imports = True + +[mypy-flux.*] +ignore_missing_imports = True + +[mypy-setproctitle.*] +ignore_missing_imports = True + +[mypy-proxystore.*] +ignore_missing_imports = True diff --git a/parsl/parsl/__init__.py b/parsl/parsl/__init__.py new file mode 100644 index 00000000..8fa723eb --- /dev/null +++ b/parsl/parsl/__init__.py @@ -0,0 +1,75 @@ +"""Parsl is a Parallel Scripting Library, designed to enable efficient workflow execution. + +Importing +--------- + +To get all the required functionality, we suggest importing the library as follows: + +>>> import parsl +>>> from parsl import * + +Constants +--------- +AUTO_LOGNAME + Special value that indicates Parsl should construct a filename for logging. + +""" +import logging +import os +import platform + +from parsl.app.app import bash_app, join_app, python_app +from parsl.config import Config +from parsl.data_provider.files import File +from parsl.dataflow.dflow import DataFlowKernel, DataFlowKernelLoader +from parsl.executors import ( + HighThroughputExecutor, + ThreadPoolExecutor, + WorkQueueExecutor, +) +from parsl.log_utils import set_file_logger, set_stream_logger +from parsl.monitoring import MonitoringHub +from parsl.version import VERSION + +__author__ = 'The Parsl Team' +__version__ = VERSION + +AUTO_LOGNAME = -1 + +__all__ = [ + + # decorators + 'bash_app', + 'python_app', + 'join_app', + + # core + 'Config', + 'DataFlowKernel', + 'File', + + # logging + 'set_stream_logger', + 'set_file_logger', + 'AUTO_LOGNAME', + + # executors + 'ThreadPoolExecutor', + 'HighThroughputExecutor', + 'WorkQueueExecutor', + 'RadicalPilotExecutor', + + # monitoring + 'MonitoringHub', +] + +clear = DataFlowKernelLoader.clear +load = DataFlowKernelLoader.load +dfk = DataFlowKernelLoader.dfk +wait_for_current_tasks = DataFlowKernelLoader.wait_for_current_tasks + + +logging.getLogger('parsl').addHandler(logging.NullHandler()) + +if platform.system() == 'Darwin': + os.environ['OBJC_DISABLE_INITIALIZE_FORK_SAFETY'] = 'YES' diff --git a/parsl/parsl/addresses.py b/parsl/parsl/addresses.py new file mode 100644 index 00000000..8f7ccb3b --- /dev/null +++ b/parsl/parsl/addresses.py @@ -0,0 +1,175 @@ +"""This module contains several helper functions which can be used to +find an address of the submitting system, for example to use as the +address parameter for HighThroughputExecutor. + +The helper to use depends on the network environment around the submitter, +so some experimentation will probably be needed to choose the correct one. +""" + +import ipaddress +import logging +import platform +import socket + +import requests + +try: + import fcntl +except ImportError: + fcntl = None # type: ignore[assignment] +import struct +from typing import Callable, List, Set, Union + +import psutil +import typeguard + +logger = logging.getLogger(__name__) + + +def address_by_route() -> str: + """Finds an address for the local host by querying the local routing table + for the route to Google DNS. + + This will return an unusable value when the internet-facing address is + not reachable from workers. + """ + logger.debug("Finding address by querying local routing table") + + # original author unknown + import socket + s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) + s.connect(("8.8.8.8", 80)) + addr = s.getsockname()[0] + s.close() + logger.debug("Address found: {}".format(addr)) + return addr + + +@typeguard.typechecked +def address_by_query(timeout: float = 30) -> str: + """Finds an address for the local host by querying ipify. This may + return an unusable value when the host is behind NAT, or when the + internet-facing address is not reachable from workers. + Parameters: + ----------- + + timeout : float + Timeout for the request in seconds. Default: 30s + """ + logger.debug("Finding address by querying remote service") + response = requests.get('https://api.ipify.org', timeout=timeout) + + if response.status_code == 200: + addr = response.text + logger.debug("Address found: {}".format(addr)) + return addr + else: + raise RuntimeError("Remote service returned unexpected HTTP status code {}".format(response.status_code)) + + +def address_by_hostname() -> str: + """Returns the hostname of the local host. + + This will return an unusable value when the hostname cannot be + resolved from workers. + """ + logger.debug("Finding address by using local hostname") + addr = platform.node() + ip_addr = socket.gethostbyname(addr) + logger.debug("Address found: {}".format(ip_addr)) + return ip_addr + + +@typeguard.typechecked +def address_by_interface(ifname: str) -> str: + """Returns the IP address of the given interface name, e.g. 'eth0' + + This is taken from a Stack Overflow answer: + https://stackoverflow.com/questions/24196932/how-can-i-get-the-ip-address-of-eth0-in-python#24196955 + + + Parameters + ---------- + ifname : str + Name of the interface whose address is to be returned. Required. + + """ + assert fcntl is not None, "This function is not supported on your OS." + s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) + return socket.inet_ntoa(fcntl.ioctl( + s.fileno(), + 0x8915, # SIOCGIFADDR + struct.pack('256s', bytes(ifname[:15], 'utf-8')) + )[20:24]) + + +def get_all_addresses() -> Set[str]: + """ Uses a combination of methods to determine possible addresses. + + Returns: + list of addresses as strings + """ + net_interfaces = psutil.net_if_addrs() + + s_addresses = set() + for interface in net_interfaces: + try: + s_addresses.add(address_by_interface(interface)) + except Exception: + logger.debug("Ignoring failure to fetch address from interface {}".format(interface)) + + resolution_functions: List[Callable[[], str]] + resolution_functions = [address_by_hostname, address_by_route, address_by_query] + for f in resolution_functions: + try: + s_addresses.add(f()) + except Exception: + logger.debug("Ignoring an address finder exception") + + return s_addresses + + +def get_any_address() -> str: + """ Uses a combination of methods to find any address of the local machine. + + Returns: + one address in string + """ + net_interfaces = psutil.net_if_addrs() + + addr = '' + for interface in net_interfaces: + try: + addr = address_by_interface(interface) + return addr + except Exception: + logger.info("Ignoring failure to fetch address from interface {}".format(interface)) + + resolution_functions: List[Callable[[], str]] + resolution_functions = [address_by_hostname, address_by_route, address_by_query] + for f in resolution_functions: + try: + addr = f() + return addr + except Exception: + logger.info("Ignoring an address finder exception") + + if addr == '': + raise Exception('Cannot find address of the local machine.') + return addr + + +def tcp_url(address: str, port: Union[str, int, None] = None) -> str: + """Construct a tcp url safe for IPv4 and IPv6""" + port_suffix = f":{port}" if port else "" + if address == "*": + return "tcp://*" + port_suffix + + ip_addr = ipaddress.ip_address(address) + + if ip_addr.version == 6 and port_suffix: + url = f"tcp://[{address}]{port_suffix}" + else: + url = f"tcp://{address}{port_suffix}" + + return url diff --git a/parsl/parsl/app/__init__.py b/parsl/parsl/app/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/app/app.py b/parsl/parsl/app/app.py new file mode 100644 index 00000000..8d0d829b --- /dev/null +++ b/parsl/parsl/app/app.py @@ -0,0 +1,197 @@ +"""Definitions for the @App decorator and the App classes. + +The App class encapsulates a generic leaf task that can be executed asynchronously. +""" +import logging +from abc import ABCMeta, abstractmethod +from inspect import signature +from typing import Any, Callable, Dict, List, Optional, Sequence, Union + +import typeguard +from typing_extensions import Literal + +from parsl.dataflow.dflow import DataFlowKernel +from parsl.dataflow.futures import AppFuture + +logger = logging.getLogger(__name__) + + +class AppBase(metaclass=ABCMeta): + """This is the base class that defines the two external facing functions that an App must define. + + The __init__ () which is called when the interpreter sees the definition of the decorated + function, and the __call__ () which is invoked when a decorated function is called by the user. + + """ + + @typeguard.typechecked + def __init__(self, func: Callable, + data_flow_kernel: Optional[DataFlowKernel] = None, + executors: Union[List[str], Literal['all']] = 'all', + cache: bool = False, + ignore_for_cache: Optional[Sequence[str]] = None) -> None: + """Construct the App object. + + Args: + - func (function): Takes the function to be made into an App + + Kwargs: + - data_flow_kernel (DataFlowKernel): The :class:`~parsl.dataflow.dflow.DataFlowKernel` responsible for + managing this app. This can be omitted only + after calling :meth:`parsl.dataflow.dflow.DataFlowKernelLoader.load`. + - executors (str|list) : Labels of the executors that this app can execute over. Default is 'all'. + - cache (Bool) : Enable caching of this app ? + - ignore_for_cache (sequence|None): Names of arguments which will be ignored by the caching mechanism. + + Returns: + - App object. + + """ + self.__name__ = func.__name__ + self.func = func + self.data_flow_kernel = data_flow_kernel + self.executors = executors + self.cache = cache + self.ignore_for_cache = ignore_for_cache + + params = signature(func).parameters + + self.kwargs: Dict[str, Any] + self.kwargs = {} + if 'stdout' in params: + self.kwargs['stdout'] = params['stdout'].default + if 'stderr' in params: + self.kwargs['stderr'] = params['stderr'].default + if 'walltime' in params: + self.kwargs['walltime'] = params['walltime'].default + if 'parsl_resource_specification' in params: + self.kwargs['parsl_resource_specification'] = params['parsl_resource_specification'].default + if 'outputs' in params: + self.kwargs['outputs'] = params['outputs'].default + if 'inputs' in params: + self.kwargs['inputs'] = params['inputs'].default + + @abstractmethod + def __call__(self, *args: Any, **kwargs: Any) -> AppFuture: + pass + + +@typeguard.typechecked +def python_app(function: Optional[Callable] = None, + data_flow_kernel: Optional[DataFlowKernel] = None, + cache: bool = False, + executors: Union[List[str], Literal['all']] = 'all', + ignore_for_cache: Optional[Sequence[str]] = None) -> Callable: + """Decorator function for making python apps. + + Parameters + ---------- + function : function + Do not pass this keyword argument directly. This is needed in order to allow for omitted parenthesis, + for example, ``@python_app`` if using all defaults or ``@python_app(walltime=120)``. If the + decorator is used alone, function will be the actual function being decorated, whereas if it + is called with arguments, function will be None. Default is None. + data_flow_kernel : DataFlowKernel + The :class:`~parsl.dataflow.dflow.DataFlowKernel` responsible for managing this app. This can + be omitted only after calling :meth:`parsl.dataflow.dflow.DataFlowKernelLoader.load`. Default is None. + executors : string or list + Labels of the executors that this app can execute over. Default is 'all'. + cache : bool + Enable caching of the app call. Default is False. + ignore_for_cache : (sequence|None) + Names of arguments which will be ignored by the caching mechanism. + """ + from parsl.app.python import PythonApp + + def decorator(func: Callable) -> Callable: + def wrapper(f: Callable) -> PythonApp: + return PythonApp(f, + data_flow_kernel=data_flow_kernel, + cache=cache, + executors=executors, + ignore_for_cache=ignore_for_cache, + join=False) + return wrapper(func) + if function is not None: + return decorator(function) + return decorator + + +@typeguard.typechecked +def join_app(function: Optional[Callable] = None, + data_flow_kernel: Optional[DataFlowKernel] = None, + cache: bool = False, + ignore_for_cache: Optional[Sequence[str]] = None) -> Callable: + """Decorator function for making join apps + + Parameters + ---------- + function : function + Do not pass this keyword argument directly. This is needed in order to allow for omitted parenthesis, + for example, ``@python_app`` if using all defaults or ``@python_app(walltime=120)``. If the + decorator is used alone, function will be the actual function being decorated, whereas if it + is called with arguments, function will be None. Default is None. + data_flow_kernel : DataFlowKernel + The :class:`~parsl.dataflow.dflow.DataFlowKernel` responsible for managing this app. This can + be omitted only after calling :meth:`parsl.dataflow.dflow.DataFlowKernelLoader.load`. Default is None. + cache : bool + Enable caching of the app call. Default is False. + ignore_for_cache : (sequence|None) + Names of arguments which will be ignored by the caching mechanism. + """ + from parsl.app.python import PythonApp + + def decorator(func: Callable) -> Callable: + def wrapper(f: Callable) -> PythonApp: + return PythonApp(f, + data_flow_kernel=data_flow_kernel, + cache=cache, + executors=["_parsl_internal"], + ignore_for_cache=ignore_for_cache, + join=True) + return wrapper(func) + if function is not None: + return decorator(function) + return decorator + + +@typeguard.typechecked +def bash_app(function: Optional[Callable] = None, + data_flow_kernel: Optional[DataFlowKernel] = None, + cache: bool = False, + executors: Union[List[str], Literal['all']] = 'all', + ignore_for_cache: Optional[Sequence[str]] = None) -> Callable: + """Decorator function for making bash apps. + + Parameters + ---------- + function : function + Do not pass this keyword argument directly. This is needed in order to allow for omitted parenthesis, + for example, ``@bash_app`` if using all defaults or ``@bash_app(walltime=120)``. If the + decorator is used alone, function will be the actual function being decorated, whereas if it + is called with arguments, function will be None. Default is None. + data_flow_kernel : DataFlowKernel + The :class:`~parsl.dataflow.dflow.DataFlowKernel` responsible for managing this app. This can + be omitted only after calling :meth:`parsl.dataflow.dflow.DataFlowKernelLoader.load`. Default is None. + walltime : int + Walltime for app in seconds. Default is 60. + executors : string or list + Labels of the executors that this app can execute over. Default is 'all'. + cache : bool + Enable caching of the app call. Default is False. + ignore_for_cache : (list|None) + Names of arguments which will be ignored by the caching mechanism. + """ + from parsl.app.bash import BashApp + + def decorator(func: Callable) -> Callable: + def wrapper(f: Callable) -> BashApp: + return BashApp(f, + data_flow_kernel=data_flow_kernel, + cache=cache, + executors=executors, + ignore_for_cache=ignore_for_cache) + return wrapper(func) + if function is not None: + return decorator(function) + return decorator diff --git a/parsl/parsl/app/bash.py b/parsl/parsl/app/bash.py new file mode 100644 index 00000000..db632e1b --- /dev/null +++ b/parsl/parsl/app/bash.py @@ -0,0 +1,162 @@ +import logging +from functools import partial +from inspect import Parameter, signature + +from parsl.app.app import AppBase +from parsl.app.errors import wrap_error +from parsl.data_provider.files import File +from parsl.dataflow.dflow import DataFlowKernelLoader + +logger = logging.getLogger(__name__) + + +def remote_side_bash_executor(func, *args, **kwargs): + """Executes the supplied function with *args and **kwargs to get a + command-line to run, and then run that command-line using bash. + """ + import os + import subprocess + + import parsl.app.errors as pe + from parsl.utils import get_std_fname_mode + + if hasattr(func, '__name__'): + func_name = func.__name__ + else: + logger.warning('No name for the function. Potentially a result of parsl#2233') + func_name = 'bash_app' + + executable = None + + # Try to run the func to compose the commandline + try: + # Execute the func to get the commandline + executable = func(*args, **kwargs) + + if not isinstance(executable, str): + raise ValueError(f"Expected a str for bash_app commandline, got {type(executable)}") + + except AttributeError as e: + if executable is not None: + raise pe.AppBadFormatting("App formatting failed for app '{}' with AttributeError: {}".format(func_name, e)) + else: + raise pe.BashAppNoReturn("Bash app '{}' did not return a value, or returned None - with this exception: {}".format(func_name, e)) + + except IndexError as e: + raise pe.AppBadFormatting("App formatting failed for app '{}' with IndexError: {}".format(func_name, e)) + except Exception as e: + raise e + + # Updating stdout, stderr if values passed at call time. + + def open_std_fd(fdname): + # fdname is 'stdout' or 'stderr' + stdfspec = kwargs.get(fdname) # spec is str name or tuple (name, mode) + if stdfspec is None: + return None + + if isinstance(stdfspec, File): + # a File is an os.PathLike and so we can use it directly for + # the subsequent file operations + fname = stdfspec + mode = "w" + else: + fname, mode = get_std_fname_mode(fdname, stdfspec) + + try: + if os.path.dirname(fname): + os.makedirs(os.path.dirname(fname), exist_ok=True) + fd = open(fname, mode) + except Exception as e: + raise pe.BadStdStreamFile(str(fname)) from e + return fd + + std_out = open_std_fd('stdout') + std_err = open_std_fd('stderr') + timeout = kwargs.get('walltime') + + if std_err is not None: + print('--> executable follows <--\n{}\n--> end executable <--'.format(executable), file=std_err, flush=True) + + returncode = None + try: + proc = subprocess.Popen(executable, stdout=std_out, stderr=std_err, shell=True, executable='/bin/bash', close_fds=False) + proc.wait(timeout=timeout) + returncode = proc.returncode + + except subprocess.TimeoutExpired: + raise pe.AppTimeout(f"App {func_name} exceeded walltime: {timeout} seconds") + + except Exception as e: + raise pe.AppException(f"App {func_name} caught exception", e) + + if returncode != 0: + raise pe.BashExitFailure(func_name, proc.returncode) + + # TODO : Add support for globs here + + missing = [] + for outputfile in kwargs.get('outputs', []): + fpath = outputfile.filepath + + if not os.path.exists(fpath): + missing.extend([outputfile]) + + if missing: + raise pe.MissingOutputs(f"Missing outputs from app {func_name}", missing) + + return returncode + + +class BashApp(AppBase): + + def __init__(self, func, data_flow_kernel=None, cache=False, executors='all', ignore_for_cache=None): + super().__init__(func, data_flow_kernel=data_flow_kernel, executors=executors, cache=cache, ignore_for_cache=ignore_for_cache) + self.kwargs = {} + + # We duplicate the extraction of parameter defaults + # to self.kwargs to ensure availability at point of + # command string format. Refer: #349 + sig = signature(func) + + for s in sig.parameters: + if sig.parameters[s].default is not Parameter.empty: + self.kwargs[s] = sig.parameters[s].default + + # partial is used to attach the first arg the "func" to the remote_side_bash_executor + # this is done to avoid passing a function type in the args which parsl.serializer + # doesn't support + remote_fn = partial(remote_side_bash_executor, self.func) + remote_fn.__name__ = self.func.__name__ + self.wrapped_remote_function = wrap_error(remote_fn) + + def __call__(self, *args, **kwargs): + """Handle the call to a Bash app. + + Args: + - Arbitrary + + Kwargs: + - Arbitrary + + Returns: + App_fut + + """ + invocation_kwargs = {} + invocation_kwargs.update(self.kwargs) + invocation_kwargs.update(kwargs) + + if self.data_flow_kernel is None: + dfk = DataFlowKernelLoader.dfk() + else: + dfk = self.data_flow_kernel + + app_fut = dfk.submit(self.wrapped_remote_function, + app_args=args, + executors=self.executors, + cache=self.cache, + ignore_for_cache=self.ignore_for_cache, + app_kwargs=invocation_kwargs) + + return app_fut diff --git a/parsl/parsl/app/errors.py b/parsl/parsl/app/errors.py new file mode 100644 index 00000000..8239c808 --- /dev/null +++ b/parsl/parsl/app/errors.py @@ -0,0 +1,141 @@ +"""Exceptions raised by Apps.""" +import logging +from functools import wraps +from types import TracebackType +from typing import Callable, List, Optional, TypeVar, Union + +from tblib import Traceback +from typing_extensions import ParamSpec + +from parsl.data_provider.files import File +from parsl.errors import ParslError + +logger = logging.getLogger(__name__) + + +class AppException(ParslError): + """An error raised during execution of an app. + + What this exception contains depends entirely on context + """ + + +class AppBadFormatting(ParslError): + """An error raised during formatting of a bash function. + """ + + +class BashExitFailure(AppException): + """A non-zero exit code returned from a @bash_app + + Contains: + app name (str) + exitcode (int) + """ + + def __init__(self, app_name: str, exitcode: int) -> None: + self.app_name = app_name + self.exitcode = exitcode + + def __str__(self) -> str: + return f"bash_app {self.app_name} failed with unix exit code {self.exitcode}" + + +class AppTimeout(AppException): + """An error raised during execution of an app when it exceeds its allotted walltime. + """ + + +class BashAppNoReturn(AppException): + """Bash app returned no string. + + Contains: + reason(string) + """ + + def __init__(self, reason: str) -> None: + super().__init__(reason) + self.reason = reason + + +class MissingOutputs(ParslError): + """Error raised at the end of app execution due to missing output files. + + Contains: + reason(string) + outputs(List of files) + """ + def __init__(self, reason: str, outputs: List[File]) -> None: + super().__init__(reason, outputs) + self.reason = reason + self.outputs = outputs + + def __str__(self) -> str: + return "Missing Outputs: {0}, Reason: {1}".format(self.outputs, self.reason) + + +class BadStdStreamFile(ParslError): + """Error raised due to bad filepaths specified for STDOUT/ STDERR. + + Contains: + reason(string) + """ + + def __init__(self, reason: str) -> None: + super().__init__(reason) + self._reason = reason + + def __str__(self) -> str: + return "Bad Stream File: {}".format(self._reason) + + +class RemoteExceptionWrapper: + def __init__(self, e_type: type, e_value: BaseException, traceback: Optional[TracebackType]) -> None: + + self.e_type = e_type + self.e_value = e_value + self.e_traceback = None if traceback is None else Traceback(traceback) + if e_value.__cause__ is None: + self.cause = None + else: + cause = e_value.__cause__ + self.cause = self.__class__(type(cause), cause, cause.__traceback__) + + def reraise(self) -> None: + + # the type is logged here before deserialising v and tb + # because occasionally there are problems deserialising the + # value (see #785, #548) and the fix is related to the + # specific exception type. + logger.debug("Reraising exception of type {}".format(self.e_type)) + + v = self.get_exception() + + raise v + + def get_exception(self) -> BaseException: + v = self.e_value + if self.cause is not None: + v.__cause__ = self.cause.get_exception() + if self.e_traceback is not None: + tb = self.e_traceback.as_traceback() + return v.with_traceback(tb) + else: + return v + + +P = ParamSpec('P') +R = TypeVar('R') + + +def wrap_error(func: Callable[P, R]) -> Callable[P, Union[R, RemoteExceptionWrapper]]: + @wraps(func) + def wrapper(*args: P.args, **kwargs: P.kwargs) -> Union[R, RemoteExceptionWrapper]: + import sys + + from parsl.app.errors import RemoteExceptionWrapper + try: + return func(*args, **kwargs) + except Exception: + return RemoteExceptionWrapper(*sys.exc_info()) + return wrapper diff --git a/parsl/parsl/app/futures.py b/parsl/parsl/app/futures.py new file mode 100644 index 00000000..d85c4f25 --- /dev/null +++ b/parsl/parsl/app/futures.py @@ -0,0 +1,99 @@ +"""This module implements DataFutures. +""" +import logging +from concurrent.futures import Future + +import typeguard + +from parsl.data_provider.files import File + +logger = logging.getLogger(__name__) + + +class DataFuture(Future): + """A datafuture points at an AppFuture. + + We are simply wrapping a AppFuture, and adding the specific case where, if + the future is resolved i.e file exists, then the DataFuture is assumed to be + resolved. + """ + + def parent_callback(self, parent_fu): + """Callback from executor future to update the parent. + + Updates the future with the result (the File object) or the parent future's + exception. + + Args: + - parent_fu (Future): Future returned by the executor along with callback + + Returns: + - None + """ + + e = parent_fu._exception + if e: + self.set_exception(e) + else: + self.set_result(self.file_obj) + + @typeguard.typechecked + def __init__(self, fut: Future, file_obj: File, tid: int) -> None: + """Construct the DataFuture object. + + If the file_obj is a string convert to a File. + + Args: + - fut (Future) : Future that this DataFuture will track. + Completion of ``fut`` indicates that the data is + ready. + - file_obj (File) : File that this DataFuture represents the availability of + + Kwargs: + - tid (task_id) : Task id that this DataFuture tracks + """ + super().__init__() + self._tid = tid + self.file_obj = file_obj + self.parent = fut + + self.parent.add_done_callback(self.parent_callback) + + logger.debug("Creating DataFuture with parent: %s and file: %s", self.parent, repr(self.file_obj)) + + @property + def tid(self): + """Returns the task_id of the task that will resolve this DataFuture.""" + return self._tid + + @property + def filepath(self): + """Filepath of the File object this datafuture represents.""" + return self.file_obj.filepath + + @property + def filename(self): + """Filepath of the File object this datafuture represents.""" + return self.filepath + + def cancel(self): + raise NotImplementedError("Cancel not implemented") + + def cancelled(self): + return False + + def running(self): + if self.parent: + return self.parent.running() + else: + return False + + def __repr__(self) -> str: + type_ = type(self) + module = type_.__module__ + qualname = type_.__qualname__ + if self.done(): + done = "done" + else: + done = "not done" + return f"<{module}.{qualname} object at {hex(id(self))} representing {repr(self.file_obj)} {done}>" diff --git a/parsl/parsl/app/python.py b/parsl/parsl/app/python.py new file mode 100644 index 00000000..58839cff --- /dev/null +++ b/parsl/parsl/app/python.py @@ -0,0 +1,84 @@ +import logging +import threading + +import tblib.pickling_support + +tblib.pickling_support.install() + +from functools import wraps + +from parsl.app.app import AppBase +from parsl.app.errors import wrap_error +from parsl.dataflow.dflow import DataFlowKernelLoader +from parsl.utils import AutoCancelTimer + +logger = logging.getLogger(__name__) + + +def timeout(f, seconds: float): + @wraps(f) + def wrapper(*args, **kwargs): + import ctypes + + import parsl.app.errors + + def inject_exception(thread): + ctypes.pythonapi.PyThreadState_SetAsyncExc( + ctypes.c_long(thread), + ctypes.py_object(parsl.app.errors.AppTimeout) + ) + + thread = threading.current_thread().ident + with AutoCancelTimer(seconds, inject_exception, args=[thread]): + return f(*args, **kwargs) + return wrapper + + +class PythonApp(AppBase): + """Extends AppBase to cover the Python App.""" + + def __init__(self, func, data_flow_kernel=None, cache=False, executors='all', ignore_for_cache=None, join=False): + super().__init__( + wrap_error(func), + data_flow_kernel=data_flow_kernel, + executors=executors, + cache=cache, + ignore_for_cache=ignore_for_cache + ) + self.join = join + + def __call__(self, *args, **kwargs): + """This is where the call to a python app is handled. + + Args: + - Arbitrary + Kwargs: + - Arbitrary + + Returns: + App_fut + + """ + invocation_kwargs = {} + invocation_kwargs.update(self.kwargs) + invocation_kwargs.update(kwargs) + + if self.data_flow_kernel is None: + dfk = DataFlowKernelLoader.dfk() + else: + dfk = self.data_flow_kernel + + walltime = invocation_kwargs.get('walltime') + if walltime is not None: + func = timeout(self.func, walltime) + else: + func = self.func + + app_fut = dfk.submit(func, app_args=args, + executors=self.executors, + cache=self.cache, + ignore_for_cache=self.ignore_for_cache, + app_kwargs=invocation_kwargs, + join=self.join) + + return app_fut diff --git a/parsl/parsl/benchmark/__init__.py b/parsl/parsl/benchmark/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/benchmark/perf.py b/parsl/parsl/benchmark/perf.py new file mode 100644 index 00000000..80347638 --- /dev/null +++ b/parsl/parsl/benchmark/perf.py @@ -0,0 +1,157 @@ +import argparse +import concurrent.futures +import importlib +import time +from typing import Any, Dict, Literal + +import parsl +from parsl.dataflow.dflow import DataFlowKernel +from parsl.errors import InternalConsistencyError + +VALID_NAMED_ITERATION_MODES = ("estimate", "exponential") + +min_iterations = 2 + + +# TODO: factor with conftest.py where this is copy/pasted from? +def load_dfk_from_config(filename: str) -> DataFlowKernel: + spec = importlib.util.spec_from_file_location('', filename) + + if spec is None: + raise RuntimeError("Could not import configuration") + + module = importlib.util.module_from_spec(spec) + + if spec.loader is None: + raise RuntimeError("Could not load configuration") + + spec.loader.exec_module(module) + + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + + if hasattr(module, 'config'): + return parsl.load(module.config) + elif hasattr(module, 'fresh_config'): + return parsl.load(module.fresh_config()) + else: + raise RuntimeError("Config module does not define config or fresh_config") + + +@parsl.python_app +def app(extra_payload: Any, parsl_resource_specification: Dict = {}) -> int: + return 7 + + +def performance(*, resources: dict, target_t: float, args_extra_size: int, iterate_mode: str | list[int]) -> None: + + delta_t: float + + iteration = 1 + + args_extra_payload = "x" * args_extra_size + + if isinstance(iterate_mode, list): + n = iterate_mode[0] + else: + n = 10 + + iterate = True + + while iterate: + print(f"==== Iteration {iteration} ====") + print(f"Will run {n} tasks") + start_t = time.time() + + fs = [] + print("Submitting tasks / invoking apps") + for _ in range(n): + fs.append(app(args_extra_payload, parsl_resource_specification=resources)) + + submitted_t = time.time() + print(f"All {n} tasks submitted ... waiting for completion") + print(f"Submission took {submitted_t - start_t:.3f} seconds = {n / (submitted_t - start_t):.3f} tasks/second") + + for f in concurrent.futures.as_completed(fs): + assert f.result() == 7 + + end_t = time.time() + + delta_t = end_t - start_t + + rate = n / delta_t + + print(f"Runtime: actual {delta_t:.3f}s vs target {target_t}s") + print(f"Tasks per second: {rate:.3f}") + + iteration += 1 + + # decide upon next iteration + + match iterate_mode: + case "estimate": + n = max(1, int(target_t * rate)) + iterate = delta_t < (0.75 * target_t) or iteration <= min_iterations + case "exponential": + n = int(n * 2) + iterate = delta_t < target_t or iteration <= min_iterations + case seq if isinstance(seq, list) and iteration <= len(seq): + n = seq[iteration - 1] + iterate = True + case seq if isinstance(seq, list): + iterate = False + case _: + raise InternalConsistencyError(f"Bad iterate mode {iterate_mode} - should have been validated at arg parse time") + + +def validate_int_list(v: str) -> list[int] | Literal[False]: + try: + return list(map(int, v.split(","))) + except ValueError: + return False + + +def iteration_mode(v: str) -> str | list[int]: + match v: + case s if s in VALID_NAMED_ITERATION_MODES: + return s + case _ if seq := validate_int_list(v): + return seq + case _: + raise argparse.ArgumentTypeError(f"Invalid iteration mode: {v}") + + +def cli_run() -> None: + parser = argparse.ArgumentParser( + prog="parsl-perf", + description="Measure performance of Parsl configurations", + epilog=""" +Example usage: python -m parsl.benchmark.perf --config parsl/tests/configs/workqueue_blocks.py --resources '{"cores":1, "memory":0, "disk":0}' + """) + + parser.add_argument("--config", required=True, help="path to Python file that defines a configuration") + parser.add_argument("--resources", metavar="EXPR", help="parsl_resource_specification dictionary") + parser.add_argument("--time", metavar="SECONDS", help="target number of seconds for an iteration", default=120, type=float) + parser.add_argument("--argsize", metavar="BYTES", help="extra bytes to add into app invocation arguments", default=0, type=int) + parser.add_argument("--version", action="version", version=f"parsl-perf from Parsl {parsl.__version__}") + parser.add_argument("--iterate", + metavar="MODE", + help="Iteration mode: " + ", ".join(VALID_NAMED_ITERATION_MODES) + ", or sequence of explicit sizes", + type=iteration_mode, + default="estimate") + + args = parser.parse_args() + + if args.resources: + resources = eval(args.resources) + else: + resources = {} + + with load_dfk_from_config(args.config): + performance(resources=resources, target_t=args.time, args_extra_size=args.argsize, iterate_mode=args.iterate) + print("Tests complete - leaving DFK block") + print("The end") + + +if __name__ == "__main__": + cli_run() diff --git a/parsl/parsl/concurrent/__init__.py b/parsl/parsl/concurrent/__init__.py new file mode 100644 index 00000000..02a9f818 --- /dev/null +++ b/parsl/parsl/concurrent/__init__.py @@ -0,0 +1,146 @@ +"""Interfaces modeled after Python's `concurrent library `_""" +import time +from concurrent.futures import Executor +from contextlib import AbstractContextManager +from typing import Callable, Dict, Iterable, Iterator, Literal, Optional, TypeVar +from warnings import warn + +from parsl import Config, DataFlowKernel, load +from parsl.app.python import PythonApp + +T = TypeVar('T') + + +class ParslPoolExecutor(Executor, AbstractContextManager): + """An executor that uses a pool of workers managed by Parsl + + Works just like a :class:`~concurrent.futures.ProcessPoolExecutor` except that tasks + are distributed across workers that can be on different machines. + + The futures returned by :meth:`submit` and :meth:`map` are Parsl futures and will work + with the same function chaining mechanisms as when using App-based Parsl. + + Parsl does not support canceling tasks. The :meth:`map` method does not cancel work + when one member of the run fails or a timeout is reached + and :meth:`shutdown` does not cancel work on completion. + """ + + def __init__(self, config: Config | None = None, dfk: DataFlowKernel | None = None, executors: Literal['all'] | list[str] = 'all'): + """Create the executor + + Args: + config: Configuration for the Parsl Data Flow Kernel (DFK) + dfk: DataFlowKernel of an already-started parsl + executors: List of executors to use for supplied functions + """ + if (config is not None) and (dfk is not None): + raise ValueError('Specify only one of config or dfk') + if (config is None) and (dfk is None): + raise ValueError('Must specify one of config or dfk') + self._config = config + self._app_cache: Dict[Callable, PythonApp] = {} # Cache specific to this instance: https://stackoverflow.com/questions/33672412 + self._dfk = dfk + self.executors = executors + + # Start workers immediately + if self._config is not None: + self._dfk = load(self._config) + + def __exit__(self, exc_type, exc_val, exc_tb): + if self._dfk is None: # Nothing has been started, do nothing + return + elif self._config is not None: # The executors are being managed by this class, shut them down + self.shutdown(wait=True) + return + else: # The DFK is managed elsewhere, do nothing + return + + @property + def app_count(self): + """Number of functions currently registered with the executor""" + return len(self._app_cache) + + def get_app(self, fn: Callable) -> PythonApp: + """Create a PythonApp for a function + + Args: + fn: Function to be turned into a Parsl app + Returns: + PythonApp version of that function + """ + if fn in self._app_cache: + return self._app_cache[fn] + app = PythonApp(fn, data_flow_kernel=self._dfk, executors=self.executors) + self._app_cache[fn] = app + return app + + def submit(self, fn, *args, **kwargs): + """Submits a callable to be executed with the given arguments. + + Schedules the callable to be executed as ``fn(*args, **kwargs)`` and returns + a Future instance representing the execution of the callable. + + Returns: + A Future representing the given call. + """ + + if self._dfk is None: + raise RuntimeError('Executor has been shut down.') + app = self.get_app(fn) + return app(*args, **kwargs) + + # TODO (wardlt): This override can go away when Parsl supports cancel + def map(self, fn: Callable[..., T], *iterables: Iterable, timeout: Optional[float] = None, + chunksize: int = 1, buffersize: Optional[int] = None) -> Iterator[T]: + """Returns an iterator equivalent to map(fn, iter). + + Args: + fn: A callable that will take as many arguments as there are + passed iterables. + timeout: The maximum number of seconds to wait. If None, then there + is no limit on the wait time. + chunksize: This parameter is ignored. Caution should be exercised + if expecting behaviour as documented in the base `concurrent.futures.Executor` class. + buffersize: This parameter is ignored. Caution should be exercised + if expecting behaviour as documented in the base `concurrent.futures.Executor` class. + + Returns: + An iterator equivalent to: map(func, ``*iterables``) but the calls may + be evaluated out-of-order. + + Raises: + TimeoutError: If the entire result iterator could not be generated + before the given timeout. + Exception: If ``fn(*args)`` raises for any values. + """ + # This is a version of the CPython 3.9 `.map` implementation modified to not use `cancel` + if timeout is not None: + end_time = timeout + time.monotonic() + + # Submit the applications + app = self.get_app(fn) + fs = [app(*args) for args in zip(*iterables)] + + # Yield the futures as completed + def result_iterator(): + # reverse to keep finishing order + fs.reverse() + while fs: + # Careful not to keep a reference to the popped future + if timeout is None: + yield fs.pop().result() + else: + yield fs.pop().result(end_time - time.monotonic()) + + return result_iterator() + + def shutdown(self, wait: bool = True, *, cancel_futures: bool = False) -> None: + if self._dfk is None: + return # Do nothing. Nothing is active + if cancel_futures: + warn(message="Canceling on-going tasks is not supported in Parsl") + if wait: + self._dfk.wait_for_current_tasks() + if self._config is not None: # The executors are being managed + self._dfk.cleanup() # Shutdown the DFK + self._dfk = None diff --git a/parsl/parsl/config.py b/parsl/parsl/config.py new file mode 100644 index 00000000..76bbb6eb --- /dev/null +++ b/parsl/parsl/config.py @@ -0,0 +1,159 @@ +import logging +from typing import Callable, Iterable, Optional, Sequence, Union + +import typeguard +from typing_extensions import Literal + +from parsl.dataflow.dependency_resolvers import DependencyResolver +from parsl.dataflow.memoization import Memoizer +from parsl.dataflow.taskrecord import TaskRecord +from parsl.errors import ConfigurationError +from parsl.executors.base import ParslExecutor +from parsl.executors.threads import ThreadPoolExecutor +from parsl.logconfigs.base import LogConfig +from parsl.monitoring import MonitoringHub +from parsl.usage_tracking.api import UsageInformation +from parsl.usage_tracking.levels import DISABLED as USAGE_TRACKING_DISABLED +from parsl.usage_tracking.levels import LEVEL_3 as USAGE_TRACKING_LEVEL_3 +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + + +class Config(RepresentationMixin, UsageInformation): + """ + Specification of Parsl configuration options. + + Parameters + ---------- + executors : sequence of ParslExecutor, optional + List (or other iterable) of `ParslExecutor` instances to use for executing tasks. + Default is (:class:`~parsl.executors.threads.ThreadPoolExecutor()`,). + dependency_resolver: plugin point for custom dependency resolvers. Default: only resolve Futures, + using the `SHALLOW_DEPENDENCY_RESOLVER`. + exit_mode: str, optional + When Parsl is used as a context manager (using ``with parsl.load`` syntax) then this parameter + controls what will happen to running tasks and exceptions at exit. The options are: + + * ``cleanup``: cleanup the DFK on exit without waiting for any tasks + * ``skip``: skip all shutdown behaviour when exiting the context manager + * ``wait``: wait for all tasks to complete when exiting normally, but exit immediately when exiting due to an exception. + + Default is ``cleanup``. + garbage_collect : bool. optional. + Delete task records from DFK when tasks have completed. Default: True + internal_tasks_max_threads : int, optional + Maximum number of threads to allocate for submit side internal tasks such as some data transfers + or @joinapps + Default is 10. + monitoring : MonitoringHub, optional + The config to use for database monitoring. Default is None which does not log to a database. + retries : int, optional + Set the number of retries (or available retry budget when using retry_handler) in case of failure. Default is 0. + retry_handler : function, optional + A user pluggable handler to decide if/how a task retry should happen. + If no handler is specified, then each task failure incurs a retry cost + of 1. + run_dir : str, optional + Path to run directory. Default is 'runinfo'. + std_autopath : function, optional + Sets the function used to generate stdout/stderr specifications when parsl.AUTO_LOGPATH is used. If no function + is specified, generates paths that look like: ``rundir/NNN/task_logs/X/task_{id}_{name}{label}.{out/err}`` + strategy : str, optional + Strategy to use for scaling blocks according to workflow needs. Can be 'simple', 'htex_auto_scale', 'none' + or `None`. + If 'none' or `None`, dynamic scaling will be disabled. Default is 'simple'. The literal value `None` is + deprecated. + strategy_period : float or int, optional + How often the scaling strategy should be executed. Default is 5 seconds. + max_idletime : float, optional + The maximum idle time allowed for an executor before strategy could shut down unused blocks. Default is 120.0 seconds. + usage_tracking : int, optional + Set this field to 1, 2, or 3 to opt-in to Parsl's usage tracking system. + The value represents the level of usage tracking detail to be collected. + Setting this field to 0 will disable usage tracking. Default (this field is not set): usage tracking is not enabled. + Parsl only collects minimal, non personally-identifiable, + information used for reporting to our funding agencies. + project_name: str, optional + Option to deanonymize usage tracking data. + If set, this value will be used as the project name in the usage tracking data and placed on the leaderboard. + initialize_logging : bool or log config, optional + Make DFK optionally not initialize any logging. Log messages + will still be passed into the python logging system under the + ``parsl`` logger name, but the logging system will not by default + perform any further log system configuration. Most noticeably, + it will not create a parsl.log logfile. The use case for this + is when parsl is used as a library in a bigger system which + wants to configure logging in a way that makes sense for that + bigger system as a whole. + """ + + @typeguard.typechecked + def __init__(self, + executors: Optional[Iterable[ParslExecutor]] = None, + memoizer: Optional[Memoizer] = None, + dependency_resolver: Optional[DependencyResolver] = None, + exit_mode: Literal['cleanup', 'skip', 'wait'] = 'cleanup', + garbage_collect: bool = True, + internal_tasks_max_threads: int = 10, + retries: int = 0, + retry_handler: Optional[Callable[[Exception, TaskRecord], float]] = None, + run_dir: str = 'runinfo', + std_autopath: Optional[Callable] = None, + strategy: Optional[str] = 'simple', + strategy_period: Union[float, int] = 5, + max_idletime: float = 120.0, + monitoring: Optional[MonitoringHub] = None, + usage_tracking: int = 0, + project_name: Optional[str] = None, + initialize_logging: Union[bool, LogConfig] = True) -> None: + + executors = tuple(executors or []) + if not executors: + executors = (ThreadPoolExecutor(),) + + self._executors: Sequence[ParslExecutor] = executors + self._validate_executors() + + self.memoizer = memoizer + self.dependency_resolver = dependency_resolver + self.exit_mode = exit_mode + self.garbage_collect = garbage_collect + self.internal_tasks_max_threads = internal_tasks_max_threads + self.retries = retries + self.retry_handler = retry_handler + self.run_dir = run_dir + self.strategy = strategy + self.strategy_period = strategy_period + self.max_idletime = max_idletime + self.validate_usage_tracking(usage_tracking) + self.usage_tracking = usage_tracking + self.project_name = project_name + self.initialize_logging = initialize_logging + self.monitoring = monitoring + self.std_autopath: Optional[Callable] = std_autopath + + @property + def executors(self) -> Sequence[ParslExecutor]: + return self._executors + + def _validate_executors(self) -> None: + + if len(self.executors) == 0: + raise ConfigurationError('At least one executor must be specified') + + labels = [e.label for e in self.executors] + duplicates = [e for n, e in enumerate(labels) if e in labels[:n]] + if len(duplicates) > 0: + raise ConfigurationError('Executors must have unique labels ({})'.format( + ', '.join(['label={}'.format(repr(d)) for d in duplicates]))) + + def validate_usage_tracking(self, level: int) -> None: + if not USAGE_TRACKING_DISABLED <= level <= USAGE_TRACKING_LEVEL_3: + raise ConfigurationError( + f"Usage Tracking values must be 0, 1, 2, or 3 and not {level}" + ) + + def get_usage_information(self): + return {"executors_len": len(self.executors), + "dependency_resolver": self.dependency_resolver is not None} diff --git a/parsl/parsl/configs/ASPIRE1.py b/parsl/parsl/configs/ASPIRE1.py new file mode 100644 index 00000000..84d131f2 --- /dev/null +++ b/parsl/parsl/configs/ASPIRE1.py @@ -0,0 +1,44 @@ +from parsl.addresses import address_by_interface +from parsl.config import Config +from parsl.dataflow.memoization import BasicMemoizer +from parsl.executors import HighThroughputExecutor +from parsl.launchers import MpiRunLauncher +from parsl.monitoring.monitoring import MonitoringHub +from parsl.providers import PBSProProvider +from parsl.usage_tracking.levels import LEVEL_1 + +config = Config( + executors=[ + HighThroughputExecutor( + label="htex", + heartbeat_period=15, + heartbeat_threshold=120, + worker_debug=True, + max_workers_per_node=4, + address=address_by_interface('ib0'), + provider=PBSProProvider( + launcher=MpiRunLauncher(), + # PBS directives (header lines): for array jobs pass '-J' option + scheduler_options='#PBS -J 1-10', + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init='', + # number of compute nodes allocated for each block + nodes_per_block=3, + min_blocks=3, + max_blocks=5, + cpus_per_node=24, + # medium queue has a max walltime of 24 hrs + walltime='24:00:00' + ), + ), + ], + monitoring=MonitoringHub( + hub_address=address_by_interface('ib0'), + resource_monitoring_interval=10, + ), + strategy='simple', + retries=3, + memoizer=BasicMemoizer(checkpoint_mode='task_exit'), + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/Azure.py b/parsl/parsl/configs/Azure.py new file mode 100644 index 00000000..2a27db3f --- /dev/null +++ b/parsl/parsl/configs/Azure.py @@ -0,0 +1,39 @@ +"""Config for Azure""" +import getpass + +from parsl.addresses import address_by_query +from parsl.config import Config +from parsl.data_provider.ftp import FTPInTaskStaging +from parsl.data_provider.http import HTTPInTaskStaging +from parsl.data_provider.rsync import RSyncStaging +from parsl.executors import HighThroughputExecutor +from parsl.providers import AzureProvider +from parsl.usage_tracking.levels import LEVEL_1 + +vm_reference = { + # All fields below are required + "admin_username": 'YOUR_ADMIN_USERNAME_FOR_INSTANCE', + "password": 'YOUR_PASSWORD_FOR_INSTANCE', + "vm_size": 'YOUR_VM_SIZE', + "disk_size_gb": 'YOUR_VM_DISK_SIZE', + "publisher": 'YOUR_IMAGE_PUBLISHER', + "offer": 'YOUR_VM_OS_OFFER', + "sku": 'YOUR_VM_OS_SKU', + "version": 'YOUR_VM_OS_VERSION', +} + +config = Config( + executors=[ + HighThroughputExecutor( + label='azure_single_node', + provider=AzureProvider( + vm_reference=vm_reference, + key_file='azure_key_file.json', + ), + storage_access=[HTTPInTaskStaging(), + FTPInTaskStaging(), + RSyncStaging(getpass.getuser() + "@" + address_by_query())], + ) + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/__init__.py b/parsl/parsl/configs/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/configs/anvil.py b/parsl/parsl/configs/anvil.py new file mode 100644 index 00000000..f187b832 --- /dev/null +++ b/parsl/parsl/configs/anvil.py @@ -0,0 +1,34 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SrunLauncher +from parsl.providers import SlurmProvider +from parsl.usage_tracking.levels import LEVEL_1 + +config = Config( + executors=[ + HighThroughputExecutor( + label='Anvil_GPU_Executor', + working_dir='/anvil/scratch/path/to/your/directory', + max_workers_per_node=1, + provider=SlurmProvider( + partition='gpu', + account='YOUR_ALLOCATION_ON_ANVIL', + nodes_per_block=1, + cores_per_node=4, + mem_per_node=50, + init_blocks=1, + max_blocks=1, + walltime='00:30:00', + # string to prepend to #SBATCH blocks in the submit + # script to the scheduler + scheduler_options='#SBATCH --gres gpu:1 --gpus-per-node=1', + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init='module load modtree/gpu', + exclusive=False, + launcher=SrunLauncher(), + ), + ) + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/bridges.py b/parsl/parsl/configs/bridges.py new file mode 100644 index 00000000..4cb0fba5 --- /dev/null +++ b/parsl/parsl/configs/bridges.py @@ -0,0 +1,40 @@ +from parsl.addresses import address_by_interface +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SrunLauncher +from parsl.providers import SlurmProvider +from parsl.usage_tracking.levels import LEVEL_1 + +""" This config assumes that it is used to launch parsl tasks from the login nodes +of Bridges at PSC. Each job submitted to the scheduler will request 2 nodes for 10 minutes. +""" + +config = Config( + executors=[ + HighThroughputExecutor( + label='Bridges_HTEX_multinode', + address=address_by_interface('ens3f0'), + max_workers_per_node=1, + provider=SlurmProvider( + 'YOUR_PARTITION_NAME', # Specify Partition / QOS, for eg. RM-small + nodes_per_block=2, + init_blocks=1, + # string to prepend to #SBATCH blocks in the submit + # script to the scheduler eg: '#SBATCH --gres=gpu:type:n' + scheduler_options='', + + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init='', + + # We request all hyperthreads on a node. + launcher=SrunLauncher(), + walltime='00:10:00', + # Slurm scheduler on Cori can be slow at times, + # increase the command timeouts + cmd_timeout=120, + ), + ) + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/cc_in2p3.py b/parsl/parsl/configs/cc_in2p3.py new file mode 100644 index 00000000..f0140da5 --- /dev/null +++ b/parsl/parsl/configs/cc_in2p3.py @@ -0,0 +1,22 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.providers import GridEngineProvider +from parsl.usage_tracking.levels import LEVEL_1 + +config = Config( + executors=[ + HighThroughputExecutor( + label='cc_in2p3_htex', + max_workers_per_node=2, + provider=GridEngineProvider( + nodes_per_block=1, + init_blocks=2, + max_blocks=2, + walltime="00:20:00", + scheduler_options='', # Input your scheduler_options if needed + worker_init='', # Input your worker_init if needed + ), + ) + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/delta.py b/parsl/parsl/configs/delta.py new file mode 100644 index 00000000..a7f421e5 --- /dev/null +++ b/parsl/parsl/configs/delta.py @@ -0,0 +1,35 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SrunLauncher +from parsl.providers import SlurmProvider +from parsl.usage_tracking.levels import LEVEL_1 + +config = Config( + executors=[ + HighThroughputExecutor( + label='Delta_GPU_Executor', + working_dir='/scratch/path/to/your/directory', + max_workers_per_node=1, + provider=SlurmProvider( + partition='gpuA100x4', + account='YOUR_ALLOCATION_ON_DELTA', + constraint="scratch", + nodes_per_block=1, + cores_per_node=32, + mem_per_node=220, + init_blocks=1, + max_blocks=1, + walltime='00:30:00', + # string to prepend to #SBATCH blocks in the submit + # script to the scheduler + scheduler_options='#SBATCH --gpus-per-node=1 --gpu-bind=closest', + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init='', + exclusive=False, + launcher=SrunLauncher(), + ), + ) + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/ec2.py b/parsl/parsl/configs/ec2.py new file mode 100644 index 00000000..8e85252a --- /dev/null +++ b/parsl/parsl/configs/ec2.py @@ -0,0 +1,30 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.providers import AWSProvider +from parsl.usage_tracking.levels import LEVEL_1 + +config = Config( + executors=[ + HighThroughputExecutor( + label='ec2_single_node', + provider=AWSProvider( + # Specify your EC2 AMI id + 'YOUR_AMI_ID', + # Specify the AWS region to provision from + # eg. us-east-1 + region='YOUR_AWS_REGION', + + # Specify the name of the key to allow ssh access to nodes + key_name='YOUR_KEY_NAME', + profile="default", + state_file='awsproviderstate.json', + nodes_per_block=1, + init_blocks=1, + max_blocks=1, + min_blocks=0, + walltime='01:00:00', + ), + ) + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/expanse.py b/parsl/parsl/configs/expanse.py new file mode 100644 index 00000000..35ef5e0f --- /dev/null +++ b/parsl/parsl/configs/expanse.py @@ -0,0 +1,30 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SrunLauncher +from parsl.providers import SlurmProvider +from parsl.usage_tracking.levels import LEVEL_1 + +config = Config( + executors=[ + HighThroughputExecutor( + label='Expanse_CPU_Multinode', + max_workers_per_node=32, + provider=SlurmProvider( + 'compute', + account='YOUR_ALLOCATION_ON_EXPANSE', + launcher=SrunLauncher(), + # string to prepend to #SBATCH blocks in the submit + # script to the scheduler + scheduler_options='', + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init='', + walltime='01:00:00', + init_blocks=1, + max_blocks=1, + nodes_per_block=2, + ), + ) + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/frontera.py b/parsl/parsl/configs/frontera.py new file mode 100644 index 00000000..25682bbe --- /dev/null +++ b/parsl/parsl/configs/frontera.py @@ -0,0 +1,35 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SrunLauncher +from parsl.providers import SlurmProvider +from parsl.usage_tracking.levels import LEVEL_1 + +""" This config assumes that it is used to launch parsl tasks from the login nodes +of Frontera at TACC. Each job submitted to the scheduler will request 2 nodes for 10 minutes. +""" +config = Config( + executors=[ + HighThroughputExecutor( + label="frontera_htex", + max_workers_per_node=1, # Set number of workers per node + provider=SlurmProvider( + cmd_timeout=60, # Add extra time for slow scheduler responses + nodes_per_block=2, + init_blocks=1, + min_blocks=1, + max_blocks=1, + partition='normal', # Replace with partition name + scheduler_options='#SBATCH -A ', # Enter scheduler_options if needed + + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init='', + + # Ideally we set the walltime to the longest supported walltime. + walltime='00:10:00', + launcher=SrunLauncher(), + ), + ) + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/gc_multisite.py b/parsl/parsl/configs/gc_multisite.py new file mode 100644 index 00000000..67862f9d --- /dev/null +++ b/parsl/parsl/configs/gc_multisite.py @@ -0,0 +1,27 @@ +from globus_compute_sdk import Executor + +from parsl.config import Config +from parsl.executors import GlobusComputeExecutor +from parsl.usage_tracking.levels import LEVEL_1 + +# Please start your own endpoint on perlmutter following instructions below to use this config: +# https://globus-compute.readthedocs.io/en/stable/endpoints/endpoint_examples.html#perlmutter-nersc +perlmutter_endpoint = 'YOUR_PERLMUTTER_ENDPOINT_UUID' + +# Please start your own endpoint on expanse following instructions below to use this config: +# https://globus-compute.readthedocs.io/en/stable/endpoints/endpoint_examples.html#expanse-sdsc +expanse_endpoint = 'YOUR_EXPANSE_ENDPOINT_UUID' + +config = Config( + executors=[ + GlobusComputeExecutor( + executor=Executor(endpoint_id=perlmutter_endpoint), + label="Perlmutter", + ), + GlobusComputeExecutor( + executor=Executor(endpoint_id=expanse_endpoint), + label="Expanse", + ), + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/gc_tutorial.py b/parsl/parsl/configs/gc_tutorial.py new file mode 100644 index 00000000..f3372a0c --- /dev/null +++ b/parsl/parsl/configs/gc_tutorial.py @@ -0,0 +1,18 @@ +from globus_compute_sdk import Executor + +from parsl.config import Config +from parsl.executors import GlobusComputeExecutor +from parsl.usage_tracking.levels import LEVEL_1 + +# Public tutorial endpoint +tutorial_endpoint = '4b116d3c-1703-4f8f-9f6f-39921e5864df' + +config = Config( + executors=[ + GlobusComputeExecutor( + executor=Executor(endpoint_id=tutorial_endpoint), + label="Tutorial_Endpoint_py3.11", + ) + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/htex_local.py b/parsl/parsl/configs/htex_local.py new file mode 100644 index 00000000..57549a47 --- /dev/null +++ b/parsl/parsl/configs/htex_local.py @@ -0,0 +1,18 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.providers import LocalProvider +from parsl.usage_tracking.levels import LEVEL_1 + +config = Config( + executors=[ + HighThroughputExecutor( + label="htex_local", + cores_per_worker=1, + provider=LocalProvider( + init_blocks=1, + max_blocks=1, + ), + ) + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/illinoiscluster.py b/parsl/parsl/configs/illinoiscluster.py new file mode 100644 index 00000000..216c910b --- /dev/null +++ b/parsl/parsl/configs/illinoiscluster.py @@ -0,0 +1,30 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SrunLauncher +from parsl.providers import SlurmProvider +from parsl.usage_tracking.levels import LEVEL_1 + +""" This config assumes that it is used to launch parsl tasks from the login nodes +of the Campus Cluster at UIUC. Each job submitted to the scheduler will request 2 nodes for 10 minutes. +""" +config = Config( + executors=[ + HighThroughputExecutor( + label="CC_htex", + worker_debug=False, + cores_per_worker=16.0, # each worker uses a full node + provider=SlurmProvider( + partition='secondary-fdr', # partition + nodes_per_block=2, # number of nodes + init_blocks=1, + max_blocks=1, + scheduler_options='', + cmd_timeout=60, + walltime='00:10:00', + launcher=SrunLauncher(), + worker_init='conda activate envParsl', # requires conda environment with parsl + ), + ) + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/improv.py b/parsl/parsl/configs/improv.py new file mode 100644 index 00000000..8a402828 --- /dev/null +++ b/parsl/parsl/configs/improv.py @@ -0,0 +1,34 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import MpiRunLauncher +from parsl.providers import PBSProProvider + +config = Config( + executors=[ + HighThroughputExecutor( + label="Improv_multinode", + max_workers_per_node=32, + provider=PBSProProvider( + account="YOUR_ALLOCATION_ON_IMPROV", + # PBS directives (header lines), for example: + # scheduler_options='#PBS -l mem=4gb', + scheduler_options='', + + queue="compute", + + # Command to be run before starting a worker: + # **WARNING** Improv requires an openmpi module to be + # loaded for the MpiRunLauncher. Add additional env + # load commands to this multiline string. + worker_init=''' +module load gcc/13.2.0; +module load openmpi/5.0.3-gcc-13.2.0; ''', + launcher=MpiRunLauncher(), + + # number of compute nodes allocated for each block + nodes_per_block=2, + walltime='00:10:00' + ), + ), + ], +) diff --git a/parsl/parsl/configs/kubernetes.py b/parsl/parsl/configs/kubernetes.py new file mode 100644 index 00000000..5a460186 --- /dev/null +++ b/parsl/parsl/configs/kubernetes.py @@ -0,0 +1,42 @@ +from parsl.addresses import address_by_route +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.providers import KubernetesProvider +from parsl.usage_tracking.levels import LEVEL_1 + +config = Config( + executors=[ + HighThroughputExecutor( + label='kube-htex', + cores_per_worker=1, + max_workers_per_node=1, + worker_logdir_root='YOUR_WORK_DIR', + + # Address for the pod worker to connect back + address=address_by_route(), + provider=KubernetesProvider( + namespace="default", + + # Docker image url to use for pods + image='YOUR_DOCKER_URL', + + # Command to be run upon pod start, such as: + # 'module load Anaconda; source activate parsl_env'. + # or 'pip install parsl' + worker_init='', + + # The secret key to download the image + secret="YOUR_KUBE_SECRET", + + # Should follow the Kubernetes naming rules + pod_name='YOUR-POD-Name', + + nodes_per_block=1, + init_blocks=1, + # Maximum number of pods to scale up + max_blocks=10, + ), + ), + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/local_threads.py b/parsl/parsl/configs/local_threads.py new file mode 100644 index 00000000..6b6561ea --- /dev/null +++ b/parsl/parsl/configs/local_threads.py @@ -0,0 +1,8 @@ +from parsl.config import Config +from parsl.executors.threads import ThreadPoolExecutor +from parsl.usage_tracking.levels import LEVEL_1 + +config = Config( + executors=[ThreadPoolExecutor()], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/midway.py b/parsl/parsl/configs/midway.py new file mode 100644 index 00000000..960c406c --- /dev/null +++ b/parsl/parsl/configs/midway.py @@ -0,0 +1,33 @@ +from parsl.addresses import address_by_interface +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SrunLauncher +from parsl.providers import SlurmProvider +from parsl.usage_tracking.levels import LEVEL_1 + +config = Config( + executors=[ + HighThroughputExecutor( + label='Midway_HTEX_multinode', + address=address_by_interface('bond0'), + worker_debug=False, + max_workers_per_node=2, + provider=SlurmProvider( + 'YOUR_PARTITION', # Partition name, e.g 'broadwl' + launcher=SrunLauncher(), + nodes_per_block=2, + init_blocks=1, + min_blocks=1, + max_blocks=1, + # string to prepend to #SBATCH blocks in the submit + # script to the scheduler eg: '#SBATCH --constraint=knl,quad,cache' + scheduler_options='', + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init='', + walltime='00:30:00' + ), + ) + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/osg.py b/parsl/parsl/configs/osg.py new file mode 100644 index 00000000..da0a1dcb --- /dev/null +++ b/parsl/parsl/configs/osg.py @@ -0,0 +1,32 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.providers import CondorProvider +from parsl.usage_tracking.levels import LEVEL_1 + +config = Config( + executors=[ + HighThroughputExecutor( + label='OSG_HTEX', + max_workers_per_node=1, + provider=CondorProvider( + nodes_per_block=1, + init_blocks=4, + max_blocks=4, + # This scheduler option string ensures that the compute nodes provisioned + # will have modules + scheduler_options=""" + +ProjectName = "MyProject" + Requirements = HAS_MODULES=?=TRUE + """, + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init='''unset HOME; unset PYTHONPATH; module load python/3.7.0; +python3 -m venv parsl_env; source parsl_env/bin/activate; python3 -m pip install parsl''', + walltime="00:20:00", + ), + worker_logdir_root='$OSG_WN_TMP', + worker_port=31000, + ) + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/polaris.py b/parsl/parsl/configs/polaris.py new file mode 100644 index 00000000..3d59991d --- /dev/null +++ b/parsl/parsl/configs/polaris.py @@ -0,0 +1,40 @@ +from parsl.addresses import address_by_interface +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import MpiExecLauncher +from parsl.providers import PBSProProvider +from parsl.usage_tracking.levels import LEVEL_1 + +# There are three user parameters to change for the PBSProProvider: +# YOUR_ACCOUNT: Account to charge usage +# YOUR_ENV: Name or path of your Parsl environment +# YOUR_WALLTIME: How long each job should run for + +# You may also need to change the filesystem if your data is not on Eagle + +config = Config( + executors=[ + HighThroughputExecutor( + available_accelerators=4, # Ensures one worker per accelerator + address=address_by_interface('bond0'), + cpu_affinity="alternating", # Prevents thread contention + prefetch_capacity=0, # Increase if you have many more tasks than workers + provider=PBSProProvider( + account="YOUR_ACCOUNT", + worker_init="module load conda; conda activate YOUR_ENV", + walltime="YOUR_WALLTIME", + queue="prod", # Small runs should use "debug" or "debug-scaling" + scheduler_options="#PBS -l filesystems=home:eagle", # Change if data on other filesystem + launcher=MpiExecLauncher( + bind_cmd="--cpu-bind", overrides="--depth=64 --ppn 1" + ), # Ensures 1 manger per node and allows it to divide work to all 64 cores + select_options="ngpus=4", + nodes_per_block=128, + min_blocks=0, + max_blocks=1, + cpus_per_node=64, + ), + ), + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/stampede2.py b/parsl/parsl/configs/stampede2.py new file mode 100644 index 00000000..b8e2aca9 --- /dev/null +++ b/parsl/parsl/configs/stampede2.py @@ -0,0 +1,39 @@ +from parsl.addresses import address_by_interface +from parsl.config import Config +from parsl.data_provider.globus import GlobusStaging +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SrunLauncher +from parsl.providers import SlurmProvider +from parsl.usage_tracking.levels import LEVEL_1 + +config = Config( + executors=[ + HighThroughputExecutor( + label='Stampede2_HTEX', + address=address_by_interface('em3'), + max_workers_per_node=2, + provider=SlurmProvider( + nodes_per_block=2, + init_blocks=1, + min_blocks=1, + max_blocks=1, + partition='YOUR_PARTITION', + # string to prepend to #SBATCH blocks in the submit + # script to the scheduler eg: '#SBATCH --constraint=knl,quad,cache' + scheduler_options='', + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init='', + launcher=SrunLauncher(), + walltime='00:30:00' + ), + storage_access=[GlobusStaging( + endpoint_uuid='ceea5ca0-89a9-11e7-a97f-22000a92523b', + endpoint_path='/', + local_path='/' + )] + ) + + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/summit.py b/parsl/parsl/configs/summit.py new file mode 100644 index 00000000..11e68ca2 --- /dev/null +++ b/parsl/parsl/configs/summit.py @@ -0,0 +1,31 @@ +from parsl.addresses import address_by_interface +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import JsrunLauncher +from parsl.providers import LSFProvider +from parsl.usage_tracking.levels import LEVEL_1 + +config = Config( + executors=[ + HighThroughputExecutor( + label='Summit_HTEX', + # On Summit ensure that the working dir is writeable from the compute nodes, + # for eg. paths below /gpfs/alpine/world-shared/ + working_dir='YOUR_WORKING_DIR_ON_SHARED_FS', + address=address_by_interface('ib0'), # This assumes Parsl is running on login node + worker_port_range=(50000, 55000), + provider=LSFProvider( + launcher=JsrunLauncher(), + walltime="00:10:00", + nodes_per_block=2, + init_blocks=1, + max_blocks=1, + worker_init='', # Input your worker environment initialization commands + project='YOUR_PROJECT_ALLOCATION', + cmd_timeout=60 + ), + ) + + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/toss3_llnl.py b/parsl/parsl/configs/toss3_llnl.py new file mode 100644 index 00000000..5c6b1c71 --- /dev/null +++ b/parsl/parsl/configs/toss3_llnl.py @@ -0,0 +1,30 @@ +from parsl.config import Config +from parsl.executors import FluxExecutor +from parsl.launchers import SrunLauncher +from parsl.providers import SlurmProvider +from parsl.usage_tracking.levels import LEVEL_1 + +config = Config( + executors=[ + FluxExecutor( + provider=SlurmProvider( + partition="YOUR_PARTITION", # e.g. "pbatch", "pdebug" + account="YOUR_ACCOUNT", + launcher=SrunLauncher(overrides="--mpibind=off"), + nodes_per_block=1, + init_blocks=1, + min_blocks=1, + max_blocks=1, + walltime="00:30:00", + # string to prepend to #SBATCH blocks in the submit + # script to the scheduler, e.g.: '#SBATCH -t 50' + scheduler_options='', + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init='', + cmd_timeout=120, + ), + ) + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/vineex_local.py b/parsl/parsl/configs/vineex_local.py new file mode 100644 index 00000000..755f1d1c --- /dev/null +++ b/parsl/parsl/configs/vineex_local.py @@ -0,0 +1,21 @@ +import uuid + +from parsl.config import Config +from parsl.executors.taskvine import TaskVineExecutor, TaskVineManagerConfig +from parsl.usage_tracking.levels import LEVEL_1 + +config = Config( + executors=[ + TaskVineExecutor( + label="parsl-vine-example", + + # If a project_name is given, then TaskVine will periodically + # report its status and performance back to the global TaskVine catalog, + # which can be viewed here: http://ccl.cse.nd.edu/software/taskvine/status + + # To disable status reporting, comment out the project_name. + manager_config=TaskVineManagerConfig(project_name="parsl-vine-" + str(uuid.uuid4())), + ) + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/configs/wqex_local.py b/parsl/parsl/configs/wqex_local.py new file mode 100644 index 00000000..fa583f38 --- /dev/null +++ b/parsl/parsl/configs/wqex_local.py @@ -0,0 +1,27 @@ +import uuid + +from parsl.config import Config +from parsl.executors import WorkQueueExecutor +from parsl.usage_tracking.levels import LEVEL_1 + +config = Config( + executors=[ + WorkQueueExecutor( + label="parsl-wq-example", + + # If a project_name is given, then Work Queue will periodically + # report its status and performance back to the global WQ catalog, + # which can be viewed here: http://ccl.cse.nd.edu/software/workqueue/status + + # To disable status reporting, comment out the project_name. + project_name="parsl-wq-" + str(uuid.uuid4()), + + # The port number that Work Queue will listen on for connecting workers. + port=9123, + + # A shared filesystem is not needed when using Work Queue. + shared_fs=False + ) + ], + usage_tracking=LEVEL_1, +) diff --git a/parsl/parsl/curvezmq.py b/parsl/parsl/curvezmq.py new file mode 100644 index 00000000..695602cb --- /dev/null +++ b/parsl/parsl/curvezmq.py @@ -0,0 +1,196 @@ +import os +from abc import ABCMeta, abstractmethod +from typing import Optional, Tuple, Union + +import zmq +import zmq.auth +from zmq.auth.thread import ThreadAuthenticator + + +def create_certificates(base_dir: Union[str, os.PathLike]): + """Create server and client certificates in a private directory. + + This will overwrite existing certificate files. + + Parameters + ---------- + base_dir : str | os.PathLike + Parent directory of the private certificates directory. + """ + cert_dir = os.path.join(base_dir, "certificates") + os.makedirs(cert_dir, mode=0o700, exist_ok=True) + + zmq.auth.create_certificates(cert_dir, name="server") + zmq.auth.create_certificates(cert_dir, name="client") + + return cert_dir + + +def _load_certificate( + cert_dir: Union[str, os.PathLike], name: str +) -> Tuple[bytes, bytes]: + if os.stat(cert_dir).st_mode & 0o777 != 0o700: + raise OSError(f"The certificates directory must be private: {cert_dir}") + + # pyzmq creates secret key files with the '.key_secret' extension + # Ref: https://github.com/zeromq/pyzmq/blob/ae615d4097ccfbc6b5c17de60355cbe6e00a6065/zmq/auth/certs.py#L73 + secret_key_file = os.path.join(cert_dir, f"{name}.key_secret") + public_key, secret_key = zmq.auth.load_certificate(secret_key_file) + if secret_key is None: + raise ValueError(f"No secret key found in {secret_key_file}") + + return public_key, secret_key + + +class BaseContext(metaclass=ABCMeta): + """Base CurveZMQ context""" + + def __init__(self, cert_dir: Optional[Union[str, os.PathLike]]) -> None: + self.cert_dir = cert_dir + self._ctx = zmq.Context() + + @property + def encrypted(self): + """Indicates whether encryption is enabled. + + False (disabled) when self.cert_dir is set to None. + """ + return self.cert_dir is not None + + @property + def closed(self): + return self._ctx.closed + + @abstractmethod + def socket(self, socket_type: int, *args, **kwargs) -> zmq.Socket: + """Create a socket associated with this context. + + This method will apply all necessary certificates and socket options. + + Parameters + ---------- + socket_type : int + The socket type, which can be any of the 0MQ socket types: REQ, REP, + PUB, SUB, PAIR, DEALER, ROUTER, PULL, PUSH, etc. + + args: + passed to the zmq.Context.socket method. + + kwargs: + passed to the zmq.Context.socket method. + """ + ... + + def term(self): + """Terminate the context.""" + self._ctx.term() + + def destroy(self, linger: Optional[int] = None): + """Close all sockets associated with this context and then terminate + the context. + + .. warning:: + + destroy involves calling ``zmq_close()``, which is **NOT** threadsafe. + If there are active sockets in other threads, this must not be called. + + Parameters + ---------- + linger : int, optional + If specified, set LINGER on sockets prior to closing them. + """ + self._ctx.destroy(linger) + + +class ServerContext(BaseContext): + """CurveZMQ server context + + We create server sockets via the `ctx.socket` method, which automatically + applies the necessary certificates and socket options. + + We handle client certificate authentication in a separate dedicated thread. + + Parameters + ---------- + cert_dir : str | os.PathLike | None + Path to the certificate directory. Setting this to None will disable encryption. + """ + + def __init__(self, cert_dir: Optional[Union[str, os.PathLike]]) -> None: + super().__init__(cert_dir) + self.auth_thread = None + if self.encrypted: + self.auth_thread = self._start_auth_thread() + + def __del__(self): + # Avoid issues in which the auth_thread attr was + # previously deleted + if getattr(self, "auth_thread", None): + self.auth_thread.stop() + self.auth_thread = None + + def _start_auth_thread(self) -> ThreadAuthenticator: + auth_thread = ThreadAuthenticator(self._ctx) + auth_thread.start() + # Only allow certs that are in the cert dir + assert self.cert_dir # For mypy + auth_thread.configure_curve(domain="*", location=self.cert_dir) + return auth_thread + + def socket(self, socket_type: int, *args, **kwargs) -> zmq.Socket: + sock = self._ctx.socket(socket_type, *args, **kwargs) + if self.encrypted: + assert self.cert_dir # For mypy + _, secret_key = _load_certificate(self.cert_dir, name="server") + try: + # Only the clients need the server's public key to encrypt + # messages and verify the server's identity. + # Ref: http://curvezmq.org/page:read-the-docs + sock.setsockopt(zmq.CURVE_SECRETKEY, secret_key) + except zmq.ZMQError as e: + raise ValueError("Invalid CurveZMQ key format") from e + sock.setsockopt(zmq.CURVE_SERVER, True) # Must come before bind + + # This flag enables IPV6 in addition to IPV4 + sock.setsockopt(zmq.IPV6, True) + return sock + + def term(self): + if self.auth_thread: + self.auth_thread.stop() + self.auth_thread = None + super().term() + + def destroy(self, linger: Optional[int] = None): + if self.auth_thread: + self.auth_thread.stop() + self.auth_thread = None + super().destroy(linger) + + +class ClientContext(BaseContext): + """CurveZMQ client context + + We create client sockets via the `ctx.socket` method, which automatically + applies the necessary certificates and socket options. + + Parameters + ---------- + cert_dir : str | os.PathLike | None + Path to the certificate directory. Setting this to None will disable encryption. + """ + + def socket(self, socket_type: int, *args, **kwargs) -> zmq.Socket: + sock = self._ctx.socket(socket_type, *args, **kwargs) + if self.encrypted: + assert self.cert_dir # For mypy + public_key, secret_key = _load_certificate(self.cert_dir, name="client") + server_public_key, _ = _load_certificate(self.cert_dir, name="server") + try: + sock.setsockopt(zmq.CURVE_PUBLICKEY, public_key) + sock.setsockopt(zmq.CURVE_SECRETKEY, secret_key) + sock.setsockopt(zmq.CURVE_SERVERKEY, server_public_key) + except zmq.ZMQError as e: + raise ValueError("Invalid CurveZMQ key format") from e + sock.setsockopt(zmq.IPV6, True) + return sock diff --git a/parsl/parsl/data_provider/__init__.py b/parsl/parsl/data_provider/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/data_provider/data_manager.py b/parsl/parsl/data_provider/data_manager.py new file mode 100644 index 00000000..b09ce2b0 --- /dev/null +++ b/parsl/parsl/data_provider/data_manager.py @@ -0,0 +1,168 @@ +import logging +from concurrent.futures import Future +from typing import TYPE_CHECKING, Any, Callable, List, Optional + +from parsl.app.futures import DataFuture +from parsl.data_provider.file_noop import NoOpFileStaging +from parsl.data_provider.files import File +from parsl.data_provider.ftp import FTPSeparateTaskStaging +from parsl.data_provider.http import HTTPSeparateTaskStaging +from parsl.data_provider.staging import Staging +from parsl.data_provider.zip import ZipFileStaging + +if TYPE_CHECKING: + from parsl.dataflow.dflow import DataFlowKernel + +logger = logging.getLogger(__name__) + +# these will be shared between all executors that do not explicitly +# override, so should not contain executor-specific state +default_staging: List[Staging] +default_staging = [NoOpFileStaging(), FTPSeparateTaskStaging(), HTTPSeparateTaskStaging(), ZipFileStaging()] + + +class DataManager: + """The DataManager is responsible for transferring input and output data. + + """ + + def __init__(self, dfk: "DataFlowKernel") -> None: + """Initialize the DataManager. + + Args: + - dfk (DataFlowKernel): The DataFlowKernel that this DataManager is managing data for. + + """ + + self.dfk = dfk + + def replace_task_stage_out(self, file: File, func: Callable, executor: str) -> Callable: + """This will give staging providers the chance to wrap (or replace entirely!) the task function.""" + executor_obj = self.dfk.executors[executor] + if hasattr(executor_obj, "storage_access") and executor_obj.storage_access is not None: + storage_access = executor_obj.storage_access # type: List[Staging] + else: + storage_access = default_staging + + for provider in storage_access: + logger.debug("stage_out checking Staging provider %r", provider) + if provider.can_stage_out(file): + newfunc = provider.replace_task_stage_out(self, executor, file, func) + if newfunc: + return newfunc + else: + return func + + logger.debug("reached end of staging provider list") + # if we reach here, we haven't found a suitable staging mechanism + raise ValueError("Executor {} cannot stage file {}".format(executor, repr(file))) + + def optionally_stage_in(self, input, func, executor): + if isinstance(input, DataFuture): + file = input.file_obj.cleancopy() + # replace the input DataFuture with a new DataFuture which will complete at + # the same time as the original one, but will contain the newly + # copied file + input = DataFuture(input, file, tid=input.tid) + elif isinstance(input, File): + file = input.cleancopy() + input = file + else: + return (input, func) + + replacement_input = self.stage_in(file, input, executor) + + func = self.replace_task(file, func, executor) + + return (replacement_input, func) + + def replace_task(self, file: File, func: Callable, executor: str) -> Callable: + """This will give staging providers the chance to wrap (or replace entirely!) the task function.""" + + executor_obj = self.dfk.executors[executor] + if hasattr(executor_obj, "storage_access") and executor_obj.storage_access is not None: + storage_access = executor_obj.storage_access + else: + storage_access = default_staging + + for provider in storage_access: + logger.debug("stage_in checking Staging provider %r", provider) + if provider.can_stage_in(file): + newfunc = provider.replace_task(self, executor, file, func) + if newfunc: + return newfunc + else: + return func + + logger.debug("reached end of staging provider list") + # if we reach here, we haven't found a suitable staging mechanism + raise ValueError("Executor {} cannot stage file {}".format(executor, repr(file))) + + def stage_in(self, file: File, input: Any, executor: str) -> Any: + """Transport the input from the input source to the executor, if it is file-like, + returning a DataFuture that wraps the stage-in operation. + + If no staging in is required - because the ``file`` parameter is not file-like, + then return that parameter unaltered. + + Args: + - self + - input (Any) : input to stage in. If this is a File or a + DataFuture, stage in tasks will be launched with appropriate + dependencies. Otherwise, no stage-in will be performed. + - executor (str) : an executor the file is going to be staged in to. + """ + + if isinstance(input, DataFuture): + parent_fut = input # type: Optional[Future] + elif isinstance(input, File): + parent_fut = None + else: + raise ValueError("Internal consistency error - should have checked DataFuture/File earlier") + + executor_obj = self.dfk.executors[executor] + if hasattr(executor_obj, "storage_access") and executor_obj.storage_access is not None: + storage_access = executor_obj.storage_access + else: + storage_access = default_staging + + for provider in storage_access: + logger.debug("stage_in checking Staging provider %r", provider) + if provider.can_stage_in(file): + staging_fut = provider.stage_in(self, executor, file, parent_fut=parent_fut) + if staging_fut: + return staging_fut + else: + return input + + logger.debug("reached end of staging provider list") + # if we reach here, we haven't found a suitable staging mechanism + raise ValueError("Executor {} cannot stage file {}".format(executor, repr(file))) + + def stage_out(self, file: File, executor: str, app_fu: Future) -> Optional[Future]: + """Transport the file from the local filesystem to the remote Globus endpoint. + + This function returns either a Future which should complete when the stageout + is complete, or None, if no staging needs to be waited for. + + Args: + - self + - file (File) - file to stage out + - executor (str) - Which executor the file is going to be staged out from. + - app_fu (Future) - a future representing the main body of the task that should + complete before stageout begins. + """ + executor_obj = self.dfk.executors[executor] + if hasattr(executor_obj, "storage_access") and executor_obj.storage_access is not None: + storage_access = executor_obj.storage_access + else: + storage_access = default_staging + + for provider in storage_access: + logger.debug("stage_out checking Staging provider %r", provider) + if provider.can_stage_out(file): + return provider.stage_out(self, executor, file, app_fu) + + logger.debug("reached end of staging provider list") + # if we reach here, we haven't found a suitable staging mechanism + raise ValueError("Executor {} cannot stage out file {}".format(executor, repr(file))) diff --git a/parsl/parsl/data_provider/file_noop.py b/parsl/parsl/data_provider/file_noop.py new file mode 100644 index 00000000..69c38669 --- /dev/null +++ b/parsl/parsl/data_provider/file_noop.py @@ -0,0 +1,17 @@ +import logging + +from parsl.data_provider.staging import Staging +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + + +class NoOpFileStaging(Staging, RepresentationMixin): + + def can_stage_in(self, file): + logger.debug("NoOpFileStaging checking file {}".format(repr(file))) + return file.scheme == 'file' + + def can_stage_out(self, file): + logger.debug("NoOpFileStaging checking file {}".format(repr(file))) + return file.scheme == 'file' diff --git a/parsl/parsl/data_provider/files.py b/parsl/parsl/data_provider/files.py new file mode 100644 index 00000000..4263753d --- /dev/null +++ b/parsl/parsl/data_provider/files.py @@ -0,0 +1,98 @@ +"""Define the File Type. + +The primary purpose of the File object is to track the protocol to be used +to transfer the file as well as to give the appropriate filepath depending +on where (client-side, remote-side, intermediary-side) the File.filepath is +being called from. +""" +import logging +import os +from typing import Optional, Union +from urllib.parse import urlparse + +import typeguard + +logger = logging.getLogger(__name__) + + +class File: + """The Parsl File Class. + + This represents the global, and sometimes local, URI/path to a file. + + Staging-in mechanisms may annotate a file with a local path recording + the path at the far end of a staging action. It is up to the user of + the File object to track which local scope that local path actually + refers to. + + """ + + @typeguard.typechecked + def __init__(self, url: Union[os.PathLike, str]): + """Construct a File object from a url string. + + Args: + - url (string or PathLike) : url of the file e.g. + - 'input.txt' + - pathlib.Path('input.txt') + - 'file:///scratch/proj101/input.txt' + - 'globus://go#ep1/~/data/input.txt' + - 'globus://ddb59aef-6d04-11e5-ba46-22000b92c6ec/home/johndoe/data/input.txt' + """ + self.url = str(url) + parsed_url = urlparse(self.url) + self.scheme = parsed_url.scheme if parsed_url.scheme else 'file' + self.netloc = parsed_url.netloc + self.path = parsed_url.path + self.filename = os.path.basename(self.path) + self.local_path: Optional[str] = None + + def cleancopy(self) -> "File": + """Returns a copy of the file containing only the global immutable state, + without any mutable site-local local_path information. The returned File + object will be as the original object was when it was constructed. + """ + logger.debug("Making clean copy of File object {}".format(repr(self))) + return File(self.url) + + def __str__(self) -> str: + return self.filepath + + def __repr__(self) -> str: + content = [ + f"{type(self).__name__}", + f"at 0x{id(self):x}", + f"url={self.url}", + f"scheme={self.scheme}", + f"netloc={self.netloc}", + f"path={self.path}", + f"filename={self.filename}", + ] + if self.local_path is not None: + content.append(f"local_path={self.local_path}") + + return f"<{' '.join(content)}>" + + def __fspath__(self) -> str: + return self.filepath + + @property + def filepath(self) -> str: + """Return the resolved filepath on the side where it is called from. + + The appropriate filepath will be returned when called from within + an app running remotely as well as regular python on the submit side. + + Only file: scheme URLs make sense to have a submit-side path, as other + URLs are not accessible through POSIX file access. + + Returns: + - filepath + """ + if self.local_path is not None: + return self.local_path + + if self.scheme in ['file']: + return self.path + else: + raise ValueError("No local_path set for {}".format(repr(self))) diff --git a/parsl/parsl/data_provider/ftp.py b/parsl/parsl/data_provider/ftp.py new file mode 100644 index 00000000..88cefe61 --- /dev/null +++ b/parsl/parsl/data_provider/ftp.py @@ -0,0 +1,82 @@ +import ftplib +import logging +import os + +import parsl +from parsl.data_provider.staging import Staging +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + + +class FTPSeparateTaskStaging(Staging, RepresentationMixin): + """Performs FTP staging as a separate parsl level task.""" + + def can_stage_in(self, file): + logger.debug("FTPSeparateTaskStaging checking file {}".format(repr(file))) + return file.scheme == 'ftp' + + def stage_in(self, dm, executor, file, parent_fut): + working_dir = dm.dfk.executors[executor].working_dir + if working_dir: + file.local_path = os.path.join(working_dir, file.filename) + else: + file.local_path = file.filename + stage_in_app = _ftp_stage_in_app(dm, executor=executor) + app_fut = stage_in_app(working_dir, outputs=[file], _parsl_staging_inhibit=True, parent_fut=parent_fut) + return app_fut._outputs[0] + + +class FTPInTaskStaging(Staging, RepresentationMixin): + """Performs FTP staging as a wrapper around the application task.""" + + def can_stage_in(self, file): + logger.debug("FTPInTaskStaging checking file {}".format(file.__repr__())) + return file.scheme == 'ftp' + + def stage_in(self, dm, executor, file, parent_fut): + working_dir = dm.dfk.executors[executor].working_dir + if working_dir: + file.local_path = os.path.join(working_dir, file.filename) + else: + file.local_path = file.filename + + return None + + def replace_task(self, dm, executor, file, f): + working_dir = dm.dfk.executors[executor].working_dir + return in_task_transfer_wrapper(f, file, working_dir) + + +def in_task_transfer_wrapper(func, file, working_dir): + def wrapper(*args, **kwargs): + import ftplib + if working_dir: + os.makedirs(working_dir, exist_ok=True) + + with open(file.local_path, 'wb') as f: + ftp = ftplib.FTP(file.netloc) + ftp.login() + ftp.cwd(os.path.dirname(file.path)) + ftp.retrbinary('RETR {}'.format(file.filename), f.write) + ftp.quit() + + result = func(*args, **kwargs) + return result + return wrapper + + +def _ftp_stage_in(working_dir, parent_fut=None, outputs=[], _parsl_staging_inhibit=True): + file = outputs[0] + if working_dir: + os.makedirs(working_dir, exist_ok=True) + with open(file.local_path, 'wb') as f: + ftp = ftplib.FTP(file.netloc) + ftp.login() + ftp.cwd(os.path.dirname(file.path)) + ftp.retrbinary('RETR {}'.format(file.filename), f.write) + ftp.quit() + + +def _ftp_stage_in_app(dm, executor): + return parsl.python_app(executors=[executor], data_flow_kernel=dm.dfk)(_ftp_stage_in) diff --git a/parsl/parsl/data_provider/globus.py b/parsl/parsl/data_provider/globus.py new file mode 100644 index 00000000..9ad3d5fe --- /dev/null +++ b/parsl/parsl/data_provider/globus.py @@ -0,0 +1,311 @@ +import json +import logging +import os +from functools import partial +from typing import Optional + +import typeguard + +import parsl +from parsl.app.app import python_app +from parsl.data_provider.staging import Staging +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + +# globus staging must be run explicitly in the same process/interpreter +# as the DFK. it relies on persisting globus state between actions in that +# process. + +""" +'Parsl Application' OAuth2 client registered with Globus Auth +by lukasz@globusid.org +""" +CLIENT_ID = '8b8060fd-610e-4a74-885e-1051c71ad473' +REDIRECT_URI = 'https://auth.globus.org/v2/web/auth-code' +SCOPES = ('openid ' + 'urn:globus:auth:scope:transfer.api.globus.org:all') + + +get_input = getattr(__builtins__, 'raw_input', input) + + +def _get_globus_provider(dfk, executor_label): + if executor_label is None: + raise ValueError("executor_label is mandatory") + executor = dfk.executors[executor_label] + if not hasattr(executor, "storage_access"): + raise ValueError("specified executor does not have storage_access attribute") + for provider in executor.storage_access: + if isinstance(provider, GlobusStaging): + return provider + + raise Exception('No suitable Globus endpoint defined for executor {}'.format(executor_label)) + + +def get_globus(): + Globus.init() + return Globus() + + +class Globus: + """ + All communication with the Globus Auth and Globus Transfer services is enclosed + in the Globus class. In particular, the Globus class is responsible for: + - managing an OAuth2 authorizer - getting access and refresh tokens, + refreshing an access token, storing to and retrieving tokens from + .globus.json file, + - submitting file transfers, + - monitoring transfers. + """ + + authorizer = None + + @classmethod + def init(cls): + token_path = os.path.join(os.path.expanduser('~'), '.parsl') + if not os.path.isdir(token_path): + os.mkdir(token_path) + cls.TOKEN_FILE = os.path.join(token_path, '.globus.json') + + if cls.authorizer: + return + cls.authorizer = cls._get_native_app_authorizer(CLIENT_ID) + + @classmethod + def get_authorizer(cls): + return cls.authorizer + + @classmethod + def transfer_file(cls, src_ep, dst_ep, src_path, dst_path): + import globus_sdk + tc = globus_sdk.TransferClient(authorizer=cls.authorizer) + td = globus_sdk.TransferData(tc, src_ep, dst_ep) + td.add_item(src_path, dst_path) + try: + task = tc.submit_transfer(td) + except Exception as e: + raise Exception('Globus transfer from {}{} to {}{} failed due to error: {}'.format( + src_ep, src_path, dst_ep, dst_path, e)) + + last_event_time = None + """ + A Globus transfer job (task) can be in one of the three states: ACTIVE, SUCCEEDED, FAILED. + Parsl every 15 seconds polls a status of the transfer job (task) from the Globus Transfer service, + with 60 second timeout limit. If the task is ACTIVE after time runs out 'task_wait' returns False, + and True otherwise. + """ + while not tc.task_wait(task['task_id'], timeout=60): + task = tc.get_task(task['task_id']) + # Get the last error Globus event + task_id = task['task_id'] + for event in tc.task_event_list(task_id): + if event['time'] != last_event_time: + last_event_time = event['time'] + logger.warning( + 'Non-critical Globus Transfer error event for globus://{}{}: "{}" at {}. Retrying...'.format( + src_ep, src_path, event['description'], event['time'])) + logger.debug('Globus Transfer error details: {}'.format(event['details'])) + """ + The Globus transfer job (task) has been terminated (is not ACTIVE). Check if the transfer + SUCCEEDED or FAILED. + """ + task = tc.get_task(task['task_id']) + if task['status'] == 'SUCCEEDED': + logger.debug('Globus transfer {}, from {}{} to {}{} succeeded'.format( + task['task_id'], src_ep, src_path, dst_ep, dst_path)) + else: + logger.debug('Globus Transfer task: {}'.format(task)) + events = tc.task_event_list(task['task_id']) + event = events.data[0] + raise Exception('Globus transfer {}, from {}{} to {}{} failed due to error: "{}"'.format( + task['task_id'], src_ep, src_path, dst_ep, dst_path, event['details'])) + + @classmethod + def _load_tokens_from_file(cls, filepath): + with open(filepath, 'r') as f: + tokens = json.load(f) + return tokens + + @classmethod + def _save_tokens_to_file(cls, filepath, tokens): + with open(filepath, 'w') as f: + json.dump(tokens, f) + + @classmethod + def _update_tokens_file_on_refresh(cls, token_response): + cls._save_tokens_to_file(cls.TOKEN_FILE, token_response.by_resource_server) + + @classmethod + def _do_native_app_authentication(cls, client_id, redirect_uri, + requested_scopes=None): + + import globus_sdk + client = globus_sdk.NativeAppAuthClient(client_id=client_id) + client.oauth2_start_flow( + requested_scopes=requested_scopes, + redirect_uri=redirect_uri, + refresh_tokens=True) + + url = client.oauth2_get_authorize_url() + print('Please visit the following URL to provide authorization: \n{}'.format(url)) + auth_code = get_input('Enter the auth code: ').strip() + token_response = client.oauth2_exchange_code_for_tokens(auth_code) + return token_response.by_resource_server + + @classmethod + def _get_native_app_authorizer(cls, client_id): + import globus_sdk + tokens = None + try: + tokens = cls._load_tokens_from_file(cls.TOKEN_FILE) + except Exception: + pass + + if not tokens: + tokens = cls._do_native_app_authentication( + client_id=client_id, + redirect_uri=REDIRECT_URI, + requested_scopes=SCOPES) + try: + cls._save_tokens_to_file(cls.TOKEN_FILE, tokens) + except Exception: + pass + + transfer_tokens = tokens['transfer.api.globus.org'] + + auth_client = globus_sdk.NativeAppAuthClient(client_id=client_id) + + return globus_sdk.RefreshTokenAuthorizer( + transfer_tokens['refresh_token'], + auth_client, + access_token=transfer_tokens['access_token'], + expires_at=transfer_tokens['expires_at_seconds'], + on_refresh=cls._update_tokens_file_on_refresh) + + +class GlobusStaging(Staging, RepresentationMixin): + """Specification for accessing data on a remote executor via Globus. + + Parameters + ---------- + endpoint_uuid : str + Universally unique identifier of the Globus endpoint at which the data can be accessed. + This can be found in the `Manage Endpoints `_ page. + endpoint_path : str, optional + FIXME + local_path : str, optional + FIXME + """ + + def can_stage_in(self, file): + logger.debug("Globus checking file {}".format(repr(file))) + return file.scheme == 'globus' + + def can_stage_out(self, file): + logger.debug("Globus checking file {}".format(repr(file))) + return file.scheme == 'globus' + + def stage_in(self, dm, executor, file, parent_fut): + globus_provider = _get_globus_provider(dm.dfk, executor) + globus_provider._update_local_path(file, executor, dm.dfk) + stage_in_app = globus_provider._globus_stage_in_app(executor=executor, dfk=dm.dfk) + app_fut = stage_in_app(outputs=[file], _parsl_staging_inhibit=True, parent_fut=parent_fut) + return app_fut._outputs[0] + + def stage_out(self, dm, executor, file, app_fu): + globus_provider = _get_globus_provider(dm.dfk, executor) + globus_provider._update_local_path(file, executor, dm.dfk) + stage_out_app = globus_provider._globus_stage_out_app(executor=executor, dfk=dm.dfk) + return stage_out_app(app_fu, _parsl_staging_inhibit=True, inputs=[file]) + + @typeguard.typechecked + def __init__(self, endpoint_uuid: str, endpoint_path: Optional[str] = None, local_path: Optional[str] = None): + self.endpoint_uuid = endpoint_uuid + self.endpoint_path = endpoint_path + self.local_path = local_path + self.globus = None + + def _globus_stage_in_app(self, executor, dfk): + executor_obj = dfk.executors[executor] + f = partial(_globus_stage_in, self, executor_obj) + return python_app(executors=['_parsl_internal'], data_flow_kernel=dfk)(f) + + def _globus_stage_out_app(self, executor, dfk): + executor_obj = dfk.executors[executor] + f = partial(_globus_stage_out, self, executor_obj) + return python_app(executors=['_parsl_internal'], data_flow_kernel=dfk)(f) + + # could this happen at __init__ time? + def initialize_globus(self): + if self.globus is None: + self.globus = get_globus() + + def _get_globus_endpoint(self, executor): + if executor.working_dir: + working_dir = os.path.normpath(executor.working_dir) + else: + raise ValueError("executor working_dir must be specified for GlobusStaging") + if self.endpoint_path and self.local_path: + endpoint_path = os.path.normpath(self.endpoint_path) + local_path = os.path.normpath(self.local_path) + common_path = os.path.commonpath((local_path, working_dir)) + if local_path != common_path: + raise Exception('"local_path" must be equal or an absolute subpath of "working_dir"') + relative_path = os.path.relpath(working_dir, common_path) + endpoint_path = os.path.join(endpoint_path, relative_path) + else: + endpoint_path = working_dir + return {'endpoint_uuid': self.endpoint_uuid, + 'endpoint_path': endpoint_path, + 'working_dir': working_dir} + + def _update_local_path(self, file, executor, dfk): + executor_obj = dfk.executors[executor] + globus_ep = self._get_globus_endpoint(executor_obj) + file.local_path = os.path.join(globus_ep['working_dir'], file.filename) + + +# this cannot be a class method, but must be a function, because I want +# to be able to use partial() on it - and partial() does not work on +# class methods +def _globus_stage_in(provider, executor, parent_fut=None, outputs=[], _parsl_staging_inhibit=True): + globus_ep = provider._get_globus_endpoint(executor) + file = outputs[0] + dst_path = os.path.join( + globus_ep['endpoint_path'], file.filename) + + provider.initialize_globus() + + provider.globus.transfer_file( + file.netloc, globus_ep['endpoint_uuid'], + file.path, dst_path) + + +def _globus_stage_out(provider, executor, app_fu, inputs=[], _parsl_staging_inhibit=True): + """ + Although app_fu isn't directly used in the stage out code, + it is needed as an input dependency to ensure this code + doesn't run until the app_fu is complete. The actual change + that is represented by the app_fu completing is that the + executor filesystem will now contain the file to stage out. + """ + globus_ep = provider._get_globus_endpoint(executor) + file = inputs[0] + src_path = os.path.join(globus_ep['endpoint_path'], file.filename) + + provider.initialize_globus() + + provider.globus.transfer_file( + globus_ep['endpoint_uuid'], file.netloc, + src_path, file.path + ) + + +def cli_run(): + parsl.set_stream_logger() + print("Parsl Globus command-line authorizer") + print("If authorization to Globus is necessary, the library will prompt you now.") + print("Otherwise it will do nothing") + get_globus() + print("Authorization complete") diff --git a/parsl/parsl/data_provider/http.py b/parsl/parsl/data_provider/http.py new file mode 100644 index 00000000..4553839b --- /dev/null +++ b/parsl/parsl/data_provider/http.py @@ -0,0 +1,88 @@ +import logging +import os + +import requests + +import parsl +from parsl.data_provider.staging import Staging +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + + +class HTTPSeparateTaskStaging(Staging, RepresentationMixin): + """A staging provider that Performs HTTP and HTTPS staging + as a separate parsl-level task. This requires a shared file + system on the executor.""" + + def can_stage_in(self, file): + logger.debug("HTTPSeparateTaskStaging checking file {}".format(repr(file))) + return file.scheme == 'http' or file.scheme == 'https' + + def stage_in(self, dm, executor, file, parent_fut): + working_dir = dm.dfk.executors[executor].working_dir + + if working_dir: + file.local_path = os.path.join(working_dir, file.filename) + else: + file.local_path = file.filename + + stage_in_app = _http_stage_in_app(dm, executor=executor) + app_fut = stage_in_app(working_dir, outputs=[file], _parsl_staging_inhibit=True, parent_fut=parent_fut) + return app_fut._outputs[0] + + +class HTTPInTaskStaging(Staging, RepresentationMixin): + """A staging provider that performs HTTP and HTTPS staging + as in a wrapper around each task. In contrast to + HTTPSeparateTaskStaging, this provider does not require a + shared file system.""" + + def can_stage_in(self, file): + logger.debug("HTTPInTaskStaging checking file {}".format(repr(file))) + return file.scheme == 'http' or file.scheme == 'https' + + def stage_in(self, dm, executor, file, parent_fut): + working_dir = dm.dfk.executors[executor].working_dir + + if working_dir: + file.local_path = os.path.join(working_dir, file.filename) + else: + file.local_path = file.filename + + return None + + def replace_task(self, dm, executor, file, f): + working_dir = dm.dfk.executors[executor].working_dir + return in_task_transfer_wrapper(f, file, working_dir) + + +def in_task_transfer_wrapper(func, file, working_dir): + def wrapper(*args, **kwargs): + import requests + if working_dir: + os.makedirs(working_dir, exist_ok=True) + resp = requests.get(file.url, stream=True) + with open(file.local_path, 'wb') as f: + for chunk in resp.iter_content(chunk_size=1024): + if chunk: + f.write(chunk) + + result = func(*args, **kwargs) + return result + return wrapper + + +def _http_stage_in(working_dir, parent_fut=None, outputs=[], _parsl_staging_inhibit=True): + file = outputs[0] + if working_dir: + os.makedirs(working_dir, exist_ok=True) + resp = requests.get(file.url, stream=True) + with open(file.local_path, 'wb') as f: + for chunk in resp.iter_content(chunk_size=1024): + if chunk: + f.write(chunk) + + +def _http_stage_in_app(dm, executor): + return parsl.python_app(executors=[executor], data_flow_kernel=dm.dfk)(_http_stage_in) diff --git a/parsl/parsl/data_provider/rsync.py b/parsl/parsl/data_provider/rsync.py new file mode 100644 index 00000000..d86b37bd --- /dev/null +++ b/parsl/parsl/data_provider/rsync.py @@ -0,0 +1,108 @@ +import logging +import os + +from parsl.data_provider.staging import Staging +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + + +class RSyncStaging(Staging, RepresentationMixin): + """ + This staging provider will execute rsync on worker nodes + to stage in files from a remote location. + + Worker nodes must be able to authenticate to the rsync server + without interactive authentication - for example, worker + initialization could include an appropriate SSH key configuration. + + The submit side will need to run an rsync-compatible server (for example, + an ssh server with the rsync binary installed) + + """ + + def __init__(self, hostname): + self.hostname = hostname + + def can_stage_in(self, file): + return file.scheme == "file" + + def can_stage_out(self, file): + return file.scheme == "file" + + def stage_in(self, dm, executor, file, parent_fut): + # we need to make path an absolute path, because + # rsync remote name needs to include absolute path + file.path = os.path.abspath(file.path) + + working_dir = dm.dfk.executors[executor].working_dir + + if working_dir: + file.local_path = os.path.join(working_dir, file.filename) + else: + file.local_path = file.filename + + return None + + def stage_out(self, dm, executor, file, parent_fut): + + file.path = os.path.abspath(file.path) + + working_dir = dm.dfk.executors[executor].working_dir + + if working_dir: + file.local_path = os.path.join(working_dir, file.filename) + else: + file.local_path = file.filename + + return None + + def replace_task(self, dm, executor, file, f): + logger.debug("Replacing task for rsync stagein") + working_dir = dm.dfk.executors[executor].working_dir + return in_task_stage_in_wrapper(f, file, working_dir, self.hostname) + + def replace_task_stage_out(self, dm, executor, file, f): + logger.debug("Replacing task for rsync stageout") + working_dir = dm.dfk.executors[executor].working_dir + return in_task_stage_out_wrapper(f, file, working_dir, self.hostname) + + +def in_task_stage_in_wrapper(func, file, working_dir, hostname): + def wrapper(*args, **kwargs): + import logging + logger = logging.getLogger(__name__) + logger.debug("rsync in_task_stage_in_wrapper start") + if working_dir: + os.makedirs(working_dir, exist_ok=True) + + logger.debug("rsync in_task_stage_in_wrapper calling rsync") + r = os.system("rsync {hostname}:{permanent_filepath} {worker_filepath}".format(hostname=hostname, + permanent_filepath=file.path, + worker_filepath=file.local_path)) + if r != 0: + raise RuntimeError("rsync returned {}, a {}".format(r, type(r))) + logger.debug("rsync in_task_stage_in_wrapper calling wrapped function") + result = func(*args, **kwargs) + logger.debug("rsync in_task_stage_in_wrapper returned from wrapped function") + return result + return wrapper + + +def in_task_stage_out_wrapper(func, file, working_dir, hostname): + def wrapper(*args, **kwargs): + import logging + logger = logging.getLogger(__name__) + logger.debug("rsync in_task_stage_out_wrapper start") + + logger.debug("rsync in_task_stage_out_wrapper calling wrapped function") + result = func(*args, **kwargs) + logger.debug("rsync in_task_stage_out_wrapper returned from wrapped function, calling rsync") + r = os.system("rsync {worker_filepath} {hostname}:{permanent_filepath}".format(hostname=hostname, + permanent_filepath=file.path, + worker_filepath=file.local_path)) + if r != 0: + raise RuntimeError("rsync returned {}, a {}".format(r, type(r))) + logger.debug("rsync in_task_stage_out_wrapper returned from rsync") + return result + return wrapper diff --git a/parsl/parsl/data_provider/staging.py b/parsl/parsl/data_provider/staging.py new file mode 100644 index 00000000..37e5c7df --- /dev/null +++ b/parsl/parsl/data_provider/staging.py @@ -0,0 +1,106 @@ +from concurrent.futures import Future +from typing import TYPE_CHECKING, Callable, Optional + +from parsl.app.futures import DataFuture +from parsl.data_provider.files import File + +if TYPE_CHECKING: + from parsl.data_provider.data_manager import DataManager + + +class Staging: + """ + This class defines the interface for file staging providers. + + For each file to be staged in, the data manager will present the file + to each configured Staging provider in turn: first, it will ask if the + provider can stage this file by calling ``can_stage_in``, and if so, it + will call both ``stage_in`` and ``replace_task`` to give the provider the + opportunity to perform staging. + + For each file to be staged out, the data manager will follow the same + pattern using the corresponding stage out methods of this class. + + The default implementation of this class rejects all files, and + performs no staging actions. + + To implement a concrete provider, one or both of the ``can_stage_*`` + methods should be overridden to match the appropriate files, and then + the corresponding ``stage_*`` and/or ``replace_task*`` methods should be + implemented. + """ + + def can_stage_in(self, file: File) -> bool: + """ + Given a File object, decide if this staging provider can + stage the file. Usually this is be based on the URL + scheme, but does not have to be. If this returns True, + then other methods of this Staging object will be called + to perform the staging. + """ + return False + + def can_stage_out(self, file: File) -> bool: + """ + Like can_stage_in, but for staging out. + """ + return False + + def stage_in(self, dm: "DataManager", executor: str, file: File, parent_fut: Optional[Future]) -> Optional[DataFuture]: + """ + This call gives the staging provider an opportunity to prepare for + stage-in and to launch arbitrary tasks which must complete as part + of stage-in. + + This call will be made with a fresh copy of the File that may be + modified for the purposes of this particular staging operation, + rather than the original application-provided File. This allows + staging specific information (primarily localpath) to be set on the + File without interfering with other stagings of the same File. + + The call can return a: + - DataFuture: the corresponding task input parameter will be + replaced by the DataFuture, and the main task will not + run until that DataFuture is complete. The DataFuture result + should be the file object as passed in. + - None: the corresponding task input parameter will be replaced + by a suitable automatically generated replacement that container + the File fresh copy, or is the fresh copy. + """ + return None + + def stage_out(self, dm: "DataManager", executor: str, file: File, app_fu: Future) -> Optional[Future]: + """ + This call gives the staging provider an opportunity to prepare for + stage-out and to launch arbitrary tasks which must complete as + part of stage-out. + + Even though it should set up stageout, it will be invoked before + the task executes. Any work which needs to happen after the main task + should depend on app_fu. + + For a given file, either return a Future which completes when stageout + is complete, or return None to indicate that no stageout action need + be waited for. When that Future completes, parsl will mark the relevant + output DataFuture complete. + + Note the asymmetry here between stage_in and stage_out: this can return + any Future, while stage_in must return a DataFuture. + """ + return None + + def replace_task(self, dm: "DataManager", executor: str, file: File, func: Callable) -> Optional[Callable]: + """ + For a file to be staged in, optionally return a replacement app + function, which usually should be the original app function wrapped + in staging code. + """ + return None + + def replace_task_stage_out(self, dm: "DataManager", executor: str, file: File, func: Callable) -> Optional[Callable]: + """ + For a file to be staged out, optionally return a replacement app + function, which usually should be the original app function wrapped + in staging code. + """ + return None diff --git a/parsl/parsl/data_provider/zip.py b/parsl/parsl/data_provider/zip.py new file mode 100644 index 00000000..167ddbbd --- /dev/null +++ b/parsl/parsl/data_provider/zip.py @@ -0,0 +1,135 @@ +import logging +import os +import zipfile +from typing import Tuple + +import filelock + +import parsl +from parsl.data_provider.files import File +from parsl.data_provider.staging import Staging +from parsl.errors import ParslError + +logger = logging.getLogger(__name__) + + +class ZipAuthorityError(ParslError): + def __init__(self, file): + self.file = file + + def __str__(self): + return f"ZipFileStaging cannot stage Files with an authority (netloc) section ({self.file.netloc}), for {self.file.url}" + + +class ZipFileStaging(Staging): + """A stage-out provider for zip files. + + This provider will stage out files by writing them into the specified zip + file. + + The filename of both the zip file and the file contained in that zip are + specified using a zip: URL, like this: + + zip:/tmp/foo/this.zip/inside/here.txt + + This URL names a zip file ``/tmp/foo/this.zip`` containing a file + ``inside/here.txt``. + + The provider will use the Python filelock package to lock the zip file so + that it does not conflict with other instances of itself. This lock will + not protect against other modifications to the zip file. + """ + + def can_stage_out(self, file: File) -> bool: + return self.is_zip_url(file) + + def can_stage_in(self, file: File) -> bool: + return self.is_zip_url(file) + + def is_zip_url(self, file: File) -> bool: + logger.debug("archive provider checking File {}".format(repr(file))) + + # First check if this is the scheme we care about + if file.scheme != "zip": + return False + + # This is some basic validation to check that the user isn't specifying + # an authority section and expecting it to mean something. + if file.netloc != "": + raise ZipAuthorityError(file) + + # If we got this far, we can stage this file + return True + + def stage_out(self, dm, executor, file, parent_fut): + assert file.scheme == 'zip' + + zip_path, inside_path = zip_path_split(file.path) + + working_dir = dm.dfk.executors[executor].working_dir + + if working_dir: + file.local_path = os.path.join(working_dir, inside_path) + + # TODO: I think its the right behaviour that a staging out provider should create the directory structure + # for the file to be placed in? + os.makedirs(os.path.dirname(file.local_path), exist_ok=True) + else: + raise RuntimeError("zip file staging requires a working_dir to be specified") + + stage_out_app = _zip_stage_out_app(dm) + app_fut = stage_out_app(zip_path, inside_path, working_dir, inputs=[file], _parsl_staging_inhibit=True, parent_fut=parent_fut) + return app_fut + + def stage_in(self, dm, executor, file, parent_fut): + assert file.scheme == 'zip' + + zip_path, inside_path = zip_path_split(file.path) + + working_dir = dm.dfk.executors[executor].working_dir + + if working_dir: + file.local_path = os.path.join(working_dir, inside_path) + + stage_in_app = _zip_stage_in_app(dm) + app_fut = stage_in_app(zip_path, inside_path, working_dir, outputs=[file], _parsl_staging_inhibit=True, parent_fut=parent_fut) + return app_fut._outputs[0] + + +def _zip_stage_out(zip_file, inside_path, working_dir, parent_fut=None, inputs=[], _parsl_staging_inhibit=True): + file = inputs[0] + + os.makedirs(os.path.dirname(zip_file), exist_ok=True) + + with filelock.FileLock(zip_file + ".lock"): + with zipfile.ZipFile(zip_file, mode='a', compression=zipfile.ZIP_DEFLATED) as z: + z.write(file, arcname=inside_path) + + os.remove(file) + + +def _zip_stage_out_app(dm): + return parsl.python_app(executors=['_parsl_internal'], data_flow_kernel=dm.dfk)(_zip_stage_out) + + +def _zip_stage_in(zip_file, inside_path, working_dir, *, parent_fut, outputs, _parsl_staging_inhibit=True): + with filelock.FileLock(zip_file + ".lock"): + with zipfile.ZipFile(zip_file, mode='r') as z: + content = z.read(inside_path) + with open(outputs[0], "wb") as of: + of.write(content) + + +def _zip_stage_in_app(dm): + return parsl.python_app(executors=['_parsl_internal'], data_flow_kernel=dm.dfk)(_zip_stage_in) + + +def zip_path_split(path: str) -> Tuple[str, str]: + """Split zip: path into a zipfile name and a contained-file name. + """ + index = path.find(".zip/") + + zip_path = path[:index + 4] + inside_path = path[index + 5:] + + return (zip_path, inside_path) diff --git a/parsl/parsl/dataflow/__init__.py b/parsl/parsl/dataflow/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/dataflow/dependency_resolvers.py b/parsl/parsl/dataflow/dependency_resolvers.py new file mode 100644 index 00000000..a2987cd7 --- /dev/null +++ b/parsl/parsl/dataflow/dependency_resolvers.py @@ -0,0 +1,115 @@ +from concurrent.futures import Future +from dataclasses import dataclass +from functools import singledispatch +from typing import Callable, Sequence + + +@dataclass +class DependencyResolver: + """A DependencyResolver describes how app dependencies can be resolved. + It is specified as two functions: `traverse_to_gather` which turns an + app parameter into a sequence of futures which must be waited for before + the task can be executed (for example, in the case of + `DEEP_DEPENDENCY_RESOLVER` this traverses structures such as lists to + find every contained ``Future``), and `traverse_to_unwrap` which turns an + app parameter into its value to be passed to the app on execution + (for example in the case of `DEEP_DEPENDENCY_RESOLVER` this replaces a + list containing futures with a new list containing the values of those + resolved futures). + + By default, Parsl will use `SHALLOW_DEPENDENCY_RESOLVER` which only + resolves Futures passed directly as arguments. + """ + traverse_to_gather: Callable[[object], Sequence[Future]] + traverse_to_unwrap: Callable[[object], object] + + +@singledispatch +def shallow_traverse_to_gather(o): + # objects in general do not expose futures that we can see + return [] + + +@singledispatch +def shallow_traverse_to_unwrap(o): + # objects in general unwrap to themselves + return o + + +@shallow_traverse_to_gather.register +def _(fut: Future): + return [fut] + + +@shallow_traverse_to_unwrap.register +@singledispatch +def _(fut: Future): + assert fut.done() + return fut.result() + + +@singledispatch +def deep_traverse_to_gather(o): + # objects in general do not expose futures that we can see + return [] + + +@singledispatch +def deep_traverse_to_unwrap(o): + # objects in general unwrap to themselves + return o + + +@deep_traverse_to_gather.register +def _(fut: Future): + return [fut] + + +@deep_traverse_to_unwrap.register +@singledispatch +def _(fut: Future): + assert fut.done() + return fut.result() + + +@deep_traverse_to_gather.register(tuple) +@deep_traverse_to_gather.register(list) +@deep_traverse_to_gather.register(set) +def _(iterable): + return [e for v in iterable for e in deep_traverse_to_gather(v)] + + +@deep_traverse_to_unwrap.register(tuple) +@deep_traverse_to_unwrap.register(list) +@deep_traverse_to_unwrap.register(set) +@singledispatch +def _(iterable): + + type_ = type(iterable) + return type_(map(deep_traverse_to_unwrap, iterable)) + + +@deep_traverse_to_gather.register(dict) +def _(dictionary): + futures = [] + for key, value in dictionary.items(): + futures.extend(deep_traverse_to_gather(key)) + futures.extend(deep_traverse_to_gather(value)) + return futures + + +@deep_traverse_to_unwrap.register(dict) +def _(dictionary): + unwrapped_dict = {} + for key, value in dictionary.items(): + key = deep_traverse_to_unwrap(key) + value = deep_traverse_to_unwrap(value) + unwrapped_dict[key] = value + return unwrapped_dict + + +DEEP_DEPENDENCY_RESOLVER = DependencyResolver(traverse_to_gather=deep_traverse_to_gather, + traverse_to_unwrap=deep_traverse_to_unwrap) + +SHALLOW_DEPENDENCY_RESOLVER = DependencyResolver(traverse_to_gather=shallow_traverse_to_gather, + traverse_to_unwrap=shallow_traverse_to_unwrap) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py new file mode 100644 index 00000000..a3aa151c --- /dev/null +++ b/parsl/parsl/dataflow/dflow.py @@ -0,0 +1,1334 @@ +from __future__ import annotations + +import atexit +import concurrent.futures as cf +import datetime +import inspect +import logging +import os +import pathlib +import random +import sys +import threading +import time +from concurrent.futures import Future +from functools import partial +from getpass import getuser +from socket import gethostname +from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union +from uuid import uuid4 + +import typeguard +from typeguard import typechecked + +import parsl +from parsl.app.errors import RemoteExceptionWrapper +from parsl.app.futures import DataFuture +from parsl.config import Config +from parsl.data_provider.data_manager import DataManager +from parsl.data_provider.files import File +from parsl.dataflow.dependency_resolvers import SHALLOW_DEPENDENCY_RESOLVER +from parsl.dataflow.errors import DependencyError, JoinError +from parsl.dataflow.futures import AppFuture +from parsl.dataflow.memoization import BasicMemoizer, Memoizer +from parsl.dataflow.rundirs import make_rundir +from parsl.dataflow.states import FINAL_FAILURE_STATES, FINAL_STATES, States +from parsl.dataflow.taskrecord import TaskRecord +from parsl.errors import ( + ConfigurationError, + InternalConsistencyError, + NoDataFlowKernelError, +) +from parsl.executors.base import ParslExecutor +from parsl.executors.status_handling import BlockProviderExecutor +from parsl.executors.threads import ThreadPoolExecutor +from parsl.jobs.job_status_poller import JobStatusPoller +from parsl.logconfigs.base import ( + LogConfig, + oneshot_initialize_logging, + oneshot_uninitialize_logging, +) +from parsl.logconfigs.file import FileLogging +from parsl.monitoring import MonitoringHub +from parsl.monitoring.errors import RadioRequiredError +from parsl.monitoring.message_type import MessageType +from parsl.monitoring.radios.multiprocessing import MultiprocessingQueueRadioSender +from parsl.monitoring.remote import monitor_wrapper +from parsl.process_loggers import wrap_with_logs +from parsl.usage_tracking.usage import UsageTracker +from parsl.utils import get_std_fname_mode, get_version + +logger = logging.getLogger(__name__) + + +class DataFlowKernel: + """The DataFlowKernel adds dependency awareness to an existing executor. + + It is responsible for managing futures, such that when dependencies are resolved, + pending tasks move to the runnable state. + + Here is a simplified diagram of what happens internally:: + + User | DFK | Executor + ---------------------------------------------------------- + | | + Task-------+> +Submit | + App_Fu<------+--| | + | Dependencies met | + | task-------+--> +Submit + | Ex_Fu<------+----| + + """ + + @typechecked + def __init__(self, config: Config) -> None: + """Initialize the DataFlowKernel. + + Parameters + ---------- + config : Config + A specification of all configuration options. For more details see the + :class:~`parsl.config.Config` documentation. + """ + + # this will be used to check cleanup only happens once + self.cleanup_called = False + + self._config = config + self.run_dir = make_rundir(config.run_dir) + + self._logging_unregister_callback: Optional[Callable[[], None]] + + self.log_config: Optional[LogConfig] + + if config.initialize_logging is True: + # This legacy behaviour deliberately does not pass log configuration + # to other components, so that they can preserve their own legacy + # logging behaviour. + self.log_config = None + dfk_log_config = FileLogging(level=logging.DEBUG) + self._logging_unregister_callback = dfk_log_config.initialize_logging(log_dir=pathlib.Path(self.run_dir), log_name="parsl") + elif config.initialize_logging is False: + self.log_config = None + self._logging_unregister_callback = None + else: + c = config.initialize_logging + assert c is not None, "type of initialize_logging was not properly narrowed to LogConfig" + self.log_config = config.initialize_logging + oneshot_initialize_logging(log_config=c, + log_dir=pathlib.Path(self.run_dir), + log_name="parsl") + self._logging_unregister_callback = lambda: oneshot_uninitialize_logging(log_config=c) + + logger.info("Starting DataFlowKernel with config\n%r", config) + + logger.info("Parsl version: %s", get_version()) + + self.usage_tracker = UsageTracker(self) + self.usage_tracker.send_start_message() + + self.task_state_counts_lock = threading.Lock() + self.task_state_counts = {state: 0 for state in States} + + # Monitoring + self.run_id = str(uuid4()) + + self.monitoring: Optional[MonitoringHub] + self.monitoring = config.monitoring + + self.monitoring_radio = None + + if self.monitoring: + self.monitoring.start(self.run_dir, self.config.run_dir, self.log_config) + self.monitoring_radio = MultiprocessingQueueRadioSender(self.monitoring.resource_msgs) + + time_began = datetime.datetime.now() + + logger.info("Run id is: " + self.run_id) + + self.workflow_name = None + if self.monitoring is not None and self.monitoring.workflow_name is not None: + self.workflow_name = self.monitoring.workflow_name + else: + for frame in inspect.stack(): + logger.debug("Considering candidate for workflow name: %s", frame.filename) + fname = os.path.basename(str(frame.filename)) + parsl_file_names = ['dflow.py', 'typeguard.py', '__init__.py'] + # Find first file name not considered a parsl file + if fname not in parsl_file_names: + self.workflow_name = fname + logger.debug("Using %s as workflow name", fname) + break + else: + logger.debug("Could not choose a name automatically") + self.workflow_name = "unnamed" + + self.workflow_version = str(time_began.replace(microsecond=0)) + if self.monitoring is not None and self.monitoring.workflow_version is not None: + self.workflow_version = self.monitoring.workflow_version + + workflow_info = { + 'python_version': "{}.{}.{}".format(sys.version_info.major, + sys.version_info.minor, + sys.version_info.micro), + 'parsl_version': get_version(), + "time_began": time_began, + 'run_id': self.run_id, + 'workflow_name': self.workflow_name, + 'workflow_version': self.workflow_version, + 'rundir': self.run_dir, + 'tasks_completed_count': self.task_state_counts[States.exec_done], + 'tasks_failed_count': self.task_state_counts[States.failed], + 'user': getuser(), + 'host': gethostname(), + } + + loggable_workflow_info = {"parsl.workflow_info." + k: v for k, v in workflow_info.items()} + logger.info("Workflow startup information: %r", workflow_info, extra={"parsl.dfk": self.run_id} | loggable_workflow_info) + if self.monitoring_radio: + self.monitoring_radio.send((MessageType.WORKFLOW_INFO, + workflow_info)) + + self.memoizer: Memoizer = config.memoizer if config.memoizer is not None else BasicMemoizer() + self.memoizer.start(run_dir=self.run_dir, config_run_dir=self.config.run_dir) + + # this must be set before executors are added since add_executors calls + # job_status_poller.add_executors. + self.job_status_poller = JobStatusPoller(strategy=self.config.strategy, + strategy_period=self.config.strategy_period, + max_idletime=self.config.max_idletime) + + self.executors: Dict[str, ParslExecutor] = {} + + self.data_manager = DataManager(self) + parsl_internal_executor = ThreadPoolExecutor(max_threads=config.internal_tasks_max_threads, + label='_parsl_internal') + self.add_executors(config.executors) + self.add_executors([parsl_internal_executor]) + + self.task_count = 0 + self.tasks: Dict[int, TaskRecord] = {} + self.submitter_lock = threading.Lock() + + self._task_launch_pool = cf.ThreadPoolExecutor(max_workers=1, thread_name_prefix="Task-Launch") + + self.dependency_resolver = self.config.dependency_resolver if self.config.dependency_resolver is not None \ + else SHALLOW_DEPENDENCY_RESOLVER + + atexit.register(self.atexit_cleanup) + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_value, traceback) -> None: + mode = self.config.exit_mode + logger.debug("Exiting context manager, with exit mode '%s'", mode) + if mode == "cleanup": + logger.info("Calling cleanup for DFK") + self.cleanup() + elif mode == "skip": + logger.info("Skipping all cleanup handling") + elif mode == "wait": + if exc_type is None: + logger.info("Waiting for all tasks to complete") + self.wait_for_current_tasks() + self.cleanup() + else: + logger.info("There was an exception - cleaning up without waiting for task completion") + self.cleanup() + else: + raise InternalConsistencyError(f"Exit case for {mode} should be unreachable, validated by typeguard on Config()") + + def _send_task_info(self, task_record: TaskRecord) -> None: + if self.monitoring_radio: + task_log_info = self._create_task_log_info(task_record) + self.monitoring_radio.send((MessageType.TASK_INFO, task_log_info)) + + def _create_task_log_info(self, task_record: TaskRecord) -> Dict[str, Any]: + """ + Create the dictionary that will be included in the log. + """ + info_to_monitor = ['func_name', 'memoize', 'hashsum', 'fail_count', 'fail_cost', 'status', + 'id', 'time_invoked', 'try_time_launched', 'time_returned', 'try_time_returned', 'executor'] + + # mypy cannot verify that these task_record[k] references are valid: + # They are valid if all entries in info_to_monitor are declared in the definition of TaskRecord + # This type: ignore[literal-required] asserts that fact. + task_log_info = {"task_" + k: task_record[k] for k in info_to_monitor} # type: ignore[literal-required] + + task_log_info['run_id'] = self.run_id + task_log_info['try_id'] = task_record['try_id'] + task_log_info['timestamp'] = datetime.datetime.now() + task_log_info['task_status_name'] = task_record['status'].name + task_log_info['tasks_failed_count'] = self.task_state_counts[States.failed] + task_log_info['tasks_completed_count'] = self.task_state_counts[States.exec_done] + task_log_info['tasks_memo_completed_count'] = self.task_state_counts[States.memo_done] + task_log_info['from_memo'] = task_record['from_memo'] + task_log_info['task_inputs'] = str(task_record['kwargs'].get('inputs', None)) + task_log_info['task_outputs'] = str(task_record['kwargs'].get('outputs', None)) + task_log_info['task_stdin'] = task_record['kwargs'].get('stdin', None) + + def std_spec_to_name(name, spec): + if spec is None: + name = "" + elif isinstance(spec, File): + name = spec.url + else: + # fallthrough case is various str, os.PathLike, tuple modes that + # can be interpreted by get_std_fname_mode. + try: + name, _ = get_std_fname_mode(name, spec) + except Exception: + logger.exception(f"Could not parse {name} specification {spec} for task {task_record['id']}") + name = "" + return name + + stdout_spec = task_record['kwargs'].get('stdout') + task_log_info['task_stdout'] = std_spec_to_name('stdout', stdout_spec) + + stderr_spec = task_record['kwargs'].get('stderr') + task_log_info['task_stderr'] = std_spec_to_name('stderr', stderr_spec) + + task_log_info['task_fail_history'] = ",".join(task_record['fail_history']) + task_log_info['task_depends'] = None + if task_record['depends'] is not None: + task_log_info['task_depends'] = ",".join([str(t.tid) for t in task_record['depends'] + if isinstance(t, AppFuture) or isinstance(t, DataFuture)]) + task_log_info['task_joins'] = None + + if isinstance(task_record['joins'], list): + task_log_info['task_joins'] = ",".join([str(t.tid) for t in task_record['joins'] + if isinstance(t, AppFuture) or isinstance(t, DataFuture)]) + elif isinstance(task_record['joins'], Future): + task_log_info['task_joins'] = ",".join([str(t.tid) for t in [task_record['joins']] + if isinstance(t, AppFuture) or isinstance(t, DataFuture)]) + + return task_log_info + + def _count_deps(self, depends: Sequence[Future]) -> int: + """Count the number of unresolved futures in the list depends. + """ + count = 0 + for dep in depends: + if not dep.done(): + count += 1 + + return count + + @property + def config(self) -> Config: + """Returns the fully initialized config that the DFK is actively using. + + Returns: + - Config object + """ + return self._config + + def handle_exec_update(self, task_record: TaskRecord, future: Future) -> None: + """This function is called only as a callback from an execution + attempt reaching a final state (either successfully or failing). + + It will launch retries if necessary, and update the task + structure. + + Args: + task_record (dict) : Task record + future (Future) : The future object corresponding to the task which + makes this callback + """ + + task_id = task_record['id'] + + task_record['try_time_returned'] = datetime.datetime.now() + + if not future.done(): + raise InternalConsistencyError("done callback called, despite future not reporting itself as done") + + try: + res = self._unwrap_remote_exception_wrapper(future) + + except Exception as e: + logger.info(f"Task {task_id} try {task_record['try_id']} failed with exception of type {type(e).__name__}") + # We keep the history separately, since the future itself could be + # tossed. + task_record['fail_history'].append(repr(e)) + task_record['fail_count'] += 1 + if self._config.retry_handler: + try: + cost = self._config.retry_handler(e, task_record) + except Exception as retry_handler_exception: + logger.exception("retry_handler raised an exception - will not retry") + + # this can be any amount > self._config.retries, to stop any more + # retries from happening + task_record['fail_cost'] = self._config.retries + 1 + + # make the reported exception be the retry handler's exception, + # rather than the execution level exception + e = retry_handler_exception + else: + task_record['fail_cost'] += cost + else: + task_record['fail_cost'] += 1 + + if isinstance(e, DependencyError): + logger.info("Task %s failed due to dependency failure so skipping retries", task_id) + self._complete_task_exception(task_record, States.dep_fail, e) + + elif task_record['fail_cost'] <= self._config.retries: + + # record the final state for this try before we mutate for retries + self._update_task_state(task_record, States.fail_retryable) + + task_record['try_id'] += 1 + task_record['try_time_launched'] = None + task_record['try_time_returned'] = None + task_record['fail_history'] = [] + self._update_task_state(task_record, States.pending) + + else: + logger.exception("Task %s failed after %s retry attempts", task_id, task_record['try_id']) + self._complete_task_exception(task_record, States.failed, e) + + else: + if task_record['from_memo']: + self._complete_task_result(task_record, States.memo_done, res) + elif not task_record['join']: + self._complete_task_result(task_record, States.exec_done, res) + else: + # This is a join task, and the original task's function code has + # completed. That means that the future returned by that code + # will be available inside the executor future, so we can now + # record the inner app ID in monitoring, and add a completion + # listener to that inner future. + + joinable = future.result() + + # Fail with a TypeError if the joinapp python body returned + # something we can't join on. + if isinstance(joinable, Future): + task_record['joins'] = joinable + task_record['join_lock'] = threading.Lock() + self._update_task_state(task_record, States.joining) + joinable.add_done_callback(partial(self.handle_join_update, task_record)) + elif joinable == []: # got a list, but it had no entries, and specifically, no Futures. + task_record['joins'] = joinable + task_record['join_lock'] = threading.Lock() + self._update_task_state(task_record, States.joining) + self.handle_join_update(task_record, None) + elif isinstance(joinable, list) and [j for j in joinable if not isinstance(j, Future)] == []: + task_record['joins'] = joinable + task_record['join_lock'] = threading.Lock() + self._update_task_state(task_record, States.joining) + for inner_future in joinable: + inner_future.add_done_callback(partial(self.handle_join_update, task_record)) + else: + self._complete_task_exception( + task_record, + States.failed, + TypeError(f"join_app body must return a Future or list of Futures, got {joinable} of type {type(joinable)}")) + + self._log_std_streams(task_record) + + # it might be that in the course of the update, we've gone back to being + # pending - in which case, we should consider ourself for relaunch + if task_record['status'] == States.pending: + self.launch_if_ready(task_record) + + def handle_join_update(self, task_record: TaskRecord, inner_app_future: Optional[AppFuture]) -> None: + with task_record['join_lock']: + # inner_app_future has completed, which is one (potentially of many) + # futures the outer task is joining on. + + # If the outer task is joining on a single future, then + # use the result of the inner_app_future as the final result of + # the outer app future. + + # If the outer task is joining on a list of futures, then + # check if the list is all done, and if so, return a list + # of the results. Otherwise, this callback can do nothing and + # processing will happen in another callback (on the final Future + # to complete) + + # There is no retry handling here: inner apps are responsible for + # their own retrying, and joining state is responsible for passing + # on whatever the result of that retrying was (if any). + + outer_task_id = task_record['id'] + logger.debug(f"Join callback for task {outer_task_id}, inner_app_future {inner_app_future}") + + if task_record['status'] != States.joining: + logger.debug(f"Join callback for task {outer_task_id} skipping because task is not in joining state") + return + + joinable = task_record['joins'] + + if isinstance(joinable, list): + for future in joinable: + if not future.done(): + logger.debug(f"A joinable future {future} is not done for task {outer_task_id} - skipping callback") + return # abandon this callback processing if joinables are not all done + + # now we know each joinable Future is done + # so now look for any exceptions + exceptions_tids: List[Tuple[BaseException, str]] + exceptions_tids = [] + if isinstance(joinable, Future): + je = joinable.exception() + if je is not None: + tid = self.render_future_description(joinable) + exceptions_tids = [(je, tid)] + elif isinstance(joinable, list): + for future in joinable: + je = future.exception() + if je is not None: + tid = self.render_future_description(future) + exceptions_tids.append((je, tid)) + else: + raise TypeError(f"Unknown joinable type {type(joinable)}") + + if exceptions_tids: + logger.debug("Task %s failed due to failure of an inner join future", outer_task_id) + e = JoinError(exceptions_tids, outer_task_id) + # We keep the history separately, since the future itself could be + # tossed. + task_record['fail_history'].append(repr(e)) + task_record['fail_count'] += 1 + # no need to update the fail cost because join apps are never + # retried + + self._complete_task_exception(task_record, States.failed, e) + + else: + # all the joinables succeeded, so construct a result: + if isinstance(joinable, Future): + assert inner_app_future is joinable + res = joinable.result() + elif isinstance(joinable, list): + res = [] + for future in joinable: + res.append(future.result()) + else: + raise TypeError(f"Unknown joinable type {type(joinable)}") + self._complete_task_result(task_record, States.exec_done, res) + + self._log_std_streams(task_record) + + def _complete_task_result(self, task_record: TaskRecord, new_state: States, result: Any) -> None: + """Set a task into a completed state + """ + assert new_state in FINAL_STATES + assert new_state not in FINAL_FAILURE_STATES + + task_record['time_returned'] = datetime.datetime.now() + + self.memoizer.update_memo_result(task_record, result) + + self._update_task_state(task_record, new_state) + + self.wipe_task(task_record['id']) + + with task_record['app_fu']._update_lock: + task_record['app_fu'].set_result(result) + + def _complete_task_exception(self, task_record: TaskRecord, new_state: States, exception: BaseException) -> None: + """Set a task into a failure state + """ + assert new_state in FINAL_STATES + assert new_state in FINAL_FAILURE_STATES + + task_record['time_returned'] = datetime.datetime.now() + + self.memoizer.update_memo_exception(task_record, exception) + + self._update_task_state(task_record, new_state) + + self.wipe_task(task_record['id']) + + with task_record['app_fu']._update_lock: + task_record['app_fu'].set_exception(exception) + + def _update_task_state(self, task_record: TaskRecord, new_state: States) -> None: + """Updates a task record state including accompanying consistency-keeping: + + * record change in task state counters + * record change in monitoring + """ + + with self.task_state_counts_lock: + extra = {"parsl.dfk": self.run_id, + "parsl.task": task_record['id'], + "parsl.task_state": new_state.name + } + if 'status' in task_record: + self.task_state_counts[task_record['status']] -= 1 + logger.info(f"Task {task_record['id']} changing state from {task_record['status'].name} to {new_state.name}", extra=extra) + else: + logger.info(f"Task {task_record['id']} initializing state to {new_state.name}", extra=extra) + + self.task_state_counts[new_state] += 1 + task_record['status'] = new_state + + self._send_task_info(task_record) + + @staticmethod + def _unwrap_remote_exception_wrapper(future: Future) -> Any: + result = future.result() + if isinstance(result, RemoteExceptionWrapper): + result.reraise() + return result + + def wipe_task(self, task_id: int) -> None: + """Remove task with task_id from the internal tasks table + """ + if self.config.garbage_collect: + del self.tasks[task_id] + + @staticmethod + def check_staging_inhibited(kwargs: Dict[str, Any]) -> bool: + return kwargs.get('_parsl_staging_inhibit', False) + + def launch_if_ready(self, task_record: TaskRecord) -> None: + """Schedules a task record for re-inspection to see if it is ready + for launch and for launch if it is ready. The call will return + immediately. + + This should be called by any piece of the DataFlowKernel that + thinks a task may have become ready to run. + + It is not an error to call launch_if_ready on a task that is not + ready to run - launch_if_ready will not incorrectly launch that + task. + + launch_if_ready is thread safe, so may be called from any thread + or callback. + """ + self._task_launch_pool.submit(self._launch_if_ready_async, task_record) + + @wrap_with_logs + def _launch_if_ready_async(self, task_record: TaskRecord) -> None: + """ + _launch_if_ready will launch the specified task, if it is ready + to run (for example, without dependencies, and in pending state). + """ + exec_fu: Future + + task_id = task_record['id'] + with task_record['task_launch_lock']: + + if task_record['status'] != States.pending: + logger.debug(f"Task {task_id} is not pending, so launch_if_ready skipping") + return + + if self._count_deps(task_record['depends']) != 0: + logger.debug(f"Task {task_id} has outstanding dependencies, so launch_if_ready skipping") + return + + # We can now launch the task or handle any dependency failures + + new_args, kwargs, exceptions_tids = self._unwrap_futures(task_record['args'], + task_record['kwargs']) + task_record['args'] = new_args + task_record['kwargs'] = kwargs + + if not exceptions_tids: + # There are no dependency errors + try: + exec_fu = self.launch_task(task_record) + assert isinstance(exec_fu, Future) + except Exception as e: + # task launched failed somehow. the execution might + # have been launched and an exception raised after + # that, though. that's hard to detect from here. + # we don't attempt retries here. This is an error with submission + # even though it might come from user code such as a plugged-in + # executor or memoization hash function. + + logger.debug("Got an exception launching task", exc_info=True) + exec_fu = Future() + exec_fu.set_exception(e) + else: + logger.info("Task %s failed due to dependency failure", task_id) + + exec_fu = Future() + exec_fu.set_exception(DependencyError(exceptions_tids, + task_id)) + + assert isinstance(exec_fu, Future), "Every code path leading here needs to define exec_fu" + + try: + exec_fu.add_done_callback(partial(self.handle_exec_update, task_record)) + except Exception: + # this exception is ignored here because it is assumed that exception + # comes from directly executing handle_exec_update (because exec_fu is + # done already). If the callback executes later, then any exception + # coming out of the callback will be ignored and not propate anywhere, + # so this block attempts to keep the same behaviour here. + logger.error("add_done_callback got an exception which will be ignored", exc_info=True) + + task_record['exec_fu'] = exec_fu + + def launch_task(self, task_record: TaskRecord) -> Future: + """Handle the actual submission of the task to the executor layer. + + Args: + task_record : The task record + + Returns: + Future that tracks the execution of the submitted function + """ + task_id = task_record['id'] + function = task_record['func'] + args = task_record['args'] + kwargs = task_record['kwargs'] + + task_record['try_time_launched'] = datetime.datetime.now() + + memo_fu = self.memoizer.check_memo(task_record) + if memo_fu: + logger.info("Reusing cached result for task %s", task_id) + task_record['from_memo'] = True + assert isinstance(memo_fu, Future) + return memo_fu + + task_record['from_memo'] = False + executor_label = task_record["executor"] + try: + executor = self.executors[executor_label] + except Exception: + logger.exception("Task %s requested invalid executor %s", task_id, executor_label) + raise ValueError("Task {} requested invalid executor {}".format(task_id, executor_label)) + + try_id = task_record['fail_count'] + + if self.monitoring is not None and self.monitoring.resource_monitoring_enabled: + if executor.remote_monitoring_radio is None: + raise RadioRequiredError() + + wrapper_logging_level = logging.DEBUG if self.monitoring.monitoring_debug else logging.INFO + (function, args, kwargs) = monitor_wrapper(f=function, + args=args, + kwargs=kwargs, + x_try_id=try_id, + x_task_id=task_id, + radio_config=executor.remote_monitoring_radio, + run_id=self.run_id, + logging_level=wrapper_logging_level, + sleep_dur=self.monitoring.resource_monitoring_interval, + monitor_resources=executor.monitor_resources(), + run_dir=self.run_dir) + + with self.submitter_lock: + exec_fu = executor.submit(function, task_record['resource_specification'], *args, **kwargs) + + self._update_task_state(task_record, States.launched) + + extra = {"parsl.dfk": self.run_id, + "parsl.task": task_id, + "parsl.try": try_id, + "parsl.executor": executor.label + } + + if hasattr(exec_fu, "parsl_executor_task_id"): + extra['parsl.executor_task'] = exec_fu.parsl_executor_task_id + logger.info( + f"Parsl task {task_id} try {try_id} launched on executor {executor.label} " + f"with executor id {exec_fu.parsl_executor_task_id}", extra=extra) + + else: + logger.info(f"Parsl task {task_id} try {try_id} launched on executor {executor.label}", extra=extra) + + self._log_std_streams(task_record) + + return exec_fu + + def _add_input_deps(self, executor: str, args: Sequence[Any], kwargs: Dict[str, Any], func: Callable) -> Tuple[Sequence[Any], Dict[str, Any], + Callable]: + """Look for inputs of the app that are files. Give the data manager + the opportunity to replace a file with a data future for that file, + for example wrapping the result of a staging action. + + Args: + - executor (str) : executor where the app is going to be launched + - args (List) : Positional args to app function + - kwargs (Dict) : Kwargs to app function + """ + + # Return if the task is a data management task, rather than doing + # data management on it. + if self.check_staging_inhibited(kwargs): + logger.debug("Not performing input staging") + return args, kwargs, func + + inputs = kwargs.get('inputs', []) + for idx, f in enumerate(inputs): + (inputs[idx], func) = self.data_manager.optionally_stage_in(f, func, executor) + + for kwarg, f in kwargs.items(): + # stdout and stderr files should not be staging in (they will be staged *out* + # in _add_output_deps) + if kwarg in ['stdout', 'stderr']: + continue + (kwargs[kwarg], func) = self.data_manager.optionally_stage_in(f, func, executor) + + newargs = list(args) + for idx, f in enumerate(newargs): + (newargs[idx], func) = self.data_manager.optionally_stage_in(f, func, executor) + + return tuple(newargs), kwargs, func + + def _add_output_deps(self, executor: str, args: Sequence[Any], kwargs: Dict[str, Any], app_fut: AppFuture, func: Callable) -> Callable: + logger.debug("Adding output dependencies") + outputs = kwargs.get('outputs', []) + app_fut._outputs = [] + + # Pass over all possible outputs: the outputs kwarg, stdout and stderr + # and for each of those, perform possible stage-out. This can result in: + # a DataFuture to be exposed in app_fut to represent the completion of + # that stageout (sometimes backed by a new sub-workflow for separate-task + # stageout), a replacement for the function to be executed (intended to + # be the original function wrapped with an in-task stageout wrapper), a + # rewritten File object to be passed to task to be executed + + def stageout_one_file(file: File, rewritable_func: Callable): + if not self.check_staging_inhibited(kwargs): + # replace a File with a DataFuture - either completing when the stageout + # future completes, or if no stage out future is returned, then when the + # app itself completes. + + # The staging code will get a clean copy which it is allowed to mutate, + # while the DataFuture-contained original will not be modified by any staging. + f_copy = file.cleancopy() + + logger.debug("Submitting stage out for output file %r", file) + stageout_fut = self.data_manager.stage_out(f_copy, executor, app_fut) + if stageout_fut: + logger.debug("Adding a dependency on stageout future for %r", file) + df = DataFuture(stageout_fut, file, tid=app_fut.tid) + else: + logger.debug("No stageout dependency for %r", file) + df = DataFuture(app_fut, file, tid=app_fut.tid) + + # this is a hook for post-task stageout + # note that nothing depends on the output - which is maybe a bug + # in the not-very-tested stageout system? + rewritable_func = self.data_manager.replace_task_stage_out(f_copy, rewritable_func, executor) + return rewritable_func, f_copy, df + else: + logger.debug("Not performing output staging for: %r", file) + return rewritable_func, file, DataFuture(app_fut, file, tid=app_fut.tid) + + for idx, file in enumerate(outputs): + func, outputs[idx], o = stageout_one_file(file, func) + app_fut._outputs.append(o) + + file = kwargs.get('stdout') + if isinstance(file, File): + func, kwargs['stdout'], app_fut._stdout_future = stageout_one_file(file, func) + + file = kwargs.get('stderr') + if isinstance(file, File): + func, kwargs['stderr'], app_fut._stderr_future = stageout_one_file(file, func) + + return func + + def _gather_all_deps(self, args: Sequence[Any], kwargs: Dict[str, Any]) -> List[Future]: + """Assemble a list of all Futures passed as arguments, kwargs or in the inputs kwarg. + + Args: + - args: The list of args pass to the app + - kwargs: The dict of all kwargs passed to the app + + Returns: + - list of dependencies + + """ + depends: List[Future] = [] + + def check_dep(d: Any) -> None: + try: + depends.extend(self.dependency_resolver.traverse_to_gather(d)) + except Exception: + logger.exception("Exception in dependency_resolver.traverse_to_gather") + raise + + # Check the positional args + for dep in args: + check_dep(dep) + + # Check for explicit kwargs ex, fu_1= + for key in kwargs: + dep = kwargs[key] + check_dep(dep) + + # Check for futures in inputs=[...] + for dep in kwargs.get('inputs', []): + check_dep(dep) + + return depends + + def _unwrap_futures(self, args: Sequence[Any], kwargs: Dict[str, Any]) \ + -> Tuple[Sequence[Any], Dict[str, Any], Sequence[Tuple[Exception, str]]]: + """This function should be called when all dependencies have completed. + + It will rewrite the arguments for that task, replacing each Future + with the result of that future. + + If the user hid futures a level below, we will not catch + it, and will (most likely) result in a type error. + + Args: + args (List) : Positional args to app function + kwargs (Dict) : Kwargs to app function + + Return: + a rewritten args list + a rewritten kwargs dict + pairs of exceptions, task ids from any Futures which stored + exceptions rather than results. + """ + dep_failures = [] + + def append_failure(e: Exception, dep: Future) -> None: + tid = self.render_future_description(dep) + dep_failures.extend([(e, tid)]) + + # Replace item in args + new_args = [] + for dep in args: + try: + new_args.extend([self.dependency_resolver.traverse_to_unwrap(dep)]) + except Exception as e: + append_failure(e, dep) + + # Check for explicit kwargs ex, fu_1= + for key in kwargs: + dep = kwargs[key] + try: + kwargs[key] = self.dependency_resolver.traverse_to_unwrap(dep) + except Exception as e: + append_failure(e, dep) + + # Check for futures in inputs=[...] + if 'inputs' in kwargs: + new_inputs = [] + for dep in kwargs['inputs']: + try: + new_inputs.extend([self.dependency_resolver.traverse_to_unwrap(dep)]) + except Exception as e: + append_failure(e, dep) + kwargs['inputs'] = new_inputs + + return new_args, kwargs, dep_failures + + def submit(self, + func: Callable, + app_args: Sequence[Any], + executors: Union[str, Sequence[str]], + cache: bool, + ignore_for_cache: Optional[Sequence[str]], + app_kwargs: Dict[str, Any], + join: bool = False) -> AppFuture: + """Add task to the dataflow system. + + If the app task has the executors attributes not set (default=='all') + the task will be launched on a randomly selected executor from the + list of executors. If the app task specifies a particular set of + executors, it will be targeted at the specified executors. + + Args: + - func : A function object + + KWargs : + - app_args : Args to the function + - executors (list or string) : List of executors this call could go to. + Default='all' + - cache (Bool) : To enable memoization or not + - ignore_for_cache (sequence) : List of kwargs to be ignored for memoization/checkpointing + - app_kwargs (dict) : Rest of the kwargs to the fn passed as dict. + + Returns: + AppFuture + + """ + + if ignore_for_cache is None: + ignore_for_cache = [] + else: + # duplicate so that it can be modified safely later + ignore_for_cache = list(ignore_for_cache) + + if self.cleanup_called: + raise NoDataFlowKernelError("Cannot submit to a DFK that has been cleaned up") + + task_id = self.task_count + self.task_count += 1 + if isinstance(executors, str) and executors.lower() == 'all': + choices = list(e for e in self.executors if e != '_parsl_internal') + elif isinstance(executors, list): + choices = executors + else: + raise ValueError("Task {} supplied invalid type for executors: {}".format(task_id, type(executors))) + executor = random.choice(choices) + logger.debug("Task %s will be sent to executor %s", task_id, executor) + + resource_specification = app_kwargs.get('parsl_resource_specification', {}) + + task_record: TaskRecord + task_record = {'args': app_args, + 'depends': [], + 'dfk': self, + 'executor': executor, + 'func': func, + 'func_name': func.__name__, + 'kwargs': app_kwargs, + 'memoize': cache, + 'hashsum': None, + 'exec_fu': None, + 'fail_count': 0, + 'fail_cost': 0, + 'fail_history': [], + 'from_memo': None, + 'ignore_for_cache': ignore_for_cache, + 'join': join, + 'joins': None, + 'try_id': 0, + 'id': task_id, + 'task_launch_lock': threading.Lock(), + 'time_invoked': datetime.datetime.now(), + 'time_returned': None, + 'try_time_launched': None, + 'try_time_returned': None, + 'resource_specification': resource_specification} + + for kw in ['stdout', 'stderr']: + if kw in app_kwargs: + if app_kwargs[kw] == parsl.AUTO_LOGNAME: + if kw not in ignore_for_cache: + ignore_for_cache += [kw] + if self.config.std_autopath is None: + app_kwargs[kw] = self.default_std_autopath(task_record, kw) + else: + app_kwargs[kw] = self.config.std_autopath(task_record, kw) + + app_fu = AppFuture(task_record) + task_record['app_fu'] = app_fu + + # Transform remote input files to data futures + app_args, app_kwargs, func = self._add_input_deps(executor, app_args, app_kwargs, func) + + func = self._add_output_deps(executor, app_args, app_kwargs, app_fu, func) + + logger.debug("Added output dependencies") + + # Replace the function invocation in the TaskRecord with whatever file-staging + # substitutions have been made. + task_record.update({ + 'args': app_args, + 'func': func, + 'kwargs': app_kwargs}) + + logger.debug("Gathering dependencies") + # Get the list of dependencies for the task + depends = self._gather_all_deps(app_args, app_kwargs) + logger.debug("Gathered dependencies") + task_record['depends'] = depends + + depend_descs = [] + for d in depends: + if isinstance(d, AppFuture) and d.task_record['dfk'] == self: + logger.info("Task %s has dependency task %s", task_id, d.task_record['id'], + extra={"parsl.task": task_id, "parsl.dependency_task": d.task_record['id']}) + else: + logger.info("Task %s has dependency future %r", task_id, d, + extra={"parsl.task": task_id, "parsl.dependency_object": repr(d)}) + + depend_descs.append(self.render_future_description(d)) + + if depend_descs != []: + waiting_message = "waiting on {}".format(", ".join(depend_descs)) + else: + waiting_message = "not waiting on any dependency" + + logger.info("Task %s submitted for App %s, %s", task_id, task_record['func_name'], waiting_message, + extra={"parsl.task": task_id, "parsl.app_name": task_record['func_name']}) + + logger.debug("Task %s has AppFuture: %r", task_id, task_record['app_fu']) + self._update_task_state(task_record, States.pending) + + assert task_id not in self.tasks + self.tasks[task_id] = task_record + + # at this point add callbacks to all dependencies to do a launch_if_ready + # call whenever a dependency completes. + + # we need to be careful about the order of setting the state to pending, + # adding the callbacks, and caling launch_if_ready explicitly once always below. + + # I think as long as we call launch_if_ready once after setting pending, then + # we can add the callback dependencies at any point: if the callbacks all fire + # before then, they won't cause a launch, but the one below will. if they fire + # after we set it pending, then the last one will cause a launch, and the + # explicit one won't. + + for d in depends: + + def callback_adapter(dep_fut: Future) -> None: + self.launch_if_ready(task_record) + + try: + d.add_done_callback(callback_adapter) + except Exception as e: + logger.error("add_done_callback got an exception %s which will be ignored", e) + + self.launch_if_ready(task_record) + + return app_fu + + # it might also be interesting to assert that all DFK + # tasks are in a "final" state (3,4,5) when the DFK + # is closed down, and report some kind of warning. + # although really I'd like this to drain properly... + # and a drain function might look like this. + # If tasks have their states changed, this won't work properly + # but we can validate that... + def log_task_states(self) -> None: + logger.info("Summary of tasks in DFK:") + + with self.task_state_counts_lock: + for state in States: + logger.info("Tasks in state %s: %s", state, self.task_state_counts[state]) + + logger.info("End of summary") + + def add_executors(self, executors: Sequence[ParslExecutor]) -> None: + for executor in executors: + logger.info("Adding executor %s of type %s.%s", executor.label, type(executor).__module__, type(executor).__name__, + extra={"parsl.executor.label": executor.label, + "parsl.executor.type": type(executor).__module__ + "." + type(executor).__name__ + }) + executor.run_id = self.run_id + executor.run_dir = self.run_dir + executor.log_config = self.log_config + if self.monitoring and executor.remote_monitoring_radio is not None: + executor.monitoring_messages = self.monitoring.resource_msgs + logger.debug("Starting monitoring receiver for executor %s " + "with remote monitoring radio config %s", + executor.label, executor.remote_monitoring_radio) + + executor.monitoring_receiver = executor.remote_monitoring_radio.create_receiver(resource_msgs=executor.monitoring_messages, + run_dir=executor.run_dir) + if hasattr(executor, 'provider'): + if hasattr(executor.provider, 'script_dir'): + executor.provider.script_dir = os.path.join(self.run_dir, 'submit_scripts') + os.makedirs(executor.provider.script_dir, exist_ok=True) + + self.executors[executor.label] = executor + executor.start() + block_executors = [e for e in executors if isinstance(e, BlockProviderExecutor)] + self.job_status_poller.add_executors(block_executors) + + def atexit_cleanup(self) -> None: + logger.warning("Python is exiting with a DFK still running. " + "You should call parsl.dfk().cleanup() before " + "exiting to release any resources") + + def wait_for_current_tasks(self) -> None: + """Waits for all tasks in the task list to be completed, by waiting for their + AppFuture to be completed. This method will not necessarily wait for any tasks + added after cleanup has started (such as data stageout?) + """ + + logger.info("Waiting for all remaining tasks to complete") + + # .values is made into a list immediately to reduce (although not + # eliminate) a race condition where self.tasks can be modified + # elsewhere by a completing task being removed from the dictionary. + task_records = list(self.tasks.values()) + for task_record in task_records: + # .exception() is a less exception throwing way of + # waiting for completion than .result() + fut = task_record['app_fu'] + if not fut.done(): + fut.exception() + # now app future is done, poll until DFK state is final: a + # DFK state being final and the app future being done do not imply each other. + while task_record['status'] not in FINAL_STATES: + time.sleep(0.1) + + logger.info("All remaining tasks completed") + + @wrap_with_logs + def cleanup(self) -> None: + """Clean-up by closing all of the components used by the DFK + """ + + logger.info("DFK cleanup initiated") + + # this check won't detect two DFK cleanups happening from + # different threads extremely close in time because of + # non-atomic read/modify of self.cleanup_called + if self.cleanup_called: + raise Exception("attempt to clean up DFK when it has already been cleaned-up") + self.cleanup_called = True + + self.log_task_states() + + self.memoizer.close() + + # Send final stats + self.usage_tracker.send_end_message() + self.usage_tracker.close() + + logger.info("Closing job status poller") + self.job_status_poller.close() + logger.info("Terminated job status poller") + + logger.info("Shutting down executors") + + for executor in self.executors.values(): + logger.info(f"Shutting down executor {executor.label}") + executor.shutdown() + logger.info(f"Shut down executor {executor.label}") + + logger.info("Terminated executors") + time_completed = datetime.datetime.now() + + workflow_info = {'tasks_failed_count': self.task_state_counts[States.failed], + 'tasks_completed_count': self.task_state_counts[States.exec_done], + 'time_completed': time_completed, + 'run_id': self.run_id, 'rundir': self.run_dir} + + loggable_workflow_info = {"parsl.workflow_info." + k: v for k, v in workflow_info.items()} + logger.info("Workflow shutdown information: %r", workflow_info, extra={"parsl.dfk": self.run_id} | loggable_workflow_info) + if self.monitoring_radio: + logger.info("Sending final monitoring message") + self.monitoring_radio.send((MessageType.WORKFLOW_INFO, workflow_info)) + + if self.monitoring: + logger.info("Terminating monitoring") + self.monitoring.close() + logger.info("Terminated monitoring") + + logger.info("Terminating task launch pool") + self._task_launch_pool.shutdown() + logger.info("Terminated task launch pool") + + logger.info("Unregistering atexit hook") + atexit.unregister(self.atexit_cleanup) + logger.info("Unregistered atexit hook") + + if DataFlowKernelLoader._dfk is self: + logger.info("Unregistering default DFK") + parsl.clear() + logger.info("Unregistered default DFK") + else: + logger.debug("Cleaning up non-default DFK - not unregistering") + + if self._logging_unregister_callback: + logger.info("Unregistering log handler") + self._logging_unregister_callback() + logger.info("Unregistered log handler") + + # This message won't go to the default parsl.log, but other handlers + # should still see it. + logger.info("DFK cleanup complete") + + @staticmethod + def _log_std_streams(task_record: TaskRecord) -> None: + tid = task_record['id'] + + def log_std_stream(name: str, target) -> None: + if target is None: + logger.info(f"{name} for task {tid} will not be redirected.") + elif isinstance(target, str): + logger.info(f"{name} for task {tid} will be redirected to {target}") + elif isinstance(target, os.PathLike): + logger.info(f"{name} for task {tid} will be redirected to {os.fspath(target)}") + elif isinstance(target, tuple) and len(target) == 2 and isinstance(target[0], str): + logger.info(f"{name} for task {tid} will be redirected to {target[0]} with mode {target[1]}") + elif isinstance(target, tuple) and len(target) == 2 and isinstance(target[0], os.PathLike): + logger.info(f"{name} for task {tid} will be redirected to {os.fspath(target[0])} with mode {target[1]}") + elif isinstance(target, DataFuture): + logger.info(f"{name} for task {tid} will staged to {target.file_obj.url}") + else: + logger.error(f"{name} for task {tid} has unknown specification: {target!r}") + + log_std_stream("Standard out", task_record['app_fu'].stdout) + log_std_stream("Standard error", task_record['app_fu'].stderr) + + def default_std_autopath(self, taskrecord, kw): + label = taskrecord['kwargs'].get('label') + task_id = taskrecord['id'] + return os.path.join( + self.run_dir, + 'task_logs', + str(int(task_id / 10000)).zfill(4), # limit logs to 10k entries per directory + 'task_{}_{}{}.{}'.format( + str(task_id).zfill(4), + taskrecord['func_name'], + '' if label is None else '_{}'.format(label), + kw)) + + def render_future_description(self, dep: Future) -> str: + """Renders a description of the future in the context of the + current DFK. + """ + if isinstance(dep, AppFuture) and dep.task_record['dfk'] == self: + tid = "task " + repr(dep.task_record['id']) + elif isinstance(dep, DataFuture): + tid = "DataFuture from task " + repr(dep.tid) + else: + tid = repr(dep) + return tid + + +class DataFlowKernelLoader: + """Manage which DataFlowKernel is active. + + This is a singleton class containing only class methods. You should not + need to instantiate this class. + """ + + _dfk: Optional[DataFlowKernel] = None + + @classmethod + def clear(cls) -> None: + """Clear the active DataFlowKernel so that a new one can be loaded.""" + cls._dfk = None + + @classmethod + @typeguard.typechecked + def load(cls, config: Optional[Config] = None) -> DataFlowKernel: + """Load a DataFlowKernel. + + Args: + - config (Config) : Configuration to load. This config will be passed to a + new DataFlowKernel instantiation which will be set as the active DataFlowKernel. + Returns: + - DataFlowKernel : The loaded DataFlowKernel object. + """ + if cls._dfk is not None: + raise ConfigurationError('Config has already been loaded') + + if config is None: + cls._dfk = DataFlowKernel(Config()) + else: + cls._dfk = DataFlowKernel(config) + + return cls._dfk + + @classmethod + def wait_for_current_tasks(cls) -> None: + """Waits for all tasks in the task list to be completed, by waiting for their + AppFuture to be completed. This method will not necessarily wait for any tasks + added after cleanup has started such as data stageout. + """ + cls.dfk().wait_for_current_tasks() + + @classmethod + def dfk(cls) -> DataFlowKernel: + """Return the currently-loaded DataFlowKernel.""" + if cls._dfk is None: + raise NoDataFlowKernelError('Must first load config') + return cls._dfk diff --git a/parsl/parsl/dataflow/errors.py b/parsl/parsl/dataflow/errors.py new file mode 100644 index 00000000..83fff06d --- /dev/null +++ b/parsl/parsl/dataflow/errors.py @@ -0,0 +1,105 @@ +from typing import List, Sequence, Tuple + +from parsl.errors import ParslError + + +class DataFlowException(ParslError): + """Base class for all exceptions. + + Only to be invoked when only a more specific error is not available. + + """ + + +class BadCheckpoint(DataFlowException): + """Error raised at the end of app execution due to missing output files. + + Args: + - reason + + Contains: + reason (string) + dependent_exceptions + """ + + def __init__(self, reason: str) -> None: + self.reason = reason + + def __str__(self) -> str: + return self.reason + + +class PropagatedException(DataFlowException): + """Error raised if an app fails because there was an error + in a related task. This is intended to be subclassed for + dependency and join_app errors. + + Args: + - dependent_exceptions_tids: List of exceptions and brief descriptions + for dependencies which failed. The description might be a task ID or + the repr of a non-AppFuture. + - task_id: Task ID of the task that failed because of the dependency error + """ + + def __init__(self, + dependent_exceptions_tids: Sequence[Tuple[BaseException, str]], + task_id: int, + *, + failure_description: str) -> None: + self.dependent_exceptions_tids = dependent_exceptions_tids + self.task_id = task_id + self._failure_description = failure_description + + (cause, cause_sequence) = self._find_any_root_cause() + self.__cause__ = cause + self._cause_sequence = cause_sequence + + def __str__(self) -> str: + sequence_text = " <- ".join(self._cause_sequence) + return f"{self._failure_description} for task {self.task_id}. " \ + f"The representative cause is via {sequence_text}" + + def _find_any_root_cause(self) -> Tuple[BaseException, List[str]]: + """Looks recursively through self.dependent_exceptions_tids to find + an exception that caused this propagated error, that is not itself + a propagated error. + """ + e: BaseException = self + dep_ids = [] + while isinstance(e, PropagatedException) and len(e.dependent_exceptions_tids) >= 1: + id_txt = e.dependent_exceptions_tids[0][1] + assert isinstance(id_txt, str) + # if there are several causes for this exception, label that + # there are more so that we know that the representative fail + # sequence is not the full story. + if len(e.dependent_exceptions_tids) > 1: + id_txt += " (+ others)" + dep_ids.append(id_txt) + e = e.dependent_exceptions_tids[0][0] + return e, dep_ids + + +class DependencyError(PropagatedException): + """Error raised if an app cannot run because there was an error + in a dependency. There can be several exceptions (one from each + dependency) and DependencyError collects them all together. + + Args: + - dependent_exceptions_tids: List of exceptions and brief descriptions + for dependencies which failed. The description might be a task ID or + the repr of a non-AppFuture. + - task_id: Task ID of the task that failed because of the dependency error + """ + def __init__(self, dependent_exceptions_tids: Sequence[Tuple[BaseException, str]], task_id: int) -> None: + super().__init__(dependent_exceptions_tids, task_id, + failure_description="Dependency failure") + + +class JoinError(PropagatedException): + """Error raised if apps joining into a join_app raise exceptions. + There can be several exceptions (one from each joining app), + and JoinError collects them all together. + """ + def __init__(self, dependent_exceptions_tids: Sequence[Tuple[BaseException, str]], task_id: int) -> None: + super().__init__(dependent_exceptions_tids, task_id, + failure_description="Join failure") diff --git a/parsl/parsl/dataflow/futures.py b/parsl/parsl/dataflow/futures.py new file mode 100644 index 00000000..616a3d3b --- /dev/null +++ b/parsl/parsl/dataflow/futures.py @@ -0,0 +1,163 @@ +from __future__ import annotations + +import logging +import threading +from concurrent.futures import Future +from typing import Any, Optional, Sequence, Union + +import parsl.app.app as app +from parsl.app.futures import DataFuture +from parsl.dataflow.taskrecord import TaskRecord + +logger = logging.getLogger(__name__) + +# Possible future states (for internal use by the futures package). +PENDING = 'PENDING' +RUNNING = 'RUNNING' +# The future was cancelled by the user... +CANCELLED = 'CANCELLED' +# ...and _Waiter.add_cancelled() was called by a worker. +CANCELLED_AND_NOTIFIED = 'CANCELLED_AND_NOTIFIED' +FINISHED = 'FINISHED' + +_STATE_TO_DESCRIPTION_MAP = { + PENDING: "pending", + RUNNING: "running", + CANCELLED: "cancelled", + CANCELLED_AND_NOTIFIED: "cancelled", + FINISHED: "finished" +} + + +class AppFuture(Future): + """An AppFuture wraps a sequence of Futures which may fail and be retried. + + The AppFuture will wait for the DFK to provide a result from an appropriate + parent future, through ``parent_callback``. It will set its result to the + result of that parent future, if that parent future completes without an + exception. This result setting should cause .result(), .exception() and done + callbacks to fire as expected. + + The AppFuture will not set its result to the result of the parent future, if + that parent future completes with an exception, and if that parent future + has retries left. In that case, no result(), exception() or done callbacks should + report a result. + + The AppFuture will set its result to the result of the parent future, if that + parent future completes with an exception and if that parent future has no + retries left, or if it has no retry field. .result(), .exception() and done callbacks + should give a result as expected when a Future has a result set + + The parent future may return a RemoteExceptionWrapper as a result + and AppFuture will treat this an an exception for the above + retry and result handling behaviour. + + """ + + def __init__(self, task_record: TaskRecord) -> None: + """Initialize the AppFuture. + + Args: + + KWargs: + - task_record : The TaskRecord for the task represented by + this future. + """ + super().__init__() + self._update_lock = threading.Lock() + self._outputs: Sequence[DataFuture] + self._outputs = [] + self.task_record = task_record + + self._stdout_future: Optional[DataFuture] = None + self._stderr_future: Optional[DataFuture] = None + + @property + def stdout(self) -> Union[None, str, DataFuture]: + """Return app stdout. If stdout was specified as a string, then this + property will return that string. If stdout was specified as a File, + then this property will return a DataFuture representing that file + stageout. + TODO: this can be a tuple too I think?""" + if self._stdout_future: + return self._stdout_future + else: + # this covers the str and None cases + return self.task_record['kwargs'].get('stdout') + + @property + def stderr(self) -> Union[None, str, DataFuture]: + """Return app stderr. If stdout was specified as a string, then this + property will return that string. If stdout was specified as a File, + then this property will return a DataFuture representing that file + stageout. + TODO: this can be a tuple too I think?""" + if self._stderr_future: + return self._stderr_future + else: + # this covers the str and None cases + return self.task_record['kwargs'].get('stderr') + + @property + def tid(self) -> int: + return self.task_record['id'] + + def cancel(self) -> bool: + raise NotImplementedError("Cancel not implemented") + + def cancelled(self) -> bool: + return False + + def task_status(self) -> str: + """Returns the status of the task that will provide the value + for this future. This may not be in-sync with the result state + of this future - for example, task_status might return 'done' but + self.done() might not be true (which in turn means self.result() + and self.exception() might block). + + The actual status description strings returned by this method are + likely to change over subsequent versions of parsl, as use-cases + and infrastructure are worked out. + + It is expected that the status values will be from a limited set + of strings (so that it makes sense, for example, to group and + count statuses from many futures). + + It is expected that might be a non-trivial cost in acquiring the + status in future (for example, by communicating with a remote + worker). + + Returns: str + """ + return self.task_record['status'].name + + @property + def outputs(self) -> Sequence[DataFuture]: + return self._outputs + + def __getitem__(self, key: Any) -> AppFuture: + # This is decorated on each invocation because the getitem task + # should be bound to the same DFK as the task associated with this + # Future. + deferred_getitem_app = app.python_app(deferred_getitem, executors=['_parsl_internal'], data_flow_kernel=self.task_record['dfk']) + + return deferred_getitem_app(self, key) + + def __getattr__(self, name: str) -> AppFuture: + # this will avoid lifting behaviour on private methods and attributes, + # including __double_underscore__ methods which implement other + # Python syntax (such as iterators in for loops) + if name.startswith("_"): + raise AttributeError() + + deferred_getattr_app = app.python_app(deferred_getattr, executors=['_parsl_internal'], data_flow_kernel=self.task_record['dfk']) + + return deferred_getattr_app(self, name) + + +def deferred_getitem(o: Any, k: Any) -> Any: + return o[k] + + +def deferred_getattr(o: Any, name: str) -> Any: + return getattr(o, name) diff --git a/parsl/parsl/dataflow/memoization.py b/parsl/parsl/dataflow/memoization.py new file mode 100644 index 00000000..15059243 --- /dev/null +++ b/parsl/parsl/dataflow/memoization.py @@ -0,0 +1,584 @@ +from __future__ import annotations + +import hashlib +import logging +import os +import pickle +import threading +import types +from abc import ABCMeta, abstractmethod +from concurrent.futures import Future +from functools import lru_cache, singledispatch +from typing import Any, Dict, List, Literal, Optional, Sequence + +import typeguard + +from parsl.dataflow.errors import BadCheckpoint +from parsl.dataflow.taskrecord import TaskRecord +from parsl.errors import ConfigurationError +from parsl.utils import Timer, get_all_checkpoints + +logger = logging.getLogger(__name__) + + +class CheckpointCommand: + def __init__(self, task_record: TaskRecord, *, result: Optional[object] = None, exception: Optional[BaseException] = None): + """Create a checkpoint command. This specifies a checkpoint entry of + either a result or an exception. If exception is set, then this object + specifies an exception checkpoint. Otherwise, it specifies a result. + This is almost: one of exception or result must be non-None, but not + quite because this data model also needs to accomodate a valid result + of None.""" + + assert result is None or exception is None, "CheckpointCommand cannot specify both a result and exception" + + self._task_record = task_record + self._result = result + self._exception = exception + + @property + def task_record(self) -> TaskRecord: + return self._task_record + + @property + def result(self) -> Optional[object]: + return self._result + + @property + def exception(self) -> Optional[BaseException]: + return self._exception + + +@singledispatch +def id_for_memo(obj: object, output_ref: bool = False) -> bytes: + """This should return a byte sequence which identifies the supplied + value for memoization purposes: for any two calls of id_for_memo, + the byte sequence should be the same when the "same" value is supplied, + and different otherwise. + + "same" is in quotes about because sameness is not as straightforward as + serialising out the content. + + For example, for two dicts x, y: + + x = {"a":3, "b":4} + y = {"b":4, "a":3} + + then: x == y, but their serialization is not equal, and some other + functions on x and y are not equal: list(x.keys()) != list(y.keys()) + + + id_for_memo is invoked with output_ref=True when the parameter is an + output reference (a value in the outputs=[] parameter of an app + invocation). + + Memo hashing might be different for such parameters: for example, a + user might choose to hash input File content so that changing the + content of an input file invalidates memoization. This does not make + sense to do for output files: there is no meaningful content stored + where an output filename points at memoization time. + """ + logger.error("id_for_memo attempted on unknown type {}".format(type(obj))) + raise ValueError("unknown type for memoization: {}".format(type(obj))) + + +@id_for_memo.register(str) +@id_for_memo.register(int) +@id_for_memo.register(float) +@id_for_memo.register(type(None)) +def id_for_memo_pickle(obj: object, output_ref: bool = False) -> bytes: + return pickle.dumps(obj) + + +@id_for_memo.register(list) +def id_for_memo_list(denormalized_list: list, output_ref: bool = False) -> bytes: + if type(denormalized_list) is not list: + raise ValueError("id_for_memo_list cannot work on subclasses of list") + + normalized_list = [] + + for e in denormalized_list: + normalized_list.append(id_for_memo(e, output_ref=output_ref)) + + return pickle.dumps(normalized_list) + + +@id_for_memo.register(tuple) +def id_for_memo_tuple(denormalized_tuple: tuple, output_ref: bool = False) -> bytes: + if type(denormalized_tuple) is not tuple: + raise ValueError("id_for_memo_tuple cannot work on subclasses of tuple") + + normalized_list = [] + + for e in denormalized_tuple: + normalized_list.append(id_for_memo(e, output_ref=output_ref)) + + return pickle.dumps(normalized_list) + + +@id_for_memo.register(dict) +def id_for_memo_dict(denormalized_dict: dict, output_ref: bool = False) -> bytes: + """This normalises the keys and values of the supplied dictionary. + + When output_ref=True, the values are normalised as output refs, but + the keys are not. + """ + if type(denormalized_dict) is not dict: + raise ValueError("id_for_memo_dict cannot work on subclasses of dict") + + keys = sorted(denormalized_dict) + + normalized_list = [] + for k in keys: + normalized_list.append(id_for_memo(k)) + normalized_list.append(id_for_memo(denormalized_dict[k], output_ref=output_ref)) + return pickle.dumps(normalized_list) + + +# the LRU cache decorator must be applied closer to the id_for_memo_function call +# that the .register() call, so that the cache-decorated version is registered. +@id_for_memo.register(types.FunctionType) +@lru_cache() +def id_for_memo_function(f: types.FunctionType, output_ref: bool = False) -> bytes: + """This will checkpoint a function based only on its name and module name. + This means that changing source code (other than the function name) will + not cause a checkpoint invalidation. + """ + return pickle.dumps(["types.FunctionType", f.__name__, f.__module__]) + + +def make_hash(task: TaskRecord) -> str: + """Create a hash of the task inputs. + + Args: + - task (dict) : Task dictionary from dfk.tasks + + Returns: + - hash (str) : A unique hash string + """ + + t: List[bytes] = [] + + # if kwargs contains an outputs parameter, that parameter is removed + # and normalised differently - with output_ref set to True. + # kwargs listed in ignore_for_cache will also be removed + + filtered_kw = task['kwargs'].copy() + + ignore_list = task['ignore_for_cache'] + + logger.debug("Ignoring these kwargs for checkpointing: %s", ignore_list) + for k in ignore_list: + logger.debug("Ignoring kwarg %s", k) + del filtered_kw[k] + + if 'outputs' in task['kwargs']: + outputs = task['kwargs']['outputs'] + del filtered_kw['outputs'] + t.append(id_for_memo(outputs, output_ref=True)) + + t.extend(map(id_for_memo, (filtered_kw, task['func'], task['args']))) + + x = b''.join(t) + return hashlib.md5(x).hexdigest() + + +class Memoizer(metaclass=ABCMeta): + """Defines the interface for the DFK to talk to the memoization/checkpoint system. + + The DFK will invoke these methods on an instance of a Memoizer at suitable points + in the lifecycle of a task. These methods are not intended for users to invoke + directly. + """ + + @abstractmethod + def update_memo_exception(self, task: TaskRecord, e: BaseException) -> None: + """Called by the DFK when a task completes with an exception. + + On every task completion, either this method or `update_memo_result` + will be called, but not both. + + This is an opportunity for the memoization/checkpoint system to record + the outcome of a task for later discovery by a call to check_memo. + """ + raise NotImplementedError + + @abstractmethod + def update_memo_result(self, task: TaskRecord, r: Any) -> None: + """Called by the DFK when a task completes with a successful result. + + On every task completion, either this method or `update_memo_exception` + will be called, but not both. + + This is an opportunity for the memoization/checkpoint system to record + the outcome of a task for later discovery by a call to check_memo. + """ + raise NotImplementedError + + @abstractmethod + def start(self, *, run_dir: str, config_run_dir: str) -> None: + """Called by the DFK when it starts up. + + This is an opportunity for the memoization/checkpoint system to + initialize itself. + + The path to the per-run run directory and the base run directory + are passed as parameters. + """ + raise NotImplementedError + + @abstractmethod + def check_memo(self, task: TaskRecord) -> Optional[Future[Any]]: + """Asks the checkpoint system for a result recorded for the described + task. ``check_memo`` should return a `Future` that will be used as + an executor future, in place of sending the task to an executor for + execution. That future should be populated with a result or exception. + """ + raise NotImplementedError + + @abstractmethod + def close(self) -> None: + """Called at DFK shutdown. This gives the checkpoint system an + opportunity for graceful shutdown. + """ + raise NotImplementedError + + +class BasicMemoizer(Memoizer): + """Memoizer is responsible for ensuring that identical work is not repeated. + + When a task is repeated, i.e., the same function is called with the same exact arguments, the + result from a previous execution is reused. `wiki `_ + + The memoizer implementation here does not collapse duplicate calls + at call time, but works **only** when the result of a previous + call is available at the time the duplicate call is made. + + For instance:: + + No advantage from Memoization helps + memoization here: here: + + TaskA TaskB + | TaskA | + | | TaskA done (TaskB) + | | | (TaskB) + done | | + done | + done + + The memoizer creates a lookup table by hashing the function name + and its inputs, and storing the results of the function. + + When a task is ready for launch, i.e., all of its arguments + have resolved, we add its hash to the task datastructure. + + """ + + run_dir: str + + def __init__(self, *, + checkpoint_files: Sequence[str] | None = None, + checkpoint_period: Optional[str] = None, + checkpoint_mode: Literal['task_exit', 'periodic', 'dfk_exit', 'manual'] | None = None, + memoize: bool = True): + """Initialize the memoizer. + + KWargs: + + - checkpoint_files : sequence of str, optional + List of paths to checkpoint files to load. By default, all checkpoints from the + run directory will be restored. This is usually the right behaviour, but this + parameter allows that behaviour to be overridden. + See :func:`parsl.utils.get_all_checkpoints` and :func:`parsl.utils.get_last_checkpoint` + for helpers. + - checkpoint_period : str, optional + Time interval (in "HH:MM:SS") at which to checkpoint completed tasks. Only has an effect if + ``checkpoint_mode='periodic'``. + - checkpoint_mode : str, optional + Checkpoint mode to use, can be ``'dfk_exit'``, ``'task_exit'``, ``'periodic'`` or ``'manual'``. + If set to `None`, checkpointing will be disabled. Default is None. + - memoize : str, enable memoization or not. + + """ + self.checkpointed_tasks = 0 + + # this lock must be held when: + # * writing to any checkpoint files + # * interacting with self.checkpointable_tasks + self._checkpoint_lock = threading.Lock() + + self.checkpoint_files = checkpoint_files + self.checkpoint_mode = checkpoint_mode + self.checkpoint_period = checkpoint_period + + self.checkpointable_tasks: List[CheckpointCommand] = [] + + self._checkpoint_timer: Timer | None = None + self.memoize = memoize + + def start(self, *, run_dir: str, config_run_dir: str) -> None: + + self.run_dir = run_dir + self.config_run_dir = config_run_dir + + if self.checkpoint_files is not None: + checkpoint_files = self.checkpoint_files + elif self.checkpoint_files is None and self.checkpoint_mode is not None: + checkpoint_files = get_all_checkpoints(self.config_run_dir) + else: + checkpoint_files = [] + + checkpoint = self.load_checkpoints(checkpoint_files) + + if self.memoize: + logger.info("App caching initialized") + self.memo_lookup_table = checkpoint + else: + logger.info("App caching disabled for all apps") + self.memo_lookup_table = {} + + if self.checkpoint_mode == "periodic": + if self.checkpoint_period is None: + raise ConfigurationError("Checkpoint period must be specified with periodic checkpoint mode") + else: + try: + h, m, s = map(int, self.checkpoint_period.split(':')) + except Exception: + raise ConfigurationError("invalid checkpoint_period provided: {0} expected HH:MM:SS".format(self.checkpoint_period)) + checkpoint_period = (h * 3600) + (m * 60) + s + self._checkpoint_timer = Timer(self.checkpoint_queue, interval=checkpoint_period, name="Checkpoint") + + def close(self) -> None: + if self.checkpoint_mode is not None: + logger.info("Making final checkpoint") + self.checkpoint_queue() + + if self._checkpoint_timer: + logger.info("Stopping checkpoint timer") + self._checkpoint_timer.close() + + def check_memo(self, task: TaskRecord) -> Optional[Future[Any]]: + """Create a hash of the task and its inputs and check the lookup table for this hash. + + If present, the results are returned. + + Args: + - task(task) : task from the dfk.tasks table + + Returns: + - Result of the function if present in table, wrapped in a Future + + This call will also set task['hashsum'] to the unique hashsum for the func+inputs. + """ + + task_id = task['id'] + + if not self.memoize or not task['memoize']: + task['hashsum'] = None + logger.debug("Task {} will not be memoized".format(task_id)) + return None + + hashsum = make_hash(task) + logger.debug("Task {} has memoization hash {}".format(task_id, hashsum)) + result = None + if hashsum in self.memo_lookup_table: + result = self.memo_lookup_table[hashsum] + logger.info("Task %s using result from cache", task_id) + else: + logger.info("Task %s had no result in cache", task_id) + + task['hashsum'] = hashsum + + assert isinstance(result, Future) or result is None + return result + + def update_memo_result(self, task: TaskRecord, r: Any) -> None: + self._update_memo(task) + if self.checkpoint_mode is not None: + self._update_checkpoint(CheckpointCommand(task, result=r)) + + def update_memo_exception(self, task: TaskRecord, e: BaseException) -> None: + self._update_memo(task) + if self.checkpoint_mode is not None: + self._update_checkpoint(CheckpointCommand(task, exception=e)) + + def _update_memo(self, task: TaskRecord) -> None: + """Updates the memoization lookup table with the result from a task. + This doesn't move any values around but associates the memoization + hashsum with the completed (by success or failure) AppFuture. + + Args: + - task (TaskRecord) : A task record from dfk.tasks + """ + task_id = task['id'] + + if not self.memoize or not task['memoize'] or 'hashsum' not in task: + return + + if not isinstance(task['hashsum'], str): + logger.error("Attempting to update app cache entry but hashsum is not a string key") + return + + if task['hashsum'] in self.memo_lookup_table: + logger.info(f"Replacing app cache entry {task['hashsum']} with result from task {task_id}") + else: + logger.debug(f"Storing app cache entry {task['hashsum']} with result from task {task_id}") + self.memo_lookup_table[task['hashsum']] = task['app_fu'] + + def _load_checkpoints(self, checkpointDirs: Sequence[str]) -> Dict[str, Future[Any]]: + """Load a checkpoint file into a lookup table. + + The data being loaded from the pickle file mostly contains input + attributes of the task: func, args, kwargs, env... + To simplify the check of whether the exact task has been completed + in the checkpoint, we hash these input params and use it as the key + for the memoized lookup table. + + Args: + - checkpointDirs (list) : List of filepaths to checkpoints + Eg. ['runinfo/001', 'runinfo/002'] + + Returns: + - memoized_lookup_table (dict) + """ + memo_lookup_table = {} + + for checkpoint_dir in checkpointDirs: + logger.info("Loading checkpoints from {}".format(checkpoint_dir)) + checkpoint_file = os.path.join(checkpoint_dir, 'tasks.pkl') + try: + with open(checkpoint_file, 'rb') as f: + while True: + try: + data = pickle.load(f) + # Copy and hash only the input attributes + memo_fu: Future = Future() + assert data['exception'] is None + memo_fu.set_result(data['result']) + memo_lookup_table[data['hash']] = memo_fu + + except EOFError: + # Done with the checkpoint file + break + except FileNotFoundError: + reason = "Checkpoint file was not found: {}".format( + checkpoint_file) + logger.error(reason) + raise BadCheckpoint(reason) + except Exception: + reason = "Failed to load checkpoint: {}".format( + checkpoint_file) + logger.error(reason) + raise BadCheckpoint(reason) + + logger.info("Completed loading checkpoint: {0} with {1} tasks".format(checkpoint_file, + len(memo_lookup_table.keys()))) + return memo_lookup_table + + @typeguard.typechecked + def load_checkpoints(self, checkpointDirs: Optional[Sequence[str]]) -> Dict[str, Future]: + """Load checkpoints from the checkpoint files into a dictionary. + + The results are used to pre-populate the memoizer's lookup_table + + Kwargs: + - checkpointDirs (list) : List of run folder to use as checkpoints + Eg. ['runinfo/001', 'runinfo/002'] + + Returns: + - dict containing, hashed -> future mappings + """ + if checkpointDirs: + return self._load_checkpoints(checkpointDirs) + else: + return {} + + def _update_checkpoint(self, command: CheckpointCommand) -> None: + if self.checkpoint_mode == 'task_exit': + self.checkpoint_one(command) + elif self.checkpoint_mode in ('manual', 'periodic', 'dfk_exit'): + with self._checkpoint_lock: + self.checkpointable_tasks.append(command) + elif self.checkpoint_mode is None: + pass + else: + assert False, "Invalid checkpoint mode {self.checkpoint_mode} - should have been validated at initialization" + + def checkpoint_one(self, cc: CheckpointCommand) -> None: + """Checkpoint a single task to a checkpoint file. + + By default the checkpoints are written to the RUNDIR of the current + run under RUNDIR/checkpoints/tasks.pkl + + Kwargs: + - task : A task to checkpoint. + + .. note:: + Checkpointing only works if memoization is enabled + + """ + with self._checkpoint_lock: + self._checkpoint_these_tasks([cc]) + + def checkpoint_queue(self) -> None: + """Checkpoint all tasks registered in self.checkpointable_tasks. + + By default the checkpoints are written to the RUNDIR of the current + run under RUNDIR/checkpoints/tasks.pkl + + .. note:: + Checkpointing only works if memoization is enabled + + """ + with self._checkpoint_lock: + self._checkpoint_these_tasks(self.checkpointable_tasks) + self.checkpointable_tasks = [] + + def checkpoint(self) -> None: + """This is the user-facing interface to manual checkpointing. + """ + self.checkpoint_queue() + + def _checkpoint_these_tasks(self, checkpoint_queue: List[CheckpointCommand]) -> None: + """Play a sequence of CheckpointCommands into a checkpoint file. + + The checkpoint lock must be held when invoking this method. + """ + + # This checks that the lock is held, at least, but does not check that + # it is held by the current thread - threading.Lock does not have a + # concept of locking thread for threading.Lock. + assert self._checkpoint_lock.locked(), "checkpoint system should be locked" + + checkpoint_dir = '{0}/checkpoint'.format(self.run_dir) + checkpoint_tasks = checkpoint_dir + '/tasks.pkl' + + if not os.path.exists(checkpoint_dir): + os.makedirs(checkpoint_dir, exist_ok=True) + + count = 0 + + with open(checkpoint_tasks, 'ab') as f: + for cc in checkpoint_queue: + if cc.exception is None: + hashsum = cc.task_record['hashsum'] + if not hashsum: + continue + t = {'hash': hashsum, 'exception': None, 'result': cc.result} + + # We are using pickle here since pickle dumps to a file in 'ab' + # mode behave like a incremental log. + pickle.dump(t, f) + count += 1 + logger.debug("Task %s checkpointed", cc.task_record['id']) + + self.checkpointed_tasks += count + + if count == 0: + if self.checkpointed_tasks == 0: + logger.warning("No tasks checkpointed so far in this run. Please ensure caching is enabled") + else: + logger.debug("No tasks checkpointed in this pass.") + else: + logger.info("Done checkpointing {} tasks".format(count)) diff --git a/parsl/parsl/dataflow/rundirs.py b/parsl/parsl/dataflow/rundirs.py new file mode 100644 index 00000000..14177308 --- /dev/null +++ b/parsl/parsl/dataflow/rundirs.py @@ -0,0 +1,39 @@ +import logging +import os +from glob import glob + +logger = logging.getLogger(__name__) + + +def make_rundir(path: str) -> str: + """Create a numbered run directory under the specified path. + + ./runinfo <- specified path + |----000 + |----001 <- Directories for each run + | .... + |----NNN + + Args: + - path (str): String path to root of all rundirs + """ + try: + if not os.path.exists(path): + os.makedirs(path) + + prev_rundirs = glob(os.path.join(path, "[0-9]*[0-9]")) + + current_rundir = os.path.join(path, '000') + + if prev_rundirs: + # Since we globbed on files named as 0-9 + x = sorted([int(os.path.basename(x)) for x in prev_rundirs])[-1] + current_rundir = os.path.join(path, '{0:03}'.format(x + 1)) + + os.makedirs(current_rundir) + logger.debug("Parsl run initializing in rundir: {0}".format(current_rundir)) + return os.path.abspath(current_rundir) + + except Exception: + logger.exception("Failed to create run directory") + raise diff --git a/parsl/parsl/dataflow/states.py b/parsl/parsl/dataflow/states.py new file mode 100644 index 00000000..d7222111 --- /dev/null +++ b/parsl/parsl/dataflow/states.py @@ -0,0 +1,75 @@ +from enum import IntEnum + + +class States(IntEnum): + """Enumerates the states a parsl task may be in. + + These states occur inside the task record for a task inside + a `DataFlowKernel` and in the monitoring database. + + In a single successful task execution, tasks will progress in this + sequence: + + pending -> launched -> running -> running_ended -> exec_done + + Other states represent deviations from this path, either due to + failures, or to deliberate changes to how tasks are executed (for + example due to join_app, or memoization). + + + All tasks should end up in one of the states listed in `FINAL_STATES`. + """ + + pending = 0 + """Task is known to parsl but cannot run yet. Usually, a task cannot + run because it is waiting for dependency tasks to complete. + """ + + running = 2 + """Task is running on a resource. This state is special - a DFK task + record never goes to States.running state; but the monitoring database + may represent a task in this state based on non-DFK information received + from monitor_wrapper.""" + + exec_done = 3 + """Task has been executed successfully.""" + + failed = 4 + """Task has failed and no more attempts will be made to run it.""" + + dep_fail = 5 + """Dependencies of this task failed, so it is marked as failed without + even an attempt to launch it.""" + + launched = 7 + """Task has been passed to a `ParslExecutor` for execution.""" + + fail_retryable = 8 + """Task has failed, but can be retried""" + + memo_done = 9 + """Task was found in the memoization table, so it is marked as done + without even an attempt to launch it.""" + + joining = 10 + """Task is a join_app, joining on internal tasks. The task has run its + own Python code, and is now waiting on other tasks before it can make + further progress (to a done/failed state).""" + + running_ended = 11 + """Like States.running, this state is also not observed by the DFK, + but instead only by monitoring. This state does not record + anything about task success or failure, merely that the wrapper + ran long enough to record it as finished.""" + + def __str__(self) -> str: + return self.__class__.__name__ + "." + self.name + + +FINAL_FAILURE_STATES = {States.failed, States.dep_fail} +"""States which are final and which indicate a failure. This must +be a subset of FINAL_STATES""" + +FINAL_STATES = {States.exec_done, States.memo_done, *FINAL_FAILURE_STATES} +"""States from which we will never move to another state, because the job has +either definitively completed or failed.""" diff --git a/parsl/parsl/dataflow/taskrecord.py b/parsl/parsl/dataflow/taskrecord.py new file mode 100644 index 00000000..0621ab8f --- /dev/null +++ b/parsl/parsl/dataflow/taskrecord.py @@ -0,0 +1,103 @@ +from __future__ import annotations + +import datetime +import threading +from concurrent.futures import Future + +# only for type checking: +from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Sequence, Union + +from typing_extensions import TypedDict + +if TYPE_CHECKING: + from parsl.dataflow.futures import AppFuture + +import parsl.dataflow.dflow as dflow +from parsl.dataflow.states import States + + +class TaskRecord(TypedDict, total=False): + """This stores most information about a Parsl task""" + + dfk: dflow.DataFlowKernel + """The DataFlowKernel which is managing this task. + """ + + func_name: str + + status: States + + depends: List[Future] + + app_fu: AppFuture + """The Future which was returned to the user when an app was invoked. + """ + + exec_fu: Optional[Future] + """When a task has been launched on an executor, stores the Future + returned by that executor. + """ + + executor: str + """The name of the executor which this task will be/is being/was + executed on. + """ + + fail_count: int + fail_cost: float + fail_history: List[str] + + checkpoint: bool + """Should this task be checkpointed? + """ + + hashsum: Optional[str] # hash for checkpointing/memoization. + """The hash used for checkpointing and memoisation. This is not known + until at least all relevant dependencies have completed, and will be + None before that. + """ + + task_launch_lock: threading.Lock + """This lock is used to ensure that task launch only happens once. + A task can be launched by dependencies completing from arbitrary + threads, and a race condition would exist when dependencies complete + in multiple threads very close together in time, which this lock + prevents. + """ + + # these three could be more strongly typed perhaps but I'm not thinking about that now + func: Callable + args: Sequence[Any] + # in some places we uses a Tuple[Any, ...] and in some places a List[Any]. + # This is an attempt to correctly type both of those. + kwargs: Dict[str, Any] + + time_invoked: Optional[datetime.datetime] + time_returned: Optional[datetime.datetime] + try_time_launched: Optional[datetime.datetime] + try_time_returned: Optional[datetime.datetime] + + memoize: bool + """Should this task be memoized?""" + ignore_for_cache: Sequence[str] + from_memo: Optional[bool] + + id: int + try_id: int + + resource_specification: Dict[str, Any] + """Dictionary containing relevant info for a task execution. + Includes resources to allocate and execution mode as a given + executor permits.""" + + join: bool + """Is this a join_app?""" + + joins: Union[None, Future, List[Future]] + """If this is a join app and the python body has executed, then this + contains the Future or list of Futures that the join app will join.""" + + join_lock: threading.Lock + """Restricts access to end-of-join behavior to ensure that joins + only complete once, even if several joining Futures complete close + together in time.""" diff --git a/parsl/parsl/errors.py b/parsl/parsl/errors.py new file mode 100644 index 00000000..5de6010d --- /dev/null +++ b/parsl/parsl/errors.py @@ -0,0 +1,37 @@ +from typing import List + + +class ParslError(Exception): + """Base class for all exceptions. + + Only to be invoked when a more specific error is not available. + """ + + +class ConfigurationError(ParslError): + """Raised when a component receives an invalid configuration. + """ + + +class OptionalModuleMissing(ParslError): + ''' Error raised when a required module is missing for a optional/extra component + ''' + + def __init__(self, module_names: List[str], reason: str): + self.module_names = module_names + self.reason = reason + + def __str__(self) -> str: + return "The functionality requested requires optional modules {0} which could not be imported, because: {1}".format( + self.module_names, self.reason + ) + + +class InternalConsistencyError(ParslError): + """Raised when a component enounters an internal inconsistency. + """ + + +class NoDataFlowKernelError(ParslError): + """Raised when no DataFlowKernel is available for an operation that needs one. + """ diff --git a/parsl/parsl/executors/__init__.py b/parsl/parsl/executors/__init__.py new file mode 100644 index 00000000..81955aab --- /dev/null +++ b/parsl/parsl/executors/__init__.py @@ -0,0 +1,13 @@ +from parsl.executors.flux.executor import FluxExecutor +from parsl.executors.globus_compute import GlobusComputeExecutor +from parsl.executors.high_throughput.executor import HighThroughputExecutor +from parsl.executors.high_throughput.mpi_executor import MPIExecutor +from parsl.executors.threads import ThreadPoolExecutor +from parsl.executors.workqueue.executor import WorkQueueExecutor + +__all__ = ['ThreadPoolExecutor', + 'HighThroughputExecutor', + 'MPIExecutor', + 'WorkQueueExecutor', + 'FluxExecutor', + 'GlobusComputeExecutor'] diff --git a/parsl/parsl/executors/base.py b/parsl/parsl/executors/base.py new file mode 100644 index 00000000..8c53b8f4 --- /dev/null +++ b/parsl/parsl/executors/base.py @@ -0,0 +1,146 @@ +from __future__ import annotations + +import logging +import os +from abc import ABCMeta, abstractmethod +from concurrent.futures import Future +from multiprocessing.queues import Queue +from typing import Any, Callable, Dict, Optional + +from typing_extensions import Literal, Self + +from parsl.logconfigs.base import LogConfig +from parsl.monitoring.radios.base import MonitoringRadioReceiver, RadioConfig +from parsl.monitoring.types import TaggedMonitoringMessage + +logger = logging.getLogger(__name__) + + +class ParslExecutor(metaclass=ABCMeta): + """Executors are abstractions that represent available compute resources + to which you could submit arbitrary App tasks. + + This is an abstract base class that only enforces concrete implementations + of functionality by the child classes. + + Can be used as a context manager. On exit, calls ``self.shutdown()`` with + no arguments and re-raises any thrown exception. + + In addition to the listed methods, a ParslExecutor instance must always + have a member field: + + label: str - a human readable label for the executor, unique + with respect to other executors. + + remote_monitoring_radio: RadioConfig describing how tasks on this executor + should report task resource status + + An executor may optionally expose: + + storage_access: List[parsl.data_provider.staging.Staging] - a list of staging + providers that will be used for file staging. In the absence of this + attribute, or if this attribute is `None`, then a default value of + ``parsl.data_provider.staging.default_staging`` will be used by the + staging code. + + Typechecker note: Ideally storage_access would be declared on executor + __init__ methods as List[Staging] - however, lists are by default + invariant, not co-variant, and it looks like @typeguard cannot be + persuaded otherwise. So if you're implementing an executor and want to + @typeguard the constructor, you'll have to use List[Any] here. + + The DataFlowKernel will set this attribute before calling .start(), + if monitoring is enabled: + + monitoring_messages: Optional[Queue[TaggedMonitoringMessage]] - an executor + can send messages to the monitoring hub by putting them into + this queue. + """ + + label: str = "undefined" + + def __init__( + self, + *, + monitoring_messages: Optional[Queue[TaggedMonitoringMessage]] = None, + run_dir: str = ".", + run_id: Optional[str] = None, + ): + self.monitoring_messages = monitoring_messages + + self.remote_monitoring_radio: Optional[RadioConfig] = None + self.monitoring_receiver: Optional[MonitoringRadioReceiver] = None + + self.run_dir = os.path.abspath(run_dir) + self.run_id = run_id + + self.log_config: Optional[LogConfig] = None + + def __enter__(self) -> Self: + return self + + def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> Literal[False]: + self.shutdown() + return False + + def start(self) -> None: + """Start the executor. + + By default, this does nothing, but this method should be overridden to + perform any spin-up operations (for example: starting thread pools). + """ + pass + + @abstractmethod + def submit(self, func: Callable, resource_specification: Dict[str, Any], *args: Any, **kwargs: Any) -> Future: + """Submit. + + The executor can optionally set a parsl_executor_task_id attribute on + the Future that it returns, and in that case, parsl will log a + relationship between the executor's task ID and parsl level try/task + IDs. + """ + pass + + @abstractmethod + def shutdown(self) -> None: + """Shutdown the executor. + + Executors should call super().shutdown() as part of their overridden + implementation. + """ + if self.monitoring_receiver is not None: + logger.info("Starting monitoring receiver shutdown") + self.monitoring_receiver.shutdown() + logger.info("Done with monitoring receiver shutdown") + + def monitor_resources(self) -> bool: + """Should resource monitoring happen for tasks on running on this executor? + + Parsl resource monitoring conflicts with execution styles which do + not directly use a process tree - for example, the ThreadPoolExecutor + and the MPIExecutor. + + This function allows resource monitoring to be disabled per executor implementation. + """ + return True + + @property + def run_dir(self) -> str: + """Path to the run directory. + """ + return self._run_dir + + @run_dir.setter + def run_dir(self, value: str) -> None: + self._run_dir = value + + @property + def run_id(self) -> Optional[str]: + """UUID for the enclosing DFK. + """ + return self._run_id + + @run_id.setter + def run_id(self, value: Optional[str]) -> None: + self._run_id = value diff --git a/parsl/parsl/executors/errors.py b/parsl/parsl/executors/errors.py new file mode 100644 index 00000000..fed43228 --- /dev/null +++ b/parsl/parsl/executors/errors.py @@ -0,0 +1,75 @@ +"""Exceptions raise by Executors.""" +from typing import Set + +from parsl.errors import ParslError +from parsl.executors.base import ParslExecutor + + +class ExecutorError(ParslError): + """Base class for executor related exceptions. + + Only to be invoked when only a more specific error is not available. + """ + + def __init__(self, executor: ParslExecutor, reason: str): + self.executor = executor + self.reason = reason + + def __str__(self): + return "Executor {0} failed due to: {1}".format(self.executor.label, self.reason) + + +class BadStateException(ExecutorError): + """Error returned by task Futures when an executor is in a bad state. + """ + + def __init__(self, executor, exception): + super().__init__(executor, str(exception)) + + +class UnsupportedFeatureError(ExecutorError): + """Error raised when attemping to use unsupported feature in an Executor""" + + def __init__(self, feature, current_executor, target_executor): + self.feature = feature + self.current_executor = current_executor + self.target_executor = target_executor + + def __str__(self): + if self.target_executor: + return "The {} feature is unsupported in {}. \ + Please checkout {} for this feature".format(self.feature, + self.current_executor, + self.target_executor) + else: + return "The {} feature is unsupported in {}.".format(self.feature, + self.current_executor) + + +class InvalidResourceSpecification(ExecutorError): + """Error raised when Invalid input is supplied via resource Specification""" + + def __init__(self, invalid_keys: Set[str], message: str = ''): + self.invalid_keys = invalid_keys + self.message = message + + def __str__(self): + return f"Invalid Resource Specification Supplied: {self.invalid_keys}. {self.message}" + + +class ScalingFailed(ExecutorError): + """Scaling failed due to error in Execution provider.""" + + def __str__(self): + return f"Executor {self.executor.label} failed to scale due to: {self.reason}" + + +class BadMessage(ParslError): + """ Mangled/Poorly formatted/Unsupported message received + """ + + def __init__(self, reason): + self.reason = reason + + def __str__(self): + return "Received an unsupported message. Reason:{}".format(self.reason) diff --git a/parsl/parsl/executors/execute_task.py b/parsl/parsl/executors/execute_task.py new file mode 100644 index 00000000..3eb8fd2d --- /dev/null +++ b/parsl/parsl/executors/execute_task.py @@ -0,0 +1,31 @@ +from parsl.serialize import unpack_apply_message + + +def execute_task(bufs: bytes): + """Deserialize the buffer and execute the task. + Returns the result or throws exception. + """ + f, args, kwargs = unpack_apply_message(bufs) + + # We might need to look into callability of the function from itself + # since we change it's name in the new namespace + prefix = "parsl_" + fname = prefix + "f" + argname = prefix + "args" + kwargname = prefix + "kwargs" + resultname = prefix + "result" + + code = "{0} = {1}(*{2}, **{3})".format(resultname, fname, + argname, kwargname) + + user_ns = locals() + user_ns.update({ + '__builtins__': __builtins__, + fname: f, + argname: args, + kwargname: kwargs, + resultname: resultname + }) + + exec(code, user_ns, user_ns) + return user_ns.get(resultname) diff --git a/parsl/parsl/executors/flux/__init__.py b/parsl/parsl/executors/flux/__init__.py new file mode 100644 index 00000000..d9324a34 --- /dev/null +++ b/parsl/parsl/executors/flux/__init__.py @@ -0,0 +1,5 @@ +"""Package defining FluxExecutor.""" + +import collections + +TaskResult = collections.namedtuple("TaskResult", ("returnval", "exception")) diff --git a/parsl/parsl/executors/flux/execute_parsl_task.py b/parsl/parsl/executors/flux/execute_parsl_task.py new file mode 100644 index 00000000..4372578e --- /dev/null +++ b/parsl/parsl/executors/flux/execute_parsl_task.py @@ -0,0 +1,45 @@ +"""Script for executing tasks inside of Flux jobs.""" + +import argparse +import logging +import os + +from parsl.executors.execute_task import execute_task +from parsl.executors.flux import TaskResult +from parsl.serialize import serialize + + +def main(): + """Execute one rank of an MPI application.""" + logging.basicConfig( + level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s" + ) + parser = argparse.ArgumentParser() + parser.add_argument("-i", "--input", required=True, help="Input pickle file") + parser.add_argument("-o", "--output", required=True, help="Output pickle file") + args = parser.parse_args() + logging.info("Input : %s", args.input) + logging.info("Output : %s", args.output) + returnval = None + exception = None + # open and deserialize the task's pickled input package + with open(args.input, "rb") as file_handle: + fn_buf = file_handle.read() + logging.info("Read input pickle file") + try: + returnval = execute_task(fn_buf) + except Exception as exc: + logging.exception("Parsl task execution failed:") + exception = exc + else: + logging.info("Finished execution") + # only rank 0 should write/return a result; other ranks exit + if int(os.environ["FLUX_TASK_RANK"]) == 0: + # write the result to the output file + result_buf = serialize(TaskResult(returnval, exception)) + with open(args.output, "wb") as file_handle: + file_handle.write(result_buf) + + +if __name__ == "__main__": + main() diff --git a/parsl/parsl/executors/flux/executor.py b/parsl/parsl/executors/flux/executor.py new file mode 100644 index 00000000..0f563384 --- /dev/null +++ b/parsl/parsl/executors/flux/executor.py @@ -0,0 +1,440 @@ +"""Defines the FluxExecutor class.""" + +import collections +import concurrent.futures as cf +import functools +import itertools +import os +import queue +import shutil +import sys +import threading +import uuid +import weakref +from collections.abc import Callable, Mapping +from socket import gethostname +from typing import Any, Dict, Optional + +import zmq + +from parsl.app.errors import AppException +from parsl.executors.base import ParslExecutor +from parsl.executors.errors import ScalingFailed +from parsl.executors.flux.execute_parsl_task import __file__ as _WORKER_PATH +from parsl.executors.flux.flux_instance_manager import __file__ as _MANAGER_PATH +from parsl.providers import LocalProvider +from parsl.providers.base import ExecutionProvider +from parsl.serialize import deserialize, pack_apply_message +from parsl.serialize.errors import SerializationError +from parsl.utils import RepresentationMixin + +_WORKER_PATH = os.path.realpath(_WORKER_PATH) +_MANAGER_PATH = os.path.realpath(_MANAGER_PATH) + + +_FluxJobInfo = collections.namedtuple( + "_FluxJobInfo", ("future", "task_id", "infile", "outfile", "resource_spec") +) + + +class FluxFutureWrapper(cf.Future): + """Wrapper class around a ``flux.job.FluxExecutorFuture.`` + + Forwards methods onto the underlying FluxExecutorFuture. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._flux_future = None + self._cancellation_lock = threading.Lock() + + def cancel(self): + # potential race condition: executor sets _flux_future after check + # that _flux_future is None. Protect the check and cancel() call with a lock. + with self._cancellation_lock: + if self._flux_future is None: + return super().cancel() + if self._flux_future.cancelled(): + # due to a bug, flux futures can only have ``cancel`` called once + return True + if self._flux_future.cancel(): # cancel underlying future and then self + if not super().cancel(): + raise RuntimeError("Unexpected state") + self.set_running_or_notify_cancel() # also should be called only once + return True + return False + + cancel.__doc__ = cf.Future.cancel.__doc__ + + def running(self): + if self._flux_future is None: + return False # no race condition since ``running()`` just advisory + return self._flux_future.running() + + running.__doc__ = cf.Future.running.__doc__ + + +def _complete_future( + expected_file: str, future_wrapper: FluxFutureWrapper, flux_future: Any +): + """Callback triggered when a FluxExecutorFuture completes. + + When the FluxExecutorFuture completes, check for the Parsl task's + output file, and assign the result to the FluxWrapperFuture future. + + Parameters + ---------- + expected_file : str + The path to the Parsl task's output file, storing the result of the task. + future_wrapper : FluxFutureWrapper + The user-facing future. + flux_future : FluxExecutorFuture + The future wrapped by ``future_wrapper``. Also accessible via + ``future_wrapper``, but the flux_future must be accepted as an argument + due to how ``concurrent.futures.add_done_callback`` works. + """ + if flux_future.cancelled(): # if underlying future was cancelled, return + return # no need to set a result on the wrapper future + try: + returncode = flux_future.result() + except Exception as unknown_err: + future_wrapper.set_exception(unknown_err) + return + if returncode == 0: + try: # look for the output file + with open(expected_file, "rb") as file_handle: + task_result = deserialize(file_handle.read()) + except FileNotFoundError: + future_wrapper.set_exception( + FileNotFoundError( + f"No result found for Parsl task, expected {expected_file}" + ) + ) + except Exception as unknown_err: + future_wrapper.set_exception(unknown_err) + else: # task package deserialized successfully + if task_result.exception is not None: + future_wrapper.set_exception(task_result.exception) + else: + future_wrapper.set_result(task_result.returnval) + else: # the job exited abnormally + future_wrapper.set_exception( + AppException(f"Parsl task exited abnormally: returned {returncode}") + ) + + +class FluxExecutor(ParslExecutor, RepresentationMixin): + """Executor that uses Flux to schedule and run jobs. + + Every callable submitted to the executor is wrapped into a Flux job. + + This executor requires that there be a Flux installation available + locally, and that it can be located either in PATH or through the + ``flux_path`` argument. + + Flux jobs are fairly heavyweight. As of Flux v0.25, a single Flux + instance is (on many systems) capped at 50 jobs per second. As such, + this executor is not a good fit for use-cases consisting of large numbers + of small, fast jobs. + + However, Flux is great at handling jobs with large resource requirements, + and collections of jobs with varying resource requirements. + + Note that due to vendor-specific extensions, on certain Cray machines like + ALCF's Theta or LANL's Trinitite/Trinity, Flux cannot run applications + that use the default MPI library. Generally the only workaround is to + recompile with another MPI library like OpenMPI. + + This executor acts as a sort of wrapper around a ``flux.job.FluxExecutor``, + which can be confusing since both wrapped and wrapper classes share the same name. + Whenever possible, the underlying executor is referred by its fully qualified name, + ``flux.job.FluxExecutor``. + + Parameters + ---------- + working_dir: str + Directory in which the executor should place its files, possibly overwriting + existing files. If ``None``, generate a unique directory. + label: str + Label for this executor instance. + flux_handle_args: collections.abc.Sequence + Positional arguments to ``flux.Flux()`` instance, if any. + The first positional argument, ``url``, is provided by this executor. + flux_executor_kwargs: collections.abc.Mapping + Keyword arguments to pass to the underlying ``flux.job.FluxExecutor()`` + instance, if any. Note that the ``handle_args`` keyword + argument is provided by this executor, + in order to supply the URL of a remote Flux instance. + flux_path: str + Path to flux installation to use, or None to search PATH for flux. + launch_cmd: str + The command to use when launching the executor's backend. The default + command is available as the ``DEFAULT_LAUNCH_COMMAND`` attribute. The + default command starts a new Flux instance, which may not be desirable + if a Flux instance will already be provisioned (this is not likely). + """ + + DEFAULT_LAUNCH_CMD = "{flux} start {python} {manager} {protocol} {hostname} {port}" + + def __init__( + self, + provider: Optional[ExecutionProvider] = None, + working_dir: Optional[str] = None, + label: str = "FluxExecutor", + flux_executor_kwargs: Mapping = {}, + flux_path: Optional[str] = None, + launch_cmd: Optional[str] = None, + ): + super().__init__() + if provider is None: + provider = LocalProvider() + self.provider = provider + self.label = label + if working_dir is None: + working_dir = self.label + "_" + str(uuid.uuid4()) + self.working_dir = os.path.abspath(working_dir) + # check that flux_path is an executable, or look for flux in PATH + if flux_path is None: + flux_path = shutil.which("flux") + if flux_path is None: + raise EnvironmentError("Cannot find Flux installation in PATH") + self.flux_path = os.path.abspath(flux_path) + self._task_id_counter = itertools.count() + # Assumes a launch command cannot be None or empty + self.launch_cmd = launch_cmd or self.DEFAULT_LAUNCH_CMD + self._submission_queue: queue.Queue = queue.Queue() + self._stop_event = threading.Event() + # lock to protect self._task_id_counter and also submission/shutdown race + self._submission_lock = threading.Lock() + self.flux_executor_kwargs = flux_executor_kwargs + self._submission_thread = threading.Thread( + target=_submit_wrapper, + args=( + self._submission_queue, + self._stop_event, + self.working_dir, + self.flux_executor_kwargs, + self.provider, + self, + self.flux_path, + self.launch_cmd, + ), + daemon=True, + ) + # add a ``weakref.finalize()`` function for joining the executor thread + weakref.finalize( + self, + lambda x, y: x.set() or y.join(), # type: ignore[func-returns-value] + self._stop_event, + self._submission_thread, + ) + + def start(self): + """Called when DFK starts the executor when the config is loaded.""" + super().start() + os.makedirs(self.working_dir, exist_ok=True) + self._submission_thread.start() + + def shutdown(self, wait=True): + """Shut down the executor, causing further calls to ``submit`` to fail. + + Parameters + ---------- + wait + If ``True``, do not return until all submitted Futures are done. + """ + with self._submission_lock: + self._stop_event.set() + if wait: + self._submission_thread.join() + + super().shutdown() + + def submit( + self, + func: Callable, + resource_specification: Dict[str, Any], + *args: Any, + **kwargs: Any, + ): + """Wrap a callable in a Flux job and submit it to Flux. + + :param func: The callable to submit as a job to Flux + + :param resource_specification: A mapping defining the resources to allocate to the Flux job. + + Only the following keys are checked for: + + - num_tasks: the number of tasks to launch (MPI ranks for an MPI job), default 1 + - cores_per_task: cores per task, default 1 + - gpus_per_task: gpus per task, default 1 + - num_nodes: if > 0, evenly distribute the allocated cores/gpus + across the given number of nodes. Does *not* give the job exclusive + access to those nodes; this option only affects distribution. + + :param args: positional arguments for the callable + + :param kwargs: keyword arguments for the callable + """ + # protect self._task_id_counter and shutdown/submit race + with self._submission_lock: + if self._stop_event.is_set(): + raise RuntimeError("`shutdown()` already called") + task_id = str(next(self._task_id_counter)) + infile = os.path.join(self.working_dir, f"{task_id}_in{os.extsep}pkl") + outfile = os.path.join(self.working_dir, f"{task_id}_out{os.extsep}pkl") + try: + fn_buf = pack_apply_message( + func, args, kwargs, buffer_threshold=1 << 20, + ) + except TypeError: + raise SerializationError(func.__name__) + with open(infile, "wb") as infile_handle: + infile_handle.write(fn_buf) + future = FluxFutureWrapper() + self._submission_queue.put( + _FluxJobInfo(future, task_id, infile, outfile, resource_specification) + ) + return future + + +def _submit_wrapper( + submission_queue: queue.Queue, stop_event: threading.Event, *args, **kwargs +): + """Wrap the ``_submit_flux_jobs`` function in a try/except. + + If an exception is thrown, error out all submitted tasks. + """ + with zmq.Context() as ctx: + with ctx.socket(zmq.REP) as socket: + try: + _submit_flux_jobs(submission_queue, stop_event, socket, *args, **kwargs) + except Exception as exc: + _error_out_jobs(submission_queue, stop_event, exc) + raise + + +def _error_out_jobs( + submission_queue: queue.Queue, stop_event: threading.Event, exc: Exception +): + """Clear out ``submission_queue``, setting errors on all futures.""" + while not stop_event.is_set() or not submission_queue.empty(): + try: + jobinfo = submission_queue.get(timeout=0.05) + except queue.Empty: + pass + else: + jobinfo.future.set_exception(exc) + + +def _submit_flux_jobs( + submission_queue: queue.Queue, + stop_event: threading.Event, + socket: zmq.Socket, + working_dir: str, + flux_executor_kwargs: Mapping, + provider: ExecutionProvider, + executor: FluxExecutor, + flux_path: str, + launch_cmd: str, +): + """Function to be run in a separate thread by executor. + + Pull ``_FluxJobInfo`` job packages from a queue and submit them to Flux. + """ + provider.script_dir = working_dir + job_id = provider.submit( + launch_cmd.format( + port=socket.bind_to_random_port("tcp://*"), + protocol="tcp", + hostname=gethostname(), + python=sys.executable, + flux=flux_path, + manager=_MANAGER_PATH, + ), + 1, + ) + if not job_id: + raise ScalingFailed( + executor, "Attempt to provision nodes via provider has failed", + ) + # wait for the flux package path to be sent + _check_provider_job(socket, provider, job_id) + # receive path to the ``flux`` package from the ZMQ socket + flux_pkg_path = socket.recv().decode() + # load the package. Unfortunately the only good way to do this is to + # modify sys.path + if flux_pkg_path not in sys.path: + sys.path.append(flux_pkg_path) + import flux.job + + socket.send(b"ack") # dummy message + # receive the URI of the Flux instance launched by provider + _check_provider_job(socket, provider, job_id) + flux_instance_uri = socket.recv() + # create a ``flux.job.FluxExecutor`` connected to remote Flux instance + with flux.job.FluxExecutor( + handle_args=(flux_instance_uri,), **flux_executor_kwargs + ) as flux_executor: + # need to ensure that no jobs submitted after stop_event set + # exit loop when event is set and queue is drained + while not stop_event.is_set() or not submission_queue.empty(): + try: + jobinfo = submission_queue.get(timeout=0.05) + except queue.Empty: + pass + else: + _submit_single_job(flux_executor, working_dir, jobinfo) + socket.send(b"shutdown") + + +def _check_provider_job(socket: zmq.Socket, provider: ExecutionProvider, job_id: Any): + """Poll for messages, checking that the provider's allocation is alive.""" + while not socket.poll(1000, zmq.POLLIN): + if provider.status([job_id])[0].terminal: + raise RuntimeError("Provider job has terminated") + + +def _submit_single_job(flux_executor: Any, working_dir: str, jobinfo: _FluxJobInfo): + """Submit a single job to Flux. Link the Flux future with a user-facing future.""" + import flux.job + + jobspec = flux.job.JobspecV1.from_command( + command=[ + sys.executable, + _WORKER_PATH, + "-i", + jobinfo.infile, + "-o", + jobinfo.outfile, + ], + num_tasks=jobinfo.resource_spec.get("num_tasks", 1), + num_nodes=jobinfo.resource_spec.get("num_nodes"), + cores_per_task=jobinfo.resource_spec.get("cores_per_task", 1), + gpus_per_task=jobinfo.resource_spec.get("gpus_per_task"), + ) + jobspec.cwd = os.getcwd() + jobspec.environment = dict(os.environ) + jobspec.stdout = os.path.abspath( + os.path.join(working_dir, f"{jobinfo.task_id}_stdout{os.extsep}txt") + ) + jobspec.stderr = os.path.abspath( + os.path.join(working_dir, f"{jobinfo.task_id}_stderr{os.extsep}txt") + ) + # need to shield user future from cancellation while setting its underlying future + with jobinfo.future._cancellation_lock: + if jobinfo.future.cancelled(): + return + try: + # flux_executor.submit() raises if the executor is broken for any reason + # most importantly, it raises if the remote flux instance dies + flux_future = flux_executor.submit(jobspec) + except Exception as exc: + jobinfo.future.set_exception(exc) + return + jobinfo.future._flux_future = flux_future + # Trigger the user-facing wrapper future to complete when the + # wrapped ``flux.job.FluxExecutor`` future completes. + flux_future.add_done_callback( + functools.partial(_complete_future, jobinfo.outfile, jobinfo.future) + ) diff --git a/parsl/parsl/executors/flux/flux_instance_manager.py b/parsl/parsl/executors/flux/flux_instance_manager.py new file mode 100644 index 00000000..e6111796 --- /dev/null +++ b/parsl/parsl/executors/flux/flux_instance_manager.py @@ -0,0 +1,56 @@ +"""Script meant to be the initial program of a Flux instance.""" + +import argparse +import logging +import os +from os.path import dirname +from socket import gethostbyname, gethostname + +import zmq + + +def main(): + """Run a Flux instance to completion. + + Send the path to the Flux Python package and the URI of the + encapsulating Flux instance. + """ + # flux imports only available when launched under Flux instance + import flux + import flux.job + + logging.basicConfig( + level=logging.DEBUG, format="%(asctime)s [%(levelname)s] %(message)s" + ) + parser = argparse.ArgumentParser() + parser.add_argument("protocol", help="Protocol of the parent executor's socket") + parser.add_argument("hostname", help="hostname of the parent executor's socket") + parser.add_argument("port", help="Port of the parent executor's socket") + args = parser.parse_args() + with zmq.Context() as context, context.socket(zmq.REQ) as socket: + socket.connect( + args.protocol + "://" + gethostbyname(args.hostname) + ":" + args.port + ) + # send the path to the ``flux.job`` package + socket.send(dirname(dirname(os.path.realpath(flux.__file__))).encode()) + logging.debug("Flux package path sent.") + # collect the encapsulating Flux instance's URI + local_uri = flux.Flux().attr_get("local-uri") + hostname = gethostname() + if args.hostname == hostname: + flux_uri = local_uri + else: + flux_uri = "ssh://" + gethostname() + local_uri.replace("local://", "") + logging.debug("Flux URI is %s", flux_uri) + response = socket.recv() # get acknowledgment + logging.debug("Received acknowledgment %s", response) + socket.send(flux_uri.encode()) # send URI + logging.debug("URI sent. Blocking for response...") + response = socket.recv() # wait for shutdown message + logging.debug("Response %s received, draining flux jobs...", response) + flux.Flux().rpc("job-manager.drain").get() + logging.debug("Flux jobs drained, exiting.") + + +if __name__ == "__main__": + main() diff --git a/parsl/parsl/executors/globus_compute.py b/parsl/parsl/executors/globus_compute.py new file mode 100644 index 00000000..2e215df7 --- /dev/null +++ b/parsl/parsl/executors/globus_compute.py @@ -0,0 +1,134 @@ +from __future__ import annotations + +import copy +from concurrent.futures import Future +from typing import Any, Callable, Dict, List, Optional + +import typeguard + +from parsl.data_provider.staging import Staging +from parsl.errors import OptionalModuleMissing +from parsl.executors.base import ParslExecutor +from parsl.utils import RepresentationMixin + +try: + from globus_compute_sdk import Executor + _globus_compute_enabled = True +except ImportError: + _globus_compute_enabled = False + + +class GlobusComputeExecutor(ParslExecutor, RepresentationMixin): + """ GlobusComputeExecutor enables remote execution on Globus Compute endpoints + + GlobusComputeExecutor is a thin wrapper over globus_compute_sdk.Executor + Refer to `globus-compute user documentation `_ + and `reference documentation `_ + for more details. + + .. note:: + As a remote execution system, Globus Compute relies on serialization to ship + tasks and results between the Parsl client side and the remote Globus Compute + Endpoint side. Serialization is unreliable across python versions, and + wrappers used by Parsl assume identical Parsl versions across on both sides. + We recommend using matching Python, Parsl and Globus Compute version on both + the client side and the endpoint side for stable behavior. + + """ + + @typeguard.typechecked + def __init__( + self, + executor: Executor, + label: str = 'GlobusComputeExecutor', + storage_access: Optional[List[Staging]] = None, + working_dir: Optional[str] = None, + ): + """ + Parameters + ---------- + + executor: globus_compute_sdk.Executor + Pass a globus_compute_sdk Executor that will be used to execute + tasks on a globus_compute endpoint. Refer to `globus-compute docs + `_ + + label: + a label to name the executor + + storage_access: + a list of staging providers that will be used for file staging + + working_dir: + The working dir to be used for file staging + """ + if not _globus_compute_enabled: + raise OptionalModuleMissing( + ['globus-compute-sdk'], + "GlobusComputeExecutor requires globus-compute-sdk installed" + ) + + super().__init__() + self.executor: Executor = executor + self.resource_specification = self.executor.resource_specification + self.user_endpoint_config = self.executor.user_endpoint_config + self.label = label + self.storage_access = storage_access + self.working_dir = working_dir + + def submit(self, func: Callable, resource_specification: Dict[str, Any], *args: Any, **kwargs: Any) -> Future: + """ Submit func to globus-compute + + + Parameters + ---------- + + func: Callable + Python function to execute remotely + + resource_specification: Dict[str, Any] + Resource specification can be used specify MPI resources required by MPI applications on + Endpoints configured to use globus compute's MPIEngine. GCE also accepts *user_endpoint_config* + to configure endpoints when the endpoint is a `Multi-User Endpoint + `_ + + args: + Args to pass to the function + + kwargs: + kwargs to pass to the function + + Returns + ------- + + Future + """ + res_spec = copy.deepcopy(resource_specification or self.resource_specification) + # Pop user_endpoint_config since it is illegal in resource_spec for globus_compute + if res_spec: + user_endpoint_config = res_spec.pop('user_endpoint_config', self.user_endpoint_config) + else: + user_endpoint_config = self.user_endpoint_config + + try: + self.executor.resource_specification = res_spec + self.executor.user_endpoint_config = user_endpoint_config + return self.executor.submit(func, *args, **kwargs) + finally: + # Reset executor state to defaults set at configuration time + self.executor.resource_specification = self.resource_specification + self.executor.user_endpoint_config = self.user_endpoint_config + + def shutdown(self): + """Clean-up the resources associated with the Executor. + + GCE.shutdown will cancel all futures that have not yet registered with + Globus Compute and will not wait for the launched futures to complete. + This method explicitly shutsdown the result_watcher thread to avoid + it waiting for outstanding futures at thread exit. + """ + self.executor.shutdown(wait=False, cancel_futures=True) + result_watcher = self.executor._get_result_watcher() + result_watcher.shutdown(wait=False, cancel_futures=True) + + super().shutdown() diff --git a/parsl/parsl/executors/high_throughput/__init__.py b/parsl/parsl/executors/high_throughput/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/executors/high_throughput/errors.py b/parsl/parsl/executors/high_throughput/errors.py new file mode 100644 index 00000000..cb2ba5d2 --- /dev/null +++ b/parsl/parsl/executors/high_throughput/errors.py @@ -0,0 +1,52 @@ +import time + + +class ManagerLost(Exception): + """ + Task lost due to manager loss. Manager is considered lost when multiple heartbeats + have been missed. + """ + def __init__(self, manager_id: bytes, hostname: str) -> None: + self.manager_id = manager_id + self.tstamp = time.time() + self.hostname = hostname + + def __str__(self) -> str: + return ( + f"Task failure due to loss of manager {self.manager_id.decode()} on" + f" host {self.hostname}" + ) + + +class VersionMismatch(Exception): + """Manager and Interchange versions do not match""" + def __init__(self, interchange_version: str, manager_version: str): + self.interchange_version = interchange_version + self.manager_version = manager_version + + def __str__(self) -> str: + return ( + f"Manager version info {self.manager_version} does not match interchange" + f" version info {self.interchange_version}" + ) + + +class WorkerLost(Exception): + """Exception raised when a worker is lost + """ + def __init__(self, worker_id, hostname): + self.worker_id = worker_id + self.hostname = hostname + + def __str__(self): + return "Task failure due to loss of worker {} on host {}".format(self.worker_id, self.hostname) + + +class CommandClientTimeoutError(Exception): + """Raised when the command client times out waiting for a response. + """ + + +class CommandClientBadError(Exception): + """Raised when the command client is bad from an earlier timeout. + """ diff --git a/parsl/parsl/executors/high_throughput/executor.py b/parsl/parsl/executors/high_throughput/executor.py new file mode 100644 index 00000000..c05ca83b --- /dev/null +++ b/parsl/parsl/executors/high_throughput/executor.py @@ -0,0 +1,966 @@ +import logging +import math +import os +import pickle +import subprocess +import threading +import typing +import warnings +from collections import defaultdict +from concurrent.futures import Future +from dataclasses import dataclass +from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union + +import typeguard + +from parsl import curvezmq +from parsl.addresses import get_all_addresses +from parsl.app.errors import RemoteExceptionWrapper +from parsl.data_provider.staging import Staging +from parsl.executors.errors import ( + BadMessage, + InvalidResourceSpecification, + ScalingFailed, +) +from parsl.executors.high_throughput import zmq_pipes +from parsl.executors.high_throughput.errors import CommandClientTimeoutError +from parsl.executors.high_throughput.manager_selector import ( + ManagerSelector, + RandomManagerSelector, +) +from parsl.executors.status_handling import BlockProviderExecutor +from parsl.jobs.states import TERMINAL_STATES, JobState, JobStatus +from parsl.monitoring.radios.base import RadioConfig +from parsl.monitoring.radios.htex import HTEXRadio +from parsl.monitoring.radios.zmq_router import ZMQRadioReceiver, start_zmq_receiver +from parsl.process_loggers import wrap_with_logs +from parsl.providers import LocalProvider +from parsl.providers.base import ExecutionProvider +from parsl.serialize import deserialize, pack_apply_message +from parsl.serialize.errors import DeserializationError, SerializationError +from parsl.usage_tracking.api import UsageInformation +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + +DEFAULT_LAUNCH_CMD = ("process_worker_pool.py {debug} {max_workers_per_node} " + "-a {addresses} " + "-p {prefetch_capacity} " + "-c {cores_per_worker} " + "-m {mem_per_worker} " + "--poll {poll_period} " + "--port={worker_port} " + "--cert_dir {cert_dir} " + "--logdir={logdir} " + "--logconf={logconf} " + "--block_id={{block_id}} " + "--hb_period={heartbeat_period} " + "{address_probe_timeout_string} " + "--hb_threshold={heartbeat_threshold} " + "--drain_period={drain_period} " + "--cpu-affinity {cpu_affinity} " + "{enable_mpi_mode} " + "--mpi-launcher={mpi_launcher} " + "--available-accelerators {accelerators}") + +DEFAULT_INTERCHANGE_LAUNCH_CMD = ["interchange.py"] + +GENERAL_HTEX_PARAM_DOCS = """provider : :class:`~parsl.providers.base.ExecutionProvider` + Provider to access computation resources. Can be one of :class:`~parsl.providers.aws.aws.EC2Provider`, + :class:`~parsl.providers.condor.condor.Condor`, + :class:`~parsl.providers.googlecloud.googlecloud.GoogleCloud`, + :class:`~parsl.providers.gridEngine.gridEngine.GridEngine`, + :class:`~parsl.providers.local.local.Local`, + :class:`~parsl.providers.sge.sge.GridEngine`, + :class:`~parsl.providers.slurm.slurm.Slurm`, or + :class:`~parsl.providers.torque.torque.Torque`. + + label : str + Label for this executor instance. + + launch_cmd : str + Command line string to launch the process_worker_pool from the provider. The command line string + will be formatted with appropriate values for the following values (debug, task_url, result_url, + cores_per_worker, nodes_per_block, heartbeat_period, heartbeat_threshold, logdir). For example: + launch_cmd="process_worker_pool.py {debug} -c {cores_per_worker} --task_url={task_url} --result_url={result_url}" + + interchange_launch_cmd : Sequence[str] + Custom sequence of command line tokens to launch the interchange process from the executor. If + undefined, the executor will use the default "interchange.py" command. + + address : string + An address to connect to the main Parsl process which is reachable from the network in which + workers will be running. This field expects an IPv4 or IPv6 address. + Most login nodes on clusters have several network interfaces available, only some of which + can be reached from the compute nodes. This field can be used to limit the executor to listen + only on a specific interface, and limiting connections to the internal network. + By default, the executor will attempt to enumerate and connect through all possible addresses. + Setting an address here overrides the default behavior. + default=None + + loopback_address: string + Specify address used for internal communication between executor and interchange. + Supports IPv4 and IPv6 addresses + default=127.0.0.1 + + worker_port : int + Specify the port to be used by workers to connect to Parsl. If this option is specified, + worker_port_range will not be honored. + + worker_port_range : (int, int) + Worker ports will be chosen between the two integers provided. + + interchange_port_range : (int, int) + Port range used by Parsl to communicate with the Interchange. + + working_dir : str + Working dir to be used by the executor. + + worker_debug : Bool + Enables worker debug logging. + + prefetch_capacity : int + Number of tasks that could be prefetched over available worker capacity. + When there are a few tasks (<100) or when tasks are long running, this option should + be set to 0 for better load balancing. Default is 0. + + address_probe_timeout : int | None + Managers attempt connecting over many different addresses to determine a viable address. + This option sets a time limit in seconds on the connection attempt. + Default of None implies 30s timeout set on worker. + + heartbeat_threshold : int + Seconds since the last message from the counterpart in the communication pair: + (interchange, manager) after which the counterpart is assumed to be un-available. Default: 120s + + heartbeat_period : int + Number of seconds after which a heartbeat message indicating liveness is sent to the + counterpart (interchange, manager). Default: 30s + + poll_period : int + Timeout period to be used by the executor components in milliseconds. Increasing poll_periods + trades performance for cpu efficiency. Default: 10ms + + drain_period : int + The number of seconds after start when workers will begin to drain + and then exit. Set this to a time that is slightly less than the + maximum walltime of batch jobs to avoid killing tasks while they + execute. For example, you could set this to the walltime minus a grace + period for the batch job to start the workers, minus the expected + maximum length of an individual task. + + worker_logdir_root : string + In case of a remote file system, specify the path to where logs will be kept. + + encrypted : bool + Flag to enable/disable encryption (CurveZMQ). Default is True. + + manager_selector: ManagerSelector + Determines what strategy the interchange uses to select managers during task distribution. + See API reference under "Manager Selectors" regarding the various manager selectors. + Default: 'RandomManagerSelector' +""" # Documentation for params used by both HTEx and MPIEx + + +class HTEXFuture(Future): + def __init__(self, task_id) -> None: + super().__init__() + self.parsl_executor_task_id = task_id + + +class HighThroughputExecutor(BlockProviderExecutor, RepresentationMixin, UsageInformation): + __doc__ = f"""Executor designed for cluster-scale + + The HighThroughputExecutor system has the following components: + 1. The HighThroughputExecutor instance which is run as part of the Parsl script. + 2. The Interchange which acts as a load-balancing proxy between workers and Parsl + 3. The multiprocessing based worker pool which coordinates task execution over several + cores on a node. + 4. ZeroMQ pipes connect the HighThroughputExecutor, Interchange and the process_worker_pool + + Here is a diagram + + .. code:: python + + + | Data | Executor | Interchange | External Process(es) + | Flow | | | + Task | Kernel | | | + +----->|-------->|------------>|->outgoing_q---|-> process_worker_pool + | | | | batching | | | + Parsl<---Fut-| | | load-balancing| result exception + ^ | | | watchdogs | | | + | | | Result | | | | + | | | Queue | | V V + | | | Thread<--|-incoming_q<---|--- +---------+ + | | | | | | + | | | | | | + +----update_fut-----+ + + + Each of the workers in each process_worker_pool has access to its local rank through + an environmental variable, ``PARSL_WORKER_RANK``. The local rank is unique for each process + and is an integer in the range from 0 to the number of workers per in the pool minus 1. + The workers also have access to the ID of the worker pool as ``PARSL_WORKER_POOL_ID`` + and the size of the worker pool as ``PARSL_WORKER_COUNT``. + + + Parameters + ---------- + + {GENERAL_HTEX_PARAM_DOCS} + + cores_per_worker : float + cores to be assigned to each worker. Oversubscription is possible + by setting cores_per_worker < 1.0. Default=1 + + mem_per_worker : float + GB of memory required per worker. If this option is specified, the node manager + will check the available memory at startup and limit the number of workers such that + the there's sufficient memory for each worker. Default: None + + max_workers_per_node : int + Caps the number of workers launched per node. Default: None + + cpu_affinity: string + Whether or how each worker process sets thread affinity. Options include "none" to forgo + any CPU affinity configuration, "block" to assign adjacent cores to workers + (ex: assign 0-1 to worker 0, 2-3 to worker 1), and + "alternating" to assign cores to workers in round-robin + (ex: assign 0,2 to worker 0, 1,3 to worker 1). + The "block-reverse" option assigns adjacent cores to workers, but assigns + the CPUs with large indices to low index workers (ex: assign 2-3 to worker 1, 0,1 to worker 2) + + available_accelerators: int | list + Accelerators available for workers to use. Each worker will be pinned to exactly one of the provided + accelerators, and no more workers will be launched than the number of accelerators. + + Either provide the list of accelerator names or the number available. If a number is provided, + Parsl will create names as integers starting with 0. + + default: empty list + + """ + + @typeguard.typechecked + def __init__(self, + label: str = 'HighThroughputExecutor', + provider: Optional[ExecutionProvider] = None, + launch_cmd: Optional[str] = None, + interchange_launch_cmd: Optional[Sequence[str]] = None, + address: Optional[str] = None, + loopback_address: str = "127.0.0.1", + worker_port: Optional[int] = None, + worker_port_range: Optional[Tuple[int, int]] = (54000, 55000), + interchange_port_range: Optional[Tuple[int, int]] = (55000, 56000), + storage_access: Optional[List[Staging]] = None, + working_dir: Optional[str] = None, + worker_debug: bool = False, + cores_per_worker: float = 1.0, + mem_per_worker: Optional[float] = None, + max_workers_per_node: Optional[int] = None, + cpu_affinity: str = 'none', + available_accelerators: Union[int, Sequence[str]] = (), + prefetch_capacity: int = 0, + heartbeat_threshold: int = 120, + heartbeat_period: int = 30, + drain_period: Optional[int] = None, + poll_period: int = 10, + address_probe_timeout: Optional[int] = None, + worker_logdir_root: Optional[str] = None, + manager_selector: ManagerSelector = RandomManagerSelector(), + block_error_handler: Union[bool, Callable[[BlockProviderExecutor, Dict[str, JobStatus]], None]] = True, + encrypted: bool = True, + remote_monitoring_radio: Optional[RadioConfig] = None): + + logger.debug("Initializing HighThroughputExecutor") + + BlockProviderExecutor.__init__(self, + provider=provider if provider else LocalProvider(), + block_error_handler=block_error_handler) + self.label = label + self.worker_debug = worker_debug + self.storage_access = storage_access + self.working_dir = working_dir + self.cores_per_worker = cores_per_worker + self.mem_per_worker = mem_per_worker + self.prefetch_capacity = prefetch_capacity + self.address = address + self.address_probe_timeout = address_probe_timeout + self.manager_selector = manager_selector + self.loopback_address = loopback_address + + if self.address: + self.all_addresses = {self.address} + else: + self.all_addresses = get_all_addresses() + + self.max_workers_per_node = max_workers_per_node or float("inf") + + mem_slots = self.max_workers_per_node + cpu_slots = self.max_workers_per_node + assert self.provider is not None, "htex always has a provider" + if hasattr(self.provider, 'mem_per_node') and \ + self.provider.mem_per_node is not None and \ + mem_per_worker is not None and \ + mem_per_worker > 0: + mem_slots = math.floor(self.provider.mem_per_node / mem_per_worker) + if hasattr(self.provider, 'cores_per_node') and \ + self.provider.cores_per_node is not None: + cpu_slots = math.floor(self.provider.cores_per_node / cores_per_worker) + + # Set the list of available accelerators + if isinstance(available_accelerators, int): + # If the user provide an integer, create some names for them + available_accelerators = list(map(str, range(available_accelerators))) + self.available_accelerators = list(available_accelerators) + + # Determine the number of workers per node + self._workers_per_node = min(self.max_workers_per_node, mem_slots, cpu_slots) + if len(self.available_accelerators) > 0: + self._workers_per_node = min(self._workers_per_node, len(available_accelerators)) + if self._workers_per_node == float('inf'): + self._workers_per_node = 1 # our best guess-- we do not have any provider hints + + self._task_counter = 0 + + if remote_monitoring_radio is not None: + self.remote_monitoring_radio = remote_monitoring_radio + else: + self.remote_monitoring_radio = HTEXRadio() + + self.worker_port = worker_port + self.worker_port_range = worker_port_range + self.interchange_proc: Optional[subprocess.Popen] = None + self.interchange_port_range = interchange_port_range + self.heartbeat_threshold = heartbeat_threshold + self.heartbeat_period = heartbeat_period + self.drain_period = drain_period + self.poll_period = poll_period + self.run_dir = '.' + self.worker_logdir_root = worker_logdir_root + self.cpu_affinity = cpu_affinity + self.encrypted = encrypted + self.cert_dir = None + + # This flag will enable/disable internal Python mismatch checks + # between the interchange and worker managers. This serves as a + # temporary workaround for Globus Compute to support different + # Python versions at the endpoint and worker layers. We can drop + # the flag once we implement modular internal message protocols. + self._check_python_mismatch: bool = True + + if not launch_cmd: + launch_cmd = DEFAULT_LAUNCH_CMD + self.launch_cmd = launch_cmd + + if not interchange_launch_cmd: + interchange_launch_cmd = DEFAULT_INTERCHANGE_LAUNCH_CMD + self.interchange_launch_cmd = interchange_launch_cmd + + self._result_queue_thread_exit = threading.Event() + self._result_queue_thread: Optional[threading.Thread] = None + + self.zmq_monitoring: Optional[ZMQRadioReceiver] + self.zmq_monitoring = None + self.hub_zmq_port = None + + enable_mpi_mode: bool = False + mpi_launcher: str = "mpiexec" + + def _warn_deprecated(self, old: str, new: str): + warnings.warn( + f"{old} is deprecated and will be removed in a future release. " + f"Please use {new} instead.", + DeprecationWarning, + stacklevel=2 + ) + + @property + def logdir(self): + return "{}/{}".format(self.run_dir, self.label) + + @property + def worker_logdir(self): + if self.worker_logdir_root is not None: + return "{}/{}".format(self.worker_logdir_root, self.label) + return self.logdir + + def validate_resource_spec(self, resource_specification: dict): + if resource_specification: + """HTEX supports the following *Optional* resource specifications: + priority: lower value is higher priority""" + acceptable_fields = {'priority'} # add new resource spec field names here to make htex accept them + keys = set(resource_specification.keys()) + invalid_keys = keys - acceptable_fields + if invalid_keys: + message = "Task resource specification only accepts these types of resources: {}".format( + ', '.join(acceptable_fields)) + logger.error(message) + raise InvalidResourceSpecification(set(invalid_keys), message) + return + + def initialize_scaling(self): + """Compose the launch command and scale out the initial blocks. + """ + debug_opts = "--debug" if self.worker_debug else "" + max_workers_per_node = "" if self.max_workers_per_node == float('inf') else "--max_workers_per_node={}".format(self.max_workers_per_node) + enable_mpi_opts = "--enable_mpi_mode " if self.enable_mpi_mode else "" + + address_probe_timeout_string = "" + if self.address_probe_timeout: + address_probe_timeout_string = "--address_probe_timeout={}".format(self.address_probe_timeout) + + l_cmd = self.launch_cmd.format(debug=debug_opts, + prefetch_capacity=self.prefetch_capacity, + address_probe_timeout_string=address_probe_timeout_string, + addresses=",".join(self.all_addresses), + worker_port=self.worker_port, + cores_per_worker=self.cores_per_worker, + mem_per_worker=self.mem_per_worker, + max_workers_per_node=max_workers_per_node, + nodes_per_block=self.provider.nodes_per_block, + heartbeat_period=self.heartbeat_period, + heartbeat_threshold=self.heartbeat_threshold, + drain_period=self.drain_period, + poll_period=self.poll_period, + cert_dir=self.cert_dir, + logdir=self.worker_logdir, + logconf=self.logconf_path, + cpu_affinity=self.cpu_affinity, + enable_mpi_mode=enable_mpi_opts, + mpi_launcher=self.mpi_launcher, + accelerators=" ".join(self.available_accelerators)) + self.launch_cmd = l_cmd + logger.debug("Launch command: {}".format(self.launch_cmd)) + + logger.debug("Starting HighThroughputExecutor with provider:\n%s", self.provider) + + def start(self): + """Create the Interchange process and connect to it. + """ + super().start() + if self.encrypted and self.cert_dir is None: + logger.debug("Creating CurveZMQ certificates") + self.cert_dir = curvezmq.create_certificates(self.logdir) + elif not self.encrypted and self.cert_dir: + raise AttributeError( + "The certificates directory path attribute (cert_dir) is defined, but the " + "encrypted attribute is set to False. You must either change cert_dir to " + "None or encrypted to True." + ) + + if self.log_config: + log_config_dir = os.path.join(self.logdir, "log_config") + os.makedirs(log_config_dir, mode=0o700, exist_ok=True) + + self.logconf_path = os.path.join(log_config_dir, "log_config.pkl") + with open(self.logconf_path, "wb") as f: + pickle.dump(self.log_config, f) + else: + self.logconf_path = None + + self.outgoing_q = zmq_pipes.TasksOutgoing( + self.loopback_address, self.interchange_port_range, self.cert_dir + ) + self.incoming_q = zmq_pipes.ResultsIncoming( + self.loopback_address, self.interchange_port_range, self.cert_dir + ) + self.command_client = zmq_pipes.CommandClient( + self.loopback_address, self.interchange_port_range, self.cert_dir + ) + + if self.monitoring_messages is not None: + self.zmq_monitoring = start_zmq_receiver(monitoring_messages=self.monitoring_messages, + loopback_address=self.loopback_address, + port_range=self.interchange_port_range, + logdir=self.logdir, + worker_debug=self.worker_debug, + cert_dir=self.cert_dir, + ) + self.hub_zmq_port = self.zmq_monitoring.port + + self._result_queue_thread = None + self._start_result_queue_thread() + self._start_local_interchange_process() + + self.initialize_scaling() + + @wrap_with_logs + def _result_queue_worker(self): + """Listen to the queue for task result messages and handle them. + + Depending on the message, tasks will be updated with results or exceptions. + + .. code:: python + + { + "task_id" : + "result" : serialized result object, if task succeeded + ... more tags could be added later + } + + { + "task_id" : + "exception" : serialized exception object, on failure + } + """ + logger.debug("Result queue worker starting") + + while not self.bad_state_is_set and not self._result_queue_thread_exit.is_set(): + try: + serialized_msg = self.incoming_q.get(timeout_ms=self.poll_period) + if serialized_msg is None: # timeout + continue + + except IOError as e: + logger.exception("Caught broken queue with exception code {}: {}".format(e.errno, e)) + return + + except Exception as e: + logger.exception("Caught unknown exception: {}".format(e)) + return + + else: + + msg = pickle.loads(serialized_msg) + + if msg['type'] == 'result': + try: + tid = msg['task_id'] + except Exception: + raise BadMessage("Message received does not contain 'task_id' field") + + if tid == -1 and 'exception' in msg: + logger.warning("Executor shutting down due to exception from interchange") + exception = deserialize(msg['exception']) + self.set_bad_state_and_fail_all(exception) + break + + task_fut = self.tasks.pop(tid) + + if 'result' in msg: + result = deserialize(msg['result']) + task_fut.set_result(result) + + elif 'exception' in msg: + try: + s = deserialize(msg['exception']) + # s should be a RemoteExceptionWrapper... so we can reraise it + if isinstance(s, RemoteExceptionWrapper): + try: + s.reraise() + except Exception as e: + task_fut.set_exception(e) + elif isinstance(s, Exception): + task_fut.set_exception(s) + else: + raise ValueError("Unknown exception-like type received: {}".format(type(s))) + except Exception as e: + # TODO could be a proper wrapped exception? + task_fut.set_exception( + DeserializationError("Received exception, but handling also threw an exception: {}".format(e))) + else: + raise BadMessage("Message received is neither result or exception") + else: + raise BadMessage("Message received with unknown type {}".format(msg['type'])) + + logger.info("Closing result ZMQ pipe") + self.incoming_q.close() + logger.info("Result queue worker finished") + + def _start_local_interchange_process(self) -> None: + """ Starts the interchange process locally + + Starts the interchange process locally and uses the command queue to + get the worker task and result ports that the interchange has bound to. + """ + + assert self.interchange_proc is None, f"Already exists! {self.interchange_proc!r}" + + interchange_config = {"client_address": self.loopback_address, + "client_ports": (self.outgoing_q.port, + self.incoming_q.port, + self.command_client.port), + "interchange_address": self.address, + "worker_port": self.worker_port, + "worker_port_range": self.worker_port_range, + "hub_address": self.loopback_address, + "hub_zmq_port": self.hub_zmq_port, + "logdir": self.logdir, + "heartbeat_threshold": self.heartbeat_threshold, + "poll_period": self.poll_period, + "logging_level": logging.DEBUG if self.worker_debug else logging.INFO, + "cert_dir": self.cert_dir, + "manager_selector": self.manager_selector, + "run_id": self.run_id, + "log_config": self.log_config, + "_check_python_mismatch": self._check_python_mismatch, + } + + config_pickle = pickle.dumps(interchange_config) + + self.interchange_proc = subprocess.Popen(self.interchange_launch_cmd, stdin=subprocess.PIPE) + stdin = self.interchange_proc.stdin + assert stdin is not None, "Popen should have created an IO object (vs default None) because of PIPE mode" + + logger.debug("Popened interchange process. Writing config object") + stdin.write(config_pickle) + stdin.flush() + stdin.close() + logger.debug("Sent config object. Requesting worker ports") + try: + self.worker_port = self.command_client.run("WORKER_BINDS", timeout_s=120) + except CommandClientTimeoutError: + logger.error("Interchange has not completed initialization. Aborting") + raise Exception("Interchange failed to start") + logger.debug( + "Interchange process started (%r). Worker port: %d", + self.interchange_proc, + self.worker_port, + ) + + def _start_result_queue_thread(self): + """Method to start the result queue thread as a daemon. + + Checks if a thread already exists, then starts it. + Could be used later as a restart if the result queue thread dies. + """ + assert self._result_queue_thread is None, f"Already exists! {self._result_queue_thread!r}" + + logger.debug("Starting result queue thread") + self._result_queue_thread = threading.Thread(target=self._result_queue_worker, name="HTEX-Result-Queue-Thread") + self._result_queue_thread.daemon = True + self._result_queue_thread.start() + logger.debug("Started result queue thread: %r", self._result_queue_thread) + + def _hold_manager(self, manager_id: str) -> None: + """Puts a manager on hold, preventing scheduling of additional tasks to it. + + This is called "hold" mostly because this only stops scheduling of tasks, + and does not actually kill the manager or workers. + + Parameters + ---------- + + manager_id : str + Manager id to be put on hold + """ + self.command_client.run(["HOLD_MANAGER", manager_id]) + logger.debug("Sent hold request to manager: {}".format(manager_id)) + + def outstanding(self) -> int: + """Returns the count of tasks outstanding across the interchange + and managers""" + return len(self.tasks) + + def connected_managers(self) -> List[Dict[str, typing.Any]]: + """Returns a list of dicts one for each connected managers. + The dict contains info on manager(str:manager_id), block_id, + worker_count, tasks(int), idle_durations(float), active(bool) + """ + return self.command_client.run("MANAGERS") + + def connected_managers_packages(self) -> Dict[str, Dict[str, str]]: + """Returns a dict mapping each manager ID to a dict of installed + packages and their versions + """ + return self.command_client.run("MANAGERS_PACKAGES") + + def connected_blocks(self) -> List[str]: + """List of connected block ids""" + return self.command_client.run("CONNECTED_BLOCKS") + + def _hold_block(self, block_id): + """ Sends hold command to all managers which are in a specific block + + Parameters + ---------- + block_id : str + Block identifier of the block to be put on hold + """ + + managers = self.connected_managers() + + for manager in managers: + if manager['block_id'] == block_id: + logger.debug("Sending hold for manager: %s", manager['manager']) + self._hold_manager(manager['manager']) + + def submit(self, func: Callable, resource_specification: dict, *args, **kwargs) -> HTEXFuture: + """Submits work to the outgoing_q. + + The outgoing_q is an external process listens on this + queue for new work. This method behaves like a submit call as described here `Python docs: `_ + + Args: + - func (callable) : Callable function + - resource_specification (dict): Dictionary containing relevant info about task that is needed by underlying executors. + - args (list) : List of arbitrary positional arguments. + + Kwargs: + - kwargs (dict) : A dictionary of arbitrary keyword args for func. + + Returns: + Future + """ + + self.validate_resource_spec(resource_specification) + + # handle people sending blobs gracefully + if logger.getEffectiveLevel() <= logging.DEBUG: + args_to_print = tuple([ar if len(ar := repr(arg)) < 100 else (ar[:100] + '...') for arg in args]) + logger.debug("Pushing function {} to queue with args {}".format(func, args_to_print)) + + try: + fn_buf = pack_apply_message(func, args, kwargs, buffer_threshold=1 << 20) + except TypeError: + raise SerializationError(func.__name__) + + context = {} + if resource_specification: + context["resource_spec"] = resource_specification + + return self.submit_payload(context, fn_buf) + + def submit_payload(self, context: dict, buffer: bytes) -> HTEXFuture: + """ + Submit specially crafted payloads. + + For use-cases where the ``HighThroughputExecutor`` consumer needs the payload + handled by the worker in a special way. For example, if the function is + serialized differently than Parsl's default approach, or if the task must + be setup more precisely than Parsl's default ``execute_task`` allows. + + An example interaction: + + .. code-block: python + + >>> htex: HighThroughputExecutor # setup prior to this example + >>> ctxt = { + ... "task_executor": { + ... "f": "full.import.path.of.custom_execute_task", + ... "a": ("additional", "arguments"), + ... "k": {"some": "keyword", "args": "here"} + ... } + ... } + >>> fn_buf = custom_serialize(task_func, *task_args, **task_kwargs) + >>> fut = htex.submit_payload(ctxt, fn_buf) + + The custom ``custom_execute_task`` would be dynamically imported, and + invoked as: + + .. code-block: python + + args = ("additional", "arguments") + kwargs = {"some": "keyword", "args": "here"} + result = custom_execute_task(fn_buf, *args, **kwargs) + + Parameters + ---------- + context: + A task-specific context associated with the function buffer. Parsl + currently implements the keys ``task_executor`` and ``resource_spec`` + + buffer: + A serialized function, that will be deserialized and executed by + ``execute_task`` (or custom function, if ``task_executor`` is specified) + + Returns + ------- + An HTEXFuture (a normal Future, with the attribute ``.parsl_executor_task_id`` + set). The future will be set to done when the associated function buffer has + been invoked and completed. + """ + if self.bad_state_is_set: + raise self.executor_exception + + self._task_counter += 1 + task_id = self._task_counter + + fut = HTEXFuture(task_id) + self.tasks[task_id] = fut + + msg = {"task_id": task_id, "context": context, "buffer": buffer} + self.outgoing_q.put(msg) + + return fut + + @property + def workers_per_node(self) -> Union[int, float]: + return self._workers_per_node + + def scale_in(self, blocks: int, max_idletime: Optional[float] = None) -> List[str]: + """Scale in the number of active blocks by specified amount. + + The scale in method here is very rude. It doesn't give the workers + the opportunity to finish current tasks or cleanup. This is tracked + in issue #530 + + Parameters + ---------- + + blocks : int + Number of blocks to terminate and scale_in by + + max_idletime: float + A time to indicate how long a block should be idle to be a + candidate for scaling in. + + If None then blocks will be force scaled in even if they are busy. + + If a float, then only idle blocks will be terminated, which may be less than + the requested number. + + Returns + ------- + List of block IDs scaled in + """ + logger.debug(f"Scale in called, blocks={blocks}") + + @dataclass + class BlockInfo: + tasks: int # sum of tasks in this block + idle: float # shortest idle time of any manager in this block + + # block_info will be populated from two sources: + # the Job Status Poller mutable block list, and the list of blocks + # which have connected to the interchange. + + def new_block_info(): + return BlockInfo(tasks=0, idle=float('inf')) + + block_info: Dict[str, BlockInfo] = defaultdict(new_block_info) + + for block_id, job_status in self._status.items(): + if job_status.state not in TERMINAL_STATES: + block_info[block_id] = new_block_info() + + managers = self.connected_managers() + for manager in managers: + if not manager['active']: + continue + b_id = manager['block_id'] + block_info[b_id].tasks += manager['tasks'] + block_info[b_id].idle = min(block_info[b_id].idle, manager['idle_duration']) + + # The scaling policy is that longest idle blocks should be scaled down + # in preference to least idle (most recently used) blocks. + # Other policies could be implemented here. + + sorted_blocks = sorted(block_info.items(), key=lambda item: (-item[1].idle, item[1].tasks)) + + logger.debug(f"Scale in selecting from {len(sorted_blocks)} blocks") + if max_idletime is None: + block_ids_to_kill = [x[0] for x in sorted_blocks[:blocks]] + else: + block_ids_to_kill = [] + for x in sorted_blocks: + if x[1].idle > max_idletime and x[1].tasks == 0: + block_ids_to_kill.append(x[0]) + if len(block_ids_to_kill) == blocks: + break + + logger.debug("Selected idle block ids to kill: {}".format( + block_ids_to_kill)) + if len(block_ids_to_kill) < blocks: + logger.warning(f"Could not find enough blocks to kill: wanted {blocks} but only selected {len(block_ids_to_kill)}") + + # Hold the block + for block_id in block_ids_to_kill: + self._hold_block(block_id) + + # Now kill via provider + # Potential issue with multiple threads trying to remove the same blocks + to_kill = [self.blocks_to_job_id[bid] for bid in block_ids_to_kill if bid in self.blocks_to_job_id] + + assert self.provider is not None, "htex always has a provider" + r = self.provider.cancel(to_kill) + job_ids = self._filter_scale_in_ids(to_kill, r) + + # to_kill block_ids are fetched from self.blocks_to_job_id + # If a block_id is in self.blocks_to_job_id, it must exist in self.job_ids_to_block + block_ids_killed = [self.job_ids_to_block[jid] for jid in job_ids] + + return block_ids_killed + + def _get_launch_command(self, block_id: str) -> str: + if self.launch_cmd is None: + raise ScalingFailed(self, "No launch command") + launch_cmd = self.launch_cmd.format(block_id=block_id) + return launch_cmd + + def status(self) -> Dict[str, JobStatus]: + job_status = super().status() + connected_blocks = self.connected_blocks() + for job_id in job_status: + job_info = job_status[job_id] + if job_info.terminal and job_id not in connected_blocks and job_info.state != JobState.SCALED_IN: + logger.debug("Rewriting job %s from status %s to MISSING", job_id, job_info) + job_status[job_id].state = JobState.MISSING + if job_status[job_id].message is None: + job_status[job_id].message = ( + "Job is marked as MISSING since the workers failed to register " + "to the executor. Check the stdout/stderr logs in the submit_scripts " + "directory for more debug information" + ) + return job_status + + def shutdown(self, timeout: float = 10.0): + """Shutdown the executor, including the interchange. This does not + shut down any workers directly - workers should be terminated by the + scaling mechanism or by heartbeat timeout. + + Parameters + ---------- + + timeout : float + Amount of time to wait for the Interchange process to terminate before + we forcefully kill it. + """ + if self.interchange_proc is None: + logger.info("HighThroughputExecutor has not started; skipping shutdown") + return + + logger.info("Attempting HighThroughputExecutor shutdown") + + logger.info("Terminating interchange and result queue thread") + self._result_queue_thread_exit.set() + self.interchange_proc.terminate() + try: + self.interchange_proc.wait(timeout=timeout) + except subprocess.TimeoutExpired: + logger.warning("Unable to terminate Interchange process; sending SIGKILL") + self.interchange_proc.kill() + + logger.info("Closing ZMQ pipes") + + # These pipes are used in a thread unsafe manner. If you have traced a + # problem to this block of code, you might consider what is happening + # with other threads that access these. + + # incoming_q is not closed here because it is used by the results queue + # worker which is not shut down at this point. + + if hasattr(self, 'outgoing_q'): + logger.info("Closing outgoing_q") + self.outgoing_q.close() + + if hasattr(self, 'command_client'): + logger.info("Closing command client") + self.command_client.close() + + logger.info("Waiting for result queue thread exit") + if self._result_queue_thread: + self._result_queue_thread.join() + + if self.zmq_monitoring: + self.zmq_monitoring.close() + + super().shutdown() + + logger.info("Finished HighThroughputExecutor shutdown attempt") + + def get_usage_information(self): + return {"mpi": self.enable_mpi_mode} diff --git a/parsl/parsl/executors/high_throughput/interchange.py b/parsl/parsl/executors/high_throughput/interchange.py new file mode 100644 index 00000000..80b9955a --- /dev/null +++ b/parsl/parsl/executors/high_throughput/interchange.py @@ -0,0 +1,607 @@ +#!/usr/bin/env python +import datetime +import logging +import os +import pathlib +import pickle +import platform +import sys +import threading +import time +from typing import Any, Dict, List, Optional, Sequence, Set, Tuple, cast + +import zmq +from sortedcontainers import SortedList + +from parsl import curvezmq +from parsl.addresses import tcp_url +from parsl.app.errors import RemoteExceptionWrapper +from parsl.executors.high_throughput.errors import ManagerLost, VersionMismatch +from parsl.executors.high_throughput.manager_record import ManagerRecord +from parsl.executors.high_throughput.manager_selector import ManagerSelector +from parsl.log_utils import set_file_logger +from parsl.logconfigs.base import LogConfig, oneshot_initialize_logging +from parsl.monitoring.message_type import MessageType +from parsl.monitoring.radios.base import MonitoringRadioSender +from parsl.monitoring.radios.zmq import ZMQRadioSender +from parsl.process_loggers import wrap_with_logs +from parsl.serialize import serialize as serialize_object +from parsl.version import VERSION as PARSL_VERSION + +PKL_HEARTBEAT_CODE = pickle.dumps((2 ** 32) - 1) +PKL_SHUTDOWN_CODE = pickle.dumps((2 ** 32) - 2) + +logger = logging.getLogger("parsl.executors.high_throughput.interchange") + + +class Interchange: + """ Interchange is a task orchestrator for distributed systems. + + 1. Asynchronously queue large volume of tasks (>100K) + 2. Allow for workers to join and leave the union + 3. Detect workers that have failed using heartbeats + """ + def __init__(self, + *, + client_address: str, + interchange_address: Optional[str], + client_ports: Tuple[int, int, int], + worker_port: Optional[int], + worker_port_range: Tuple[int, int], + hub_address: Optional[str], + hub_zmq_port: Optional[int], + heartbeat_threshold: int, + logdir: str, + logging_level: int, + poll_period: int, + cert_dir: Optional[str], + manager_selector: ManagerSelector, + run_id: str, + log_config: Optional[LogConfig], + _check_python_mismatch: bool, + ) -> None: + """ + Parameters + ---------- + client_address : str + The ip address at which the parsl client can be reached. Default: "127.0.0.1" + + interchange_address : Optional str + If specified the interchange will only listen on this address for connections from workers + else, it binds to all addresses. + + client_ports : tuple(int, int, int) + The ports at which the client can be reached + + worker_port : int + The specific port to which workers will connect to the Interchange. + + worker_port_range : tuple(int, int) + The interchange picks ports at random from the range which will be used by workers. + This is overridden when the worker_port option is set. + + hub_address : str + The IP address at which the interchange can send info about managers to when monitoring is enabled. + When None, monitoring is disabled. + + hub_zmq_port : str + The port at which the interchange can send info about managers to when monitoring is enabled. + When None, monitoring is disabled. + + heartbeat_threshold : int + Number of seconds since the last heartbeat after which worker is considered lost. + + logdir : str + Parsl log directory paths. Logs and temp files go here. + + logging_level : int + Logging level as defined in the logging module. + + poll_period : int + The main thread polling period, in milliseconds. + + cert_dir : str | None + Path to the certificate directory. + + _check_python_mismatch : bool + If True, the interchange and worker managers must run the same version of + Python and Parsl. Running different versions can cause inter-process + communication errors, so proceed with caution. + Default: True + """ + self.cert_dir = cert_dir + self.logdir = logdir + os.makedirs(self.logdir, exist_ok=True) + + if log_config: + oneshot_initialize_logging(log_config=log_config, log_dir=pathlib.Path(self.logdir), log_name="interchange") + # discard the returned callback because the interchange doesn't usually do + # a normal shutdown + else: + set_file_logger("{}/interchange.log".format(self.logdir), level=logging_level) + + logger.debug("Initializing Interchange process") + + self.client_address = client_address + self.interchange_address: str = interchange_address or "*" + self.poll_period = poll_period + + logger.info("Attempting connection to client at {} on ports: {},{},{}".format( + client_address, client_ports[0], client_ports[1], client_ports[2])) + self.zmq_context = curvezmq.ServerContext(self.cert_dir) + self.task_incoming = self.zmq_context.socket(zmq.DEALER) + self.task_incoming.set_hwm(0) + self.task_incoming.connect(tcp_url(client_address, client_ports[0])) + self.results_outgoing = self.zmq_context.socket(zmq.DEALER) + self.results_outgoing.set_hwm(0) + self.results_outgoing.connect(tcp_url(client_address, client_ports[1])) + + self.command_channel = self.zmq_context.socket(zmq.REP) + self.command_channel.connect(tcp_url(client_address, client_ports[2])) + logger.info("Connected to client") + + self.run_id = run_id + self._check_python_mismatch = _check_python_mismatch + + self.hub_address = hub_address + self.hub_zmq_port = hub_zmq_port + + self.pending_task_queue: SortedList[Any] = SortedList(key=lambda tup: (tup[0], tup[1])) + + # count of tasks that have been sent out to worker pools + self.count = 0 + + self.manager_sock = self.zmq_context.socket(zmq.ROUTER) + self.manager_sock.set_hwm(0) + + if worker_port: + task_addy = tcp_url(self.interchange_address, worker_port) + self.manager_sock.bind(task_addy) + + else: + worker_port = self.manager_sock.bind_to_random_port( + tcp_url(self.interchange_address), + min_port=worker_port_range[0], + max_port=worker_port_range[1], + max_tries=100, + ) + self.worker_port = worker_port + + logger.info(f"Bound to port {worker_port} for incoming worker connections") + + self._ready_managers: Dict[bytes, ManagerRecord] = {} + self._logged_manager_count_token: object = None + self.connected_block_history: List[str] = [] + + self.heartbeat_threshold = heartbeat_threshold + + self.manager_selector = manager_selector + + self.current_platform = {'parsl_v': PARSL_VERSION, + 'python_v': "{}.{}.{}".format(sys.version_info.major, + sys.version_info.minor, + sys.version_info.micro), + 'os': platform.system(), + 'hostname': platform.node(), + 'dir': os.getcwd()} + + logger.info("Platform info: {}".format(self.current_platform)) + + def get_tasks(self, count: int) -> Sequence[dict]: + """ Obtains a batch of tasks from the internal pending_task_queue + + Parameters + ---------- + count: int + Count of tasks to get from the queue + + Returns + ------- + List of upto count tasks. May return fewer than count down to an empty list + eg. [{'task_id':, 'buffer':} ... ] + """ + tasks = [] + try: + for _ in range(count): + *_, task = self.pending_task_queue.pop() + tasks.append(task) + except IndexError: + pass + + return tasks + + def _send_monitoring_info(self, monitoring_radio: Optional[MonitoringRadioSender], manager: ManagerRecord) -> None: + if monitoring_radio: + logger.info("Sending message {} to MonitoringHub".format(manager)) + + d: Dict = cast(Dict, manager.copy()) + d['timestamp'] = datetime.datetime.now() + d['last_heartbeat'] = datetime.datetime.fromtimestamp(d['last_heartbeat']) + d['run_id'] = self.run_id + + monitoring_radio.send((MessageType.NODE_INFO, d)) + + def process_command(self, monitoring_radio: Optional[MonitoringRadioSender]) -> None: + """ Command server to run async command to the interchange + """ + + reply: Any # the type of reply depends on the command_req received (aka this needs dependent types...) + + if self.socks.get(self.command_channel) == zmq.POLLIN: + logger.debug("entering command_server section") + + command_req = self.command_channel.recv_pyobj() + logger.debug("Received command request: {}".format(command_req)) + if command_req == "CONNECTED_BLOCKS": + reply = self.connected_block_history + + elif command_req == "MANAGERS": + reply = [] + now = time.time() + for manager_id, m in self._ready_managers.items(): + idle_duration = now - (m['idle_since'] or now) + resp = { + 'manager': manager_id.decode('utf-8'), + 'block_id': m['block_id'], + 'worker_count': m['worker_count'], + 'tasks': len(m['tasks']), + 'idle_duration': idle_duration, + 'active': m['active'], + 'parsl_version': m['parsl_version'], + 'python_version': m['python_version'], + 'draining': m['draining'] + } + reply.append(resp) + + elif command_req == "MANAGERS_PACKAGES": + reply = {} + for manager_id, m in self._ready_managers.items(): + manager_id_str = manager_id.decode('utf-8') + reply[manager_id_str] = m["packages"] + + elif isinstance(command_req, list) and len(command_req) == 2 and command_req[0] == "HOLD_MANAGER": + manager_id = command_req[1].encode('utf-8') + logger.info("Received HOLD_MANAGER for {!r}".format(manager_id)) + if manager_id in self._ready_managers: + m = self._ready_managers[manager_id] + m['active'] = False + self._send_monitoring_info(monitoring_radio, m) + else: + logger.warning("Worker to hold was not in ready managers list") + + reply = None + + elif command_req == "WORKER_BINDS": + reply = self.worker_port + + else: + logger.error(f"Received unknown command: {command_req}") + reply = None + + logger.debug("Reply: {}".format(reply)) + self.command_channel.send_pyobj(reply) + + @wrap_with_logs + def start(self) -> None: + """ Start the interchange + """ + + logger.info("Starting main interchange method") + + if self.hub_address is not None and self.hub_zmq_port is not None: + logger.debug("Creating monitoring radio to %s:%s", self.hub_address, self.hub_zmq_port) + monitoring_radio = ZMQRadioSender(self.hub_address, self.hub_zmq_port, self.cert_dir) + logger.debug("Created monitoring radio") + else: + monitoring_radio = None + + poll_period = self.poll_period + + start = time.time() + + kill_event = threading.Event() + + poller = zmq.Poller() + poller.register(self.manager_sock, zmq.POLLIN) + poller.register(self.task_incoming, zmq.POLLIN) + poller.register(self.command_channel, zmq.POLLIN) + + # These are managers which we should examine in an iteration + # for scheduling a job (or maybe any other attention?). + # Anything altering the state of the manager should add it + # onto this list. + interesting_managers: Set[bytes] = set() + + while not kill_event.is_set(): + self.socks = dict(poller.poll(timeout=poll_period)) + + self.process_command(monitoring_radio) + self.process_task_incoming() + self.process_manager_socket_message(interesting_managers, monitoring_radio, kill_event) + self.expire_bad_managers(interesting_managers, monitoring_radio) + self.expire_drained_managers(interesting_managers, monitoring_radio) + self.log_manager_counts(interesting_managers) + self.process_tasks_to_send(interesting_managers, monitoring_radio) + + self.zmq_context.destroy() + delta = time.time() - start + logger.info(f"Processed {self.count} tasks in {delta} seconds") + logger.warning("Exiting") + + def process_task_incoming(self) -> None: + """Process incoming task message(s). + """ + + if self.socks.get(self.task_incoming) == zmq.POLLIN: + logger.debug("start task_incoming section") + msg = self.task_incoming.recv_pyobj() + + # Process priority, higher number = lower priority + task_id = msg['task_id'] + resource_spec = msg['context'].get('resource_spec', {}) + priority = resource_spec.get('priority', float('inf')) + queue_entry = (-priority, -task_id, msg) + + logger.debug("Putting task %s onto pending_task_queue", task_id) + + self.pending_task_queue.add(queue_entry) + logger.debug("Put task %s onto pending_task_queue", task_id) + + def process_manager_socket_message( + self, + interesting_managers: Set[bytes], + monitoring_radio: Optional[MonitoringRadioSender], + kill_event: threading.Event, + ) -> None: + """Process one message from manager on the manager_sock channel.""" + if not self.socks.get(self.manager_sock) == zmq.POLLIN: + return + + logger.debug('starting worker message section') + msg_parts = self.manager_sock.recv_multipart() + try: + manager_id, msg_b = msg_parts + msg = pickle.loads(msg_b) + mtype = msg['type'] + except Exception as e: + logger.warning( + 'Failed to read manager message; ignoring message' + f' (Exception: [{type(e).__name__}] {e})' + ) + logger.debug('Raw message bytes:\n %r\n', msg_parts, exc_info=e) + return + + logger.debug( + 'Processing message type %r from manager %r', mtype, manager_id + ) + + if mtype == 'connection_probe': + self.manager_sock.send_multipart([manager_id, b'']) + return + + elif mtype == 'registration': + ix_minor_py = self.current_platform['python_v'].rsplit('.', 1)[0] + ix_parsl_v = self.current_platform['parsl_v'] + mgr_minor_py = msg['python_v'].rsplit('.', 1)[0] + mgr_parsl_v = msg['parsl_v'] + + # legacy draining handling. remove in ~6 months (ca Apr 2027) + drain_time = msg.get('drain_time', float('inf')) + + new_rec = ManagerRecord( + block_id=None, + start_time=msg['start_time'], + tasks=[], + worker_count=0, + max_capacity=0, + active=True, + draining=False, + drain_time=drain_time, + last_heartbeat=time.time(), + idle_since=time.time(), + parsl_version=mgr_parsl_v, + python_version=msg['python_v'], + ) + + # m is a ManagerRecord, but meta is a dict[Any,Any] and so can + # contain arbitrary fields beyond those in ManagerRecord (and + # indeed does - for example, python_v) which are then ignored + # later. + new_rec.update(msg) + + logger.info(f'Registration info for manager {manager_id!r}: {msg}') + self._send_monitoring_info(monitoring_radio, new_rec) + + python_mismatch: bool = ix_minor_py != mgr_minor_py + parsl_mismatch: bool = ix_parsl_v != mgr_parsl_v + if self._check_python_mismatch and (parsl_mismatch or python_mismatch): + kill_event.set() + vm_exc = VersionMismatch( + f"py.v={ix_minor_py} parsl.v={ix_parsl_v}", + f"py.v={mgr_minor_py} parsl.v={mgr_parsl_v}", + ) + result_package = { + 'type': 'result', + 'task_id': -1, + 'exception': serialize_object(vm_exc), + } + pkl_package = pickle.dumps(result_package) + self.results_outgoing.send(pkl_package) + logger.error( + 'Manager has incompatible version info with the interchange;' + ' sending failure reports and shutting down:' + f'\n Interchange: {vm_exc.interchange_version}' + f'\n Manager: {vm_exc.manager_version}' + ) + + else: + # We really should update the associated data structure; but not + # at this time. *kicks can down the road* + assert new_rec['block_id'] is not None, 'Verified externally' + + # set up entry only if we accept the registration + self._ready_managers[manager_id] = new_rec + self.connected_block_history.append(new_rec['block_id']) + + interesting_managers.add(manager_id) + + logger.info( + f"Registered manager {manager_id!r} (py{mgr_minor_py}," + f" {mgr_parsl_v}) and added to ready queue" + ) + logger.debug("Manager %r -> %s", manager_id, new_rec) + + return + + if not (m := self._ready_managers.get(manager_id)): + logger.warning(f"Ignoring message from unknown manager: {manager_id!r}") + return + + if mtype == 'result': + # process this for task ID and forward to executor + tid = msg['task_id'] + logger.debug("Removing task %s from manager", tid) + try: + m['tasks'].remove(tid) + except Exception: + logger.exception( + 'Ignoring exception removing task_id %s from manager' + ' task list %s', + tid, + m['tasks'] + ) + return + + logger.debug('Sending result on results_outgoing') + self.results_outgoing.send(msg_b) + logger.debug('Sent result on results_outgoing') + + # Result received, so manager now has idle capacity + interesting_managers.add(manager_id) + + if len(m['tasks']) == 0 and m['idle_since'] is None: + m['idle_since'] = time.time() + + self._send_monitoring_info(monitoring_radio, m) + + elif mtype == 'monitoring': + # the monitoring code makes the assumption that no + # monitoring messages will be received if monitoring + # is not configured, and that monitoring_radio will only + # be None when monitoring is not configurated. + assert monitoring_radio is not None + + monitoring_radio.send(msg['payload']) + + elif mtype == 'heartbeat': + m['last_heartbeat'] = time.time() + self.manager_sock.send_multipart([manager_id, PKL_HEARTBEAT_CODE]) + + # legacy draining handling. remove in ~6 months (ca Apr 2027) + elif mtype == 'drain': + m['draining'] = True + + else: + logger.error(f"Unexpected message type received from manager: {mtype}") + + logger.debug("leaving worker message section") + + def expire_drained_managers(self, interesting_managers: Set[bytes], monitoring_radio: Optional[MonitoringRadioSender]) -> None: + now = time.time() + for manager_id, m in list(self._ready_managers.items()): + if not m['draining'] and now >= m['drain_time']: + logger.info(f"Manager {manager_id!r} has reached its drain time - draining") + m['draining'] = True + + if m['draining'] and len(m['tasks']) == 0: + logger.info(f"Manager {manager_id!r} is drained - telling manager to shutdown") + self.manager_sock.send_multipart([manager_id, PKL_SHUTDOWN_CODE]) + interesting_managers.discard(manager_id) + self._ready_managers.pop(manager_id) + + m['active'] = False + self._send_monitoring_info(monitoring_radio, m) + + def log_manager_counts(self, interesting_managers: Set[bytes]) -> None: + count_interesting = len(interesting_managers) + count_ready = len(self._ready_managers) + + new_logged_manager_count_token = (count_interesting, count_ready) + + if self._logged_manager_count_token != new_logged_manager_count_token: + + logger.debug( + "Managers count (interesting/total): %d/%d", + count_interesting, + count_ready + ) + self._logged_manager_count_token = new_logged_manager_count_token + + def process_tasks_to_send(self, interesting_managers: Set[bytes], monitoring_radio: Optional[MonitoringRadioSender]) -> None: + # Check if there are tasks that could be sent to managers + + if interesting_managers and self.pending_task_queue: + shuffled_managers = self.manager_selector.sort_managers(self._ready_managers, interesting_managers) + + while shuffled_managers and self.pending_task_queue: # cf. the if statement above... + manager_id = shuffled_managers.pop() + m = self._ready_managers[manager_id] + tasks_inflight = len(m['tasks']) + real_capacity = m['max_capacity'] - tasks_inflight + + if real_capacity and m["active"] and not m["draining"]: + tasks = self.get_tasks(real_capacity) + if tasks: + self.manager_sock.send_multipart([manager_id, pickle.dumps(tasks)]) + task_count = len(tasks) + self.count += task_count + tids = [t['task_id'] for t in tasks] + m['tasks'].extend(tids) + m['idle_since'] = None + logger.debug("Sent tasks: %s to manager %r", tids, manager_id) + # recompute real_capacity after sending tasks + real_capacity -= task_count + if real_capacity > 0: + logger.debug("Manager %r has free capacity %s", manager_id, real_capacity) + # ... so keep it in the interesting_managers list + else: + logger.debug("Manager %r is now saturated", manager_id) + interesting_managers.remove(manager_id) + self._send_monitoring_info(monitoring_radio, m) + else: + interesting_managers.remove(manager_id) + logger.debug("leaving _ready_managers section, with %s managers still interesting", len(interesting_managers)) + + def expire_bad_managers(self, interesting_managers: Set[bytes], monitoring_radio: Optional[MonitoringRadioSender]) -> None: + bad_managers = [(manager_id, m) for (manager_id, m) in self._ready_managers.items() if + time.time() - m['last_heartbeat'] > self.heartbeat_threshold] + for (manager_id, m) in bad_managers: + logger.debug("Last: {} Current: {}".format(m['last_heartbeat'], time.time())) + logger.warning(f"Too many heartbeats missed for manager {manager_id!r} - removing manager") + if m['active']: + m['active'] = False + self._send_monitoring_info(monitoring_radio, m) + + logger.warning(f"Cancelling htex tasks {m['tasks']} on removed manager") + for tid in m['tasks']: + try: + raise ManagerLost(manager_id, m['hostname']) + except Exception: + result_package = {'type': 'result', 'task_id': tid, 'exception': serialize_object(RemoteExceptionWrapper(*sys.exc_info()))} + pkl_package = pickle.dumps(result_package) + self.results_outgoing.send(pkl_package) + logger.warning("Sent failure reports, unregistering manager") + self._ready_managers.pop(manager_id, 'None') + if manager_id in interesting_managers: + interesting_managers.remove(manager_id) + + +if __name__ == "__main__": + from parsl.utils import setproctitle + + setproctitle("parsl: HTEX interchange") + + config = pickle.load(sys.stdin.buffer) + + ic = Interchange(**config) + ic.start() diff --git a/parsl/parsl/executors/high_throughput/manager_record.py b/parsl/parsl/executors/high_throughput/manager_record.py new file mode 100644 index 00000000..8f16716a --- /dev/null +++ b/parsl/parsl/executors/high_throughput/manager_record.py @@ -0,0 +1,22 @@ +from datetime import datetime +from typing import Any, Dict, List, Optional + +from typing_extensions import TypedDict + + +class ManagerRecord(TypedDict, total=False): + block_id: Optional[str] + start_time: float + tasks: List[Any] + worker_count: int + max_capacity: int + active: bool + draining: bool + drain_time: float + hostname: str + last_heartbeat: float + idle_since: Optional[float] + timestamp: datetime + parsl_version: str + python_version: str + packages: Dict[str, str] diff --git a/parsl/parsl/executors/high_throughput/manager_selector.py b/parsl/parsl/executors/high_throughput/manager_selector.py new file mode 100644 index 00000000..25a9c49e --- /dev/null +++ b/parsl/parsl/executors/high_throughput/manager_selector.py @@ -0,0 +1,55 @@ +import random +from abc import ABCMeta, abstractmethod +from typing import Dict, List, Set + +from parsl.executors.high_throughput.manager_record import ManagerRecord + + +class ManagerSelector(metaclass=ABCMeta): + + @abstractmethod + def sort_managers(self, ready_managers: Dict[bytes, ManagerRecord], manager_list: Set[bytes]) -> List[bytes]: + """ Sort a given list of managers. + + Any operations pertaining to the sorting and rearrangement of the + interesting_managers Set should be performed here. + """ + pass + + +class RandomManagerSelector(ManagerSelector): + + """Returns a shuffled list of interesting_managers + + By default this strategy is used by the interchange. Works well + in distributing workloads equally across all availble compute + resources. The random workload strategy is not effective in + conjunction with elastic scaling behavior as the even task + distribution does not allow the scaling down of blocks, leading + to wasted resource consumption. + """ + + def sort_managers(self, ready_managers: Dict[bytes, ManagerRecord], manager_list: Set[bytes]) -> List[bytes]: + c_manager_list = list(manager_list) + random.shuffle(c_manager_list) + return c_manager_list + + +class BlockIdManagerSelector(ManagerSelector): + + """Returns an interesting_managers list sorted by block ID + + Observations: + 1. BlockID manager selector helps with workloads that see a varying + amount of tasks over time. New blocks are prioritized with the + blockID manager selector, when used with 'htex_auto_scaling', results + in compute cost savings. + + 2. Doesn't really work with bag-of-tasks workloads. When all the tasks + are put into the queue upfront, all blocks operate at near full + utilization for the majority of the workload, which task goes where + doesn't really matter. + """ + + def sort_managers(self, ready_managers: Dict[bytes, ManagerRecord], manager_list: Set[bytes]) -> List[bytes]: + return sorted(manager_list, key=lambda x: (ready_managers[x]['block_id'] is not None, ready_managers[x]['block_id'])) diff --git a/parsl/parsl/executors/high_throughput/monitoring_info.py b/parsl/parsl/executors/high_throughput/monitoring_info.py new file mode 100644 index 00000000..f39f4693 --- /dev/null +++ b/parsl/parsl/executors/high_throughput/monitoring_info.py @@ -0,0 +1,9 @@ +# this is a global that will be worker-specific +# and can be set to the result queue which can +# then be acquired by any other code running in +# a worker context - specifically the monitoring +# wrapper code. +from queue import Queue +from typing import Optional + +result_queue: Optional[Queue] = None diff --git a/parsl/parsl/executors/high_throughput/mpi_executor.py b/parsl/parsl/executors/high_throughput/mpi_executor.py new file mode 100644 index 00000000..de1aeb8e --- /dev/null +++ b/parsl/parsl/executors/high_throughput/mpi_executor.py @@ -0,0 +1,125 @@ +"""A simplified interface for HTEx when running in MPI mode""" +from typing import Callable, Dict, List, Optional, Tuple, Union + +import typeguard + +from parsl.data_provider.staging import Staging +from parsl.executors.high_throughput.executor import ( + GENERAL_HTEX_PARAM_DOCS, + HighThroughputExecutor, +) +from parsl.executors.high_throughput.mpi_prefix_composer import ( + VALID_LAUNCHERS, + validate_resource_spec, +) +from parsl.executors.status_handling import BlockProviderExecutor +from parsl.jobs.states import JobStatus +from parsl.launchers import SimpleLauncher +from parsl.monitoring.radios.base import RadioConfig +from parsl.providers.base import ExecutionProvider + + +class MPIExecutor(HighThroughputExecutor): + __doc__ = f"""A version of :class:`~parsl.HighThroughputExecutor` tuned for executing multi-node (e.g., MPI) tasks. + + The Provider _must_ use the :class:`~parsl.launchers.SimpleLauncher`, + which places a single pool of workers on the first node of a block. + Each worker can then make system calls which use an MPI launcher (e.g., ``mpirun``, ``srun``) + to spawn multi-node tasks. + + Specify the maximum number of multi-node tasks to run at once using ``max_workers_per_block``. + The value should be less than or equal to the ``nodes_per_block`` in the Provider. + + Parameters + ---------- + max_workers_per_block: int + Maximum number of MPI applications to run at once per block + + mpi_launcher: str + Select one from the list of supported MPI launchers: + ("srun", "aprun", "mpiexec"). + default: "mpiexec" + + {GENERAL_HTEX_PARAM_DOCS} + """ + + @typeguard.typechecked + def __init__(self, + label: str = 'MPIExecutor', + provider: Optional[ExecutionProvider] = None, + launch_cmd: Optional[str] = None, + interchange_launch_cmd: Optional[str] = None, + address: Optional[str] = None, + loopback_address: str = "127.0.0.1", + worker_port: Optional[int] = None, + worker_port_range: Optional[Tuple[int, int]] = (54000, 55000), + interchange_port_range: Optional[Tuple[int, int]] = (55000, 56000), + storage_access: Optional[List[Staging]] = None, + working_dir: Optional[str] = None, + worker_debug: bool = False, + max_workers_per_block: int = 1, + prefetch_capacity: int = 0, + heartbeat_threshold: int = 120, + heartbeat_period: int = 30, + drain_period: Optional[int] = None, + poll_period: int = 10, + address_probe_timeout: Optional[int] = None, + worker_logdir_root: Optional[str] = None, + mpi_launcher: str = "mpiexec", + block_error_handler: Union[bool, Callable[[BlockProviderExecutor, Dict[str, JobStatus]], None]] = True, + encrypted: bool = False, + remote_monitoring_radio: Optional[RadioConfig] = None): + super().__init__( + # Hard-coded settings + cores_per_worker=1e-9, # Ensures there will be at least an absurd number of workers + max_workers_per_node=max_workers_per_block, + + # Everything else + label=label, + provider=provider, + launch_cmd=launch_cmd, + interchange_launch_cmd=interchange_launch_cmd, + address=address, + loopback_address=loopback_address, + worker_port=worker_port, + worker_port_range=worker_port_range, + interchange_port_range=interchange_port_range, + storage_access=storage_access, + working_dir=working_dir, + worker_debug=worker_debug, + prefetch_capacity=prefetch_capacity, + heartbeat_threshold=heartbeat_threshold, + heartbeat_period=heartbeat_period, + drain_period=drain_period, + poll_period=poll_period, + address_probe_timeout=address_probe_timeout, + worker_logdir_root=worker_logdir_root, + block_error_handler=block_error_handler, + encrypted=encrypted, + remote_monitoring_radio=remote_monitoring_radio + ) + self.enable_mpi_mode = True + self.mpi_launcher = mpi_launcher + + self.max_workers_per_block = max_workers_per_block + + assert isinstance(self.provider, ExecutionProvider), \ + "HTEX initialization should always make self.provider a provider" + + if not hasattr(self.provider, 'launcher') or not isinstance(self.provider.launcher, SimpleLauncher): + raise TypeError("mpi_mode requires the provider to be configured to use a SimpleLauncher") + + if mpi_launcher not in VALID_LAUNCHERS: + raise ValueError(f"mpi_launcher set to:{mpi_launcher} must be set to one of {VALID_LAUNCHERS}") + + self.mpi_launcher = mpi_launcher + + def validate_resource_spec(self, resource_specification: dict): + return validate_resource_spec(resource_specification) + + def monitor_resources(self): + """Resource monitoring does not make sense when using the + MPIExecutor, as the process tree launched for each task is spread + across multiple OS images/worker nodes. + """ + return False diff --git a/parsl/parsl/executors/high_throughput/mpi_prefix_composer.py b/parsl/parsl/executors/high_throughput/mpi_prefix_composer.py new file mode 100644 index 00000000..689fbb77 --- /dev/null +++ b/parsl/parsl/executors/high_throughput/mpi_prefix_composer.py @@ -0,0 +1,136 @@ +import logging +from typing import Dict, List, Tuple + +from parsl.executors.errors import InvalidResourceSpecification + +logger = logging.getLogger(__name__) + +VALID_LAUNCHERS = ('srun', + 'aprun', + 'mpiexec') + + +def validate_resource_spec(resource_spec: Dict[str, str]): + """Basic validation of keys in the resource_spec + + Raises: InvalidResourceSpecification if the resource_spec + is invalid (e.g, contains invalid keys) + """ + user_keys = set(resource_spec.keys()) + + # empty resource_spec when mpi_mode is set causes parsl to hang + # ref issue #3427 + if len(user_keys) == 0: + raise InvalidResourceSpecification(user_keys, + 'MPI mode requires optional parsl_resource_specification keyword argument to be configured') + + legal_keys = set(("ranks_per_node", + "num_nodes", + "num_ranks", + "launcher_options", + )) + invalid_keys = user_keys - legal_keys + if invalid_keys: + raise InvalidResourceSpecification(invalid_keys) + if "num_nodes" in resource_spec: + if not resource_spec.get("num_ranks") and resource_spec.get("ranks_per_node"): + resource_spec["num_ranks"] = str(int(resource_spec["num_nodes"]) * int(resource_spec["ranks_per_node"])) + if not resource_spec.get("ranks_per_node") and resource_spec.get("num_ranks"): + resource_spec["ranks_per_node"] = str(int(resource_spec["num_ranks"]) / int(resource_spec["num_nodes"])) + return + + +def compose_mpiexec_launch_cmd( + resource_spec: Dict, node_hostnames: List[str] +) -> Tuple[str, str]: + """Compose mpiexec launch command prefix""" + + node_str = ",".join(node_hostnames) + args = [ + "mpiexec", + "-n", + resource_spec.get("num_ranks"), + "-ppn", + resource_spec.get("ranks_per_node"), + "-hosts", + node_str, + resource_spec.get("launcher_options", ""), + ] + prefix = " ".join(str(arg) for arg in args) + return "PARSL_MPIEXEC_PREFIX", prefix + + +def compose_srun_launch_cmd( + resource_spec: Dict, node_hostnames: List[str] +) -> Tuple[str, str]: + """Compose srun launch command prefix""" + + num_nodes = str(len(node_hostnames)) + args = [ + "srun", + "--ntasks", + resource_spec.get("num_ranks"), + "--ntasks-per-node", + resource_spec.get("ranks_per_node"), + "--nodelist", + ",".join(node_hostnames), + "--nodes", + num_nodes, + resource_spec.get("launcher_options", ""), + ] + + prefix = " ".join(str(arg) for arg in args) + return "PARSL_SRUN_PREFIX", prefix + + +def compose_aprun_launch_cmd( + resource_spec: Dict, node_hostnames: List[str] +) -> Tuple[str, str]: + """Compose aprun launch command prefix""" + + node_str = ",".join(node_hostnames) + args = [ + "aprun", + "-n", + resource_spec.get("num_ranks"), + "-N", + resource_spec.get("ranks_per_node"), + "-node-list", + node_str, + resource_spec.get("launcher_options", ""), + ] + prefix = " ".join(str(arg) for arg in args) + return "PARSL_APRUN_PREFIX", prefix + + +def compose_all( + mpi_launcher: str, resource_spec: Dict, node_hostnames: List[str] +) -> Dict[str, str]: + """Compose all launch command prefixes and set the default""" + + all_prefixes = {} + composers = [ + compose_aprun_launch_cmd, + compose_srun_launch_cmd, + compose_mpiexec_launch_cmd, + ] + for composer in composers: + try: + key, prefix = composer(resource_spec, node_hostnames) + all_prefixes[key] = prefix + except Exception: + logging.exception( + f"Failed to compose launch prefix with {composer} from {resource_spec}" + ) + pass + + if mpi_launcher == "srun": + all_prefixes["PARSL_MPI_PREFIX"] = all_prefixes["PARSL_SRUN_PREFIX"] + elif mpi_launcher == "aprun": + all_prefixes["PARSL_MPI_PREFIX"] = all_prefixes["PARSL_APRUN_PREFIX"] + elif mpi_launcher == "mpiexec": + all_prefixes["PARSL_MPI_PREFIX"] = all_prefixes["PARSL_MPIEXEC_PREFIX"] + else: + raise RuntimeError(f"Unknown mpi_launcher:{mpi_launcher}") + + return all_prefixes diff --git a/parsl/parsl/executors/high_throughput/mpi_resource_management.py b/parsl/parsl/executors/high_throughput/mpi_resource_management.py new file mode 100644 index 00000000..0d14b2d1 --- /dev/null +++ b/parsl/parsl/executors/high_throughput/mpi_resource_management.py @@ -0,0 +1,211 @@ +import logging +import multiprocessing +import os +import pickle +import queue +import subprocess +from dataclasses import dataclass, field +from enum import Enum +from typing import Dict, List, Optional + +from parsl.multiprocessing import SpawnContext + +logger = logging.getLogger(__name__) + + +class Scheduler(Enum): + Unknown = 0 + Slurm = 1 + PBS = 2 + + +def get_slurm_hosts_list() -> List[str]: + """Get list of slurm hosts from scontrol""" + cmd = "scontrol show hostname $SLURM_NODELIST" + b_output = subprocess.check_output( + cmd, stderr=subprocess.STDOUT, shell=True + ) # bytes + output = b_output.decode().strip().split() + return output + + +def get_pbs_hosts_list() -> List[str]: + """Get list of PBS hosts from envvar: PBS_NODEFILE""" + nodefile_name = os.environ["PBS_NODEFILE"] + with open(nodefile_name) as f: + return [line.strip() for line in f.readlines()] + + +def get_nodes_in_batchjob(scheduler: Scheduler) -> List[str]: + """Get nodelist from all supported schedulers""" + nodelist = [] + if scheduler == Scheduler.Slurm: + nodelist = get_slurm_hosts_list() + elif scheduler == Scheduler.PBS: + nodelist = get_pbs_hosts_list() + else: + raise RuntimeError(f"mpi_mode does not support scheduler:{scheduler}") + return nodelist + + +def identify_scheduler() -> Scheduler: + """Use envvars to determine batch scheduler""" + if os.environ.get("SLURM_NODELIST"): + return Scheduler.Slurm + elif os.environ.get("PBS_NODEFILE"): + return Scheduler.PBS + else: + return Scheduler.Unknown + + +class MPINodesUnavailable(Exception): + """Raised if there are no free nodes available for an MPI request""" + + def __init__(self, requested: int, available: int): + self.requested = requested + self.available = available + + def __str__(self): + return f"MPINodesUnavailable(requested={self.requested} available={self.available})" + + +@dataclass(order=True) +class PrioritizedTask: + # Comparing dict will fail since they are unhashable + # This dataclass limits comparison to the priority field + priority: int + task: Dict = field(compare=False) + + +class TaskScheduler: + """Default TaskScheduler that does no taskscheduling + + This class simply acts as an abstraction over the task_q and result_q + that can be extended to implement more complex task scheduling logic + """ + def __init__( + self, + pending_task_q: multiprocessing.Queue, + pending_result_q: multiprocessing.Queue, + ): + self.pending_task_q = pending_task_q + self.pending_result_q = pending_result_q + + def put_task(self, task) -> None: + return self.pending_task_q.put(task) + + def get_result(self, block: bool = True, timeout: Optional[float] = None): + return self.pending_result_q.get(block, timeout) + + +class MPITaskScheduler(TaskScheduler): + """Extends TaskScheduler to schedule MPI functions over provisioned nodes + The MPITaskScheduler runs on a Manager on the lead node of a batch job, as + such it is expected to control task placement over this single batch job. + + The MPITaskScheduler adds the following functionality: + 1) Determine list of nodes attached to current batch job + 2) put_task for execution onto workers: + a) if resources are available attach resource list + b) if unavailable place tasks into backlog + 3) get_result will fetch a result and relinquish nodes, + and attempt to schedule tasks in backlog if any. + """ + def __init__( + self, + pending_task_q: multiprocessing.Queue, + pending_result_q: multiprocessing.Queue, + ): + super().__init__(pending_task_q, pending_result_q) + self.scheduler = identify_scheduler() + # PriorityQueue is threadsafe + self._backlog_queue: queue.PriorityQueue[PrioritizedTask] = queue.PriorityQueue() + self._map_tasks_to_nodes: Dict[str, List[str]] = {} + self.available_nodes = get_nodes_in_batchjob(self.scheduler) + self._free_node_counter = SpawnContext.Value("i", len(self.available_nodes)) + # mp.Value has issues with mypy + # issue https://github.com/python/typeshed/issues/8799 + # from mypy 0.981 onwards + self.nodes_q: queue.Queue = queue.Queue() + for node in self.available_nodes: + self.nodes_q.put(node) + + logger.info( + f"Starting MPITaskScheduler with {len(self.available_nodes)}" + ) + + def _get_nodes(self, num_nodes: int) -> List[str]: + """Thread safe method to acquire num_nodes from free resources + + Raises: MPINodesUnavailable if there aren't enough resources + Returns: List of nodenames:str + """ + logger.debug( + f"Requesting : {num_nodes=} we have {self._free_node_counter}" + ) + acquired_nodes = [] + with self._free_node_counter.get_lock(): + if num_nodes <= self._free_node_counter.value: + self._free_node_counter.value -= num_nodes + else: + raise MPINodesUnavailable( + requested=num_nodes, available=self._free_node_counter.value + ) + + for i in range(num_nodes): + node = self.nodes_q.get() + acquired_nodes.append(node) + return acquired_nodes + + def _return_nodes(self, nodes: List[str]) -> None: + """Threadsafe method to return a list of nodes""" + for node in nodes: + self.nodes_q.put(node) + with self._free_node_counter.get_lock(): + self._free_node_counter.value += len(nodes) + + def put_task(self, task_package: dict): + """Schedule task if resources are available otherwise backlog the task""" + resource_spec = task_package.get("context", {}).get("resource_spec", {}) + + if nodes_needed := resource_spec.get("num_nodes"): + tid = task_package["task_id"] + try: + allocated_nodes = self._get_nodes(nodes_needed) + except MPINodesUnavailable: + logger.info(f"Not enough resources, placing task {tid} into backlog") + self._backlog_queue.put(PrioritizedTask(nodes_needed, task_package)) + return + else: + resource_spec["MPI_NODELIST"] = ",".join(allocated_nodes) + self._map_tasks_to_nodes[tid] = allocated_nodes + + self.pending_task_q.put(task_package) + + def _schedule_backlog_tasks(self): + """Attempt to schedule backlogged tasks""" + try: + prioritized_task = self._backlog_queue.get(block=False) + self.put_task(prioritized_task.task) + except queue.Empty: + return + else: + # Keep attempting to schedule tasks till we are out of resources + self._schedule_backlog_tasks() + + def get_result(self, block: bool = True, timeout: Optional[float] = None): + """Return result and relinquish provisioned nodes""" + result_pkl = self.pending_result_q.get(block, timeout) + if result_pkl is None: + return None + result_dict = pickle.loads(result_pkl) + # TODO (wardlt): If the task did not request nodes, it won't be in `self._map_tasks_to_nodes`. + # Causes Parsl to hang. See Issue #3427 + if result_dict["type"] == "result": + task_id = result_dict["task_id"] + assert task_id in self._map_tasks_to_nodes, "You are about to experience issue #3427" + nodes_to_reallocate = self._map_tasks_to_nodes[task_id] + self._return_nodes(nodes_to_reallocate) + self._schedule_backlog_tasks() + + return result_pkl diff --git a/parsl/parsl/executors/high_throughput/probe.py b/parsl/parsl/executors/high_throughput/probe.py new file mode 100644 index 00000000..52582b7d --- /dev/null +++ b/parsl/parsl/executors/high_throughput/probe.py @@ -0,0 +1,65 @@ +import logging +import pickle +from contextlib import ExitStack +from typing import Optional + +import zmq + +from parsl import curvezmq + +logger = logging.getLogger(__name__) + + +def probe_addresses( + zmq_context: curvezmq.ClientContext, + addresses: set[str], + timeout_ms: int = 120_000, + identity: Optional[bytes] = None, +): + """ + Given a set of addresses, return the first proven valid address. + + This function will connect to each address in ``addresses`` and attempt to send a + CONNECTION_PROBE packet. Returns the first address that receives a response. + + If no address receives a response within the ``timeout_ms`` (specified in + milliseconds), then raise ``ConnectionError``. + + :param zmq_context: A ZMQ Context; the call-site may provide an encrypted ZMQ + context for assurance that the returned address is the expected and correct + endpoint + :param addresses: a set of addresses to attempt. Example: + ``{"tcp://127.0.0.1:1234", "tcp://[3812::03aa]:5678"}`` + :param timeout_ms: how long to wait for a response from the probes. The probes + are initiated and await concurrently, so this timeout will be the total wall + time in the worst case of "no addresses are valid." + :param identity: a ZMQ connection identity; used for logging connection probes + at the interchange + :raises: ``ConnectionError`` if no addresses are determined valid + :returns: a single address, the first one that received a response + """ + if not addresses: + raise ValueError("No address to probe!") + + sock_map = {} + with ExitStack() as stk: + poller = zmq.Poller() + for url in addresses: + logger.debug("Testing ZMQ connection to url: %s", url) + s: zmq.Socket = stk.enter_context(zmq_context.socket(zmq.DEALER)) + s.setsockopt(zmq.LINGER, 0) + s.setsockopt(zmq.IPV6, True) + if identity: + s.setsockopt(zmq.IDENTITY, identity) + stk.enter_context(s.connect(url)) + poller.register(s, zmq.POLLIN) + sock_map[s] = url + + s.send(pickle.dumps({'type': 'connection_probe'})) + + for sock, evt in poller.poll(timeout=timeout_ms): + sock.recv() # clear the buffer for good netizenry + return sock_map.get(sock) + + addys = ", ".join(addresses) # just slightly more human friendly + raise ConnectionError(f"No viable ZMQ url from: {addys}") diff --git a/parsl/parsl/executors/high_throughput/process_worker_pool.py b/parsl/parsl/executors/high_throughput/process_worker_pool.py new file mode 100755 index 00000000..31af7a79 --- /dev/null +++ b/parsl/parsl/executors/high_throughput/process_worker_pool.py @@ -0,0 +1,1018 @@ +#!/usr/bin/env python3 + +import argparse +import importlib +import logging +import math +import multiprocessing +import os +import pathlib +import pickle +import platform +import queue +import subprocess +import sys +import threading +import time +import uuid +from importlib.metadata import distributions +from multiprocessing.context import SpawnProcess +from multiprocessing.managers import DictProxy +from multiprocessing.sharedctypes import Synchronized +from typing import Callable, Dict, List, Optional, Sequence, Union + +import psutil +import zmq + +from parsl import curvezmq +from parsl.addresses import tcp_url +from parsl.app.errors import RemoteExceptionWrapper +from parsl.executors.execute_task import execute_task +from parsl.executors.high_throughput.errors import WorkerLost +from parsl.executors.high_throughput.mpi_prefix_composer import ( + VALID_LAUNCHERS, + compose_all, +) +from parsl.executors.high_throughput.mpi_resource_management import ( + MPITaskScheduler, + TaskScheduler, +) +from parsl.executors.high_throughput.probe import probe_addresses +from parsl.log_utils import set_file_logger +from parsl.logconfigs.base import LogConfig, oneshot_initialize_logging +from parsl.multiprocessing import SpawnContext +from parsl.process_loggers import wrap_with_logs +from parsl.serialize import serialize +from parsl.version import VERSION as PARSL_VERSION + +HEARTBEAT_CODE = (2 ** 32) - 1 +SHUTDOWN_CODE = (2 ** 32) - 2 + +# this is specified explicitly because when this file is run as a script (the +# expected use), __name__ which is the normal idiom here is set to __main__, +# rather than a parsl submodule name. +logger = logging.getLogger("parsl.executors.high_throughput.process_worker_pool") + + +class Manager: + """ Manager manages task execution by the workers + + | zmq | Manager | Worker Processes + | | | + | <-----Register with -----+ | Request task<--+ + | N task capacity | | | | + Interchange | -------------------------+->Receive task batch| | | + | | Distribute tasks--+----> Get(block) & | + | | | Execute task | + | | | | | + | <------------------------+--Return results----+---- Post result | + | | | | | + | | | +----------+ + | | IPC-Qeueues + + """ + def __init__(self, *, + addresses: str, + address_probe_timeout: int, + port: int, + cores_per_worker, + mem_per_worker: Optional[float], + max_workers_per_node, + prefetch_capacity: int, + uid: str, + block_id: str, + heartbeat_threshold: int, + heartbeat_period: int, + poll_period: int, + cpu_affinity: str, + enable_mpi_mode: bool = False, + mpi_launcher: str = "mpiexec", + available_accelerators: Sequence[str], + cert_dir: Optional[str], + drain_period: Optional[int], + log_config: Optional[LogConfig]) -> None: + """ + Parameters + ---------- + addresses : str + comma separated list of addresses for the interchange + + address_probe_timeout : int + Timeout in seconds for the address probe to detect viable addresses + to the interchange. + + uid : str + string unique identifier + + block_id : str + Block identifier that maps managers to the provider blocks they belong to. + + cores_per_worker : float + cores to be assigned to each worker. Oversubscription is possible + by setting cores_per_worker < 1.0. + + mem_per_worker : optional float + GB of memory required per worker. If this option is specified, the node manager + will check the available memory at startup and limit the number of workers such that + the there's sufficient memory for each worker. If set to None, memory on node is not + considered in the determination of workers to be launched on node by the manager. + + max_workers_per_node : int | float + Caps the maximum number of workers that can be launched. + + prefetch_capacity : int + Number of tasks that could be prefetched over available worker capacity. + When there are a few tasks (<100) or when tasks are long running, this option should + be set to 0 for better load balancing. + + heartbeat_threshold : int + Seconds since the last message from the interchange after which the + interchange is assumed to be un-available, and the manager initiates shutdown. + + Number of seconds since the last message from the interchange after which the worker + assumes that the interchange is lost and the manager shuts down. + + heartbeat_period : int + Number of seconds after which a heartbeat message is sent to the interchange, and workers + are checked for liveness. + + poll_period : int + Timeout period used by the manager in milliseconds. + + cpu_affinity : str + Whether or how each worker should force its affinity to different CPUs + + available_accelerators: list of str + List of accelerators available to the workers. + + enable_mpi_mode: bool + When set to true, the manager assumes ownership of the batch job and each worker + claims a subset of nodes from a shared pool to execute multi-node mpi tasks. Node + info is made available to workers via env vars. + + mpi_launcher: str + Set to one of the supported MPI launchers: ("srun", "aprun", "mpiexec") + + cert_dir : str | None + Path to the certificate directory. + + drain_period: int | None + Number of seconds to drain after TODO: could be a nicer timespec involving m,s,h qualifiers for user friendliness? + """ + + logger.info("Manager initializing") + + self._start_time = time.time() + + self.log_config = log_config + + self.cert_dir = cert_dir + self.zmq_context = curvezmq.ClientContext(self.cert_dir) + + address_set = {tcp_url(a, port) for a in addresses.split(',')} + try: + self._ix_url = probe_addresses( + self.zmq_context, + address_set, + timeout_ms=1_000 * address_probe_timeout, + identity=uid.encode('utf-8'), + ) + except ConnectionError: + addys = ", ".join(addresses.split(",")) + logger.error(f"Unable to connect to interchange; attempted addresses: {addys}") + raise + + logger.info(f"Probe discovered interchange url: {self._ix_url}") + + self.uid = uid + self.block_id = block_id + self.start_time = time.time() + + self.enable_mpi_mode = enable_mpi_mode + self.mpi_launcher = mpi_launcher + + if os.environ.get('PARSL_CORES'): + cores_on_node = int(os.environ['PARSL_CORES']) + else: + cores_on_node = SpawnContext.cpu_count() + + if os.environ.get('PARSL_MEMORY_GB'): + available_mem_on_node = float(os.environ['PARSL_MEMORY_GB']) + else: + available_mem_on_node = round(psutil.virtual_memory().available / (2**30), 1) + + self.max_workers_per_node = max_workers_per_node + self.prefetch_capacity = prefetch_capacity + + mem_slots = max_workers_per_node + # Avoid a divide by 0 error. + if mem_per_worker and mem_per_worker > 0: + mem_slots = math.floor(available_mem_on_node / mem_per_worker) + + self.worker_count: int = min(max_workers_per_node, + mem_slots, + math.floor(cores_on_node / cores_per_worker)) + + self._mp_manager = SpawnContext.Manager() # Starts a server process + self._tasks_in_progress = self._mp_manager.dict() + self._stop_event = threading.Event() # when set, will begin shutdown process + + self.monitoring_queue = self._mp_manager.Queue() + self.pending_task_queue = SpawnContext.Queue() + self.pending_result_queue = SpawnContext.Queue() + self.task_scheduler: TaskScheduler + if self.enable_mpi_mode: + self.task_scheduler = MPITaskScheduler( + self.pending_task_queue, + self.pending_result_queue, + ) + else: + self.task_scheduler = TaskScheduler( + self.pending_task_queue, + self.pending_result_queue + ) + self.ready_worker_count = SpawnContext.Value("i", 0) + + self.heartbeat_period = heartbeat_period + self.heartbeat_threshold = heartbeat_threshold + self.poll_period = poll_period + + self.drain_time: float + if drain_period: + self.drain_time = self._start_time + drain_period + logger.info(f"Will request drain at {self.drain_time}") + else: + self.drain_time = float('inf') + + self.cpu_affinity = cpu_affinity + + # Define accelerator available, adjust worker count accordingly + self.available_accelerators = available_accelerators + self.accelerators_available = len(available_accelerators) > 0 + if self.accelerators_available: + self.worker_count = min(len(self.available_accelerators), self.worker_count) + logger.info("Manager will spawn {} workers".format(self.worker_count)) + + def create_reg_message(self) -> dict: + """ Creates a registration message to identify the worker to the interchange + """ + return { + 'type': 'registration', + 'parsl_v': PARSL_VERSION, + 'python_v': "{}.{}.{}".format( + sys.version_info.major, + sys.version_info.minor, + sys.version_info.micro + ), + 'packages': {d.metadata['Name']: d.version for d in distributions()}, + 'worker_count': self.worker_count, + 'uid': self.uid, + 'block_id': self.block_id, + 'start_time': self.start_time, + 'drain_time': self.drain_time, + 'prefetch_capacity': self.prefetch_capacity, + 'max_capacity': self.worker_count + self.prefetch_capacity, + 'os': platform.system(), + 'hostname': platform.node(), + 'dir': os.getcwd(), + 'cpu_count': psutil.cpu_count(logical=False), + 'total_memory': psutil.virtual_memory().total, + } + + @staticmethod + def heartbeat_to_incoming(task_incoming: zmq.Socket) -> None: + """ Send heartbeat to the incoming task queue + """ + # don't need to dumps and encode this every time - could do as a global on import? + b_msg = pickle.dumps({'type': 'heartbeat'}) + task_incoming.send(b_msg) + logger.debug("Sent heartbeat") + + @wrap_with_logs + def interchange_communicator(self, pair_setup: threading.Event) -> None: + """ Pull tasks from the incoming tasks zmq pipe onto the internal + pending task queue + """ + logger.info("starting") + + results_sock = self.zmq_context.socket(zmq.PAIR) + results_sock.setsockopt(zmq.LINGER, 0) + results_sock.bind("inproc://results") + pair_setup.set() + + # Linger is set to 0, so that the manager can exit even when there might be + # messages in the pipe + ix_sock = self.zmq_context.socket(zmq.DEALER) + ix_sock.setsockopt(zmq.IDENTITY, self.uid.encode('utf-8')) + ix_sock.setsockopt(zmq.LINGER, 0) + ix_sock.connect(self._ix_url) + logger.info("Manager task pipe connected to interchange") + + poller = zmq.Poller() + poller.register(results_sock, zmq.POLLIN) + poller.register(ix_sock, zmq.POLLIN) + + ix_sock.send(pickle.dumps({"type": "connection_probe"})) + evts = dict(poller.poll(timeout=self.heartbeat_period)) + if evts.get(ix_sock) is None: + logger.error(f"Failed to connect to interchange ({self._ix_url}") + + ix_sock.recv() + logger.info(f"Successfully connected to interchange via URL: {self._ix_url}") + + # Send a registration message + msg = self.create_reg_message() + logger.debug("Sending registration message: %s", msg) + ix_sock.send(pickle.dumps(msg)) + last_beat = time.time() + last_interchange_contact = time.time() + task_recv_counter = 0 + + while not self._stop_event.is_set(): + + # This loop will sit inside poller.poll until either a message + # arrives or one of these event times is reached. This code + # assumes that the event times won't change except on iteration + # of this loop - so will break if a different thread does + # anything to bring one of the event times earlier - and that the + # time here are correctly copy-pasted from the relevant if + # statements. + next_interesting_event_time = min(last_beat + self.heartbeat_period, + self.drain_time, # legacy, remove ca Apr 2027 + last_interchange_contact + self.heartbeat_threshold) + try: + pending_task_count = self.pending_task_queue.qsize() + except NotImplementedError: + # Ref: https://github.com/python/cpython/blob/6d5e0dc0e330f4009e8dc3d1642e46b129788877/Lib/multiprocessing/queues.py#L125 + pending_task_count = f"pending task count is not available on {platform.system()}" # type: ignore[assignment] + + logger.debug( + 'ready workers: %d, pending tasks: %d', + self.ready_worker_count.value, + pending_task_count, + ) + + if time.time() >= last_beat + self.heartbeat_period: + self.heartbeat_to_incoming(ix_sock) + last_beat = time.time() + + # legacy draining handling. remove in ~6 months (ca Apr 2027) + if time.time() > self.drain_time: + # ask to start draining (old interchanges don't track that for us). this + # stops ours from sending more tasks; it will tell us to shut down when + # we finish all active work. + logger.info("Requesting drain") + ix_sock.send(pickle.dumps({'type': 'drain'})) + logger.debug("Sent drain") + + # now set drain time to the far future so we don't send a drain + # message every iteration. + self.drain_time = float('inf') + + poll_duration_s = max(0, next_interesting_event_time - time.time()) + socks = dict(poller.poll(timeout=math.floor(poll_duration_s * 1000))) + + if socks.get(ix_sock) == zmq.POLLIN: + pkl_msg = ix_sock.recv() + tasks = pickle.loads(pkl_msg) + del pkl_msg + + last_interchange_contact = time.time() + + if tasks == HEARTBEAT_CODE: + logger.debug("Got heartbeat response from interchange") + elif tasks == SHUTDOWN_CODE: + logger.info("Got shutdown message from interchange - setting kill flag") + self._stop_event.set() + else: + task_recv_counter += len(tasks) + logger.debug("Got executor tasks: {}, cumulative count of tasks: {}".format( + [t['task_id'] for t in tasks], task_recv_counter + )) + + for task in tasks: + self.task_scheduler.put_task(task) + + elif socks.get(results_sock) == zmq.POLLIN: + ix_sock.send(results_sock.recv()) + logger.debug("Result sent to interchange") + + else: + logger.debug("No incoming tasks") + + # Only check if no messages were received. + if time.time() >= last_interchange_contact + self.heartbeat_threshold: + logger.critical("Missing contact with interchange beyond heartbeat_threshold") + self._stop_event.set() + logger.critical("Exiting") + break + + ix_sock.close() + logger.info("Exiting") + + @wrap_with_logs + def ferry_result(self, may_connect: threading.Event) -> None: + """ Listens on the pending_result_queue and ferries results to the interchange + connected thread + """ + logger.debug("Begin") + + # Linger is set to 0, so that the manager can exit even when there might be + # messages in the pipe + notify_sock = self.zmq_context.socket(zmq.PAIR) + notify_sock.setsockopt(zmq.LINGER, 0) + may_connect.wait() + notify_sock.connect("inproc://results") + + while not self._stop_event.is_set(): + try: + r = self.task_scheduler.get_result() + if r is None: + continue + logger.debug("Result received from worker") + notify_sock.send(r) + except Exception: + logger.exception("Failed to send result to interchange") + + notify_sock.close() + logger.debug("Exiting") + + def worker_watchdog(self, procs: dict[int, SpawnProcess]) -> None: + """Keeps workers alive.""" + logger.debug("Starting worker watchdog") + + while not self._stop_event.wait(self.heartbeat_period): + for worker_id, p in procs.items(): + if not p.is_alive(): + logger.error("Worker {} has died".format(worker_id)) + try: + task = self._tasks_in_progress.pop(worker_id) + logger.info("Worker {} was busy when it died".format(worker_id)) + try: + raise WorkerLost(worker_id, platform.node()) + except Exception: + logger.info("Putting exception for executor task {} in the pending result queue".format(task['task_id'])) + result_package = {'type': 'result', + 'task_id': task['task_id'], + 'exception': serialize(RemoteExceptionWrapper(*sys.exc_info()))} + pkl_package = pickle.dumps(result_package) + self.pending_result_queue.put(pkl_package) + del pkl_package + except KeyError: + logger.info("Worker {} was not busy when it died".format(worker_id)) + + procs[worker_id] = self._start_worker(worker_id) + logger.info("Worker {} has been restarted".format(worker_id)) + + logger.debug("Exiting") + + @wrap_with_logs + def handle_monitoring_messages(self) -> None: + """Transfer messages from the managed monitoring queue to the result queue. + + We separate the queues so that the result queue does not rely on a manager + process, which adds overhead that causes slower queue operations but enables + use across processes started in fork and spawn contexts. + + We transfer the messages to the result queue to reuse the ZMQ connection between + the manager and the interchange. + """ + logger.debug("Starting monitoring handler thread") + + while not self._stop_event.is_set(): + try: + logger.debug("Starting monitor_queue.get()") + msg = self.monitoring_queue.get(block=True) + if msg is None: + continue + logger.debug("Got a monitoring message") + self.pending_result_queue.put(msg) + logger.debug("Put monitoring message on pending_result_queue") + except Exception: + logger.exception("Failed to forward monitoring message") + + logger.debug("Exiting") + + def start(self) -> None: + """ Start the worker processes. + """ + procs: dict[int, SpawnProcess] = {} + for worker_id in range(self.worker_count): + procs[worker_id] = self._start_worker(worker_id) + + logger.debug("Workers started") + + pair_setup = threading.Event() + + thr_task_puller = threading.Thread( + target=self.interchange_communicator, + args=(pair_setup,), + name="Interchange-Communicator", + ) + thr_result_ferry = threading.Thread( + target=self.ferry_result, args=(pair_setup,), name="Result-Shovel") + thr_worker_watchdog = threading.Thread( + target=self.worker_watchdog, args=(procs,), name="worker-watchdog" + ) + thr_monitoring_handler = threading.Thread( + target=self.handle_monitoring_messages, name="Monitoring-Handler" + ) + + thr_task_puller.start() + thr_result_ferry.start() + thr_worker_watchdog.start() + thr_monitoring_handler.start() + + logger.info("Manager threads started") + + # This might need a multiprocessing event to signal back. + self._stop_event.wait() + logger.info("Stop event set; terminating worker processes") + + # Invite blocking threads to quit + self.monitoring_queue.put(None) + self.pending_result_queue.put(None) + + thr_monitoring_handler.join() + thr_worker_watchdog.join() + thr_result_ferry.join() + thr_task_puller.join() + + for worker_id in procs: + p = procs[worker_id] + proc_info = f"(PID: {p.pid}, Worker ID: {worker_id})" + logger.debug(f"Signaling worker {p.name} (TERM). {proc_info}") + p.terminate() + + self.zmq_context.term() + + # give processes 1 second to gracefully shut themselves down, based on the + # SIGTERM (.terminate()) just sent; after then, we pull the plug. + force_child_shutdown_at = time.monotonic() + 1 + while procs: + worker_id, p = procs.popitem() + timeout = max(force_child_shutdown_at - time.monotonic(), 0.000001) + p.join(timeout=timeout) + proc_info = f"(PID: {p.pid}, Worker ID: {worker_id})" + if p.exitcode is not None: + logger.debug( + "Worker joined successfully. %s (exitcode: %s)", proc_info, p.exitcode + ) + + else: + logger.warning( + f"Worker {p.name} ({worker_id}) failed to terminate in a timely" + f" manner; sending KILL signal to process. {proc_info}" + ) + p.kill() + p.join() + p.close() + + delta = time.time() - self._start_time + logger.info("process_worker_pool ran for {} seconds".format(delta)) + + def _start_worker(self, worker_id: int) -> SpawnProcess: + p = SpawnContext.Process( + target=worker, + args=( + worker_id, + self.uid, + self.worker_count, + self.pending_task_queue, + self.pending_result_queue, + self.monitoring_queue, + self.ready_worker_count, + self._tasks_in_progress, + self.cpu_affinity, + self.available_accelerators[worker_id] if self.accelerators_available else None, + self.block_id, + self.heartbeat_period, + os.getpid(), + args.logdir, + self.log_config, + args.debug, + self.mpi_launcher, + ), + name="HTEX-Worker-{}".format(worker_id), + ) + p.start() + return p + + +def update_resource_spec_env_vars(mpi_launcher: str, resource_spec: Dict, node_info: List[str]) -> None: + prefix_table = compose_all(mpi_launcher, resource_spec=resource_spec, node_hostnames=node_info) + for key in prefix_table: + os.environ[key] = prefix_table[key] + + +def _init_mpi_env(mpi_launcher: str, resource_spec: Dict) -> None: + for varname in resource_spec: + envname = "PARSL_" + str(varname).upper() + os.environ[envname] = str(resource_spec[varname]) + + node_list = resource_spec.get("MPI_NODELIST") + if node_list is None: + return + nodes_for_task = node_list.split(',') + logger.info(f"Launching task on provisioned nodes: {nodes_for_task}") + update_resource_spec_env_vars(mpi_launcher=mpi_launcher, resource_spec=resource_spec, node_info=nodes_for_task) + + +@wrap_with_logs +def worker( + worker_id: int, + pool_id: str, + pool_size: int, + task_queue: multiprocessing.Queue, + result_queue: multiprocessing.Queue, + monitoring_queue: queue.Queue, + ready_worker_count: Synchronized, + tasks_in_progress: DictProxy, + cpu_affinity: str, + accelerator: Optional[str], + block_id: str, + task_queue_timeout: int, + manager_pid: int, + logdir: str, + log_config: LogConfig, + debug: bool, + mpi_launcher: str, +) -> None: + + if log_config: + path = pathlib.Path(logdir) / f"block-{block_id}" / pool_id + oneshot_initialize_logging(log_config=log_config, log_dir=path, log_name=f"worker_{worker_id}") + else: + set_file_logger('{}/block-{}/{}/worker_{}.log'.format(logdir, block_id, pool_id, worker_id), + level=logging.DEBUG if debug else logging.INFO) + + # Store worker ID as an environment variable + os.environ['PARSL_WORKER_RANK'] = str(worker_id) + os.environ['PARSL_WORKER_COUNT'] = str(pool_size) + os.environ['PARSL_WORKER_POOL_ID'] = str(pool_id) + os.environ['PARSL_WORKER_BLOCK_ID'] = str(block_id) + + import parsl.executors.high_throughput.monitoring_info as mi + mi.result_queue = monitoring_queue + + logger.info('Worker {} started'.format(worker_id)) + if debug: + logger.debug("Debug logging enabled") + + # If desired, set process affinity + if cpu_affinity != "none": + # Count the number of cores per worker + # OSX does not implement os.sched_getaffinity + avail_cores = sorted(os.sched_getaffinity(0)) # type: ignore[attr-defined, unused-ignore] + cores_per_worker = len(avail_cores) // pool_size + assert cores_per_worker > 0, "Affinity does not work if there are more workers than cores" + + # Determine this worker's cores + if cpu_affinity == "block": + my_cores = avail_cores[cores_per_worker * worker_id:cores_per_worker * (worker_id + 1)] + elif cpu_affinity == "block-reverse": + cpu_worker_id = pool_size - worker_id - 1 # To assign in reverse order + my_cores = avail_cores[cores_per_worker * cpu_worker_id:cores_per_worker * (cpu_worker_id + 1)] + elif cpu_affinity == "alternating": + my_cores = avail_cores[worker_id::pool_size] + elif cpu_affinity[0:4] == "list": + thread_ranks = cpu_affinity.split(":")[1:] + if len(thread_ranks) != pool_size: + raise ValueError("Affinity list {} has wrong number of thread ranks".format(cpu_affinity)) + threads = thread_ranks[worker_id] + thread_list = threads.split(",") + my_cores = [] + for tl in thread_list: + thread_range = tl.split("-") + if len(thread_range) == 1: + my_cores.append(int(thread_range[0])) + elif len(thread_range) == 2: + start_thread = int(thread_range[0]) + end_thread = int(thread_range[1]) + 1 + my_cores += list(range(start_thread, end_thread)) + else: + raise ValueError("Affinity list formatting is not expected {}".format(cpu_affinity)) + else: + raise ValueError("Affinity strategy {} is not supported".format(cpu_affinity)) + + # Set the affinity for OpenMP + # See: https://hpc-tutorials.llnl.gov/openmp/ProcessThreadAffinity.pdf + proc_list = ",".join(map(str, my_cores)) + os.environ["OMP_NUM_THREADS"] = str(len(my_cores)) + os.environ["GOMP_CPU_AFFINITY"] = proc_list # Compatible with GCC OpenMP + os.environ["KMP_AFFINITY"] = f"explicit,proclist=[{proc_list}]" # For Intel OpenMP + + # Set the affinity for this worker + # OSX does not implement os.sched_setaffinity so type checking + # is ignored here in two ways: + # On a platform without sched_setaffinity, that attribute will not + # be defined, so ignore[attr-defined] will tell mypy to ignore this + # incorrect-for-OS X attribute access. + # On a platform with sched_setaffinity, that type: ignore message + # will be redundant, and ignore[unused-ignore] tells mypy to ignore + # that this ignore is unneeded. + os.sched_setaffinity(0, my_cores) # type: ignore[attr-defined, unused-ignore] + logger.info("Set worker CPU affinity to {}".format(my_cores)) + + # If desired, pin to accelerator + if accelerator is not None: + + # If CUDA devices, find total number of devices to allow for MPS + # See: https://developer.nvidia.com/system-management-interface + nvidia_smi_cmd = "nvidia-smi -L > /dev/null && nvidia-smi -L | wc -l" + nvidia_smi_ret = subprocess.run(nvidia_smi_cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + if nvidia_smi_ret.returncode == 0: + num_cuda_devices = int(nvidia_smi_ret.stdout.split()[0]) + else: + num_cuda_devices = None + + try: + if num_cuda_devices is not None: + procs_per_cuda_device = pool_size // num_cuda_devices + partitioned_accelerator = str(int(accelerator) // procs_per_cuda_device) # multiple workers will share a GPU + os.environ["CUDA_VISIBLE_DEVICES"] = partitioned_accelerator + logger.info(f'Pinned worker to partitioned cuda device: {partitioned_accelerator}') + else: + os.environ["CUDA_VISIBLE_DEVICES"] = accelerator + except (TypeError, ValueError, ZeroDivisionError): + os.environ["CUDA_VISIBLE_DEVICES"] = accelerator + os.environ["ROCR_VISIBLE_DEVICES"] = accelerator + os.environ["ZE_AFFINITY_MASK"] = accelerator + os.environ["ZE_ENABLE_PCI_ID_DEVICE_ORDER"] = '1' + + logger.info(f'Pinned worker to accelerator: {accelerator}') + + def manager_is_alive() -> bool: + try: + # This does not kill the process, but instead raises + # an exception if the process doesn't exist + os.kill(manager_pid, 0) + except OSError: + logger.critical(f"Manager ({manager_pid}) died; worker {worker_id} shutting down") + return False + else: + return True + + worker_enqueued = False + while manager_is_alive(): + if not worker_enqueued: + with ready_worker_count.get_lock(): + ready_worker_count.value += 1 + worker_enqueued = True + + try: + req = task_queue.get(timeout=task_queue_timeout) + # req is {'task_id':, 'buffer':, 'resource_spec':} + except queue.Empty: + continue + + tasks_in_progress[worker_id] = req + tid = req['task_id'] + logger.info("Received executor task {}".format(tid)) + + with ready_worker_count.get_lock(): + ready_worker_count.value -= 1 + worker_enqueued = False + + ctxt = req["context"] + res_spec = ctxt.get("resource_spec", {}) + + _init_mpi_env(mpi_launcher=mpi_launcher, resource_spec=res_spec) + + exec_func: Callable = execute_task + exec_args = () + exec_kwargs = {} + + try: + if task_executor := ctxt.get("task_executor", None): + mod_name, _, fn_name = task_executor["f"].rpartition(".") + exec_mod = importlib.import_module(mod_name) + exec_func = getattr(exec_mod, fn_name) + + exec_args = task_executor.get("a", ()) + exec_kwargs = task_executor.get("k", {}) + + result = exec_func(req['buffer'], *exec_args, **exec_kwargs) + serialized_result = serialize(result, buffer_threshold=1000000) + except Exception as e: + logger.info('Caught an exception: {}'.format(e)) + result_package = {'type': 'result', 'task_id': tid, 'exception': serialize(RemoteExceptionWrapper(*sys.exc_info()))} + else: + result_package = {'type': 'result', 'task_id': tid, 'result': serialized_result} + del serialized_result + del req + + logger.info("Completed executor task {}".format(tid)) + try: + pkl_package = pickle.dumps(result_package) + except Exception: + logger.exception("Caught exception while trying to pickle the result package") + pkl_package = pickle.dumps({'type': 'result', 'task_id': tid, + 'exception': serialize(RemoteExceptionWrapper(*sys.exc_info())) + }) + + result_queue.put(pkl_package) + del pkl_package, result_package + tasks_in_progress.pop(worker_id) + logger.info("All processing finished for executor task {}".format(tid)) + + +def get_arg_parser() -> argparse.ArgumentParser: + + def strategyorlist(s: str) -> Union[str, list]: + s = s.lower() + allowed_strategies = ("none", "block", "alternating", "block-reverse") + if s in allowed_strategies: + return s + elif s[0:4] == "list": + return s + err_msg = f"cpu-affinity must be one of {allowed_strategies} or a list format" + raise argparse.ArgumentTypeError(err_msg) + + parser = argparse.ArgumentParser() + parser.add_argument( + "-d", "--debug", action='store_true', help="Enable logging at DEBUG level", + ) + parser.add_argument( + "-a", + "--addresses", + required=True, + help="Comma separated list of addresses at which the interchange could be reached", + ) + parser.add_argument( + "--cert_dir", required=True, help="Path to certificate directory." + ) + parser.add_argument( + "-l", + "--logdir", + default="process_worker_pool_logs", + help="Process worker pool log directory", + ) + parser.add_argument( + "-L", + "--logconf", + default=None, + help="Path to pickled log configuration object, default=None", + ) + parser.add_argument( + "-u", + "--uid", + default=str(uuid.uuid4()).split('-')[-1], + help="Unique identifier string for Manager", + ) + parser.add_argument( + "-b", "--block_id", default=None, help="Block identifier for Manager" + ) + parser.add_argument( + "-c", + "--cores_per_worker", + default="1.0", + help="Number of cores assigned to each worker process. Default=1.0", + ) + parser.add_argument( + "-m", + "--mem_per_worker", + help="GB of memory assigned to each worker process. 'None' means no assignment.", + ) + parser.add_argument( + "-P", + "--port", + required=True, + help="Port for communication with the interchange", + ) + parser.add_argument( + "--max_workers_per_node", + default=float('inf'), + help="Caps the maximum workers that can be launched, default:infinity", + ) + parser.add_argument( + "-p", + "--prefetch_capacity", + default=0, + help="Number of tasks that can be prefetched to the manager. Default is 0.", + ) + parser.add_argument( + "--hb_period", + default=30, + help="Heartbeat period in seconds. Uses manager default unless set", + ) + parser.add_argument( + "--hb_threshold", + default=120, + help="Heartbeat threshold in seconds. Uses manager default unless set", + ) + parser.add_argument( + "--drain_period", + default=None, + help="Drain this pool after specified number of seconds. By default, does not drain.", + ) + parser.add_argument( + "--address_probe_timeout", + default=30, + help="Timeout to probe for viable address to interchange. Default: 30s", + ) + parser.add_argument( + "--poll", default=10, help="Poll period used in milliseconds" + ) + parser.add_argument( + "--cpu-affinity", + type=strategyorlist, + required=True, + help="Whether/how workers should control CPU affinity.", + ) + parser.add_argument( + "--available-accelerators", + type=str, + nargs="*", + default=[], + help="Names of available accelerators, if not given assumed to be zero accelerators available", + ) + parser.add_argument( + "--enable_mpi_mode", action='store_true', help="Enable MPI mode" + ) + parser.add_argument( + "--mpi-launcher", + type=str, + choices=VALID_LAUNCHERS, + help="MPI launcher to use iff enable_mpi_mode=true", + ) + + return parser + + +if __name__ == "__main__": + parser = get_arg_parser() + args = parser.parse_args() + + if args.logconf is None or args.logconf == "None": + os.makedirs(os.path.join(args.logdir, "block-{}".format(args.block_id), args.uid), exist_ok=True) + + set_file_logger( + f'{args.logdir}/block-{args.block_id}/{args.uid}/manager.log', + level=logging.DEBUG if args.debug is True else logging.INFO + ) + logconf = None + else: + with open(args.logconf, "rb") as f: + # This deliberately does not make the path directory. Creating directories + # can be extremely expensive on shared filesystems, and avoiding this for + # logging is a specific use case for this log configurability. + path = os.path.join(args.logdir, "block-{}".format(args.block_id), args.uid) + logconf = pickle.load(f) + oneshot_initialize_logging(log_config=logconf, log_dir=pathlib.Path(path), log_name="manager") + + logger.info( + f"\n Python version: {sys.version}" + f"\n Debug logging: {args.debug}" + f"\n Certificates dir: {args.cert_dir}" + f"\n Log dir: {args.logdir}" + f"\n Log config path: {args.logconf}" + f"\n Manager ID: {args.uid}" + f"\n Block ID: {args.block_id}" + f"\n cores_per_worker: {args.cores_per_worker}" + f"\n mem_per_worker: {args.mem_per_worker}" + f"\n Interchange port: {args.port}" + f"\n addresses: {args.addresses}" + f"\n max_workers_per_node: {args.max_workers_per_node}" + f"\n poll_period: {args.poll}" + f"\n address_probe_timeout: {args.address_probe_timeout}" + f"\n Prefetch capacity: {args.prefetch_capacity}" + f"\n Heartbeat threshold: {args.hb_threshold}" + f"\n Heartbeat period: {args.hb_period}" + f"\n Drain period: {args.drain_period}" + f"\n CPU affinity: {args.cpu_affinity}" + f"\n Accelerators: {' '.join(args.available_accelerators)}" + f"\n enable_mpi_mode: {args.enable_mpi_mode}" + f"\n mpi_launcher: {args.mpi_launcher}" + ) + try: + manager = Manager(port=args.port, + addresses=args.addresses, + address_probe_timeout=int(args.address_probe_timeout), + uid=args.uid, + block_id=args.block_id, + cores_per_worker=float(args.cores_per_worker), + mem_per_worker=None if args.mem_per_worker == 'None' else float(args.mem_per_worker), + max_workers_per_node=( + args.max_workers_per_node if args.max_workers_per_node == float('inf') + else int(args.max_workers_per_node) + ), + prefetch_capacity=int(args.prefetch_capacity), + heartbeat_threshold=int(args.hb_threshold), + heartbeat_period=int(args.hb_period), + drain_period=None if args.drain_period == "None" else int(args.drain_period), + poll_period=int(args.poll), + cpu_affinity=args.cpu_affinity, + enable_mpi_mode=args.enable_mpi_mode, + mpi_launcher=args.mpi_launcher, + available_accelerators=args.available_accelerators, + cert_dir=None if args.cert_dir == "None" else args.cert_dir, + log_config=logconf) + manager.start() + + except Exception: + logger.critical("Process worker pool exiting with an exception", exc_info=True) + raise + else: + logger.info("Process worker pool exiting normally") + print("Process worker pool exiting normally") diff --git a/parsl/parsl/executors/high_throughput/zmq_pipes.py b/parsl/parsl/executors/high_throughput/zmq_pipes.py new file mode 100644 index 00000000..b06cc05c --- /dev/null +++ b/parsl/parsl/executors/high_throughput/zmq_pipes.py @@ -0,0 +1,195 @@ +#!/usr/bin/env python3 + +import logging +import threading +import time +from typing import Optional + +import zmq + +from parsl import curvezmq +from parsl.addresses import tcp_url +from parsl.errors import InternalConsistencyError +from parsl.executors.high_throughput.errors import ( + CommandClientBadError, + CommandClientTimeoutError, +) + +logger = logging.getLogger(__name__) + + +class CommandClient: + """ CommandClient + """ + def __init__(self, ip_address, port_range, cert_dir: Optional[str] = None): + """ + Parameters + ---------- + + ip_address: str + IP address of the client (where Parsl runs) + + port_range: tuple(int, int) + Port range for the comms between client and interchange + + cert_dir: str | None + Path to the certificate directory. Setting this to None will disable encryption. + default: None + + """ + self.zmq_context = curvezmq.ClientContext(cert_dir) + self.ip_address = ip_address + self.port_range = port_range + self.port = None + self.create_socket_and_bind() + self._lock = threading.Lock() + self.ok = True + + def create_socket_and_bind(self): + """ Creates socket and binds to a port. + + Upon recreating the socket, we bind to the same port. + """ + self.zmq_socket = self.zmq_context.socket(zmq.REQ) + self.zmq_socket.setsockopt(zmq.LINGER, 0) + if self.port is None: + self.port = self.zmq_socket.bind_to_random_port(tcp_url(self.ip_address), + min_port=self.port_range[0], + max_port=self.port_range[1]) + else: + self.zmq_socket.bind(tcp_url(self.ip_address, self.port)) + + def run(self, message, max_retries=3, timeout_s=None): + """ This function needs to be fast at the same time aware of the possibility of + ZMQ pipes overflowing. + + We could set copy=False and get slightly better latency but this results + in ZMQ sockets reaching a broken state once there are ~10k tasks in flight. + This issue can be magnified if each the serialized buffer itself is larger. + """ + if not self.ok: + raise CommandClientBadError() + + start_time_s = time.monotonic() + + reply = '__PARSL_ZMQ_PIPES_MAGIC__' + with self._lock: + logger.debug("Sending command client command") + + if timeout_s is not None: + remaining_time_s = start_time_s + timeout_s - time.monotonic() + poll_result = self.zmq_socket.poll(timeout=remaining_time_s * 1000, flags=zmq.POLLOUT) + if poll_result == zmq.POLLOUT: + pass # this is OK, so continue + elif poll_result == 0: + raise CommandClientTimeoutError("Waiting for command channel to be ready for a command") + else: + raise InternalConsistencyError(f"ZMQ poll returned unexpected value: {poll_result}") + + self.zmq_socket.send_pyobj(message, copy=True) + + if timeout_s is not None: + logger.debug("Polling for command client response or timeout") + remaining_time_s = start_time_s + timeout_s - time.monotonic() + poll_result = self.zmq_socket.poll(timeout=remaining_time_s * 1000, flags=zmq.POLLIN) + if poll_result == zmq.POLLIN: + pass # this is OK, so continue + elif poll_result == 0: + logger.error("Command timed-out - command client is now bad forever") + self.ok = False + raise CommandClientTimeoutError("Waiting for a reply from command channel") + else: + raise InternalConsistencyError(f"ZMQ poll returned unexpected value: {poll_result}") + + logger.debug("Receiving command client response") + reply = self.zmq_socket.recv_pyobj() + logger.debug("Received command client response") + return reply + + def close(self): + self.zmq_socket.close() + self.zmq_context.term() + + +class TasksOutgoing: + """ Outgoing task queue from the executor to the Interchange + """ + def __init__(self, ip_address, port_range, cert_dir: Optional[str] = None): + """ + Parameters + ---------- + + ip_address: str + IP address of the client (where Parsl runs) + + port_range: tuple(int, int) + Port range for the comms between client and interchange + + cert_dir: str | None + Path to the certificate directory. Setting this to None will disable encryption. + default: None + + """ + self.zmq_context = curvezmq.ClientContext(cert_dir) + self.zmq_socket = self.zmq_context.socket(zmq.DEALER) + self.zmq_socket.set_hwm(0) + self.port = self.zmq_socket.bind_to_random_port(tcp_url(ip_address), + min_port=port_range[0], + max_port=port_range[1]) + + def put(self, message): + """ This function needs to be fast at the same time aware of the possibility of + ZMQ pipes overflowing. + + We could set copy=False and get slightly better latency but this results + in ZMQ sockets reaching a broken state once there are ~10k tasks in flight. + This issue can be magnified if each the serialized buffer itself is larger. + """ + logger.debug("Sending TasksOutgoing message") + self.zmq_socket.send_pyobj(message) + logger.debug("Sent TasksOutgoing message") + + def close(self): + self.zmq_socket.close() + self.zmq_context.term() + + +class ResultsIncoming: + """ Incoming results queue from the Interchange to the executor + """ + + def __init__(self, ip_address, port_range, cert_dir: Optional[str] = None): + """ + Parameters + ---------- + + ip_address: str + IP address of the client (where Parsl runs) + + port_range: tuple(int, int) + Port range for the comms between client and interchange + + cert_dir: str | None + Path to the certificate directory. Setting this to None will disable encryption. + default: None + + """ + self.zmq_context = curvezmq.ClientContext(cert_dir) + self.results_receiver = self.zmq_context.socket(zmq.DEALER) + self.results_receiver.set_hwm(0) + self.port = self.results_receiver.bind_to_random_port(tcp_url(ip_address), + min_port=port_range[0], + max_port=port_range[1]) + + def get(self, timeout_ms=None): + """Get a message from the queue, returning None if timeout expires + without a message. timeout is measured in milliseconds. + """ + if zmq.POLLIN == self.results_receiver.poll(timeout_ms, zmq.POLLIN): + logger.debug("Receiving ResultsIncoming message") + return self.results_receiver.recv() + return None + + def close(self): + self.results_receiver.close() + self.zmq_context.term() diff --git a/parsl/parsl/executors/radical/__init__.py b/parsl/parsl/executors/radical/__init__.py new file mode 100644 index 00000000..43b61e81 --- /dev/null +++ b/parsl/parsl/executors/radical/__init__.py @@ -0,0 +1,4 @@ +from parsl.executors.radical.executor import RadicalPilotExecutor +from parsl.executors.radical.rpex_resources import ResourceConfig + +__all__ = ['RadicalPilotExecutor', 'ResourceConfig'] diff --git a/parsl/parsl/executors/radical/executor.py b/parsl/parsl/executors/radical/executor.py new file mode 100644 index 00000000..48597f92 --- /dev/null +++ b/parsl/parsl/executors/radical/executor.py @@ -0,0 +1,605 @@ +"""RadicalPilotExecutor builds on the RADICAL-Pilot/Parsl +""" +import inspect +import logging +import os +import queue +import sys +import threading as mt +import time +from concurrent.futures import Future +from functools import partial +from pathlib import PosixPath +from typing import Dict, Optional + +import requests +import typeguard + +import parsl +from parsl.app.errors import BashExitFailure, RemoteExceptionWrapper +from parsl.app.python import timeout +from parsl.data_provider.files import File +from parsl.executors.base import ParslExecutor +from parsl.serialize import deserialize, pack_apply_message +from parsl.serialize.errors import DeserializationError, SerializationError +from parsl.utils import RepresentationMixin + +from .rpex_resources import CLIENT, MPI, ResourceConfig + +try: + import radical.pilot as rp + import radical.utils as ru +except ImportError: + _rp_enabled = False +else: + _rp_enabled = True + + +RPEX = 'RPEX' +BASH = 'bash' +PYTHON = 'python' + +CWD = os.getcwd() +PWD = os.path.abspath(os.path.dirname(__file__)) + +PARSL_RP_RESOURCE_MAP = {'cores': 'ranks', + 'disk': 'lfs_per_rank', + 'memory': 'mem_per_rank'} + +logger = logging.getLogger(__name__) + + +class RadicalPilotExecutor(ParslExecutor, RepresentationMixin): + """Executor is designed for executing heterogeneous tasks + in terms of type/resource. + + The RadicalPilotExecutor system has the following main components: + + 1. "start": Create and start the RADICAL-Pilot runtime components ``rp.Session``, + ``rp.PilotManager`` and ``rp.TaskManager``. + 2. "translate": Unwrap, identify, and parse Parsl ``apps`` into ``rp.TaskDescription``. + 3. "submit": Submit Parsl apps to ``rp.TaskManager``. + 4. "shutdown": Shut down the RADICAL-Pilot runtime and all associated components. + + Here is a diagram + + .. code:: python + + ---------------------------------------------------------------------------- + Parsl Data Flow Kernel | Task Translator | rp.TaskManager + ---------------------------------------|-------------------|---------------- + | | + -> Dep. check ------> Parsl_tasks{} <--+--> Parsl Task | + Data management +dfk.submit | | | + | v | + | RP Task(s) -> | submit(task) + ---------------------------------------------------------------------------- + + The RadicalPilotExecutor creates a ``rp.Session``, ``rp.TaskManager``, + and ``rp.PilotManager``. The executor receives the Parsl apps from the + DFK and translates these apps (in-memory) into ``rp.TaskDescription`` + object to be passed to the ``rp.TaskManager``. This executor has two + submission mechanisms: + + 1. Default_mode: where the executor submits the tasks directly to + RADICAL-Pilot. + + 2. Bulk_mode: where the executor accumulates N tasks (functions and + executables) and submit them. + + Parameters + ---------- + rpex_cfg : :class: `~parsl.executors.rpex_resources.ResourceConfig` + a dataclass specifying resource configuration. + Default is ResourceConfig instance. + + label : str + Label for this executor instance. + Default is "RPEX". + + bulk_mode : bool + Enable bulk mode submission and execution. Default is False (stream). + + resource : Optional[str] + The resource name of the targeted HPC machine or cluster. + Default is local.localhost (user local machine). + + runtime : int + The maximum runtime for the entire job in minutes. + Default is 30. + + working_dir : str + The working dir to be used by the executor. + + rpex_pilot_kwargs: Dict of kwargs that are passed directly to the rp.PilotDescription object. + + For more information: https://radicalpilot.readthedocs.io/en/stable/ + """ + + @typeguard.typechecked + def __init__(self, + resource: str, + label: str = RPEX, + bulk_mode: bool = False, + working_dir: Optional[str] = None, + rpex_cfg: Optional[ResourceConfig] = None, **rpex_pilot_kwargs): + + super().__init__() + self.pmgr = None + self.tmgr = None + self.run_dir = '.' + self.label = label + self.session = None + self.resource = resource + self._uid = RPEX.lower() + self.bulk_mode = bulk_mode + self._terminate = mt.Event() + self.working_dir = working_dir + self.pilot_kwargs = rpex_pilot_kwargs + self.future_tasks: Dict[str, Future] = {} + + if rpex_cfg: + self.rpex_cfg = rpex_cfg.get_config() + elif not rpex_cfg and 'local' in resource: + self.rpex_cfg = ResourceConfig().get_config() + else: + raise ValueError('Resource config must be ' + 'specified for a non-local resources') + + def task_state_cb(self, task, state): + """ + Update the state of Parsl Future apps + Based on RP task state callbacks. + """ + # check the Master/Worker state + if task.mode in [rp.RAPTOR_MASTER, rp.RAPTOR_WORKER]: + if state == rp.FAILED: + exception = RuntimeError(f'{task.uid} failed with internal error: {task.stderr}') + self._fail_all_tasks(exception) + + # check all other tasks state + else: + parsl_task = self.future_tasks[task.uid] + + if state == rp.DONE: + if task.description['mode'] in [rp.TASK_EXEC, + rp.TASK_PROC, + rp.TASK_EXECUTABLE]: + parsl_task.set_result(int(task.exit_code)) + else: + # we do not support MPI function output + # serialization. TODO: To be fixed soon. + if not task.description.get('use_mpi'): + result = deserialize(eval(task.return_value)) + parsl_task.set_result(result) + else: + parsl_task.set_result(task.return_value) + + elif state == rp.CANCELED: + parsl_task.cancel() + + elif state == rp.FAILED: + if task.description['mode'] in [rp.TASK_EXEC, + rp.TASK_EXECUTABLE]: + parsl_task.set_exception(BashExitFailure(task.name, + task.exit_code)) + else: + if task.exception: + # unpack a serialized exception + if not task.description.get('use_mpi') or task.description['mode'] == rp.TASK_PROC: + self._unpack_and_set_parsl_exception(parsl_task, task.exception) + # we do not serialize mpi function exception + else: + parsl_task.set_exception(eval(task.exception)) + else: + parsl_task.set_exception('Task failed for an unknown reason') + + def _fail_all_tasks(self, exception): + """ + Fail all outstanding tasks with the given exception. + + This method iterates through all outstanding tasks in the + `_future_tasks` dictionary, which have not yet completed, + and sets the provided exception as their result, indicating + a failure. + + Parameters: + - exception: The exception to be set as the result for all + outstanding tasks. + """ + for fut_task in self.future_tasks.values(): + if not fut_task.done(): + fut_task.set_exception(exception) + + def start(self): + """Create the Pilot component and pass it. + """ + logger.info("starting RadicalPilotExecutor") + super().start() + logger.info('Parsl: {0}'.format(parsl.__version__)) + logger.info('RADICAL pilot: {0}'.format(rp.version)) + self.session = rp.Session(cfg={'base': self.run_dir}, + uid=ru.generate_id('rpex.session', + mode=ru.ID_PRIVATE)) + logger.info("RPEX session is created: {0}".format(self.session.path)) + + pd_init = {**self.pilot_kwargs, + 'exit_on_error': True, + 'resource': self.resource} + + if not self.resource or 'local' in self.resource: + os.environ["RADICAL_LOG_LVL"] = "DEBUG" + logger.info("RPEX will be running in local mode") + + pd = rp.PilotDescription(pd_init) + pd.verify() + + # start RP's main components TMGR, PMGR and Pilot + self.tmgr = rp.TaskManager(session=self.session) + self.pmgr = rp.PilotManager(session=self.session) + self.pilot = self.pmgr.submit_pilots(pd) + + if not self.pilot.description.get('cores') or not self.pilot.description.get('nodes'): + logger.warning('no "cores/nodes" per pilot were set, using default resources') + + self.tmgr.add_pilots(self.pilot) + self.tmgr.register_callback(self.task_state_cb) + + worker_path = '{0}/rpex_worker.py'.format(PWD) + + self.masters = [] + + logger.info(f'Starting {self.rpex_cfg.n_masters} masters and {self.rpex_cfg.n_workers} workers for each master') + + # create N masters + for _ in range(self.rpex_cfg.n_masters): + md = rp.TaskDescription(self.rpex_cfg.master_descr) + md.uid = ru.generate_id('rpex.master.%(item_counter)06d', ru.ID_CUSTOM, + ns=self.session.uid) + + # submit the master to the TMGR + master = self.tmgr.submit_raptors(md)[0] + self.masters.append(master) + + workers = [] + # create N workers for each master and submit them to the TMGR + for _ in range(self.rpex_cfg.n_workers): + wd = rp.TaskDescription(self.rpex_cfg.worker_descr) + wd.uid = ru.generate_id('rpex.worker.%(item_counter)06d', ru.ID_CUSTOM, + ns=self.session.uid) + wd.raptor_id = master.uid + wd.input_staging = self._stage_files([File(worker_path)], mode='in') + workers.append(wd) + + self.tmgr.submit_workers(workers) + + self.select_master = self._cyclic_master_selector() + + # prepare or use the current env for the agent/pilot side environment + if self.rpex_cfg.pilot_env_mode != CLIENT: + logger.info("creating {0} environment for the executor".format(self.rpex_cfg.pilot_env.name)) + self.pilot.prepare_env(env_name=self.rpex_cfg.pilot_env.name, + env_spec=self.rpex_cfg.pilot_env.as_dict()) + else: + client_env = sys.prefix + logger.info("reusing ({0}) environment for the executor".format(client_env)) + + # create a bulking thread to run the actual task submission + # to RP in bulks + if self.bulk_mode: + self._max_bulk_size = 1024 + self._max_bulk_time = 3 # seconds + self._min_bulk_time = 0.1 # seconds + + self._bulk_queue = queue.Queue() + self._bulk_thread = mt.Thread(target=self._bulk_collector) + + self._bulk_thread.daemon = True + self._bulk_thread.start() + + logger.info('bulk mode is on, submitting tasks in bulks') + + return True + + def _cyclic_master_selector(self): + """ + Balance tasks submission across N masters and N workers + """ + current_master = 0 + masters_uids = [m.uid for m in self.masters] + + while True: + yield masters_uids[current_master] + current_master = (current_master + 1) % len(self.masters) + + def unwrap(self, func, args): + """ + Unwrap a Parsl app and its args for further processing. + + Parameters + ---------- + func : callable + The function to be unwrapped. + + args : tuple + The arguments associated with the function. + + Returns + ------- + tuple + A tuple containing the unwrapped function, adjusted arguments, + and task type information. + """ + + task_type = '' + + while hasattr(func, '__wrapped__'): + func = func.__wrapped__ + + try: + if isinstance(func, partial): + try: + task_type = inspect.getsource(func.args[0]).split('\n')[0] + if BASH in task_type: + task_type = BASH + func = func.args[0] + else: + task_type = PYTHON + + except Exception: + logger.exception('unwrap failed') + + return func, args, task_type + + else: + task_type = inspect.getsource(func).split('\n')[0] + if PYTHON in task_type: + task_type = PYTHON + else: + task_type = '' + except Exception as e: + raise Exception('failed to obtain task type: {0}'.format(e)) + + return func, args, task_type + + def task_translate(self, tid, func, parsl_resource_specification, args, kwargs): + """ + Convert Parsl function to RADICAL-Pilot rp.TaskDescription + """ + + task = rp.TaskDescription() + task.name = func.__name__ + + if parsl_resource_specification and isinstance(parsl_resource_specification, dict): + logger.debug('mapping Parsl resource specifications >> RP resource specifications') + for key, val in parsl_resource_specification.items(): + if key not in task.as_dict(): + key = PARSL_RP_RESOURCE_MAP.get(key, None) + if not key: + logger.warning('ignoring "{0}" key from task resource specification as it is not supported by RP'.format(key)) + continue + setattr(task, key, val) + + func, args, task_type = self.unwrap(func, args) + + if BASH in task_type: + if callable(func): + # if the user specifies the executable mode then we expect the + # a code in a file that need to be executed in an isolated env. + if parsl_resource_specification.get('mode') == rp.TASK_EXECUTABLE: + # These lines of code are from parsl/app/bash.py + try: + # Execute the func to get the command + bash_app = func(*args, **kwargs) + if not isinstance(bash_app, str): + raise ValueError("Expected a str for bash_app cmd," + "got: {0}".format(type(bash_app))) + except AttributeError as e: + raise Exception("failed to obtain bash app cmd") from e + + task.executable = bash_app + task.mode = rp.TASK_EXECUTABLE + + # This is the default mode where the bash_app will be executed as + # as a single core process by RP. For cores > 1 the user must use + # task.mode=rp.TASK_EXECUTABLE (above) or use MPI functions if their + # code is Python. + else: + task.mode = rp.TASK_PROC + task.raptor_id = next(self.select_master) + task.executable = self._pack_and_apply_message(func, args, kwargs) + + elif PYTHON in task_type or not task_type: + task.mode = rp.TASK_FUNCTION + task.raptor_id = next(self.select_master) + if kwargs.get('walltime'): + func = timeout(func, kwargs['walltime']) + + # Check how to serialize the function object + if MPI in self.rpex_cfg.worker_type.lower(): + task.use_mpi = True + task.function = rp.PythonTask(func, *args, **kwargs) + else: + task.use_mpi = False + task.function = self._pack_and_apply_message(func, args, kwargs) + + task.input_staging = self._stage_files(kwargs.get("inputs", []), + mode='in') + task.output_staging = self._stage_files(kwargs.get("outputs", []), + mode='out') + + task.input_staging.extend(self._stage_files(list(args), mode='in')) + + self._set_stdout_stderr(task, kwargs) + + try: + task.verify() + except ru.typeddict.TDKeyError as e: + raise Exception(f'{e}. Please check: https://radicalpilot.readthedocs.io/en/stable/ documentation') + + return task + + def _pack_and_apply_message(self, func, args, kwargs): + try: + buffer = pack_apply_message(func, args, kwargs, buffer_threshold=1 << 20) + task_func = rp.utils.serialize_bson(buffer) + except TypeError: + raise SerializationError(func.__name__) + + return task_func + + def _unpack_and_set_parsl_exception(self, parsl_task, exception): + try: + try: + s = rp.utils.deserialize_bson(exception) + except Exception: + s = exception + + if isinstance(s, RemoteExceptionWrapper): + try: + s.reraise() + except Exception as e: + parsl_task.set_exception(e) + elif isinstance(s, Exception): + parsl_task.set_exception(s) + elif isinstance(s, str): + parsl_task.set_exception(eval(s)) + else: + raise ValueError("Unknown exception-like type received: {}".format(type(s))) + except Exception as e: + parsl_task.set_exception( + DeserializationError("Received exception, but handling also threw an exception: {}".format(e))) + + def _set_stdout_stderr(self, task, kwargs): + """ + set the stdout and stderr of a task + """ + for k in ['stdout', 'stderr']: + k_val = kwargs.get(k, '') + if k_val: + # check the type of the stderr/out + if isinstance(k_val, File): + k_val = k_val.filepath + elif isinstance(k_val, PosixPath): + k_val = k_val.__str__() + + # set the stderr/out to the desired + # name by the user + setattr(task, k, k_val) + task.sandbox = CWD + + def _stage_files(self, files, mode): + """ + a function to stage list of input/output + files between two locations. + """ + to_stage = [] + files = [f for f in files if isinstance(f, File)] + for file in files: + if mode == 'in': + # a workaround RP not supporting + # staging https file + if file.scheme == 'https': + r = requests.get(file.url) + p = CWD + '/' + file.filename + with open(p, 'wb') as ff: + ff.write(r.content) + file = File(p) + + f = {'source': file.url, + 'action': rp.TRANSFER} + to_stage.append(f) + + elif mode == 'out': + # this indicates that the user + # did not provided a specific + # output file and RP will stage out + # the task.stdout from pilot://task_folder + # to the CWD or file.url + if '/' not in file.url: + f = {'source': file.filename, + 'target': file.url, + 'action': rp.TRANSFER} + to_stage.append(f) + else: + raise ValueError('unknown staging mode') + + return to_stage + + def _bulk_collector(self): + + bulk = list() + + while not self._terminate.is_set(): + + now = time.time() # time of last submission + + # collect tasks for min bulk time + # NOTE: total collect time could actually be max_time + min_time + while time.time() - now < self._max_bulk_time: + + try: + task = self._bulk_queue.get(block=True, + timeout=self._min_bulk_time) + except queue.Empty: + task = None + + if task: + bulk.append(task) + + if len(bulk) >= self._max_bulk_size: + break + + if self._terminate.is_set(): + break + + if bulk: + logger.debug('submit bulk: %d', len(bulk)) + self.tmgr.submit_tasks(bulk) + bulk = list() + + def submit(self, func, resource_specification, *args, **kwargs): + """ + Submits tasks in stream mode or bulks (bulk mode) + to RADICAL-Pilot rp.TaskManager. + """ + rp_tid = ru.generate_id('task.%(item_counter)06d', ru.ID_CUSTOM, + ns=self.session.uid) + parsl_tid = int(rp_tid.split('task.')[1]) + + logger.debug("got Task {0} from Parsl-dfk".format(parsl_tid)) + task = self.task_translate(parsl_tid, func, resource_specification, args, kwargs) + + # assign task id for rp task + task.uid = rp_tid + + # set the future with corresponding id + self.future_tasks[rp_tid] = Future() + + if self.bulk_mode: + # push task to rp submit thread + self._bulk_queue.put(task) + else: + # submit the task to rp + logger.debug("put {0} to rp-TMGR".format(rp_tid)) + self.tmgr.submit_tasks(task) + + return self.future_tasks[rp_tid] + + def shutdown(self, hub=True, targets='all', block=False): + """Shutdown the executor, including all RADICAL-Pilot components.""" + logger.info("RadicalPilotExecutor is terminating...") + + self._terminate.set() + + # ensure we are in the bulk submssion mode + if self.bulk_mode: + self._bulk_thread.join() + + self.session.close(download=True) + + super().shutdown() + + logger.info("RadicalPilotExecutor is terminated.") + + return True diff --git a/parsl/parsl/executors/radical/rpex_resources.py b/parsl/parsl/executors/radical/rpex_resources.py new file mode 100644 index 00000000..c337ee33 --- /dev/null +++ b/parsl/parsl/executors/radical/rpex_resources.py @@ -0,0 +1,167 @@ +import json +import sys +from typing import List + +_setup_paths: List[str] = [] +try: + import radical.pilot as rp + import radical.utils as ru +except ImportError: + pass + + +MPI = "mpi" +RP_ENV = "rp" +CLIENT = "client" +RPEX_ENV = "ve_rpex" +MPI_WORKER = "MPIWorker" +DEFAULT_WORKER = "DefaultWorker" + + +class ResourceConfig: + """ + This ResourceConfig class is an abstraction of the resource + configuration of the RAPTOR layer in the RADICAL-Pilot runtime system. + + This class sets up the default configuration values for the executor and + allows the user to specify different resource requirements flexibly. + + For more information: + https://radicalpilot.readthedocs.io/en/stable/tutorials/raptor.html + + Parameters + ---------- + masters : int + The number of masters to be deployed by RAPTOR. + Default is 1. + + workers : int + The number of workers to be deployed by RAPTOR. + Default is 1. + + worker_gpus_per_node : int + The number of GPUs a worker will operate on per node. + Default is 0. + + worker_cores_per_node : int + The number of CPU cores a worker will operate on per node. + Default is 4. + + cores_per_master : int + The number of cores a master will operate on per node. + Default is 1. + + nodes_per_worker : int + The number of nodes to be occupied by every worker. + Default is 1. + + pilot_env_path : str + The path to an exisitng pilot environment. + Default is an empty string (RADICAL-Pilot will create one). + + pilot_env_name : str + The name of the pilot environment. + Default is "ve_rpex". + + pilot_env_pre_exec : list + List of commands to be executed before starting the pilot environment. + Default is an empty list. + + pilot_env_type : str + The type of the pilot environment (e.g., 'venv', 'conda'). + Default is "venv". + + pilot_env_setup : list + List of setup commands/packages for the pilot environment. + Default is an empty list. + + python_v : str + The Python version to be used in the pilot environment. + Default is determined by the system's Python version. + + worker_type : str + The type of worker(s) to be deployed by RAPTOR on the compute + resources. + Default is "DefaultWorker". + """ + + masters: int = 1 + workers: int = 1 + + worker_gpus_per_node: int = 0 + worker_cores_per_node: int = 4 + + cores_per_master: int = 1 + nodes_per_worker: int = 1 + + pilot_env_mode: str = CLIENT + pilot_env_path: str = "" + pilot_env_type: str = "venv" + pilot_env_name: str = RP_ENV + pilot_env_pre_exec: List[str] = [] + pilot_env_setup: List[str] = _setup_paths + + python_v: str = f'{sys.version_info[0]}.{sys.version_info[1]}' + worker_type: str = DEFAULT_WORKER + + def get_config(cls, path=None): + + # Default ENV mode for RP is to reuse + # the client side. If this is not the case, + # then RP will create a new env named ve_rpex + # The user need to make sure that under: + # $HOME/.radical/pilot/configs/*_resource.json + # that virtenv_mode = local + if cls.pilot_env_mode != CLIENT: + cls.pilot_env_name = RPEX_ENV + + if MPI in cls.worker_type.lower() and \ + "mpi4py" not in cls.pilot_env_setup: + cls.pilot_env_setup.append("mpi4py") + + cfg = { + 'n_masters': cls.masters, + 'n_workers': cls.workers, + 'worker_type': cls.worker_type, + 'gpus_per_node': cls.worker_gpus_per_node, + 'cores_per_node': cls.worker_cores_per_node, + 'cores_per_master': cls.cores_per_master, + 'nodes_per_worker': cls.nodes_per_worker, + + 'pilot_env': { + "version": cls.python_v, + "name": cls.pilot_env_name, + "path": cls.pilot_env_path, + "type": cls.pilot_env_type, + "setup": cls.pilot_env_setup, + "pre_exec": cls.pilot_env_pre_exec + }, + + 'pilot_env_mode': cls.pilot_env_mode, + + 'master_descr': { + "ranks": 1, + "cores_per_rank": 1, + "mode": rp.RAPTOR_MASTER, + "named_env": cls.pilot_env_name, + }, + + 'worker_descr': { + "mode": rp.RAPTOR_WORKER, + "named_env": cls.pilot_env_name, + "raptor_file": "./rpex_worker.py", + "raptor_class": cls.worker_type if + cls.worker_type.lower() != MPI else MPI_WORKER, + "ranks": cls.nodes_per_worker * cls.worker_cores_per_node, + "gpus_per_rank": cls.nodes_per_worker * cls.worker_gpus_per_node, + }} + + # Convert the class instance to a Json file or a Config dict. + if path: + config_path = 'rpex.cfg' + config_path = path + '/' + config_path + with open(config_path, 'w') as f: + json.dump(cfg, f, indent=4) + else: + config_obj = ru.Config(from_dict=cfg) + return config_obj diff --git a/parsl/parsl/executors/radical/rpex_worker.py b/parsl/parsl/executors/radical/rpex_worker.py new file mode 100644 index 00000000..9e4f17aa --- /dev/null +++ b/parsl/parsl/executors/radical/rpex_worker.py @@ -0,0 +1,62 @@ +import sys + +import radical.pilot as rp + +import parsl.app.errors as pe +from parsl.app.bash import remote_side_bash_executor +from parsl.executors.execute_task import execute_task +from parsl.serialize import serialize, unpack_apply_message + + +class ParslWorker: + + def _dispatch_func(self, task): + + try: + buffer = rp.utils.deserialize_bson(task['description']['function']) + result = execute_task(buffer) + val = str(serialize(result, buffer_threshold=1000000)) + exc = (None, None) + ret = 0 + out = None + err = None + except Exception: + val = None + exc = (rp.utils.serialize_bson(pe.RemoteExceptionWrapper(*sys.exc_info())), None) + ret = 1 + out = None + err = None + + return out, err, ret, val, exc + + def _dispatch_proc(self, task): + + try: + buffer = rp.utils.deserialize_bson(task['description']['executable']) + func, args, kwargs = unpack_apply_message(buffer) + ret = remote_side_bash_executor(func, *args, **kwargs) + exc = (None, None) + val = None + out = None + err = None + except Exception: + val = None + exc = (rp.utils.serialize_bson(pe.RemoteExceptionWrapper(*sys.exc_info())), None) + ret = 1 + out = None + err = None + + return out, err, ret, val, exc + + +class MPIWorker(rp.raptor.MPIWorker): + def _dispatch_func(self, task): + return super()._dispatch_func(task) + + +class DefaultWorker(rp.raptor.DefaultWorker): + def _dispatch_func(self, task): + return ParslWorker()._dispatch_func(task) + + def _dispatch_proc(self, task): + return ParslWorker()._dispatch_proc(task) diff --git a/parsl/parsl/executors/status_handling.py b/parsl/parsl/executors/status_handling.py new file mode 100644 index 00000000..5d6063ff --- /dev/null +++ b/parsl/parsl/executors/status_handling.py @@ -0,0 +1,373 @@ +from __future__ import annotations + +import datetime +import logging +import threading +import time +from abc import abstractmethod, abstractproperty +from concurrent.futures import Future +from functools import cached_property +from itertools import compress +from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union + +from parsl.executors.base import ParslExecutor +from parsl.executors.errors import BadStateException, ScalingFailed +from parsl.jobs.error_handlers import noop_error_handler, simple_error_handler +from parsl.jobs.states import TERMINAL_STATES, JobState, JobStatus +from parsl.monitoring.message_type import MessageType +from parsl.monitoring.radios.multiprocessing import MultiprocessingQueueRadioSender +from parsl.providers.base import ExecutionProvider +from parsl.utils import AtomicIDCounter + +logger = logging.getLogger(__name__) + + +class BlockProviderExecutor(ParslExecutor): + """A base class for executors which scale using blocks. + + This base class is intended to help with executors which: + + - use blocks of workers to execute tasks + - blocks of workers are launched on a batch system through + an `ExecutionProvider` + + An implementing class should implement the abstract methods required by + `ParslExecutor` to submit tasks, as well as BlockProviderExecutor + abstract methods to provide the executor-specific command to start a block + of workers (the ``_get_launch_command`` method), and some basic scaling + information (``outstanding`` and ``workers_per_node`` properties). + + This base class provides a ``scale_out`` method which will launch new + blocks. It does not provide a ``scale_in`` method, because scale-in + behaviour is not well defined in the Parsl scaling model and so behaviour + is left to individual executors. + + Parsl scaling will provide scaling between min_blocks and max_blocks by + invoking scale_out, but it will not initialize the blocks requested by + any init_blocks parameter. Subclasses must implement that behaviour + themselves. + """ + + def __init__(self, *, + provider: Optional[ExecutionProvider], + block_error_handler: Union[bool, Callable[[BlockProviderExecutor, Dict[str, JobStatus]], None]]): + super().__init__() + self._provider = provider + self.block_error_handler: Callable[[BlockProviderExecutor, Dict[str, JobStatus]], None] + if isinstance(block_error_handler, bool): + if block_error_handler: + self.block_error_handler = simple_error_handler + else: + self.block_error_handler = noop_error_handler + else: + self.block_error_handler = block_error_handler + + self._executor_bad_state = threading.Event() + self._executor_exception: Optional[Exception] = None + + self._block_id_counter = AtomicIDCounter() + + self._tasks = {} # type: Dict[object, Future] + + self._last_poll_time = 0.0 + + # these four structures track, in loosely coordinated fashion, the + # existence of blocks and jobs and how to map between their + # identifiers. + self.blocks_to_job_id = {} # type: Dict[str, str] + self.job_ids_to_block = {} # type: Dict[str, str] + + # errors can happen during the submit call to the provider; this is used + # to keep track of such errors so that they can be handled in one place + # together with errors reported by status() + self._simulated_status: Dict[str, JobStatus] = {} + + # this stores an approximation (sometimes delayed) of the latest status + # of pending, active and recently terminated blocks + self._status = {} # type: Dict[str, JobStatus] + + self.submit_monitoring_radio: Optional[MultiprocessingQueueRadioSender] = None + + @cached_property + def _scale_prefix(self) -> str: + """Prefix used for logging messages about scaling""" + return f'[Scaling executor {self.label}]' + + def start(self): + super().start() + if self.monitoring_messages: + self.submit_monitoring_radio = MultiprocessingQueueRadioSender(self.monitoring_messages) + + def _make_status_dict(self, block_ids: Sequence[str], status_list: Sequence[JobStatus]) -> Dict[str, JobStatus]: + """Given a sequence of block ids and a sequence of corresponding status strings, + returns a dictionary mapping each block id to the corresponding status + + :param block_ids: the sequence of block ids + :param status_list: the sequence of job status strings + :return: the resulting dictionary + """ + if len(block_ids) != len(status_list): + raise IndexError("block id list and status string list differ in size") + d = {} + for i in range(len(block_ids)): + d[block_ids[i]] = status_list[i] + + return d + + @property + def status_polling_interval(self): + """Returns the interval, in seconds, at which the status method should be called. The + assumption here is that, once initialized, an executor's polling interval is fixed. + In practice, at least given the current situation, the executor uses a single task provider + and this method is a delegate to the corresponding method in the provider. + + :return: the number of seconds to wait between calls to status() or zero if no polling + should be done + """ + if self._provider is None: + return 0 + else: + return self._provider.status_polling_interval + + @abstractmethod + def outstanding(self) -> int: + """This should return the number of tasks that the executor has been given to run (waiting to run, and running now)""" + + raise NotImplementedError("Classes inheriting from BlockProviderExecutor must implement " + "outstanding()") + + def set_bad_state_and_fail_all(self, exception: Exception): + """Allows external error handlers to mark this executor as irrecoverably bad and cause + all tasks submitted to it now and in the future to fail. The executor is responsible + for checking :method:bad_state_is_set() in the :method:submit() method and raising the + appropriate exception, which is available through :method:executor_exception(). + """ + logger.exception("Setting bad state due to exception", exc_info=exception) + self._executor_exception = exception + # Set bad state to prevent new tasks from being submitted + self._executor_bad_state.set() + # We set all current tasks to this exception to make sure that + # this is raised in the main context. + for task in self._tasks: + self._tasks[task].set_exception(BadStateException(self, self._executor_exception)) + + @property + def bad_state_is_set(self): + """Returns true if this executor is in an irrecoverable error state. If this method + returns true, :property:executor_exception should contain an exception indicating the + cause. + """ + return self._executor_bad_state.is_set() + + @property + def executor_exception(self): + """Returns an exception that indicates why this executor is in an irrecoverable state.""" + return self._executor_exception + + def handle_errors(self, status: Dict[str, JobStatus]) -> None: + """This method is called by the error management infrastructure after a status poll. The + executor implementing this method is then responsible for detecting abnormal conditions + based on the status of submitted jobs. If the executor does not implement any special + error handling, this method should return False, in which case a generic error handling + scheme will be used. + :param status: status of all jobs launched by this executor + """ + self.block_error_handler(self, status) + + @property + def tasks(self) -> Dict[object, Future]: + return self._tasks + + @property + def provider(self) -> Optional[ExecutionProvider]: + return self._provider + + def _filter_scale_in_ids(self, to_kill: Sequence[Any], killed: Sequence[bool]) -> Sequence[Any]: + """ Filter out job id's that were not killed + """ + assert len(to_kill) == len(killed) + + if False in killed: + killed_job_ids = [jid for jid, k in zip(to_kill, killed) if k] + not_killed_job_ids = [jid for jid, k in zip(to_kill, killed) if not k] + logger.warning("Some jobs were not killed successfully: " + f"killed jobs: {killed_job_ids}, " + f"not-killed jobs: {not_killed_job_ids}") + + # Filters first iterable by bool values in second + return list(compress(to_kill, killed)) + + def scale_out_facade(self, n: int) -> List[str]: + """Scales out the number of blocks by "blocks" + """ + assert self.provider is not None, \ + "scale_out_facade should only be called on executors with a valid provider" + block_ids = [] + monitoring_status_changes = {} + logger.info("%s Scaling out by %d blocks", self._scale_prefix, n) + for _ in range(n): + block_id = str(self._block_id_counter.get_id()) + logger.info("%s Allocated block ID %s", self._scale_prefix, block_id) + try: + job_id = self._launch_block(block_id) + + pending_status = JobStatus(JobState.PENDING) + + self.blocks_to_job_id[block_id] = job_id + self.job_ids_to_block[job_id] = block_id + self._status[block_id] = pending_status + + monitoring_status_changes[block_id] = pending_status + block_ids.append(block_id) + + except Exception as ex: + failed_status = JobStatus(JobState.FAILED, "Failed to start block {}: {}".format(block_id, ex)) + self._simulated_status[block_id] = failed_status + self._status[block_id] = failed_status + + self.send_monitoring_info(monitoring_status_changes) + return block_ids + + def scale_in(self, blocks: int) -> List[str]: + """Scale in method. + + Cause the executor to reduce the number of blocks by count. + + The default implementation will kill blocks without regard to their + status or whether they are executing tasks. Executors with more + nuanced scaling strategies might overload this method to work with + that strategy - see the HighThroughputExecutor for an example of that. + + :return: A list of block ids corresponding to the blocks that were removed. + """ + + active_blocks = [block_id for block_id, status in self._status.items() + if status.state not in TERMINAL_STATES] + + block_ids_to_kill = active_blocks[:blocks] + + job_ids_to_kill = [self.blocks_to_job_id[block] for block in block_ids_to_kill] + + # Cancel the blocks provisioned + if self.provider: + logger.info("%s Scaling in jobs: %s", self._scale_prefix, str(job_ids_to_kill)) + r = self.provider.cancel(job_ids_to_kill) + job_ids = self._filter_scale_in_ids(job_ids_to_kill, r) + block_ids_killed = [self.job_ids_to_block[job_id] for job_id in job_ids] + return block_ids_killed + else: + logger.error("%s No execution provider available to scale in", self._scale_prefix) + return [] + + def _launch_block(self, block_id: str) -> Any: + assert self.provider is not None, \ + "_launch_block should only be called on executors with a valid provider" + + launch_cmd = self._get_launch_command(block_id) + job_name = f"parsl.{self.label}.block-{block_id}" + logger.debug("%s Submitting to provider with job_name %s", self._scale_prefix, job_name) + job_id = self.provider.submit(launch_cmd, 1, job_name) + if job_id: + logger.debug("%s Launched block %s with job ID %s", self._scale_prefix, block_id, str(job_id)) + else: + raise ScalingFailed(self, "Attempt to provision nodes did not return a job ID") + return job_id + + @abstractmethod + def _get_launch_command(self, block_id: str) -> str: + pass + + def _get_block_and_job_ids(self) -> Tuple[List[str], List[Any]]: + # Not using self.blocks.keys() and self.blocks.values() simultaneously + # The dictionary may be changed during invoking this function + # As scale_in and scale_out are invoked in multiple threads + block_ids = list(self.blocks_to_job_id.keys()) + job_ids = [] # types: List[Any] + for bid in block_ids: + job_ids.append(self.blocks_to_job_id[bid]) + return block_ids, job_ids + + @abstractproperty + def workers_per_node(self) -> Union[int, float]: + pass + + def send_monitoring_info(self, status: Dict) -> None: + # Send monitoring info for HTEX when monitoring enabled + if self.submit_monitoring_radio: + msg = self.create_monitoring_info(status) + logger.debug("Sending block monitoring message: %r", msg) + self.submit_monitoring_radio.send((MessageType.BLOCK_INFO, msg)) + + def create_monitoring_info(self, status: Dict[str, JobStatus]) -> Sequence[Dict[str, Any]]: + """Create a monitoring message for each block based on the poll status. + """ + return [ + { + "run_id": self.run_id, + "status": s.status_name, + "timestamp": datetime.datetime.now(), + "executor_label": self.label, + "job_id": self.blocks_to_job_id.get(bid, None), + "block_id": bid + } + for bid, s in status.items() + ] + + def poll_facade(self) -> None: + now = time.time() + if now >= self._last_poll_time + self.status_polling_interval: + previous_status = self._status + self._status = self.status() + self._last_poll_time = now + delta_status = {} + for block_id in self._status: + if block_id not in previous_status \ + or previous_status[block_id].state != self._status[block_id].state: + delta_status[block_id] = self._status[block_id] + + if delta_status: + self.send_monitoring_info(delta_status) + + def status(self) -> Dict[str, JobStatus]: + """Return the status of all jobs/blocks currently known to this executor. + + :return: a dictionary mapping block ids (in string) to job status + """ + if self._provider: + block_ids, job_ids = self._get_block_and_job_ids() + status = self._make_status_dict(block_ids, self._provider.status(job_ids)) + else: + status = {} + status.update(self._simulated_status) + + return status + + @property + def status_facade(self) -> Dict[str, JobStatus]: + """Return the status of all jobs/blocks of the executor of this poller. + + :return: a dictionary mapping block ids (in string) to job status + """ + return self._status + + def scale_in_facade(self, n: int, max_idletime: Optional[float] = None) -> List[str]: + + if max_idletime is None: + block_ids = self.scale_in(n) + else: + # This is a HighThroughputExecutor-specific interface violation. + # This code hopes, through pan-codebase reasoning, that this + # scale_in method really does come from HighThroughputExecutor, + # and so does have an extra max_idletime parameter not present + # in the executor interface. + block_ids = self.scale_in(n, max_idletime=max_idletime) # type: ignore[call-arg] + if block_ids is not None: + new_status = {} + for block_id in block_ids: + logger.debug("Marking block %s as SCALED_IN", block_id) + s = JobStatus(JobState.SCALED_IN) + new_status[block_id] = s + self._status[block_id] = s + self._simulated_status[block_id] = s + self.send_monitoring_info(new_status) + return block_ids diff --git a/parsl/parsl/executors/taskvine/__init__.py b/parsl/parsl/executors/taskvine/__init__.py new file mode 100644 index 00000000..a9f0c723 --- /dev/null +++ b/parsl/parsl/executors/taskvine/__init__.py @@ -0,0 +1,5 @@ +from parsl.executors.taskvine.executor import TaskVineExecutor +from parsl.executors.taskvine.factory_config import TaskVineFactoryConfig +from parsl.executors.taskvine.manager_config import TaskVineManagerConfig + +__all__ = ['TaskVineExecutor', 'TaskVineManagerConfig', 'TaskVineFactoryConfig'] diff --git a/parsl/parsl/executors/taskvine/errors.py b/parsl/parsl/executors/taskvine/errors.py new file mode 100644 index 00000000..3d7aac94 --- /dev/null +++ b/parsl/parsl/executors/taskvine/errors.py @@ -0,0 +1,29 @@ +from parsl.app.errors import AppException +from parsl.errors import ParslError + + +class TaskVineTaskFailure(AppException): + """A failure executing a task in TaskVine + + Contains: + reason(string) + status(int) + """ + + def __init__(self, reason: str, status: int): + self.reason = reason + self.status = status + + +class TaskVineManagerFailure(ParslError): + """A failure in the taskvine executor that prevented the task to be + executed. + """ + pass + + +class TaskVineFactoryFailure(ParslError): + """A failure in the TaskVine factory that prevents the factory from + supplying workers to the manager. + """ + pass diff --git a/parsl/parsl/executors/taskvine/exec_parsl_function.py b/parsl/parsl/executors/taskvine/exec_parsl_function.py new file mode 100644 index 00000000..cf02ced0 --- /dev/null +++ b/parsl/parsl/executors/taskvine/exec_parsl_function.py @@ -0,0 +1,185 @@ +import sys +import traceback + +from parsl.app.errors import RemoteExceptionWrapper +from parsl.data_provider.files import File +from parsl.serialize import deserialize, serialize +from parsl.utils import get_std_fname_mode + +# This scripts executes a parsl function which is pickled in 4 files: +# +# exec_parsl_function.py map_file function_file argument_file result_file +# +# map_file: Contains a pickled dictionary that indicates which local_paths the +# parsl Files should take. +# +# function_file: Contains a pickle parsl function. Function might be serialized in advance. +# See @parsl.serialize.concretes.py +# +# argument_file: Contains the serialized arguments to the function call. +# +# result_file: A file path, whose content will contain the result of the function, including any +# exception generated. Exceptions will be wrapped with RemoteExceptionWrapper. +# +# Exit codes: +# 0: The function was evaluated to completion. The result or any exception +# wrapped with RemoteExceptionWrapper were written to result_file. +# anything else: There was an error that prevented writing to the result file altogether. +# The exit code corresponds to whatever the python interpreter gives. +# + + +def dump_result_to_file(result_file: str, result): + """ Dump a result to the given result file.""" + with open(result_file, "wb") as f_out: + f_out.write(serialize(result)) + + +def remap_location(mapping, parsl_file): + """ Remap files from local name (on manager) to remote name (on worker).""" + if not isinstance(parsl_file, File): + return + # Below we rewrite .local_path when scheme != file only when the local_name + # was given by the main parsl process. This is the case when scheme != + # 'file' but .local_path (via filepath) is in mapping. + if parsl_file.scheme == 'file' or parsl_file.local_path: + master_location = parsl_file.filepath + if master_location in mapping: + parsl_file.local_path = mapping[master_location] + + +def remap_list_of_files(mapping, maybe_files): + """ Remap a list of potential files.""" + for maybe_file in maybe_files: + remap_location(mapping, maybe_file) + + +def remap_all_files(mapping, fn_args, fn_kwargs): + # remap any positional argument given to the function that looks like a + # File + remap_list_of_files(mapping, fn_args) + + # remap any keyword argument in the same way, but we need to treat + # "inputs" and "outputs" specially because they are lists, and + # "stdout" and "stderr", because they are not File's. + for kwarg, maybe_file in fn_kwargs.items(): + if kwarg in ["inputs", "outputs"]: + remap_list_of_files(mapping, maybe_file) + if kwarg in ["stdout", "stderr"]: + if maybe_file: + (fname, mode) = get_std_fname_mode(kwarg, maybe_file) + if fname in mapping: + fn_kwargs[kwarg] = (mapping[fname], mode) + else: + # Treat anything else as a possible File to be remapped. + remap_location(mapping, maybe_file) + + +def unpack_object_from_file(path): + with open(path, 'rb') as f: + obj = deserialize(f.read()) + return obj + + +def encode_function(user_namespace, fn, fn_name, fn_args, fn_kwargs): + """ Register the given function to the given namespace.""" + # Returns a tuple (code, result_name) + # code can be exec in the user_namespace to produce result_name. + prefix = "parsl_" + args_name = prefix + "args" + kwargs_name = prefix + "kwargs" + result_name = prefix + "result" + + # Add variables to the namespace to make function call + user_namespace.update({args_name: fn_args, + kwargs_name: fn_kwargs, + result_name: result_name}) + + if callable(fn): + code = encode_byte_code_function(user_namespace, fn, fn_name, args_name, kwargs_name, result_name) + else: + raise ValueError("Function object does not look like a function.") + + return (code, result_name) + + +def encode_byte_code_function(user_namespace, fn, fn_name, args_name, kwargs_name, result_name): + user_namespace.update({fn_name: fn}) + code = "{0} = {1}(*{2}, **{3})".format(result_name, fn_name, args_name, kwargs_name) + return code + + +def load_function(map_file, function_file, argument_file): + # Decodes the function and its file arguments to be executed into + # function_code, and updates a user namespace with the function name and + # the variable named `result_name`. When the function is executed, its result + # will be stored in this variable in the user namespace. + # Returns (namespace, function_code, result_name) + + fn = unpack_object_from_file(function_file) + args_dict = unpack_object_from_file(argument_file) + fn_args = args_dict['args'] + fn_kwargs = args_dict['kwargs'] + fn_name = 'parsl_tmp_func_name' + + mapping = unpack_object_from_file(map_file) + remap_all_files(mapping, fn_args, fn_kwargs) + + # Create the namespace to isolate the function execution. + user_ns = locals() + user_ns.update({'__builtins__': __builtins__}) + + (code, result_name) = encode_function(user_ns, fn, fn_name, fn_args, fn_kwargs) + + return (user_ns, code, result_name) + + +def execute_function(namespace, function_code, result_name): + # On executing the function inside the namespace, its result will be in a + # variable named result_name. + exec(function_code, namespace, namespace) + result = namespace.get(result_name) + + return result + + +def run(map_file, function_file, argument_file, result_file): + try: + try: + (namespace, function_code, result_name) = load_function(map_file, function_file, argument_file) + except Exception: + print("There was an error setting up the function for execution.") + raise + + try: + result = execute_function(namespace, function_code, result_name) + except Exception: + print("There was an error executing the function.") + raise + except Exception: + traceback.print_exc() + result = RemoteExceptionWrapper(*sys.exc_info()) + + # Write out function result to the result file + try: + dump_result_to_file(result_file, result) + except Exception: + print("Could not write to result file.") + traceback.print_exc() + sys.exit(1) + + +if __name__ == "__main__": + # parse the four required command line arguments: + # map_file: contains a pickled dictionary to map original names to + # names at the execution site. + # function_file: contains the pickled parsl function to execute. + # argument_file: contains the pickled arguments to the function call. + # result_file: any output (including exceptions) will be written to + # this file. + try: + (map_file, function_file, argument_file, result_file) = sys.argv[1:] + except ValueError: + print("Usage:\n\t{} function argument result mapping\n".format(sys.argv[0])) + raise + run(map_file, function_file, argument_file, result_file) diff --git a/parsl/parsl/executors/taskvine/executor.py b/parsl/parsl/executors/taskvine/executor.py new file mode 100644 index 00000000..1da10415 --- /dev/null +++ b/parsl/parsl/executors/taskvine/executor.py @@ -0,0 +1,673 @@ +""" TaskVineExecutor utilizes the TaskVine distributed framework developed by the +Cooperative Computing Lab (CCL) at Notre Dame to provide a fault-tolerant, +high-throughput system for delegating Parsl tasks to thousands of remote machines +""" + +import getpass +import hashlib +import inspect +import itertools +import logging +import multiprocessing +import os +import queue +import shutil +import subprocess +import tempfile + +# Import Python built-in libraries +import threading +import uuid +from concurrent.futures import Future +from datetime import datetime +from typing import List, Literal, Optional, Union + +# Import other libraries +import typeguard + +# Import Parsl constructs +import parsl.utils as putils +from parsl.addresses import get_any_address +from parsl.data_provider.files import File +from parsl.data_provider.staging import Staging +from parsl.errors import OptionalModuleMissing +from parsl.executors.errors import ExecutorError +from parsl.executors.status_handling import BlockProviderExecutor +from parsl.executors.taskvine import exec_parsl_function +from parsl.executors.taskvine.errors import TaskVineManagerFailure, TaskVineTaskFailure +from parsl.executors.taskvine.factory import _taskvine_factory +from parsl.executors.taskvine.factory_config import TaskVineFactoryConfig +from parsl.executors.taskvine.manager import _taskvine_submit_wait +from parsl.executors.taskvine.manager_config import TaskVineManagerConfig +from parsl.executors.taskvine.utils import ParslFileToVine, ParslTaskToVine +from parsl.monitoring.radios.base import RadioConfig +from parsl.monitoring.radios.filesystem import FilesystemRadio +from parsl.multiprocessing import SpawnContext +from parsl.process_loggers import wrap_with_logs +from parsl.providers import CondorProvider, LocalProvider +from parsl.providers.base import ExecutionProvider +from parsl.serialize import deserialize, serialize + +logger = logging.getLogger(__name__) + + +class TaskVineExecutor(BlockProviderExecutor, putils.RepresentationMixin): + """Executor to use TaskVine dynamic workflow system + + The TaskVineExecutor system utilizes the TaskVine framework to + efficiently delegate Parsl apps to remote machines in clusters and + grids using a fault-tolerant system. Users can run the + vine_worker program on remote machines to connect to the + TaskVineExecutor, and Parsl apps will then be sent out to these + machines for execution and retrieval. + + This Executor sets up configurations for the TaskVine manager, TaskVine + factory, and run both in separate processes. Sending tasks and receiving + results are done through multiprocessing module native to Python. + + Parameters + ---------- + + label: str + A human readable label for the executor, unique + with respect to other executors. + Default is "TaskVineExecutor". + + worker_launch_method: Union[Literal['provider'], Literal['factory'], Literal['manual']] + Choose to use Parsl provider, TaskVine factory, or + manual user-provided workers to scale workers. + Options are among {'provider', 'factory', 'manual'}. + Default is 'factory'. + + function_exec_mode: Union[Literal['regular'], Literal['serverless']] + Choose to execute functions with a regular fresh python process or a + pre-warmed forked python process. + Default is 'regular'. + + manager_config: TaskVineManagerConfig + Configuration for the TaskVine manager. Default + + factory_config: TaskVineFactoryConfig + Configuration for the TaskVine factory. + Use of factory is disabled by default. + + provider: ExecutionProvider + The Parsl provider that will spawn worker processes. + Default to spawning one local vine worker process. + + storage_access: List[Staging] + Define Parsl file staging providers for this executor. + Default is None. + """ + + @typeguard.typechecked + def __init__(self, + label: str = "TaskVineExecutor", + worker_launch_method: Union[Literal['provider'], Literal['factory'], Literal['manual']] = 'factory', + function_exec_mode: Union[Literal['regular'], Literal['serverless']] = 'regular', + manager_config: TaskVineManagerConfig = TaskVineManagerConfig(), + factory_config: TaskVineFactoryConfig = TaskVineFactoryConfig(), + provider: Optional[ExecutionProvider] = None, + storage_access: Optional[List[Staging]] = None, + remote_monitoring_radio: Optional[RadioConfig] = None): + + # Set worker launch option for this executor + if worker_launch_method == 'factory' or worker_launch_method == 'manual': + provider = None + elif worker_launch_method == 'provider' and provider is None: + # provider method chosen, but no explicit provider supplied to __init__ + # so default to LocalProvider + provider = LocalProvider(init_blocks=1) + + # Initialize the parent class with the execution provider and block error handling enabled. + # If provider is None, then no worker is launched via the provider method. + BlockProviderExecutor.__init__(self, provider=provider, + block_error_handler=True) + + # Raise an exception if there's a problem importing TaskVine + try: + import ndcctools.taskvine + logger.debug(f'TaskVine default port: {ndcctools.taskvine.cvine.VINE_DEFAULT_PORT}') + except ImportError: + raise OptionalModuleMissing(['taskvine'], "TaskVineExecutor requires the taskvine module.") + + # Executor configurations + self.label = label + self.worker_launch_method = worker_launch_method + self.function_exec_mode = function_exec_mode + self.manager_config = manager_config + self.factory_config = factory_config + self.storage_access = storage_access + + if remote_monitoring_radio is not None: + self.remote_monitoring_radio = remote_monitoring_radio + else: + self.remote_monitoring_radio = FilesystemRadio() + + # Queue to send ready tasks from TaskVine executor process to TaskVine manager process + self._ready_task_queue: multiprocessing.Queue = SpawnContext.Queue() + + # Queue to send finished tasks from TaskVine manager process to TaskVine executor process + self._finished_task_queue: multiprocessing.Queue = SpawnContext.Queue() + + # Event to signal whether the manager and factory processes should stop running + self._should_stop = SpawnContext.Event() + + # TaskVine manager process + self._submit_process = None + + # TaskVine factory process + self._factory_process = None + + # Executor thread to collect results from TaskVine manager and set + # tasks' futures to done status. + self._collector_thread = None + + # track task id of submitted parsl tasks + # task ids are incremental and start from 0 + self._executor_task_counter = 0 + + # track number of tasks that are waiting/running + self._outstanding_tasks = 0 + + # Lock for threads to access self._outstanding_tasks attribute + self._outstanding_tasks_lock = threading.Lock() + + # Threading lock to manage self.tasks dictionary object, which maps a task id + # to its future object. + self._tasks_lock = threading.Lock() + + # Worker command to be given to an execution provider (e.g., local or Condor) + self._worker_command = "" + + # Path to directory that holds all tasks' data and results. + self._function_data_dir = "" + + # Helper scripts to prepare package tarballs for Parsl apps + self._package_analyze_script = shutil.which("poncho_package_analyze") + self._package_create_script = shutil.which("poncho_package_create") + if self._package_analyze_script is None or self._package_create_script is None: + self._poncho_available = False + else: + self._poncho_available = True + + def _get_launch_command(self, block_id): + # Implements BlockProviderExecutor's abstract method. + # This executor uses different terminology for worker/launch + # commands than in htex. + return f"PARSL_WORKER_BLOCK_ID={block_id} {self._worker_command}" + + def __synchronize_manager_factory_comm_settings(self): + # Synchronize the communication settings between the manager and the factory + # so the factory can direct workers to contact the manager. + + # If the manager can choose any available port (port number = 0), + # then it must have a project name + # so the factory can look it up. Otherwise the factory process will not know the + # port number as it's only chosen when the TaskVine manager process is run. + if self.manager_config.port == 0 and self.manager_config.project_name is None: + self.manager_config.project_name = "parsl-vine-" + str(uuid.uuid4()) + + # guess the host name if the project name is not given and none has been supplied + # explicitly in the manager config. + if not self.manager_config.project_name and self.manager_config.address is None: + self.manager_config.address = get_any_address() + + # Factory communication settings are overridden by manager communication settings. + self.factory_config._project_port = self.manager_config.port + self.factory_config._project_address = self.manager_config.address + self.factory_config._project_name = self.manager_config.project_name + self.factory_config._project_password_file = self.manager_config.project_password_file + logger.debug('Communication settings between TaskVine manager and factory synchronized.') + + def __create_data_and_logging_dirs(self): + # Create neccessary data and logging directories + + # Use the current run directory from Parsl + run_dir = self.run_dir + + # Create directories for data and results + log_dir = os.path.join(run_dir, self.label) + os.makedirs(log_dir) + tmp_prefix = f'{self.label}-{getpass.getuser()}-{datetime.now().strftime("%Y%m%d%H%M%S%f")}-' + self._function_data_dir = tempfile.TemporaryDirectory(prefix=tmp_prefix) + + # put TaskVine logs outside of a Parsl run as TaskVine caches between runs while + # Parsl does not. + vine_log_dir = os.path.join(os.path.dirname(run_dir), self.label) + if self.manager_config.vine_log_dir is None: + self.manager_config.vine_log_dir = vine_log_dir + + # factory logs go with manager logs regardless + self.factory_config.scratch_dir = self.manager_config.vine_log_dir + logger.debug(f"Function data directory: {self._function_data_dir.name}, log directory: {log_dir}") + logger.debug( + f"TaskVine manager log directory: {self.manager_config.vine_log_dir}, " + f"factory log directory: {self.factory_config.scratch_dir}") + + def start(self): + """Create submit process and collector thread to create, send, and + retrieve Parsl tasks within the TaskVine system. + """ + + super().start() + # Synchronize connection and communication settings between the manager and factory + self.__synchronize_manager_factory_comm_settings() + + # Create data and logging dirs + self.__create_data_and_logging_dirs() + + logger.debug("Starting TaskVineExecutor") + + # Create a process to run the TaskVine manager. + submit_process_kwargs = {"ready_task_queue": self._ready_task_queue, + "finished_task_queue": self._finished_task_queue, + "should_stop": self._should_stop, + "manager_config": self.manager_config} + self._submit_process = SpawnContext.Process(target=_taskvine_submit_wait, + name="TaskVine-Submit-Process", + kwargs=submit_process_kwargs) + + # Create a process to run the TaskVine factory if enabled. + if self.worker_launch_method == 'factory': + factory_process_kwargs = {"should_stop": self._should_stop, + "factory_config": self.factory_config} + self._factory_process = SpawnContext.Process(target=_taskvine_factory, + name="TaskVine-Factory-Process", + kwargs=factory_process_kwargs) + + # Run thread to collect results and set tasks' futures. + self._collector_thread = threading.Thread(target=self._collect_taskvine_results, + name="TaskVine-Collector-Thread") + # Interpreter can exit without waiting for this thread. + self._collector_thread.daemon = True + + # Begin work + self._submit_process.start() + + # Run worker scaler either with Parsl provider or TaskVine factory. + # Skip if workers are launched manually. + if self.worker_launch_method == 'factory': + self._factory_process.start() + elif self.worker_launch_method == 'provider': + self.initialize_scaling() + + self._collector_thread.start() + + logger.debug("All components in TaskVineExecutor started") + + def _path_in_task(self, executor_task_id, *path_components): + """ + Returns a filename fixed and specific to a task. + It is used for the following filename's: + (not given): The subdirectory per task that contains function, result, etc. + 'function': Pickled file that contains the function to be executed. + 'argument': Pickled file that contains the arguments of the function call. + 'result': Pickled file that (will) contain the result of the function. + 'map': Pickled file with a dict between local parsl names, and remote taskvine names. + """ + task_dir = "{:04d}".format(executor_task_id) + return os.path.join(self._function_data_dir.name, task_dir, *path_components) + + def submit(self, func, resource_specification, *args, **kwargs): + """Processes the Parsl app by its arguments and submits the function + information to the task queue, to be executed using the TaskVine + system. The args and kwargs are processed for input and output files to + the Parsl app, so that the files are appropriately specified for the TaskVine task. + + Parameters + ---------- + + func : function + Parsl app to be submitted to the TaskVine system + resource_specification: dict + Dictionary containing relevant info about task. + Include information about resources of task, execution mode + of task (out of {regular, serverless}). + args : list + Arguments to the Parsl app + kwargs : dict + Keyword arguments to the Parsl app + """ + + logger.debug(f'Got resource specification: {resource_specification}') + + # Default execution mode of apps is regular + exec_mode = resource_specification.get('exec_mode', self.function_exec_mode) + + # Detect resources and features of a submitted Parsl app + cores = None + memory = None + disk = None + gpus = None + priority = None + category = None + running_time_min = None + if resource_specification and isinstance(resource_specification, dict): + for k in resource_specification: + if k == 'cores': + cores = resource_specification[k] + elif k == 'memory': + memory = resource_specification[k] + elif k == 'disk': + disk = resource_specification[k] + elif k == 'gpus': + gpus = resource_specification[k] + elif k == 'priority': + priority = resource_specification[k] + elif k == 'category': + category = resource_specification[k] + elif k == 'running_time_min': + running_time_min = resource_specification[k] + + # Assign executor task id to app + executor_task_id = self._executor_task_counter + self._executor_task_counter += 1 + + # Create a per task directory for the function, argument, map, and result files + os.mkdir(self._path_in_task(executor_task_id)) + + input_files = [] + output_files = [] + + # Determine whether to stage input files that will exist at the workers + # Input and output files are always cached + input_files += [self._register_file(f) for f in kwargs.get("inputs", []) if isinstance(f, File)] + output_files += [self._register_file(f) for f in kwargs.get("outputs", []) if isinstance(f, File)] + + # Also consider any *arg that looks like a file as an input: + input_files += [self._register_file(f) for f in args if isinstance(f, File)] + + for kwarg, maybe_file in kwargs.items(): + # Add appropriate input and output files from "stdout" and "stderr" keyword arguments + if kwarg == "stdout" or kwarg == "stderr": + if maybe_file: + output_files.append(self._std_output_to_vine(kwarg, maybe_file)) + # For any other keyword that looks like a file, assume it is an input file + elif isinstance(maybe_file, File): + input_files.append(self._register_file(maybe_file)) + + # Create a Future object and have it be mapped from the task ID in the tasks dictionary + fu = Future() + fu.parsl_executor_task_id = executor_task_id + with self._tasks_lock: + self.tasks[str(executor_task_id)] = fu + + # Setup files to be used on a worker to execute the function + function_file = None + argument_file = None + result_file = None + map_file = None + + # Get path to files that will contain the pickled function, + # arguments, result, and map of input and output files + function_file = self._path_in_task(executor_task_id, "function") + argument_file = self._path_in_task(executor_task_id, "argument") + result_file = self._path_in_task(executor_task_id, "result") + map_file = self._path_in_task(executor_task_id, "map") + + logger.debug("Creating executor task {} with function at: {}, argument at: {}, \ + and result to be found at: {}".format(executor_task_id, function_file, argument_file, result_file)) + + # Serialize function object and arguments, separately + self._serialize_object_to_file(function_file, func) + args_dict = {'args': args, 'kwargs': kwargs} + self._serialize_object_to_file(argument_file, args_dict) + + # Construct the map file of local filenames at worker + self._construct_map_file(map_file, input_files, output_files) + + # Register a tarball containing all package dependencies for this app if instructed + if self.manager_config.app_pack: + env_pkg = self._prepare_package(func, self.extra_pkgs) + else: + env_pkg = None + + # Create message to put into the message queue + logger.debug("Placing task {} on message queue".format(executor_task_id)) + + # Put apps into their categories based on function name if enabled + if category is None: + category = func.__name__ if self.manager_config.autocategory else 'parsl-default' + + task_info = ParslTaskToVine(executor_id=executor_task_id, + exec_mode=exec_mode, + category=category, + input_files=input_files, + output_files=output_files, + map_file=map_file, + function_file=function_file, + argument_file=argument_file, + result_file=result_file, + cores=cores, + memory=memory, + disk=disk, + gpus=gpus, + priority=priority, + running_time_min=running_time_min, + env_pkg=env_pkg) + + # Send ready task to manager process + if not self._submit_process.is_alive(): + raise ExecutorError(self, "taskvine Submit Process is not alive") + + self._ready_task_queue.put_nowait(task_info) + + # Increment outstanding task counter + with self._outstanding_tasks_lock: + self._outstanding_tasks += 1 + + # Return the future for this function, will be set by collector thread when result + # comes back from the TaskVine manager. + return fu + + def _construct_worker_command(self): + worker_command = self.factory_config.worker_executable + if self.factory_config._project_password_file: + worker_command += ' --password {}'.format(self.factory_config._project_password_file) + if self.factory_config.worker_options: + worker_command += ' {}'.format(self.factory_config.worker_options) + if self.factory_config._project_name: + worker_command += ' -M {}'.format(self.factory_config._project_name) + else: + worker_command += ' {} {}'.format(self.factory_config._project_address, self.factory_config._project_port) + + logger.debug("Using worker command: {}".format(worker_command)) + return worker_command + + def _patch_providers(self): + # Add the worker and password file to files that the provider needs to stage. + # (Currently only for the CondorProvider) + if isinstance(self.provider, CondorProvider): + path_to_worker = shutil.which('vine_worker') + self._worker_command = './' + self._worker_command + self.provider.transfer_input_files.append(path_to_worker) + if self.project_password_file: + self.provider.transfer_input_files.append(self.project_password_file) + + def _serialize_object_to_file(self, path, obj): + """Takes any object and serializes it to the file path.""" + serialized_obj = serialize(obj, buffer_threshold=1024 * 1024) + with open(path, 'wb') as f_out: + written = 0 + while written < len(serialized_obj): + written += f_out.write(serialized_obj[written:]) + + def _construct_map_file(self, map_file, input_files, output_files): + """ Map local filepath of parsl files to the filenames at the execution worker. + If using a shared filesystem, the filepath is mapped to its absolute filename. + Otherwise, to its original relative filename. In this later case, taskvine + recreates any directory hierarchy needed.""" + file_translation_map = {} + for spec in itertools.chain(input_files, output_files): + local_name = spec.parsl_name + if self.manager_config.shared_fs: + remote_name = os.path.abspath(local_name) + else: + remote_name = local_name + file_translation_map[local_name] = remote_name + self._serialize_object_to_file(map_file, file_translation_map) + + def _register_file(self, parsl_file): + """Generates a tuple (parsl_file.filepath, stage, cache) to give to + taskvine. cache is always True. + stage is True if the file has a relative path. (i.e., not + a URL or an absolute path)""" + to_cache = True + to_stage = False + if parsl_file.scheme == 'file' or \ + (parsl_file.local_path and os.path.exists(parsl_file.local_path)): + to_stage = not os.path.isabs(parsl_file.filepath) + + return ParslFileToVine(parsl_file.filepath, to_stage, to_cache) + + def _std_output_to_vine(self, fdname, stdfspec): + """Find the name of the file that will contain stdout or stderr and + return a ParslFileToVine with it. These files are never cached""" + fname, mode = putils.get_std_fname_mode(fdname, stdfspec) + to_stage = not os.path.isabs(fname) + return ParslFileToVine(fname, stage=to_stage, cache=False) + + def _prepare_package(self, fn, extra_pkgs): + """ Look at source code of apps to figure out their package depedencies + and output a tarball containing those to send along with tasks for execution.""" + + if not self._poncho_available: + raise ExecutorError(self, 'poncho package is not available to individually pack apps.') + + fn_id = id(fn) + fn_name = fn.__name__ + if fn_id in self.cached_envs: + logger.debug("Skipping analysis of %s, previously got %s", fn_name, self.cached_envs[fn_id]) + return self.cached_envs[fn_id] + source_code = inspect.getsource(fn).encode() + pkg_dir = os.path.join(tempfile.gettempdir(), "python_package-{}".format(os.geteuid())) + os.makedirs(pkg_dir, exist_ok=True) + with tempfile.NamedTemporaryFile(suffix='.yaml') as spec: + logger.info("Analyzing dependencies of %s", fn_name) + analyze_cmdline = [self._package_analyze_script, exec_parsl_function.__file__, '-', spec.name] + for p in extra_pkgs: + analyze_cmdline += ["--extra-pkg", p] + subprocess.run(analyze_cmdline, input=source_code, check=True) + with open(spec.name, mode='rb') as f: + spec_hash = hashlib.sha256(f.read()).hexdigest() + logger.debug("Spec hash for %s is %s", fn_name, spec_hash) + pkg = os.path.join(pkg_dir, "pack-{}.tar.gz".format(spec_hash)) + if os.access(pkg, os.R_OK): + self.cached_envs[fn_id] = pkg + logger.debug("Cached package for %s found at %s", fn_name, pkg) + return pkg + (fd, tarball) = tempfile.mkstemp(dir=pkg_dir, prefix='.tmp', suffix='.tar.gz') + os.close(fd) + logger.info("Creating dependency package for %s", fn_name) + logger.debug("Writing deps for %s to %s", fn_name, tarball) + subprocess.run([self._package_create_script, spec.name, tarball], stdout=subprocess.DEVNULL, check=True) + logger.debug("Done with conda-pack; moving %s to %s", tarball, pkg) + os.rename(tarball, pkg) + self.cached_envs[fn_id] = pkg + return pkg + + def initialize_scaling(self): + """ Compose the launch command and call scale out + + Scales the workers to the appropriate nodes with provider + """ + # Start scaling in/out + logger.debug("Starting TaskVineExecutor with provider: %s", self.provider) + self._worker_command = self._construct_worker_command() + self._patch_providers() + + def outstanding(self) -> int: + """Count the number of outstanding tasks.""" + logger.debug(f"Counted {self._outstanding_tasks} outstanding tasks") + return self._outstanding_tasks + + @property + def workers_per_node(self) -> Union[int, float]: + return 1 + + def shutdown(self, *args, **kwargs): + """Shutdown the executor. Sets flag to cancel the submit process and + collector thread, which shuts down the TaskVine system submission. + """ + logger.debug("TaskVine shutdown started") + self._should_stop.set() + + # Join all processes before exiting + logger.debug("Joining on submit process") + self._submit_process.join() + self._submit_process.close() + logger.debug("Joining on collector thread") + self._collector_thread.join() + if self.worker_launch_method == 'factory': + logger.debug("Joining on factory process") + self._factory_process.join() + self._factory_process.close() + + # Shutdown multiprocessing queues + self._ready_task_queue.close() + self._ready_task_queue.join_thread() + self._finished_task_queue.close() + self._finished_task_queue.join_thread() + + super().shutdown() + + logger.debug("TaskVine shutdown completed") + + @wrap_with_logs + def _collect_taskvine_results(self): + """Sets the values of tasks' futures completed by taskvine. + """ + logger.debug("Starting Collector Thread") + try: + while not self._should_stop.is_set(): + if not self._submit_process.is_alive(): + raise ExecutorError(self, "taskvine Submit Process is not alive") + + # Get the result message from the _finished_task_queue + try: + task_report = self._finished_task_queue.get(timeout=1) + except queue.Empty: + continue + + # Obtain the future from the tasks dictionary + with self._tasks_lock: + future = self.tasks.pop(task_report.executor_id) + + logger.debug(f'Updating Future for Parsl Task: {task_report.executor_id}. ' + f'Task {task_report.executor_id} has result_received set to {task_report.result_received}') + if task_report.result_received: + try: + with open(task_report.result_file, 'rb') as f_in: + result = deserialize(f_in.read()) + except Exception as e: + logger.error(f'Cannot load result from result file {task_report.result_file}. Exception: {e}') + ex = TaskVineTaskFailure('Cannot load result from result file', None) + ex.__cause__ = e + future.set_exception(ex) + else: + if isinstance(result, Exception): + ex = TaskVineTaskFailure('Task execution raises an exception', result) + ex.__cause__ = result + future.set_exception(ex) + else: + future.set_result(result) + else: + # If there are no results, then the task failed according to one of + # taskvine modes, such as resource exhaustion. + ex = TaskVineTaskFailure(task_report.reason, None) + future.set_exception(ex) + + # decrement outstanding task counter + with self._outstanding_tasks_lock: + self._outstanding_tasks -= 1 + finally: + logger.debug(f"Marking all {self.outstanding()} outstanding tasks as failed") + logger.debug("Acquiring tasks_lock") + with self._tasks_lock: + logger.debug("Acquired tasks_lock") + # set exception for tasks waiting for results that taskvine did not execute + for fu in self.tasks.values(): + if not fu.done(): + fu.set_exception(TaskVineManagerFailure("taskvine executor failed to execute the task.")) + logger.debug("Exiting Collector Thread") diff --git a/parsl/parsl/executors/taskvine/factory.py b/parsl/parsl/executors/taskvine/factory.py new file mode 100644 index 00000000..0790af51 --- /dev/null +++ b/parsl/parsl/executors/taskvine/factory.py @@ -0,0 +1,73 @@ +import logging +import os + +from parsl.executors.taskvine.errors import TaskVineFactoryFailure +from parsl.process_loggers import wrap_with_logs + +# This try except clause prevents import errors +# when TaskVine is not used in Parsl. +try: + from ndcctools.taskvine import Factory + taskvine_available = True +except ImportError: + taskvine_available = False + +logger = logging.getLogger(__name__) + + +@wrap_with_logs +def _taskvine_factory(should_stop, factory_config): + if not taskvine_available: + logger.debug("TaskVine package cannot be found. Please install the ndcctools package.") + return + logger.debug("Starting TaskVine factory process") + + try: + # create the factory according to the project name if given + if factory_config._project_name: + factory = Factory(batch_type=factory_config.batch_type, + manager_name=factory_config._project_name, + ) + else: + factory = Factory(batch_type=factory_config.batch_type, + manager_host_port=f"{factory_config._project_address}:{factory_config._project_port}", + ) + except Exception as e: + raise TaskVineFactoryFailure(f'Cannot create factory with exception {e}') + + # Set attributes of this factory + if factory_config._project_password_file: + factory.password = factory_config._project_password_file + factory.factory_timeout = factory_config.factory_timeout + factory.scratch_dir = factory_config.scratch_dir + factory.min_workers = factory_config.min_workers + factory.max_workers = factory_config.max_workers + factory.workers_per_cycle = factory_config.workers_per_cycle + + # create scratch dir if factory process gets ahead of the manager. + os.makedirs(factory.scratch_dir, exist_ok=True) + + if factory_config.worker_options: + factory.extra_options = factory_config.worker_options + factory.timeout = factory_config.worker_timeout + if factory_config.cores: + factory.cores = factory_config.cores + if factory_config.gpus: + factory.gpus = factory_config.gpus + if factory_config.memory: + factory.memory = factory_config.memory + if factory_config.disk: + factory.disk = factory_config.disk + if factory_config.python_env: + factory.python_env = factory_config.python_env + + if factory_config.condor_requirements: + factory.condor_requirements = factory_config.condor_requirements + if factory_config.batch_options: + factory.batch_options = factory_config.batch_options + + # run factory through Python context and wait for signal to stop. + with factory: + should_stop.wait() + + logger.debug("Exiting TaskVine factory process") diff --git a/parsl/parsl/executors/taskvine/factory_config.py b/parsl/parsl/executors/taskvine/factory_config.py new file mode 100644 index 00000000..f198e874 --- /dev/null +++ b/parsl/parsl/executors/taskvine/factory_config.py @@ -0,0 +1,121 @@ +from dataclasses import dataclass +from typing import Optional + + +@dataclass +class TaskVineFactoryConfig: + """ + Configuration of a TaskVine factory. + Default is to spawn one local worker. + + Parameters + ---------- + factory_timeout: int + Exit if no manager seen in $factory_timeout seconds. + Kill all workers on upon exit. + + scratch_dir: Optional[str] + Directory for factory to operate. + Default is None, which TaskVine factory will use a directory + residing inside Parsl logging directory. + + min_workers: int + Number of minimum workers connecting to manager at all times. + Default is 1. + + max_workers: int + Number of maximum workers connecting to manager at all times. + Default is 1. + + workers_per_cycle: int + Maximum number of new workers per 30s. + + worker_options: Optional[str] + Additional options to pass to workers. Run + ``vine_worker --help`` for more details. + Default is None. + + worker_executable: str + Executable to run workers. + Default is 'vine_worker', which finds the executable + on $PATH. + + worker_timeout: int + Number of seconds for workers to wait for manager's contact + before exiting. + Default is 300. + + cores: Optional[int] + Number of cores a worker should have. + Default is None, which allows a worker to use all available + cores of the machine it lands on. + + gpus: Optional[int] + Number of gpus a worker should have. + Default is None, which allows a worker to use all available + gpus of the machine it lands on. + + memory: Optional[int] + Amount of memory in MBs a worker should have. + Default is None, which allows a worker to use all available + memory of the machine it lands on. + + disk: Optional[int] + Amount of disk in MBs a worker should have. + Default is None, which allows a worker to use all available + disk space of the machine it lands on. + + python_env: Optional[str] + Run workers inside this python environment tarball. + Default is None. + + batch_type: str + Batch systems the factory should submit workers as jobs. + 'local': spawn local workers. + 'condor': Submit workers through HTCondor batch system. + 'sge': Submit workers through SGE batch system. + 'slurm': Submit workers through SLURM batch system. + Default is 'local'. + + condor_requirements: Optional[str] + String describing the condor requirements for slots + that workers should land on. + Default is None. + + batch_options: Optional[str] + String describing generic batch options. Varies with the + type of batch system. + Default is None. + """ + + # Connection and communication settings + factory_timeout: int = 300 + + # Logging settings + scratch_dir: Optional[str] = None + + # Scale settings + min_workers: int = 1 + max_workers: int = 1 + workers_per_cycle: int = 1 + + # Worker settings + worker_options: Optional[str] = None + worker_executable: str = 'vine_worker' + worker_timeout: int = 300 + cores: Optional[int] = None + gpus: Optional[int] = None + memory: Optional[int] = None + disk: Optional[int] = None + python_env: Optional[str] = None + + # Batch settings + batch_type: str = "local" + condor_requirements: Optional[str] = None + batch_options: Optional[str] = None + + # Private attributes, users should not touch + _project_port: int = 0 + _project_address: Optional[str] = None + _project_name: Optional[str] = None + _project_password_file: Optional[str] = None diff --git a/parsl/parsl/executors/taskvine/manager.py b/parsl/parsl/executors/taskvine/manager.py new file mode 100644 index 00000000..d11e81be --- /dev/null +++ b/parsl/parsl/executors/taskvine/manager.py @@ -0,0 +1,490 @@ +import hashlib +import logging +import os +import queue +import shutil +import subprocess +import uuid + +import parsl +from parsl.executors.taskvine import exec_parsl_function +from parsl.executors.taskvine.utils import VineTaskToParsl, run_parsl_function +from parsl.process_loggers import wrap_with_logs +from parsl.utils import setproctitle + +try: + from ndcctools.taskvine import FunctionCall, Manager, Task, cvine + from ndcctools.taskvine.cvine import ( + VINE_ALLOCATION_MODE_EXHAUSTIVE_BUCKETING, + VINE_ALLOCATION_MODE_MAX, + VINE_ALLOCATION_MODE_MAX_THROUGHPUT, + ) +except ImportError: + _taskvine_enabled = False +else: + _taskvine_enabled = True + +logger = logging.getLogger(__name__) + + +def _set_manager_attributes(m, config): + """ Set various manager global attributes.""" + if config.project_password_file: + m.set_password_file(config.project_password_file) + + # Autolabeling resources require monitoring to be enabled + if config.autolabel: + m.enable_monitoring() + if config.autolabel_window is not None: + m.tune('category-steady-n-tasks', config.autolabel_window) + + # Specify number of workers to wait for before sending the first task + if config.wait_for_workers: + m.tune("wait-for-workers", config.wait_for_workers) + + # Enable peer transfer feature between workers if specified + if config.enable_peer_transfers: + m.enable_peer_transfers() + else: + m.disable_peer_transfers() + + # Set catalog report to parsl if project name exists + if m.name: + m.set_property("framework", "parsl") + + if config.tune_parameters is not None: + for k, v in config.tune_parameters.items(): + m.tune(k, v) + + +def _prepare_environment_serverless(manager_config, env_cache_dir, poncho_create_script): + # Return path to a packaged poncho environment + poncho_env_path = '' + if not manager_config.shared_fs: + if manager_config.env_pack is None: + raise Exception('TaskVine manager needs env_pack to be specified when running tasks in serverless mode and with no shared_fs') + + poncho_env_path = manager_config.env_pack + + # If a conda environment name or path is given, then use the hash of the headers of + # all contained packages as the name of the to-be-packaged poncho tarball, + # and package it if it's not cached. + if not poncho_env_path.endswith('tar.gz'): + if os.path.isabs(poncho_env_path): + conda_env_signature = hashlib.md5(subprocess.check_output(['conda', 'list', '-p', poncho_env_path, '--json'])).hexdigest() + logger.debug(f'Signature of conda environment at {poncho_env_path}: {conda_env_signature}') + else: + conda_env_signature = hashlib.md5(subprocess.check_output(['conda', 'list', '-n', poncho_env_path, '--json'])).hexdigest() + logger.debug(f'Signature of conda environment named {poncho_env_path}: {conda_env_signature}') + + # If env is cached then use it, + # else create a new env tarball + poncho_env_path = os.path.join(env_cache_dir, '.'.join([conda_env_signature, 'tar.gz'])) + if not os.path.isfile(poncho_env_path): + logger.debug(f'No cached poncho environment. Creating poncho environment for library task at {poncho_env_path}') + try: + subprocess.run([poncho_create_script, manager_config.env_pack, poncho_env_path], stdout=subprocess.DEVNULL, check=True) + except Exception: + logger.error('Cannot create a poncho environment. Removing it.') + if os.path.isfile(poncho_env_path): + os.remove(poncho_env_path) + raise + else: + logger.debug(f'Found cached poncho environment at {poncho_env_path}. Reusing it.') + else: + logger.debug(f'Use the given poncho environment at {manager_config.env_pack} to setup library task.') + return poncho_env_path + + +def _prepare_environment_regular(m, manager_config, t, task, poncho_env_to_file, poncho_create_script): + # Add environment file to the task if possible + # Prioritize local poncho environment over global poncho environment + # (local: use app_pack, global: use env_pack) + poncho_env_file = None + + # check if env_pack is specified + if manager_config.env_pack is not None: + + # check if the environment file is not already created + if manager_config.env_pack not in poncho_env_to_file: + + # if the environment is already packaged as a tarball, then add the file + # otherwise it is an environment name or path, so create a poncho tarball then add it + if not manager_config.env_pack.endswith('.tar.gz'): + env_tarball = str(uuid.uuid4()) + '.tar.gz' + logger.debug(f'Creating a poncho environment at {env_tarball} from conda environment {manager_config.env_pack}') + subprocess.run([poncho_create_script, manager_config.env_pack, env_tarball], stdout=subprocess.DEVNULL, check=True) + else: + env_tarball = manager_config.env_pack + poncho_env_file = m.declare_poncho(env_tarball, cache=True, peer_transfer=True) + poncho_env_to_file[manager_config.env_pack] = poncho_env_file + else: + poncho_env_file = poncho_env_to_file[manager_config.env_pack] + logger.debug(f'Found cached poncho environment for {manager_config.env_pack}. Reusing it.') + + # check if app_pack is used, override if possible + if task.env_pkg is not None: + if task.env_pkg not in poncho_env_to_file: + poncho_env_file = m.declare_poncho(task.env_pkg, cache=True, peer_transfer=True) + poncho_env_to_file[task.env_pkg] = poncho_env_file + else: + poncho_env_file = poncho_env_to_file[task.env_pkg] + + # Add environment to the task + if poncho_env_file is not None: + t.add_environment(poncho_env_file) + + +@wrap_with_logs +def _taskvine_submit_wait(ready_task_queue=None, + finished_task_queue=None, + should_stop=None, + manager_config=None + ): + """Process to handle Parsl app submissions to the TaskVine objects. + Takes in Parsl functions submitted using submit(), and creates a + TaskVine task with the appropriate specifications, which is then + submitted to TaskVine. After tasks are completed, processes the + exit status and exit code of the task, and sends results to the + TaskVine collector thread. + To avoid python's global interpreter lock with taskvine's wait, this + function should be launched as a process, not as a lightweight thread. This + means that any communication should be done using the multiprocessing + module capabilities, rather than shared memory. + """ + logger.debug("Starting TaskVine Submit/Wait Process") + setproctitle("parsl: TaskVine submit/wait") + + # Enable debugging flags and create logging file + if manager_config.vine_log_dir is not None: + logger.debug("Setting debugging flags and creating logging file at {}".format(manager_config.vine_log_dir)) + + # Create TaskVine queue object + logger.debug("Creating TaskVine Object") + try: + logger.debug("Listening on port {}".format(manager_config.port)) + m = Manager(port=manager_config.port, + name=manager_config.project_name, + run_info_path=manager_config.vine_log_dir) + except Exception as e: + logger.error("Unable to create TaskVine object: {}".format(e)) + raise e + + # Specify TaskVine manager attributes + _set_manager_attributes(m, manager_config) + + # Get parent pid, useful to shutdown this process when its parent, the taskvine + # executor process, exits. + orig_ppid = os.getppid() + + result_file_of_task_id = {} # Mapping executor task id -> result file. + + poncho_env_to_file = {} # Mapping poncho_env id to File object in TaskVine + + # Mapping of parsl local file name to TaskVine File object + # dict[str] -> vine File object + parsl_file_name_to_vine_file = {} + + # Mapping of tasks from vine id to parsl id + # Dict[str] -> str + vine_id_to_executor_task_id = {} + + # Find poncho scripts to create and activate an environment tarball + poncho_create_script = shutil.which("poncho_package_create") + + # Declare helper script as cache-able and peer-transferable + exec_parsl_function_file = m.declare_file(exec_parsl_function.__file__, cache=True, peer_transfer=True) + + # Flag to make sure library for serverless tasks is declared and installed only once. + lib_installed = False + + # Create cache dir for environment files + env_cache_dir = os.path.join(manager_config.vine_log_dir, 'vine-cache', 'vine-poncho-env-cache') + os.makedirs(env_cache_dir, exist_ok=True) + + logger.debug("Entering main loop of TaskVine manager") + + while not should_stop.is_set(): + # Check if executor process is still running + ppid = os.getppid() + if ppid != orig_ppid: + logger.debug("Executor process is detected to have exited. Exiting..") + break + + # Submit tasks + while ready_task_queue.qsize() > 0 or m.empty() and not should_stop.is_set(): + # Obtain task from ready_task_queue + try: + task = ready_task_queue.get(timeout=1) + logger.debug("Removing executor task from queue") + except queue.Empty: + logger.debug("Queue is empty") + continue + if task.exec_mode == 'regular': + # Create command string + launch_cmd = "python3 exec_parsl_function.py {mapping} {function} {argument} {result}" + if manager_config.init_command != '': + launch_cmd = "{init_cmd} " + launch_cmd + command_str = launch_cmd.format(init_cmd=manager_config.init_command, + mapping=os.path.basename(task.map_file), + function=os.path.basename(task.function_file), + argument=os.path.basename(task.argument_file), + result=os.path.basename(task.result_file)) + logger.debug("Sending executor task {} (mode: regular) with command: {}".format(task.executor_id, command_str)) + try: + t = Task(command_str) + except Exception as e: + logger.error("Unable to create executor task (mode:regular): {}".format(e)) + finished_task_queue.put_nowait(VineTaskToParsl(executor_id=task.executor_id, + result_received=False, + result_file=None, + reason="task could not be created by taskvine", + status=-1)) + continue + elif task.exec_mode == 'serverless': + if not lib_installed: + # Declare and install common library for serverless tasks. + # Library requires an environment setup properly, which is + # different from setup of regular tasks. + # If shared_fs is True, then no environment preparation is done. + # Only the core serverless code is created. + poncho_env_path = _prepare_environment_serverless(manager_config, env_cache_dir, poncho_create_script) + + # Don't automatically add environment so manager can declare and cache the vine file associated with the environment file + add_env = False + serverless_lib = m.create_library_from_functions('common-parsl-taskvine-lib', + run_parsl_function, + poncho_env=poncho_env_path, + init_command=manager_config.init_command, + add_env=add_env, + hoisting_modules=[parsl.serialize, run_parsl_function]) + + # Configure the library if provided + if manager_config.library_config: + lib_cores = manager_config.library_config.get('cores', None) + lib_memory = manager_config.library_config.get('memory', None) + lib_disk = manager_config.library_config.get('disk', None) + lib_slots = manager_config.library_config.get('num_slots', None) + if lib_cores: + serverless_lib.set_cores(lib_cores) + if lib_memory: + serverless_lib.set_memory(lib_memory) + if lib_disk: + serverless_lib.set_disk(lib_disk) + if lib_slots: + serverless_lib.set_function_slots(lib_slots) + + if poncho_env_path: + serverless_lib_env_file = m.declare_poncho(poncho_env_path, cache=True, peer_transfer=True) + serverless_lib.add_environment(serverless_lib_env_file) + poncho_env_to_file[manager_config.env_pack] = serverless_lib_env_file + logger.debug(f'Created library task using poncho environment at {poncho_env_path}.') + else: + logger.debug('Created minimal library task with no environment.') + + m.install_library(serverless_lib) + lib_installed = True + try: + # run_parsl_function only needs remote names of map_file, function_file, argument_file, + # and result_file, which are simply named map, function, argument, result. + # These names are given when these files are declared below. + t = FunctionCall('common-parsl-taskvine-lib', run_parsl_function.__name__, 'map', 'function', 'argument', 'result') + except Exception as e: + logger.error("Unable to create executor task (mode:serverless): {}".format(e)) + finished_task_queue.put_nowait(VineTaskToParsl(executor_id=task.executor_id, + result_received=False, + result_file=None, + reason="task could not be created by taskvine", + status=-1)) + else: + raise Exception(f'Unrecognized task mode {task.exec_mode}. Exiting...') + + # prepare environment for regular tasks if not using shared_fs + if task.exec_mode == 'regular' and not manager_config.shared_fs: + _prepare_environment_regular(m, manager_config, t, task, poncho_env_to_file, poncho_create_script) + + t.set_category(task.category) + + # Set autolabel mode + if manager_config.autolabel: + if manager_config.autolabel_algorithm == 'max-xput': + m.set_category_mode(task.category, VINE_ALLOCATION_MODE_MAX_THROUGHPUT) + elif manager_config.autolabel_algorithm == 'bucketing': + m.set_category_mode(task.category, VINE_ALLOCATION_MODE_EXHAUSTIVE_BUCKETING) + elif manager_config.autolabel_algorithm == 'max': + m.set_category_mode(task.category, VINE_ALLOCATION_MODE_MAX) + else: + logger.warning(f'Unrecognized autolabeling algorithm named {manager_config.autolabel_algorithm} for taskvine manager.') + raise Exception(f'Unrecognized autolabeling algorithm named {manager_config.autolabel_algorithm} for taskvine manager.') + + if task.cores is not None: + t.set_cores(task.cores) + if task.memory is not None: + t.set_memory(task.memory) + if task.disk is not None: + t.set_disk(task.disk) + if task.gpus is not None: + t.set_gpus(task.gpus) + if task.priority is not None: + t.set_priority(task.priority) + if task.running_time_min is not None: + t.set_time_min(task.running_time_min) + + if manager_config.max_retries is not None: + logger.debug(f"Specifying max_retries {manager_config.max_retries}") + t.set_retries(manager_config.max_retries) + + # Specify environment variables for the task + if manager_config.env_vars is not None: + for var in manager_config.env_vars: + t.set_env_var(str(var), str(manager_config.env_vars[var])) + + if task.exec_mode == 'regular': + # Add helper files that execute parsl functions on remote nodes + # only needed to add as file for tasks with 'regular' mode + t.add_input(exec_parsl_function_file, "exec_parsl_function.py") + + # Declare and add task-specific function, data, and result files to task + task_function_file = m.declare_file(task.function_file, cache=False, peer_transfer=False) + t.add_input(task_function_file, "function") + + task_argument_file = m.declare_file(task.argument_file, cache=False, peer_transfer=False) + t.add_input(task_argument_file, "argument") + + task_map_file = m.declare_file(task.map_file, cache=False, peer_transfer=False) + t.add_input(task_map_file, "map") + + task_result_file = m.declare_file(task.result_file, cache=False, peer_transfer=False) + t.add_output(task_result_file, "result") + + result_file_of_task_id[str(task.executor_id)] = task.result_file + + logger.debug("Executor task id: {}".format(task.executor_id)) + + # Specify input/output files that need to be staged. + # Absolute paths are assumed to be in shared filesystem, and thus + # not staged by taskvine. + # Files that share the same local path are assumed to be the same + # and thus use the same Vine File object if detected. + if not manager_config.shared_fs: + for spec in task.input_files: + if spec.stage: + if spec.parsl_name in parsl_file_name_to_vine_file: + task_in_file = parsl_file_name_to_vine_file[spec.parsl_name] + else: + task_in_file = m.declare_file(spec.parsl_name, cache=spec.cache, peer_transfer=True) + parsl_file_name_to_vine_file[spec.parsl_name] = task_in_file + t.add_input(task_in_file, spec.parsl_name) + + for spec in task.output_files: + if spec.stage: + if spec.parsl_name in parsl_file_name_to_vine_file: + task_out_file = parsl_file_name_to_vine_file[spec.parsl_name] + else: + task_out_file = m.declare_file(spec.parsl_name, cache=spec.cache, peer_transfer=True) + parsl_file_name_to_vine_file[spec.parsl_name] = task_out_file + t.add_output(task_out_file, spec.parsl_name) + + # Submit the task to the TaskVine object + logger.debug("Submitting executor task {}, {} to TaskVine".format(task.executor_id, t)) + try: + vine_id = m.submit(t) + logger.debug("Submitted executor task {} to TaskVine".format(task.executor_id)) + vine_id_to_executor_task_id[str(vine_id)] = str(task.executor_id), task.exec_mode + except Exception as e: + logger.error("Unable to submit task to taskvine: {}".format(e)) + finished_task_queue.put_nowait(VineTaskToParsl(executor_id=task.executor_id, + result_received=False, + result_file=None, + reason="task could not be submited to taskvine", + status=-1)) + continue + + logger.debug("Executor task {} submitted as TaskVine task with id {}".format(task.executor_id, vine_id)) + + # If the queue is not empty wait on the TaskVine queue for a task + task_found = True + while not m.empty() and task_found and not should_stop.is_set(): + # Obtain the task from the queue + t = m.wait(1) + if t is None: + task_found = False + continue + logger.debug('Found a task') + executor_task_id = vine_id_to_executor_task_id[str(t.id)][0] + vine_id_to_executor_task_id.pop(str(t.id)) + + # When a task is found + result_file = result_file_of_task_id.pop(executor_task_id) + + logger.debug(f"completed executor task info: {executor_task_id}, {t.category}, {t.command}, {t.std_output}") + + # A tasks completes 'succesfully' if it has result file. + # A check whether the Python object represented using this file can be + # deserialized happens later in the collector thread of the executor + # process. + logger.debug("Looking for result in {}".format(result_file)) + if os.path.exists(result_file): + logger.debug("Found result in {}".format(result_file)) + finished_task_queue.put_nowait(VineTaskToParsl(executor_id=executor_task_id, + result_received=True, + result_file=result_file, + reason=None, + status=t.exit_code)) + # If a result file could not be generated, explain the + # failure according to taskvine error codes. + else: + reason = _explain_taskvine_result(t) + logger.debug("Did not find result in {}".format(result_file)) + logger.debug("Wrapper Script status: {}\nTaskVine Status: {}".format(t.exit_code, t.result)) + logger.debug("Task with executor id {} / vine id {} failed because:\n{}".format(executor_task_id, t.id, reason)) + finished_task_queue.put_nowait(VineTaskToParsl(executor_id=executor_task_id, + result_received=False, + result_file=None, + reason=reason, + status=t.exit_code)) + + logger.debug("Exiting TaskVine Monitoring Process") + return 0 + + +def _explain_taskvine_result(vine_task): + """Returns a string with the reason why a task failed according to taskvine.""" + + vine_result = vine_task.result + reason = "taskvine result: " + if vine_result == cvine.VINE_RESULT_SUCCESS: + reason += "succesful execution with exit code {}".format(vine_task.return_status) + elif vine_result == cvine.VINE_RESULT_OUTPUT_MISSING: + reason += "The result file was not transfered from the worker.\n" + reason += "This usually means that there is a problem with the python setup,\n" + reason += "or the wrapper that executes the function." + reason += "\nTrace:\n" + str(vine_task.output) + elif vine_result == cvine.VINE_RESULT_INPUT_MISSING: + reason += "missing input file" + elif vine_result == cvine.VINE_RESULT_STDOUT_MISSING: + reason += "stdout has been truncated" + elif vine_result == cvine.VINE_RESULT_SIGNAL: + reason += "task terminated with a signal" + elif vine_result == cvine.VINE_RESULT_RESOURCE_EXHAUSTION: + reason += "task used more resources than requested" + elif vine_result == cvine.VINE_RESULT_MAX_END_TIME: + reason += "task ran past the specified end time" + elif vine_result == cvine.VINE_RESULT_UNKNOWN: + reason += "result could not be classified" + elif vine_result == cvine.VINE_RESULT_FORSAKEN: + reason += "task failed, but not a task error" + elif vine_result == cvine.VINE_RESULT_MAX_RETRIES: + reason += "unable to complete after specified number of retries" + elif vine_result == cvine.VINE_RESULT_MAX_WALL_TIME: + reason += "task ran for more than the specified time" + elif vine_result == cvine.VINE_RESULT_RMONITOR_ERROR: + reason += "task failed because the monitor did not produce an output" + elif vine_result == cvine.VINE_RESULT_OUTPUT_TRANSFER_ERROR: + reason += "task failed because output transfer fails" + elif vine_result == cvine.VINE_RESULT_FIXED_LOCATION_MISSING: + reason += "task failed because no worker could satisfy the fixed \n" + reason += "location input file requirements" + else: + reason += "unable to process TaskVine system failure" + return reason diff --git a/parsl/parsl/executors/taskvine/manager_config.py b/parsl/parsl/executors/taskvine/manager_config.py new file mode 100644 index 00000000..26b1e460 --- /dev/null +++ b/parsl/parsl/executors/taskvine/manager_config.py @@ -0,0 +1,191 @@ +from dataclasses import dataclass +from typing import Optional + +# This try except clause prevents import errors +# when TaskVine is not used in Parsl. +try: + from ndcctools.taskvine.cvine import VINE_DEFAULT_PORT +except ImportError: + VINE_DEFAULT_PORT = 0 # use any available port. + + +@dataclass +class TaskVineManagerConfig: + """ + Configuration of a TaskVine manager + + Parameters + ---------- + + port: int + Network port for the manager to communicate with workers. + A value of 0 means TaskVine chooses any available port. + Default is VINE_DEFAULT_PORT. + + address: Optional[str] + Address of the local machine. + If None, :py:func:`parsl.addresses.get_any_address` will be used to determine the address. + + project_name: Optional[str] + If given, TaskVine will periodically report its status and performance + back to the global TaskVine catalog at + http://ccl.cse.nd.edu/software/taskvine/status. + Recommended mode of communication between manager and workers. + Default is None. Overrides address. + + project_password_file: Optional[str] + If given, manager and workers will authenticate each other + with the given password file. + Default is None. + + env_vars: Optional[dict] + Dictionary of environment variables to set in a shell + before executing a task. + Default is None + + init_command: str + Command line to run before executing a task in a worker. + Default is ''. + + env_pack: Optional[str] + Used to encapsulate package dependencies of tasks to + execute them remotely without needing a shared filesystem. + Recommended way to manage tasks' dependency requirements. + All tasks will be executed in the encapsulated environment. + If an absolute path to a conda environment or a conda + environment name is given, TaskVine will package the conda + environment in a tarball and send it along with tasks to be + executed in a replicated conda environment. + If a tarball of packages (``*.tar.gz``) is given, TaskVine + skips the packaging step and sends the tarball along with + tasks to be executed in a replicated conda environment. + + app_pack: bool + Use conda-pack to prepare a self-contained Python environment + for each app. Enabling this increases first task latency but + does not require a common environment or a shared filesystem + on workers. + If env_pack is specified, app_pack if set to True will override + env_pack. + Default is False. + + extra_pkgs: Optional[list] + Used when app_pack is True. + List of extra pip/conda package names to include when packing + the environment. Useful when app executes other (possibly + non-Python) programs provided via pip or conda. + Default is None. + + max_retries: int + Set the number of retries that TaskVine will make when a task + fails. This is distinct from Parsl level retries configured in + parsl.config.Config. Set to None to allow TaskVine to retry tasks + forever. + Default is 1. + + library_config: Optional[dict] + Only and must specify when functions are executed in the serverless mode. + Configure the number of function slots and amount of resources + a library task can run. A library task is a stateful object that executes + functions in the serverless way. Accept the following keywords: + 'num_slots', 'cores', 'memory (MBs)', 'disk (MBs)'. + Default is {'num_slots': 1, 'cores': None, 'memory': None, 'disk': None}, + which will take all resources of a worker node and run at most 1 function + invocation at any given time. + E.g., {'num_slots': 4, 'cores': 16, 'memory': 16000, 'disk': 16000} will + reserve those resources to the library task to run at most 4 function + invocations. + + shared_fs: bool + Whether workers will use a shared filesystem or not. If so, TaskVine + will not track and transfer files for execution, in exchange for + I/O pressure on the shared filesystem. + Default is False. + + autolabel: bool + Use the Resource Monitor to automatically determine resource + labels based on observed task behavior. + Default is False. + + autolabel_window: int + Set the number of tasks considered for autolabeling. TaskVine + will wait for a series of N tasks with steady resource + requirements before making a decision on labels. Increasing + this parameter will reduce the number of failed tasks due to + resource exhaustion when autolabeling, at the cost of increased + resources spent collecting stats. + + autolabel_algorithm: str + Choose an autolabeling algorithm to label resources to tasks. + 'max-xput': smartly guess a resource value, then fall back if task + fails due to overconsumption. + 'bucketing': group tasks into buckets based on resource consumption + similarity, and randomly choose a bucket to assign resources to the + next task. + 'max': always allocate max resources seen so far. Allocate a whole + machine if task fails. + Default is max-xput. + + autolabel_window: Optional[int] + Only apply to 'max-xput' and 'max'. + Set the number of tasks considered for autolabeling. TaskVine + will wait for a series of N tasks with steady resource + requirements before making a decision on labels. Increasing + this parameter will reduce the number of failed tasks due to + resource exhaustion when autolabeling, at the cost of increased + resources spent collecting stats. + + autocategory: bool + Place each app in its own category by default. If all + invocations of an app have similar performance characteristics, + this will provide a reasonable set of categories automatically. + Default is True. + + enable_peer_transfers: bool + Whether to enable workers' capability to transfer files among + themselves or not. Helpful if TaskVine manages all files + tasks in the workflow use. Require that workers can communicate + with each other via TCP ports. + Default is True. + + wait_for_workers: Optional[int] + Number of workers to wait for before the manager starts sending + tasks for execution. + + vine_log_dir: Optional[str] + Directory to store TaskVine logging facilities. + Default is None, in which all TaskVine logs will be contained + in the Parsl logging directory. + + tune_parameters: Optional[dict] + Extended vine_tune parameters, expressed in a dictionary + by { 'tune-parameter' : value }. + """ + + # Connection and communication settings + port: int = VINE_DEFAULT_PORT + address: Optional[str] = None + project_name: Optional[str] = None + project_password_file: Optional[str] = None + + # Global task settings + env_vars: Optional[dict] = None + init_command: str = "" + env_pack: Optional[str] = None + app_pack: bool = False + extra_pkgs: Optional[list] = None + max_retries: int = 1 + library_config: Optional[dict] = None + + # Performance-specific settings + shared_fs: bool = False + autolabel: bool = False + autolabel_algorithm: str = 'max-xput' + autolabel_window: Optional[int] = None + autocategory: bool = True + enable_peer_transfers: bool = True + wait_for_workers: Optional[int] = None + tune_parameters: Optional[dict] = None + + # Logging settings + vine_log_dir: Optional[str] = None diff --git a/parsl/parsl/executors/taskvine/utils.py b/parsl/parsl/executors/taskvine/utils.py new file mode 100644 index 00000000..86cf446b --- /dev/null +++ b/parsl/parsl/executors/taskvine/utils.py @@ -0,0 +1,85 @@ +from typing import Optional + + +class ParslTaskToVine: + """ Helper class to transform a Parsl function into a TaskVine task.""" + + def __init__(self, + executor_id: int, # executor id of Parsl function + exec_mode: str, # execution mode of function, out of {regular, python, serverless} + category: str, # category of Parsl function + input_files: list, # list of input files to this function + output_files: list, # list of output files to this function + map_file: Optional[str], # pickled file containing mapping of local to remote names of files + function_file: Optional[str], # pickled file containing the function information + argument_file: Optional[str], # pickled file containing the arguments to the function call + result_file: Optional[str], # path to the pickled result object of the function execution + cores: Optional[float], # number of cores to allocate + memory: Optional[int], # amount of memory in MBs to allocate + disk: Optional[int], # amount of disk in MBs to allocate + gpus: Optional[float], # number of gpus to allocate + priority: Optional[float], # priority of function, the higher the more priority + running_time_min: Optional[int], # minimum amount of time for the function to run on a worker + env_pkg: Optional[str], # path to a poncho environment tarball + ): + self.executor_id = executor_id + self.exec_mode = exec_mode + self.category = category + self.map_file = map_file + self.function_file = function_file + self.argument_file = argument_file + self.result_file = result_file + self.input_files = input_files + self.output_files = output_files + self.cores = cores + self.memory = memory + self.disk = disk + self.gpus = gpus + self.priority = priority + self.running_time_min = running_time_min + self.env_pkg = env_pkg + + +class VineTaskToParsl: + """ + Support structure to communicate final status of TaskVine tasks to Parsl. + result_file is only valid if result_received is True. + Reason and status are only valid if result_received is False. + """ + def __init__(self, + executor_id: int, # executor id of task + result_received: bool, # whether result is received or not + result_file: Optional[str], # path to file that contains the serialized result object + reason: Optional[str], # string describing why execution fails + status: Optional[int] # exit code of execution of task + ): + self.executor_id = executor_id + self.result_received = result_received + self.result_file = result_file + self.reason = reason + self.status = status + + +class ParslFileToVine: + """ + Support structure to report Parsl filenames to TaskVine. + parsl_name is the local_name or filepath attribute of a Parsl file object. + stage tells whether the file should be copied by TaskVine to the workers. + cache tells whether the file should be cached at workers. Only valid if stage == True + """ + def __init__(self, + parsl_name: str, # name of file + stage: bool, # whether TaskVine should know about this file + cache: bool # whether TaskVine should cache this file + ): + self.parsl_name = parsl_name + self.stage = stage + self.cache = cache + + +def run_parsl_function(map_file, function_file, argument_file, result_file): + """ + Wrapper function to deploy with FunctionCall as serverless tasks. + """ + from parsl.executors.taskvine.exec_parsl_function import run + run(map_file, function_file, argument_file, result_file) diff --git a/parsl/parsl/executors/threads.py b/parsl/parsl/executors/threads.py new file mode 100644 index 00000000..3b53a7f7 --- /dev/null +++ b/parsl/parsl/executors/threads.py @@ -0,0 +1,100 @@ +import concurrent.futures as cf +import logging +from typing import List, Optional + +import typeguard + +from parsl.data_provider.staging import Staging +from parsl.executors.base import ParslExecutor +from parsl.executors.errors import InvalidResourceSpecification +from parsl.monitoring.radios.base import RadioConfig +from parsl.monitoring.radios.multiprocessing import MultiprocessingQueueRadio +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + + +class ThreadPoolExecutor(ParslExecutor, RepresentationMixin): + """A thread-based executor. + + Parameters + ---------- + max_threads : Optional[int] + Number of threads. Default is 2. + thread_name_prefix : string + Thread name prefix + storage_access : list of :class:`~parsl.data_provider.staging.Staging` + Specifications for accessing data this executor remotely. + """ + + @typeguard.typechecked + def __init__(self, label: str = 'threads', max_threads: Optional[int] = 2, + thread_name_prefix: str | None = None, storage_access: Optional[List[Staging]] = None, + working_dir: Optional[str] = None, remote_monitoring_radio: Optional[RadioConfig] = None): + ParslExecutor.__init__(self) + self.label = label + self.max_threads = max_threads + if thread_name_prefix is None: + self.thread_name_prefix = "ThreadPoolExecutor-" + label + else: + self.thread_name_prefix = thread_name_prefix + + # we allow storage_access to be None now, which means something else to [] now + # None now means that a default storage access list will be used, while + # [] is a list with no storage access in it at all + self.storage_access = storage_access + self.working_dir = working_dir + + if remote_monitoring_radio is not None: + self.remote_monitoring_radio = remote_monitoring_radio + else: + self.remote_monitoring_radio = MultiprocessingQueueRadio() + + def start(self): + self.executor = cf.ThreadPoolExecutor(max_workers=self.max_threads, + thread_name_prefix=self.thread_name_prefix) + + def submit(self, func, resource_specification, *args, **kwargs): + """Submits work to the thread pool. + + This method is simply pass through and behaves like a submit call as described + here `Python docs: `_ + + """ + if resource_specification: + logger.error("Ignoring the resource specification. " + "Parsl resource specification is not supported in ThreadPool Executor.") + raise InvalidResourceSpecification(set(resource_specification.keys()), + "Parsl resource specification is not supported in ThreadPool Executor.") + + return self.executor.submit(func, *args, **kwargs) + + def shutdown(self, block=True): + """Shutdown the ThreadPool. The underlying concurrent.futures thread pool + implementation will not terminate tasks that are being executed, because it + does not provide a mechanism to do that. With block set to false, this will + return immediately and it will appear as if the DFK is shut down, but + the python process will not be able to exit until the thread pool has + emptied out by task completions. In either case, this can be a very long wait. + + Kwargs: + - block (Bool): To block for confirmations or not + + """ + logger.debug("Shutting down executor, which involves waiting for running tasks to complete") + self.executor.shutdown(wait=block) + + super().shutdown() + + logger.debug("Done with executor shutdown") + + def monitor_resources(self): + """Resource monitoring does not make sense when using the + ThreadPoolExecutor, as there is no per-task process tree: all tasks + run inside the same single submitting process. + + In addition, the use of fork-based multiprocessing in the remote + wrapper in parsl/monitoring/remote.py was especially prone to deadlock + with this executor. + """ + return False diff --git a/parsl/parsl/executors/workqueue/__init__.py b/parsl/parsl/executors/workqueue/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/executors/workqueue/errors.py b/parsl/parsl/executors/workqueue/errors.py new file mode 100644 index 00000000..3b12d1ad --- /dev/null +++ b/parsl/parsl/executors/workqueue/errors.py @@ -0,0 +1,24 @@ +from typing import Optional + +from parsl.app.errors import AppException +from parsl.errors import ParslError + + +class WorkQueueTaskFailure(AppException): + """A failure executing a task in workqueue + + Contains: + reason(string) + status(optional exception) + """ + + def __init__(self, reason: str, status: Optional[Exception]): + self.reason = reason + self.status = status + + +class WorkQueueFailure(ParslError): + """A failure in the work queue executor that prevented the task to be + executed."" + """ + pass diff --git a/parsl/parsl/executors/workqueue/exec_parsl_function.py b/parsl/parsl/executors/workqueue/exec_parsl_function.py new file mode 100644 index 00000000..d19d92ef --- /dev/null +++ b/parsl/parsl/executors/workqueue/exec_parsl_function.py @@ -0,0 +1,209 @@ +import pickle +import sys +import traceback + +from parsl.app.errors import RemoteExceptionWrapper +from parsl.data_provider.files import File +from parsl.serialize import serialize +from parsl.utils import get_std_fname_mode + +# This scripts executes a parsl function which is pickled in a file: +# +# exec_parsl_function.py map_file function_file result_file +# +# map_file: Contains a pickled dictionary that indicates which local_paths the +# parsl Files should take. +# +# function_file: Contains a pickle parsl function. +# +# result_file: It will contain the result of the function, including any +# exception generated. Exceptions will be wrapped with RemoteExceptionWrapper. +# +# Exit codes: +# 0: The function was evaluated to completion. The result or any exception +# wrapped with RemoteExceptionWrapper were written to result_file. +# anything else: There was an error that prevented writing to the result file altogether. +# The exit code corresponds to whatever the python interpreter gives. +# + + +def load_pickled_file(filename): + with open(filename, "rb") as f_in: + return pickle.load(f_in) + + +def dump_result_to_file(result_file, result_package): + with open(result_file, "wb") as f_out: + f_out.write(serialize(result_package)) + + +def remap_location(mapping, parsl_file): + if not isinstance(parsl_file, File): + return + # Below we rewrite .local_path when scheme != file only when the local_name + # was given by the main parsl process. This is the case when scheme != + # 'file' but .local_path (via filepath) is in mapping. + if parsl_file.scheme == 'file' or parsl_file.local_path: + master_location = parsl_file.filepath + if master_location in mapping: + parsl_file.local_path = mapping[master_location] + + +def remap_list_of_files(mapping, maybe_files): + for maybe_file in maybe_files: + remap_location(mapping, maybe_file) + + +def remap_all_files(mapping, fn_args, fn_kwargs): + # remap any positional argument given to the function that looks like a + # File + remap_list_of_files(mapping, fn_args) + + # remap any keyword argument in the same way, but we need to treat + # "inputs" and "outputs" specially because they are lists, and + # "stdout" and "stderr", because they are not File's. + for kwarg, maybe_file in fn_kwargs.items(): + if kwarg in ["inputs", "outputs"]: + remap_list_of_files(mapping, maybe_file) + if kwarg in ["stdout", "stderr"]: + if maybe_file: + (fname, mode) = get_std_fname_mode(kwarg, maybe_file) + if fname in mapping: + fn_kwargs[kwarg] = (mapping[fname], mode) + else: + # Treat anything else as a possible File to be remapped. + remap_location(mapping, maybe_file) + + +def unpack_function(function_info, user_namespace): + if "source code" in function_info: + return unpack_source_code_function(function_info, user_namespace) + elif "byte code" in function_info: + return unpack_byte_code_function(function_info, user_namespace) + else: + raise ValueError("Function file does not have a valid function representation.") + + +def unpack_source_code_function(function_info, user_namespace): + source_code = function_info["source code"] + name = function_info["name"] + args = function_info["args"] + kwargs = function_info["kwargs"] + return (source_code, name, args, kwargs) + + +def unpack_byte_code_function(function_info, user_namespace): + from parsl.serialize import unpack_apply_message + func, args, kwargs = unpack_apply_message(function_info["byte code"]) + return (func, 'parsl_function_name', args, kwargs) + + +def encode_function(user_namespace, fn, fn_name, fn_args, fn_kwargs): + # Returns a tuple (code, result_name) + # code can be exec in the user_namespace to produce result_name. + prefix = "parsl_" + args_name = prefix + "args" + kwargs_name = prefix + "kwargs" + result_name = prefix + "result" + + # Add variables to the namespace to make function call + user_namespace.update({args_name: fn_args, + kwargs_name: fn_kwargs, + result_name: result_name}) + + if isinstance(fn, str): + code = encode_source_code_function(user_namespace, fn, fn_name, args_name, kwargs_name, result_name) + elif callable(fn): + code = encode_byte_code_function(user_namespace, fn, fn_name, args_name, kwargs_name, result_name) + else: + raise ValueError("Function object does not look like a function.") + + return (code, result_name) + + +def encode_source_code_function(user_namespace, fn, fn_name, args_name, kwargs_name, result_name): + # We drop the first line as it names the parsl decorator used (i.e., @python_app) + source = fn.split('\n')[1:] + fn_app = "{0} = {1}(*{2}, **{3})".format(result_name, fn_name, args_name, kwargs_name) + + source.append(fn_app) + + code = "\n".join(source) + return code + + +def encode_byte_code_function(user_namespace, fn, fn_name, args_name, kwargs_name, result_name): + user_namespace.update({fn_name: fn}) + code = "{0} = {1}(*{2}, **{3})".format(result_name, fn_name, args_name, kwargs_name) + return code + + +def load_function(map_file, function_file): + # Decodes the function and its file arguments to be executed into + # function_code, and updates a user namespace with the function name and + # the variable named result_name. When the function is executed, its result + # will be stored in this variable in the user namespace. + # Returns (namespace, function_code, result_name) + + # Create the namespace to isolate the function execution. + user_ns = locals() + user_ns.update({'__builtins__': __builtins__}) + + function_info = load_pickled_file(function_file) + + (fn, fn_name, fn_args, fn_kwargs) = unpack_function(function_info, user_ns) + + mapping = load_pickled_file(map_file) + remap_all_files(mapping, fn_args, fn_kwargs) + + (code, result_name) = encode_function(user_ns, fn, fn_name, fn_args, fn_kwargs) + + return (user_ns, code, result_name) + + +def execute_function(namespace, function_code, result_name): + # On executing the function inside the namespace, its result will be in a + # variable named result_name. + + exec(function_code, namespace, namespace) + result = namespace.get(result_name) + + return result + + +if __name__ == "__main__": + try: + # parse the three required command line arguments: + # map_file: contains a pickled dictionary to map original names to + # names at the execution site. + # function_file: contains the pickled parsl function to execute. + # result_file: any output (including exceptions) will be written to + # this file. + try: + (map_file, function_file, result_file) = sys.argv[1:] + except ValueError: + print("Usage:\n\t{} function result mapping\n".format(sys.argv[0])) + raise + + try: + (namespace, function_code, result_name) = load_function(map_file, function_file) + except Exception: + print("There was an error setting up the function for execution.") + raise + + try: + result = execute_function(namespace, function_code, result_name) + except Exception: + print("There was an error executing the function.") + raise + except Exception: + traceback.print_exc() + result = RemoteExceptionWrapper(*sys.exc_info()) + + # Write out function result to the result file + try: + dump_result_to_file(result_file, result) + except Exception: + print("Could not write to result file.") + traceback.print_exc() + sys.exit(1) diff --git a/parsl/parsl/executors/workqueue/executor.py b/parsl/parsl/executors/workqueue/executor.py new file mode 100644 index 00000000..5370ad6d --- /dev/null +++ b/parsl/parsl/executors/workqueue/executor.py @@ -0,0 +1,1054 @@ +""" WorkQueueExecutor utilizes the Work Queue distributed framework developed by the +Cooperative Computing Lab (CCL) at Notre Dame to provide a fault-tolerant, +high-throughput system for delegating Parsl tasks to thousands of remote machines +""" + +import hashlib +import inspect +import itertools +import logging +import multiprocessing +import os +import pickle +import queue +import shutil +import socket +import subprocess +import tempfile +import threading +import time +from collections import namedtuple +from concurrent.futures import Future +from ctypes import c_bool +from typing import Dict, List, Optional, Set, Union + +import typeguard + +import parsl.utils as putils +from parsl.data_provider.files import File +from parsl.data_provider.staging import Staging +from parsl.errors import OptionalModuleMissing +from parsl.executors.errors import ExecutorError, InvalidResourceSpecification +from parsl.executors.status_handling import BlockProviderExecutor +from parsl.executors.workqueue import exec_parsl_function +from parsl.monitoring.radios.base import RadioConfig +from parsl.monitoring.radios.filesystem import FilesystemRadio +from parsl.multiprocessing import SpawnContext, SpawnProcess +from parsl.process_loggers import wrap_with_logs +from parsl.providers import CondorProvider, LocalProvider +from parsl.providers.base import ExecutionProvider +from parsl.serialize import deserialize, pack_apply_message +from parsl.utils import setproctitle + +from .errors import WorkQueueFailure, WorkQueueTaskFailure + +IMPORT_EXCEPTION = None +try: + from ndcctools import work_queue as wq + from ndcctools.work_queue import ( + WORK_QUEUE_ALLOCATION_MODE_MAX_THROUGHPUT, + WORK_QUEUE_DEFAULT_PORT, + WorkQueue, + ) +except ImportError as e: + _work_queue_enabled = False + WORK_QUEUE_DEFAULT_PORT = 0 + IMPORT_EXCEPTION = e +else: + _work_queue_enabled = True + +package_analyze_script = shutil.which("python_package_analyze") +package_create_script = shutil.which("python_package_create") +package_run_script = shutil.which("python_package_run") + +logger = logging.getLogger(__name__) + + +# Support structure to communicate parsl tasks to the work queue submit thread. +ParslTaskToWq = namedtuple( + 'ParslTaskToWq', + 'id ' + 'category ' + 'cores memory disk gpus priority running_time_min env_pkg map_file function_file result_file input_files output_files') + +# Support structure to communicate final status of work queue tasks to parsl +# if result_received is True: +# result_file is the path to the file containing the result. +# if result_received is False: +# reason and status are only valid if result_received is False +# result_file is None +WqTaskToParsl = namedtuple('WqTaskToParsl', 'id result_received result_file reason status') + +# Support structure to report parsl filenames to work queue. +# parsl_name is the local_name or filepath attribute of a parsl file object. +# stage tells whether the file should be copied by work queue to the workers. +# cache tells whether the file should be cached at workers. Only valid if stage == True +ParslFileToWq = namedtuple('ParslFileToWq', 'parsl_name stage cache') + + +class WorkQueueExecutor(BlockProviderExecutor, putils.RepresentationMixin): + """Executor to use Work Queue batch system + + The WorkQueueExecutor system utilizes the Work Queue framework to + efficiently delegate Parsl apps to remote machines in clusters and + grids using a fault-tolerant system. Users can run the + work_queue_worker program on remote machines to connect to the + WorkQueueExecutor, and Parsl apps will then be sent out to these + machines for execution and retrieval. + + + Parameters + ---------- + + label: str + A human readable label for the executor, unique + with respect to other Work Queue master programs. + Default is "WorkQueueExecutor". + + working_dir: str + Location for Parsl to perform app delegation to the Work + Queue system. Defaults to current directory. + + project_name: str + If a project_name is given, then Work Queue will periodically + report its status and performance back to the global WQ catalog, + which can be viewed here: http://ccl.cse.nd.edu/software/workqueue/status + Default is None. Overrides address. + + project_password_file: str + Optional password file for the work queue project. Default is None. + + address: str + The ip to contact this work queue master process. + If not given, uses the address of the current machine as returned + by socket.gethostname(). + Ignored if project_name is specified. + + port: int + TCP port on Parsl submission machine for Work Queue workers + to connect to. Workers will connect to Parsl using this port. + + If 0, Work Queue will allocate a port number automatically. + In this case, environment variables can be used to influence the + choice of port, documented here: + https://ccl.cse.nd.edu/software/manuals/api/html/work__queue_8h.html#a21714a10bcdfcf5c3bd44a96f5dcbda6 + Default: WORK_QUEUE_DEFAULT_PORT. + + env: dict{str} + Dictionary that contains the environmental variables that + need to be set on the Work Queue worker machine. + + shared_fs: bool + Define if working in a shared file system or not. If Parsl + and the Work Queue workers are on a shared file system, Work + Queue does not need to transfer and rename files for execution. + Default is False. + + use_cache: bool + Whether workers should cache files that are common to tasks. + Warning: Two files are considered the same if they have the same + filepath name. Use with care when reusing the executor instance + across multiple parsl workflows. Default is False. + + source: bool + Choose whether to transfer parsl app information as + source code. (Note: this increases throughput for + @python_apps, but the implementation does not include + functionality for @bash_apps, and thus source=False + must be used for programs utilizing @bash_apps.) + Default is False. Set to True if pack is True + + pack: bool + Use conda-pack to prepare a self-contained Python evironment for + each task. This greatly increases task latency, but does not + require a common environment or shared FS on execution nodes. + Implies source=True. + + extra_pkgs: list + List of extra pip/conda package names to include when packing + the environment. This may be useful if the app executes other + (possibly non-Python) programs provided via pip or conda. + Scanning the app source for imports would not detect these + dependencies, so they need to be manually specified. + + autolabel: bool + Use the Resource Monitor to automatically determine resource + labels based on observed task behavior. + + autolabel_window: int + Set the number of tasks considered for autolabeling. Work Queue + will wait for a series of N tasks with steady resource + requirements before making a decision on labels. Increasing + this parameter will reduce the number of failed tasks due to + resource exhaustion when autolabeling, at the cost of increased + resources spent collecting stats. + + autocategory: bool + Place each app in its own category by default. If all + invocations of an app have similar performance characteristics, + this will provide a reasonable set of categories automatically. + + max_retries: int + Set the number of retries that Work Queue will make when a task + fails. This is distinct from Parsl level retries configured in + parsl.config.Config. Set to None to allow Work Queue to retry + tasks forever. By default, this is set to 1, so that all retries + will be managed by Parsl. + + init_command: str + Command line to run before executing a task in a worker. + Default is ''. + + worker_options: str + Extra options passed to work_queue_worker. Default is ''. + + worker_executable: str + The command used to invoke work_queue_worker. This can be used + when the worker needs to be wrapped inside some other command + (for example, to run the worker inside a container). Default is + 'work_queue_worker'. + + function_dir: str + The directory where serialized function invocations are placed + to be sent to workers. If undefined, this defaults to a directory + under runinfo/. If shared_filesystem=True, then this directory + must be visible from both the submitting side and workers. + + coprocess: bool + Use Work Queue's coprocess facility to avoid launching a new Python + process for each task. Experimental. + This requires a version of Work Queue / cctools after commit + 874df524516441da531b694afc9d591e8b134b73 (release 7.5.0 is too early). + Default is False. + + scaling_cores_per_worker: int + When using Parsl scaling, this specifies the number of cores that a + worker is expected to have available for computation. Default 1. This + parameter can be ignored when using a fixed number of blocks, or when + using one task per worker (by omitting a ``cores`` resource + specifiation for each task). + """ + + @typeguard.typechecked + def __init__(self, + label: str = "WorkQueueExecutor", + provider: ExecutionProvider = LocalProvider(), + working_dir: str = ".", + project_name: Optional[str] = None, + project_password_file: Optional[str] = None, + address: Optional[str] = None, + port: int = WORK_QUEUE_DEFAULT_PORT, + env: Optional[Dict] = None, + shared_fs: bool = False, + storage_access: Optional[List[Staging]] = None, + use_cache: bool = False, + source: bool = False, + pack: bool = False, + extra_pkgs: Optional[List[str]] = None, + autolabel: bool = False, + autolabel_window: int = 1, + autocategory: bool = True, + max_retries: int = 1, + init_command: str = "", + worker_options: str = "", + full_debug: bool = True, + worker_executable: str = 'work_queue_worker', + function_dir: Optional[str] = None, + coprocess: bool = False, + scaling_cores_per_worker: int = 1, + remote_monitoring_radio: Optional[RadioConfig] = None): + BlockProviderExecutor.__init__(self, provider=provider, + block_error_handler=True) + if not _work_queue_enabled: + raise OptionalModuleMissing(['work_queue'], f"WorkQueueExecutor requires the work_queue module. More info: {IMPORT_EXCEPTION}") + + self.scaling_cores_per_worker = scaling_cores_per_worker + self.label = label + self.task_queue: multiprocessing.Queue = SpawnContext.Queue() + self.collector_queue: multiprocessing.Queue = SpawnContext.Queue() + self.address = address + self.port = port + self.executor_task_counter = -1 + self.project_name = project_name + self.project_password_file = project_password_file + self.env = env + self.init_command = init_command + self.shared_fs = shared_fs + self.storage_access = storage_access + self.use_cache = use_cache + self.working_dir = working_dir + self.registered_files: Set[str] = set() + self.full_debug = full_debug + self.source = True if pack else source + self.pack = pack + self.extra_pkgs = extra_pkgs or [] + self.autolabel = autolabel + self.autolabel_window = autolabel_window + self.autocategory = autocategory + self.max_retries = max_retries + self.should_stop = SpawnContext.Value(c_bool, False) + self.cached_envs = {} # type: Dict[int, str] + self.worker_options = worker_options + self.worker_executable = worker_executable + self.function_dir = function_dir + self.coprocess = coprocess + + if not self.address: + self.address = socket.gethostname() + + if self.project_password_file is not None and not os.path.exists(self.project_password_file): + raise WorkQueueFailure('Could not find password file: {}'.format(self.project_password_file)) + + if self.project_password_file is not None: + if os.path.exists(self.project_password_file) is False: + logger.debug("Password File does not exist, no file used") + self.project_password_file = None + + # Build foundations of the launch command + self.launch_cmd = ("{package_prefix}python3 exec_parsl_function.py {mapping} {function} {result}") + if self.init_command != "": + self.launch_cmd = self.init_command + "; " + self.launch_cmd + + if remote_monitoring_radio is not None: + self.remote_monitoring_radio = remote_monitoring_radio + else: + self.remote_monitoring_radio = FilesystemRadio() + + def _get_launch_command(self, block_id): + # this executor uses different terminology for worker/launch + # commands than in htex + return f"PARSL_WORKER_BLOCK_ID={block_id} {self.worker_command}" + + def start(self): + """Create submit process and collector thread to create, send, and + retrieve Parsl tasks within the Work Queue system. + """ + super().start() + self.tasks_lock = threading.Lock() + + # Create directories for data and results + if not self.function_dir: + self.function_data_dir = os.path.join(self.run_dir, self.label, "function_data") + else: + tp = str(time.time()) + tx = os.path.join(self.function_dir, tp) + os.makedirs(tx) + self.function_data_dir = os.path.join(self.function_dir, tp, self.label, "function_data") + self.package_dir = os.path.join(self.run_dir, self.label, "package_data") + self.wq_log_dir = os.path.join(self.run_dir, self.label) + logger.debug("function data directory: {}\nlog directory: {}".format(self.function_data_dir, self.wq_log_dir)) + os.makedirs(self.wq_log_dir) + os.makedirs(self.function_data_dir) + os.makedirs(self.package_dir) + + logger.debug("Starting WorkQueueExecutor") + + port_mailbox = SpawnContext.Queue() + + # Create a Process to perform WorkQueue submissions + submit_process_kwargs = {"task_queue": self.task_queue, + "launch_cmd": self.launch_cmd, + "data_dir": self.function_data_dir, + "collector_queue": self.collector_queue, + "full": self.full_debug, + "shared_fs": self.shared_fs, + "autolabel": self.autolabel, + "autolabel_window": self.autolabel_window, + "autocategory": self.autocategory, + "max_retries": self.max_retries, + "should_stop": self.should_stop, + "port": self.port, + "wq_log_dir": self.wq_log_dir, + "project_password_file": self.project_password_file, + "project_name": self.project_name, + "port_mailbox": port_mailbox, + "coprocess": self.coprocess + } + self.submit_process = SpawnProcess(target=_work_queue_submit_wait, + name="WorkQueue-Submit-Process", + kwargs=submit_process_kwargs) + + self.collector_thread = threading.Thread(target=self._collect_work_queue_results, + name="WorkQueue-collector-thread") + self.collector_thread.daemon = True + + # Begin both processes + self.submit_process.start() + self.collector_thread.start() + + self._chosen_port = port_mailbox.get(timeout=60) + + logger.debug(f"Chosen listening port is {self._chosen_port}") + + # Initialize scaling for the provider + self.initialize_scaling() + + def _path_in_task(self, executor_task_id, *path_components): + """Returns a filename specific to a task. + It is used for the following filename's: + (not given): The subdirectory per task that contains function, result, etc. + 'function': Pickled file that contains the function to be executed. + 'result': Pickled file that (will) contain the result of the function. + 'map': Pickled file with a dict between local parsl names, and remote work queue names. + """ + task_dir = "{:04d}".format(executor_task_id) + return os.path.join(self.function_data_dir, task_dir, *path_components) + + def submit(self, func, resource_specification, *args, **kwargs): + """Processes the Parsl app by its arguments and submits the function + information to the task queue, to be executed using the Work Queue + system. The args and kwargs are processed for input and output files to + the Parsl app, so that the files are appropriately specified for the Work + Queue task. + + Parameters + ---------- + + func : function + Parsl app to be submitted to the Work Queue system + args : list + Arguments to the Parsl app + kwargs : dict + Keyword arguments to the Parsl app + """ + cores = None + memory = None + disk = None + gpus = None + priority = None + category = None + running_time_min = None + if resource_specification and isinstance(resource_specification, dict): + logger.debug("Got resource_specification: {}".format(resource_specification)) + + required_resource_types = set(['cores', 'memory', 'disk']) + acceptable_fields = set(['cores', 'memory', 'disk', 'gpus', 'priority', 'running_time_min']) + keys = set(resource_specification.keys()) + + if not keys.issubset(acceptable_fields): + message = "Task resource specification only accepts these types of resources: {}".format( + ', '.join(acceptable_fields)) + logger.error(message) + raise InvalidResourceSpecification(keys, message) + + # this checks that either all of the required resource types are specified, or + # that none of them are: the `required_resource_types` are not actually required, + # but if one is specified, then they all must be. + key_check = required_resource_types.intersection(keys) + required_keys_ok = len(key_check) == 0 or key_check == required_resource_types + if not self.autolabel and not required_keys_ok: + logger.error("Running with `autolabel=False`. In this mode, " + "task resource specification requires " + "three resources to be specified simultaneously: cores, memory, and disk") + raise InvalidResourceSpecification(keys, + "Task resource specification requires " + "three resources to be specified simultaneously: cores, memory, and disk. " + "Try setting autolabel=True if you are unsure of the resource usage") + + for k in keys: + if k == 'cores': + cores = resource_specification[k] + elif k == 'memory': + memory = resource_specification[k] + elif k == 'disk': + disk = resource_specification[k] + elif k == 'gpus': + gpus = resource_specification[k] + elif k == 'priority': + priority = resource_specification[k] + elif k == 'category': + category = resource_specification[k] + elif k == 'running_time_min': + running_time_min = resource_specification[k] + + self.executor_task_counter += 1 + executor_task_id = self.executor_task_counter + + # Create a per task directory for the function, result, map, and result files + os.mkdir(self._path_in_task(executor_task_id)) + + input_files = [] + output_files = [] + + # Determine the input and output files that will exist at the workers: + input_files += [self._register_file(f) for f in kwargs.get("inputs", []) if isinstance(f, File)] + output_files += [self._register_file(f) for f in kwargs.get("outputs", []) if isinstance(f, File)] + + # Also consider any *arg that looks like a file as an input: + input_files += [self._register_file(f) for f in args if isinstance(f, File)] + + for kwarg, maybe_file in kwargs.items(): + # Add appropriate input and output files from "stdout" and "stderr" keyword arguments + if kwarg == "stdout" or kwarg == "stderr": + if maybe_file: + output_files.append(self._std_output_to_wq(kwarg, maybe_file)) + # For any other keyword that looks like a file, assume it is an input file + elif isinstance(maybe_file, File): + input_files.append(self._register_file(maybe_file)) + + # Create a Future object and have it be mapped from the task ID in the tasks dictionary + fu = Future() + fu.parsl_executor_task_id = executor_task_id + assert isinstance(resource_specification, dict) + fu.resource_specification = resource_specification + logger.debug("Getting tasks_lock to set WQ-level task entry") + with self.tasks_lock: + logger.debug("Got tasks_lock to set WQ-level task entry") + self.tasks[str(executor_task_id)] = fu + + logger.debug("Creating executor task {} for function {} with args {}".format(executor_task_id, func, args)) + + function_file = self._path_in_task(executor_task_id, "function") + result_file = self._path_in_task(executor_task_id, "result") + map_file = self._path_in_task(executor_task_id, "map") + + logger.debug("Creating executor task {} with function at: {}".format(executor_task_id, function_file)) + logger.debug("Creating executor task {} with result to be found at: {}".format(executor_task_id, result_file)) + + self._serialize_function(function_file, func, args, kwargs) + + if self.pack: + env_pkg = self._prepare_package(func, self.extra_pkgs) + else: + env_pkg = None + + logger.debug("Constructing map for local filenames at worker for executor task {}".format(executor_task_id)) + self._construct_map_file(map_file, input_files, output_files) + + if not self.submit_process.is_alive(): + raise ExecutorError(self, "Workqueue Submit Process is not alive") + + # Create message to put into the message queue + logger.debug("Placing executor task {} on message queue".format(executor_task_id)) + if category is None: + category = func.__name__ if self.autocategory else 'parsl-default' + self.task_queue.put_nowait(ParslTaskToWq(executor_task_id, + category, + cores, + memory, + disk, + gpus, + priority, + running_time_min, + env_pkg, + map_file, + function_file, + result_file, + input_files, + output_files)) + + return fu + + def _construct_worker_command(self): + worker_command = self.worker_executable + if self.coprocess: + worker_command += " --coprocess parsl_coprocess.py" + if self.project_password_file: + worker_command += ' --password {}'.format(self.project_password_file) + if self.worker_options: + worker_command += ' {}'.format(self.worker_options) + if self.project_name: + worker_command += ' -M {}'.format(self.project_name) + else: + worker_command += ' {} {}'.format(self.address, self._chosen_port) + + logger.debug("Using worker command: {}".format(worker_command)) + return worker_command + + def _patch_providers(self): + # Add the worker and password file to files that the provider needs to stage. + # (Currently only for the CondorProvider) + if isinstance(self.provider, CondorProvider): + path_to_worker = shutil.which('work_queue_worker') + self.worker_command = './' + self.worker_command + self.provider.transfer_input_files.append(path_to_worker) + if self.project_password_file: + self.provider.transfer_input_files.append(self.project_password_file) + + def _serialize_function(self, fn_path, parsl_fn, parsl_fn_args, parsl_fn_kwargs): + """Takes the function application parsl_fn(*parsl_fn_args, **parsl_fn_kwargs) + and serializes it to the file fn_path.""" + + # Either build a dictionary with the source of the function, or pickle + # the function directly: + if self.source: + function_info = {"source code": inspect.getsource(parsl_fn), + "name": parsl_fn.__name__, + "args": parsl_fn_args, + "kwargs": parsl_fn_kwargs} + else: + function_info = {"byte code": pack_apply_message(parsl_fn, parsl_fn_args, parsl_fn_kwargs, + buffer_threshold=1024 * 1024)} + + with open(fn_path, "wb") as f_out: + pickle.dump(function_info, f_out) + + def _construct_map_file(self, map_file, input_files, output_files): + """ Map local filepath of parsl files to the filenames at the execution worker. + If using a shared filesystem, the filepath is mapped to its absolute filename. + Otherwise, to its original relative filename. In this later case, work queue + recreates any directory hierarchy needed.""" + file_translation_map = {} + for spec in itertools.chain(input_files, output_files): + local_name = spec[0] + if self.shared_fs: + remote_name = os.path.abspath(local_name) + else: + remote_name = local_name + file_translation_map[local_name] = remote_name + with open(map_file, "wb") as f_out: + pickle.dump(file_translation_map, f_out) + + def _register_file(self, parsl_file): + """Generates a tuple (parsl_file.filepath, stage, cache) to give to + work queue. cache is always False if self.use_cache is False. + Otherwise, it is set to True if parsl_file is used more than once. + stage is True if the file needs to be copied by work queue. (i.e., not + a URL or an absolute path) + + It has the side-effect of adding parsl_file to a list of registered + files. + + Note: The first time a file is used cache is set to False. Since + tasks are generated dynamically, without other information this is + the best we can do.""" + to_cache = False + if self.use_cache: + to_cache = parsl_file in self.registered_files + + to_stage = False + if parsl_file.scheme == 'file' or (parsl_file.local_path and os.path.exists(parsl_file.local_path)): + to_stage = not os.path.isabs(parsl_file.filepath) + + self.registered_files.add(parsl_file) + + return ParslFileToWq(parsl_file.filepath, to_stage, to_cache) + + def _std_output_to_wq(self, fdname, stdfspec): + """Find the name of the file that will contain stdout or stderr and + return a ParslFileToWq with it. These files are never cached""" + fname, mode = putils.get_std_fname_mode(fdname, stdfspec) + to_stage = not os.path.isabs(fname) + return ParslFileToWq(fname, stage=to_stage, cache=False) + + def _prepare_package(self, fn, extra_pkgs): + fn_id = id(fn) + fn_name = fn.__name__ + if fn_id in self.cached_envs: + logger.debug("Skipping analysis of %s, previously got %s", fn_name, self.cached_envs[fn_id]) + return self.cached_envs[fn_id] + source_code = inspect.getsource(fn).encode() + pkg_dir = os.path.join(tempfile.gettempdir(), "python_package-{}".format(os.geteuid())) + os.makedirs(pkg_dir, exist_ok=True) + with tempfile.NamedTemporaryFile(suffix='.yaml') as spec: + logger.info("Analyzing dependencies of %s", fn_name) + analyze_cmdline = [package_analyze_script, exec_parsl_function.__file__, '-', spec.name] + for p in extra_pkgs: + analyze_cmdline += ["--extra-pkg", p] + subprocess.run(analyze_cmdline, input=source_code, check=True) + with open(spec.name, mode='rb') as f: + spec_hash = hashlib.sha256(f.read()).hexdigest() + logger.debug("Spec hash for %s is %s", fn_name, spec_hash) + pkg = os.path.join(pkg_dir, "pack-{}.tar.gz".format(spec_hash)) + if os.access(pkg, os.R_OK): + self.cached_envs[fn_id] = pkg + logger.debug("Cached package for %s found at %s", fn_name, pkg) + return pkg + (fd, tarball) = tempfile.mkstemp(dir=pkg_dir, prefix='.tmp', suffix='.tar.gz') + os.close(fd) + logger.info("Creating dependency package for %s", fn_name) + logger.debug("Writing deps for %s to %s", fn_name, tarball) + subprocess.run([package_create_script, spec.name, tarball], stdout=subprocess.DEVNULL, check=True) + logger.debug("Done with conda-pack; moving %s to %s", tarball, pkg) + os.rename(tarball, pkg) + self.cached_envs[fn_id] = pkg + return pkg + + def initialize_scaling(self): + """ Compose the launch command and call scale out + + Scales the workers to the appropriate nodes with provider + """ + # Start scaling in/out + logger.debug("Starting WorkQueueExecutor with provider: %s", self.provider) + self.worker_command = self._construct_worker_command() + self._patch_providers() + + def outstanding(self) -> int: + """Count the number of outstanding slots required. This is inefficiently + implemented and probably could be replaced with a counter. + """ + logger.debug("Calculating outstanding task slot load") + outstanding = 0 + tasks = 0 # only for log message... + with self.tasks_lock: + for fut in self.tasks.values(): + if not fut.done(): + # if a task does not specify a core count, Work Queue will allocate an entire + # worker node to that task. That's approximated here by saying that it uses + # scaling_cores_per_worker. + resource_spec = getattr(fut, 'resource_specification', {}) + cores = resource_spec.get('cores', self.scaling_cores_per_worker) + + outstanding += cores + tasks += 1 + logger.debug(f"Counted {tasks} outstanding tasks with {outstanding} outstanding slots") + return outstanding + + @property + def workers_per_node(self) -> Union[int, float]: + return self.scaling_cores_per_worker + + def shutdown(self, *args, **kwargs): + """Shutdown the executor. Sets flag to cancel the submit process and + collector thread, which shuts down the Work Queue system submission. + """ + logger.debug("Work Queue shutdown started") + self.should_stop.value = True + + logger.debug("Joining on submit process") + self.submit_process.join() + self.submit_process.close() + + logger.debug("Joining on collector thread") + self.collector_thread.join() + + logger.debug("Closing multiprocessing queues") + self.task_queue.close() + self.task_queue.join_thread() + self.collector_queue.close() + self.collector_queue.join_thread() + + super().shutdown() + + logger.debug("Work Queue shutdown completed") + + @wrap_with_logs + def _collect_work_queue_results(self): + """Sets the values of tasks' futures of tasks completed by work queue. + """ + logger.debug("Starting Collector Thread") + try: + while not self.should_stop.value: + if not self.submit_process.is_alive(): + raise ExecutorError(self, "Workqueue Submit Process is not alive") + + # Get the result message from the collector_queue + try: + task_report = self.collector_queue.get(timeout=1) + except queue.Empty: + continue + + # Obtain the future from the tasks dictionary + with self.tasks_lock: + future = self.tasks.pop(task_report.id) + logger.debug("Updating Future for executor task {}".format(task_report.id)) + # If result_received, then there's a result file. The object inside the file + # may be a valid result or an exception caused within the function invocation. + # Otherwise there's no result file, implying errors from WorkQueue. + if task_report.result_received: + try: + with open(task_report.result_file, 'rb') as f_in: + result = deserialize(f_in.read()) + except Exception as e: + logger.error(f'Cannot load result from result file {task_report.result_file}. Exception: {e}') + ex = WorkQueueTaskFailure('Cannot load result from result file', None) + ex.__cause__ = e + future.set_exception(ex) + else: + if isinstance(result, Exception): + ex = WorkQueueTaskFailure('Task execution raises an exception', result) + ex.__cause__ = result + future.set_exception(ex) + else: + future.set_result(result) + else: + # If there are no results, then the task failed according to one of + # work queue modes, such as resource exhaustion. + ex = WorkQueueTaskFailure(task_report.reason, None) + future.set_exception(ex) + finally: + logger.debug("Marking all outstanding tasks as failed") + logger.debug("Acquiring tasks_lock") + with self.tasks_lock: + logger.debug("Acquired tasks_lock") + # set exception for tasks waiting for results that work queue did not execute + for fu in self.tasks.values(): + if not fu.done(): + fu.set_exception(WorkQueueFailure("work queue executor failed to execute the task.")) + logger.debug("Exiting Collector Thread") + + +@wrap_with_logs +def _work_queue_submit_wait(*, + port_mailbox: multiprocessing.Queue, + task_queue: multiprocessing.Queue, + launch_cmd: str, + collector_queue: multiprocessing.Queue, + data_dir: str, + full: bool, + shared_fs: bool, + autolabel: bool, + autolabel_window: int, + autocategory: bool, + max_retries: Optional[int], + should_stop, # multiprocessing.Value is an awkward type alias from inside multiprocessing + port: int, + wq_log_dir: str, + project_password_file: Optional[str], + project_name: Optional[str], + coprocess: bool) -> int: + """Thread to handle Parsl app submissions to the Work Queue objects. + Takes in Parsl functions submitted using submit(), and creates a + Work Queue task with the appropriate specifications, which is then + submitted to Work Queue. After tasks are completed, processes the + exit status and exit code of the task, and sends results to the + Work Queue collector thread. + To avoid python's global interpreter lock with work queue's wait, this + function should be launched as a process, not as a lightweight thread. This + means that any communication should be done using the multiprocessing + module capabilities, rather than shared memory. + """ + logger.debug("Starting WorkQueue Submit/Wait Process") + setproctitle("parsl: Work Queue submit/wait") + + # Enable debugging flags and create logging file + wq_debug_log = None + if wq_log_dir is not None: + logger.debug("Setting debugging flags and creating logging file") + wq_debug_log = os.path.join(wq_log_dir, "debug_log") + + # Create WorkQueue queue object + logger.debug("Creating WorkQueue Object") + try: + logger.debug("Requested port {}".format(port)) + q = WorkQueue(port, debug_log=wq_debug_log) + port_mailbox.put(q.port) + logger.debug("Listening on port {}".format(q.port)) + except Exception as e: + logger.error("Unable to create WorkQueue object: {}".format(e)) + port_mailbox.put(None) + raise e + + # Specify WorkQueue queue attributes + if project_name: + q.specify_name(project_name) + + if project_password_file: + q.specify_password_file(project_password_file) + + if autolabel: + q.enable_monitoring() + if autolabel_window is not None: + q.tune('category-steady-n-tasks', autolabel_window) + + # Only write logs when the wq_log_dir is specified, which it most likely will be + if wq_log_dir is not None: + wq_master_log = os.path.join(wq_log_dir, "master_log") + wq_trans_log = os.path.join(wq_log_dir, "transaction_log") + if full and autolabel: + wq_resource_log = os.path.join(wq_log_dir, "resource_logs") + q.enable_monitoring_full(dirname=wq_resource_log) + q.specify_log(wq_master_log) + q.specify_transactions_log(wq_trans_log) + + orig_ppid = os.getppid() + + result_file_of_task_id = {} # Mapping executor task id -> result file for active tasks. + + while not should_stop.value: + # Monitor the task queue + ppid = os.getppid() + if ppid != orig_ppid: + logger.debug("new Process") + break + + # Submit tasks + while task_queue.qsize() > 0 or q.empty() and not should_stop.value: + # Obtain task from task_queue + try: + task = task_queue.get(timeout=1) + logger.debug("Removing task from queue") + except queue.Empty: + continue + + try: + pkg_pfx = "" + if task.env_pkg is not None: + if package_run_script is None: + raise ValueError("package_run_script must be specified") + pkg_pfx = "./{} -e {} ".format(os.path.basename(package_run_script), + os.path.basename(task.env_pkg)) + + if not coprocess: + # Create command string + logger.debug(launch_cmd) + command_str = launch_cmd.format(package_prefix=pkg_pfx, + mapping=os.path.basename(task.map_file), + function=os.path.basename(task.function_file), + result=os.path.basename(task.result_file)) + logger.debug(command_str) + + # Create WorkQueue task for the command + logger.debug("Sending executor task {} with command: {}".format(task.id, command_str)) + t = wq.Task(command_str) + else: + t = wq.RemoteTask("run_parsl_task", + "parsl_coprocess", + os.path.basename(task.map_file), + os.path.basename(task.function_file), + os.path.basename(task.result_file)) + t.specify_exec_method("direct") + logger.debug("Sending executor task {} to coprocess".format(task.id)) + + except Exception as e: + logger.error("Unable to create task: {}".format(e)) + collector_queue.put_nowait(WqTaskToParsl(id=task.id, + result_received=False, + result_file=None, + reason="task could not be created by work queue", + status=-1)) + continue + + t.specify_category(task.category) + if autolabel: + q.specify_category_mode(task.category, WORK_QUEUE_ALLOCATION_MODE_MAX_THROUGHPUT) + + if task.cores is not None: + t.specify_cores(task.cores) + if task.memory is not None: + t.specify_memory(task.memory) + if task.disk is not None: + t.specify_disk(task.disk) + if task.gpus is not None: + t.specify_gpus(task.gpus) + if task.priority is not None: + t.specify_priority(task.priority) + if task.running_time_min is not None: + t.specify_running_time_min(task.running_time_min) + + if max_retries is not None: + logger.debug(f"Specifying max_retries {max_retries}") + t.specify_max_retries(max_retries) + else: + logger.debug("Not specifying max_retries") + + if task.env_pkg is not None: + t.specify_input_file(package_run_script, cache=True) + t.specify_input_file(task.env_pkg, cache=True) + + # Specify script, and data/result files for task + t.specify_input_file(exec_parsl_function.__file__, cache=True) + t.specify_input_file(task.function_file, cache=False) + t.specify_input_file(task.map_file, cache=False) + t.specify_output_file(task.result_file, cache=False) + t.specify_tag(str(task.id)) + result_file_of_task_id[str(task.id)] = task.result_file + + logger.debug("Executor task id: {}".format(task.id)) + + # Specify input/output files that need to be staged. + # Absolute paths are assumed to be in shared filesystem, and thus + # not staged by work queue. + if not shared_fs: + for spec in task.input_files: + if spec.stage: + t.specify_input_file(spec.parsl_name, spec.parsl_name, cache=spec.cache) + for spec in task.output_files: + if spec.stage: + t.specify_output_file(spec.parsl_name, spec.parsl_name, cache=spec.cache) + + # Submit the task to the WorkQueue object + logger.debug("Submitting executor task {} to WorkQueue".format(task.id)) + try: + wq_id = q.submit(t) + except Exception as e: + logger.error("Unable to submit task to work queue: {}".format(e)) + collector_queue.put_nowait(WqTaskToParsl(id=task.id, + result_received=False, + result_file=None, + reason="task could not be submited to work queue", + status=-1)) + continue + logger.info("Executor task {} submitted as Work Queue task {}".format(task.id, wq_id)) + + # If the queue is not empty wait on the WorkQueue queue for a task + task_found = True + while not q.empty() and task_found and not should_stop.value: + # Obtain the task from the queue + t = q.wait(1) + if t is None: + task_found = False + continue + # When a task is found: + executor_task_id = t.tag + logger.info("Completed Work Queue task {}, executor task {}".format(t.id, t.tag)) + result_file = result_file_of_task_id.pop(t.tag) + + # A tasks completes 'succesfully' if it has result file. + # The check whether this file can load a serialized Python object + # happens later in the collector thread of the executor process. + logger.debug("Looking for result in {}".format(result_file)) + if os.path.exists(result_file): + logger.debug("Found result in {}".format(result_file)) + collector_queue.put_nowait(WqTaskToParsl(id=executor_task_id, + result_received=True, + result_file=result_file, + reason=None, + status=t.return_status)) + # If a result file could not be generated, explain the + # failure according to work queue error codes. + else: + reason = _explain_work_queue_result(t) + logger.debug("Did not find result in {}".format(result_file)) + logger.debug("Wrapper Script status: {}\nWorkQueue Status: {}" + .format(t.return_status, t.result)) + logger.debug("Task with executor id {} / Work Queue id {} failed because:\n{}" + .format(executor_task_id, t.id, reason)) + collector_queue.put_nowait(WqTaskToParsl(id=executor_task_id, + result_received=False, + result_file=None, + reason=reason, + status=t.return_status)) + logger.debug("Exiting WorkQueue Monitoring Process") + return 0 + + +def _explain_work_queue_result(wq_task): + """Returns a string with the reason why a task failed according to work queue.""" + + wq_result = wq_task.result + + reason = "work queue result: " + if wq_result == wq.WORK_QUEUE_RESULT_SUCCESS: + reason += "succesful execution with exit code {}".format(wq_task.return_status) + elif wq_result == wq.WORK_QUEUE_RESULT_OUTPUT_MISSING: + reason += "The result file was not transfered from the worker.\n" + reason += "This usually means that there is a problem with the python setup,\n" + reason += "or the wrapper that executes the function." + reason += "\nTrace:\n" + str(wq_task.output) + elif wq_result == wq.WORK_QUEUE_RESULT_INPUT_MISSING: + reason += "missing input file" + elif wq_result == wq.WORK_QUEUE_RESULT_STDOUT_MISSING: + reason += "stdout has been truncated" + elif wq_result == wq.WORK_QUEUE_RESULT_SIGNAL: + reason += "task terminated with a signal" + elif wq_result == wq.WORK_QUEUE_RESULT_RESOURCE_EXHAUSTION: + reason += "task used more resources than requested" + elif wq_result == wq.WORK_QUEUE_RESULT_TASK_TIMEOUT: + reason += "task ran past the specified end time" + elif wq_result == wq.WORK_QUEUE_RESULT_UNKNOWN: + reason += "result could not be classified" + elif wq_result == wq.WORK_QUEUE_RESULT_FORSAKEN: + reason += "task failed, but not a task error" + elif wq_result == wq.WORK_QUEUE_RESULT_MAX_RETRIES: + reason += "unable to complete after specified number of retries" + elif wq_result == wq.WORK_QUEUE_RESULT_TASK_MAX_RUN_TIME: + reason += "task ran for more than the specified time" + elif wq_result == wq.WORK_QUEUE_RESULT_DISK_ALLOC_FULL: + reason += "task needed more space to complete task" + elif wq_result == wq.WORK_QUEUE_RESULT_RMONITOR_ERROR: + reason += "task failed because the monitor did not produce an output" + else: + reason += "unable to process Work Queue system failure" + return reason diff --git a/parsl/parsl/executors/workqueue/parsl_coprocess.py b/parsl/parsl/executors/workqueue/parsl_coprocess.py new file mode 100755 index 00000000..0bbcde89 --- /dev/null +++ b/parsl/parsl/executors/workqueue/parsl_coprocess.py @@ -0,0 +1,166 @@ +#! /usr/bin/env python3 + +import json +import os +import socket +import sys + +# If enabled, coprocess will print to stdout +debug_mode = False + +# Send a message on a binary I/O stream by sending the message length and then the (string) message. +def send_message(stream, data): + size = len(data) + size_msg = "{}\n".format(size) + stream.write(size_msg) + stream.write(data) + +# Receive a standard message from a binary I/O stream by reading length and then returning the (string) message +def recv_message(stream): + line = stream.readline() + length = int(line) + return stream.read(length) + +# Decorator for remotely execution functions to package things as json. +def remote_execute(func): + def remote_wrapper(event): + kwargs = event["fn_kwargs"] + args = event["fn_args"] + try: + response = { + "Result": func(*args, **kwargs), + "StatusCode": 200 + } + except Exception as e: + response = { + "Result": str(e), + "StatusCode": 500 + } + return response + return remote_wrapper + +# Main loop of coprocess for executing network functions. +def main(): + # Listen on an arbitrary port to be reported to the worker. + s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + try: + s.bind(('localhost', 0)) + except Exception as e: + s.close() + print(e, file=sys.stderr) + sys.exit(1) + + # Inform the worker of name and port for later connection. + config = { + "name": name(), # noqa: F821 + "port": s.getsockname()[1], + } + send_message(sys.stdout, json.dumps(config)) + sys.stdout.flush() + + # Remember original working directory b/c we change for each invocation. + abs_working_dir = os.getcwd() + + # Create pipe for communication with child process + rpipe, wpipe = os.pipe() + rpipestream = os.fdopen(rpipe, "r") + + while True: + s.listen() + conn, addr = s.accept() + connstream = conn.makefile("rw", encoding="utf-8") + + if debug_mode: + print('Network function: connection from {}'.format(addr), file=sys.stderr) + + while True: + # Read the invocation header from the worker + line = connstream.readline() + + # If end of file, then break out and accept again + if not line: + break + + # Parse the invocation header. + input_spec = line.split() + function_name = input_spec[0] + task_id = int(input_spec[1]) + event_size = int(input_spec[2]) + + # then read the contents of the event itself + event_str = connstream.read(event_size) + event = json.loads(event_str) + exec_method = event.get("remote_task_exec_method", None) + + try: + # First move to target directory (is undone in finally block) + os.chdir(os.path.join(abs_working_dir, f't.{task_id}')) + + # Then invoke function by desired method, resulting in + # response containing the text representation of the result. + + if exec_method == "direct": + response = json.dumps(globals()[function_name](event)) + else: + p = os.fork() + if p == 0: + response = globals()[function_name](event) + wpipestream = os.fdopen(wpipe, "w") + send_message(wpipestream, json.dumps(response)) + wpipestream.flush() + os._exit(0) + elif p < 0: + if debug_mode: + print(f'Network function: unable to fork to execute {function_name}', file=sys.stderr) + response = { + "Result": "unable to fork", + "StatusCode": 500 + } + response = json.dumps(response) + else: + # Get response string from child process. + response = recv_message(rpipestream) + # Wait for child process to complete + os.waitpid(p, 0) + + # At this point, response is set to a value one way or the other + + except Exception as e: + if debug_mode: + print("Network function encountered exception ", str(e), file=sys.stderr) + response = { + 'Result': f'network function encountered exception {e}', + 'Status Code': 500 + } + response = json.dumps(response) + finally: + # Restore the working directory, no matter how the function ended. + os.chdir(abs_working_dir) + + # Send response string back to parent worker process. + send_message(connstream, response) + connstream.flush() + + return 0 +def name(): + return 'parsl_coprocess' +@remote_execute +def run_parsl_task(a, b, c): + import parsl.executors.workqueue.exec_parsl_function as epf + try: + (map_file, function_file, result_file) = (a, b, c) + try: + (namespace, function_code, result_name) = epf.load_function(map_file, function_file) + except Exception: + raise + try: + result = epf.execute_function(namespace, function_code, result_name) + except Exception: + raise + except Exception: + result = RemoteExceptionWrapper(*sys.exc_info()) + epf.dump_result_to_file(result_file, result) + return None +if __name__ == "__main__": + main() + diff --git a/parsl/parsl/executors/workqueue/parsl_coprocess_stub.py b/parsl/parsl/executors/workqueue/parsl_coprocess_stub.py new file mode 100755 index 00000000..e914f6fa --- /dev/null +++ b/parsl/parsl/executors/workqueue/parsl_coprocess_stub.py @@ -0,0 +1,31 @@ +#! /usr/bin/env python3 + +import sys + +from parsl.app.errors import RemoteExceptionWrapper + + +def name(): + return 'parsl_coprocess' + + +def run_parsl_task(a, b, c): + import parsl.executors.workqueue.exec_parsl_function as epf + try: + (map_file, function_file, result_file) = (a, b, c) + + try: + (namespace, function_code, result_name) = epf.load_function(map_file, function_file) + except Exception: + raise + + try: + result = epf.execute_function(namespace, function_code, result_name) + except Exception: + raise + except Exception: + result = RemoteExceptionWrapper(*sys.exc_info()) + + epf.dump_result_to_file(result_file, result) + + return None diff --git a/parsl/parsl/jobs/__init__.py b/parsl/parsl/jobs/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/jobs/error_handlers.py b/parsl/parsl/jobs/error_handlers.py new file mode 100644 index 00000000..246f8c30 --- /dev/null +++ b/parsl/parsl/jobs/error_handlers.py @@ -0,0 +1,68 @@ +from __future__ import annotations + +from typing import Dict, Tuple + +import parsl.executors.status_handling as status_handling +from parsl.jobs.errors import TooManyJobFailuresError +from parsl.jobs.states import JobState, JobStatus + + +def noop_error_handler(executor: status_handling.BlockProviderExecutor, status: Dict[str, JobStatus], threshold: int = 3) -> None: + pass + + +def simple_error_handler(executor: status_handling.BlockProviderExecutor, status: Dict[str, JobStatus], threshold: int = 3) -> None: + (total_jobs, failed_jobs) = _count_jobs(status) + assert executor.provider is not None, "error handlers should only be called on executors which have a provider" + if hasattr(executor.provider, "init_blocks"): + threshold = max(1, executor.provider.init_blocks) + + if total_jobs >= threshold and failed_jobs == total_jobs: + executor.set_bad_state_and_fail_all(_get_error(status)) + + +def windowed_error_handler(executor: status_handling.BlockProviderExecutor, status: Dict[str, JobStatus], threshold: int = 3) -> None: + sorted_status = [(key, status[key]) for key in sorted(status, key=lambda x: int(x))] + current_window = dict(sorted_status[-threshold:]) + total, failed = _count_jobs(current_window) + if failed == threshold: + executor.set_bad_state_and_fail_all(_get_error(status)) + + +def _count_jobs(status: Dict[str, JobStatus]) -> Tuple[int, int]: + total = 0 + failed = 0 + for js in status.values(): + total += 1 + if js.state == JobState.FAILED or js.state == JobState.MISSING: + failed += 1 + return total, failed + + +def _get_error(status: Dict[str, JobStatus]) -> Exception: + """Concatenate all errors.""" + err = "" + count = 1 + for js in status.values(): + err = err + f"Error {count}:\n" + count += 1 + + if js.message is not None: + err = err + f"\t{js.message}\n" + + if js.exit_code is not None: + err = err + f"\tEXIT CODE: {js.exit_code}\n" + + stdout = js.stdout_summary + if stdout: + err = err + "\tSTDOUT: {}\n".format(stdout) + + stderr = js.stderr_summary + if stderr: + err = err + "\tSTDERR: {}\n".format(stderr) + + if len(err) == 0: + err = "No error messages received" + # wrapping things in an exception here doesn't really help in providing more information + # than the string itself + return TooManyJobFailuresError(err) diff --git a/parsl/parsl/jobs/errors.py b/parsl/parsl/jobs/errors.py new file mode 100644 index 00000000..6d42f429 --- /dev/null +++ b/parsl/parsl/jobs/errors.py @@ -0,0 +1,7 @@ +from parsl.errors import ParslError + + +class TooManyJobFailuresError(ParslError): + """Indicates that executor is shut down because of too many block failures. + """ + pass diff --git a/parsl/parsl/jobs/job_status_poller.py b/parsl/parsl/jobs/job_status_poller.py new file mode 100644 index 00000000..c5d86b63 --- /dev/null +++ b/parsl/parsl/jobs/job_status_poller.py @@ -0,0 +1,66 @@ +import logging +from typing import List, Optional, Sequence, Union + +from parsl.executors.status_handling import BlockProviderExecutor +from parsl.jobs.strategy import Strategy +from parsl.utils import Timer + +logger = logging.getLogger(__name__) + + +class JobStatusPoller(Timer): + + def __init__(self, *, strategy: Optional[str], max_idletime: float, + strategy_period: Union[float, int]) -> None: + """Initalize the poller. + + strategy: str + names the scaling strategy to use + + max_idletime: float, seconds + used by some scaling strategies to decide how long a block is idle + + strategy_period: float|int, seconds + how often the scaling strategy should be executed + """ + # only executors which should be polled and which have a valid + # provider should be added to this list: so it is safe to assume + # that e.provider is not None for e in self._executors + self._executors: List[BlockProviderExecutor] = [] + + self._strategy = Strategy(strategy=strategy, + max_idletime=max_idletime) + super().__init__(self.poll, interval=strategy_period, name="JobStatusPoller") + + def poll(self) -> None: + for executor in self._executors: + # Update status + executor.poll_facade() + executor.handle_errors(executor.status_facade) + + self._strategy.strategize(self._executors) + + def add_executors(self, executors: Sequence[BlockProviderExecutor]) -> None: + for executor in executors: + if executor.status_polling_interval > 0: + assert executor.provider is not None, \ + "BlockProviderExecutor.status_polling_interval implementation guarantee: None => status_polling_interval == 0" + logger.debug("Adding executor {}".format(executor.label)) + self._executors.append(executor) + self._strategy.add_executors(executors) + + def close(self, timeout: Optional[float] = None) -> None: + super().close(timeout) + for executor in self._executors: + if not executor.bad_state_is_set: + logger.info(f"Scaling in executor {executor.label}") + + # this code needs to be at least as many blocks as need + # cancelling, but it is safe to be more, as the scaling + # code will cope with being asked to cancel more blocks + # than exist. + block_count = len(executor.status_facade) + executor.scale_in_facade(block_count) + + else: # and bad_state_is_set + logger.warning(f"Not scaling in executor {executor.label} because it is in bad state") diff --git a/parsl/parsl/jobs/states.py b/parsl/parsl/jobs/states.py new file mode 100644 index 00000000..b1cbdc82 --- /dev/null +++ b/parsl/parsl/jobs/states.py @@ -0,0 +1,150 @@ +import logging +import os +from enum import IntEnum +from typing import Optional + +logger = logging.getLogger(__name__) + + +class JobState(IntEnum): + """Defines a set of states that a job can be in""" + + UNKNOWN = 0 + """The batch provider is unable to determine a state for this job""" + + PENDING = 1 + """"This job is in the batch queue but has not started running""" + + RUNNING = 2 + """This job is running in the batch system""" + + CANCELLED = 3 + """This job has been cancelled""" + + COMPLETED = 4 + """This job has completed""" + + FAILED = 5 + """This job has failed""" + + TIMEOUT = 6 + """This job has ended due to walltime expiry. This is different from + other error states, because in the pilot job model, a timeout is usually + expected and not a failure. Timeouts should not be reported with FAILED + state: if they are reported as FAILED, Parsl's error handling code + in `simple_error_handler` will eventually regard the batch system as + broken and shut down. + """ + + HELD = 7 + """This job is held/suspended in the batch system""" + + MISSING = 8 + """This job has reached a terminal state without the resources (managers/workers) + launched in the job connecting back to the Executor. This state is set by HTEX + when it is able to infer that the block failed to start workers for eg due to + bad worker environment or network connectivity issues. + """ + + SCALED_IN = 9 + """This job has been deliberately scaled in. Scaling code should not be concerned + that the job never ran (for example for error handling purposes). + """ + + def __str__(self) -> str: + return f"{self.__class__.__name__}.{self.name}" + + +TERMINAL_STATES = [JobState.CANCELLED, JobState.COMPLETED, JobState.FAILED, + JobState.TIMEOUT, JobState.MISSING, JobState.SCALED_IN] + + +class JobStatus: + """Encapsulates a job state together with other details: + + Args: + state: The machine-readable state of the job this status refers to + message: Optional human readable message + exit_code: Optional exit code + stdout_path: Optional path to a file containing the job's stdout + stderr_path: Optional path to a file containing the job's stderr + """ + SUMMARY_TRUNCATION_THRESHOLD = 2048 + + def __init__(self, state: JobState, message: Optional[str] = None, exit_code: Optional[int] = None, + stdout_path: Optional[str] = None, stderr_path: Optional[str] = None): + self.state = state + self.message = message + self.exit_code = exit_code + self.stdout_path = stdout_path + self.stderr_path = stderr_path + + @property + def terminal(self) -> bool: + return self.state in TERMINAL_STATES + + @property + def status_name(self) -> str: + return self.state.name + + def __repr__(self) -> str: + if self.message is not None: + extra = f"state={self.state} message={self.message}" + else: + extra = f"state={self.state}" + return f"<{type(self).__module__}.{type(self).__qualname__} object at {hex(id(self))}, {extra}>" + + def __str__(self) -> str: + if self.message is not None: + return f"{self.state} ({self.message})" + else: + return f"{self.state}" + + @property + def stdout(self) -> Optional[str]: + return self._read_file(self.stdout_path) + + @property + def stderr(self) -> Optional[str]: + return self._read_file(self.stderr_path) + + def _read_file(self, path: Optional[str]) -> Optional[str]: + if path is None: + return None + try: + with open(path, 'r') as f: + return f.read() + except Exception: + logger.exception("Converting exception to None") + return None + + @property + def stdout_summary(self) -> Optional[str]: + return self._read_summary(self.stdout_path) + + @property + def stderr_summary(self) -> Optional[str]: + return self._read_summary(self.stderr_path) + + def _read_summary(self, path: Optional[str]) -> Optional[str]: + if not path: + # can happen for synthetic job failures + return None + try: + with open(path, 'r') as f: + f.seek(0, os.SEEK_END) + size = f.tell() + f.seek(0, os.SEEK_SET) + if size > JobStatus.SUMMARY_TRUNCATION_THRESHOLD: + half_threshold = int(JobStatus.SUMMARY_TRUNCATION_THRESHOLD / 2) + head = f.read(half_threshold) + f.seek(size - half_threshold, os.SEEK_SET) + tail = f.read(half_threshold) + return head + '\n...\n' + tail + else: + f.seek(0, os.SEEK_SET) + return f.read() + except FileNotFoundError: + # When output is redirected to a file, but the process does not produce any output + # bytes, no file is actually created. This handles that case. + return None diff --git a/parsl/parsl/jobs/strategy.py b/parsl/parsl/jobs/strategy.py new file mode 100644 index 00000000..aa9d36f4 --- /dev/null +++ b/parsl/parsl/jobs/strategy.py @@ -0,0 +1,316 @@ +from __future__ import annotations + +import logging +import math +import time +import warnings +from typing import Dict, List, Optional, Sequence, TypedDict + +from parsl.executors import HighThroughputExecutor +from parsl.executors.base import ParslExecutor +from parsl.executors.status_handling import BlockProviderExecutor +from parsl.jobs.states import JobState +from parsl.process_loggers import wrap_with_logs + +logger = logging.getLogger(__name__) + + +class ExecutorState(TypedDict): + """Strategy relevant state for an executor + """ + + idle_since: Optional[float] + """The timestamp at which an executor became idle. + If the executor is not idle, then None. + """ + + first: bool + """True if this executor has not yet had a strategy poll. + """ + + +class Strategy: + """Scaling strategy. + + As a workflow dag is processed by Parsl, new tasks are added and completed + asynchronously. Parsl interfaces executors with execution providers to construct + scalable executors to handle the variable work-load generated by the + workflow. This component is responsible for periodically checking outstanding + tasks and available compute capacity and trigger scaling events to match + workflow needs. + + Here's a diagram of an executor. An executor consists of blocks, which are usually + created by single requests to a Local Resource Manager (LRM) such as slurm, + condor, torque, or even AWS API. The blocks could contain several task blocks + which are separate instances on workers. + + + .. code:: python + + |<--min_blocks |<-init_blocks max_blocks-->| + +----------------------------------------------------------+ + | +--------block----------+ +--------block--------+ | + executor = | | task task | ... | task task | | + | +-----------------------+ +---------------------+ | + +----------------------------------------------------------+ + + The relevant specification options are: + 1. min_blocks: Minimum number of blocks to maintain + 2. init_blocks: number of blocks to provision at initialization of workflow + 3. max_blocks: Maximum number of blocks that can be active due to one workflow + + + .. code:: python + + active_tasks = pending_tasks + running_tasks + + Parallelism = slots / tasks + = [0, 1] (i.e, 0 <= p <= 1) + + For example: + + When p = 0, + => compute with the least resources possible. + infinite tasks are stacked per slot. + + .. code:: python + + blocks = min_blocks { if active_tasks = 0 + max(min_blocks, 1) { else + + When p = 1, + => compute with the most resources. + one task is stacked per slot. + + .. code:: python + + blocks = min ( max_blocks, + ceil( active_tasks / slots ) ) + + + When p = 1/2, + => We stack upto 2 tasks per slot before we overflow + and request a new block + + + let's say min:init:max = 0:0:4 and task_blocks=2 + Consider the following example: + min_blocks = 0 + init_blocks = 0 + max_blocks = 4 + tasks_per_node = 2 + nodes_per_block = 1 + + In the diagram, X <- task + + at 2 tasks: + + .. code:: python + + +---Block---| + | | + | X X | + |slot slot| + +-----------+ + + at 5 tasks, we overflow as the capacity of a single block is fully used. + + .. code:: python + + +---Block---| +---Block---| + | X X | ----> | | + | X X | | X | + |slot slot| |slot slot| + +-----------+ +-----------+ + + """ + + def __init__(self, *, strategy: Optional[str], max_idletime: float) -> None: + """Initialize strategy.""" + self.executors: Dict[str, ExecutorState] + self.executors = {} + self.max_idletime = max_idletime + + self.strategies = {None: self._strategy_init_only, + 'none': self._strategy_init_only, + 'simple': self._strategy_simple, + 'htex_auto_scale': self._strategy_htex_auto_scale} + + if strategy is None: + warnings.warn("literal None for strategy choice is deprecated. Use string 'none' instead.", + DeprecationWarning) + + self.strategize = self.strategies[strategy] + + logger.debug("Scaling strategy: {0}".format(strategy)) + + def add_executors(self, executors: Sequence[ParslExecutor]) -> None: + for executor in executors: + self.executors[executor.label] = {'idle_since': None, 'first': True} + + def _strategy_init_only(self, executors: List[BlockProviderExecutor]) -> None: + """Scale up to init_blocks at the start, then nothing more. + """ + for executor in executors: + if self.executors[executor.label]['first']: + assert executor.provider is not None, \ + "strategy must only be called with executors that have a provider." + logger.debug(f"strategy_init_only: scaling out {executor.provider.init_blocks} initial blocks for {executor.label}") + executor.scale_out_facade(executor.provider.init_blocks) + self.executors[executor.label]['first'] = False + else: + logger.debug("strategy_init_only: doing nothing") + + def _strategy_simple(self, executors: List[BlockProviderExecutor]) -> None: + self._general_strategy(executors, strategy_type='simple') + + def _strategy_htex_auto_scale(self, executors: List[BlockProviderExecutor]) -> None: + """HTEX specific auto scaling strategy + + This strategy works only for HTEX. This strategy will scale out by + requesting additional compute resources via the provider when the + workload requirements exceed the provisioned capacity. The scale out + behavior is exactly like the 'simple' strategy. + + If there are idle blocks during execution, this strategy will terminate + those idle blocks specifically. When # of tasks >> # of blocks, HTEX places + tasks evenly across blocks, which makes it rather difficult to ensure that + some blocks will reach 0% utilization. Consequently, this strategy can be + expected to scale in effectively only when # of workers, or tasks executing + per block is close to 1. + """ + self._general_strategy(executors, strategy_type='htex') + + @wrap_with_logs + def _general_strategy(self, executors: List[BlockProviderExecutor], *, strategy_type: str) -> None: + logger.debug(f"general strategy starting with strategy_type {strategy_type} for {len(executors)} executors") + + for executor in executors: + assert executor.provider is not None, \ + "strategy must only be called with executors that have a provider." + label = executor.label + prefix = f"[Scaling executor {label}]" + + if executor.bad_state_is_set: + logger.info("%s Not strategizing for executor because bad state is set", prefix) + continue + + logger.debug("%s Strategizing for executor", prefix) + + if self.executors[label]['first']: + logger.debug("%s Scaling out %d initial blocks", prefix, executor.provider.init_blocks) + executor.scale_out_facade(executor.provider.init_blocks) + self.executors[label]['first'] = False + + # Tasks that are either pending completion + active_tasks = executor.outstanding() + + status = executor.status_facade + + # FIXME we need to handle case where provider does not define these + # FIXME probably more of this logic should be moved to the provider + min_blocks = executor.provider.min_blocks + max_blocks = executor.provider.max_blocks + tasks_per_node = executor.workers_per_node + + nodes_per_block = executor.provider.nodes_per_block + parallelism = executor.provider.parallelism + + running = sum([1 for x in status.values() if x.state == JobState.RUNNING]) + pending = sum([1 for x in status.values() if x.state == JobState.PENDING]) + active_blocks = running + pending + active_slots = active_blocks * tasks_per_node * nodes_per_block + + logger.debug('%s Executor %d active tasks, %d active slots, and %d/%d running/pending blocks', + prefix, active_tasks, active_slots, running, pending) + + # reset idle timer if executor has active tasks + if active_tasks > 0 and self.executors[executor.label]['idle_since']: + self.executors[executor.label]['idle_since'] = None + + # Case 1 + # No tasks. + if active_tasks == 0: + # Case 1a + # Fewer blocks that min_blocks + if active_blocks <= min_blocks: + logger.debug("%s Strategy case 1a: Executor has no active tasks and minimum blocks. Taking no action.", prefix) + # Case 1b + # More blocks than min_blocks. Scale in + else: + # We want to make sure that max_idletime is reached + # before killing off resources + logger.debug("%s Strategy case 1b: Executor has no active tasks, and more (%d)" + " than minimum blocks (%d)", prefix, active_blocks, min_blocks) + + if not self.executors[executor.label]['idle_since']: + logger.debug("%s Starting idle timer. If idle time exceeds %.1fs, blocks will be scaled in", prefix, self.max_idletime) + self.executors[executor.label]['idle_since'] = time.time() + idle_since = self.executors[executor.label]['idle_since'] + assert idle_since is not None, "The `if` statement above this assert should have forced idle time to be not-None" + + idle_duration = time.time() - idle_since + if idle_duration > self.max_idletime: + # We have resources idle for the max duration, + # we have to scale_in now. + logger.debug("%s Idle time has reached %.1fs for executor; scaling in", prefix, self.max_idletime) + executor.scale_in_facade(active_blocks - min_blocks) + + else: + logger.debug( + "%s Idle time %.1fs is less than max_idletime %.1fs; not scaling in", prefix, idle_duration, self.max_idletime, + ) + + # Case 2 + # More tasks than the available slots. + elif (float(active_slots) / active_tasks) < parallelism: + logger.debug("%s Strategy case 2: slots are overloaded - (slot_ratio = active_slots/active_tasks) < parallelism", prefix) + + # Case 2a + # We have the max blocks possible + if active_blocks >= max_blocks: + # Ignore since we already have the max nodes + logger.debug("%s Strategy case 2a: active_blocks %d >= max_blocks %d so not scaling out", prefix, active_blocks, max_blocks) + # Case 2b + else: + logger.debug("%s Strategy case 2b: active_blocks %d < max_blocks %d so scaling out", prefix, active_blocks, max_blocks) + excess_slots = math.ceil((active_tasks * parallelism) - active_slots) + excess_blocks = math.ceil(float(excess_slots) / (tasks_per_node * nodes_per_block)) + excess_blocks = min(excess_blocks, max_blocks - active_blocks) + logger.debug("%s Requesting %d more blocks", prefix, excess_blocks) + executor.scale_out_facade(excess_blocks) + + # Case 4a + # Tasks but no slots + elif active_slots == 0 and active_tasks > 0: + logger.debug("%s Strategy case 4a: No active slots but some active tasks - could scale out by a single block", prefix) + + if active_blocks < max_blocks: + logger.debug("%s Requesting single block", prefix) + + executor.scale_out_facade(1) + else: + logger.debug("%s Not requesting any blocks, because at maxblocks already", prefix) + + # Case 4b + # More slots than tasks + elif active_slots > 0 and active_slots > active_tasks: + logger.debug("%s Strategy case 4b: more slots than tasks", prefix) + if strategy_type == 'htex': + # Scale in for htex + if isinstance(executor, HighThroughputExecutor): + if active_blocks > min_blocks: + excess_slots = math.floor(active_slots - (active_tasks * parallelism)) + excess_blocks = math.floor(float(excess_slots) / (tasks_per_node * nodes_per_block)) + excess_blocks = min(excess_blocks, active_blocks - min_blocks) + logger.debug("%s Requesting scaling in by %d blocks with idle time %.1fs", prefix, excess_blocks, self.max_idletime) + executor.scale_in_facade(excess_blocks, max_idletime=self.max_idletime) + else: + logger.error("%s This strategy only supports scaling in for HighThroughputExecutor - taking no action", prefix) + else: + logger.debug("%s This strategy does not support scaling in", prefix) + + # Case 3 + # tasks ~ slots + else: + logger.debug("%s Strategy case 3: no changes necessary to current block load", prefix) diff --git a/parsl/parsl/launchers/__init__.py b/parsl/parsl/launchers/__init__.py new file mode 100644 index 00000000..155ed556 --- /dev/null +++ b/parsl/parsl/launchers/__init__.py @@ -0,0 +1,24 @@ +from parsl.launchers.launchers import ( + AprunLauncher, + GnuParallelLauncher, + JsrunLauncher, + MpiExecLauncher, + MpiRunLauncher, + SimpleLauncher, + SingleNodeLauncher, + SrunLauncher, + SrunMPILauncher, + WrappedLauncher, +) + +__all__ = ['SimpleLauncher', + 'WrappedLauncher', + 'SingleNodeLauncher', + 'SrunLauncher', + 'AprunLauncher', + 'SrunMPILauncher', + 'JsrunLauncher', + 'GnuParallelLauncher', + 'MpiExecLauncher', + 'MpiRunLauncher', + 'WrappedLauncher'] diff --git a/parsl/parsl/launchers/base.py b/parsl/parsl/launchers/base.py new file mode 100644 index 00000000..a4ee7e52 --- /dev/null +++ b/parsl/parsl/launchers/base.py @@ -0,0 +1,17 @@ +from abc import ABCMeta, abstractmethod + +from parsl.utils import RepresentationMixin + + +class Launcher(RepresentationMixin, metaclass=ABCMeta): + """Launchers are basically wrappers for user submitted scripts as they + are submitted to a specific execution resource. + """ + def __init__(self, debug: bool = True): + self.debug = debug + + @abstractmethod + def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: + """ Wraps the command with the Launcher calls. + """ + pass diff --git a/parsl/parsl/launchers/errors.py b/parsl/parsl/launchers/errors.py new file mode 100644 index 00000000..2882a85a --- /dev/null +++ b/parsl/parsl/launchers/errors.py @@ -0,0 +1,13 @@ +from parsl.launchers.base import Launcher +from parsl.providers.errors import ExecutionProviderException + + +class BadLauncher(ExecutionProviderException, TypeError): + """Error raised when an object of inappropriate type is supplied as a Launcher + """ + + def __init__(self, launcher: Launcher): + self.launcher = launcher + + def __str__(self) -> str: + return f"Bad Launcher provided: {self.launcher}, expecting a parsl.launcher.launcher.Launcher" diff --git a/parsl/parsl/launchers/launchers.py b/parsl/parsl/launchers/launchers.py new file mode 100644 index 00000000..75312754 --- /dev/null +++ b/parsl/parsl/launchers/launchers.py @@ -0,0 +1,534 @@ +import logging + +from parsl.launchers.base import Launcher + +logger = logging.getLogger(__name__) + + +class SimpleLauncher(Launcher): + """ Does no wrapping. Just returns the command as-is + """ + def __init__(self, debug: bool = True) -> None: + super().__init__(debug=debug) + + def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: + + if nodes_per_block > 1: + logger.warning('Simple Launcher only supports single node per block. ' + f'Requested nodes: {nodes_per_block}. ' + 'You may be getting fewer workers than expected') + + return command + + +class WrappedLauncher(Launcher): + """Wraps the command by prepending commands before a user's command + + As an example, the wrapped launcher can be used to launch a command + inside a docker container by prepending the proper docker invocation""" + + def __init__(self, prepend: str, debug: bool = True): + """ + Args: + prepend (str): Command to use before the launcher (e.g., ``time``) + """ + super().__init__(debug=debug) + self.prepend = prepend + + def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: + if tasks_per_node > 1: + logger.warning('WrappedLauncher ignores the number of tasks per node. ' + 'You may be getting fewer workers than expected') + if nodes_per_block > 1: + logger.warning('WrappedLauncher ignores the number of nodes per block. ' + 'You may be getting fewer workers than expected') + return "{0} {1}".format(self.prepend, command) + + +class SingleNodeLauncher(Launcher): + """ Worker launcher that wraps the user's command with the framework to + launch multiple command invocations in parallel. This wrapper sets the + bash env variable CORES to the number of cores on the machine. By setting + task_blocks to an integer or to a bash expression the number of invocations + of the command to be launched can be controlled. + """ + + def __init__(self, debug: bool = True, fail_on_any: bool = False): + super().__init__(debug=debug) + self.fail_on_any = fail_on_any + + def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: + """ + Args: + - command (str): The command string to be launched + - fail_on_any: If True, return a nonzero exit code if any worker failed, otherwise zero; + if False, return a nonzero exit code if all workers failed, otherwise zero. + - script_dir (str): Path to the submit script directory + + """ + task_blocks = tasks_per_node * nodes_per_block + fail_on_any_num = int(self.fail_on_any) + debug_num = int(self.debug) + + x = '''set -e +export CORES=$(getconf _NPROCESSORS_ONLN) +[[ "{debug}" == "1" ]] && echo "Found cores : $CORES" +WORKERCOUNT={task_blocks} +FAILONANY={fail_on_any} +PIDS="" + +CMD() {{ +{command} +}} +for COUNT in $(seq 1 1 $WORKERCOUNT); do + [[ "{debug}" == "1" ]] && echo "Launching worker: $COUNT" + CMD $COUNT & + PIDS="$PIDS $!" +done + +ALLFAILED=1 +ANYFAILED=0 +for PID in $PIDS ; do + wait $PID + if [ "$?" != "0" ]; then + ANYFAILED=1 + else + ALLFAILED=0 + fi +done + +[[ "{debug}" == "1" ]] && echo "All workers done" +if [ "$FAILONANY" == "1" ]; then + exit $ANYFAILED +else + exit $ALLFAILED +fi +'''.format(command=command, + task_blocks=task_blocks, + debug=debug_num, + fail_on_any=fail_on_any_num) + return x + + +class GnuParallelLauncher(Launcher): + """ Worker launcher that wraps the user's command with the framework to + launch multiple command invocations via GNU parallel sshlogin. + + This wrapper sets the bash env variable CORES to the number of cores on the + machine. + + This launcher makes the following assumptions: + + - GNU parallel is installed and can be located in $PATH + - Paswordless SSH login is configured between the controller node and the + target nodes. + - The provider makes available the $PBS_NODEFILE environment variable + """ + def __init__(self, debug: bool = True): + super().__init__(debug=debug) + + def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: + """ + Args: + - command (str): The command string to be launched + - script_dir (str): Path to the submit script directory + + """ + task_blocks = tasks_per_node * nodes_per_block + debug_num = int(self.debug) + + x = '''set -e +export CORES=$(getconf _NPROCESSORS_ONLN) +[[ "{debug}" == "1" ]] && echo "Found cores : $CORES" +WORKERCOUNT={task_blocks} +SCRIPT_DIR="{script_dir}" +[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR="" + +# Deduplicate the nodefile +SSHLOGINFILE="$JOBNAME.nodes" +if [ -z "$PBS_NODEFILE" ]; then + echo "localhost" > $SSHLOGINFILE +else + sort -u $PBS_NODEFILE > $SSHLOGINFILE +fi + +cat << PARALLEL_CMD_EOF > ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh +{command} +PARALLEL_CMD_EOF +chmod u+x ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh + +#file to contain the commands to parallel +PFILE=${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_${{JOBNAME}}.sh.parallel + +# Truncate the file +cp /dev/null $PFILE + +for COUNT in $(seq 1 1 $WORKERCOUNT) +do + echo "sh ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh" >> $PFILE +done + +parallel --env _ --joblog "$JOBNAME.sh.parallel.log" \ + --sshloginfile $SSHLOGINFILE --jobs {tasks_per_node} < $PFILE + +[[ "{debug}" == "1" ]] && echo "All workers done" +'''.format(command=command, + tasks_per_node=tasks_per_node, + task_blocks=task_blocks, + debug=debug_num, + script_dir=script_dir) + return x + + +class MpiExecLauncher(Launcher): + """ Worker launcher that wraps the user's command with the framework to + launch multiple command invocations via mpiexec. + + This wrapper sets the bash env variable CORES to the number of cores on the + machine. + + This launcher makes the following assumptions: + - mpiexec is installed and can be located in $PATH + - The provider makes available the $PBS_NODEFILE environment variable + """ + def __init__(self, debug: bool = True, bind_cmd: str = '--bind-to', overrides: str = ''): + """ + + Parameters + ---------- + bind_cmd: str + Name of the argument for binding ranks to CPUs + + overrides: str + Additional arguments to add to the invocation + + """ + super().__init__(debug=debug) + self.bind_cmd = bind_cmd + self.overrides = overrides + + def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: + """ + Args: + - command (str): The command string to be launched + - script_dir (str): Path to the submit script directory + + """ + task_blocks = tasks_per_node * nodes_per_block + debug_num = int(self.debug) + + x = '''set -e +export CORES=$(getconf _NPROCESSORS_ONLN) +[[ "{debug}" == "1" ]] && echo "Found cores : $CORES" +WORKERCOUNT={task_blocks} +SCRIPT_DIR="{script_dir}" +[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR="" + +# Deduplicate the nodefile +HOSTFILE="$JOBNAME.nodes" +if [ -z "$PBS_NODEFILE" ]; then + echo "localhost" > $HOSTFILE +else + sort -u $PBS_NODEFILE > $HOSTFILE +fi + +cat << MPIEXEC_EOF > ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh +{command} +MPIEXEC_EOF +chmod u+x ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh + +mpiexec {bind_cmd} none {overrides} -n $WORKERCOUNT --hostfile $HOSTFILE /usr/bin/sh ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh + +[[ "{debug}" == "1" ]] && echo "All workers done" +'''.format(command=command, + task_blocks=task_blocks, + debug=debug_num, + overrides=self.overrides, + bind_cmd=self.bind_cmd, + script_dir=script_dir) + return x + + +class MpiRunLauncher(Launcher): + """ Worker launcher that wraps the user's command with the framework to + launch multiple command invocations via mpirun. + + This wrapper sets the bash env variable CORES to the number of cores on the + machine. + + This launcher makes the following assumptions: + - mpirun is installed and can be located in $PATH + - The provider makes available the $PBS_NODEFILE environment variable + """ + def __init__(self, debug: bool = True, bash_location: str = '/bin/bash', overrides: str = ''): + super().__init__(debug=debug) + self.bash_location = bash_location + self.overrides = overrides + + def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: + """ + Args: + - command (str): The command string to be launched + - script_dir (str): Path to the submit script directory + + """ + task_blocks = tasks_per_node * nodes_per_block + debug_num = int(self.debug) + + x = '''set -e +export CORES=$(getconf _NPROCESSORS_ONLN) +[[ "{debug}" == "1" ]] && echo "Found cores : $CORES" +WORKERCOUNT={task_blocks} +SCRIPT_DIR="{script_dir}" +[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR="" + +cat << MPIRUN_EOF > ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh +{command} +MPIRUN_EOF +chmod u+x ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh + +mpirun -np $WORKERCOUNT {overrides} {bash_location} ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh + +[[ "{debug}" == "1" ]] && echo "All workers done" +'''.format(command=command, + task_blocks=task_blocks, + overrides=self.overrides, + bash_location=self.bash_location, + debug=debug_num, + script_dir=script_dir) + return x + + +class SrunLauncher(Launcher): + """ Worker launcher that wraps the user's command with the SRUN launch framework + to launch multiple cmd invocations in parallel on a single job allocation. + """ + + def __init__(self, debug: bool = True, overrides: str = ''): + """ + Parameters + ---------- + + overrides: str + This string will be passed to the srun launcher. Default: '' + """ + + super().__init__(debug=debug) + self.overrides = overrides + + def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: + """ + Args: + - command (str): The command string to be launched + - script_dir (str): Path to the submit script directory + + """ + task_blocks = tasks_per_node * nodes_per_block + debug_num = int(self.debug) + + x = '''set -e +export CORES=$SLURM_CPUS_ON_NODE +export NODES=$SLURM_JOB_NUM_NODES + +[[ "{debug}" == "1" ]] && echo "Found cores : $CORES" +[[ "{debug}" == "1" ]] && echo "Found nodes : $NODES" +WORKERCOUNT={task_blocks} +SCRIPT_DIR="{script_dir}" +[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR="" + +cat << SLURM_EOF > ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$SLURM_JOB_NAME.sh +{command} +SLURM_EOF +chmod a+x ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$SLURM_JOB_NAME.sh + +srun --ntasks {task_blocks} -l {overrides} bash ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$SLURM_JOB_NAME.sh + +[[ "{debug}" == "1" ]] && echo "Done" +'''.format(command=command, + task_blocks=task_blocks, + overrides=self.overrides, + debug=debug_num, + script_dir=script_dir) + return x + + +class SrunMPILauncher(Launcher): + """Launches as many workers as MPI tasks to be executed concurrently within a block. + + Use this launcher instead of SrunLauncher if each block will execute multiple MPI applications + at the same time. Workers should be launched with independent Srun calls so as to setup the + environment for MPI application launch. + """ + def __init__(self, debug: bool = True, overrides: str = ''): + """ + Parameters + ---------- + + overrides: str + This string will be passed to the launcher. Default: '' + """ + + super().__init__(debug=debug) + self.overrides = overrides + + def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: + """ + Args: + - command (str): The command string to be launched + - script_dir (str): Path to the submit script directory + + """ + task_blocks = tasks_per_node * nodes_per_block + debug_num = int(self.debug) + + x = '''set -e +export CORES=$SLURM_CPUS_ON_NODE +export NODES=$SLURM_JOB_NUM_NODES + +[[ "{debug}" == "1" ]] && echo "Found cores : $CORES" +[[ "{debug}" == "1" ]] && echo "Found nodes : $NODES" +WORKERCOUNT={task_blocks} +SCRIPT_DIR="{script_dir}" +[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR="" + +cat << SLURM_EOF > ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$SLURM_JOB_NAME.sh +{command} +SLURM_EOF +chmod a+x ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$SLURM_JOB_NAME.sh + +TASKBLOCKS={task_blocks} + +# If there are more taskblocks to be launched than nodes use +if (( "$TASKBLOCKS" > "$NODES" )) +then + [[ "{debug}" == "1" ]] && echo "TaskBlocks:$TASKBLOCKS > Nodes:$NODES" + CORES_PER_BLOCK=$(($NODES * $CORES / $TASKBLOCKS)) + for blk in $(seq 1 1 $TASKBLOCKS): + do + srun --ntasks $CORES_PER_BLOCK -l {overrides} bash ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$SLURM_JOB_NAME.sh & + done + wait +else + # A Task block could be integer multiples of Nodes + [[ "{debug}" == "1" ]] && echo "TaskBlocks:$TASKBLOCKS <= Nodes:$NODES" + NODES_PER_BLOCK=$(( $NODES / $TASKBLOCKS )) + for blk in $(seq 1 1 $TASKBLOCKS): + do + srun --exclusive --nodes $NODES_PER_BLOCK -l {overrides} bash ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$SLURM_JOB_NAME.sh & + done + wait + +fi + + +[[ "{debug}" == "1" ]] && echo "Done" +'''.format(command=command, + task_blocks=task_blocks, + overrides=self.overrides, + debug=debug_num, + script_dir=script_dir) + return x + + +class AprunLauncher(Launcher): + """ Worker launcher that wraps the user's command with the Aprun launch framework + to launch multiple cmd invocations in parallel on a single job allocation + + """ + def __init__(self, debug: bool = True, overrides: str = ''): + """ + Parameters + ---------- + + overrides: str + This string will be passed to the aprun launcher. Default: '' + """ + super().__init__(debug=debug) + self.overrides = overrides + + def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: + """ + Args: + - command (str): The command string to be launched + - tasks_per_node (int) : Workers to launch per node + - nodes_per_block (int) : Number of nodes in a block + - script_dir (str): Path to the submit script directory + + """ + + tasks_per_block = tasks_per_node * nodes_per_block + debug_num = int(self.debug) + + x = '''set -e +WORKERCOUNT={tasks_per_block} +SCRIPT_DIR="{script_dir}" +[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR="" + +cat << APRUN_EOF > ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh +{command} +APRUN_EOF +chmod a+x ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh + +aprun -n {tasks_per_block} -N {tasks_per_node} {overrides} /bin/bash ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh & +wait + +[[ "{debug}" == "1" ]] && echo "Done" +'''.format(command=command, + tasks_per_block=tasks_per_block, + tasks_per_node=tasks_per_node, + overrides=self.overrides, + debug=debug_num, + script_dir=script_dir) + return x + + +class JsrunLauncher(Launcher): + """ Worker launcher that wraps the user's command with the Jsrun launch framework + to launch multiple cmd invocations in parallel on a single job allocation + + """ + def __init__(self, debug: bool = True, overrides: str = ''): + """ + Parameters + ---------- + + overrides: str + This string will be passed to the JSrun launcher. Default: '' + """ + super().__init__(debug=debug) + self.overrides = overrides + + def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: + """ + Args: + - command (str): The command string to be launched + - tasks_per_node (int) : Workers to launch per node + - nodes_per_block (int) : Number of nodes in a block + - script_dir (str): Path to the submit script directory + + """ + + tasks_per_block = tasks_per_node * nodes_per_block + debug_num = int(self.debug) + + x = '''set -e +WORKERCOUNT={tasks_per_block} +SCRIPT_DIR="{script_dir}" +[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR="" + +cat << JSRUN_EOF > ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh +{command} +JSRUN_EOF +chmod a+x ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh + +jsrun -n {tasks_per_block} -r {tasks_per_node} {overrides} /bin/bash ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh & +wait + +[[ "{debug}" == "1" ]] && echo "Done" +'''.format(command=command, + tasks_per_block=tasks_per_block, + tasks_per_node=tasks_per_node, + overrides=self.overrides, + debug=debug_num, + script_dir=script_dir) + return x diff --git a/parsl/parsl/log_utils.py b/parsl/parsl/log_utils.py new file mode 100644 index 00000000..f9a93afb --- /dev/null +++ b/parsl/parsl/log_utils.py @@ -0,0 +1,99 @@ +"""This module contains helpers for configuring logging. By default, +`set_file_logger` is invoked by the DataFlowKernel initializer to log +parsl messages to parsl.log. + +`set_stream_logger` which by default logs to stderr, can be useful +when working in a Jupyter notebook. +""" +import io +import logging +from typing import Callable, Optional + +import typeguard + +DEFAULT_FORMAT = ( + "%(created)f %(asctime)s %(processName)s-%(process)d " + "%(threadName)s-%(thread)d %(name)s:%(lineno)d %(funcName)s %(levelname)s: " + "%(message)s" +) + + +@typeguard.typechecked +def set_stream_logger(name: str = 'parsl', + level: int = logging.DEBUG, + format_string: Optional[str] = None, + stream: Optional[io.TextIOBase] = None) -> Callable[[], None]: + """Add a stream log handler. + + Args: + - name (string) : Set the logger name. + - level (logging.LEVEL) : Set to logging.DEBUG by default. + - format_string (string) : Set to None by default. + - stream (io.TextIOWrapper) : Specify sys.stdout or sys.stderr for stream. + If not specified, the default stream for logging.StreamHandler is used. + """ + if format_string is None: + # format_string = "%(asctime)s %(name)s [%(levelname)s] Thread:%(thread)d %(message)s" + format_string = "%(asctime)s %(name)s:%(lineno)d [%(levelname)s] %(message)s" + + logger = logging.getLogger(name) + logger.setLevel(logging.DEBUG) + handler = logging.StreamHandler(stream) + handler.setLevel(level) + formatter = logging.Formatter(format_string, datefmt='%Y-%m-%d %H:%M:%S') + handler.setFormatter(formatter) + logger.addHandler(handler) + + # Concurrent.futures errors are also of interest, as exceptions + # which propagate out of the top of a callback are logged this way + # and then discarded. (see #240) + futures_logger = logging.getLogger("concurrent.futures") + futures_logger.addHandler(handler) + + def unregister_callback(): + logger.removeHandler(handler) + futures_logger.removeHandler(handler) + + return unregister_callback + + +@typeguard.typechecked +def set_file_logger(filename: str, + name: str = 'parsl', + level: int = logging.DEBUG, + format_string: Optional[str] = None) -> Callable[[], None]: + """Add a file log handler. + + Args: + - filename (string): Name of the file to write logs to + - name (string): Logger name + - level (logging.LEVEL): Set the logging level. + - format_string (string): Set the format string + + Returns: + - a callable which, when invoked, will reverse the log handler + attachments made by this call. (compare to how object based pieces + of parsl model this as a close/shutdown/cleanup method on the + object)) + """ + if format_string is None: + format_string = DEFAULT_FORMAT + + logger = logging.getLogger(name) + logger.setLevel(logging.DEBUG) + handler = logging.FileHandler(filename) + handler.setLevel(level) + formatter = logging.Formatter(format_string, datefmt='%Y-%m-%d %H:%M:%S') + handler.setFormatter(formatter) + logger.addHandler(handler) + + # see note in set_stream_logger for notes about logging + # concurrent.futures + futures_logger = logging.getLogger("concurrent.futures") + futures_logger.addHandler(handler) + + def unregister_callback(): + logger.removeHandler(handler) + futures_logger.removeHandler(handler) + + return unregister_callback diff --git a/parsl/parsl/logconfigs/__init__.py b/parsl/parsl/logconfigs/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/logconfigs/base.py b/parsl/parsl/logconfigs/base.py new file mode 100644 index 00000000..de8788a9 --- /dev/null +++ b/parsl/parsl/logconfigs/base.py @@ -0,0 +1,118 @@ +from __future__ import annotations + +import abc +import logging +import pathlib +import threading +import uuid +from collections.abc import Callable + +logger = logging.getLogger(__name__) + + +class LogConfig(abc.ABC): + """Implementations of this class can initialize Parsl logging. + + Parsl logging is built around Python's `logging` system, with + a Parsl-provided configuration system that allows multiple + configurations to be initialized and deinitialized within the + same process. + + Configurations which might be shared between processes should expect to + be pickled/unpickled multiple times as they move around a Parsl + distributed system. + """ + + def __init__(self) -> None: + self.uuid = uuid.uuid4() + + @abc.abstractmethod + def initialize_logging(self, *, log_dir: pathlib.Path, log_name: str) -> Callable[[], None]: + """Initialize logging in current process. + + This should be implemented by users wanting to define their own log + configuration policies. + + This should be called by Parsl components to initialize logging + according to a user supplied policy, rather than a hard-coded log + policy. + + This should return a callback to uninitialize the logging initialized + by this call. + """ + ... + + +class _ConfigReference: + def __init__(self, uninit_callback: Callable) -> None: + self.uninit_callback = uninit_callback + self.count: int = 1 + + +# This holds the collection of initialized configurations for the current +# Python interpreter. It is scoped at the interpreter level because that is +# the scope of Python `logging` module log configuration. +_initialized_log_contexts: dict[uuid.UUID, _ConfigReference] = {} +_initialized_log_context_lock: threading.Lock = threading.Lock() + + +def oneshot_initialize_logging(*, log_config: LogConfig, log_dir: pathlib.Path, log_name: str) -> None: + """Initialized the LogConfig if it is not currently initialized. + + This is scoped per-interpreter, the same as the global state for the + Python logging module. + + A first oneshot_initialize_logging for any given LogConfig will call + the initialize_logging method of that config. Subsequent calls will + be reference counted, but not cause a reinitialization. + + Calling the callback will decrease the reference count, and when that + count reaches zero, the LogConfig will be uninitialized via its own + callback. + + The API syntax mimics the syntax of the LogConfig abstract class, with + these reference counting semantics added on top. + + The intention is to allow a log configuration to be initialized by + multiple pieces of Parsl without duplicating logs: for example, when + using the thread pool executor, a task does not need the global log + configuration to be initialized (again) because the task runs in the + same process as the DFK; but in, for example, the Work Queue executor + (at least now), there is no per-worker log configuration, and the task + *does* need to initialize logging. This reference counting is the + mechanism by which that different behaviour can occur. + + There is an ambiguity on the use of log_name and log_dir across multiple + initializations: the name and directory of the first initialization + will be used and the name and directory of subsequent initializations + will be ignored. + """ + with _initialized_log_context_lock: + if log_config.uuid in _initialized_log_contexts: + _initialized_log_contexts[log_config.uuid].count += 1 + logger.debug('Configuration %r already initialized, now with %s references', + log_config, + _initialized_log_contexts[log_config.uuid].count) + else: + logger.debug('Initializing %r', log_config) + _initialized_log_contexts[log_config.uuid] = _ConfigReference( + log_config.initialize_logging(log_dir=log_dir, log_name=log_name) + ) + assert callable(_initialized_log_contexts[log_config.uuid].uninit_callback), ( + f'Log config {log_config} should have returned a callable on initialization' + ) + logger.debug('Initialized %r', log_config) + + assert log_config.uuid in _initialized_log_contexts + + +def oneshot_uninitialize_logging(*, log_config: LogConfig) -> None: + with _initialized_log_context_lock: + assert log_config.uuid in _initialized_log_contexts, f"log context for {log_config} not found, perhaps too many uninitializations" + assert _initialized_log_contexts[log_config.uuid].count >= 1, \ + f"log context for {log_config} has bad reference count {_initialized_log_contexts[log_config.uuid].count}" + _initialized_log_contexts[log_config.uuid].count -= 1 + if _initialized_log_contexts[log_config.uuid].count < 1: + logger.debug('All references removed for %r', log_config) + _initialized_log_contexts[log_config.uuid].uninit_callback() + del _initialized_log_contexts[log_config.uuid] diff --git a/parsl/parsl/logconfigs/file.py b/parsl/parsl/logconfigs/file.py new file mode 100644 index 00000000..c3bf8bb5 --- /dev/null +++ b/parsl/parsl/logconfigs/file.py @@ -0,0 +1,50 @@ +import logging +import pathlib +from typing import Callable + +from parsl.log_utils import DEFAULT_FORMAT +from parsl.logconfigs.base import LogConfig + + +class FileLogging(LogConfig): + """A log configuration which configures traditional logging to files.""" + + def __init__(self, *, format_string: str = DEFAULT_FORMAT, level: int = logging.DEBUG): + super().__init__() + self.format_string = format_string + self.level = level + + def initialize_logging(self, *, log_dir: pathlib.Path, log_name: str) -> Callable[[], None]: + + log_dir.mkdir(exist_ok=True, parents=True) + filename = log_dir.joinpath(log_name + ".log") + + handler = logging.FileHandler(filename) + handler.setLevel(self.level) + formatter = logging.Formatter(self.format_string, datefmt='%Y-%m-%d %H:%M:%S') + handler.setFormatter(formatter) + + logger = logging.getLogger("parsl") + + if logger.level == logging.NOTSET: + logger.setLevel(self.level) + else: + logger.setLevel(min(logger.level, self.level)) + + logger.addHandler(handler) + + # Concurrent.futures errors are also of interest, as exceptions + # which propagate out of the top of a callback are logged this way + # and then discarded. (see #240) + futures_logger = logging.getLogger("concurrent.futures") + if futures_logger.level == logging.NOTSET: + futures_logger.setLevel(self.level) + else: + futures_logger.setLevel(min(futures_logger.level, self.level)) + futures_logger.addHandler(handler) + + def unregister_callback() -> None: + logger.removeHandler(handler) + futures_logger.removeHandler(handler) + + return unregister_callback diff --git a/parsl/parsl/logconfigs/json.py b/parsl/parsl/logconfigs/json.py new file mode 100644 index 00000000..860e2543 --- /dev/null +++ b/parsl/parsl/logconfigs/json.py @@ -0,0 +1,74 @@ +import json +import logging +import pathlib +from typing import Any, Callable + +from parsl.logconfigs.base import LogConfig + + +class JSONLogging(LogConfig): + """A log configuration which logs as JSON objects. + + This configuration will write each log event as a JSON object into a + log file, one per line. + """ + + def __init__(self, *, level: int = logging.DEBUG): + super().__init__() + self.level = level + + def initialize_logging(self, *, log_dir: pathlib.Path, log_name: str) -> Callable[[], None]: + log_dir.mkdir(exist_ok=True, parents=True) + + filename = log_dir.joinpath(log_name + ".jsonlog") + + logger = logging.getLogger("") + if logger.level == logging.NOTSET: + logger.setLevel(self.level) + else: + logger.setLevel(min(logger.level, self.level)) + + handler = JSONHandler(filename) + handler.setLevel(self.level) + formatter = logging.Formatter("%(message)s", datefmt='%Y-%m-%d %H:%M:%S') + handler.setFormatter(formatter) + logger.addHandler(handler) + + def unregister_callback() -> None: + logger.removeHandler(handler) + + return unregister_callback + + +class JSONHandler(logging.Handler): + def __init__(self, filename: pathlib.Path, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.f = open(filename, "w") + + def emit(self, record: logging.LogRecord) -> None: + + d: dict[str, object] = { + "created": record.created, + "process": record.process, + "processName": record.processName, + "threadName": record.threadName, + "thread": record.thread, + "name": record.name, + "lineno": record.lineno, + "funcname": record.funcName, + "levelname": record.levelname, + "msg": record.msg, + "formatted": self.format(record) + } + + for (k, v) in record.__dict__.items(): + if k.startswith("parsl."): + try: + d[k] = str(v) + except Exception as e: + # present the exception rather than omitting + d[k] = f"UNREPRESENTABLE: {e!r}" + + json.dump(d, fp=self.f) + print("", file=self.f) + self.f.flush() diff --git a/parsl/parsl/logconfigs/noop.py b/parsl/parsl/logconfigs/noop.py new file mode 100644 index 00000000..dc678e17 --- /dev/null +++ b/parsl/parsl/logconfigs/noop.py @@ -0,0 +1,20 @@ +import pathlib +from typing import Callable + +from parsl.logconfigs.base import LogConfig + + +class NoopLogging(LogConfig): + """A log configuration which doesn't configure any logging. + + This is intended to help with benchmarking, but can also be used to + configure an environment with no logging from any LogConfig aware + component. + """ + + def initialize_logging(self, *, log_dir: pathlib.Path, log_name: str) -> Callable[[], None]: + + def unregister_callback() -> None: + pass + + return unregister_callback diff --git a/parsl/parsl/monitoring/__init__.py b/parsl/parsl/monitoring/__init__.py new file mode 100644 index 00000000..4f72c82e --- /dev/null +++ b/parsl/parsl/monitoring/__init__.py @@ -0,0 +1,3 @@ +from parsl.monitoring.monitoring import MonitoringHub + +__all__ = ['MonitoringHub'] diff --git a/parsl/parsl/monitoring/db_manager.py b/parsl/parsl/monitoring/db_manager.py new file mode 100644 index 00000000..12921add --- /dev/null +++ b/parsl/parsl/monitoring/db_manager.py @@ -0,0 +1,724 @@ +import datetime +import logging +import multiprocessing.queues as mpq +import multiprocessing.synchronize as mpe +import os +import pathlib +import queue +import threading +import time +from typing import Any, Dict, List, Optional, Set, Tuple, TypeVar, cast + +import typeguard + +from parsl.dataflow.states import States +from parsl.errors import OptionalModuleMissing +from parsl.log_utils import set_file_logger +from parsl.logconfigs.base import LogConfig, oneshot_initialize_logging +from parsl.monitoring.message_type import MessageType +from parsl.monitoring.types import MonitoringMessage, TaggedMonitoringMessage +from parsl.process_loggers import wrap_with_logs +from parsl.utils import setproctitle + +logger = logging.getLogger(__name__) + +X = TypeVar('X') + +try: + import sqlalchemy as sa + from sqlalchemy import ( + BigInteger, + Boolean, + Column, + DateTime, + Float, + Integer, + PrimaryKeyConstraint, + Table, + Text, + ) + from sqlalchemy.orm import Mapper, declarative_base, mapperlib, sessionmaker +except ImportError: + _sqlalchemy_enabled = False +else: + _sqlalchemy_enabled = True + + +WORKFLOW = 'workflow' # Workflow table includes workflow metadata +TASK = 'task' # Task table includes task metadata +TRY = 'try' # Try table includes information about each attempt to run a task +STATUS = 'status' # Status table includes task status +RESOURCE = 'resource' # Resource table includes task resource utilization +NODE = 'node' # Node table include node info +BLOCK = 'block' # Block table include the status for block polling + + +class Database: + + if not _sqlalchemy_enabled: + raise OptionalModuleMissing(['sqlalchemy'], + ("Monitoring requires the sqlalchemy library." + " Install monitoring dependencies with: pip install 'parsl[monitoring]'")) + Base = declarative_base() + + def __init__(self, + url: str = 'sqlite:///runinfomonitoring.db', + ): + + self.eng = sa.create_engine(url) + self.meta = self.Base.metadata + + # TODO: this code wants a read lock on the sqlite3 database, and fails if it cannot + # - for example, if someone else is querying the database at the point that the + # monitoring system is initialized. See PR #1917 for related locked-for-read fixes + # elsewhere in this file. + self.meta.create_all(self.eng) + + self.meta.reflect(bind=self.eng) + + Session = sessionmaker(bind=self.eng) + self.session = Session() + + def _get_mapper(self, table_obj: Table) -> Mapper: + all_mappers: Set[Mapper] = set() + for mapper_registry in mapperlib._all_registries(): + all_mappers.update(mapper_registry.mappers) + mapper_gen = ( + mapper for mapper in all_mappers + if table_obj in mapper.tables + ) + try: + mapper = next(mapper_gen) + second_mapper = next(mapper_gen, False) + except StopIteration: + raise ValueError(f"Could not get mapper for table {table_obj}") + + if second_mapper: + raise ValueError(f"Multiple mappers for table {table_obj}") + return mapper + + def update(self, *, table: str, columns: List[str], messages: List[MonitoringMessage]) -> None: + table_obj = self.meta.tables[table] + mappings = self._generate_mappings(table_obj, columns=columns, + messages=messages) + mapper = self._get_mapper(table_obj) + self.session.bulk_update_mappings(mapper, mappings) + self.session.commit() + + def insert(self, *, table: str, messages: List[MonitoringMessage]) -> None: + table_obj = self.meta.tables[table] + mappings = self._generate_mappings(table_obj, messages=messages) + mapper = self._get_mapper(table_obj) + self.session.bulk_insert_mappings(mapper, mappings) + self.session.commit() + + def rollback(self) -> None: + self.session.rollback() + + def _generate_mappings( + self, + table: Table, + columns: Optional[List[str]] = None, + messages: List[MonitoringMessage] = [], + ) -> List[Dict[str, Any]]: + + mappings = [] + for msg in messages: + m = {} + if columns is None: + columns = table.c.keys() + for column in columns: + m[column] = msg.get(column, None) + mappings.append(m) + return mappings + + class Workflow(Base): + __tablename__ = WORKFLOW + run_id = Column(Text, nullable=False, primary_key=True) + workflow_name = Column(Text, nullable=True) + workflow_version = Column(Text, nullable=True) + time_began = Column(DateTime, nullable=False) + time_completed = Column(DateTime, nullable=True) + host = Column(Text, nullable=False) + user = Column(Text, nullable=False) + rundir = Column(Text, nullable=False) + tasks_failed_count = Column(Integer, nullable=False) + tasks_completed_count = Column(Integer, nullable=False) + + class Status(Base): + __tablename__ = STATUS + task_id = Column(Integer, nullable=False) + task_status_name = Column(Text, nullable=False) + timestamp = Column(DateTime, nullable=False) + run_id = Column(Text, sa.ForeignKey('workflow.run_id'), nullable=False) + try_id = Column('try_id', Integer, nullable=False) + __table_args__ = ( + PrimaryKeyConstraint('task_id', 'run_id', + 'task_status_name', 'timestamp'), + ) + + class Task(Base): + __tablename__ = TASK + task_id = Column('task_id', Integer, nullable=False) + run_id = Column('run_id', Text, nullable=False) + task_depends = Column('task_depends', Text, nullable=True) + task_func_name = Column('task_func_name', Text, nullable=False) + task_memoize = Column('task_memoize', Text, nullable=False) + task_hashsum = Column('task_hashsum', Text, nullable=True, index=True) + task_inputs = Column('task_inputs', Text, nullable=True) + task_outputs = Column('task_outputs', Text, nullable=True) + task_stdin = Column('task_stdin', Text, nullable=True) + task_stdout = Column('task_stdout', Text, nullable=True) + task_stderr = Column('task_stderr', Text, nullable=True) + + task_time_invoked = Column( + 'task_time_invoked', DateTime, nullable=True) + + task_time_returned = Column( + 'task_time_returned', DateTime, nullable=True) + + task_fail_count = Column('task_fail_count', Integer, nullable=False) + task_fail_cost = Column('task_fail_cost', Float, nullable=False) + + __table_args__ = ( + PrimaryKeyConstraint('task_id', 'run_id'), + ) + + class Try(Base): + __tablename__ = TRY + try_id = Column('try_id', Integer, nullable=False) + task_id = Column('task_id', Integer, nullable=False) + run_id = Column('run_id', Text, nullable=False) + + block_id = Column('block_id', Text, nullable=True) + hostname = Column('hostname', Text, nullable=True) + + task_executor = Column('task_executor', Text, nullable=False) + + task_try_time_launched = Column( + 'task_try_time_launched', DateTime, nullable=True) + + task_try_time_running = Column( + 'task_try_time_running', DateTime, nullable=True) + + task_try_time_returned = Column( + 'task_try_time_returned', DateTime, nullable=True) + + task_fail_history = Column('task_fail_history', Text, nullable=True) + + task_joins = Column('task_joins', Text, nullable=True) + + __table_args__ = ( + PrimaryKeyConstraint('try_id', 'task_id', 'run_id'), + ) + + class Node(Base): + __tablename__ = NODE + id = Column('id', Integer, nullable=False, primary_key=True, autoincrement=True) + run_id = Column('run_id', Text, nullable=False) + hostname = Column('hostname', Text, nullable=False) + uid = Column('uid', Text, nullable=False) + block_id = Column('block_id', Text, nullable=False) + cpu_count = Column('cpu_count', Integer, nullable=False) + total_memory = Column('total_memory', BigInteger, nullable=False) + active = Column('active', Boolean, nullable=False) + worker_count = Column('worker_count', Integer, nullable=False) + python_v = Column('python_v', Text, nullable=False) + timestamp = Column('timestamp', DateTime, nullable=False) + last_heartbeat = Column('last_heartbeat', DateTime, nullable=False) + + class Block(Base): + __tablename__ = BLOCK + run_id = Column('run_id', Text, nullable=False) + executor_label = Column('executor_label', Text, nullable=False) + block_id = Column('block_id', Text, nullable=False) + job_id = Column('job_id', Text, nullable=True) + timestamp = Column('timestamp', DateTime, nullable=False) + status = Column("status", Text, nullable=False) + __table_args__ = ( + PrimaryKeyConstraint('run_id', 'block_id', 'executor_label', 'timestamp'), + ) + + class Resource(Base): + __tablename__ = RESOURCE + try_id = Column('try_id', Integer, nullable=False) + task_id = Column('task_id', Integer, nullable=False) + run_id = Column('run_id', Text, sa.ForeignKey( + 'workflow.run_id'), nullable=False) + timestamp = Column('timestamp', DateTime, nullable=False) + resource_monitoring_interval = Column( + 'resource_monitoring_interval', Float, nullable=True) + psutil_process_pid = Column( + 'psutil_process_pid', Integer, nullable=True) + psutil_process_memory_percent = Column( + 'psutil_process_memory_percent', Float, nullable=True) + psutil_process_children_count = Column( + 'psutil_process_children_count', Float, nullable=True) + psutil_process_time_user = Column( + 'psutil_process_time_user', Float, nullable=True) + psutil_process_time_system = Column( + 'psutil_process_time_system', Float, nullable=True) + psutil_process_memory_virtual = Column( + 'psutil_process_memory_virtual', Float, nullable=True) + psutil_process_memory_resident = Column( + 'psutil_process_memory_resident', Float, nullable=True) + psutil_process_disk_read = Column( + 'psutil_process_disk_read', Float, nullable=True) + psutil_process_disk_write = Column( + 'psutil_process_disk_write', Float, nullable=True) + psutil_process_status = Column( + 'psutil_process_status', Text, nullable=True) + psutil_cpu_num = Column( + 'psutil_cpu_num', Text, nullable=True) + psutil_process_num_ctx_switches_voluntary = Column( + 'psutil_process_num_ctx_switches_voluntary', Float, nullable=True) + psutil_process_num_ctx_switches_involuntary = Column( + 'psutil_process_num_ctx_switches_involuntary', Float, nullable=True) + __table_args__ = ( + PrimaryKeyConstraint('try_id', 'task_id', 'run_id', 'timestamp'), + ) + + +class DatabaseManager: + def __init__(self, + *, + db_url: str = 'sqlite:///runinfo/monitoring.db', + run_dir: str = '.', + logging_level: int = logging.INFO, + log_config: Optional[LogConfig] = None, + batching_interval: float = 1, + batching_threshold: float = 99999, + exit_event: mpe.Event + ): + + self.workflow_end = False + self.workflow_start_message: Optional[MonitoringMessage] = None + self.run_dir = run_dir + + if log_config: + oneshot_initialize_logging(log_config=log_config, log_dir=pathlib.Path(self.run_dir), log_name="database_manager") + else: + os.makedirs(self.run_dir, exist_ok=True) + + set_file_logger(f"{self.run_dir}/database_manager.log", level=logging_level, + format_string="%(asctime)s.%(msecs)03d %(name)s:%(lineno)d [%(levelname)s] [%(threadName)s %(thread)d] %(message)s") + + logger.info("Initializing Database Manager process") + + self.db = Database(db_url) + self.batching_interval = batching_interval + self.batching_threshold = batching_threshold + + self.pending_priority_queue: queue.Queue[TaggedMonitoringMessage] = queue.Queue() + self.pending_node_queue: queue.Queue[MonitoringMessage] = queue.Queue() + self.pending_block_queue: queue.Queue[MonitoringMessage] = queue.Queue() + self.pending_resource_queue: queue.Queue[MonitoringMessage] = queue.Queue() + self.pending_worker_task_queue: queue.Queue[MonitoringMessage] = queue.Queue() + + self.external_exit_event = exit_event + + def start(self, + resource_queue: mpq.Queue) -> None: + + self._kill_event = threading.Event() + + self._resource_queue_pull_thread = threading.Thread(target=self._migrate_logs_to_internal, + args=( + resource_queue, self._kill_event,), + name="Monitoring-migrate-resource", + daemon=True, + ) + self._resource_queue_pull_thread.start() + + # The following three variables work together to maintain sets to + # track the tasks and tries that are already INSERTed into database + # to address a race condition that the first resource message + # (indicating that the task has begun running on a worker) + # arrives before the first task message from the DFK. In such a case, + # the code cannot UPDATE the relevant try table row, because it + # does not exist yet. + + # When such a message arrives, it is added to deferred_resource_messages + # and processed later, after the relevant DFK-side message has been + # received and processed to create a row in the try table. + + # Exactly one message per task/try can be deferred, which may not be + # enough when a try happens several times (for example when an + # executor performs its own Parsl-try unaware retries. + inserted_tasks: Set[object] = set() + inserted_tries: Set[object] = set() + deferred_worker_task_messages: dict[str, MonitoringMessage] = {} + + exception_happened = False + + while (not self._kill_event.is_set() or + not self.pending_priority_queue.empty() or not self.pending_resource_queue.empty() or + not self.pending_worker_task_queue.empty() or + not self.pending_node_queue.empty() or not self.pending_block_queue.empty() or + not resource_queue.empty()): + + """ + WORKFLOW_INFO and TASK_INFO messages (i.e. priority messages) + + """ + try: + logger.debug("""Checking STOP conditions: {}, {}, {}, {}, {}, {}""".format( + self._kill_event.is_set(), + not self.pending_priority_queue.empty(), not self.pending_resource_queue.empty(), + not self.pending_node_queue.empty(), not self.pending_block_queue.empty(), + not resource_queue.empty())) + + # This is the list of resource messages which can be reprocessed as if they + # had just arrived because the corresponding first task message has been + # processed (corresponding by task id) + reprocessable_first_worker_task_messages = [] + + # end-of-task-run status messages - handled in similar way as + # for last resource messages to try to have symmetry... this + # needs a type annotation though reprocessable_first_worker_task_messages + # doesn't... not sure why. Too lazy right now to figure out what, + # if any, more specific type than Any the messages have. + reprocessable_last_worker_task_messages: List[Any] = [] + + # Get a batch of priority messages + priority_messages = self._get_messages_in_batch(self.pending_priority_queue) + if priority_messages: + logger.debug( + "Got {} messages from priority queue".format(len(priority_messages))) + task_info_update_messages, task_info_insert_messages, task_info_all_messages = [], [], [] + try_update_messages, try_insert_messages, try_all_messages = [], [], [] + for msg_type, msg in priority_messages: + if msg_type == MessageType.WORKFLOW_INFO: + if "python_version" in msg: # workflow start message + logger.debug( + "Inserting workflow start info to WORKFLOW table") + self._insert(table=WORKFLOW, messages=[msg]) + self.workflow_start_message = msg + else: # workflow end message + logger.debug( + "Updating workflow end info to WORKFLOW table") + self._update(table=WORKFLOW, + columns=['run_id', 'tasks_failed_count', + 'tasks_completed_count', 'time_completed'], + messages=[msg]) + self.workflow_end = True + + elif msg_type == MessageType.TASK_INFO: + task_try_id = str(msg['task_id']) + "." + str(msg['try_id']) + task_info_all_messages.append(msg) + if msg['task_id'] in inserted_tasks: + task_info_update_messages.append(msg) + else: + inserted_tasks.add(msg['task_id']) + task_info_insert_messages.append(msg) + + try_all_messages.append(msg) + if task_try_id in inserted_tries: + try_update_messages.append(msg) + else: + inserted_tries.add(task_try_id) + try_insert_messages.append(msg) + + # check if there is a deferred message for this task + if task_try_id in deferred_worker_task_messages: + reprocessable_first_worker_task_messages.append( + deferred_worker_task_messages.pop(task_try_id)) + else: + raise RuntimeError("Unexpected message type {} received on priority queue".format(msg_type)) + + logger.debug("Updating and inserting TASK_INFO to all tables") + logger.debug("Updating {} TASK_INFO into workflow table".format(len(task_info_update_messages))) + self._update(table=WORKFLOW, + columns=['run_id', 'tasks_failed_count', + 'tasks_completed_count'], + messages=task_info_all_messages) + + if task_info_insert_messages: + self._insert(table=TASK, messages=task_info_insert_messages) + logger.debug( + "There are {} inserted task records".format(len(inserted_tasks))) + + if task_info_update_messages: + logger.debug("Updating {} TASK_INFO into task table".format(len(task_info_update_messages))) + self._update(table=TASK, + columns=['task_time_invoked', + 'task_time_returned', + 'run_id', 'task_id', + 'task_fail_count', + 'task_fail_cost', + 'task_hashsum', + 'task_inputs'], + messages=task_info_update_messages) + logger.debug("Inserting {} task_info_all_messages into status table".format(len(task_info_all_messages))) + + self._insert(table=STATUS, messages=task_info_all_messages) + + if try_insert_messages: + logger.debug("Inserting {} TASK_INFO to try table".format(len(try_insert_messages))) + self._insert(table=TRY, messages=try_insert_messages) + logger.debug( + "There are {} inserted task records".format(len(inserted_tasks))) + + if try_update_messages: + logger.debug("Updating {} TASK_INFO into try table".format(len(try_update_messages))) + self._update(table=TRY, + columns=['run_id', 'task_id', 'try_id', + 'task_fail_history', + 'task_try_time_launched', + 'task_try_time_returned', + 'task_joins'], + messages=try_update_messages) + + """ + NODE_INFO messages + + """ + node_info_messages = self._get_messages_in_batch(self.pending_node_queue) + if node_info_messages: + logger.debug( + "Got {} messages from node queue".format(len(node_info_messages))) + self._insert(table=NODE, messages=node_info_messages) + + """ + BLOCK_INFO messages + + """ + block_info_messages = self._get_messages_in_batch(self.pending_block_queue) + if block_info_messages: + logger.debug( + "Got {} messages from block queue".format(len(block_info_messages))) + # block_info_messages is possibly a nested list of dict (at different polling times) + # Each dict refers to the info of a job/block at one polling time + block_messages_to_insert: List[Any] = [] + for block_msg in block_info_messages: + block_messages_to_insert.extend(block_msg) + self._insert(table=BLOCK, messages=block_messages_to_insert) + + """ + Worker task status messages + """ + worker_task_messages = self._get_messages_in_batch(self.pending_worker_task_queue) + + if worker_task_messages: + logger.debug( + "Got {} messages from worker task queue, " + "{} reprocessable as first messages, " + "{} reprocessable as last messages".format(len(worker_task_messages), + len(reprocessable_first_worker_task_messages), + len(reprocessable_last_worker_task_messages))) + + for msg in worker_task_messages: + task_try_id = str(msg['task_id']) + "." + str(msg['try_id']) + if msg['first_msg']: + # Update the running time to try table if first message + msg['task_status_name'] = States.running.name + msg['task_try_time_running'] = msg['timestamp'] + + if task_try_id in inserted_tries: + reprocessable_first_worker_task_messages.append(msg) + else: + if task_try_id in deferred_worker_task_messages: + # this might happen if a task/try is run multiple + # times by an executor (for example, the work queue + # executor can be configured to do this) + logger.error( + "Task {} already has a deferred resource message. " + "Discarding previous message.".format(msg['task_id']) + ) + deferred_worker_task_messages[task_try_id] = msg + elif msg['last_msg']: + # This assumes that the primary key has been added + # to the try table already, so doesn't use the same + # deferral logic as the first_msg case. + msg['task_status_name'] = States.running_ended.name + reprocessable_last_worker_task_messages.append(msg) + else: + raise RuntimeError("Bad worker task message that is neither first nor last") + + if reprocessable_first_worker_task_messages: + self._insert(table=STATUS, messages=reprocessable_first_worker_task_messages) + self._update(table=TRY, + columns=['task_try_time_running', + 'run_id', 'task_id', 'try_id', + 'block_id', 'hostname'], + messages=reprocessable_first_worker_task_messages) + + if reprocessable_last_worker_task_messages: + self._insert(table=STATUS, messages=reprocessable_last_worker_task_messages) + + """ + Resource info messages + + """ + resource_messages = self._get_messages_in_batch(self.pending_resource_queue) + if resource_messages: + logger.debug("Got {} messages from resource queue".format(len(resource_messages))) + self._insert(table=RESOURCE, messages=resource_messages) + + except Exception: + logger.exception( + "Exception in db loop: this might have been a malformed message, " + "or some other error. monitoring data may have been lost" + ) + exception_happened = True + + if self.external_exit_event.is_set(): + self.close() + + if exception_happened: + raise RuntimeError("An exception happened sometime during database processing and should have been logged in database_manager.log") + + @wrap_with_logs + def _migrate_logs_to_internal(self, logs_queue: mpq.Queue, kill_event: threading.Event) -> None: + logger.info("Starting _migrate_logs_to_internal") + + while not kill_event.is_set() or not logs_queue.empty(): + logger.debug("Checking STOP conditions: kill event: %s, queue has entries: %s", + kill_event.is_set(), not logs_queue.empty()) + + try: + x = logs_queue.get(timeout=0.1) + except queue.Empty: + continue + else: + self._dispatch_to_internal(x) + + def _dispatch_to_internal(self, x: Tuple) -> None: + assert isinstance(x, tuple) + assert len(x) == 2, "expected message tuple to have exactly two elements" + + if x[0] in [MessageType.WORKFLOW_INFO, MessageType.TASK_INFO]: + self.pending_priority_queue.put(cast(Any, x)) + elif x[0] == MessageType.RESOURCE_INFO: + self.pending_resource_queue.put(x[1]) + elif x[0] == MessageType.WORKER_TASK_INFO: + self.pending_worker_task_queue.put(x[1]) + elif x[0] == MessageType.NODE_INFO: + logger.debug("Will put {} to pending node queue".format(x[1])) + self.pending_node_queue.put(x[1]) + elif x[0] == MessageType.BLOCK_INFO: + logger.debug("Will put {} to pending block queue".format(x[1])) + self.pending_block_queue.put(x[1]) + else: + logger.error("Discarding message of unknown type {}".format(x[0])) + + def _update(self, table: str, columns: List[str], messages: List[MonitoringMessage]) -> None: + try: + done = False + while not done: + try: + self.db.update(table=table, columns=columns, messages=messages) + done = True + except sa.exc.OperationalError as e: + # This code assumes that an OperationalError is something that will go away eventually + # if retried - for example, the database being locked because someone else is readying + # the tables we are trying to write to. If that assumption is wrong, then this loop + # may go on forever. + logger.warning("Got a database OperationalError. " + "Ignoring and retrying on the assumption that it is recoverable: {}".format(e)) + self.db.rollback() + time.sleep(1) # hard coded 1s wait - this should be configurable or exponential backoff or something + + except KeyboardInterrupt: + logger.exception("KeyboardInterrupt when trying to update Table {}".format(table)) + try: + self.db.rollback() + except Exception: + logger.exception("Rollback failed") + raise + except Exception: + logger.exception("Got exception when trying to update table {}".format(table)) + try: + self.db.rollback() + except Exception: + logger.exception("Rollback failed") + + def _insert(self, table: str, messages: List[MonitoringMessage]) -> None: + try: + done = False + while not done: + try: + self.db.insert(table=table, messages=messages) + done = True + except sa.exc.OperationalError as e: + # hoping that this is a database locked error during _update, not some other problem + logger.warning("Got a database OperationalError. " + "Ignoring and retrying on the assumption that it is recoverable: {}".format(e)) + self.db.rollback() + time.sleep(1) # hard coded 1s wait - this should be configurable or exponential backoff or something + except KeyboardInterrupt: + logger.exception("KeyboardInterrupt when trying to update Table {}".format(table)) + try: + self.db.rollback() + except Exception: + logger.exception("Rollback failed") + raise + except Exception: + logger.exception("Got exception when trying to insert to table {}".format(table)) + try: + self.db.rollback() + except Exception: + logger.exception("Rollback failed") + + def _get_messages_in_batch(self, msg_queue: "queue.Queue[X]") -> List[X]: + messages: List[X] = [] + start = time.time() + while True: + if time.time() - start >= self.batching_interval or len(messages) >= self.batching_threshold: + break + try: + x = msg_queue.get(timeout=0.1) + # logger.debug("Database manager receives a message {}".format(x)) + except queue.Empty: + logger.debug("Database manager has not received any message.") + break + else: + messages.append(x) + return messages + + def close(self) -> None: + logger.info("Database Manager cleanup initiated.") + if not self.workflow_end and self.workflow_start_message: + logger.info("Logging workflow end info to database due to abnormal exit") + time_completed = datetime.datetime.now() + msg = {'time_completed': time_completed, + 'workflow_duration': (time_completed - self.workflow_start_message['time_began']).total_seconds()} + self.workflow_start_message.update(msg) + self._update(table=WORKFLOW, + columns=['run_id', 'time_completed', + 'workflow_duration'], + messages=[self.workflow_start_message]) + self.batching_interval = float('inf') + self.batching_threshold = float('inf') + self._kill_event.set() + + +@wrap_with_logs +@typeguard.typechecked +def dbm_starter(resource_msgs: mpq.Queue, + db_url: str, + run_dir: str, + logging_level: int, + exit_event: mpe.Event, + log_config: Optional[LogConfig]) -> None: + """Start the database manager process + """ + setproctitle("parsl: monitoring database") + + try: + dbm = DatabaseManager(db_url=db_url, + run_dir=run_dir, + logging_level=logging_level, + exit_event=exit_event, + log_config=log_config) + logger.info("Starting dbm in dbm starter") + dbm.start(resource_msgs) + except KeyboardInterrupt: + logger.exception("KeyboardInterrupt signal caught") + dbm.close() + raise + except Exception: + logger.exception("dbm.start exception") + dbm.close() + + logger.info("End of dbm_starter") diff --git a/parsl/parsl/monitoring/errors.py b/parsl/parsl/monitoring/errors.py new file mode 100644 index 00000000..c5a85425 --- /dev/null +++ b/parsl/parsl/monitoring/errors.py @@ -0,0 +1,11 @@ +from parsl.errors import ParslError + + +class MonitoringRouterStartError(ParslError): + def __str__(self) -> str: + return "Monitoring router failed to start" + + +class RadioRequiredError(ParslError): + def __str__(self) -> str: + return "A radio must be configured for remote task monitoring" diff --git a/parsl/parsl/monitoring/message_type.py b/parsl/parsl/monitoring/message_type.py new file mode 100644 index 00000000..26c1633e --- /dev/null +++ b/parsl/parsl/monitoring/message_type.py @@ -0,0 +1,22 @@ +from enum import Enum + + +class MessageType(Enum): + + # Reports any task related info such as launch, completion etc. + TASK_INFO = 0 + + # Reports of resource utilization on a per-task basis + RESOURCE_INFO = 1 + + # Top level workflow information + WORKFLOW_INFO = 2 + + # Reports of the resource capacity for each node + NODE_INFO = 3 + + # Reports of the block info + BLOCK_INFO = 4 + + # Reports of a task starting or ending execution on a worker + WORKER_TASK_INFO = 5 diff --git a/parsl/parsl/monitoring/monitoring.py b/parsl/parsl/monitoring/monitoring.py new file mode 100644 index 00000000..f4171795 --- /dev/null +++ b/parsl/parsl/monitoring/monitoring.py @@ -0,0 +1,162 @@ +from __future__ import annotations + +import logging +import multiprocessing.synchronize as ms +import os +import warnings +from multiprocessing.queues import Queue +from typing import Any, Optional, Union + +import typeguard + +from parsl.logconfigs.base import LogConfig +from parsl.monitoring.types import TaggedMonitoringMessage +from parsl.multiprocessing import ( + SpawnEvent, + SpawnProcess, + SpawnQueue, + join_terminate_close_proc, +) +from parsl.utils import RepresentationMixin + +_db_manager_excepts: Optional[Exception] + + +try: + from parsl.monitoring.db_manager import dbm_starter +except Exception as e: + _db_manager_excepts = e +else: + _db_manager_excepts = None + +logger = logging.getLogger(__name__) + + +@typeguard.typechecked +class MonitoringHub(RepresentationMixin): + def __init__(self, + hub_address: Any = None, # unused, so no type enforcement + hub_port_range: Any = None, # unused, so no type enforcement + hub_port: Any = None, # unused, so no type enforcement + + workflow_name: Optional[str] = None, + workflow_version: Optional[str] = None, + logging_endpoint: Optional[str] = None, + monitoring_debug: bool = False, + resource_monitoring_enabled: bool = True, + resource_monitoring_interval: float = 30): # in seconds + """ + Parameters + ---------- + hub_address : unused + hub_port : unused + Unused, but probably retained until 2026-06-01 to give deprecation warning. + These two values previously configured UDP parameters when UDP was used + for monitoring messages from workers. These are now configured on the + relevant UDPRadio. + hub_port_range : unused + Unused, but probably retained until 2026-06-01 to give deprecation warning. + This value previously configured one ZMQ channel inside the + HighThroughputExecutor. That ZMQ channel is now configured by the + interchange_port_range parameter of HighThroughputExecutor. + workflow_name : str + The name for the workflow. Default to the name of the parsl script + workflow_version : str + The version of the workflow. Default to the beginning datetime of the parsl script + logging_endpoint : str + The database connection url for monitoring to log the information. + These URLs follow RFC-1738, and can include username, password, hostname, database name. + Default: sqlite, in the configured run_dir. + monitoring_debug : Bool + Enable monitoring debug logging. Default: False + resource_monitoring_enabled : boolean + Set this field to True to enable logging of information from the worker side. + This will include environment information such as start time, hostname and block id, + along with periodic resource usage of each task. Default: True + resource_monitoring_interval : float + The time interval, in seconds, at which the monitoring records the resource usage of each task. + If set to 0, only start and end information will be logged, and no periodic monitoring will + be made. + Default: 30 seconds + """ + + if _db_manager_excepts: + raise _db_manager_excepts + + # The following three parameters need to exist as attributes to be + # output by RepresentationMixin. + if hub_address is not None: + message = "Instead of MonitoringHub.hub_address, specify UDPRadio(address=...)" + warnings.warn(message, DeprecationWarning) + logger.warning(message) + + self.hub_address = hub_address + + if hub_port is not None: + message = "Instead of MonitoringHub.hub_port, specify UDPRadio(port=...)" + warnings.warn(message, DeprecationWarning) + logger.warning(message) + + self.hub_port = hub_port + + if hub_port_range is not None: + message = "Instead of MonitoringHub.hub_port_range, Use HighThroughputExecutor.interchange_port_range" + warnings.warn(message, DeprecationWarning) + logger.warning(message) + + self.hub_port_range = hub_port_range + + self.logging_endpoint = logging_endpoint + self.monitoring_debug = monitoring_debug + + self.workflow_name = workflow_name + self.workflow_version = workflow_version + + self.resource_monitoring_enabled = resource_monitoring_enabled + self.resource_monitoring_interval = resource_monitoring_interval + + def start(self, dfk_run_dir: str, config_run_dir: Union[str, os.PathLike], log_config: Optional[LogConfig]) -> None: + + logger.debug("Starting MonitoringHub") + + if self.logging_endpoint is None: + self.logging_endpoint = f"sqlite:///{os.fspath(config_run_dir)}/monitoring.db" + + os.makedirs(dfk_run_dir, exist_ok=True) + + self.monitoring_hub_active = True + + self.resource_msgs: Queue[TaggedMonitoringMessage] + self.resource_msgs = SpawnQueue() + + self.dbm_exit_event: ms.Event + self.dbm_exit_event = SpawnEvent() + + self.dbm_proc = SpawnProcess(target=dbm_starter, + args=(self.resource_msgs,), + kwargs={"run_dir": dfk_run_dir, + "logging_level": logging.DEBUG if self.monitoring_debug else logging.INFO, + "db_url": self.logging_endpoint, + "exit_event": self.dbm_exit_event, + "log_config": log_config, + }, + name="Monitoring-DBM-Process", + daemon=True, + ) + self.dbm_proc.start() + logger.info("Started DBM process %s", self.dbm_proc.pid) + logger.info("Monitoring Hub initialized") + + def close(self) -> None: + logger.info("Terminating Monitoring Hub") + if self.monitoring_hub_active: + self.monitoring_hub_active = False + logger.debug("Waiting for DB termination") + self.dbm_exit_event.set() + join_terminate_close_proc(self.dbm_proc) + logger.debug("Finished waiting for DBM termination") + + logger.info("Closing monitoring multiprocessing queues") + self.resource_msgs.close() + self.resource_msgs.join_thread() + logger.info("Closed monitoring multiprocessing queues") diff --git a/parsl/parsl/monitoring/queries/__init__.py b/parsl/parsl/monitoring/queries/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/monitoring/queries/pandas.py b/parsl/parsl/monitoring/queries/pandas.py new file mode 100644 index 00000000..9bda8422 --- /dev/null +++ b/parsl/parsl/monitoring/queries/pandas.py @@ -0,0 +1,73 @@ +from typing import Any + +import pandas as pd + +# pandas can take several different types of database connection, +# and itself exposes its connection parameters as "Any". +# This alias might help things be a bit clearer in our documentation. +DB = Any + + +def app_counts_for_workflow(workflow_id: Any, db: DB) -> pd.DataFrame: + return pd.read_sql_query(""" + SELECT task_func_name, count(*) as 'frequency' + FROM task + WHERE run_id='%s' + GROUP BY task_func_name; + """ % workflow_id, db) + + +def nodes_for_workflow(workflow_id: Any, db: Any) -> pd.DataFrame: + return pd.read_sql_query(""" + SELECT * + FROM node + WHERE run_id='%s'; + """ % (workflow_id), db) + + +def resources_for_workflow(workflow_id: Any, db: Any) -> pd.DataFrame: + return pd.read_sql_query(""" + SELECT * + FROM resource + WHERE run_id='%s'; + """ % (workflow_id), db) + + +def resources_for_task(workflow_id: Any, task_id: Any, db: Any) -> pd.DataFrame: + return pd.read_sql_query(""" + SELECT * + FROM resource + WHERE run_id='%s' AND task_id='%s'; + """ % (workflow_id, task_id), db) + + +def status_for_workflow(workflow_id: Any, db: Any) -> pd.DataFrame: + return pd.read_sql_query(""" + SELECT run_id, task_id, task_status_name, timestamp + FROM status + WHERE run_id='%s'; + """ % workflow_id, db) + + +def completion_times_for_workflow(workflow_id: Any, db: Any) -> pd.DataFrame: + return pd.read_sql_query(""" + SELECT task_id, task_func_name, task_time_returned + FROM task + WHERE run_id='%s' + """ % (workflow_id), db) + + +def tasks_for_workflow(workflow_id: Any, db: Any) -> pd.DataFrame: + return pd.read_sql_query(""" + SELECT * + FROM task + WHERE run_id='%s' + """ % (workflow_id), db) + + +def tries_for_workflow(workflow_id: Any, db: Any) -> pd.DataFrame: + return pd.read_sql_query(""" + SELECT task.task_id, task_func_name, task_time_returned, task_try_time_running, task_try_time_returned + FROM task, try + WHERE task.task_id = try.task_id AND task.run_id='%s' AND try.run_id='%s' + """ % (workflow_id, workflow_id), db) diff --git a/parsl/parsl/monitoring/radios/__init__.py b/parsl/parsl/monitoring/radios/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/monitoring/radios/base.py b/parsl/parsl/monitoring/radios/base.py new file mode 100644 index 00000000..c870a07c --- /dev/null +++ b/parsl/parsl/monitoring/radios/base.py @@ -0,0 +1,71 @@ +from abc import ABCMeta, abstractmethod +from multiprocessing.queues import Queue + + +class MonitoringRadioReceiver(metaclass=ABCMeta): + @abstractmethod + def shutdown(self) -> None: + pass + + +class MonitoringRadioSender(metaclass=ABCMeta): + @abstractmethod + def send(self, message: object) -> None: + pass + + +class RadioConfig(metaclass=ABCMeta): + """Base class for radio plugin configuration. + + This provides the configuration for a particular way of sending monitoring + messages from a source of monitoring messages into the submit side + monitoring database. + + This uses staged initialization like lots of Parsl configuration, but in + a slightly different form. + + A RadioConfig object must be pickleable, because it will be sent to remote + workers to configure senders. The MonitoringRadioSender and + MonitoringRadioReceiver objects do not need to be pickleable (and often + will not be - for example, when they hold references to other processes). + + The RadioConfig object will be used by Parsl in this sequence: + + * A user creates a RadioConfig object from the appropriate subclass for + radio mechanism they want to use, and specifies it as part of their + executor configuration. + + Methods on the RadioConfig will then be invoked by Parsl like this: + + * one create_receiver call, on the submit side + - this call can modify the state of radioconfig to contain information + about how a sender can connect back to the receiver. for example, + after binding to a particular port, can store that port so that the + sender knows which port to connect to. + + * Possibly many serializations to get the RadioConfig to remote workers + + * Many (0 or more) create_sender calls, possibly on remote workers, to + create the sending side of the radio (MonitoringRadioSender instances) + + * Those senders are used to send messages + + * At executor shutdown, the receiver is shut down. + + This object cannot be re-used across parsl configurations - like many other + pieces of parsl config it is single use in that respect. + """ + + @abstractmethod + def create_receiver(self, *, run_dir: str, resource_msgs: Queue) -> MonitoringRadioReceiver: + """Create a receiver for this RadioConfig, and update this RadioConfig + with enough context to create senders. + """ + pass + + @abstractmethod + def create_sender(self) -> MonitoringRadioSender: + """Create a sender to connect to the receiver created by an + earlier call to create_receiver. + """ + pass diff --git a/parsl/parsl/monitoring/radios/filesystem.py b/parsl/parsl/monitoring/radios/filesystem.py new file mode 100644 index 00000000..7967949e --- /dev/null +++ b/parsl/parsl/monitoring/radios/filesystem.py @@ -0,0 +1,68 @@ +import logging +import os +import pickle +import uuid +from multiprocessing.queues import Queue + +from parsl.monitoring.radios.base import ( + MonitoringRadioReceiver, + MonitoringRadioSender, + RadioConfig, +) +from parsl.monitoring.radios.filesystem_router import FilesystemRadioReceiver +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + + +class FilesystemRadio(RadioConfig, RepresentationMixin): + """A MonitoringRadioSender that sends messages over a shared filesystem. + + The messsage directory structure is based on maildir, + https://en.wikipedia.org/wiki/Maildir + + The writer creates a message in tmp/ and then when it is fully + written, moves it atomically into new/ + + The reader ignores tmp/ and only reads and deletes messages from + new/ + + This avoids a race condition of reading partially written messages. + + This radio is likely to give higher shared filesystem load compared to + the UDP radio, but should be much more reliable. + """ + + def create_sender(self) -> MonitoringRadioSender: + return FilesystemRadioSender(run_dir=self.run_dir) + + def create_receiver(self, *, run_dir: str, resource_msgs: Queue) -> MonitoringRadioReceiver: + self.run_dir = run_dir + return FilesystemRadioReceiver(resource_msgs, run_dir) + + +class FilesystemRadioSender(MonitoringRadioSender): + def __init__(self, *, run_dir: str): + logger.info("filesystem based monitoring radio initializing") + self.base_path = f"{run_dir}/monitor-fs-radio/" + self.tmp_path = f"{self.base_path}/tmp" + self.new_path = f"{self.base_path}/new" + + os.makedirs(self.tmp_path, exist_ok=True) + os.makedirs(self.new_path, exist_ok=True) + + def send(self, message: object) -> None: + logger.info("Sending a monitoring message via filesystem") + + unique_id = str(uuid.uuid4()) + + tmp_filename = f"{self.tmp_path}/{unique_id}" + new_filename = f"{self.new_path}/{unique_id}" + buffer = message + + # this will write the message out then atomically + # move it into new/, so that a partially written + # file will never be observed in new/ + with open(tmp_filename, "wb") as f: + pickle.dump(buffer, f) + os.rename(tmp_filename, new_filename) diff --git a/parsl/parsl/monitoring/radios/filesystem_router.py b/parsl/parsl/monitoring/radios/filesystem_router.py new file mode 100644 index 00000000..2ae5c3fc --- /dev/null +++ b/parsl/parsl/monitoring/radios/filesystem_router.py @@ -0,0 +1,84 @@ +from __future__ import annotations + +import logging +import os +import pickle +from multiprocessing.queues import Queue +from multiprocessing.synchronize import Event +from typing import cast + +from parsl.log_utils import set_file_logger +from parsl.monitoring.radios.base import MonitoringRadioReceiver +from parsl.monitoring.radios.multiprocessing import MultiprocessingQueueRadioSender +from parsl.monitoring.types import TaggedMonitoringMessage +from parsl.multiprocessing import SpawnEvent, SpawnProcess, join_terminate_close_proc +from parsl.process_loggers import wrap_with_logs +from parsl.utils import setproctitle + +logger = logging.getLogger(__name__) + +# how often the router will scan the new message directory +POLL_PERIOD_S = 10 + + +@wrap_with_logs +def filesystem_router_starter(*, q: Queue[TaggedMonitoringMessage], run_dir: str, exit_event: Event) -> None: + set_file_logger(f"{run_dir}/monitoring_filesystem_radio.log", + level=logging.INFO) + + logger.info("Starting filesystem radio receiver") + setproctitle("parsl: monitoring filesystem receiver") + base_path = f"{run_dir}/monitor-fs-radio/" + tmp_dir = f"{base_path}/tmp/" + new_dir = f"{base_path}/new/" + logger.debug("Creating new and tmp paths under %s", base_path) + + target_radio = MultiprocessingQueueRadioSender(q) + + os.makedirs(tmp_dir, exist_ok=True) + os.makedirs(new_dir, exist_ok=True) + + loop = True + + while loop: + logger.debug("Start filesystem radio receiver loop") + + # this happens before the final poll of the directory so that + # one complete pass over the new_dir will happen strictly after + # the exit_event is set. Without forcing that final pass, there + # can be a race between exiting on exit_event after the files + # are added. + if exit_event.wait(POLL_PERIOD_S): + loop = False + + # iterate over files in new_dir + for filename in os.listdir(new_dir): + try: + logger.info("Processing filesystem radio file %s", filename) + full_path_filename = f"{new_dir}/{filename}" + with open(full_path_filename, "rb") as f: + message = pickle.load(f) + logger.debug("Message received is: %s", message) + assert isinstance(message, tuple) + target_radio.send(cast(TaggedMonitoringMessage, message)) + os.remove(full_path_filename) + except Exception: + logger.exception("Exception processing %s - probably will be retried next iteration", filename) + + logger.info("Ending filesystem radio receiver") + + +class FilesystemRadioReceiver(MonitoringRadioReceiver): + def __init__(self, resource_msgs: Queue, run_dir: str) -> None: + self.exit_event = SpawnEvent() + self.process = SpawnProcess(target=filesystem_router_starter, + kwargs={"q": resource_msgs, "run_dir": run_dir, "exit_event": self.exit_event}, + name="Monitoring-Filesystem-Process", + daemon=True + ) + self.process.start() + logger.info("Started filesystem radio receiver process %s", self.process.pid) + + def shutdown(self) -> None: + self.exit_event.set() + join_terminate_close_proc(self.process) diff --git a/parsl/parsl/monitoring/radios/htex.py b/parsl/parsl/monitoring/radios/htex.py new file mode 100644 index 00000000..6d42a753 --- /dev/null +++ b/parsl/parsl/monitoring/radios/htex.py @@ -0,0 +1,65 @@ +import logging +import pickle +from multiprocessing.queues import Queue + +from parsl.monitoring.radios.base import ( + MonitoringRadioReceiver, + MonitoringRadioSender, + RadioConfig, +) +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + + +class HTEXRadio(RadioConfig, RepresentationMixin): + def create_sender(self) -> MonitoringRadioSender: + return HTEXRadioSender() + + def create_receiver(self, *, run_dir: str, resource_msgs: Queue) -> MonitoringRadioReceiver: + return HTEXRadioReceiver() + + +class HTEXRadioSender(MonitoringRadioSender): + + def __init__(self) -> None: + # there is nothing to initialize + pass + + def send(self, message: object) -> None: + """ Sends a message via HTEX result channel. + + Parameter + --------- + + message: object + Arbitrary pickle-able object that is to be sent + + Returns: + None + """ + + import parsl.executors.high_throughput.monitoring_info + + result_queue = parsl.executors.high_throughput.monitoring_info.result_queue + + # this message needs to go in the result queue tagged so that it is treated + # as a monitoring message by the interchange. + + # for the interchange, the outer wrapper, this needs to be a dict: + + interchange_msg = { + 'type': 'monitoring', + 'payload': message + } + + if result_queue: + result_queue.put(pickle.dumps(interchange_msg)) + else: + logger.error("result_queue is uninitialized - cannot put monitoring message") + + +class HTEXRadioReceiver(MonitoringRadioReceiver): + def shutdown(self) -> None: + # there is nothing to shut down + pass diff --git a/parsl/parsl/monitoring/radios/multiprocessing.py b/parsl/parsl/monitoring/radios/multiprocessing.py new file mode 100644 index 00000000..52773a73 --- /dev/null +++ b/parsl/parsl/monitoring/radios/multiprocessing.py @@ -0,0 +1,38 @@ +from multiprocessing import Queue + +from parsl.monitoring.radios.base import ( + MonitoringRadioReceiver, + MonitoringRadioSender, + RadioConfig, +) +from parsl.utils import RepresentationMixin + + +class MultiprocessingQueueRadioSender(MonitoringRadioSender, RepresentationMixin): + """A monitoring radio which connects over a multiprocessing Queue. + This radio is intended to be used on the submit side, where components + in the submit process, or processes launched by multiprocessing, will have + access to a Queue shared with the monitoring database code (bypassing the + monitoring router). + """ + def __init__(self, queue: Queue) -> None: + self.queue = queue + + def send(self, message: object) -> None: + self.queue.put(message) + + +class MultiprocessingQueueRadio(RadioConfig): + def create_sender(self) -> MonitoringRadioSender: + return MultiprocessingQueueRadioSender(self._queue) + + def create_receiver(self, *, run_dir: str, resource_msgs: Queue) -> MonitoringRadioReceiver: + # This object is only for use with an in-process thread-pool so it + # is fine to store a reference to the message queue directly. + self._queue = resource_msgs + return MultiprocessingQueueRadioReceiver() + + +class MultiprocessingQueueRadioReceiver(MonitoringRadioReceiver): + def shutdown(self) -> None: + pass diff --git a/parsl/parsl/monitoring/radios/udp.py b/parsl/parsl/monitoring/radios/udp.py new file mode 100644 index 00000000..54b32fa7 --- /dev/null +++ b/parsl/parsl/monitoring/radios/udp.py @@ -0,0 +1,95 @@ +import hashlib +import hmac +import logging +import pickle +import secrets +import socket +from multiprocessing.queues import Queue +from typing import Optional + +from parsl.monitoring.radios.base import ( + MonitoringRadioReceiver, + MonitoringRadioSender, + RadioConfig, +) +from parsl.monitoring.radios.udp_router import start_udp_receiver +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + + +class UDPRadio(RadioConfig, RepresentationMixin): + def __init__(self, *, port: Optional[int] = None, atexit_timeout: int = 3, address: str, debug: bool = False, hmac_digest: str = 'sha512'): + self.port = port + self.atexit_timeout = atexit_timeout + self.address = address + self.debug = debug + self.hmac_digest = hmac_digest + + def create_sender(self) -> MonitoringRadioSender: + assert self.port is not None, "self.port should have been initialized by create_receiver" + return UDPRadioSender(self.address, self.port, self.hmac_key, self.hmac_digest) + + def create_receiver(self, run_dir: str, resource_msgs: Queue) -> MonitoringRadioReceiver: + # RFC 2104 section 2 recommends that the key length be at + # least as long as the hash output (64 bytes in the case of SHA512). + # RFC 2014 section 3 talks about periodic key refreshing. This key is + # not refreshed inside a workflow run, but each separate workflow run + # uses a new key. + keysize = hashlib.new(self.hmac_digest).digest_size + self.hmac_key = secrets.token_bytes(keysize) + + udp_receiver = start_udp_receiver(logdir=run_dir, + monitoring_messages=resource_msgs, + port=self.port, + debug=self.debug, + atexit_timeout=self.atexit_timeout, + hmac_key=self.hmac_key, + hmac_digest=self.hmac_digest + ) + self.port = udp_receiver.port + return udp_receiver + + +class UDPRadioSender(MonitoringRadioSender): + + def __init__(self, address: str, port: int, hmac_key: bytes, hmac_digest: str, *, timeout: int = 10) -> None: + self.sock_timeout = timeout + self.address = address + self.port = port + self.hmac_key = hmac_key + self.hmac_digest = hmac_digest + + self.sock = socket.socket(socket.AF_INET, + socket.SOCK_DGRAM, + socket.IPPROTO_UDP) # UDP + self.sock.settimeout(self.sock_timeout) + + def send(self, message: object) -> None: + """ Sends a message to the UDP receiver + + Parameter + --------- + + message: object + Arbitrary pickle-able object that is to be sent + + Returns: + None + """ + logger.info("Starting UDP radio message send") + try: + data = pickle.dumps(message) + origin_hmac = hmac.digest(self.hmac_key, data, self.hmac_digest) + buffer = origin_hmac + data + except Exception: + logging.exception("Exception during pickling", exc_info=True) + return + + try: + self.sock.sendto(buffer, (self.address, self.port)) + except socket.timeout: + logging.error("Could not send message within timeout limit") + return + logger.info("Normal ending for UDP radio message send") + return diff --git a/parsl/parsl/monitoring/radios/udp_router.py b/parsl/parsl/monitoring/radios/udp_router.py new file mode 100644 index 00000000..edfac5ee --- /dev/null +++ b/parsl/parsl/monitoring/radios/udp_router.py @@ -0,0 +1,233 @@ +from __future__ import annotations + +import hmac +import logging +import multiprocessing.queues as mpq +import os +import pickle +import queue +import socket +import time +from multiprocessing.context import SpawnProcess as SpawnProcessType +from multiprocessing.queues import Queue +from multiprocessing.synchronize import Event +from multiprocessing.synchronize import Event as EventType +from typing import Optional, Union + +import typeguard + +from parsl.log_utils import set_file_logger +from parsl.monitoring.errors import MonitoringRouterStartError +from parsl.monitoring.radios.base import MonitoringRadioReceiver +from parsl.monitoring.radios.multiprocessing import MultiprocessingQueueRadioSender +from parsl.multiprocessing import ( + SpawnEvent, + SpawnProcess, + SpawnQueue, + join_terminate_close_proc, +) +from parsl.process_loggers import wrap_with_logs +from parsl.utils import setproctitle + +logger = logging.getLogger(__name__) + + +class MonitoringRouter: + + def __init__(self, + *, + udp_port: Optional[int] = None, + run_dir: str = ".", + logging_level: int = logging.INFO, + atexit_timeout: int, # in seconds + resource_msgs: mpq.Queue, + exit_event: Event, + hmac_key: bytes, + hmac_digest: str, + ): + """ Initializes a monitoring configuration class. + + Parameters + ---------- + udp_port : int + The specific port at which workers will be able to reach the Hub via UDP. Default: None + run_dir : str + Parsl log directory paths. Logs and temp files go here. Default: '.' + logging_level : int + Logging level as defined in the logging module. Default: logging.INFO + atexit_timeout : float, optional + The amount of time in seconds to terminate the hub without receiving any messages, after the last dfk workflow message is received. + resource_msgs : multiprocessing.Queue + A multiprocessing queue to receive messages to be routed onwards to the database process + exit_event : Event + An event that the main Parsl process will set to signal that the monitoring router should shut down. + """ + os.makedirs(run_dir, exist_ok=True) + set_file_logger(f"{run_dir}/monitoring_udp_router.log", + level=logging_level) + logger.debug("Monitoring router starting") + + self.atexit_timeout = atexit_timeout + + self.hmac_key = hmac_key + self.hmac_digest = hmac_digest + + self.loop_freq = 10.0 # milliseconds + + # Initialize the UDP socket + self.udp_sock = socket.socket(socket.AF_INET, + socket.SOCK_DGRAM, + socket.IPPROTO_UDP) + + # We are trying to bind to all interfaces with 0.0.0.0 + if not udp_port: + self.udp_sock.bind(('0.0.0.0', 0)) + self.udp_port = self.udp_sock.getsockname()[1] + else: + self.udp_port = udp_port + try: + self.udp_sock.bind(('0.0.0.0', self.udp_port)) + except Exception as e: + raise RuntimeError(f"Could not bind to udp_port {udp_port} because: {e}") + self.udp_sock.settimeout(self.loop_freq / 1000) + logger.info("Initialized the UDP socket on 0.0.0.0:{}".format(self.udp_port)) + + self.target_radio = MultiprocessingQueueRadioSender(resource_msgs) + self.exit_event = exit_event + + @wrap_with_logs + def start(self) -> None: + logger.info("Starting UDP listener") + try: + while not self.exit_event.is_set(): + try: + self.process_message() + except socket.timeout: + pass + + logger.info("UDP listener draining") + last_msg_received_time = time.time() + while time.time() - last_msg_received_time < self.atexit_timeout: + try: + self.process_message() + last_msg_received_time = time.time() + except socket.timeout: + pass + + logger.info("UDP listener finishing normally") + finally: + logger.info("UDP listener finished") + + def process_message(self) -> None: + hmdata, addr = self.udp_sock.recvfrom(2048) + h = hmac.HMAC(key=self.hmac_key, digestmod=self.hmac_digest) + origin_hmac = hmdata[0:h.digest_size] + h.update(hmdata[h.digest_size:]) + data = hmdata[h.digest_size:] + + # Check hmac before pickle load. + # If data is wrong, do not log it because it is suspect, + # but it should be safe to log the addr, at error level. + + recomputed_hmac = h.digest() + + if not hmac.compare_digest(origin_hmac, recomputed_hmac): + logger.error("HMAC does not match on received message") + # No exception, because this can be arbitrary network noise + # that shouldn't break the receiver. + else: + resource_msg = pickle.loads(data) + logger.debug("Got UDP Message from {}: {}".format(addr, resource_msg)) + self.target_radio.send(resource_msg) + + +@wrap_with_logs +@typeguard.typechecked +def udp_router_starter(*, + comm_q: mpq.Queue, + resource_msgs: mpq.Queue, + exit_event: Event, + + udp_port: Optional[int], + hmac_key: bytes, + run_dir: str, + logging_level: int, + atexit_timeout: int, + hmac_digest: str) -> None: + setproctitle("parsl: monitoring UDP router") + try: + router = MonitoringRouter(udp_port=udp_port, + run_dir=run_dir, + logging_level=logging_level, + resource_msgs=resource_msgs, + exit_event=exit_event, + atexit_timeout=atexit_timeout, + hmac_key=hmac_key, + hmac_digest=hmac_digest) + except Exception as e: + logger.error("MonitoringRouter construction failed.", exc_info=True) + comm_q.put(f"Monitoring router construction failed: {e}") + else: + comm_q.put(router.udp_port) + + logger.info("Starting MonitoringRouter in router_starter") + try: + router.start() + except Exception: + logger.exception("UDP router start exception") + + +class UDPRadioReceiver(MonitoringRadioReceiver): + def __init__(self, *, process: SpawnProcessType, exit_event: EventType, port: int) -> None: + self.process = process + self.exit_event = exit_event + self.port = port + + def shutdown(self) -> None: + self.exit_event.set() + join_terminate_close_proc(self.process) + + +def start_udp_receiver(*, + monitoring_messages: Queue, + port: Optional[int], + logdir: str, + debug: bool, + atexit_timeout: int, + hmac_key: bytes, + hmac_digest: str) -> UDPRadioReceiver: + + udp_comm_q: Queue[Union[int, str]] + udp_comm_q = SpawnQueue(maxsize=10) + + router_exit_event = SpawnEvent() + + router_proc = SpawnProcess(target=udp_router_starter, + kwargs={"comm_q": udp_comm_q, + "resource_msgs": monitoring_messages, + "exit_event": router_exit_event, + "udp_port": port, + "run_dir": logdir, + "logging_level": logging.DEBUG if debug else logging.INFO, + "atexit_timeout": atexit_timeout, + "hmac_key": hmac_key, + "hmac_digest": hmac_digest, + }, + name="Monitoring-UDP-Router-Process", + daemon=True, + ) + router_proc.start() + + try: + udp_comm_q_result = udp_comm_q.get(block=True, timeout=120) + udp_comm_q.close() + udp_comm_q.join_thread() + except queue.Empty: + logger.error("Monitoring UDP router has not reported port in 120s. Aborting") + raise MonitoringRouterStartError() + + if isinstance(udp_comm_q_result, str): + logger.error("MonitoringRouter sent an error message: %s", udp_comm_q_result) + raise RuntimeError(f"MonitoringRouter failed to start: {udp_comm_q_result}") + + return UDPRadioReceiver(process=router_proc, exit_event=router_exit_event, port=udp_comm_q_result) diff --git a/parsl/parsl/monitoring/radios/zmq.py b/parsl/parsl/monitoring/radios/zmq.py new file mode 100644 index 00000000..cb3bf94e --- /dev/null +++ b/parsl/parsl/monitoring/radios/zmq.py @@ -0,0 +1,20 @@ +from typing import Optional + +import zmq + +import parsl.curvezmq as curvezmq +from parsl.monitoring.radios.base import MonitoringRadioSender + + +class ZMQRadioSender(MonitoringRadioSender): + """A monitoring radio which connects over ZMQ. This radio is not + thread-safe, because its use of ZMQ is not thread-safe. + """ + + def __init__(self, hub_address: str, hub_zmq_port: int, cert_dir: Optional[str]) -> None: + self._hub_channel = curvezmq.ClientContext(cert_dir=cert_dir).socket(zmq.DEALER) + self._hub_channel.set_hwm(0) + self._hub_channel.connect(f"tcp://{hub_address}:{hub_zmq_port}") + + def send(self, message: object) -> None: + self._hub_channel.send_pyobj(message) diff --git a/parsl/parsl/monitoring/radios/zmq_router.py b/parsl/parsl/monitoring/radios/zmq_router.py new file mode 100644 index 00000000..0774f896 --- /dev/null +++ b/parsl/parsl/monitoring/radios/zmq_router.py @@ -0,0 +1,198 @@ +from __future__ import annotations + +import logging +import multiprocessing.queues as mpq +import os +import queue +import time +from multiprocessing.context import SpawnProcess as SpawnProcessType +from multiprocessing.queues import Queue as QueueType +from multiprocessing.synchronize import Event as EventType +from typing import Optional, Tuple + +import typeguard +import zmq + +import parsl.curvezmq as curvezmq +from parsl.addresses import tcp_url +from parsl.log_utils import set_file_logger +from parsl.monitoring.errors import MonitoringRouterStartError +from parsl.monitoring.radios.multiprocessing import MultiprocessingQueueRadioSender +from parsl.monitoring.types import TaggedMonitoringMessage +from parsl.multiprocessing import ( + SpawnEvent, + SpawnProcess, + SpawnQueue, + join_terminate_close_proc, +) +from parsl.process_loggers import wrap_with_logs +from parsl.utils import setproctitle + +logger = logging.getLogger(__name__) + + +class MonitoringRouter: + + def __init__(self, + *, + address: str, + port_range: Tuple[int, int] = (55050, 56000), + + run_dir: str = ".", + logging_level: int = logging.INFO, + resource_msgs: mpq.Queue, + exit_event: EventType, + cert_dir: Optional[str], + ): + """ Initializes a monitoring configuration class. + + Parameters + ---------- + address : str + The ip address at which the workers will be able to reach the Hub. + port_range : tuple(int, int) + The MonitoringHub picks ports at random from the range which will be used by Hub. + Default: (55050, 56000) + run_dir : str + Parsl log directory paths. Logs and temp files go here. Default: '.' + logging_level : int + Logging level as defined in the logging module. Default: logging.INFO + resource_msgs : multiprocessing.Queue + A multiprocessing queue to receive messages to be routed onwards to the database process + exit_event : Event + An event that the main Parsl process will set to signal that the monitoring router should shut down. + """ + os.makedirs(run_dir, exist_ok=True) + set_file_logger(f"{run_dir}/monitoring_zmq_router.log", + level=logging_level) + logger.debug("Monitoring router starting") + + self.address = address + + self.loop_freq = 10.0 # milliseconds + + self._context = curvezmq.ServerContext(cert_dir=cert_dir) + self.zmq_receiver_channel = self._context.socket(zmq.DEALER) + self.zmq_receiver_channel.setsockopt(zmq.LINGER, 0) + self.zmq_receiver_channel.set_hwm(0) + self.zmq_receiver_channel.RCVTIMEO = int(self.loop_freq) # in milliseconds + logger.debug("address: {}. port_range {}".format(address, port_range)) + self.zmq_receiver_port = self.zmq_receiver_channel.bind_to_random_port(tcp_url(address), + min_port=port_range[0], + max_port=port_range[1]) + + self.target_radio = MultiprocessingQueueRadioSender(resource_msgs) + self.exit_event = exit_event + + @wrap_with_logs + def start(self) -> None: + logger.info("Starting ZMQ listener") + try: + while not self.exit_event.is_set(): + try: + dfk_loop_start = time.time() + while time.time() - dfk_loop_start < 1.0: # TODO make configurable + # note that nothing checks that msg really is of the annotated type + msg: TaggedMonitoringMessage + msg = self.zmq_receiver_channel.recv_pyobj() + + assert isinstance(msg, tuple), "ZMQ Receiver expects only tuples, got {}".format(msg) + assert len(msg) >= 1, "ZMQ Receiver expects tuples of length at least 1, got {}".format(msg) + assert len(msg) == 2, "ZMQ Receiver expects message tuples of exactly length 2, got {}".format(msg) + + self.target_radio.send(msg) + except zmq.Again: + pass + except Exception: + # This will catch malformed messages. What happens if the + # channel is broken in such a way that it always raises + # an exception? Looping on this would maybe be the wrong + # thing to do. + logger.warning("Failure processing a ZMQ message", exc_info=True) + + logger.info("ZMQ listener finishing normally") + finally: + logger.info("ZMQ listener finished") + + +@wrap_with_logs +@typeguard.typechecked +def zmq_router_starter(*, + comm_q: mpq.Queue, + resource_msgs: mpq.Queue, + exit_event: EventType, + + address: str, + port_range: Tuple[int, int], + + run_dir: str, + logging_level: int, + cert_dir: Optional[str]) -> None: + setproctitle("parsl: monitoring zmq router") + try: + router = MonitoringRouter(address=address, + port_range=port_range, + run_dir=run_dir, + logging_level=logging_level, + resource_msgs=resource_msgs, + exit_event=exit_event, + cert_dir=cert_dir) + except Exception as e: + logger.error("MonitoringRouter construction failed.", exc_info=True) + comm_q.put(f"Monitoring router construction failed: {e}") + else: + comm_q.put(router.zmq_receiver_port) + router.start() + + +class ZMQRadioReceiver(): + def __init__(self, *, process: SpawnProcessType, exit_event: EventType, port: int) -> None: + self.process = process + self.exit_event = exit_event + self.port = port + + def close(self) -> None: + self.exit_event.set() + join_terminate_close_proc(self.process) + + +def start_zmq_receiver(*, + monitoring_messages: QueueType, + loopback_address: str, + port_range: Tuple[int, int], + logdir: str, + worker_debug: bool, + cert_dir: Optional[str]) -> ZMQRadioReceiver: + comm_q = SpawnQueue(maxsize=10) + + router_exit_event = SpawnEvent() + + router_proc = SpawnProcess(target=zmq_router_starter, + kwargs={"comm_q": comm_q, + "resource_msgs": monitoring_messages, + "exit_event": router_exit_event, + "address": loopback_address, + "port_range": port_range, + "run_dir": logdir, + "logging_level": logging.DEBUG if worker_debug else logging.INFO, + "cert_dir": cert_dir, + }, + name="Monitoring-ZMQ-Router-Process", + daemon=True, + ) + router_proc.start() + + try: + logger.debug("Waiting for router process to report port") + comm_q_result = comm_q.get(block=True, timeout=120) + comm_q.close() + comm_q.join_thread() + except queue.Empty: + logger.error("Monitoring ZMQ Router has not reported port in 120s") + raise MonitoringRouterStartError() + + if isinstance(comm_q_result, str): + logger.error("MonitoringRouter sent an error message: %s", comm_q_result) + raise RuntimeError(f"MonitoringRouter failed to start: {comm_q_result}") + + return ZMQRadioReceiver(process=router_proc, exit_event=router_exit_event, port=comm_q_result) diff --git a/parsl/parsl/monitoring/remote.py b/parsl/parsl/monitoring/remote.py new file mode 100644 index 00000000..121b60ea --- /dev/null +++ b/parsl/parsl/monitoring/remote.py @@ -0,0 +1,292 @@ +import datetime +import logging +import os +import time +from functools import wraps +from multiprocessing import Event +from typing import Any, Callable, Dict, List, Sequence, Tuple + +from parsl.monitoring.message_type import MessageType +from parsl.monitoring.radios.base import RadioConfig +from parsl.multiprocessing import ForkProcess +from parsl.process_loggers import wrap_with_logs + +logger = logging.getLogger(__name__) + + +def monitor_wrapper(*, + f: Any, # per app + args: Sequence, # per invocation + kwargs: Dict, # per invocation + x_try_id: int, # per invocation + x_task_id: int, # per invocation + radio_config: RadioConfig, # per executor + run_id: str, # per workflow + logging_level: int, # per workflow + sleep_dur: float, # per workflow + monitor_resources: bool, # per workflow + run_dir: str) -> Tuple[Callable, Sequence, Dict]: + """Wrap the Parsl app with a function that will call the monitor function and point it at the correct pid when the task begins. + """ + + @wraps(f) + def wrapped(*args: List[Any], **kwargs: Dict[str, Any]) -> Any: + task_id = kwargs.pop('_parsl_monitoring_task_id') + try_id = kwargs.pop('_parsl_monitoring_try_id') + terminate_event = Event() + # Send first message to monitoring router + send_first_message(try_id, + task_id, + radio_config, + run_id, + run_dir) + + if monitor_resources and sleep_dur > 0: + # create the monitor process and start + pp = ForkProcess(target=monitor, + args=(os.getpid(), + try_id, + task_id, + radio_config, + run_id, + logging_level, + sleep_dur, + run_dir, + terminate_event), + daemon=True, + name="Monitor-Wrapper-{}".format(task_id)) + pp.start() + p = pp + # TODO: awkwardness because ForkProcess is not directly a constructor + # and type-checking is expecting p to be optional and cannot + # narrow down the type of p in this block. + + else: + p = None + + try: + return f(*args, **kwargs) + finally: + # There's a chance of zombification if the workers are killed by some signals (?) + if p: + terminate_event.set() + p.join(30) # 30 second delay for this -- this timeout will be hit in the case of an unusually long end-of-loop + if p.exitcode is None: + logger.warn("Event-based termination of monitoring helper took too long. Using process-based termination.") + p.terminate() + # DANGER: this can corrupt shared queues according to docs. + # So, better that the above termination event worked. + # This is why this log message is a warning + p.join() + + send_last_message(try_id, + task_id, + radio_config, + run_id, + run_dir) + + new_kwargs = kwargs.copy() + new_kwargs['_parsl_monitoring_task_id'] = x_task_id + new_kwargs['_parsl_monitoring_try_id'] = x_try_id + + return (wrapped, args, new_kwargs) + + +@wrap_with_logs +def send_first_message(try_id: int, + task_id: int, + radio_config: RadioConfig, + run_id: str, run_dir: str) -> None: + send_first_last_message(try_id, task_id, radio_config, run_id, + run_dir, False) + + +@wrap_with_logs +def send_last_message(try_id: int, + task_id: int, + radio_config: RadioConfig, + run_id: str, run_dir: str) -> None: + send_first_last_message(try_id, task_id, radio_config, run_id, + run_dir, True) + + +def send_first_last_message(try_id: int, + task_id: int, + radio_config: RadioConfig, + run_id: str, run_dir: str, + is_last: bool) -> None: + import os + import platform + + radio = radio_config.create_sender() + + msg = (MessageType.WORKER_TASK_INFO, + {'run_id': run_id, + 'try_id': try_id, + 'task_id': task_id, + 'hostname': platform.node(), + 'block_id': os.environ.get('PARSL_WORKER_BLOCK_ID'), + 'first_msg': not is_last, + 'last_msg': is_last, + 'timestamp': datetime.datetime.now() + }) + radio.send(msg) + return + + +@wrap_with_logs +def monitor(pid: int, + try_id: int, + task_id: int, + radio_config: RadioConfig, + run_id: str, + logging_level: int, + sleep_dur: float, + run_dir: str, + # removed all defaults because unused and there's no meaningful default for terminate_event. + # these probably should become named arguments, with a *, and named at invocation. + terminate_event: Any) -> None: # cannot be Event because of multiprocessing type weirdness. + """Monitors the Parsl task's resources by pointing psutil to the task's pid and watching it and its children. + + This process makes calls to logging, but deliberately does not attach + any log handlers. Previously, there was a handler which logged to a + file in /tmp, but this was usually not useful or even accessible. + In some circumstances, it might be useful to hack in a handler so the + logger calls remain in place. + """ + import logging + import platform + + import psutil + + from parsl.utils import setproctitle + + setproctitle("parsl: task resource monitor") + + radio = radio_config.create_sender() + + logging.debug("start of monitor") + + # these values are simple to log. Other information is available in special formats such as memory below. + simple = ["cpu_num", 'create_time', 'cwd', 'exe', 'memory_percent', 'nice', 'name', 'num_threads', 'pid', 'ppid', 'status', 'username'] + # values that can be summed up to see total resources used by task process and its children + summable_values = ['memory_percent', 'num_threads'] + + pm = psutil.Process(pid) + + children_user_time: Dict[int, float] = {} + children_system_time: Dict[int, float] = {} + children_num_ctx_switches_voluntary: Dict[int, float] = {} + children_num_ctx_switches_involuntary: Dict[int, float] = {} + + def accumulate_and_prepare() -> Dict[str, Any]: + d = {"psutil_process_" + str(k): v for k, v in pm.as_dict().items() if k in simple} + d["run_id"] = run_id + d["task_id"] = task_id + d["try_id"] = try_id + d['resource_monitoring_interval'] = sleep_dur + d['hostname'] = platform.node() + d['timestamp'] = datetime.datetime.now() + + logging.debug("getting children") + children = pm.children(recursive=True) + logging.debug("got children") + + d["psutil_cpu_count"] = psutil.cpu_count() + + # note that this will be the CPU number of the base process, not anything launched by it + d["psutil_cpu_num"] = pm.cpu_num() + + pctxsw = pm.num_ctx_switches() + + d["psutil_process_num_ctx_switches_voluntary"] = pctxsw.voluntary + d["psutil_process_num_ctx_switches_involuntary"] = pctxsw.involuntary + + d['psutil_process_memory_virtual'] = pm.memory_info().vms + d['psutil_process_memory_resident'] = pm.memory_info().rss + d['psutil_process_time_user'] = pm.cpu_times().user + d['psutil_process_time_system'] = pm.cpu_times().system + d['psutil_process_children_count'] = len(children) + try: + d['psutil_process_disk_write'] = pm.io_counters().write_chars + d['psutil_process_disk_read'] = pm.io_counters().read_chars + except Exception: + # occasionally pid temp files that hold this information are unvailable to be read so set to zero + logging.exception("Exception reading IO counters for main process. Recorded IO usage may be incomplete", exc_info=True) + d['psutil_process_disk_write'] = 0 + d['psutil_process_disk_read'] = 0 + for child in children: + for k, v in child.as_dict(attrs=summable_values).items(): + d['psutil_process_' + str(k)] += v + child_user_time = child.cpu_times().user + child_system_time = child.cpu_times().system + children_user_time[child.pid] = child_user_time + children_system_time[child.pid] = child_system_time + + pctxsw = child.num_ctx_switches() + children_num_ctx_switches_voluntary[child.pid] = pctxsw.voluntary + children_num_ctx_switches_involuntary[child.pid] = pctxsw.involuntary + + d['psutil_process_memory_virtual'] += child.memory_info().vms + d['psutil_process_memory_resident'] += child.memory_info().rss + try: + d['psutil_process_disk_write'] += child.io_counters().write_chars + d['psutil_process_disk_read'] += child.io_counters().read_chars + except Exception: + # occassionally pid temp files that hold this information are unvailable to be read so add zero + logging.exception("Exception reading IO counters for child {k}. Recorded IO usage may be incomplete".format(k=k), exc_info=True) + d['psutil_process_disk_write'] += 0 + d['psutil_process_disk_read'] += 0 + + total_children_user_time = 0.0 + for child_pid in children_user_time: + total_children_user_time += children_user_time[child_pid] + + total_children_system_time = 0.0 + for child_pid in children_system_time: + total_children_system_time += children_system_time[child_pid] + + total_children_num_ctx_switches_voluntary = 0.0 + for child_pid in children_num_ctx_switches_voluntary: + total_children_num_ctx_switches_voluntary += children_num_ctx_switches_voluntary[child_pid] + + total_children_num_ctx_switches_involuntary = 0.0 + for child_pid in children_num_ctx_switches_involuntary: + total_children_num_ctx_switches_involuntary += children_num_ctx_switches_involuntary[child_pid] + + d['psutil_process_time_user'] += total_children_user_time + d['psutil_process_time_system'] += total_children_system_time + d['psutil_process_num_ctx_switches_voluntary'] += total_children_num_ctx_switches_voluntary + d['psutil_process_num_ctx_switches_involuntary'] += total_children_num_ctx_switches_involuntary + logging.debug("sending message") + return d + + next_send = time.time() + accumulate_dur = 5.0 # TODO: make configurable? + + while not terminate_event.is_set() and pm.is_running(): + logging.debug("start of monitoring loop") + try: + d = accumulate_and_prepare() + if time.time() >= next_send: + logging.debug("Sending intermediate resource message") + radio.send((MessageType.RESOURCE_INFO, d)) + next_send += sleep_dur + except Exception: + logging.exception("Exception getting the resource usage. Not sending usage to Hub", exc_info=True) + logging.debug("sleeping") + + # wait either until approx next send time, or the accumulation period + # so the accumulation period will not be completely precise. + # but before this, the sleep period was also not completely precise. + # with a minimum floor of 0 to not upset wait + + terminate_event.wait(max(0, min(next_send - time.time(), accumulate_dur))) + + logging.debug("Sending final resource message") + try: + d = accumulate_and_prepare() + radio.send((MessageType.RESOURCE_INFO, d)) + except Exception: + logging.exception("Exception getting the resource usage. Not sending final usage to Hub", exc_info=True) + logging.debug("End of monitoring helper") diff --git a/parsl/parsl/monitoring/types.py b/parsl/parsl/monitoring/types.py new file mode 100644 index 00000000..662d1421 --- /dev/null +++ b/parsl/parsl/monitoring/types.py @@ -0,0 +1,11 @@ +from typing import Any, Dict, Tuple + +from typing_extensions import TypeAlias + +from parsl.monitoring.message_type import MessageType + +# A MonitoringMessage dictionary can be tagged, giving a +# TaggedMonitoringMessage. + +MonitoringMessage: TypeAlias = Dict[str, Any] +TaggedMonitoringMessage: TypeAlias = Tuple[MessageType, MonitoringMessage] diff --git a/parsl/parsl/monitoring/visualization/__init__.py b/parsl/parsl/monitoring/visualization/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/monitoring/visualization/app.py b/parsl/parsl/monitoring/visualization/app.py new file mode 100644 index 00000000..e0c9510e --- /dev/null +++ b/parsl/parsl/monitoring/visualization/app.py @@ -0,0 +1,36 @@ +import argparse +import os + +from flask import Flask + +from parsl.monitoring.visualization.models import db + + +def cli_run(): + """ Instantiates the Monitoring viz server + """ + parser = argparse.ArgumentParser(description='Parsl visualization tool') + parser.add_argument('db_path', type=str, default="sqlite:///{cwd}/runinfo/monitoring.db".format(cwd=os.getcwd()), + nargs="?", help='Database path in the format sqlite:///') + parser.add_argument('-p', '--port', type=int, default=8080, + help='Port at which the monitoring Viz Server is hosted. Default: 8080') + parser.add_argument("-d", "--debug", action='store_true', + help="Enable debug logging") + parser.add_argument("-l", "--listen", type=str, default="127.0.0.1", metavar="ADDRESS", + help="Choose address to listen for connections on. Default: 127.0.0.1. Choose 0.0.0.0 to listen on all addresses.") + args = parser.parse_args() + + app = Flask(__name__) + app.config['SQLALCHEMY_DATABASE_URI'] = args.db_path + app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False + db.init_app(app) + + with app.app_context(): + db.create_all() + from parsl.monitoring.visualization import views + views.dummy = False + app.run(host=args.listen, port=args.port, debug=args.debug) + + +if __name__ == "__main__": + cli_run() diff --git a/parsl/parsl/monitoring/visualization/models.py b/parsl/parsl/monitoring/visualization/models.py new file mode 100644 index 00000000..dd39c1d9 --- /dev/null +++ b/parsl/parsl/monitoring/visualization/models.py @@ -0,0 +1,112 @@ +from flask_sqlalchemy import SQLAlchemy + +WORKFLOW = 'workflow' # Workflow table includes workflow metadata +TASK = 'task' # Task table includes task metadata +STATUS = 'status' # Status table includes task status +RESOURCE = 'resource' # Resource table includes task resource utilization +NODE = 'node' # Node table include node info + +db = SQLAlchemy() + + +class Workflow(db.Model): + __tablename__ = WORKFLOW + run_id = db.Column(db.Text, nullable=False, primary_key=True) + workflow_name = db.Column(db.Text, nullable=True) + workflow_version = db.Column(db.Text, nullable=True) + time_began = db.Column(db.DateTime, nullable=False) # Why not date? + time_completed = db.Column(db.DateTime) + host = db.Column(db.Text, nullable=False) + user = db.Column(db.Text, nullable=False) + rundir = db.Column(db.Text, nullable=False) + tasks_failed_count = db.Column(db.Integer, nullable=False) + tasks_completed_count = db.Column(db.Integer, nullable=False) + + +class Node(db.Model): + __tablename__ = NODE + id = db.Column('id', db.Integer, nullable=False, primary_key=True, autoincrement=True) + run_id = db.Column('run_id', db.Text, nullable=False) + hostname = db.Column('hostname', db.Text, nullable=False) + cpu_count = db.Column('cpu_count', db.Integer, nullable=False) + total_memory = db.Column('total_memory', db.Integer, nullable=False) + active = db.Column('active', db.Boolean, nullable=False) + worker_count = db.Column('worker_count', db.Integer, nullable=False) + python_v = db.Column('python_v', db.Text, nullable=False) + reg_time = db.Column('reg_time', db.DateTime, nullable=False) + + +# TODO: expand to full set of info +class Status(db.Model): + __tablename__ = STATUS + task_id = db.Column(db.Integer, db.ForeignKey( + 'task.task_id'), nullable=False) + task_status_name = db.Column(db.Text, nullable=False) + timestamp = db.Column(db.DateTime, nullable=False) + run_id = db.Column(db.Text, db.ForeignKey( + 'workflow.run_id'), nullable=False) + __table_args__ = ( + db.PrimaryKeyConstraint('task_id', 'run_id', + 'task_status_name', 'timestamp'), + ) + + +class Task(db.Model): + __tablename__ = TASK + task_id = db.Column('task_id', db.Integer, nullable=False) + run_id = db.Column('run_id', db.Text, nullable=False) + task_func_name = db.Column('task_func_name', db.Text, nullable=False) + task_depends = db.Column('task_depends', db.Text, nullable=True) + task_time_invoked = db.Column( + 'task_time_invoked', db.DateTime, nullable=True) + task_time_returned = db.Column( + 'task_time_returned', db.DateTime, nullable=True) + task_memoize = db.Column('task_memoize', db.Text, nullable=False) + task_inputs = db.Column('task_inputs', db.Text, nullable=True) + task_outputs = db.Column('task_outputs', db.Text, nullable=True) + task_stdin = db.Column('task_stdin', db.Text, nullable=True) + task_stdout = db.Column('task_stdout', db.Text, nullable=True) + task_stderr = db.Column('task_stderr', db.Text, nullable=True) + __table_args__ = ( + db.PrimaryKeyConstraint('task_id', 'run_id'), + ) + + +class Resource(db.Model): + __tablename__ = RESOURCE + task_id = db.Column('task_id', db.Integer, db.ForeignKey( + 'task.task_id'), nullable=False) + timestamp = db.Column('timestamp', db.DateTime, nullable=False) + run_id = db.Column('run_id', db.Text, db.ForeignKey( + 'workflow.run_id'), nullable=False) + resource_monitoring_interval = db.Column( + 'resource_monitoring_interval', db.Float, nullable=True) + psutil_process_pid = db.Column( + 'psutil_process_pid', db.Integer, nullable=True) + psutil_process_memory_percent = db.Column( + 'psutil_process_memory_percent', db.Float, nullable=True) + psutil_process_children_count = db.Column( + 'psutil_process_children_count', db.Float, nullable=True) + psutil_process_time_user = db.Column( + 'psutil_process_time_user', db.Float, nullable=True) + psutil_process_time_system = db.Column( + 'psutil_process_time_system', db.Float, nullable=True) + psutil_process_memory_virtual = db.Column( + 'psutil_process_memory_virtual', db.Float, nullable=True) + psutil_process_memory_resident = db.Column( + 'psutil_process_memory_resident', db.Float, nullable=True) + psutil_process_disk_read = db.Column( + 'psutil_process_disk_read', db.Float, nullable=True) + psutil_process_disk_write = db.Column( + 'psutil_process_disk_write', db.Float, nullable=True) + psutil_process_status = db.Column( + 'psutil_process_status', db.Text, nullable=True) + psutil_cpu_num = db.Column( + 'psutil_cpu_num', db.Text, nullable=True) + psutil_process_num_ctx_switches_voluntary = db.Column( + 'psutil_process_num_ctx_switches_voluntary', db.Float, nullable=True) + psutil_process_num_ctx_switches_involuntary = db.Column( + 'psutil_process_num_ctx_switches_involuntary', db.Float, nullable=True) + + __table_args__ = ( + db.PrimaryKeyConstraint('task_id', 'run_id', 'timestamp'),) diff --git a/parsl/parsl/monitoring/visualization/plots/__init__.py b/parsl/parsl/monitoring/visualization/plots/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/monitoring/visualization/plots/default/__init__.py b/parsl/parsl/monitoring/visualization/plots/default/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/monitoring/visualization/plots/default/task_plots.py b/parsl/parsl/monitoring/visualization/plots/default/task_plots.py new file mode 100644 index 00000000..9885859a --- /dev/null +++ b/parsl/parsl/monitoring/visualization/plots/default/task_plots.py @@ -0,0 +1,33 @@ +import plotly.graph_objs as go +from plotly.offline import plot + + +def time_series_cpu_per_task_plot(df_resources, resource_type, label): + yaxis = dict(title='Accumulated CPU user time (seconds)') + fig = go.Figure(data=[go.Scatter(x=df_resources['timestamp'], + y=df_resources[resource_type])], + layout=go.Layout(xaxis=dict(tickformat='%m-%d\n%H:%M:%S', + autorange=True, + title='Time'), + yaxis=yaxis, + title=label)) + + return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) + + +def time_series_memory_per_task_plot(df_resources, resource_type, label): + if resource_type == "psutil_process_memory_percent": + yaxis = dict(title="Memory utilization") + data = [go.Scatter(x=df_resources['timestamp'], + y=df_resources[resource_type])] + else: + yaxis = dict(title='Memory usage (GB)') + data = [go.Scatter(x=df_resources['timestamp'], + y=[num / 1000000000 for num in df_resources[resource_type].astype(float)])] + fig = go.Figure(data=data, + layout=go.Layout(xaxis=dict(tickformat='%m-%d\n%H:%M:%S', + autorange=True, + title='Time'), + yaxis=yaxis, + title=label)) + return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) diff --git a/parsl/parsl/monitoring/visualization/plots/default/workflow_plots.py b/parsl/parsl/monitoring/visualization/plots/default/workflow_plots.py new file mode 100644 index 00000000..7c972374 --- /dev/null +++ b/parsl/parsl/monitoring/visualization/plots/default/workflow_plots.py @@ -0,0 +1,294 @@ +import math + +import networkx as nx +import numpy as np +import pandas as pd +import plotly.figure_factory as ff +import plotly.graph_objs as go +from plotly.offline import plot + +from parsl.dataflow.states import States +from parsl.monitoring.visualization.utils import ( + DB_DATE_FORMAT, + num_to_timestamp, + timestamp_to_int, +) + +gantt_colors = { + "pending": 'rgb(168, 168, 168)', + "launched": 'rgb(100, 255, 255)', + "running": 'rgb(0, 0, 255)', + "dep_fail": 'rgb(255, 128, 255)', + "failed": 'rgb(200, 0, 0)', + "exec_done": 'rgb(0, 200, 0)', + "memo_done": 'rgb(64, 200, 64)', + "fail_retryable": 'rgb(200, 128, 128)', + "joining": 'rgb(128, 128, 255)', + "running_ended": 'rgb(64, 64, 255)', +} +dag_state_colors = {} +for enum_i, (state, color) in enumerate(gantt_colors.items()): + dag_state_colors[state] = (enum_i, color) + +assert len(States) == len(gantt_colors), "must assign color for every state" +assert all(s.name in gantt_colors for s in States), "must assign color for every state" + + +def task_gantt_plot(df_task, df_status, time_completed=None): + + if df_task.empty: + return None + + # if the workflow is not recorded as completed, then assume + # that tasks should continue in their last state until now, + # rather than the workflow end time. + if not time_completed: + time_completed = df_status['timestamp'].max() + + df_task = df_task.sort_values(by=['task_id'], ascending=False) + + parsl_tasks = [] + for i, task in df_task.iterrows(): + task_id = task['task_id'] + + description = "Task ID: {}, app: {}".format(task['task_id'], task['task_func_name']) + + statuses = df_status.loc[df_status['task_id'] == task_id].sort_values(by=['timestamp']) + + last_status = None + for j, status in statuses.iterrows(): + if last_status is not None: + last_status_bar = {'Task': description, + 'Start': last_status['timestamp'], + 'Finish': status['timestamp'], + 'Resource': last_status['task_status_name'] + } + parsl_tasks.extend([last_status_bar]) + last_status = status + + # TODO: factor with above? + if last_status is not None: + last_status_bar = {'Task': description, + 'Start': last_status['timestamp'], + 'Finish': time_completed, + 'Resource': last_status['task_status_name'] + } + parsl_tasks.extend([last_status_bar]) + + fig = ff.create_gantt(parsl_tasks, + title="", + colors=gantt_colors, + group_tasks=True, + show_colorbar=True, + index_col='Resource', + ) + fig['layout']['yaxis']['title'] = 'Task' + fig['layout']['yaxis']['showticklabels'] = False + fig['layout']['xaxis']['title'] = 'Time' + return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) + + +def task_per_app_plot(task, status, time_completed): + + try: + task['epoch_time_running'] = (pd.to_datetime( + task['task_try_time_running']) - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') + task['epoch_time_returned'] = (pd.to_datetime( + task['task_time_returned']) - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') + start = int(task['epoch_time_running'].min()) + + end = int(task['epoch_time_returned'].max()) + + tasks_per_app = {} + all_tasks = [0] * (end - start + 1) + for i, row in task.iterrows(): + if math.isnan(row['epoch_time_running']): + # Skip rows with no running start time. + continue + if math.isnan(row['epoch_time_returned']): + time_returned = end + else: + time_returned = int(row['epoch_time_returned']) + + if row['task_func_name'] not in tasks_per_app: + tasks_per_app[row['task_func_name']] = [0] * (end - start + 1) + for j in range(int(row['epoch_time_running']) + 1, time_returned + 1): + tasks_per_app[row['task_func_name']][j - start] += 1 + all_tasks[j - start] += 1 + fig = go.Figure( + data=[go.Scatter(x=list(range(0, end - start + 1)), + y=all_tasks, + name='All', + )] + + [go.Scatter(x=list(range(0, end - start + 1)), + y=tasks_per_app[app], + name=app, + ) for app in tasks_per_app], + + layout=go.Layout(xaxis=dict(autorange=True, + title='Time (seconds)'), + yaxis=dict(title='Number of tasks'), + title="Execution tries per app")) + return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) + except Exception as e: + return "The tasks per app plot cannot be generated because of exception {}.".format(e) + + +def total_tasks_plot(df_task, df_status, columns=20): + + min_time = timestamp_to_int(min(df_status['timestamp'])) + max_time = timestamp_to_int(max(df_status['timestamp'])) + time_step = (max_time - min_time) / columns + + x_axis = [] + for i in np.arange(min_time, max_time + time_step, time_step): + x_axis.append(num_to_timestamp(i).strftime(DB_DATE_FORMAT)) + + # Fill up dict "apps" like: {app1: [#task1, #task2], app2: [#task4], app3: [#task3]} + apps_dict = dict() + for i in range(len(df_task)): + row = df_task.iloc[i] + if row['task_func_name'] in apps_dict: + apps_dict[row['task_func_name']].append(row['task_id']) + else: + apps_dict[row['task_func_name']] = [row['task_id']] + + def y_axis_setup(value): + items = [] + for app, tasks in apps_dict.items(): + tmp = [] + task = df_status[df_status['task_id'].isin(tasks)] + for i in range(len(x_axis) - 1): + x = task['timestamp'] >= x_axis[i] + y = task['timestamp'] < x_axis[i + 1] + tmp.append(sum(task.loc[x & y]['task_status_name'] == value)) + items = np.sum([items, tmp], axis=0) + + return items + + y_axis_done = y_axis_setup('done') + y_axis_failed = y_axis_setup('failed') + + fig = go.Figure(data=[go.Bar(x=x_axis[:-1], + y=y_axis_done, + name='done'), + go.Bar(x=x_axis[:-1], + y=y_axis_failed, + name='failed')], + layout=go.Layout(xaxis=dict(tickformat='%m-%d\n%H:%M:%S', + autorange=True, + title='Time'), + yaxis=dict(tickformat=',d', + title='Running tasks.' ' Bin width: ' + num_to_timestamp(time_step).strftime('%Mm%Ss')), + annotations=[ + dict( + x=0, + y=1.07, + showarrow=False, + text='Total Done: ' + + str(sum(y_axis_done)), + xref='paper', + yref='paper' + ), + dict( + x=0, + y=1.05, + showarrow=False, + text='Total Failed: ' + + str(sum(y_axis_failed)), + xref='paper', + yref='paper' + ), + ], + barmode='stack', + title="Total tasks")) + + return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) + + +def workflow_dag_plot(df_tasks, group_by_apps=True): + G = nx.DiGraph(directed=True) + nodes = df_tasks['task_id'].unique() + dic = df_tasks.set_index('task_id').to_dict() + G.add_nodes_from(nodes) + + # Add edges or links between the nodes: + edges = [] + for k, v in dic['task_depends'].items(): + if v: + adj = v.split(",") + for e in adj: + edges.append((int(e), k)) + G.add_edges_from(edges) + + node_positions = nx.nx_pydot.pydot_layout(G, prog='dot') + + if group_by_apps: + groups_list = {app: (i, None) for i, app in enumerate( + df_tasks['task_func_name'].unique())} + else: + groups_list = dag_state_colors + + node_traces = [...] * len(groups_list) + + for k, (index, color) in groups_list.items(): + node_trace = go.Scatter( + x=[], + y=[], + text=[], + mode='markers', + textposition='top center', + textfont=dict( + family='arial', + size=18, + color='rgb(0,0,0)' + ), + hoverinfo='text', + name=k, # legend app_name here + marker=dict( + showscale=False, + color=color, + size=11, + line=dict(width=1, color='rgb(0,0,0)'))) + node_traces[index] = node_trace + + for node in node_positions: + x, y = node_positions[node] + if group_by_apps: + name = dic['task_func_name'][node] + else: + name = dic['task_status_name'][node] + index, _ = groups_list[name] + node_traces[index]['x'] += tuple([x]) + node_traces[index]['y'] += tuple([y]) + node_traces[index]['text'] += tuple( + ["{}:{}".format(dic['task_func_name'][node], node)]) + + # The edges will be drawn as lines: + edge_trace = go.Scatter( + x=[], + y=[], + line=dict(width=1, color='rgb(160,160,160)'), + hoverinfo='none', + # showlegend=False, + name='Dependency', + mode='lines') + + for edge in G.edges: + x0, y0 = node_positions[edge[0]] + x1, y1 = node_positions[edge[1]] + edge_trace['x'] += tuple([x0, x1, None]) + edge_trace['y'] += tuple([y0, y1, None]) + + # Create figure: + title = go.layout.Title(text='Workflow DAG', font=dict(size=16)) + fig = go.Figure(data=[edge_trace] + node_traces, + layout=go.Layout( + title=title, + showlegend=True, + hovermode='closest', + margin=dict(b=20, l=5, r=5, t=40), # noqa: E741 + xaxis=dict(showgrid=False, zeroline=False, + showticklabels=False), + yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))) + return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) diff --git a/parsl/parsl/monitoring/visualization/plots/default/workflow_resource_plots.py b/parsl/parsl/monitoring/visualization/plots/default/workflow_resource_plots.py new file mode 100644 index 00000000..c7cd776d --- /dev/null +++ b/parsl/parsl/monitoring/visualization/plots/default/workflow_resource_plots.py @@ -0,0 +1,242 @@ +import math + +import numpy as np +import pandas as pd +import plotly.graph_objs as go +from plotly.offline import plot + + +def resource_distribution_plot(df_resources, df_task, type, label, option, columns=20,): + assert type == "psutil_process_time_user" or type == "psutil_process_memory_resident" + assert option == "avg" or option == "max" + + min_range = min(df_resources[type].astype('float')) + max_range = max(df_resources[type].astype('float')) + time_step = (max_range - min_range) / columns + + if min_range == max_range: + x_axis = [min_range] + else: + x_axis = [] + for i in np.arange(min_range, max_range + time_step, time_step): + x_axis.append(i) + + tasks_dict = dict() + for i in range(len(df_task)): + row = df_task.iloc[i] + tasks_dict[row['task_id']] = [] + + def y_axis_setup(): + items = [0] * len(x_axis) + + for task_id, tasks in tasks_dict.items(): + if option == 'avg': + task = df_resources[df_resources['task_id'] == + task_id][type].astype('float').mean() + elif option == 'max': + task = df_resources[df_resources['task_id'] == task_id][type].astype('float').max() + else: + raise ValueError(f"Cannot plot unknown aggregation option {option}") + + for i in range(len(x_axis) - 1): + a = task >= x_axis[i] + b = task < x_axis[i + 1] + if a and b: + items[i] += 1 + break + if task >= x_axis[-1]: + items[-1] += 1 + return items + + if type == "psutil_process_time_user": + xaxis = dict(autorange=True, + title='CPU user time (seconds)') + elif type == "psutil_process_memory_resident": + xaxis = dict(autorange=True, + title='Memory usage (bytes)') + else: + raise ValueError(f"Cannot plot unknown type {type}") + + fig = go.Figure( + data=[go.Bar(x=x_axis, + y=y_axis_setup(), + name='tasks')], + layout=go.Layout(xaxis=xaxis, + yaxis=dict(title='Tasks'), + title=label + '(' + option + ')')) + + return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) + + +def resource_time_series(tasks, type='psutil_process_time_user', label='CPU user time'): + tasks['epoch_time'] = (pd.to_datetime( + tasks['timestamp']) - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') + step = int(tasks['resource_monitoring_interval'][0]) + start = tasks['epoch_time'].min() + end = tasks['epoch_time'].max() + tasks['relative_time'] = tasks['epoch_time'] - start + if end != start: + bins = pd.cut(tasks['relative_time'], + range(0, end - start + 1, step), + include_lowest=True) + df = tasks.groupby(bins, as_index=False)[type].mean() + df['time'] = step * df.index + fig = go.Figure( + data=[go.Scatter(x=df['time'], + y=df[type], + )], + layout=go.Layout(xaxis=dict(autorange=True, + title='Time (seconds)'), + yaxis=dict(title=label), + title=label)) + else: + fig = go.Figure( + data=[go.Scatter(x=[0], + y=[tasks[type].mean()], + )], + layout=go.Layout(xaxis=dict(autorange=True, + title='Time (seconds)'), + yaxis=dict(title=label), + title=label)) + return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) + + +def worker_efficiency(task, node): + try: + node['epoch_time'] = (pd.to_datetime( + node['timestamp']) - pd.Timestamp("1970-01-01")) / pd.Timedelta('1s') + task['epoch_time_start'] = (pd.to_datetime( + task['task_try_time_launched']) - pd.Timestamp("1970-01-01")) / pd.Timedelta('1s') + task['epoch_time_running'] = (pd.to_datetime( + task['task_try_time_running']) - pd.Timestamp("1970-01-01")) / pd.Timedelta('1s') + task['epoch_time_returned'] = (pd.to_datetime( + task['task_try_time_returned']) - pd.Timestamp("1970-01-01")) / pd.Timedelta('1s') + start = int(min(task['epoch_time_start'].min(), node['epoch_time'].min())) + end = int(task['epoch_time_returned'].max()) + + # worker_plot will be used to make a histogram of worker usage, one bucket per + # second + worker_plot = [0] * (end - start + 1) + total_workers = node['worker_count'].sum() + + for i, row in task.iterrows(): + if math.isnan(row['epoch_time_running']): + # skip tasks with no running start time + continue + if math.isnan(row['epoch_time_returned']): + # there is no end time for this, so we should assume the "end" time + etr = end + else: + etr = row['epoch_time_returned'] + + # there's a two cases here: + # The task starts and ends in same one-second bucket: + # populate one bucket with the fraction of second between + # the start and end + # + # or + # + # The task starts in one bucket and ends in another: + # populate middle buckets with 1, and start/end buckets with fraction + + start_bucket = int(row['epoch_time_running']) - start + end_bucket = int(etr) - start + + if start_bucket == end_bucket: + fraction = etr - row['epoch_time_running'] + worker_plot[start_bucket] += fraction + + else: + fraction_before = row['epoch_time_running'] - int(row['epoch_time_running']) + worker_plot[start_bucket] += (1 - fraction_before) + + for j in range(start_bucket + 1, end_bucket): + worker_plot[j] += 1 + + fraction_after = etr - int(etr) + worker_plot[end_bucket] += fraction_after + + fig = go.Figure( + data=[go.Scatter(x=list(range(0, end - start + 1)), + y=worker_plot, + name='Total busy workers', + ), + go.Scatter(x=list(range(0, end - start + 1)), + y=[total_workers] * (end - start + 1), + name='Total of workers in whole run', + ) + ], + layout=go.Layout(xaxis=dict(autorange=True, + title='Time (seconds)'), + yaxis=dict(title='Number of workers'), + title="Worker efficiency")) + return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) + except Exception as e: + print(e) + return "The worker efficiency plot cannot be generated due to missing data." + + +def resource_efficiency(resource, node, label): + try: + resource['epoch_time'] = (pd.to_datetime( + resource['timestamp']) - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') + node['epoch_time'] = (pd.to_datetime( + node['timestamp']) - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') + resource = resource.sort_values(by='epoch_time') + start = min(resource['epoch_time'].min(), node['epoch_time'].min()) + end = resource['epoch_time'].max() + resource['relative_time'] = resource['epoch_time'] - start + node['relative_time'] = node['epoch_time'] - start + resource['key'] = resource['task_id'].astype(str) + "-" + resource['try_id'].astype(str) + + task_plot = [0] * (end - start + 1) + if label == 'CPU': + total = node['cpu_count'].sum() + elif label == 'mem': + total = node['total_memory'].sum() / 1024 / 1024 / 1024 + + resource['total_cpu_time'] = resource['psutil_process_time_user'] + resource['psutil_process_time_system'] + for key in resource['key'].unique(): + tmp = resource[resource['key'] == key] + tmp['last_timestamp'] = tmp['relative_time'].shift(1) + if label == 'CPU': + tmp['last_cputime'] = tmp['total_cpu_time'].shift(1) + for index, row in tmp.iterrows(): + if np.isnan(row['last_timestamp']): + continue + for i in range(int(row['last_timestamp']), int(row['relative_time'])): + if label == 'CPU': + diff = (row['total_cpu_time'] - row['last_cputime']) / (row['relative_time'] - row['last_timestamp']) + elif label == 'mem': + diff = row['psutil_process_memory_resident'] / 1024 / 1024 / 1024 + task_plot[i] += diff + + if label == 'CPU': + name1 = 'Used CPU cores' + name2 = 'Total CPU cores' + yaxis = 'Number of CPU cores' + title = 'CPU usage' + elif label == 'mem': + name1 = 'Used memory' + name2 = 'Total memory' + yaxis = 'Memory (GB)' + title = 'Memory usage' + + fig = go.Figure( + data=[go.Scatter(x=list(range(0, end - start + 1)), + y=task_plot, + name=name1, + ), + go.Scatter(x=list(range(0, end - start + 1)), + y=[total] * (end - start + 1), + name=name2, + ) + ], + layout=go.Layout(xaxis=dict(autorange=True, + title='Time (seconds)'), + yaxis=dict(title=yaxis), + title=title)) + return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) + except Exception as e: + print(e) + return "The resource efficiency 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+.task{ + border: 2px solid; + font-size: 10px; + padding: 5px; + margin-right: 3px; +} + +.task-complete{ + border-radius: 50%!important; + border-color: #006600; +} + +.task-running{ + border-radius: 50%!important; + border: 3px dashed; + border-color: #66ff66; +} + +.task-failed{ + border-color: red; +} \ No newline at end of file diff --git a/parsl/parsl/monitoring/visualization/templates/app.html b/parsl/parsl/monitoring/visualization/templates/app.html new file mode 100644 index 00000000..ab63b9b4 --- /dev/null +++ b/parsl/parsl/monitoring/visualization/templates/app.html @@ -0,0 +1,53 @@ +{% extends "layout.html" %} + +{% block content %} + +
    +

    {{ app_name }}

    +
    + + +     + + + + + + + + + + + + + + {% for t in task_summary %} + + + + + + + {% endfor %} + +
    NameTask IDDependenciesCompleted
    {{ t.task_func_name }}{{ t['task_id'] }} + {% if t['task_depends'] %} + {% for id in t['task_depends'].split(",") %} + {{ id }} + {% endfor %} + {% else %} + None + {% endif %} + {{ t['task_time_returned'] | timeformat }}
    + +{% endblock %} diff --git a/parsl/parsl/monitoring/visualization/templates/dag.html b/parsl/parsl/monitoring/visualization/templates/dag.html new file mode 100644 index 00000000..3fdc4b4a --- /dev/null +++ b/parsl/parsl/monitoring/visualization/templates/dag.html @@ -0,0 +1,34 @@ +{% extends "layout.html" %} + +{% block content %} + +
    +

    {{ workflow_details['workflow_name'] }}

    + +
    + +
      +
    • Started: {{ workflow_details['time_began'] | timeformat }}
      • +
    • Completed: {{ workflow_details['time_completed'] | timeformat }}
      • +
    • Workflow duration: {{ (workflow_details['time_began'], workflow_details['time_completed']) | durationformat }}
      • +
    • Owner: {{ workflow_details['user'] }}
      • +
    • Host: {{ workflow_details['host'] }}
      • +
    • Run directory: {{ workflow_details['rundir'] }}
      • +
    • Number of completed tasks: {{ workflow_details['tasks_completed_count'] }}
      • +
    • Number of failed tasks: {{ workflow_details['tasks_failed_count'] }}
      • +
    + +{% if not group_by_apps %} +     View workflow DAG -- colored by apps +{% else %} + View workflow DAG -- colored by task states +{% endif%} +
    View workflow task summary + +
    +
    + {{ workflow_dag_plot | safe }} +
    +
    + +{% endblock %} diff --git a/parsl/parsl/monitoring/visualization/templates/error.html b/parsl/parsl/monitoring/visualization/templates/error.html new file mode 100644 index 00000000..fbf54c39 --- /dev/null +++ b/parsl/parsl/monitoring/visualization/templates/error.html @@ -0,0 +1,9 @@ +{% extends "layout.html" %} + +{% block content %} + +
    +

    {{ message }}

    + + +{% endblock %} diff --git a/parsl/parsl/monitoring/visualization/templates/layout.html b/parsl/parsl/monitoring/visualization/templates/layout.html new file mode 100644 index 00000000..0b25df42 --- /dev/null +++ b/parsl/parsl/monitoring/visualization/templates/layout.html @@ -0,0 +1,54 @@ + + + + + + + + + Parsl Monitoring Interface + + + + + + + + + + + + + + + + + + + +
    + + {% block content %}{% endblock %} + + +
    + + + + diff --git a/parsl/parsl/monitoring/visualization/templates/resource_usage.html b/parsl/parsl/monitoring/visualization/templates/resource_usage.html new file mode 100644 index 00000000..fb97cb9f --- /dev/null +++ b/parsl/parsl/monitoring/visualization/templates/resource_usage.html @@ -0,0 +1,62 @@ +{% extends "layout.html" %} + +{% block content %} + +
    +

    {{ workflow_details['workflow_name'] }}

    + +
    + +
      +
    • Started: {{ workflow_details['time_began'] | timeformat }}
      • +
    • Completed: {{ workflow_details['time_completed'] | timeformat }}
      • +
    • Workflow duration: {{ (workflow_details['time_began'], workflow_details['time_completed']) | durationformat }}
      • +
    • Owner: {{ workflow_details['user'] }}
      • +
    • Host: {{ workflow_details['host'] }}
      • +
    • Run directory: {{ workflow_details['rundir'] }}
      • +
    • Number of completed tasks: {{ workflow_details['tasks_completed_count'] }}
      • +
    • Number of failed tasks: {{ workflow_details['tasks_failed_count'] }}
      • +
    + +View workflow DAG -- colored by apps +
    View workflow DAG -- colored by task states +
    View workflow task summary + + +
    Worker usage
    +
    +{{ worker_efficiency|safe }} +
    + +
    CPU usage
    +
    +{{ cpu_efficiency|safe }} +
    + +
    CPU Usage
    +
    +
    +
    +{{ user_time_distribution_max_plot|safe }} +
    +
    +
    + +
    Memory usage
    +
    +{{ memory_efficiency|safe }} +
    + +
    Memory Usage
    +
    +
    +
    +{{ memory_usage_distribution_avg_plot|safe }} +
    +
    +{{ memory_usage_distribution_max_plot|safe }} +
    +
    +
    + +{% endblock %} diff --git a/parsl/parsl/monitoring/visualization/templates/task.html b/parsl/parsl/monitoring/visualization/templates/task.html new file mode 100644 index 00000000..3e763401 --- /dev/null +++ b/parsl/parsl/monitoring/visualization/templates/task.html @@ -0,0 +1,80 @@ +{% extends "layout.html" %} + +{% block content %} + +
    +

    {{ task_details['task_func_name'] }} ({{ task_details['task_id'] }})

    +
    +
    +
    +
    + +
    +
    +
    Task State
    + + + + + + + + + + {% for t in task_status %} + + + + + {% endfor %} + +
    TimeState
    {{ t.timestamp | timeformat }}{{ t.task_status_name }}
    +
    +
    +
    + +
    +
    +
    +{{ time_series_memory_resident|safe }} +
    +
    +
    + + +{% endblock %} diff --git a/parsl/parsl/monitoring/visualization/templates/workflow.html b/parsl/parsl/monitoring/visualization/templates/workflow.html new file mode 100644 index 00000000..d8b78a18 --- /dev/null +++ b/parsl/parsl/monitoring/visualization/templates/workflow.html @@ -0,0 +1,55 @@ +{% extends "layout.html" %} + +{% block content %} + +
    +

    {{ workflow_details['workflow_name'] }}

    +
    + +
    +
    +
    +
    Workflow Summary
    + +
      +
    • Started: {{ workflow_details['time_began'] | timeformat}}
      • +
    • Completed: {{ workflow_details['time_completed'] | timeformat }}
      • +
    • Workflow duration: {{ (workflow_details['time_began'], workflow_details['time_completed']) | durationformat }}
      • +
    • Owner: {{ workflow_details['user'] }}
      • +
    • Host: {{ workflow_details['host'] }}
      • +
    • Run directory: {{ workflow_details['rundir'] }}
      • +
    • Number of completed tasks: {{ workflow_details['tasks_completed_count'] }}
      • +
    • Number of failed tasks: {{ workflow_details['tasks_failed_count'] }}
      • +
    +
    +
    +
    App Summary
    + + + + + + + + + + {% for (_, t) in task_summary.iterrows() %} + + + + + {% endfor %} + +
    NameCount
    {{ t['task_func_name'] }}{{ t.frequency }}
    +
    +
    +
    + +View workflow DAG -- colored by apps +
    View workflow DAG -- colored by task states +
    View workflow resource usage + +{{ task_gantt | safe }} +{{ task_per_app |safe }} + +{% endblock %} diff --git a/parsl/parsl/monitoring/visualization/templates/workflows_summary.html b/parsl/parsl/monitoring/visualization/templates/workflows_summary.html new file mode 100644 index 00000000..277e2fea --- /dev/null +++ b/parsl/parsl/monitoring/visualization/templates/workflows_summary.html @@ -0,0 +1,37 @@ +{% extends "layout.html" %} + +{% block content %} + +
    +

    Workflows

    + + + + + + + + + + + + + + {% for w in workflows %} + + + + + + + + + {% endfor %} + +
    NameVersionOwnerStatusRuntime (s)Tasks
    {{ w.workflow_name }}{{ w.workflow_version }}{{ w.user }}{{ w.status }}{{ (w.time_began, w.time_completed) | durationformat }} + {{ w['tasks_completed_count'] }} + {{ w['tasks_failed_count'] }} + +
    + +{% endblock %} diff --git a/parsl/parsl/monitoring/visualization/utils.py b/parsl/parsl/monitoring/visualization/utils.py new file mode 100644 index 00000000..a2438c5c --- /dev/null +++ b/parsl/parsl/monitoring/visualization/utils.py @@ -0,0 +1,15 @@ +from datetime import datetime + +DB_DATE_FORMAT = '%Y-%m-%d %H:%M:%S' + + +def timestamp_to_float(time, format=DB_DATE_FORMAT): + return datetime.strptime(time, format).timestamp() + + +def timestamp_to_int(time): + return int(timestamp_to_float(time)) + + +def num_to_timestamp(n): + return datetime.fromtimestamp(n) diff --git a/parsl/parsl/monitoring/visualization/version.py b/parsl/parsl/monitoring/visualization/version.py new file mode 100644 index 00000000..e7800716 --- /dev/null +++ b/parsl/parsl/monitoring/visualization/version.py @@ -0,0 +1,6 @@ +"""Set module version. + +..[alpha/beta/..] +Alphas will be numbered like this -> 0.4.0a0 +""" +VERSION = '0.1' diff --git a/parsl/parsl/monitoring/visualization/views.py b/parsl/parsl/monitoring/visualization/views.py new file mode 100644 index 00000000..02e7b04a --- /dev/null +++ b/parsl/parsl/monitoring/visualization/views.py @@ -0,0 +1,187 @@ +import pandas as pd +import sqlalchemy +from flask import current_app as app +from flask import render_template + +import parsl.monitoring.queries.pandas as queries +from parsl.monitoring.visualization.models import Status, Task, Workflow, db +from parsl.monitoring.visualization.plots.default.task_plots import ( + time_series_memory_per_task_plot, +) +from parsl.monitoring.visualization.plots.default.workflow_plots import ( + task_gantt_plot, + task_per_app_plot, + workflow_dag_plot, +) +from parsl.monitoring.visualization.plots.default.workflow_resource_plots import ( + resource_distribution_plot, + resource_efficiency, + worker_efficiency, +) + +dummy = True + +import datetime + + +def format_time(value): + if value is None: + return value + elif isinstance(value, float): + return str(datetime.timedelta(seconds=round(value))) + elif isinstance(value, datetime.datetime): + return value.replace(microsecond=0) + elif isinstance(value, datetime.timedelta): + rounded_timedelta = datetime.timedelta(days=value.days, seconds=value.seconds) + return rounded_timedelta + else: + return "Incorrect time format found (neither float nor datetime.datetime object)" + + +def format_duration(value): + (start, end) = value + if start and end: + return format_time(end - start) + else: + return "-" + + +app.jinja_env.filters['timeformat'] = format_time +app.jinja_env.filters['durationformat'] = format_duration + + +@app.route('/') +def index(): + workflows = Workflow.query.all() + for workflow in workflows: + workflow.status = 'Running' + if workflow.time_completed is not None: + workflow.status = 'Completed' + return render_template('workflows_summary.html', workflows=workflows) + + +@app.route('/workflow//') +def workflow(workflow_id): + workflow_details = Workflow.query.filter_by(run_id=workflow_id).first() + + if workflow_details is None: + return render_template('error.html', message="Workflow %s could not be found" % workflow_id) + + df_status = queries.status_for_workflow(workflow_id, db.engine) + df_task = queries.completion_times_for_workflow(workflow_id, db.engine) + df_task_tries = queries.tries_for_workflow(workflow_id, db.engine) + task_summary = queries.app_counts_for_workflow(workflow_id, db.engine) + + return render_template('workflow.html', + workflow_details=workflow_details, + task_summary=task_summary, + task_gantt=task_gantt_plot(df_task, df_status, time_completed=workflow_details.time_completed), + task_per_app=task_per_app_plot(df_task_tries, df_status, time_completed=workflow_details.time_completed)) + + +@app.route('/workflow//app/') +def parsl_app(workflow_id, app_name): + workflow_details = Workflow.query.filter_by(run_id=workflow_id).first() + + if workflow_details is None: + return render_template('error.html', message="Workflow %s could not be found" % workflow_id) + + task_summary = Task.query.filter_by( + run_id=workflow_id, task_func_name=app_name) + return render_template('app.html', + app_name=app_name, + workflow_details=workflow_details, + task_summary=task_summary) + + +@app.route('/workflow//task/') +def parsl_apps(workflow_id): + workflow_details = Workflow.query.filter_by(run_id=workflow_id).first() + + if workflow_details is None: + return render_template('error.html', message="Workflow %s could not be found" % workflow_id) + + task_summary = Task.query.filter_by(run_id=workflow_id) + return render_template('app.html', + app_name="All Apps", + workflow_details=workflow_details, + task_summary=task_summary) + + +@app.route('/workflow//task/') +def task(workflow_id, task_id): + workflow_details = Workflow.query.filter_by(run_id=workflow_id).first() + + if workflow_details is None: + return render_template('error.html', message="Workflow %s could not be found" % workflow_id) + + task_details = Task.query.filter_by( + run_id=workflow_id, task_id=task_id).first() + task_status = Status.query.filter_by( + run_id=workflow_id, task_id=task_id).order_by(Status.timestamp) + + df_resources = queries.resources_for_task(workflow_id, task_id, db.engine) + + return render_template('task.html', + workflow_details=workflow_details, + task_details=task_details, + task_status=task_status, + time_series_memory_resident=time_series_memory_per_task_plot( + df_resources, 'psutil_process_memory_resident', 'Memory Usage'), + ) + + +@app.route('/workflow//dag_') +def workflow_dag_details(workflow_id, path): + assert path == "group_by_apps" or path == "group_by_states" + + workflow_details = Workflow.query.filter_by(run_id=workflow_id).first() + query = """SELECT task.task_id, task.task_func_name, task.task_depends, status.task_status_name + FROM task LEFT JOIN status + ON task.task_id = status.task_id + AND task.run_id = status.run_id + AND status.timestamp = (SELECT MAX(status.timestamp) + FROM status + WHERE status.task_id = task.task_id and status.run_id = task.run_id + ) + WHERE task.run_id=:run_id""" + + df_tasks = pd.read_sql_query(sqlalchemy.text(query), db.engine, params={"run_id": workflow_id}) + + group_by_apps = (path == "group_by_apps") + return render_template('dag.html', + workflow_details=workflow_details, + group_by_apps=group_by_apps, + workflow_dag_plot=workflow_dag_plot(df_tasks, group_by_apps=group_by_apps)) + + +@app.route('/workflow//resource_usage') +def workflow_resources(workflow_id): + workflow_details = Workflow.query.filter_by(run_id=workflow_id).first() + if workflow_details is None: + return render_template('error.html', message="Workflow %s could not be found" % workflow_id) + + df_resources = queries.resources_for_workflow(workflow_id, db.engine) + if df_resources.empty: + return render_template('error.html', + message="Workflow %s does not have any resource usage records." % workflow_id) + + df_task = queries.tasks_for_workflow(workflow_id, db.engine) + + query = """SELECT task.task_id, task_func_name, + task_try_time_launched, task_try_time_running, task_try_time_returned from task, try + WHERE task.task_id = try.task_id AND task.run_id=:run_id and try.run_id=task.run_id""" + df_task_tries = pd.read_sql_query(query, db.engine, params={"run_id": workflow_id}) + df_node = queries.nodes_for_workflow(workflow_id, db.engine) + + return render_template('resource_usage.html', workflow_details=workflow_details, + user_time_distribution_max_plot=resource_distribution_plot( + df_resources, df_task, type='psutil_process_time_user', label='CPU Time Distribution', option='max'), + memory_usage_distribution_avg_plot=resource_distribution_plot( + df_resources, df_task, type='psutil_process_memory_resident', label='Memory Distribution', option='avg'), + memory_usage_distribution_max_plot=resource_distribution_plot( + df_resources, df_task, type='psutil_process_memory_resident', label='Memory Distribution', option='max'), + cpu_efficiency=resource_efficiency(df_resources, df_node, label='CPU'), + memory_efficiency=resource_efficiency(df_resources, df_node, label='mem'), + worker_efficiency=worker_efficiency(df_task_tries, df_node), + ) diff --git a/parsl/parsl/multiprocessing.py b/parsl/parsl/multiprocessing.py new file mode 100644 index 00000000..6f3ec7fc --- /dev/null +++ b/parsl/parsl/multiprocessing.py @@ -0,0 +1,57 @@ +"""Helpers for cross-platform multiprocessing support. +""" + +import logging +import multiprocessing +import multiprocessing.queues +from multiprocessing.context import SpawnProcess as SpawnProcessType +import platform +from typing import Callable + +logger = logging.getLogger(__name__) + +if platform.system() != 'Windows': + from multiprocessing.context import ForkProcess as ForkProcessType + ForkContext = multiprocessing.get_context("fork") + ForkProcess: Callable[..., ForkProcessType] = ForkContext.Process + +SpawnContext = multiprocessing.get_context("spawn") +SpawnProcess: Callable[..., SpawnProcessType] = SpawnContext.Process + +SpawnEvent = SpawnContext.Event +SpawnQueue = SpawnContext.Queue + + +def join_terminate_close_proc(process: SpawnProcessType, *, timeout: int = 30) -> None: + """Increasingly aggressively terminate a process. + + This function assumes that the process is likely to exit before + the join timeout, driven by some other means, such as the + MonitoringHub router_exit_event. If the process does not exit, then + first terminate() and then kill() will be used to end the process. + + In the case of a very mis-behaving process, this function might take + up to 3*timeout to exhaust all termination methods and return. + """ + logger.debug("Joining process") + process.join(timeout) + + # run a sequence of increasingly aggressive steps to shut down the process. + if process.is_alive(): + logger.error("Process did not join. Terminating.") + process.terminate() + process.join(timeout) + if process.is_alive(): + logger.error("Process did not join after terminate. Killing.") + process.kill() + process.join(timeout) + # This kill should not be caught by any signal handlers so it is + # unlikely that this join will timeout. If it does, there isn't + # anything further to do except log an error in the next if-block. + + if process.is_alive(): + logger.error("Process failed to end") + # don't call close if the process hasn't ended: + # process.close() doesn't work on a running process. + else: + process.close() diff --git a/parsl/parsl/process_loggers.py b/parsl/parsl/process_loggers.py new file mode 100644 index 00000000..a7352de7 --- /dev/null +++ b/parsl/parsl/process_loggers.py @@ -0,0 +1,38 @@ +import functools +import logging +import threading +from typing import Callable, Optional + + +def wrap_with_logs(fn: Optional[Callable] = None, target: str = __name__) -> Callable: + """Calls the supplied function, and logs whether that + function raised an exception or terminated normally. + + This is intended to be used around the top level functions of + processes and threads, where exceptions would normally not + go to a log. + """ + + def decorator(func): + + @functools.wraps(func) + def wrapped(*args, **kwargs): + assert func is not None + thread = threading.current_thread() + name = f"{func.__name__} on thread {thread.name}" + logger = logging.getLogger(target) + + try: + r = func(*args, **kwargs) + logger.debug("Normal ending for {}".format(name)) + return r + except Exception: + logger.error("Exceptional ending for {}".format(name), exc_info=True) + raise + + return wrapped + + if fn is not None: + return decorator(fn) + else: + return decorator diff --git a/parsl/parsl/providers/__init__.py b/parsl/parsl/providers/__init__.py new file mode 100644 index 00000000..6855915b --- /dev/null +++ b/parsl/parsl/providers/__init__.py @@ -0,0 +1,26 @@ +# Cloud Providers +from parsl.providers.aws.aws import AWSProvider +from parsl.providers.azure.azure import AzureProvider +from parsl.providers.condor.condor import CondorProvider +from parsl.providers.googlecloud.googlecloud import GoogleCloudProvider +from parsl.providers.grid_engine.grid_engine import GridEngineProvider + +# Kubernetes +from parsl.providers.kubernetes.kube import KubernetesProvider +from parsl.providers.local.local import LocalProvider +from parsl.providers.lsf.lsf import LSFProvider +from parsl.providers.pbspro.pbspro import PBSProProvider +from parsl.providers.slurm.slurm import SlurmProvider +from parsl.providers.torque.torque import TorqueProvider + +__all__ = ['LocalProvider', + 'CondorProvider', + 'GridEngineProvider', + 'SlurmProvider', + 'TorqueProvider', + 'LSFProvider', + 'PBSProProvider', + 'AWSProvider', + 'GoogleCloudProvider', + 'KubernetesProvider', + 'AzureProvider'] diff --git a/parsl/parsl/providers/aws/__init__.py b/parsl/parsl/providers/aws/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/providers/aws/aws.py b/parsl/parsl/providers/aws/aws.py new file mode 100644 index 00000000..62af6553 --- /dev/null +++ b/parsl/parsl/providers/aws/aws.py @@ -0,0 +1,790 @@ +import json +import logging +import os +import time +from string import Template + +from parsl.errors import ConfigurationError, OptionalModuleMissing +from parsl.jobs.states import JobState, JobStatus +from parsl.launchers import SingleNodeLauncher +from parsl.providers.aws.template import template_string +from parsl.providers.base import ExecutionProvider +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + +try: + import boto3 + from botocore.exceptions import ClientError + +except ImportError: + _boto_enabled = False +else: + _boto_enabled = True + +translate_table = { + 'pending': JobState.PENDING, + 'running': JobState.RUNNING, + 'terminated': JobState.COMPLETED, + 'shutting-down': JobState.COMPLETED, # (configuring), + 'stopping': JobState.COMPLETED, # We shouldn't really see this state + 'stopped': JobState.COMPLETED, # We shouldn't really see this state +} + + +class AWSProvider(ExecutionProvider, RepresentationMixin): + """A provider for using Amazon Elastic Compute Cloud (EC2) resources. + + One of 3 methods are required to authenticate: keyfile, profile or environment + variables. If neither keyfile or profile are set, the following environment + variables must be set: ``AWS_ACCESS_KEY_ID`` (the access key for your AWS account), + ``AWS_SECRET_ACCESS_KEY`` (the secret key for your AWS account), and (optionaly) the + ``AWS_SESSION_TOKEN`` (the session key for your AWS account). + + Parameters + ---------- + image_id : str + Identification of the Amazon Machine Image (AMI). + worker_init : str + String to append to the Userdata script executed in the cloudinit phase of + instance initialization. + walltime : str + Walltime requested per block in HH:MM:SS. + key_file : str + Path to json file that contains 'AWSAccessKeyId' and 'AWSSecretKey'. + nodes_per_block : int + This is always 1 for ec2. Nodes to provision per block. + profile : str + Profile to be used from the standard aws config file ~/.aws/config. + nodes_per_block : int + Nodes to provision per block. Default is 1. + init_blocks : int + Number of blocks to provision at the start of the run. Default is 1. + min_blocks : int + Minimum number of blocks to maintain. Default is 0. + max_blocks : int + Maximum number of blocks to maintain. Default is 10. + instance_type : str + EC2 instance type. Instance types comprise varying combinations of CPU, memory, + storage, and networking capacity For more information on possible instance types, + see `here `_. Default is 't2.small'. + region : str + Amazon Web Service (AWS) region to launch machines. Default is 'us-east-2'. + key_name : str + Name of the AWS private key (.pem file) that is usually generated on the console + to allow SSH access to the EC2 instances. This is mostly used for debugging. + spot_max_bid : float + Maximum bid price (if requesting spot market machines). + iam_instance_profile_arn : str + Launch instance with a specific role. + state_file : str + Path to the state file from a previous run to re-use. + walltime : str + Walltime requested per block in HH:MM:SS. This option is not currently honored by this provider. + launcher : Launcher + Launcher for this provider. With AWS, usually the default + :class:`~parsl.launchers.SingleNodeLauncher` will be appropriate. + linger : Bool + When set to True, the workers will not ``halt``. The user is responsible for shutting + down the node. + """ + + def __init__(self, + image_id, + key_name, + init_blocks=1, + min_blocks=0, + max_blocks=10, + nodes_per_block=1, + parallelism=1, + + worker_init='', + instance_type='t2.small', + region='us-east-2', + spot_max_bid=0, + + key_file=None, + profile=None, + iam_instance_profile_arn='', + + state_file=None, + walltime="01:00:00", + linger=False, + launcher=SingleNodeLauncher()): + if not _boto_enabled: + raise OptionalModuleMissing(['boto3'], "AWS Provider requires the boto3 module.") + + self.image_id = image_id + self._label = 'ec2' + self.init_blocks = init_blocks + self.min_blocks = min_blocks + self.max_blocks = max_blocks + self.nodes_per_block = nodes_per_block + self.max_nodes = max_blocks * nodes_per_block + self.parallelism = parallelism + + self.worker_init = worker_init + self.instance_type = instance_type + self.region = region + self.spot_max_bid = spot_max_bid + + self.key_name = key_name + self.key_file = key_file + self.profile = profile + self.iam_instance_profile_arn = iam_instance_profile_arn + + self.walltime = walltime + self.launcher = launcher + self.linger = linger + self.resources = {} + self.script_dir = None + self.state_file = state_file if state_file is not None else 'awsproviderstate.json' + + env_specified = os.getenv("AWS_ACCESS_KEY_ID") is not None and os.getenv("AWS_SECRET_ACCESS_KEY") is not None + if profile is None and key_file is None and not env_specified: + raise ConfigurationError("Must specify either profile', 'key_file', or " + "'AWS_ACCESS_KEY_ID' and 'AWS_SECRET_ACCESS_KEY' environment variables.") + + try: + self.initialize_boto_client() + except Exception as e: + logger.error("{} failed to initialize.".format(self)) + raise e + + state_file_exists = False + try: + self.read_state_file(self.state_file) + state_file_exists = True + except Exception: + logger.info("No state file found. Cannot load previous options. Creating new infrastructure.") + + if not state_file_exists: + try: + self.create_vpc().id + except Exception as e: + logger.info("Failed to create ec2 infrastructure: {0}".format(e)) + raise + else: + self.write_state_file() + + def initialize_boto_client(self): + """Initialize the boto client.""" + + self.session = self.create_session() + self.client = self.session.client('ec2') + self.ec2 = self.session.resource('ec2') + self.instances = [] + self.instance_states = {} + self.vpc_id = 0 + self.sg_id = 0 + self.sn_ids = [] + + def read_state_file(self, state_file): + """Read the state file, if it exists. + + If this script has been run previously, resource IDs will have been written to a + state file. On starting a run, a state file will be looked for before creating new + infrastructure. Information on VPCs, security groups, and subnets are saved, as + well as running instances and their states. + + AWS has a maximum number of VPCs per region per account, so we do not want to + clutter users' AWS accounts with security groups and VPCs that will be used only + once. + """ + try: + fh = open(state_file, 'r') + state = json.load(fh) + self.vpc_id = state['vpcID'] + self.sg_id = state['sgID'] + self.sn_ids = state['snIDs'] + self.instances = state['instances'] + except Exception as e: + logger.debug("Caught exception while reading state file: {0}".format(e)) + raise e + logger.debug("Done reading state from the local state file.") + + def write_state_file(self): + """Save information that must persist to a file. + + We do not want to create a new VPC and new identical security groups, so we save + information about them in a file between runs. + """ + fh = open(self.state_file, 'w') + state = {} + state['vpcID'] = self.vpc_id + state['sgID'] = self.sg_id + state['snIDs'] = self.sn_ids + state['instances'] = self.instances + state["instanceState"] = self.instance_states + fh.write(json.dumps(state, indent=4)) + + def create_session(self): + """Create a session. + + First we look in self.key_file for a path to a json file with the + credentials. The key file should have 'AWSAccessKeyId' and 'AWSSecretKey'. + + Next we look at self.profile for a profile name and try + to use the Session call to automatically pick up the keys for the profile from + the user default keys file ~/.aws/config. + + Finally, boto3 will look for the keys in environment variables: + AWS_ACCESS_KEY_ID: The access key for your AWS account. + AWS_SECRET_ACCESS_KEY: The secret key for your AWS account. + AWS_SESSION_TOKEN: The session key for your AWS account. + This is only needed when you are using temporary credentials. + The AWS_SECURITY_TOKEN environment variable can also be used, + but is only supported for backwards compatibility purposes. + AWS_SESSION_TOKEN is supported by multiple AWS SDKs besides python. + """ + + session = None + + if self.key_file is not None: + credfile = os.path.expandvars(os.path.expanduser(self.key_file)) + + try: + with open(credfile, 'r') as f: + creds = json.load(f) + except json.JSONDecodeError as e: + logger.error( + "EC2Provider '{}': json decode error in credential file {}".format(self.label, credfile) + ) + raise e + + except Exception as e: + logger.debug( + "EC2Provider '{0}' caught exception while reading credential file: {1}".format( + self.label, credfile + ) + ) + raise e + + logger.debug("EC2Provider '{}': Using credential file to create session".format(self.label)) + session = boto3.session.Session(region_name=self.region, **creds) + elif self.profile is not None: + logger.debug("EC2Provider '{}': Using profile name to create session".format(self.label)) + session = boto3.session.Session( + profile_name=self.profile, region_name=self.region + ) + else: + logger.debug("EC2Provider '{}': Using environment variables to create session".format(self.label)) + session = boto3.session.Session(region_name=self.region) + + return session + + def create_vpc(self): + """Create and configure VPC + + We create a VPC with CIDR 10.0.0.0/16, which provides up to 64,000 instances. + + We attach a subnet for each availability zone within the region specified in the + config. We give each subnet an ip range like 10.0.X.0/20, which is large enough + for approx. 4000 instances. + + Security groups are configured in function security_group. + """ + + vpc_name = self.generate_aws_id() + + try: + tag_spec = self.create_name_tag_spec('vpc', vpc_name) + + # We use a large VPC so that the cluster can get large + vpc = self.ec2.create_vpc( + CidrBlock='10.0.0.0/16', + AmazonProvidedIpv6CidrBlock=False, + TagSpecifications=tag_spec, + ) + except Exception as e: + # This failure will cause a full abort + logger.error("{}\n".format(e)) + raise e + + # Attach internet gateway so that our cluster can + # talk to the outside internet + internet_gateway = self.ec2.create_internet_gateway() + internet_gateway.attach_to_vpc(VpcId=vpc.vpc_id) # Returns None + self.internet_gateway = internet_gateway.id + + # Create and configure route table to allow proper traffic + route_table = self.config_route_table(vpc, internet_gateway) + self.route_table = route_table.id + + # Get all avaliability zones + availability_zones = self.client.describe_availability_zones() + + # go through AZs and set up a subnet per + for num, zone in enumerate(availability_zones['AvailabilityZones']): + if zone['State'] == "available": + zone_name = zone['ZoneName'] + tag_spec = self.create_name_tag_spec('subnet', '{0}.{1}'.format(vpc_name, zone_name)) + + # Create a large subnet (4000 max nodes) + subnet = vpc.create_subnet( + CidrBlock='10.0.{}.0/20'.format(16 * num), + AvailabilityZone=zone_name, + TagSpecifications=tag_spec, + ) + + # Make subnet accessible + subnet.meta.client.modify_subnet_attribute( + SubnetId=subnet.id, MapPublicIpOnLaunch={"Value": True} + ) + + route_table.associate_with_subnet(SubnetId=subnet.id) + self.sn_ids.append(subnet.id) + else: + logger.info("{} unavailable".format(zone['ZoneName'])) + # Security groups + security_group_iterator = vpc.security_groups.all() + for security_group in security_group_iterator: + # this can only be the default security group for this VPC, + # since no other security groups have been created yet + security_group.create_tags( + Tags=[{'Key': 'Name', 'Value': '{0}.default'.format(vpc_name)}], + ) + + self.security_group(vpc, vpc_name) + self.vpc_id = vpc.id + return vpc + + def security_group(self, vpc, name): + """Create and configure a new security group. + + Allows all ICMP in, all TCP and UDP in within VPC. + + This security group is very open. It allows all incoming ping requests on all + ports. It also allows all outgoing traffic on all ports. This can be limited by + changing the allowed port ranges. + + Parameters + ---------- + vpc : VPC instance + VPC in which to set up security group. + name : str + Name tag for the newly created security group. + """ + + tag_spec = self.create_name_tag_spec('security-group', name) + + sg = vpc.create_security_group( + GroupName="private-subnet", + Description="security group for remote executors", + TagSpecifications=tag_spec, + ) + + ip_ranges = [{'CidrIp': '10.0.0.0/16'}] + + # Allows all ICMP in, all TCP and UDP in within VPC + in_permissions = [ + { + 'IpProtocol': 'TCP', + 'FromPort': 0, + 'ToPort': 65535, + 'IpRanges': ip_ranges, + }, { + 'IpProtocol': 'UDP', + 'FromPort': 0, + 'ToPort': 65535, + 'IpRanges': ip_ranges, + }, { + 'IpProtocol': 'ICMP', + 'FromPort': -1, + 'ToPort': -1, + 'IpRanges': [{ + 'CidrIp': '0.0.0.0/0' + }], + }, { + 'IpProtocol': 'TCP', + 'FromPort': 22, + 'ToPort': 22, + 'IpRanges': [{ + 'CidrIp': '0.0.0.0/0' + }], + } + ] + + # Allows all TCP out, all TCP and UDP out within VPC + out_permissions = [ + { + 'IpProtocol': 'TCP', + 'FromPort': 0, + 'ToPort': 65535, + 'IpRanges': [{ + 'CidrIp': '0.0.0.0/0' + }], + }, + { + 'IpProtocol': 'TCP', + 'FromPort': 0, + 'ToPort': 65535, + 'IpRanges': ip_ranges, + }, + { + 'IpProtocol': 'UDP', + 'FromPort': 0, + 'ToPort': 65535, + 'IpRanges': ip_ranges, + }, + ] + + sg.authorize_ingress(IpPermissions=in_permissions) + sg.authorize_egress(IpPermissions=out_permissions) + self.sg_id = sg.id + return sg + + def config_route_table(self, vpc, internet_gateway): + """Configure route table for Virtual Private Cloud (VPC). + + Parameters + ---------- + vpc : dict + Representation of the VPC (created by create_vpc()). + internet_gateway : dict + Representation of the internet gateway (created by create_vpc()). + """ + route_table = vpc.create_route_table() + route_table.create_route( + DestinationCidrBlock='0.0.0.0/0', GatewayId=internet_gateway.internet_gateway_id + ) + return route_table + + def xstr(self, s): + return '' if s is None else s + + def spin_up_instance(self, command, job_name): + """Start an instance in the VPC in the first available subnet. + + N instances will be started if nodes_per_block > 1. + Not supported. We only do 1 node per block. + + Parameters + ---------- + command : str + Command string to execute on the node. + job_name : str + Name associated with the instances. + """ + + command = Template(template_string).substitute(jobname=job_name, + user_script=command, + linger=str(self.linger).lower(), + worker_init=self.worker_init) + instance_type = self.instance_type + ami_id = self.image_id + total_instances = len(self.instances) + + # not all availability zones support all instance types, + # so we need to check which subnets can actually be used given the + # user-provided instance type + + # this finds the availability zones in a region that support + # the user-provided instance type + az_offerings = self.client.describe_instance_type_offerings( + LocationType='availability-zone', + Filters=[ + { + 'Name': 'instance-type', + 'Values': [ + instance_type, + ] + }, + ], + ) + + allowed_azs = [] + for az in az_offerings['InstanceTypeOfferings']: + allowed_azs.append(az['Location']) + + # this is used to retrieve the availability zone of each subnet + subnets = self.client.describe_subnets( + SubnetIds=list(self.sn_ids), + ) + + subnet = None + for sn in subnets['Subnets']: + sn_id = sn['SubnetId'] + az = sn['AvailabilityZone'] + # a subnet has been found with an availability zone + # that is supported for the instance type, so we should use + # this subnet + if az in allowed_azs: + subnet = sn_id + break + + if subnet is None: + logger.error("No subnet in region {0} supports instance type {1}".format(self.region, instance_type)) + return [None] + + if float(self.spot_max_bid) > 0: + spot_options = { + 'MarketType': 'spot', + 'SpotOptions': { + 'MaxPrice': str(self.spot_max_bid), + 'SpotInstanceType': 'one-time', + 'InstanceInterruptionBehavior': 'terminate' + } + } + else: + spot_options = {} + + if total_instances > self.max_nodes: + logger.warning("Exceeded instance limit ({}). Cannot continue\n".format(self.max_nodes)) + return [None] + try: + tag_spec = self.create_name_tag_spec('instance', job_name) + + instance = self.ec2.create_instances( + MinCount=1, + MaxCount=1, + InstanceType=instance_type, + ImageId=ami_id, + KeyName=self.key_name, + SubnetId=subnet, + SecurityGroupIds=[self.sg_id], + TagSpecifications=tag_spec, + InstanceMarketOptions=spot_options, + InstanceInitiatedShutdownBehavior='terminate', + IamInstanceProfile={'Arn': self.iam_instance_profile_arn}, + UserData=command, + ) + except ClientError as e: + print(e) + logger.error(e.response) + return [None] + + except Exception as e: + logger.error("Request for EC2 resources failed : {0}".format(e)) + return [None] + + self.instances.append(instance[0].id) + logger.info( + "Started up 1 instance {}. Instance type: {}".format(instance[0].id, instance_type) + ) + return instance + + def shut_down_instance(self, instances=None): + """Shut down a list of instances, if provided. + + If no instance is provided, the last instance started up will be shut down. + """ + + if instances and len(self.instances) > 0: + print(instances) + try: + print([i.id for i in instances]) + except Exception as e: + print(e) + term = self.client.terminate_instances(InstanceIds=instances) + logger.info("Shut down {} instances (ids:{}".format(len(instances), str(instances))) + elif len(self.instances) > 0: + instance = self.instances.pop() + term = self.client.terminate_instances(InstanceIds=[instance]) + logger.info("Shut down 1 instance (id:{})".format(instance)) + else: + logger.warning("No Instances to shut down.\n") + return -1 + self.get_instance_state() + return term + + def get_instance_state(self, instances=None): + """Get states of all instances on EC2 which were started by this file.""" + if instances: + desc = self.client.describe_instances(InstanceIds=instances) + else: + desc = self.client.describe_instances(InstanceIds=self.instances) + # pprint.pprint(desc['Reservations'],indent=4) + for i in range(len(desc['Reservations'])): + instance = desc['Reservations'][i]['Instances'][0] + self.instance_states[instance['InstanceId']] = instance['State']['Name'] + return self.instance_states + + def status(self, job_ids): + """Get the status of a list of jobs identified by their ids. + + Parameters + ---------- + job_ids : list of str + Identifiers for the jobs. + + Returns + ------- + list of int + The status codes of the requsted jobs. + """ + + all_states = [] + + status = self.client.describe_instances(InstanceIds=list(job_ids)) + for r in status['Reservations']: + for i in r['Instances']: + instance_id = i['InstanceId'] + instance_state = translate_table.get(i['State']['Name'], JobState.UNKNOWN) + instance_status = JobStatus(instance_state) + self.resources[instance_id]['status'] = instance_status + all_states.extend([instance_status]) + + return all_states + + def submit(self, command='sleep 1', tasks_per_node=1, job_name="parsl.aws"): + """Submit the command onto a freshly instantiated AWS EC2 instance. + + Submit returns an ID that corresponds to the task that was just submitted. + + Parameters + ---------- + command : str + Command to be invoked on the remote side. + tasks_per_node : int (default=1) + Number of command invocations to be launched per node + job_name : str + Prefix for the job name. + + Returns + ------- + None or str + If at capacity, None will be returned. Otherwise, the job identifier will be returned. + """ + + job_name = self.generate_aws_id() + wrapped_cmd = self.launcher(command, + tasks_per_node, + self.nodes_per_block, + self.script_dir) + [instance, *rest] = self.spin_up_instance(command=wrapped_cmd, job_name=job_name) + + if not instance: + logger.error("Failed to submit request to EC2") + return None + + logger.debug("Started instance_id: {0}".format(instance.instance_id)) + + state = translate_table.get(instance.state['Name'], JobState.PENDING) + + self.resources[instance.instance_id] = { + "job_id": instance.instance_id, + "instance": instance, + "status": JobStatus(state) + } + + return instance.instance_id + + def cancel(self, job_ids): + """Cancel the jobs specified by a list of job ids. + + Parameters + ---------- + job_ids : list of str + List of of job identifiers + + Returns + ------- + list of bool + Each entry in the list will contain False if the operation fails. Otherwise, the entry will be True. + """ + + if self.linger is True: + logger.debug("Ignoring cancel requests due to linger mode") + return [False for x in job_ids] + + try: + self.client.terminate_instances(InstanceIds=list(job_ids)) + except Exception as e: + logger.error("Caught error while attempting to remove instances: {0}".format(job_ids)) + raise e + else: + logger.debug("Removed the instances: {0}".format(job_ids)) + + for job_id in job_ids: + self.resources[job_id]["status"] = JobStatus(JobState.COMPLETED) + + for job_id in job_ids: + self.instances.remove(job_id) + + return [True for x in job_ids] + + def show_summary(self): + """Print human readable summary of current AWS state to log and to console.""" + self.get_instance_state() + status_string = "EC2 Summary:\n\tVPC IDs: {}\n\tSubnet IDs: \ +{}\n\tSecurity Group ID: {}\n\tRunning Instance IDs: {}\n".format( + self.vpc_id, self.sn_ids, self.sg_id, self.instances + ) + status_string += "\tInstance States:\n\t\t" + self.get_instance_state() + for state in self.instance_states.keys(): + status_string += "Instance ID: {} State: {}\n\t\t".format( + state, self.instance_states[state] + ) + status_string += "\n" + logger.info(status_string) + return status_string + + def teardown(self): + """Teardown the EC2 infastructure. + + Terminate all EC2 instances, delete all subnets, delete security group, delete VPC, + and reset all instance variables. + """ + + self.shut_down_instance(self.instances) + self.instances = [] + try: + self.client.delete_internet_gateway(InternetGatewayId=self.internet_gateway) + self.internet_gateway = None + self.client.delete_route_table(RouteTableId=self.route_table) + self.route_table = None + for subnet in list(self.sn_ids): + # Cast to list ensures that this is a copy + # Which is important because it means that + # the length of the list won't change during iteration + self.client.delete_subnet(SubnetId=subnet) + self.sn_ids.remove(subnet) + self.client.delete_security_group(GroupId=self.sg_id) + self.sg_id = None + self.client.delete_vpc(VpcId=self.vpc_id) + self.vpc_id = None + except Exception as e: + logger.error("{}".format(e)) + raise e + self.show_summary() + os.remove(self.config['state_file_path']) + + def generate_aws_id(self): + """Generate a new ID for AWS resources. + + Returns + ------- + str + An ID of the form 'parsl.aws.123456.789' for giving resources unique identifiers. + """ + return "parsl.aws.{0}".format(time.time()) + + def create_name_tag_spec(self, resource_type, name): + """Create a new tag specification for a resource name. + + Parameters + ---------- + resource_type : str + The AWS resource type + name : str + The name to assign to the resource + + Returns + ------- + record + A TagSpecifications record to be passed into the creation of a new AWS resource. + """ + return [{"ResourceType": resource_type, "Tags": [{'Key': 'Name', 'Value': name}]}] + + @property + def label(self): + return self._label + + def goodbye(self): + self.teardown() + + @property + def status_polling_interval(self): + return 60 diff --git a/parsl/parsl/providers/aws/template.py b/parsl/parsl/providers/aws/template.py new file mode 100644 index 00000000..41332dd0 --- /dev/null +++ b/parsl/parsl/providers/aws/template.py @@ -0,0 +1,17 @@ +template_string = """#!/bin/bash +cd ~ +export DEBIAN_FRONTEND=noninteractive +apt-get update -y +apt-get install -y python3 python3-pip libffi-dev g++ libssl-dev +pip3 install numpy scipy parsl +$worker_init + +$user_script + +# Shutdown the instance as soon as the worker scripts exits +# or times out to avoid EC2 costs. +if ! $linger +then + halt +fi +""" diff --git a/parsl/parsl/providers/azure/__init__.py b/parsl/parsl/providers/azure/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/providers/azure/azure.py b/parsl/parsl/providers/azure/azure.py new file mode 100644 index 00000000..ee4391a5 --- /dev/null +++ b/parsl/parsl/providers/azure/azure.py @@ -0,0 +1,488 @@ +import json +import logging +import os +import time +from string import Template + +from parsl.errors import ConfigurationError, OptionalModuleMissing +from parsl.jobs.states import JobState, JobStatus +from parsl.launchers import SingleNodeLauncher +from parsl.providers.azure.template import template_string +from parsl.providers.base import ExecutionProvider +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + +try: + from azure.common.credentials import ServicePrincipalCredentials + from azure.mgmt.compute import ComputeManagementClient + from azure.mgmt.compute.models import DiskCreateOption + from azure.mgmt.network import NetworkManagementClient + from azure.mgmt.resource import ResourceManagementClient + from msrestazure.azure_exceptions import CloudError + + _api_enabled = True + +except ImportError: + _api_enabled = False +else: + _api_enabled = True + +translate_table = { + 'VM pending': JobState.PENDING, + 'VM running': JobState.RUNNING, + 'VM deallocated': JobState.COMPLETED, + 'VM stopping': JobState.COMPLETED, # We shouldn't really see this state + 'VM stopped': JobState.COMPLETED, # We shouldn't really see this state +} + + +class AzureProvider(ExecutionProvider, RepresentationMixin): + """ + A Provider for using Microsoft Azure Resources + + One of 2 methods are required to authenticate: `key_file`, or environment + variables. If `key_file` is not set, the following environment + variables must be set: `AZURE_CLIENT_ID` (the access key for + your azure account), + `AZURE_CLIENT_SECRET` (the secret key for your azure account), + `AZURE_TENANT_ID` (the session key for your azure account), and + `AZURE_SUBSCRIPTION_ID`. + + The tenant ID is also known as the directory ID here: + in https://portal.azure.com/#blade/Microsoft_AAD_IAM/ActiveDirectoryMenuBlade/Properties + A tenant ID is a GUID. + + A client ID and secret can be created by following these instructions: + https://docs.microsoft.com/en-us/azure/active-directory/develop/howto-create-service-principal-portal + A client ID is a GUID. + + The subscription ID can be found here: + https://portal.azure.com/#blade/Microsoft_Azure_Billing/SubscriptionsBlade + + Parameters + ---------- + vm_reference : dict + Dictionary describing the parameters of the Azure VM. + + Required structure: + { + 'publisher': VM OS publisher + 'offer': VM OS offer + 'sku': VM OS SKU + 'version': VM OS version + 'vm_size': VM Size, analogous to instance type for AWS + 'disk_size_gb': (int) size of VM disk in gb + 'admin_username': (str) admin username of VM instances, + 'password': (str) admin password for VM instances + } + + VM Publisher, author, SKU, and Version can be found in the Azure marketplace. + One way to do so is to use the CLI, as described + [here](https://docs.microsoft.com/en-us/azure/virtual-machines/linux/cli-ps-findimage) + + Another way is to look in the marketplace's GUI, + [here](https://azuremarketplace.microsoft.com/en-us/marketplace/apps?filters=virtual-machine-images%3Blinux&page=1). + + worker_init : str + String to append to the Userdata script executed in the cloudinit phase of + instance initialization. + key_file : str + Path to JSON file that contains 'Azure keys' + The structure of the key file is as follows: + { + "AZURE_CLIENT_ID": (str) azure client id [from account principal], + "AZURE_CLIENT_SECRET":(str) azure client secret [from account principal], + "AZURE_TENANT_ID": (str) azure tenant [account] id, + "AZURE_SUBSCRIPTION_ID": (str) azure subscription id + } + init_blocks : int + Number of blocks to provision at the start of the run. Default is 1. + min_blocks : int + Minimum number of blocks to maintain. Default is 0. + max_blocks : int + Maximum number of blocks to maintain. Default is 10. + region : str + Azure region to launch machines. Default is 'westus'. + key_name : str + Name of the Azure private key (.pem file) that is usually generated on the console + to allow SSH access to the Azure instances. This is mostly used for debugging. + launcher : Launcher + Launcher for this provider. Possible launchers include + :class:`~parsl.launchers.SingleNodeLauncher` (the default), + :class:`~parsl.launchers.SrunLauncher`, or + :class:`~parsl.launchers.AprunLauncher` + linger : Bool + When set to True, the workers will not `halt`. The user is responsible for shutting + down the nodes. + """ + + def __init__(self, + vm_reference, + init_blocks=1, + min_blocks=0, + max_blocks=10, + parallelism=1, + worker_init='', + location='westus', + group_name='parsl.group', + key_name=None, + key_file=None, + vnet_name="parsl.vnet", + linger=False, + launcher=SingleNodeLauncher()): + if not _api_enabled: + raise OptionalModuleMissing( + ['azure', 'msrestazure'], "Azure Provider requires the azure module.") + + self._label = 'azure' + self.init_blocks = init_blocks + self.min_blocks = min_blocks + self.max_blocks = max_blocks + self.max_nodes = max_blocks + self.parallelism = parallelism + self.nodes_per_block = 1 + + self.worker_init = worker_init + self.vm_reference = vm_reference + self.region = location + self.vnet_name = vnet_name + + self.key_name = key_name + self.key_file = key_file + self.location = location + self.group_name = group_name + + self.launcher = launcher + self.linger = linger + self.resources = {} + self.script_dir = None + self.instances = [] + + env_specified = os.getenv("AZURE_CLIENT_ID") is not None and os.getenv( + "AZURE_CLIENT_SECRET") is not None and os.getenv( + "AZURE_TENANT_ID") is not None and os.getenv("AZURE_SUBSCRIPTION_ID") is not None + + if key_file is None and not env_specified: + raise ConfigurationError(("Must specify either: 'key_file', or " + "`AZURE_CLIENT_ID`, `AZURE_CLIENT_SECRET`, " + "and `AZURE_TENANT_ID` environment variables.")) + + if key_file is None: + self.clientid = os.getenv("AZURE_CLIENT_ID") + self.clientsecret = os.getenv("AZURE_CLIENT_SECRET") + self.tenantid = os.getenv("AZURE_TENANT_ID") + self.subid = os.getenv("AZURE_SUBSCRIPTION_ID") + else: + with open(key_file) as fh: + keys = json.load(fh) + self.clientid = keys.get("AZURE_CLIENT_ID") + self.clientsecret = keys.get("AZURE_CLIENT_SECRET") + self.tenantid = keys.get("AZURE_TENANT_ID") + self.subid = keys.get("AZURE_SUBSCRIPTION_ID") + + self.get_clients() + + def get_clients(self): + """ + Set up access to Azure API clients + """ + credentials, subscription_id = self.get_credentials() + self.resource_client = ResourceManagementClient( + credentials, subscription_id) + self.compute_client = ComputeManagementClient(credentials, + subscription_id) + self.network_client = NetworkManagementClient(credentials, + subscription_id) + + def get_credentials(self): + """ + Authenticate to the Azure API + """ + subscription_id = self.subid + credentials = ServicePrincipalCredentials( + client_id=self.clientid, + secret=self.clientsecret, + tenant=self.tenantid) + return credentials, subscription_id + + def submit(self, + command='sleep 1', + tasks_per_node=1, + job_name="parsl.azure"): + """ + Submit the command onto a freshly instantiated Azure VM + Submit returns an ID that corresponds to the task that was just submitted. + Parameters + ---------- + command : str + Command to be invoked on the remote side. + tasks_per_node : int (default=1) + Number of command invocations to be launched per node + job_name : str + Prefix for the job name. + Returns + ------- + None or str + If at capacity, None will be returned. Otherwise, the job identifier will be returned. + """ + self.resource_client.resource_groups.create_or_update( + self.group_name, {'location': self.location}) + self.resources["group"] = self.group_name + + logger.info('Creating NIC') + nic = self.create_nic(self.network_client) + + wrapped_cmd = self.launcher(command, tasks_per_node, 1, self.script_dir) + + cmd_str = Template(template_string).substitute(jobname=job_name, + user_script=wrapped_cmd, + linger=str(self.linger).lower(), + worker_init=self.worker_init) + + logger.info('Creating Linux Virtual Machine') + vm_parameters = self.create_vm_parameters(nic.id, + self.vm_reference) + + # Uniqueness strategy from AWS provider + job_name = "{0}-parsl-azure".format(str(time.time()).replace(".", "")) + + async_vm_creation = self.compute_client.\ + virtual_machines.create_or_update( + self.vnet_name, job_name, vm_parameters) + + vm_info = async_vm_creation.result() + self.instances.append(vm_info.name) + + try: + + logger.debug("Started instance_id: {0}".format(vm_info.id)) + disk, d_name = self.create_disk() + + self.resources[vm_info.id] = { + "job_id": vm_info.id, + "instance": vm_info, + "status": JobStatus(JobState.PENDING) + } + + vm_info.storage_profile.data_disks.append({ + 'lun': + 12, + 'name': + d_name, + 'create_option': + DiskCreateOption.attach, + 'managed_disk': { + 'id': disk.id + } + }) + async_disk_attach = self.\ + compute_client.virtual_machines.create_or_update( + self.group_name, vm_info.name, vm_info) + async_disk_attach.wait() + + async_vm_start = self.compute_client.virtual_machines.start( + self.group_name, job_name) + async_vm_start.wait() + + logger.debug("attempting to connect instance to Parsl master") + run_command_parameters = { + 'command_id': 'RunShellScript', + 'script': cmd_str.split("\n") + } + self.compute_client.virtual_machines.run_command( + self.group_name, + vm_info.name, + run_command_parameters) + except KeyboardInterrupt: + self.cancel([vm_info.name]) + raise KeyboardInterrupt + + return vm_info.name + + def status(self, job_ids): + """Get the status of a list of jobs identified by their ids. + Parameters + ---------- + job_ids : list of str + Identifiers for the jobs. + Returns + ------- + list of int + The status codes of the requsted jobs. + """ + statuses = [] + logger.info('List VMs in resource group') + for job_id in job_ids: + try: + vm = self.compute_client.virtual_machines.get( + self.group_name, job_id, expand='instanceView') + status = vm.instance_view.statuses[1].display_status + statuses.append(JobStatus(translate_table.get(status, JobState.UNKNOWN))) + # This only happens when it is in ProvisionState/Pending + except IndexError: + statuses.append(JobStatus(JobState.PENDING)) + return statuses + + def cancel(self, job_ids): + """Cancel the jobs specified by a list of job ids. + Parameters + ---------- + job_ids : list of str + List of of job identifiers + Returns + ------- + list of bool + Each entry in the list will contain False if the operation fails. Otherwise, the entry will be True. + """ + + return_vals = [] + + if self.linger: + logger.debug("Ignoring cancel requests due to linger mode") + return [False for x in job_ids] + + for job_id in job_ids: + try: + logger.debug('Delete VM {}'.format(job_id)) + async_vm_delete = self.compute_client.virtual_machines.delete( + self.group_name, job_id) + async_vm_delete.wait() + self.instances.remove(job_id) + return_vals.append(True) + except Exception: + return_vals.append(False) + + return return_vals + + @property + def label(self): + return self._label + + def create_nic(self, network_client): + """Create (or update, if it exists already) a Network Interface for a VM. + + Also ensures that there's a virtual network available. + + We create a VPC with CIDR 10.0.0.0/16, which provides up to 64,000 instances. + + We attach a subnet for each availability zone within the region specified in the + config. We give each subnet an ip range like 10.0.X.0/20, which is large enough + for approx. 4000 instances. + + + """ + try: + logger.info('Creating (or updating) Vnet') + async_vnet_creation = self.network_client.virtual_networks.\ + create_or_update( + self.group_name, self.vnet_name, { + 'location': self.location, + 'address_space': { + 'address_prefixes': ['10.0.0.0/16'] + } + }) + vnet_info = async_vnet_creation.result() + self.resources["vnet"] = vnet_info + + except CloudError as e: + + if "InUse" in str(e): + logger.info('Found Existing Vnet. Proceeding.') + else: + raise e + + if not self.resources.get("subnets", None): + self.resources["subnets"] = {} + + try: + logger.info('Creating (or updating) Subnet') + async_subnet_creation = self.network_client.subnets.create_or_update( + self.group_name, self.vnet_name, "{}.subnet".format( + self.group_name), {'address_prefix': '10.0.0.0/20'}) + subnet_info = async_subnet_creation.result() + + self.resources["subnets"][subnet_info.id] = subnet_info + + except CloudError as e: + if "InUse" in str(e): + subnet_info = self.network_client.subnets.get(self.group_name, self.vnet_name, "{}.subnet".format( + self.group_name)) + self.resources["subnets"][subnet_info.id] = subnet_info + logger.info('Found Existing Subnet. Proceeding.') + else: + raise e + + logger.info('Creating (or updating) NIC') + async_nic_creation = self.network_client.network_interfaces.\ + create_or_update( + self.group_name, + "{}.{}.nic".format(self.group_name, time.time()), { + 'location': + self.location, + 'ip_configurations': [{ + 'name': + "{}.ip.config".format(self.group_name), + 'subnet': { + 'id': subnet_info.id + } + }] + }) + + nic_info = async_nic_creation.result() + + if not self.resources.get("nics", None): + self.resources["nics"] = {} + + self.resources["nics"][nic_info.id] = nic_info + + return nic_info + + def create_vm_parameters(self, nic_id, vm_reference): + """Create the VM parameters structure. + """ + return { + 'location': self.region, + 'os_profile': { + 'computer_name': "{}.{}".format(self.vnet_name, time.time()), + 'admin_username': self.vm_reference["admin_username"], + 'admin_password': self.vm_reference["password"] + }, + 'hardware_profile': { + 'vm_size': vm_reference["vm_size"] + }, + 'storage_profile': { + 'image_reference': { + 'publisher': vm_reference['publisher'], + 'offer': vm_reference['offer'], + 'sku': vm_reference['sku'], + 'version': vm_reference['version'] + }, + }, + 'network_profile': { + 'network_interfaces': [{ + 'id': nic_id, + }] + } + } + + def create_disk(self): + """Create a managed data disk of size specified in config. + + Each instance gets one disk""" + logger.info('Create (empty) managed Data Disk') + name = '{}.{}'.format(self.group_name, time.time()) + async_disk_creation = self.compute_client.disks.create_or_update( + self.group_name, name, { + 'location': self.location, + 'disk_size_gb': self.vm_reference["disk_size_gb"], + 'creation_data': { + 'create_option': DiskCreateOption.empty + } + }) + data_disk = async_disk_creation.result() + return data_disk, name + + @property + def status_polling_interval(self): + return 60 diff --git a/parsl/parsl/providers/azure/template.py b/parsl/parsl/providers/azure/template.py new file mode 100644 index 00000000..92efc65d --- /dev/null +++ b/parsl/parsl/providers/azure/template.py @@ -0,0 +1,15 @@ +template_string = """#!/bin/bash +cd ~ +export DEBIAN_FRONTEND=noninteractive +apt-get update -y +apt-get install -y python3 python3-pip libffi-dev g++ libssl-dev +pip3 install numpy scipy parsl +$worker_init +$user_script +# Shutdown the instance as soon as the worker scripts exits +# or times out to avoid Azure costs. +if ! $linger +then + halt +fi +""" diff --git a/parsl/parsl/providers/base.py b/parsl/parsl/providers/base.py new file mode 100644 index 00000000..66c58f83 --- /dev/null +++ b/parsl/parsl/providers/base.py @@ -0,0 +1,142 @@ +import logging +from abc import ABCMeta, abstractmethod, abstractproperty +from typing import Any, Dict, Optional, Sequence + +from parsl.jobs.states import JobStatus + +logger = logging.getLogger(__name__) + + +class ExecutionProvider(metaclass=ABCMeta): + """Execution providers are responsible for managing execution resources + that have a Local Resource Manager (LRM). For instance, campus clusters + and supercomputers generally have LRMs (schedulers) such as Slurm, + Torque/PBS, and Condor. Clouds, on the other hand, have API + interfaces that allow much more fine-grained composition of an execution + environment. An execution provider abstracts these types of resources and + provides a single uniform interface to them. + + The providers abstract away the interfaces provided by various systems to + request, monitor, and cancel compute resources. + + .. code:: python + + +------------------ + | + script_string ------->| submit + id <--------|---+ + | + [ ids ] ------->| status + [statuses] <--------|----+ + | + [ ids ] ------->| cancel + [cancel] <--------|----+ + | + +------------------- + + In addition to the listed methods, an ExecutionProvider instance must always + have these attributes, which both default to `None`: + + mem_per_node: Real memory to provision per node in GB. + + Providers which set this attribute should ask for mem_per_node of memory + when provisioning resources, and set the corresponding environment + variable PARSL_MEMORY_GB before executing submitted commands. + + If this attribute is set, executors may use it to calculate how many tasks can + run concurrently per node. + + cores_per_node: Number of cores to provision per node. + + Providers which set this attribute should ask for cores_per_node cores + when provisioning resources, and set the corresponding environment + variable PARSL_CORES before executing submitted commands. + + If this attribute is set, executors may use it to calculate how many tasks can + run concurrently per node. + """ + + @abstractmethod + def __init__(self) -> None: + self.min_blocks: int + self.max_blocks: int + self.init_blocks: int + self.nodes_per_block: int + self.script_dir: Optional[str] + self.parallelism: float + self.resources: Dict[object, Any] + self.cores_per_node: Optional[int] = None + self.mem_per_node: Optional[float] = None + pass + + @abstractmethod + def submit(self, command: str, tasks_per_node: int, job_name: str = "parsl.auto") -> object: + ''' The submit method takes the command string to be executed upon + instantiation of a resource most often to start a pilot (such as for + HighThroughputExecutor or WorkQueueExecutor). + + Args : + - command (str) : The bash command string to be executed + - tasks_per_node (int) : command invocations to be launched per node + + KWargs: + - job_name (str) : Human friendly name to be assigned to the job request + + Returns: + - A job identifier, this could be an integer, string etc + or None or any other object that evaluates to boolean false + if submission failed but an exception isn't thrown. + + Raises: + - ExecutionProviderException or its subclasses + ''' + + pass + + @abstractmethod + def status(self, job_ids: Sequence[object]) -> Sequence[JobStatus]: + ''' Get the status of a list of jobs identified by the job identifiers + returned from the submit request. + + Args: + - job_ids (list) : A list of job identifiers + + Returns: + - A list of JobStatus objects corresponding to each job_id in the job_ids list. + + Raises: + - ExecutionProviderException or its subclasses + + ''' + + pass + + @abstractmethod + def cancel(self, job_ids: Sequence[object]) -> Sequence[bool]: + ''' Cancels the resources identified by the job_ids provided by the user. + + Args: + - job_ids (list): A list of job identifiers + + Returns: + - A list of status from cancelling the job which can be True, False + + Raises: + - ExecutionProviderException or its subclasses + ''' + + pass + + @abstractproperty + def label(self) -> str: + ''' Provides the label for this provider ''' + pass + + @property + @abstractmethod + def status_polling_interval(self) -> int: + """Returns the interval, in seconds, at which the status method should be called. + + :return: the number of seconds to wait between calls to status() + """ + pass diff --git a/parsl/parsl/providers/cluster_provider.py b/parsl/parsl/providers/cluster_provider.py new file mode 100644 index 00000000..db7ef9ea --- /dev/null +++ b/parsl/parsl/providers/cluster_provider.py @@ -0,0 +1,138 @@ +import logging +from abc import abstractmethod +from string import Template + +from parsl.launchers.base import Launcher +from parsl.launchers.errors import BadLauncher +from parsl.providers.base import ExecutionProvider +from parsl.providers.errors import SchedulerMissingArgs, ScriptPathError +from parsl.utils import execute_wait + +logger = logging.getLogger(__name__) + + +class ClusterProvider(ExecutionProvider): + """ This class defines behavior common to all cluster/supercompute-style scheduler systems. + + Parameters + ---------- + label : str + Label for this provider. + walltime : str + Walltime requested per block in HH:MM:SS. + launcher : Launcher + Launcher for this provider. + cmd_timeout : int + Timeout for commands made to the scheduler in seconds + + .. code:: python + + +------------------ + | + script_string ------->| submit + id <--------|---+ + | + [ ids ] ------->| status + [statuses] <--------|----+ + | + [ ids ] ------->| cancel + [cancel] <--------|----+ + | + +------------------- + """ + + def __init__(self, + label, + nodes_per_block, + init_blocks, + min_blocks, + max_blocks, + parallelism, + walltime, + launcher, + cmd_timeout=10): + + self._label = label + self.nodes_per_block = nodes_per_block + self.init_blocks = init_blocks + self.min_blocks = min_blocks + self.max_blocks = max_blocks + self.parallelism = parallelism + self.launcher = launcher + self.walltime = walltime + self.cmd_timeout = cmd_timeout + if not isinstance(self.launcher, Launcher): + raise BadLauncher(self.launcher) + + self.script_dir = None + + # Dictionary that keeps track of jobs, keyed on job_id + self.resources = {} + + def execute_wait(self, cmd, timeout=None): + t = self.cmd_timeout + if timeout is not None: + t = timeout + return execute_wait(cmd, t) + + def _write_submit_script(self, template, script_filename, job_name, configs): + """Generate submit script and write it to a file. + + Args: + - template (string) : The template string to be used for the writing submit script + - script_filename (string) : Name of the submit script + - job_name (string) : job name + - configs (dict) : configs that get pushed into the template + + Returns: + - None + + Raises: + SchedulerMissingArgs : If template is missing args + ScriptPathError : Unable to write submit script out + """ + + try: + submit_script = Template(template).substitute(jobname=job_name, **configs) + with open(script_filename, 'w') as f: + f.write(submit_script) + + except KeyError as e: + logger.error("Missing keys for submit script : %s", e) + raise SchedulerMissingArgs(e.args, self.label) + + except IOError as e: + logger.error("Failed writing to submit script: %s", script_filename) + raise ScriptPathError(script_filename, e) + except Exception as e: + print("Template : ", template) + print("Args : ", job_name) + print("Kwargs : ", configs) + logger.error("Uncategorized error: %s", e) + raise e + + @abstractmethod + def _status(self): + pass + + def status(self, job_ids): + """ Get the status of a list of jobs identified by the job identifiers + returned from the submit request. + + Args: + - job_ids (list) : A list of job identifiers + + Returns: + - A list of JobStatus objects corresponding to each job_id in the job_ids list. + + Raises: + - ExecutionProviderException or its subclasses + + """ + if job_ids: + self._status() + return [self.resources[jid]['status'] for jid in job_ids] + + @property + def label(self): + return self._label diff --git a/parsl/parsl/providers/condor/__init__.py b/parsl/parsl/providers/condor/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/providers/condor/condor.py b/parsl/parsl/providers/condor/condor.py new file mode 100644 index 00000000..e92e0920 --- /dev/null +++ b/parsl/parsl/providers/condor/condor.py @@ -0,0 +1,330 @@ +import logging +import os +import re +import time + +import typeguard + +from parsl.jobs.states import JobState, JobStatus +from parsl.launchers import SingleNodeLauncher +from parsl.launchers.base import Launcher +from parsl.providers.cluster_provider import ClusterProvider +from parsl.providers.condor.template import template_string +from parsl.providers.errors import ScaleOutFailed +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + +from typing import Dict, List, Optional + +# See http://pages.cs.wisc.edu/~adesmet/status.html +translate_table = { + '1': JobState.PENDING, + '2': JobState.RUNNING, + '3': JobState.CANCELLED, + '4': JobState.COMPLETED, + '5': JobState.FAILED, + '6': JobState.FAILED, +} + + +class CondorProvider(RepresentationMixin, ClusterProvider): + """HTCondor Execution Provider. + + Parameters + ---------- + nodes_per_block : int + Nodes to provision per block. + cores_per_slot : int + Specify the number of cores to provision per slot. If set to None, executors + will assume all cores on the node are available for computation. Default is None. + mem_per_slot : float + Specify the real memory to provision per slot in GB. If set to None, no + explicit request to the scheduler will be made. Default is None. + init_blocks : int + Number of blocks to provision at time of initialization + min_blocks : int + Minimum number of blocks to maintain + max_blocks : int + Maximum number of blocks to maintain. + parallelism : float + Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive + scaling where as many resources as possible are used; parallelism close to 0 represents + the opposite situation in which as few resources as possible (i.e., min_blocks) are used. + environment : dict of str + A dictionary of environmant variable name and value pairs which will be set before + running a task. + project : str + Project which the job will be charged against + scheduler_options : str + String to add specific condor attributes to the HTCondor submit script. + transfer_input_files : list(str) + List of strings of paths to additional files or directories to transfer to the job + worker_init : str + Command to be run before starting a worker. + requirements : str + Condor requirements. + launcher : Launcher + Launcher for this provider. Possible launchers include + :class:`~parsl.launchers.SingleNodeLauncher` (the default), + cmd_timeout : int + Timeout for commands made to the scheduler in seconds. + cmd_chunk_size : int + Calls to the scheduler will be made for chunks of blocks with this size. + """ + @typeguard.typechecked + def __init__(self, + nodes_per_block: int = 1, + cores_per_slot: Optional[int] = None, + mem_per_slot: Optional[float] = None, + init_blocks: int = 1, + min_blocks: int = 0, + max_blocks: int = 1, + parallelism: float = 1, + environment: Optional[Dict[str, str]] = None, + project: str = '', + scheduler_options: str = '', + transfer_input_files: List[str] = [], + walltime: str = "00:10:00", + worker_init: str = '', + launcher: Launcher = SingleNodeLauncher(), + requirements: str = '', + cmd_timeout: int = 60, + cmd_chunk_size: int = 100) -> None: + + label = 'condor' + super().__init__(label, + nodes_per_block, + init_blocks, + min_blocks, + max_blocks, + parallelism, + walltime, + launcher, + cmd_timeout=cmd_timeout) + self.cores_per_slot = cores_per_slot + self.mem_per_slot = mem_per_slot + self.cmd_chunk_size = cmd_chunk_size + + # To Parsl, Condor slots should be treated equivalently to nodes + self.cores_per_node = cores_per_slot + self.mem_per_node = mem_per_slot + + self.environment = environment if environment is not None else {} + for key, value in self.environment.items(): + # To escape literal quote marks, double them + # See: http://research.cs.wisc.edu/htcondor/manual/v8.6/condor_submit.html + try: + self.environment[key] = "'{}'".format(value.replace("'", '"').replace('"', '""')) + except AttributeError: + pass + + self.project = project + self.scheduler_options = scheduler_options + '\n' + self.worker_init = worker_init + '\n' + self.requirements = requirements + self.transfer_input_files = transfer_input_files + + def _status(self): + """Update the resource dictionary with job statuses.""" + + for job_id_chunk in _chunker(self.resources.keys(), self.cmd_chunk_size): + job_id_list = ' '.join(job_id_chunk) + cmd = "condor_q {0} -af:jr JobStatus".format(job_id_list) + retcode, stdout, stderr = self.execute_wait(cmd) + """ + Example output: + + $ condor_q 34524642.0 34524643.0 -af:jr JobStatus + 34524642.0 2 + 34524643.0 1 + """ + + for line in stdout.splitlines(): + parts = line.strip().split() + job_id = parts[0] + state = translate_table.get(parts[1], JobState.UNKNOWN) + if job_id in self.resources: + self.resources[job_id]['status'] = JobStatus(state) + + def status(self, job_ids): + """Get the status of a list of jobs identified by their ids. + + Parameters + ---------- + job_ids : list of int + Identifiers of jobs for which the status will be returned. + + Returns + ------- + List of int + Status codes for the requested jobs. + + """ + self._status() + return [self.resources[jid]['status'] for jid in job_ids] + + def submit(self, command, tasks_per_node, job_name="parsl.condor"): + """Submits the command onto an Local Resource Manager job. + + example file with the complex case of multiple submits per job: + Universe =vanilla + output = out.$(Cluster).$(Process) + error = err.$(Cluster).$(Process) + log = log.$(Cluster) + leave_in_queue = true + executable = test.sh + queue 5 + executable = foo + queue 1 + + $ condor_submit test.sub + Submitting job(s)...... + 5 job(s) submitted to cluster 118907. + 1 job(s) submitted to cluster 118908. + + Parameters + ---------- + command : str + Command to execute + job_name : str + Job name prefix. + tasks_per_node : int + command invocations to be launched per node + Returns + ------- + None or str + None if at capacity and cannot provision more; otherwise the identifier for the job. + """ + + logger.debug("Attempting to launch") + + job_name = "parsl.{0}.{1}".format(job_name, time.time()) + + scheduler_options = self.scheduler_options + worker_init = self.worker_init + if self.mem_per_slot is not None: + scheduler_options += 'RequestMemory = {}\n'.format(self.mem_per_slot * 1024) + worker_init += 'export PARSL_MEMORY_GB={}\n'.format(self.mem_per_slot) + if self.cores_per_slot is not None: + scheduler_options += 'RequestCpus = {}\n'.format(self.cores_per_slot) + worker_init += 'export PARSL_CORES={}\n'.format(self.cores_per_slot) + + script_path = "{0}/{1}.submit".format(self.script_dir, job_name) + script_path = os.path.abspath(script_path) + userscript_path = "{0}/{1}.script".format(self.script_dir, job_name) + userscript_path = os.path.abspath(userscript_path) + + self.environment["JOBNAME"] = "'{}'".format(job_name) + + job_config = {} + job_config["job_name"] = job_name + job_config["submit_script_dir"] = self.script_dir + job_config["project"] = self.project + job_config["nodes"] = self.nodes_per_block + job_config["scheduler_options"] = scheduler_options + job_config["worker_init"] = worker_init + job_config["user_script"] = command + job_config["tasks_per_node"] = tasks_per_node + job_config["requirements"] = self.requirements + job_config["environment"] = ' '.join(['{}={}'.format(key, value) for key, value in self.environment.items()]) + + # Move the user script + # This is where the command should be wrapped by the launchers. + wrapped_command = self.launcher(command, + tasks_per_node, + self.nodes_per_block, + self.script_dir) + + with open(userscript_path, 'w') as f: + f.write(job_config["worker_init"] + '\n' + wrapped_command) + + the_input_files = [userscript_path] + self.transfer_input_files + job_config["input_files"] = ','.join(the_input_files) + job_config["job_script"] = os.path.basename(userscript_path) + + # Construct and move the submit script + self._write_submit_script(template_string, script_path, job_name, job_config) + + cmd = "condor_submit {0}".format(script_path) + try: + retcode, stdout, stderr = self.execute_wait(cmd) + except Exception as e: + raise ScaleOutFailed(self.label, str(e)) + + job_id = [] + + if retcode == 0: + for line in stdout.split('\n'): + if re.match('^[0-9]', line) is not None: + cluster = line.split(" ")[5] + # We know the first job id ("process" in condor terms) within a + # cluster is 0 and we know the total number of jobs from + # condor_submit, so we use some list comprehensions to expand + # the condor_submit output into job IDs + # e.g., ['118907.0', '118907.1', '118907.2', '118907.3', '118907.4', '118908.0'] + processes = [str(x) for x in range(0, int(line[0]))] + job_id += [cluster + process for process in processes] + + self._add_resource(job_id) + return job_id[0] + else: + message = "Command '{}' failed with return code {}".format(cmd, retcode) + message += " and standard output '{}'".format(stdout.strip()) if stdout is not None else '' + message += " and standard error '{}'".format(stderr.strip()) if stderr is not None else '' + raise ScaleOutFailed(self.label, message) + + def cancel(self, job_ids): + """Cancels the jobs specified by a list of job IDs. + + Parameters + ---------- + job_ids : list of str + The job IDs to cancel. + + Returns + ------- + list of bool + Each entry in the list will be True if the job is cancelled succesfully, otherwise False. + """ + rets = [] + for job_id_chunk in _chunker(job_ids, self.cmd_chunk_size): + job_id_list = ' '.join(job_id_chunk) + cmd = "condor_rm {0}; condor_rm -forcex {0}".format(job_id_list) + logger.debug("Attempting removal of jobs : {0}".format(cmd)) + retcode, stdout, stderr = self.execute_wait(cmd) + if retcode == 0: + for jid in job_id_chunk: + if jid in self.resources: + self.resources[jid]['status'] = JobStatus(JobState.CANCELLED) + rets.extend([True for i in job_id_chunk]) + else: + rets.extend([False for i in job_id_chunk]) + + return rets + + def _add_resource(self, job_id): + for jid in job_id: + self.resources[jid] = {'status': JobStatus(JobState.PENDING), 'size': 1} + return True + + @property + def status_polling_interval(self): + return 60 + + +# see https://stackoverflow.com/a/18243990 +def _chunker(seq, size): + res = [] + for el in seq: + res.append(el) + if len(res) == size: + yield res + res = [] + if res: + yield res + + +if __name__ == "__main__": + + print("None") diff --git a/parsl/parsl/providers/condor/template.py b/parsl/parsl/providers/condor/template.py new file mode 100644 index 00000000..a31b29d5 --- /dev/null +++ b/parsl/parsl/providers/condor/template.py @@ -0,0 +1,28 @@ +template_string = ''' +universe = vanilla +should_transfer_files = YES +when_to_transfer_output = ON_EXIT_OR_EVICT +Transfer_Executable = false +transfer_input_files = ${input_files} +machine_count = ${nodes} +output = ${submit_script_dir}/${job_name}.stdout +error = ${submit_script_dir}/${job_name}.stderr +log = ${submit_script_dir}/${job_name}.log +executable = /bin/bash +arguments = ${job_script} +requirements = ${requirements} ++projectname = ${project} +leave_in_queue = TRUE +environment = "${environment}" +${scheduler_options} + +queue + +''' + +# for later, +# if we want to remove on preemption, this might work: +# PERIODIC_REMOVE = (NumJobstarts > 1) +# or if the pilot can trap signals, then we can send a special exit code on +# sigterm/sigkill and remove that way. but then we still need to be careful in +# cases where the worker dies, for example-- no signal is sent diff --git a/parsl/parsl/providers/errors.py b/parsl/parsl/providers/errors.py new file mode 100644 index 00000000..c3aa7e59 --- /dev/null +++ b/parsl/parsl/providers/errors.py @@ -0,0 +1,68 @@ +import warnings + +from parsl.errors import ParslError + + +class ExecutionProviderException(ParslError): + """ Base class for all exceptions + Only to be invoked when only a more specific error is not available. + + """ + pass + + +class ScaleOutFailed(ExecutionProviderException): + ''' Scale out failed in the submit phase on the provider side + ''' + + def __init__(self, provider, reason): + self.provider = provider + self.reason = reason + + def __str__(self): + return "Unable to scale out {} provider: {}".format(self.provider, self.reason) + + +class SchedulerMissingArgs(ExecutionProviderException): + ''' Error raised when the template used to compose the submit script to the local resource manager is missing required arguments + ''' + + def __init__(self, missing_keywords, sitename): + self.missing_keywords = missing_keywords + self.sitename = sitename + + def __str__(self): + return "SchedulerMissingArgs: Pool:{0} Arg:{1}".format(self.sitename, self.missing_keywords) + + +class ScriptPathError(ExecutionProviderException): + ''' Error raised when the template used to compose the submit script to the local resource manager is missing required arguments + ''' + + def __init__(self, script_path, reason): + self.script_path = script_path + self.reason = reason + + def __str__(self): + return "Unable to write submit script:{0} Reason:{1}".format(self.script_path, self.reason) + + +class SubmitException(ExecutionProviderException): + '''Raised by the submit() method of a provider if there is an error in launching a job. + ''' + + def __init__(self, job_name, message, stdout=None, stderr=None, retcode=None): + self.job_name = job_name + self.message = message + self.stdout = stdout + self.stderr = stderr + self.retcode = retcode + + @property + def task_name(self) -> str: + warnings.warn("task_name is deprecated; use .job_name instead. This will be removed after 2024-06.", DeprecationWarning) + return self.job_name + + def __str__(self) -> str: + # TODO: make this more user-friendly + return f"Cannot launch job {self.job_name}: {self.message}; recode={self.retcode}, stdout={self.stdout}, stderr={self.stderr}" diff --git a/parsl/parsl/providers/googlecloud/__init__.py b/parsl/parsl/providers/googlecloud/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/providers/googlecloud/googlecloud.py b/parsl/parsl/providers/googlecloud/googlecloud.py new file mode 100644 index 00000000..1b860b76 --- /dev/null +++ b/parsl/parsl/providers/googlecloud/googlecloud.py @@ -0,0 +1,238 @@ +import atexit +import logging +import os + +from parsl.jobs.states import JobState, JobStatus +from parsl.launchers import SingleNodeLauncher + +logger = logging.getLogger(__name__) + +try: + import googleapiclient.discovery + +except ImportError: + _google_enabled = False +else: + _google_enabled = True + +translate_table = { + 'PENDING': JobState.PENDING, + 'PROVISIONING': JobState.PENDING, + "STAGING": JobState.PENDING, + 'RUNNING': JobState.RUNNING, + 'DONE': JobState.COMPLETED, + 'STOPPING': JobState.COMPLETED, + 'STOPPED': JobState.COMPLETED, + 'TERMINATED': JobState.COMPLETED, + 'SUSPENDING': JobState.COMPLETED, + 'SUSPENDED': JobState.COMPLETED, +} + + +class GoogleCloudProvider(): + """A provider for using resources from the Google Compute Engine. + + Parameters + ---------- + project_id : str + Project ID from Google compute engine. + key_file : str + Path to authorization private key json file. This is required for auth. + A new one can be generated here: https://console.cloud.google.com/apis/credentials + region : str + Region in which to start instances + os_project : str + OS project code for Google compute engine. + os_family : str + OS family to request. + google_version : str + Google compute engine version to use. Possibilies include 'v1' (default) or 'beta'. + instance_type: str + 'n1-standard-1', + init_blocks : int + Number of blocks to provision immediately. Default is 1. + min_blocks : int + Minimum number of blocks to maintain. Default is 0. + max_blocks : int + Maximum number of blocks to maintain. Default is 10. + parallelism : float + Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive + scaling where as many resources as possible are used; parallelism close to 0 represents + the opposite situation in which as few resources as possible (i.e., min_blocks) are used. + + """ + + def __init__(self, + project_id, + key_file, + region, + os_project, + os_family, + google_version='v1', + instance_type='n1-standard-1', + init_blocks=1, + min_blocks=0, + max_blocks=10, + launcher=SingleNodeLauncher(), + parallelism=1): + self.project_id = project_id + os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = key_file + self.zone = self.get_zone(region) + self.os_project = os_project + self.os_family = os_family + self.label = 'google_cloud' + self.client = googleapiclient.discovery.build('compute', google_version) + self.instance_type = instance_type + self.init_blocks = init_blocks + self.min_blocks = min_blocks + self.max_blocks = max_blocks + self.parallelism = parallelism + self.num_instances = 0 + self.launcher = launcher + self.script_dir = None + + # Dictionary that keeps track of jobs, keyed on job_id + self.resources = {} + atexit.register(self.bye) + + def submit(self, command, tasks_per_node, job_name="parsl.gcs"): + ''' The submit method takes the command string to be executed upon + instantiation of a resource most often to start a pilot. + + Args : + - command (str) : The bash command string to be executed. + - tasks_per_node (int) : command invocations to be launched per node + + KWargs: + - job_name (str) : Human friendly name to be assigned to the job request + + Returns: + - A job identifier, this could be an integer, string etc + + Raises: + - ExecutionProviderException or its subclasses + ''' + wrapped_cmd = self.launcher(command, + tasks_per_node, + 1, + self.script_dir) + + instance, name = self.create_instance(command=wrapped_cmd) + self.resources[name] = {"job_id": name, "status": JobStatus(translate_table[instance['status']])} + return name + + def status(self, job_ids): + ''' Get the status of a list of jobs identified by the job identifiers + returned from the submit request. + + Args: + - job_ids (list) : A list of job identifiers + + Returns: + - A list of JobStatus objects corresponding to each job_id in the job_ids list. + + Raises: + - ExecutionProviderException or its subclasses + + ''' + statuses = [] + for job_id in job_ids: + instance = self.client.instances().get(instance=job_id, project=self.project_id, zone=self.zone).execute() + job_status = JobStatus(translate_table[instance['status']]) + self.resources[job_id]['status'] = job_status + statuses.append(job_status) + return statuses + + def cancel(self, job_ids): + ''' Cancels the resources identified by the job_ids provided by the user. + + Args: + - job_ids (list): A list of job identifiers + + Returns: + - A list of status from cancelling the job which can be True, False + + Raises: + - ExecutionProviderException or its subclasses + ''' + statuses = [] + for job_id in job_ids: + try: + self.delete_instance(job_id) + statuses.append(True) + except Exception: + statuses.append(False) + return statuses + + def bye(self): + self.cancel([i for i in list(self.resources)]) + + def create_instance(self, command=""): + name = "parslauto{}".format(self.num_instances) + self.num_instances += 1 + compute = self.client + project = self.project_id + image_response = compute.images().getFromFamily( + project=self.os_project, family=self.os_family).execute() + source_disk_image = image_response['selfLink'] + + # Configure the machine + machine_type = "zones/{}/machineTypes/{}".format(self.zone, self.instance_type) + startup_script = command + + config = { + 'name': name, + 'machineType': machine_type, + + # Specify the boot disk and the image to use as a source. + 'disks': [{ + 'boot': True, + 'autoDelete': True, + 'initializeParams': { + 'sourceImage': source_disk_image, + } + }], + 'networkInterfaces': [{ + 'network': 'global/networks/default', + 'accessConfigs': [{ + 'type': 'ONE_TO_ONE_NAT', + 'name': 'External NAT' + }] + }], + 'serviceAccounts': [{ + 'email': + 'default', + 'scopes': [ + 'https://www.googleapis.com/auth/devstorage.read_write', + 'https://www.googleapis.com/auth/logging.write' + ] + }], + 'metadata': { + 'items': [{ + # Startup script is automatically executed by the + # instance upon startup. + 'key': 'startup-script', + 'value': startup_script + }] + } + } + + return compute.instances().insert(project=project, zone=self.zone, body=config).execute(), name + + def get_zone(self, region): + res = self.client.zones().list(project=self.project_id).execute() + for zone in res['items']: + if region in zone['name'] and zone['status'] == "UP": + return zone["name"] + + def delete_instance(self, name): + + compute = self.client + project = self.project_id + zone = self.zone + + return compute.instances().delete(project=project, zone=zone, instance=name).execute() + + @property + def status_polling_interval(self): + return 60 diff --git a/parsl/parsl/providers/grid_engine/__init__.py b/parsl/parsl/providers/grid_engine/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/providers/grid_engine/grid_engine.py b/parsl/parsl/providers/grid_engine/grid_engine.py new file mode 100644 index 00000000..afb3e583 --- /dev/null +++ b/parsl/parsl/providers/grid_engine/grid_engine.py @@ -0,0 +1,223 @@ +import logging +import os +import time + +from parsl.jobs.states import JobState, JobStatus +from parsl.launchers import SingleNodeLauncher +from parsl.providers.cluster_provider import ClusterProvider +from parsl.providers.grid_engine.template import template_string +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + +translate_table = { + 'qw': JobState.PENDING, + 'hqw': JobState.PENDING, + 'hrwq': JobState.PENDING, + 'r': JobState.RUNNING, + 's': JobState.FAILED, # obsuspended + 'ts': JobState.FAILED, + 't': JobState.FAILED, # Suspended by alarm + 'eqw': JobState.FAILED, # Error states + 'ehqw': JobState.FAILED, # .. + 'ehrqw': JobState.FAILED, # .. + 'd': JobState.COMPLETED, + 'dr': JobState.COMPLETED, + 'dt': JobState.COMPLETED, + 'drt': JobState.COMPLETED, + 'ds': JobState.COMPLETED, + 'drs': JobState.COMPLETED, +} + + +class GridEngineProvider(ClusterProvider, RepresentationMixin): + """A provider for the Grid Engine scheduler. + + Parameters + ---------- + nodes_per_block : int + Nodes to provision per block. + min_blocks : int + Minimum number of blocks to maintain. + max_blocks : int + Maximum number of blocks to maintain. + parallelism : float + Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive + scaling where as many resources as possible are used; parallelism close to 0 represents + the opposite situation in which as few resources as possible (i.e., min_blocks) are used. + walltime : str + Walltime requested per block in HH:MM:SS. + scheduler_options : str + String to prepend to the #$$ blocks in the submit script to the scheduler. + worker_init : str + Command to be run before starting a worker, such as 'module load Anaconda; source activate env'. + launcher : Launcher + Launcher for this provider. Possible launchers include + :class:`~parsl.launchers.SingleNodeLauncher` (the default), + cmd_timeout : int + Timeout for commands made to the scheduler in seconds + """ + + def __init__(self, + nodes_per_block=1, + init_blocks=1, + min_blocks=0, + max_blocks=1, + parallelism=1, + walltime="00:10:00", + scheduler_options='', + worker_init='', + launcher=SingleNodeLauncher(), + cmd_timeout: int = 60, + queue=None): + label = 'grid_engine' + super().__init__(label, + nodes_per_block, + init_blocks, + min_blocks, + max_blocks, + parallelism, + walltime, + launcher, + cmd_timeout=cmd_timeout) + self.scheduler_options = scheduler_options + self.worker_init = worker_init + self.queue = queue + + if launcher in ['srun', 'srun_mpi']: + logger.warning("Use of {} launcher is usually appropriate for Slurm providers. " + "Recommended options include 'single_node' or 'aprun'.".format(launcher)) + + def get_configs(self, command, tasks_per_node): + """Compose a dictionary with information for writing the submit script.""" + + logger.debug("Requesting one block with {} nodes per block and {} tasks per node".format( + self.nodes_per_block, tasks_per_node)) + + job_config = {} + job_config["submit_script_dir"] = self.script_dir + job_config["nodes"] = self.nodes_per_block + job_config["walltime"] = self.walltime + job_config["scheduler_options"] = self.scheduler_options + job_config["worker_init"] = self.worker_init + job_config["user_script"] = command + + job_config["user_script"] = self.launcher(command, + tasks_per_node, + self.nodes_per_block, + self.script_dir) + return job_config + + def submit(self, command, tasks_per_node, job_name="parsl.sge"): + ''' The submit method takes the command string to be executed upon + instantiation of a resource most often to start a pilot. + + Args : + - command (str) : The bash command string to be executed. + - tasks_per_node (int) : command invocations to be launched per node + + KWargs: + - job_name (str) : Human friendly name to be assigned to the job request + + Returns: + - A job identifier, this could be an integer, string etc + + Raises: + - ExecutionProviderException or its subclasses + ''' + + # Set job name + job_name = "{0}.{1}".format(job_name, time.time()) + + # Set script path + script_path = "{0}/{1}.submit".format(self.script_dir, job_name) + script_path = os.path.abspath(script_path) + + job_config = self.get_configs(command, tasks_per_node) + + logger.debug("Writing submit script") + self._write_submit_script(template_string, script_path, job_name, job_config) + + if self.queue is not None: + cmd = "qsub -q {0} -terse {1}".format(self.queue, script_path) + else: + cmd = "qsub -terse {0}".format(script_path) + retcode, stdout, stderr = self.execute_wait(cmd) + + if retcode == 0: + for line in stdout.split('\n'): + job_id = line.strip() + if not job_id: + continue + self.resources[job_id] = {'job_id': job_id, 'status': JobStatus(JobState.PENDING)} + return job_id + else: + logger.error("Submit command failed") + logger.error("Retcode:%s STDOUT:%s STDERR:%s", retcode, stdout.strip(), stderr.strip()) + + def _status(self): + ''' Get the status of a list of jobs identified by the job identifiers + returned from the submit request. + + Returns: + - A list of JobStatus objects corresponding to each job_id in the job_ids list. + + Raises: + - ExecutionProviderException or its subclasses + + ''' + + cmd = "qstat" + + retcode, stdout, stderr = self.execute_wait(cmd) + + # Execute_wait failed. Do no update + if retcode != 0: + return + + jobs_missing = list(self.resources.keys()) + for line in stdout.split('\n'): + parts = line.split() + if parts and parts[0].lower().lower() != 'job-id' \ + and not parts[0].startswith('----'): + job_id = parts[0] + state = translate_table.get(parts[4].lower(), JobState.UNKNOWN) + if job_id in self.resources: + self.resources[job_id]['status'] = JobStatus(state) + jobs_missing.remove(job_id) + + # Filling in missing blanks for jobs that might have gone missing + # we might lose some information about why the jobs failed. + for missing_job in jobs_missing: + self.resources[missing_job]['status'] = JobStatus(JobState.COMPLETED) + + def cancel(self, job_ids): + ''' Cancels the resources identified by the job_ids provided by the user. + + Args: + - job_ids (list): A list of job identifiers + + Returns: + - A list of status from cancelling the job which can be True, False + + Raises: + - ExecutionProviderException or its subclasses + ''' + + job_id_list = ' '.join(job_ids) + cmd = "qdel {}".format(job_id_list) + retcode, stdout, stderr = self.execute_wait(cmd) + + rets = None + if retcode == 0: + for jid in job_ids: + self.resources[jid]['status'] = JobStatus(JobState.COMPLETED) + rets = [True for i in job_ids] + else: + rets = [False for i in job_ids] + + return rets + + @property + def status_polling_interval(self): + return 60 diff --git a/parsl/parsl/providers/grid_engine/template.py b/parsl/parsl/providers/grid_engine/template.py new file mode 100644 index 00000000..6ba32100 --- /dev/null +++ b/parsl/parsl/providers/grid_engine/template.py @@ -0,0 +1,14 @@ +template_string = """#!/bin/bash +#$$ -S /bin/bash +#$$ -o ${submit_script_dir}/${jobname}.submit.stdout +#$$ -e ${submit_script_dir}/${jobname}.submit.stderr +#$$ -cwd +#$$ -l h_rt=${walltime} +${scheduler_options} + +${worker_init} + +export JOBNAME="${jobname}" + +$user_script +""" diff --git a/parsl/parsl/providers/kubernetes/__init__.py b/parsl/parsl/providers/kubernetes/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/providers/kubernetes/kube.py b/parsl/parsl/providers/kubernetes/kube.py new file mode 100644 index 00000000..2f70d2ad --- /dev/null +++ b/parsl/parsl/providers/kubernetes/kube.py @@ -0,0 +1,366 @@ +import logging +import uuid +from typing import Any, Dict, List, Optional, Tuple + +import typeguard + +from parsl.errors import OptionalModuleMissing +from parsl.jobs.states import JobState, JobStatus +from parsl.providers.base import ExecutionProvider +from parsl.providers.kubernetes.template import template_string +from parsl.utils import RepresentationMixin, sanitize_dns_subdomain_rfc1123 + +try: + from kubernetes import client, config + _kubernetes_enabled = True +except (ImportError, NameError, FileNotFoundError): + _kubernetes_enabled = False + +logger = logging.getLogger(__name__) + +translate_table = { + 'Running': JobState.RUNNING, + 'Pending': JobState.PENDING, + 'Succeeded': JobState.COMPLETED, + 'Failed': JobState.FAILED, + 'Unknown': JobState.UNKNOWN, +} + + +class KubernetesProvider(ExecutionProvider, RepresentationMixin): + """ Kubernetes execution provider + + Parameters + ---------- + + namespace : str + Kubernetes namespace to create deployments. + image : str + Docker image to use in the deployment. + nodes_per_block : int + Nodes to provision per block. + init_blocks : int + Number of blocks to provision at the start of the run. Default is 1. + min_blocks : int + Minimum number of blocks to maintain. + max_blocks : int + Maximum number of blocks to maintain. + max_cpu : float + CPU limits of the blocks (pods), in cpu units. + This is the cpu "limits" option for resource specification. + Check kubernetes docs for more details. Default is 2. + max_mem : str + Memory limits of the blocks (pods), in Mi or Gi. + This is the memory "limits" option for resource specification on kubernetes. + Check kubernetes docs for more details. Default is 500Mi. + init_cpu : float + CPU limits of the blocks (pods), in cpu units. + This is the cpu "requests" option for resource specification. + Check kubernetes docs for more details. Default is 1. + init_mem : str + Memory limits of the blocks (pods), in Mi or Gi. + This is the memory "requests" option for resource specification on kubernetes. + Check kubernetes docs for more details. Default is 250Mi. + extra_requests: Dict[str, str] + Extra resource requests of the blocks (pods). Check kubernetes docs for more details. + extra_limits: Dict[str, str] + Extra resource limits of the blocks (pods). Check kubernetes docs for more details. + parallelism : float + Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive + scaling where as many resources as possible are used; parallelism close to 0 represents + the opposite situation in which as few resources as possible (i.e., min_blocks) are used. + worker_init : str + Command to be run first for the workers, such as ``python start.py``. + secret : str + The Kubernetes ImagePullSecret secret to use to pull images + pod_name : str + The name for the pod, will be appended with a timestamp. + Default is None, meaning parsl automatically names the pod. + user_id : str + Unix user id to run the container as. + group_id : str + Unix group id to run the container as. + run_as_non_root : bool + Run as non-root (True) or run as root (False). + persistent_volumes: list[(str, str)] + List of tuples describing persistent volumes to be mounted in the pod. + The tuples consist of (PVC Name, Mount Directory). + service_account_name: str + Name of the service account to run the pod as. + annotations: Dict[str, str] + Annotations to set on the pod. + """ + @typeguard.typechecked + def __init__(self, + image: str, + namespace: str = 'default', + nodes_per_block: int = 1, + init_blocks: int = 4, + min_blocks: int = 0, + max_blocks: int = 10, + max_cpu: float = 2, + max_mem: str = "500Mi", + init_cpu: float = 1, + init_mem: str = "250Mi", + extra_requests: Optional[Dict[str, str]] = None, + extra_limits: Optional[Dict[str, str]] = None, + parallelism: float = 1, + worker_init: str = "", + pod_name: Optional[str] = None, + user_id: Optional[str] = None, + group_id: Optional[str] = None, + run_as_non_root: bool = False, + secret: Optional[str] = None, + persistent_volumes: List[Tuple[str, str]] = [], + service_account_name: Optional[str] = None, + annotations: Optional[Dict[str, str]] = None) -> None: + if not _kubernetes_enabled: + raise OptionalModuleMissing(['kubernetes'], + "Kubernetes provider requires kubernetes module and config.") + try: + config.load_kube_config() + except config.config_exception.ConfigException: + # `load_kube_config` assumes a local kube-config file, and fails if not + # present, raising: + # + # kubernetes.config.config_exception.ConfigException: Invalid + # kube-config file. No configuration found. + # + # Since running a parsl driver script on a kubernetes cluster is a common + # pattern to enable worker-interchange communication, this enables an + # in-cluster config to be loaded if a kube-config file isn't found. + # + # Based on: https://github.com/kubernetes-client/python/issues/1005 + try: + config.load_incluster_config() + except config.config_exception.ConfigException: + raise config.config_exception.ConfigException( + "Failed to load both kube-config file and in-cluster configuration." + ) + + self.namespace = namespace + self.image = image + self.nodes_per_block = nodes_per_block + self.init_blocks = init_blocks + self.min_blocks = min_blocks + self.max_blocks = max_blocks + self.max_cpu = max_cpu + self.max_mem = max_mem + self.init_cpu = init_cpu + self.init_mem = init_mem + self.extra_requests = extra_requests if extra_requests else {} + self.extra_limits = extra_limits if extra_limits else {} + self.parallelism = parallelism + self.worker_init = worker_init + self.secret = secret + self.pod_name = pod_name + self.user_id = user_id + self.group_id = group_id + self.run_as_non_root = run_as_non_root + self.persistent_volumes = persistent_volumes + self.service_account_name = service_account_name + self.annotations = annotations + + self.kube_client = client.CoreV1Api() + + # Dictionary that keeps track of jobs, keyed on job_id + self.resources: Dict[object, Dict[str, Any]] + self.resources = {} + + def submit(self, cmd_string: str, tasks_per_node: int, job_name: str = "parsl.kube"): + """ Submit a job + Args: + - cmd_string :(String) - Name of the container to initiate + - tasks_per_node (int) : command invocations to be launched per node + + Kwargs: + - job_name (String): Name for job + + Returns: + - job_id: (string) Identifier for the job + """ + job_id = uuid.uuid4().hex[:8] + + pod_name = self.pod_name or job_name + try: + pod_name = sanitize_dns_subdomain_rfc1123(pod_name) + except ValueError: + logger.warning( + f"Invalid pod name '{pod_name}' for job '{job_id}', falling back to 'parsl.kube'" + ) + pod_name = "parsl.kube" + pod_name = pod_name[:253 - 1 - len(job_id)] # Leave room for the job ID + pod_name = pod_name.rstrip(".-") # Remove trailing dot or hyphen after trim + pod_name = f"{pod_name}.{job_id}" + + formatted_cmd = template_string.format(command=cmd_string, + worker_init=self.worker_init) + + logger.debug("Pod name: %s", pod_name) + self._create_pod(image=self.image, + pod_name=pod_name, + job_id=job_id, + cmd_string=formatted_cmd, + volumes=self.persistent_volumes, + service_account_name=self.service_account_name, + annotations=self.annotations) + self.resources[job_id] = {'status': JobStatus(JobState.RUNNING), 'pod_name': pod_name} + + return job_id + + def status(self, job_ids): + """ Get the status of a list of jobs identified by the job identifiers + returned from the submit request. + Args: + - job_ids (list) : A list of job identifiers + Returns: + - A list of JobStatus objects corresponding to each job_id in the job_ids list. + Raises: + - ExecutionProviderExceptions or its subclasses + """ + if job_ids: + self._status() + return [self.resources[jid]['status'] for jid in job_ids] + + def _get_pod_name(self, job_id: str) -> str: + return self.resources[job_id]['pod_name'] + + def cancel(self, job_ids): + """ Cancels the jobs specified by a list of job ids + Args: + job_ids : [ ...] + Returns : + [True/False...] : If the cancel operation fails the entire list will be False. + """ + for job in job_ids: + logger.debug("Terminating job/pod: {0}".format(job)) + pod_name = self._get_pod_name(job) + self._delete_pod(pod_name) + + self.resources[job]['status'] = JobStatus(JobState.CANCELLED) + rets = [True for i in job_ids] + + return rets + + def _status(self): + """Returns the status list for a list of job_ids + Args: + self + Returns: + [status...] : Status list of all jobs + """ + + job_ids = list(self.resources.keys()) + to_poll_job_ids = [jid for jid in job_ids if not self.resources[jid]['status'].terminal] + logger.debug("Polling Kubernetes pod status: {}".format(to_poll_job_ids)) + for jid in to_poll_job_ids: + phase = None + try: + pod_name = self._get_pod_name(jid) + pod = self.kube_client.read_namespaced_pod(name=pod_name, namespace=self.namespace) + except Exception: + logger.exception("Failed to poll pod {} status, most likely because pod was terminated".format(jid)) + if self.resources[jid]['status'] is JobStatus(JobState.RUNNING): + phase = 'Unknown' + else: + phase = pod.status.phase + if phase: + status = translate_table.get(phase, JobState.UNKNOWN) + logger.debug("Updating pod {} with status {} to parsl status {}".format(jid, + phase, + status)) + self.resources[jid]['status'] = JobStatus(status) + + def _create_pod(self, + image: str, + pod_name: str, + job_id: str, + port: int = 80, + cmd_string=None, + volumes=[], + service_account_name=None, + annotations=None): + """ Create a kubernetes pod for the job. + Args: + - image (string) : Docker image to launch + - pod_name (string) : Name of the pod + - job_id (string) : Job ID + KWargs: + - port (integer) : Container port + Returns: + - None + """ + + security_context = None + if self.user_id and self.group_id: + security_context = client.V1SecurityContext(run_as_group=int(self.group_id), + run_as_user=int(self.user_id), + run_as_non_root=self.run_as_non_root) + + # Create the environment variables and command to initiate IPP + environment_vars = client.V1EnvVar(name="TEST", value="SOME DATA") + + launch_args = ["-c", "{0}".format(cmd_string)] + + volume_mounts = [] + # Create mount paths for the volumes + for volume in volumes: + volume_mounts.append(client.V1VolumeMount(mount_path=volume[1], + name=volume[0])) + resources = client.V1ResourceRequirements(limits={'cpu': str(self.max_cpu), + 'memory': self.max_mem} | self.extra_limits, + requests={'cpu': str(self.init_cpu), + 'memory': self.init_mem} | self.extra_requests, + ) + # Configure Pod template container + container = client.V1Container( + name=job_id, + image=image, + resources=resources, + ports=[client.V1ContainerPort(container_port=port)], + volume_mounts=volume_mounts, + command=['/bin/bash'], + args=launch_args, + env=[environment_vars], + security_context=security_context) + + # Create a secret to enable pulling images from secure repositories + secrets = None + if self.secret: + secrets = [client.V1LocalObjectReference(name=self.secret)] + + # Create list of volumes from (pvc, mount) tuples + volume_defs = [] + for volume in volumes: + volume_defs.append(client.V1Volume(name=volume[0], + persistent_volume_claim=client.V1PersistentVolumeClaimVolumeSource( + claim_name=volume[0]))) + + metadata = client.V1ObjectMeta(name=pod_name, + labels={"parsl-job-id": job_id}, + annotations=annotations) + spec = client.V1PodSpec(containers=[container], + image_pull_secrets=secrets, + volumes=volume_defs, + service_account_name=service_account_name) + + pod = client.V1Pod(spec=spec, metadata=metadata) + api_response = self.kube_client.create_namespaced_pod(namespace=self.namespace, + body=pod) + logger.debug("Pod created. status='{0}'".format(str(api_response.status))) + + def _delete_pod(self, pod_name): + """Delete a pod""" + + api_response = self.kube_client.delete_namespaced_pod(name=pod_name, + namespace=self.namespace, + body=client.V1DeleteOptions()) + logger.debug("Pod deleted. status='{0}'".format(str(api_response.status))) + + @property + def label(self): + return "kubernetes" + + @property + def status_polling_interval(self): + return 60 diff --git a/parsl/parsl/providers/kubernetes/template.py b/parsl/parsl/providers/kubernetes/template.py new file mode 100644 index 00000000..6693b1ba --- /dev/null +++ b/parsl/parsl/providers/kubernetes/template.py @@ -0,0 +1,4 @@ +template_string = """{worker_init} + +{command} +""" diff --git a/parsl/parsl/providers/local/__init__.py b/parsl/parsl/providers/local/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py new file mode 100644 index 00000000..259885fb --- /dev/null +++ b/parsl/parsl/providers/local/local.py @@ -0,0 +1,275 @@ +import logging +import os +import time + +from parsl.jobs.states import JobState, JobStatus +from parsl.launchers import SingleNodeLauncher +from parsl.providers.base import ExecutionProvider +from parsl.providers.errors import ( + SchedulerMissingArgs, + ScriptPathError, + SubmitException, +) +from parsl.utils import RepresentationMixin, execute_wait + +logger = logging.getLogger(__name__) + + +class LocalProvider(ExecutionProvider, RepresentationMixin): + """ Local Execution Provider + + This provider is used to provide execution resources from the localhost. + + Parameters + ---------- + + min_blocks : int + Minimum number of blocks to maintain. + max_blocks : int + Maximum number of blocks to maintain. + parallelism : float + Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive + scaling where as many resources as possible are used; parallelism close to 0 represents + the opposite situation in which as few resources as possible (i.e., min_blocks) are used. + worker_init : str + Command to be run before starting a worker, such as 'module load Anaconda; source activate env'. + """ + + def __init__(self, + nodes_per_block=1, + launcher=SingleNodeLauncher(), + init_blocks=1, + min_blocks=0, + max_blocks=1, + worker_init='', + cmd_timeout=30, + parallelism=1): + self._label = 'local' + self.nodes_per_block = nodes_per_block + self.launcher = launcher + self.worker_init = worker_init + self.init_blocks = init_blocks + self.min_blocks = min_blocks + self.max_blocks = max_blocks + self.parallelism = parallelism + self.script_dir = None + self.cmd_timeout = cmd_timeout + + # Dictionary that keeps track of jobs, keyed on job_id + self.resources = {} + + def status(self, job_ids): + ''' Get the status of a list of jobs identified by their ids. + + Args: + - job_ids (List of ids) : List of identifiers for the jobs + + Returns: + - List of status codes. + + ''' + + for job_id in self.resources: + # This job dict should really be a class on its own + job_dict = self.resources[job_id] + if job_dict['status'] and job_dict['status'].terminal: + # We already checked this and it can't change after that + continue + script_path = job_dict['script_path'] + + alive = self._is_alive(job_dict) + str_ec = self._read_job_file(script_path, '.ec').strip() + + status = None + if str_ec == '-': + if alive: + status = JobStatus(JobState.RUNNING) + else: + # not alive but didn't get to write an exit code + if 'cancelled' in job_dict: + # because we cancelled it + status = JobStatus(JobState.CANCELLED) + else: + # we didn't cancel it, so it must have been killed by something outside + # parsl; we don't have a state for this, but we'll use CANCELLED with + # a specific message + status = JobStatus(JobState.CANCELLED, message='Killed') + else: + try: + # TODO: ensure that these files are only read once and clean them + ec = int(str_ec) + stdout_path = self._job_file_path(script_path, '.out') + stderr_path = self._job_file_path(script_path, '.err') + if ec == 0: + state = JobState.COMPLETED + else: + state = JobState.FAILED + status = JobStatus(state, exit_code=ec, + stdout_path=stdout_path, stderr_path=stderr_path) + except Exception: + status = JobStatus(JobState.FAILED, + 'Cannot parse exit code: {}'.format(str_ec)) + + job_dict['status'] = status + + return [self.resources[jid]['status'] for jid in job_ids] + + @staticmethod + def _is_alive(job_dict) -> bool: + try: + os.kill(job_dict['remote_pid'], 0) + except ProcessLookupError: + return False + except PermissionError: + pass # exists; just no permissions to send signal + return True + + def _job_file_path(self, script_path: str, suffix: str) -> str: + path = '{0}{1}'.format(script_path, suffix) + return path + + def _read_job_file(self, script_path: str, suffix: str) -> str: + path = self._job_file_path(script_path, suffix) + + with open(path, 'r') as f: + return f.read() + + def _write_submit_script(self, script_string, script_filename): + ''' + Load the template string with config values and write the generated submit script to + a submit script file. + + Args: + - template_string (string) : The template string to be used for the writing submit script + - script_filename (string) : Name of the submit script + + Returns: + - True: on success + + Raises: + SchedulerMissingArgs : If template is missing args + ScriptPathError : Unable to write submit script out + ''' + + try: + with open(script_filename, 'w') as f: + f.write(script_string) + + except KeyError as e: + logger.error("Missing keys for submit script: %s", e) + raise SchedulerMissingArgs(e.args, self.label) + + except IOError as e: + logger.error("Failed writing to submit script: %s", script_filename) + raise ScriptPathError(script_filename, e) + + return True + + def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): + ''' Submits the command onto an Local Resource Manager job. + Submit returns an ID that corresponds to the task that was just submitted. + + If tasks_per_node < 1: + 1/tasks_per_node is provisioned + + If tasks_per_node == 1: + A single node is provisioned + + If tasks_per_node > 1 : + tasks_per_node nodes are provisioned. + + Args: + - command :(String) Commandline invocation to be made on the remote side. + - tasks_per_node (int) : command invocations to be launched per node + + Kwargs: + - job_name (String): Name for job, must be unique + + Returns: + - None: At capacity, cannot provision more + - job_id: (string) Identifier for the job + + ''' + + job_name = "{0}.{1}".format(job_name, time.time()) + + # Set script path + script_path = "{0}/{1}.sh".format(self.script_dir, job_name) + script_path = os.path.abspath(script_path) + + wrap_command = (self.worker_init + f'\nexport JOBNAME={job_name}\n' + + self.launcher(command, tasks_per_node, self.nodes_per_block, self.script_dir)) + + self._write_submit_script(wrap_command, script_path) + + job_id = None + remote_pid = None + + logger.debug("Launching") + # We need to capture the exit code and the streams, so we put them in files. We also write + # '-' to the exit code file to isolate potential problems with writing to files in the + # script directory + # + # The basic flow is: + # 1. write "-" to the exit code file. If this fails, exit + # 2. Launch the following sequence in the background: + # a. the command to run + # b. write the exit code of the command from (a) to the exit code file + # 3. Write the PID of the background sequence on stdout. The PID is needed if we want to + # cancel the task later. + # + # We need to do the >/dev/null 2>&1 so that bash closes stdout, otherwise + # execute_wait hangs reading the process stdout until all the + # background commands complete. + cmd = '/bin/bash -c \'echo - >{0}.ec && {{ {{ bash {0} 1>{0}.out 2>{0}.err ; ' \ + 'echo $? > {0}.ec ; }} >/dev/null 2>&1 & echo "PID:$!" ; }}\''.format(script_path) + retcode, stdout, stderr = execute_wait(cmd, self.cmd_timeout) + print(retcode) + print('stdout:', stdout) + print('stderr:', stderr) + if retcode != 0: + raise SubmitException(job_name, "Launch command exited with code {0}".format(retcode), + stdout, stderr) + for line in stdout.split('\n'): + if line.startswith("PID:"): + job_id = line.split("PID:")[1].strip() + remote_pid = int(job_id) + break + if job_id is None: + raise SubmitException(job_name, "Channel failed to start remote command/retrieve PID") + + self.resources[job_id] = {'job_id': job_id, 'status': JobStatus(JobState.RUNNING), + 'remote_pid': remote_pid, 'script_path': script_path} + + return job_id + + def cancel(self, job_ids): + ''' Cancels the jobs specified by a list of job ids + + Args: + job_ids : [ ...] + + Returns: [True] Always returns true for every job_id, regardless of + whether an individual cancel failed (unless an + exception is raised) + ''' + for job in job_ids: + job_dict = self.resources[job] + job_dict['cancelled'] = True + logger.debug("Terminating job/process ID: {0}".format(job)) + cmd = "kill -- -$(ps -o pgid= {} | grep -o '[0-9]*')".format(job_dict['remote_pid']) + retcode, stdout, stderr = execute_wait(cmd, self.cmd_timeout) + if retcode != 0: + logger.warning("Failed to kill PID: {} and child processes on {}".format(job_dict['remote_pid'], + self.label)) + + rets = [True for i in job_ids] + return rets + + @property + def label(self): + return self._label + + @property + def status_polling_interval(self): + return 5 diff --git a/parsl/parsl/providers/lsf/__init__.py b/parsl/parsl/providers/lsf/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/providers/lsf/lsf.py b/parsl/parsl/providers/lsf/lsf.py new file mode 100644 index 00000000..4c76ef6d --- /dev/null +++ b/parsl/parsl/providers/lsf/lsf.py @@ -0,0 +1,267 @@ +import logging +import math +import os +import time + +from parsl.jobs.states import JobState, JobStatus +from parsl.launchers import SingleNodeLauncher +from parsl.providers.cluster_provider import ClusterProvider +from parsl.providers.lsf.template import template_string +from parsl.utils import RepresentationMixin, wtime_to_minutes + +logger = logging.getLogger(__name__) + +translate_table = { + 'PEND': JobState.PENDING, + 'RUN': JobState.RUNNING, + 'DONE': JobState.COMPLETED, + 'EXIT': JobState.FAILED, # (failed), + 'PSUSP': JobState.CANCELLED, + 'USUSP': JobState.CANCELLED, + 'SSUSP': JobState.CANCELLED, +} + + +class LSFProvider(ClusterProvider, RepresentationMixin): + """LSF Execution Provider + + This provider uses bsub to submit, bjobs for status and bkill to cancel + jobs. The bsub script to be used is created from a template file in this + same module. + + Parameters + ---------- + nodes_per_block : int + Nodes to provision per block. + When request_by_nodes is False, it is computed by cores_per_block / cores_per_node. + cores_per_block : int + Cores to provision per block. Enabled only when request_by_nodes is False. + cores_per_node: int + Cores to provision per node. Enabled only when request_by_nodes is False. + init_blocks : int + Number of blocks to request at the start of the run. + min_blocks : int + Minimum number of blocks to maintain. + max_blocks : int + Maximum number of blocks to maintain. + parallelism : float + Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive + scaling where as many resources as possible are used; parallelism close to 0 represents + the opposite situation in which as few resources as possible (i.e., min_blocks) are used. + walltime : str + Walltime requested per block in HH:MM:SS. + project : str + Project to which the resources must be charged + queue : str + Queue to which to submit the job request + scheduler_options : str + String to prepend to the #BSUB blocks in the submit script to the scheduler. + worker_init : str + Command to be run before starting a worker, such as 'module load Anaconda; source activate env'. + cmd_timeout : int + Seconds after which requests to the scheduler will timeout. Default: 120s + launcher : Launcher + Launcher for this provider. Possible launchers include + :class:`~parsl.launchers.SingleNodeLauncher` (the default), + :class:`~parsl.launchers.SrunLauncher`, or + :class:`~parsl.launchers.AprunLauncher` + bsub_redirection: Bool + Should a redirection symbol "<" be included when submitting jobs, i.e., Bsub < job_script. + request_by_nodes: Bool + Request by nodes or request by cores per block. + When this is set to false, nodes_per_block is computed by cores_per_block / cores_per_node. + Default is True. + """ + + def __init__(self, + nodes_per_block=1, + cores_per_block=None, + cores_per_node=None, + init_blocks=1, + min_blocks=0, + max_blocks=1, + parallelism=1, + walltime="00:10:00", + scheduler_options='', + worker_init='', + project=None, + queue=None, + cmd_timeout=120, + bsub_redirection=False, + request_by_nodes=True, + launcher=SingleNodeLauncher()): + label = 'LSF' + super().__init__(label, + nodes_per_block, + init_blocks, + min_blocks, + max_blocks, + parallelism, + walltime, + cmd_timeout=cmd_timeout, + launcher=launcher) + + self.project = project + self.queue = queue + self.cores_per_block = cores_per_block + self.cores_per_node = cores_per_node + self.bsub_redirection = bsub_redirection + self.request_by_nodes = request_by_nodes + + # Update scheduler options + self.scheduler_options = scheduler_options + "\n" + if project: + self.scheduler_options += "#BSUB -P {}\n".format(project) + if queue: + self.scheduler_options += "#BSUB -q {}\n".format(queue) + if request_by_nodes: + self.scheduler_options += "#BSUB -nnodes {}\n".format(nodes_per_block) + else: + assert cores_per_block is not None and cores_per_node is not None, \ + "Requesting resources by the number of cores. " \ + "Need to specify cores_per_block and cores_per_node in the LSF provider." + + self.scheduler_options += "#BSUB -n {}\n".format(cores_per_block) + self.scheduler_options += '#BSUB -R "span[ptile={}]"\n'.format(cores_per_node) + + # Set nodes_per_block manually for Parsl strategy + assert cores_per_node != 0, "Need to specify a non-zero cores_per_node." + self.nodes_per_block = int(math.ceil(cores_per_block / cores_per_node)) + + self.worker_init = worker_init + + def _status(self): + '''Returns the status list for a list of job_ids + + Args: + self + + Returns: + [status...] : Status list of all jobs + ''' + logger.debug(f"Resources: {self.resources}") + job_id_list = [jid for jid, job in self.resources.items() if not job['status'].terminal] + if not job_id_list: + logger.debug('No active jobs, skipping status update') + return + logger.debug(f"job_id_list: {job_id_list}") + # only request the JOBID and STAT columns from LSF + cmd = f"bjobs -noheader -o 'jobid stat' {' '.join(job_id_list)}" + logger.debug(f"Executing command: {cmd}") + retcode, stdout, stderr = self.execute_wait(cmd) + logger.debug(f"bjobs returned:\nstdout=\n{stdout}stderr=\n{stderr}") + # Execute_wait failed. Do no update + if retcode != 0: + logger.warning(f"bjobs failed with non-zero exit code: {retcode}") + return + + jobs_missing = set(job_id_list) + bjobs_lines = stdout.rstrip('\n').split('\n') + + for line in bjobs_lines: + line_list = line.split() + if len(line_list) != 2: + logger.debug(f"{line_list} length not equal to 2, skipping") + continue + job_id, lsf_state = line_list + if job_id not in job_id_list: + logger.debug(f"job_id {job_id} not in job_id_list, skipping") + continue + if lsf_state not in translate_table: + logger.warning(f"LSF status {lsf_state} is not recognized") + state = translate_table.get(lsf_state, JobState.UNKNOWN) + logger.debug(f"Updating job {job_id} with LSF status {lsf_state} " + f"to parsl state {state}") + self.resources[job_id]['status'] = JobStatus(state) + jobs_missing.remove(job_id) + + # bjobs does not report on jobs that are not running. So we are filling in the + # blanks for missing jobs, we might lose some information about why the jobs failed. + for missing_job in jobs_missing: + logger.debug(f"Updating missing job {missing_job} to completed status") + self.resources[missing_job]['status'] = JobStatus(JobState.COMPLETED) + + def submit(self, command, tasks_per_node, job_name="parsl.lsf"): + """Submit the command as an LSF job. + + Parameters + ---------- + command : str + Command to be made on the remote side. + tasks_per_node : int + Command invocations to be launched per node + job_name : str + Name for the job (must be unique). + Returns + ------- + None or str + If at capacity, returns None; otherwise, a string identifier for the job + """ + + job_name = "{0}.{1}".format(job_name, time.time()) + + script_path = "{0}/{1}.submit".format(self.script_dir, job_name) + script_path = os.path.abspath(script_path) + + logger.debug("Requesting one block with {} nodes".format(self.nodes_per_block)) + + job_config = {} + job_config["submit_script_dir"] = self.script_dir + job_config["nodes"] = self.nodes_per_block + job_config["tasks_per_node"] = tasks_per_node + job_config["walltime"] = wtime_to_minutes(self.walltime) + job_config["scheduler_options"] = self.scheduler_options + job_config["worker_init"] = self.worker_init + job_config["user_script"] = command + + # Wrap the command + job_config["user_script"] = self.launcher(command, + tasks_per_node, + self.nodes_per_block, + self.script_dir) + + logger.debug("Writing submit script") + self._write_submit_script(template_string, script_path, job_name, job_config) + + if self.bsub_redirection: + cmd = "bsub < {0}".format(script_path) + else: + cmd = "bsub {0}".format(script_path) + retcode, stdout, stderr = super().execute_wait(cmd) + + job_id = None + if retcode == 0: + for line in stdout.split('\n'): + if line.lower().startswith("job") and "is submitted to" in line.lower(): + job_id = line.split()[1].strip('<>') + self.resources[job_id] = {'job_id': job_id, 'status': JobStatus(JobState.PENDING)} + else: + logger.warning("Submit command failed") + logger.error("Retcode:%s STDOUT:%s STDERR:%s", retcode, stdout.strip(), stderr.strip()) + return job_id + + def cancel(self, job_ids): + ''' Cancels the jobs specified by a list of job ids + + Args: + job_ids : [ ...] + + Returns : + [True/False...] : If the cancel operation fails the entire list will be False. + ''' + + job_id_list = ' '.join(job_ids) + retcode, stdout, stderr = self.execute_wait("bkill {0}".format(job_id_list)) + rets = None + if retcode == 0: + for jid in job_ids: + self.resources[jid]['status'] = JobStatus(JobState.CANCELLED) # Setting state to cancelled + rets = [True for i in job_ids] + else: + rets = [False for i in job_ids] + + return rets + + @property + def status_polling_interval(self): + return 60 diff --git a/parsl/parsl/providers/lsf/template.py b/parsl/parsl/providers/lsf/template.py new file mode 100644 index 00000000..c3db6ac5 --- /dev/null +++ b/parsl/parsl/providers/lsf/template.py @@ -0,0 +1,14 @@ +template_string = '''#!/bin/bash + +#BSUB -W ${walltime} +#BSUB -J ${jobname} +#BSUB -o ${submit_script_dir}/${jobname}.submit.stdout +#BSUB -e ${submit_script_dir}/${jobname}.submit.stderr +${scheduler_options} + +${worker_init} + +export JOBNAME="${jobname}" + +$user_script +''' diff --git a/parsl/parsl/providers/pbspro/__init__.py b/parsl/parsl/providers/pbspro/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/providers/pbspro/pbspro.py b/parsl/parsl/providers/pbspro/pbspro.py new file mode 100644 index 00000000..5d614412 --- /dev/null +++ b/parsl/parsl/providers/pbspro/pbspro.py @@ -0,0 +1,229 @@ +import json +import logging +import os +import time + +from parsl.jobs.states import JobState, JobStatus +from parsl.launchers import SingleNodeLauncher +from parsl.providers.errors import SubmitException +from parsl.providers.pbspro.template import template_string +from parsl.providers.torque.torque import TorqueProvider, translate_table + +logger = logging.getLogger(__name__) + + +class PBSProProvider(TorqueProvider): + """PBS Pro Execution Provider + + Parameters + ---------- + account : str + Account the job will be charged against. + queue : str + Queue to request blocks from. + nodes_per_block : int + Nodes to provision per block. + cpus_per_node : int + CPUs to provision per node. + init_blocks : int + Number of blocks to provision at the start of the run. Default is 1. + min_blocks : int + Minimum number of blocks to maintain. Default is 0. + max_blocks : int + Maximum number of blocks to maintain. + parallelism : float + Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive + scaling where as many resources as possible are used; parallelism close to 0 represents + the opposite situation in which as few resources as possible (i.e., min_blocks) are used. + walltime : str + Walltime requested per block in HH:MM:SS. + scheduler_options : str + String to prepend to the #PBS blocks in the submit script to the scheduler. + select_options : str + String to append to the #PBS -l select block in the submit script to the scheduler. This can be used to + specify ngpus. + worker_init : str + Command to be run before starting a worker, such as 'module load Anaconda; source activate env'. + launcher : Launcher + Launcher for this provider. The default is + :class:`~parsl.launchers.SingleNodeLauncher`. + """ + def __init__(self, + account=None, + queue=None, + scheduler_options='', + select_options='', + worker_init='', + nodes_per_block=1, + cpus_per_node=1, + init_blocks=1, + min_blocks=0, + max_blocks=1, + parallelism=1, + launcher=SingleNodeLauncher(), + walltime="00:20:00", + cmd_timeout=120): + super().__init__(account, + queue, + scheduler_options, + worker_init, + nodes_per_block, + init_blocks, + min_blocks, + max_blocks, + parallelism, + launcher, + walltime, + cmd_timeout=cmd_timeout) + + self.template_string = template_string + self._label = 'pbspro' + self.cpus_per_node = cpus_per_node + self.select_options = select_options + + def _status(self): + '''Returns the status list for a list of job_ids + + Args: + self + + Returns: + [status...] : Status list of all jobs + ''' + + job_ids = list(self.resources.keys()) + job_id_list = ' '.join(self.resources.keys()) + + jobs_missing = list(self.resources.keys()) + + retcode, stdout, stderr = self.execute_wait("qstat -x -f -F json {0}".format(job_id_list)) + + # If qstat failed do not update job state + if retcode != 0: + logger.warning("qstat failed with retcode:%s STDOUT:%s STDERR:%s", + retcode, + stdout.strip(), + stderr.strip()) + return + + job_statuses = json.loads(stdout) + + if 'Jobs' in job_statuses: + for job_id, job in job_statuses['Jobs'].items(): + for long_job_id in job_ids: + if long_job_id.startswith(job_id): + logger.debug('coerced job_id %s -> %s', job_id, long_job_id) + job_id = long_job_id + break + + job_state = job.get('job_state', JobState.UNKNOWN) + state = translate_table.get(job_state, JobState.UNKNOWN) + self.resources[job_id]['status'] = JobStatus(state, + stdout_path=self.resources[job_id]['job_stdout_path'], + stderr_path=self.resources[job_id]['job_stderr_path']) + jobs_missing.remove(job_id) + + # squeue does not report on jobs that are not running. So we are filling in the + # blanks for missing jobs, we might lose some information about why the jobs failed. + for missing_job in jobs_missing: + self.resources[missing_job]['status'] = JobStatus(JobState.COMPLETED, + stdout_path=self.resources[missing_job]['job_stdout_path'], + stderr_path=self.resources[missing_job]['job_stderr_path']) + + def submit(self, command, tasks_per_node, job_name="parsl"): + """Submits the command job. + + Parameters + ---------- + command : str + Command to be executed on the remote side. + tasks_per_node : int + Command invocations to be launched per node. + job_name : str + Identifier for job. + + Returns + ------- + None + If at capacity and cannot provision more + job_id : str + Identifier for the job + """ + + job_name = "{0}.{1}".format(job_name, time.time()) + + assert self.script_dir, "Expected script_dir to be set" + script_path = os.path.join(self.script_dir, job_name) + script_path = os.path.abspath(script_path) + job_stdout_path = script_path + ".stdout" + job_stderr_path = script_path + ".stderr" + + logger.debug("Requesting {} nodes_per_block, {} tasks_per_node".format( + self.nodes_per_block, tasks_per_node) + ) + + job_config = {} + job_config["submit_script_dir"] = self.script_dir + job_config["nodes_per_block"] = self.nodes_per_block + job_config["ncpus"] = self.cpus_per_node + job_config["walltime"] = self.walltime + job_config["scheduler_options"] = self.scheduler_options + job_config["worker_init"] = self.worker_init + job_config["user_script"] = command + job_config["job_stdout_path"] = job_stdout_path + job_config["job_stderr_path"] = job_stderr_path + + # Add a colon to select_options if one isn't included + if self.select_options and not self.select_options.startswith(":"): + self.select_options = ":" + self.select_options + + job_config["select_options"] = self.select_options + + # Wrap the command + job_config["user_script"] = self.launcher(command, + tasks_per_node, + self.nodes_per_block, + self.script_dir) + + logger.debug("Writing submit script") + self._write_submit_script(self.template_string, script_path, job_name, job_config) + + submit_options = '' + if self.queue is not None: + submit_options = '{0} -q {1}'.format(submit_options, self.queue) + if self.account is not None: + submit_options = '{0} -A {1}'.format(submit_options, self.account) + + launch_cmd = "qsub {0} {1}".format(submit_options, script_path) + retcode, stdout, stderr = self.execute_wait(launch_cmd) + + job_id = None + if retcode == 0: + for line in stdout.split('\n'): + if line.strip(): + job_id = line.strip() + self.resources[job_id] = {'job_id': job_id, + 'status': JobStatus(JobState.PENDING), + 'job_stdout_path': job_stdout_path, + 'job_stderr_path': job_stderr_path, + } + else: + message = f"Submit command '{launch_cmd}' failed" + logger.error( + f"{message}\n" + f" Return code: {retcode}\n" + f" STDOUT: {stdout.strip()}\n" + f" STDERR: {stderr.strip()}" + ) + raise SubmitException( + job_name=job_name, + message=message, + stdout=stdout, + stderr=stderr, + ) + + return job_id + + @property + def status_polling_interval(self): + return 60 diff --git a/parsl/parsl/providers/pbspro/template.py b/parsl/parsl/providers/pbspro/template.py new file mode 100644 index 00000000..1bccad8f --- /dev/null +++ b/parsl/parsl/providers/pbspro/template.py @@ -0,0 +1,18 @@ +template_string = '''#!/bin/bash + +#PBS -S /bin/bash +#PBS -N ${jobname} +#PBS -m n +#PBS -l walltime=$walltime +#PBS -l select=${nodes_per_block}:ncpus=${ncpus}${select_options} +#PBS -o ${job_stdout_path} +#PBS -e ${job_stderr_path} +${scheduler_options} + +${worker_init} + +export JOBNAME="${jobname}" + +${user_script} + +''' diff --git a/parsl/parsl/providers/slurm/__init__.py b/parsl/parsl/providers/slurm/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/providers/slurm/slurm.py b/parsl/parsl/providers/slurm/slurm.py new file mode 100644 index 00000000..3086e3e7 --- /dev/null +++ b/parsl/parsl/providers/slurm/slurm.py @@ -0,0 +1,414 @@ +import logging +import math +import os +import re +import sys +import time +from typing import Any, Dict, Optional + +import typeguard + +from parsl.jobs.states import JobState, JobStatus +from parsl.launchers import SingleNodeLauncher +from parsl.launchers.base import Launcher +from parsl.providers.cluster_provider import ClusterProvider +from parsl.providers.errors import SubmitException +from parsl.providers.slurm.template import template_string +from parsl.utils import RepresentationMixin, wtime_to_minutes + +logger = logging.getLogger(__name__) + +# From https://slurm.schedmd.com/sacct.html#SECTION_JOB-STATE-CODES +sacct_translate_table = { + 'PENDING': JobState.PENDING, + 'RUNNING': JobState.RUNNING, + 'CANCELLED': JobState.CANCELLED, + 'COMPLETED': JobState.COMPLETED, + 'FAILED': JobState.FAILED, + 'NODE_FAIL': JobState.FAILED, + 'BOOT_FAIL': JobState.FAILED, + 'DEADLINE': JobState.TIMEOUT, + 'TIMEOUT': JobState.TIMEOUT, + 'REVOKED': JobState.FAILED, + 'OUT_OF_MEMORY': JobState.FAILED, + 'SUSPENDED': JobState.HELD, + 'PREEMPTED': JobState.TIMEOUT, + 'REQUEUED': JobState.PENDING +} + +squeue_translate_table = { + 'PD': JobState.PENDING, + 'R': JobState.RUNNING, + 'CA': JobState.CANCELLED, + 'CF': JobState.PENDING, # (configuring), + 'CG': JobState.RUNNING, # (completing), + 'CD': JobState.COMPLETED, + 'F': JobState.FAILED, # (failed), + 'TO': JobState.TIMEOUT, # (timeout), + 'NF': JobState.FAILED, # (node failure), + 'RV': JobState.FAILED, # (revoked) and + 'SE': JobState.FAILED # (special exit state) +} + + +if sys.version_info < (3, 12): + from itertools import islice + from typing import Iterable + + def batched( + iterable: Iterable[tuple[object, Any]], n: int, *, strict: bool = False + ): + """Batched + Turns a list into a batch of size n. This code is from + https://docs.python.org/3.12/library/itertools.html#itertools.batched + and in versions 3.12+ this can be replaced with + itertools.batched + """ + if n < 1: + raise ValueError("n must be at least one") + iterator = iter(iterable) + while batch := tuple(islice(iterator, n)): + yield batch +else: + from itertools import batched + + +class SlurmProvider(ClusterProvider, RepresentationMixin): + """Slurm Execution Provider + + This provider uses sbatch to submit, sacct for status and scancel to cancel + jobs. The sbatch script to be used is created from a template file in this + same module. + + Parameters + ---------- + partition : str + Slurm partition to request blocks from. If unspecified or ``None``, no partition slurm directive will be specified. + account : str + Slurm account to which to charge resources used by the job. If unspecified or ``None``, the job will use the + user's default account. + qos : str + Slurm queue to place job in. If unspecified or ``None``, no queue slurm directive will be specified. + constraint : str + Slurm job constraint, often used to choose cpu or gpu type. If unspecified or ``None``, no constraint slurm directive will be added. + clusters : str + Slurm cluster name, or comma seperated cluster list, used to choose between different clusters in a federated Slurm instance. + If unspecified or ``None``, no slurm directive for clusters will be added. + nodes_per_block : int + Nodes to provision per block. + cores_per_node : int + Specify the number of cores to provision per node. If set to None, executors + will assume all cores on the node are available for computation. Default is None. + mem_per_node : int + Specify the real memory to provision per node in GB. If set to None, no + explicit request to the scheduler will be made. Default is None. + gpus_per_node: int + Specify the number of GPUs to provision per node. If set to None, executors + request no gpus. Default: None + gres: str + Slurm GRES options to request arbitrary resources. + `Refer to slurm docs `_ + init_blocks : int + Number of blocks to provision at the start of the run. Default is 1. + min_blocks : int + Minimum number of blocks to maintain. + max_blocks : int + Maximum number of blocks to maintain. + parallelism : float + Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive + scaling where as many resources as possible are used; parallelism close to 0 represents + the opposite situation in which as few resources as possible (i.e., min_blocks) are used. + walltime : str + Walltime requested per block in HH:MM:SS. + scheduler_options : str + String to prepend to the #SBATCH blocks in the submit script to the scheduler. + regex_job_id : str + The regular expression used to extract the job ID from the ``sbatch`` standard output. + The default is ``r"Submitted batch job (?P\\S*)"``, where ``id`` is the regular expression + symbolic group for the job ID. + worker_init : str + Command to be run before starting a worker, such as 'module load Anaconda; source activate env'. + cmd_timeout : int (Default = 10) + Number of seconds to wait for slurm commands to finish. For schedulers with many this + may need to be increased to wait longer for scheduler information. + status_batch_size: int (Default = 50) + Number of jobs to batch together in calls to the scheduler status. For schedulers + with many jobs this may need to be decreased to get jobs in smaller batches. + exclusive : bool (Default = True) + Requests nodes which are not shared with other running jobs. + launcher : Launcher + Launcher for this provider. Possible launchers include + :class:`~parsl.launchers.SingleNodeLauncher` (the default), + :class:`~parsl.launchers.SrunLauncher`, or + :class:`~parsl.launchers.AprunLauncher` + """ + + @typeguard.typechecked + def __init__(self, + partition: Optional[str] = None, + account: Optional[str] = None, + qos: Optional[str] = None, + constraint: Optional[str] = None, + clusters: Optional[str] = None, + nodes_per_block: int = 1, + cores_per_node: Optional[int] = None, + mem_per_node: Optional[int] = None, + gpus_per_node: Optional[int] = None, + gres: Optional[str] = None, + init_blocks: int = 1, + min_blocks: int = 0, + max_blocks: int = 1, + parallelism: float = 1, + walltime: str = "00:10:00", + scheduler_options: str = '', + regex_job_id: str = r"Submitted batch job (?P\S*)", + worker_init: str = '', + cmd_timeout: int = 10, + status_batch_size: int = 50, + exclusive: bool = True, + launcher: Launcher = SingleNodeLauncher()): + label = 'slurm' + super().__init__(label, + nodes_per_block, + init_blocks, + min_blocks, + max_blocks, + parallelism, + walltime, + cmd_timeout=cmd_timeout, + launcher=launcher) + + self.partition = partition + self.cores_per_node = cores_per_node + self.mem_per_node = mem_per_node + self.gpus_per_node = gpus_per_node + self.gres = gres + self.exclusive = exclusive + self.account = account + self.qos = qos + self.constraint = constraint + self.clusters = clusters + # Used to batch requests to sacct/squeue for long jobs lists + self.status_batch_size = status_batch_size + self.scheduler_options = scheduler_options + '\n' + if exclusive: + self.scheduler_options += "#SBATCH --exclusive\n" + if partition: + self.scheduler_options += "#SBATCH --partition={}\n".format(partition) + if account: + self.scheduler_options += "#SBATCH --account={}\n".format(account) + if qos: + self.scheduler_options += "#SBATCH --qos={}\n".format(qos) + if constraint: + self.scheduler_options += "#SBATCH --constraint={}\n".format(constraint) + if clusters: + self.scheduler_options += "#SBATCH --clusters={}\n".format(clusters) + if gpus_per_node: + self.scheduler_options += "#SBATCH --gpus-per-node={}\n".format(gpus_per_node) + if gres: + self.scheduler_options += "#SBATCH --gres={}\n".format(gres) + + self.regex_job_id = regex_job_id + self.worker_init = worker_init + '\n' + # Check if sacct works and if not fall back to squeue + cmd = "sacct -X" + logger.debug("Executing %s", cmd) + retcode, stdout, stderr = self.execute_wait(cmd) + # If sacct fails it should return retcode=1 stderr="Slurm accounting storage is disabled" + logger.debug(f"sacct returned retcode={retcode} stderr={stderr}") + if retcode == 0: + logger.debug("using sacct to get job status") + _cmd = "sacct" + # Add clusters option to sacct if provided + if self.clusters: + _cmd += f" --clusters={self.clusters}" + # Using state%20 to get enough characters to not truncate output + # of the state. Without output can look like " CANCELLED+" + self._cmd = _cmd + " -X --noheader --format=jobid,state%20 --job '{0}'" + self._translate_table = sacct_translate_table + else: + logger.debug(f"sacct failed with retcode={retcode}") + logger.debug("falling back to using squeue to get job status") + _cmd = "squeue" + # Add clusters option to squeue if provided + if self.clusters: + _cmd += f" --clusters={self.clusters}" + self._cmd = _cmd + " --noheader --format='%i %t' --job '{0}'" + self._translate_table = squeue_translate_table + + def _status(self): + '''Returns the status list for a list of job_ids + + Args: + self + + Returns: + [status...] : Status list of all jobs + ''' + active_jobs = {jid: job for jid, job in self.resources.items() if not job["status"].terminal} + if len(active_jobs) == 0: + logger.debug("No active jobs, skipping status update") + return + + stdout = "" + for job_batch in batched(active_jobs.items(), self.status_batch_size): + job_id_list = ",".join(jid for jid, job in job_batch if not job["status"].terminal) + cmd = self._cmd.format(job_id_list) + logger.debug("Executing %s", cmd) + retcode, batch_stdout, batch_stderr = self.execute_wait(cmd) + logger.debug(f"sacct/squeue returned {retcode} {batch_stdout} {batch_stderr}") + stdout += batch_stdout + # Execute_wait failed. Do no update + if retcode != 0: + logger.warning("sacct/squeue failed with non-zero exit code {}".format(retcode)) + return + + jobs_missing = set(self.resources.keys()) + for line in stdout.split('\n'): + if not line: + # Blank line + continue + # Sacct includes extra information in some outputs + # For example " CANCELLED by " + # This splits and ignores anything past the first two unpacked values + job_id, slurm_state, *ignore = line.split() + if slurm_state not in self._translate_table: + logger.warning(f"Slurm status {slurm_state} is not recognized") + status = self._translate_table.get(slurm_state, JobState.UNKNOWN) + logger.debug("Updating job {} with slurm status {} to parsl state {!s}".format(job_id, slurm_state, status)) + self.resources[job_id]['status'] = JobStatus(status, + stdout_path=self.resources[job_id]['job_stdout_path'], + stderr_path=self.resources[job_id]['job_stderr_path']) + jobs_missing.remove(job_id) + + # sacct can get job info after jobs have completed so this path shouldn't be hit + # squeue does not report on jobs that are not running. So we are filling in the + # blanks for missing jobs, we might lose some information about why the jobs failed. + for missing_job in jobs_missing: + logger.debug("Updating missing job {} to completed status".format(missing_job)) + self.resources[missing_job]['status'] = JobStatus( + JobState.COMPLETED, stdout_path=self.resources[missing_job]['job_stdout_path'], + stderr_path=self.resources[missing_job]['job_stderr_path']) + + def submit(self, command: str, tasks_per_node: int, job_name="parsl.slurm") -> str: + """Submit the command as a slurm job. + + Parameters + ---------- + command : str + Command to be made on the remote side. + tasks_per_node : int + Command invocations to be launched per node + job_name : str + Name for the job + Returns + ------- + job id : str + A string identifier for the job + """ + + scheduler_options = self.scheduler_options + worker_init = self.worker_init + if self.mem_per_node is not None: + scheduler_options += '#SBATCH --mem={}g\n'.format(self.mem_per_node) + worker_init += 'export PARSL_MEMORY_GB={}\n'.format(self.mem_per_node) + if self.cores_per_node is not None: + cpus_per_task = math.floor(self.cores_per_node / tasks_per_node) + scheduler_options += '#SBATCH --cpus-per-task={}'.format(cpus_per_task) + worker_init += 'export PARSL_CORES={}\n'.format(cpus_per_task) + + job_name = "{0}.{1}".format(job_name, time.time()) + + assert self.script_dir, "Expected script_dir to be set" + script_path = os.path.join(self.script_dir, job_name) + script_path = os.path.abspath(script_path) + job_stdout_path = script_path + ".stdout" + job_stderr_path = script_path + ".stderr" + + logger.debug("Requesting one block with {} nodes".format(self.nodes_per_block)) + + job_config: Dict[str, Any] = {} + job_config["submit_script_dir"] = self.script_dir + job_config["nodes"] = self.nodes_per_block + job_config["tasks_per_node"] = tasks_per_node + job_config["walltime"] = wtime_to_minutes(self.walltime) + job_config["scheduler_options"] = scheduler_options + job_config["worker_init"] = worker_init + job_config["user_script"] = command + job_config["job_stdout_path"] = job_stdout_path + job_config["job_stderr_path"] = job_stderr_path + + # Wrap the command + job_config["user_script"] = self.launcher(command, + tasks_per_node, + self.nodes_per_block, + self.script_dir) + + logger.debug("Writing submit script") + self._write_submit_script(template_string, script_path, job_name, job_config) + + retcode, stdout, stderr = self.execute_wait("sbatch {0}".format(script_path)) + + if retcode == 0: + for line in stdout.split('\n'): + match = re.match(self.regex_job_id, line) + if match: + job_id = match.group("id") + self.resources[job_id] = {'job_id': job_id, + 'status': JobStatus(JobState.PENDING), + 'job_stdout_path': job_stdout_path, + 'job_stderr_path': job_stderr_path, + } + return job_id + else: + logger.error("Could not read job ID from submit command standard output.") + logger.error("Retcode:%s STDOUT:%s STDERR:%s", retcode, stdout.strip(), stderr.strip()) + raise SubmitException( + job_name, + "Could not read job ID from submit command standard output", + stdout=stdout, + stderr=stderr, + retcode=retcode + ) + else: + logger.error("Submit command failed") + logger.error("Retcode:%s STDOUT:%s STDERR:%s", retcode, stdout.strip(), stderr.strip()) + raise SubmitException( + job_name, "Could not read job ID from submit command standard output", + stdout=stdout, + stderr=stderr, + retcode=retcode + ) + + def cancel(self, job_ids): + ''' Cancels the jobs specified by a list of job ids + + Args: + job_ids : [ ...] + + Returns : + [True/False...] : If the cancel operation fails the entire list will be False. + ''' + + job_id_list = ' '.join(job_ids) + + # Make the command to cancel jobs + _cmd = "scancel" + if self.clusters: + _cmd += f" --clusters={self.clusters}" + _cmd += " {0}" + + retcode, stdout, stderr = self.execute_wait(_cmd.format(job_id_list)) + rets = None + if retcode == 0: + for jid in job_ids: + self.resources[jid]['status'] = JobStatus(JobState.CANCELLED) # Setting state to cancelled + rets = [True for i in job_ids] + else: + rets = [False for i in job_ids] + + return rets + + @property + def status_polling_interval(self): + return 60 diff --git a/parsl/parsl/providers/slurm/template.py b/parsl/parsl/providers/slurm/template.py new file mode 100644 index 00000000..3b66d274 --- /dev/null +++ b/parsl/parsl/providers/slurm/template.py @@ -0,0 +1,16 @@ +template_string = '''#!/bin/bash + +#SBATCH --job-name=${jobname} +#SBATCH --output=${job_stdout_path} +#SBATCH --error=${job_stderr_path} +#SBATCH --nodes=${nodes} +#SBATCH --time=${walltime} +#SBATCH --ntasks-per-node=${tasks_per_node} +${scheduler_options} + +${worker_init} + +export JOBNAME="${jobname}" + +$user_script +''' diff --git a/parsl/parsl/providers/torque/__init__.py b/parsl/parsl/providers/torque/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/providers/torque/template.py b/parsl/parsl/providers/torque/template.py new file mode 100644 index 00000000..c01f86e6 --- /dev/null +++ b/parsl/parsl/providers/torque/template.py @@ -0,0 +1,18 @@ +template_string = '''#!/bin/bash + +${scheduler_options} +#PBS -S /bin/bash +#PBS -N ${jobname} +#PBS -m n +#PBS -l walltime=$walltime +#PBS -l nodes=${nodes_per_block}:ppn=${tasks_per_node} +#PBS -o ${submit_script_dir}/${jobname}.submit.stdout +#PBS -e ${submit_script_dir}/${jobname}.submit.stderr + +${worker_init} + +export JOBNAME="${jobname}" + +${user_script} + +''' diff --git a/parsl/parsl/providers/torque/torque.py b/parsl/parsl/providers/torque/torque.py new file mode 100644 index 00000000..0bbf28ec --- /dev/null +++ b/parsl/parsl/providers/torque/torque.py @@ -0,0 +1,238 @@ +import logging +import os +import time + +from parsl.jobs.states import JobState, JobStatus +from parsl.launchers import AprunLauncher +from parsl.providers.cluster_provider import ClusterProvider +from parsl.providers.torque.template import template_string +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + +# From the man pages for qstat for PBS/Torque systems +translate_table = { + 'B': JobState.RUNNING, # This state is returned for running array jobs + 'R': JobState.RUNNING, + 'C': JobState.COMPLETED, # Completed after having run (TORQUE) + 'E': JobState.COMPLETED, # Exiting after having run + 'F': JobState.COMPLETED, # Finished (PBS Pro) + 'H': JobState.HELD, # Held + 'Q': JobState.PENDING, # Queued, and eligible to run + 'W': JobState.PENDING, # Job is waiting for it's execution time (-a option) to be reached + 'S': JobState.HELD, # Suspended + 'T': JobState.PENDING, # Transiting (PBS Pro) + 'U': JobState.HELD, # User-suspended (PBS Pro) + 'X': JobState.COMPLETED, # Subjob finished (PBS Pro) +} + + +class TorqueProvider(ClusterProvider, RepresentationMixin): + """Torque Execution Provider + + This provider uses qsub to submit, qstat for status, and qdel to cancel + jobs. The qsub script to be used is created from a template file in this + same module. + + Parameters + ---------- + account : str + Account the job will be charged against. + queue : str + Torque queue to request blocks from. + nodes_per_block : int + Nodes to provision per block. + init_blocks : int + Number of blocks to provision at the start of the run. Default is 1. + min_blocks : int + Minimum number of blocks to maintain. Default is 0. + max_blocks : int + Maximum number of blocks to maintain. + parallelism : float + Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive + scaling where as many resources as possible are used; parallelism close to 0 represents + the opposite situation in which as few resources as possible (i.e., min_blocks) are used. + walltime : str + Walltime requested per block in HH:MM:SS. + scheduler_options : str + String to prepend to the #PBS blocks in the submit script to the scheduler. + WARNING: scheduler_options should only be given #PBS strings, and should not have trailing newlines. + worker_init : str + Command to be run before starting a worker, such as 'module load Anaconda; source activate env'. + launcher : Launcher + Launcher for this provider. Possible launchers include + :class:`~parsl.launchers.AprunLauncher` (the default), or + :class:`~parsl.launchers.SingleNodeLauncher`, + + """ + def __init__(self, + account=None, + queue=None, + scheduler_options='', + worker_init='', + nodes_per_block=1, + init_blocks=1, + min_blocks=0, + max_blocks=1, + parallelism=1, + launcher=AprunLauncher(), + walltime="00:20:00", + cmd_timeout=120): + label = 'torque' + super().__init__(label, + nodes_per_block, + init_blocks, + min_blocks, + max_blocks, + parallelism, + walltime, + launcher, + cmd_timeout=cmd_timeout) + + self.account = account + self.queue = queue + self.scheduler_options = scheduler_options + self.worker_init = worker_init + self.template_string = template_string + + # Dictionary that keeps track of jobs, keyed on job_id + self.resources = {} + + def _status(self): + '''Returns the status list for a list of job_ids + + Args: + self + + Returns: + [status...] : Status list of all jobs + ''' + + job_ids = list(self.resources.keys()) + job_id_list = ' '.join(self.resources.keys()) + + jobs_missing = list(self.resources.keys()) + + retcode, stdout, stderr = self.execute_wait("qstat {0}".format(job_id_list)) + for line in stdout.split('\n'): + parts = line.split() + if not parts or parts[0].upper().startswith('JOB') or parts[0].startswith('---'): + continue + job_id = parts[0] # likely truncated + for long_job_id in job_ids: + if long_job_id.startswith(job_id): + logger.debug('coerced job_id %s -> %s', job_id, long_job_id) + job_id = long_job_id + break + state = translate_table.get(parts[4], JobState.UNKNOWN) + self.resources[job_id]['status'] = JobStatus(state) + jobs_missing.remove(job_id) + + # squeue does not report on jobs that are not running. So we are filling in the + # blanks for missing jobs, we might lose some information about why the jobs failed. + for missing_job in jobs_missing: + self.resources[missing_job]['status'] = JobStatus(JobState.COMPLETED) + + def submit(self, command, tasks_per_node, job_name="parsl.torque"): + ''' Submits the command onto an Local Resource Manager job. + Submit returns an ID that corresponds to the task that was just submitted. + + If tasks_per_node < 1 : ! This is illegal. tasks_per_node should be integer + + If tasks_per_node == 1: + A single node is provisioned + + If tasks_per_node > 1 : + tasks_per_node number of nodes are provisioned. + + Args: + - command :(String) Commandline invocation to be made on the remote side. + - tasks_per_node (int) : command invocations to be launched per node + + Kwargs: + - job_name (String): Name for job, must be unique + + Returns: + - None: At capacity, cannot provision more + - job_id: (string) Identifier for the job + + ''' + + # Set job name + job_name = "parsl.{0}.{1}".format(job_name, time.time()) + + # Set script path + script_path = "{0}/{1}.submit".format(self.script_dir, job_name) + script_path = os.path.abspath(script_path) + + logger.debug("Requesting nodes_per_block:%s tasks_per_node:%s", self.nodes_per_block, + tasks_per_node) + + job_config = {} + job_config["submit_script_dir"] = self.script_dir + job_config["nodes"] = self.nodes_per_block + job_config["task_blocks"] = self.nodes_per_block * tasks_per_node + job_config["nodes_per_block"] = self.nodes_per_block + job_config["tasks_per_node"] = tasks_per_node + job_config["walltime"] = self.walltime + job_config["scheduler_options"] = self.scheduler_options + job_config["worker_init"] = self.worker_init + job_config["user_script"] = command + + # Wrap the command + job_config["user_script"] = self.launcher(command, + tasks_per_node, + self.nodes_per_block, + self.script_dir) + + logger.debug("Writing submit script") + self._write_submit_script(self.template_string, script_path, job_name, job_config) + + submit_options = '' + if self.queue is not None: + submit_options = '{0} -q {1}'.format(submit_options, self.queue) + if self.account is not None: + submit_options = '{0} -A {1}'.format(submit_options, self.account) + + launch_cmd = "qsub {0} {1}".format(submit_options, script_path) + retcode, stdout, stderr = self.execute_wait(launch_cmd) + + job_id = None + if retcode == 0: + for line in stdout.split('\n'): + if line.strip(): + job_id = line.strip() + self.resources[job_id] = {'job_id': job_id, 'status': JobStatus(JobState.PENDING)} + else: + message = "Command '{}' failed with return code {}".format(launch_cmd, retcode) + if (stdout is not None) and (stderr is not None): + message += "\nstderr:{}\nstdout{}".format(stderr.strip(), stdout.strip()) + logger.error(message) + + return job_id + + def cancel(self, job_ids): + ''' Cancels the jobs specified by a list of job ids + + Args: + job_ids : [ ...] + + Returns : + [True/False...] : If the cancel operation fails the entire list will be False. + ''' + + job_id_list = ' '.join(job_ids) + retcode, stdout, stderr = self.execute_wait("qdel {0}".format(job_id_list)) + rets = None + if retcode == 0: + for jid in job_ids: + self.resources[jid]['status'] = JobStatus(JobState.COMPLETED) # Setting state to exiting + rets = [True for i in job_ids] + else: + rets = [False for i in job_ids] + + return rets + + @property + def status_polling_interval(self): + return 60 diff --git a/parsl/parsl/py.typed b/parsl/parsl/py.typed new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/serialize/__init__.py b/parsl/parsl/serialize/__init__.py new file mode 100644 index 00000000..9f8f6c9c --- /dev/null +++ b/parsl/parsl/serialize/__init__.py @@ -0,0 +1,13 @@ +from parsl.serialize.facade import ( + deserialize, + pack_apply_message, + serialize, + unpack_apply_message, +) + +__all__ = ( + "serialize", + "deserialize", + "pack_apply_message", + "unpack_apply_message", +) diff --git a/parsl/parsl/serialize/base.py b/parsl/parsl/serialize/base.py new file mode 100644 index 00000000..188c68db --- /dev/null +++ b/parsl/parsl/serialize/base.py @@ -0,0 +1,38 @@ +import logging +from abc import abstractmethod +from functools import cached_property +from typing import Any + +logger = logging.getLogger(__name__) + + +class SerializerBase: + """ Adds shared functionality for all serializer implementations + """ + + @cached_property + def identifier(self) -> bytes: + """Compute identifier used in serialization header. + This will be used to indicate in byte streams that this class should + be used for deserialization. + + Serializers that use identifiers that don't align with the way this is + computed (such as the default concretes.py implementations) should + override this property with their own identifier. + + Returns + ------- + identifier : bytes + """ + t = type(self) + m = bytes(t.__module__, encoding="utf-8") + c = bytes(t.__name__, encoding="utf-8") + return m + b' ' + c + + @abstractmethod + def serialize(self, data: Any) -> bytes: + pass + + @abstractmethod + def deserialize(self, payload: bytes) -> Any: + pass diff --git a/parsl/parsl/serialize/concretes.py b/parsl/parsl/serialize/concretes.py new file mode 100644 index 00000000..40ad08be --- /dev/null +++ b/parsl/parsl/serialize/concretes.py @@ -0,0 +1,65 @@ +import functools +import logging +import pickle + +import dill + +logger = logging.getLogger(__name__) +from typing import Any + +from parsl.serialize.base import SerializerBase + + +class PickleSerializer(SerializerBase): + """ Pickle serialization covers most python objects, with some notable exceptions: + + * functions defined in a interpreter/notebook + * classes defined in local context and not importable using a fully qualified name + * closures, generators and coroutines + """ + + identifier = b'01' + + def serialize(self, data: Any) -> bytes: + return pickle.dumps(data) + + def deserialize(self, body: bytes) -> Any: + return pickle.loads(body) + + +class DillSerializer(SerializerBase): + """ Dill serialization works on a superset of object including the ones covered by pickle. + However for most cases pickle is faster. For most callable objects the additional overhead + of dill can be amortized with an lru_cache. Here's items that dill handles that pickle + doesn't: + + * functions defined in a interpreter/notebook + * classes defined in local context and not importable using a fully qualified name + * functions that are wrapped/decorated by other functions/classes + * closures + """ + + identifier = b'02' + + def serialize(self, data: Any) -> bytes: + return dill.dumps(data) + + def deserialize(self, body: bytes) -> Any: + return dill.loads(body) + + +class DillCallableSerializer(SerializerBase): + """This serializer is a variant of the DillSerializer that will + serialize and deserialize callables using an lru_cache, under the + assumption that callables are immutable and so can be cached. + """ + + identifier = b'C2' + + @functools.lru_cache + def serialize(self, data: Any) -> bytes: + return dill.dumps(data) + + @functools.lru_cache + def deserialize(self, body: bytes) -> Any: + return dill.loads(body) diff --git a/parsl/parsl/serialize/errors.py b/parsl/parsl/serialize/errors.py new file mode 100644 index 00000000..21cfeefb --- /dev/null +++ b/parsl/parsl/serialize/errors.py @@ -0,0 +1,35 @@ +from parsl.errors import ParslError + + +class DeserializationError(ParslError): + """Failure at the deserialization of results/exceptions from remote workers. + """ + + def __init__(self, reason: str) -> None: + self.reason = reason + + def __str__(self) -> str: + return f"Failed to deserialize objects. Reason: {self.reason}" + + +class SerializationError(ParslError): + """Failure to serialize task objects. + """ + + def __init__(self, fname: str) -> None: + self.fname = fname + self.troubleshooting = "https://parsl.readthedocs.io/en/latest/faq.html#addressing-serializationerror" + + def __str__(self) -> str: + return f"Failed to serialize objects for an invocation of function {self.fname}. Refer {self.troubleshooting}" + + +class DeserializerPluginError(ParslError): + """Failure to dynamically load a deserializer plugin. + """ + + def __init__(self, header: bytes) -> None: + self.header = header + + def __str__(self) -> str: + return f"Failed to load deserializer plugin for header {self.header!r}" diff --git a/parsl/parsl/serialize/facade.py b/parsl/parsl/serialize/facade.py new file mode 100644 index 00000000..87165795 --- /dev/null +++ b/parsl/parsl/serialize/facade.py @@ -0,0 +1,180 @@ +import importlib +import logging +from typing import Any, Dict, List, Union + +import parsl.serialize.concretes as concretes +from parsl.serialize.base import SerializerBase +from parsl.serialize.errors import DeserializerPluginError + +logger = logging.getLogger(__name__) + + +methods_for_code: Dict[bytes, SerializerBase] = {} + + +def register_method_for_code(s: SerializerBase) -> None: + methods_for_code[s.identifier] = s + + +register_method_for_code(concretes.DillCallableSerializer()) + + +methods_for_data: Dict[bytes, SerializerBase] = {} + + +def register_method_for_data(s: SerializerBase) -> None: + methods_for_data[s.identifier] = s + + +register_method_for_data(concretes.DillSerializer()) + + +# When deserialize dynamically loads a deserializer, it will be stored here, +# rather than in the methods_for_* dictionaries, so that loading does not +# cause it to be used for future serializations. +additional_methods_for_deserialization: Dict[bytes, SerializerBase] = {} + + +def pack_apply_message(func: Any, args: Any, kwargs: Any, buffer_threshold: int = int(128 * 1e6)) -> bytes: + """Serialize and pack function and parameters + + Parameters + ---------- + + func: Function + A function to ship + + args: Tuple/list of objects + positional parameters as a list + + kwargs: Dict + Dict containing named parameters + + buffer_threshold: int + Limits buffer to specified size in bytes. Exceeding this limit would give you + a warning in the log. Default is 128MB. + """ + b_func = serialize(func, buffer_threshold=buffer_threshold) + b_args = serialize(args, buffer_threshold=buffer_threshold) + b_kwargs = serialize(kwargs, buffer_threshold=buffer_threshold) + packed_buffer = pack_buffers([b_func, b_args, b_kwargs]) + return packed_buffer + + +def unpack_apply_message(packed_buffer: bytes) -> List[Any]: + """ Unpack and deserialize function and parameters + """ + return [deserialize(buf) for buf in unpack_buffers(packed_buffer)] + + +def serialize(obj: Any, buffer_threshold: int = int(1e6)) -> bytes: + """ Try available serialization methods one at a time + + Individual serialization methods might raise a TypeError (eg. if objects are non serializable) + This method will raise the exception from the last method that was tried, if all methods fail. + """ + result: Union[bytes, Exception] + if callable(obj): + methods = methods_for_code + else: + methods = methods_for_data + + for method in methods.values(): + try: + result = method.identifier + b'\n' + method.serialize(obj) + except Exception as e: + result = e + continue + else: + break + + if isinstance(result, BaseException): + raise result + else: + if len(result) > buffer_threshold: + logger.warning(f"Serialized object exceeds buffer threshold of {buffer_threshold} bytes, this could cause overflows") + return result + + +def deserialize(payload: bytes) -> Any: + """ + Parameters + ---------- + payload : str + Payload object to be deserialized + + """ + header, body = payload.split(b'\n', 1) + + if header in methods_for_code: + deserializer = methods_for_code[header] + elif header in methods_for_data: + deserializer = methods_for_data[header] + elif header in additional_methods_for_deserialization: + deserializer = additional_methods_for_deserialization[header] + else: + logger.info("Trying to dynamically load deserializer: {!r}".format(header)) + # This is a user plugin point, so expect exceptions to happen. + try: + module_name, class_name = header.split(b' ', 1) + decoded_module_name = module_name.decode('utf-8') + module = importlib.import_module(decoded_module_name) + deserializer_class = getattr(module, class_name.decode('utf-8')) + deserializer = deserializer_class() + additional_methods_for_deserialization[header] = deserializer + except Exception as e: + raise DeserializerPluginError(header) from e + + result = deserializer.deserialize(body) + + return result + + +def pack_buffers(buffers: List[bytes]) -> bytes: + """ + Parameters + ---------- + buffers: list of byte strings + """ + packed = b'' + for buf in buffers: + s_length = bytes(str(len(buf)) + '\n', 'utf-8') + packed += s_length + buf + + return packed + + +def unpack_buffers(packed_buffer: bytes) -> List[bytes]: + """ + Parameters + ---------- + packed_buffers : packed buffer as byte sequence + """ + unpacked = [] + while packed_buffer: + s_length, buf = packed_buffer.split(b'\n', 1) + i_length = int(s_length.decode('utf-8')) + current, packed_buffer = buf[:i_length], buf[i_length:] + unpacked.extend([current]) + + return unpacked + + +def unpack_and_deserialize(packed_buffer: bytes) -> Any: + """ Unpacks a packed buffer of 3 byte sequences and returns the + deserialized contents for use in function application. + Parameters + ---------- + packed_buffers : packed buffer of 3 byte sequences + """ + unpacked = [] + while packed_buffer: + s_length, buf = packed_buffer.split(b'\n', 1) + i_length = int(s_length.decode('utf-8')) + current, packed_buffer = buf[:i_length], buf[i_length:] + deserialized = deserialize(current) + unpacked.extend([deserialized]) + + assert len(unpacked) == 3, "Unpack expects 3 buffers, got {}".format(len(unpacked)) + + return unpacked diff --git a/parsl/parsl/serialize/proxystore.py b/parsl/parsl/serialize/proxystore.py new file mode 100644 index 00000000..fb1d77e0 --- /dev/null +++ b/parsl/parsl/serialize/proxystore.py @@ -0,0 +1,50 @@ +import io +import typing as t + +import dill +from proxystore.store import Store + +from parsl.serialize.base import SerializerBase + + +class ProxyStoreDeepPickler(dill.Pickler): + """This class extends dill so that certain objects will be stored into + ProxyStore rather than serialized directly. The selection of objects is + made by a user-specified policy. + """ + + def __init__(self, *args: t.Any, should_proxy: t.Callable[[t.Any], bool], store: Store, **kwargs: t.Any) -> None: + super().__init__(*args, **kwargs) + self._store = store + self._should_proxy = should_proxy + + def reducer_override(self, o: t.Any) -> t.Any: + if self._should_proxy(o): + proxy = self._store.proxy(o) + return proxy.__reduce__() + else: + # fall through to dill + return NotImplemented + + +class ProxyStoreSerializer(SerializerBase): + + def __init__(self, *, should_proxy: t.Optional[t.Callable[[t.Any], bool]] = None, store: t.Optional[Store] = None) -> None: + self._store = store + self._should_proxy = should_proxy + + def serialize(self, data: t.Any) -> bytes: + assert self._store is not None + assert self._should_proxy is not None + + assert data is not None + + f = io.BytesIO() + pickler = ProxyStoreDeepPickler(file=f, store=self._store, should_proxy=self._should_proxy) + pickler.dump(data) + return f.getvalue() + + def deserialize(self, body: bytes) -> t.Any: + # because we aren't customising deserialization, use regular + # dill for deserialization + return dill.loads(body) diff --git a/parsl/parsl/tests/__init__.py b/parsl/parsl/tests/__init__.py new file mode 100644 index 00000000..7018ff6a --- /dev/null +++ b/parsl/parsl/tests/__init__.py @@ -0,0 +1,7 @@ +from parsl.data_provider.files import File +from parsl.dataflow.memoization import id_for_memo + + +@id_for_memo.register(File) +def id_for_memo_file(file: File, output_ref: bool = False): + return file.url diff --git a/parsl/parsl/tests/callables_helper.py b/parsl/parsl/tests/callables_helper.py new file mode 100644 index 00000000..c7a859de --- /dev/null +++ b/parsl/parsl/tests/callables_helper.py @@ -0,0 +1,2 @@ +def some_aux_func(): + pass diff --git a/parsl/parsl/tests/cleanup b/parsl/parsl/tests/cleanup new file mode 100755 index 00000000..2b7556f4 --- /dev/null +++ b/parsl/parsl/tests/cleanup @@ -0,0 +1,11 @@ +rm -rf *~ *txt *out *err outputs* data runinfo __pycache__ + +for dir in */ + do + echo "Cleaning dir: $dir" + cd $dir + rm -rf *txt *out *err outputs* *~ runinfo* ipengine.json __pycache__ + cd .. + done + +find . -path '*/runinfo*' -type d | xargs rm -rf diff --git a/parsl/parsl/tests/configs/__init__.py b/parsl/parsl/tests/configs/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/configs/azure_single_node.py b/parsl/parsl/tests/configs/azure_single_node.py new file mode 100644 index 00000000..32b6a5d3 --- /dev/null +++ b/parsl/parsl/tests/configs/azure_single_node.py @@ -0,0 +1,46 @@ +"""Config for Azure""" +import getpass + +from parsl.config import Config +from parsl.data_provider.ftp import FTPInTaskStaging +from parsl.data_provider.http import HTTPInTaskStaging +from parsl.data_provider.rsync import RSyncStaging +from parsl.executors import HighThroughputExecutor +from parsl.providers import AzureProvider + +# If you are a developer running tests, make sure to update parsl/tests/configs/user_opts.py +# If you are a user copying-and-pasting this as an example, make sure to either +# 1) create a local `user_opts.py`, or +# 2) delete the user_opts import below and replace all appearances of `user_opts` with the literal value +# (i.e., user_opts['swan']['username'] -> 'your_username') +from parsl.tests.configs.user_opts import user_opts + +vm_reference = { + + # these fields gave me key errors unless I specified them: + "admin_username": user_opts['azure']['admin_username'], + "password": user_opts['azure']['password'], + "vm_size": user_opts['azure']['vm_size'], + "disk_size_gb": user_opts['azure']['disk_size_gb'], + "publisher": user_opts['azure']['publisher'], + "offer": user_opts['azure']['offer'], + "sku": user_opts['azure']['sku'], + "version": user_opts['azure']['version'] +} + +provider = AzureProvider( + vm_reference=vm_reference, + key_file='azure_key_file.json', + ) + +config = Config( + executors=[ + HighThroughputExecutor( + storage_access=[HTTPInTaskStaging(), FTPInTaskStaging(), RSyncStaging(getpass.getuser() + "@" + user_opts['public_ip'])], + label='azure_single_node', + address=user_opts['public_ip'], + encrypted=True, + provider=provider + ) + ] +) diff --git a/parsl/parsl/tests/configs/bluewaters.py b/parsl/parsl/tests/configs/bluewaters.py new file mode 100644 index 00000000..67d21e18 --- /dev/null +++ b/parsl/parsl/tests/configs/bluewaters.py @@ -0,0 +1,40 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import AprunLauncher +from parsl.providers import TorqueProvider + +from .user_opts import user_opts + + +def fresh_config(): + config = Config( + executors=[ + HighThroughputExecutor( + label="bw_htex", + cores_per_worker=1, + worker_debug=False, + max_workers_per_node=1, + encrypted=True, + provider=TorqueProvider( + queue='normal', + launcher=AprunLauncher(overrides="-b -- bwpy-environ --"), + # string to prepend to #SBATCH blocks in the submit + # script to the scheduler eg: '#SBATCH --constraint=knl,quad,cache' + scheduler_options='', + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init=user_opts['bluewaters']['worker_init'], + init_blocks=1, + max_blocks=1, + min_blocks=1, + nodes_per_block=2, + walltime='00:30:00', + cmd_timeout=120, + ), + ) + ], + ) + return config + + +config = fresh_config() diff --git a/parsl/parsl/tests/configs/bridges.py b/parsl/parsl/tests/configs/bridges.py new file mode 100644 index 00000000..21feec19 --- /dev/null +++ b/parsl/parsl/tests/configs/bridges.py @@ -0,0 +1,40 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SrunLauncher +from parsl.providers import SlurmProvider + +from .user_opts import user_opts + + +def fresh_config(): + return Config( + executors=[ + HighThroughputExecutor( + label='Bridges_HTEX_multinode', + # This is the network interface on the login node to + # which compute nodes can communicate + # address=address_by_interface('bond0.144'), + max_workers_per_node=1, + encrypted=True, + provider=SlurmProvider( + user_opts['bridges']['partition'], # Partition / QOS + nodes_per_block=2, + init_blocks=1, + # string to prepend to #SBATCH blocks in the submit + # script to the scheduler eg: '#SBATCH --constraint=knl,quad,cache' + scheduler_options=user_opts['bridges']['scheduler_options'], + + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init=user_opts['bridges']['worker_init'], + + # We request all hyperthreads on a node. + launcher=SrunLauncher(), + walltime='00:10:00', + # Slurm scheduler on Cori can be slow at times, + # increase the command timeouts + cmd_timeout=120, + ), + ) + ] + ) diff --git a/parsl/parsl/tests/configs/cc_in2p3.py b/parsl/parsl/tests/configs/cc_in2p3.py new file mode 100644 index 00000000..5d9edf9d --- /dev/null +++ b/parsl/parsl/tests/configs/cc_in2p3.py @@ -0,0 +1,28 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.providers import GridEngineProvider + +from .user_opts import user_opts + + +def fresh_config(): + return Config( + executors=[ + HighThroughputExecutor( + label='cc_in2p3_htex', + max_workers_per_node=1, + encrypted=True, + provider=GridEngineProvider( + nodes_per_block=2, + init_blocks=2, + max_blocks=2, + walltime="00:20:00", + scheduler_options=user_opts['cc_in2p3']['scheduler_options'], + worker_init=user_opts['cc_in2p3']['worker_init'], + ), + ) + ], + ) + + +config = fresh_config() diff --git a/parsl/parsl/tests/configs/comet.py b/parsl/parsl/tests/configs/comet.py new file mode 100644 index 00000000..3a946b5c --- /dev/null +++ b/parsl/parsl/tests/configs/comet.py @@ -0,0 +1,36 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SrunLauncher +from parsl.providers import SlurmProvider + +from .user_opts import user_opts + + +def fresh_config(): + return Config( + executors=[ + HighThroughputExecutor( + label='Comet_HTEX_multinode', + max_workers_per_node=1, + encrypted=True, + provider=SlurmProvider( + 'debug', + launcher=SrunLauncher(), + # string to prepend to #SBATCH blocks in the submit + # script to the scheduler + scheduler_options=user_opts['comet']['scheduler_options'], + + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init=user_opts['comet']['worker_init'], + walltime='00:10:00', + init_blocks=1, + max_blocks=1, + nodes_per_block=2, + ), + ) + ] + ) + + +config = fresh_config() diff --git a/parsl/parsl/tests/configs/ec2_single_node.py b/parsl/parsl/tests/configs/ec2_single_node.py new file mode 100644 index 00000000..6222dbfa --- /dev/null +++ b/parsl/parsl/tests/configs/ec2_single_node.py @@ -0,0 +1,45 @@ +"""Config for EC2. + +Block {Min:0, init:1, Max:1} +================== +| ++++++++++++++ | +| | Node | | +| | | | +| | Task Task | | +| | | | +| ++++++++++++++ | +================== + +""" +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.providers import AWSProvider + +# If you are a developer running tests, make sure to update parsl/tests/configs/user_opts.py +# If you are a user copying-and-pasting this as an example, make sure to either +# 1) create a local `user_opts.py`, or +# 2) delete the user_opts import below and replace all appearances of `user_opts` with the literal value +# (i.e., user_opts['swan']['username'] -> 'your_username') +from parsl.tests.configs.user_opts import user_opts + +config = Config( + executors=[ + HighThroughputExecutor( + label='ec2_single_node', + address=user_opts['public_ip'], + encrypted=True, + provider=AWSProvider( + user_opts['ec2']['image_id'], + region=user_opts['ec2']['region'], + key_name=user_opts['ec2']['key_name'], + profile="default", + state_file='awsproviderstate.json', + nodes_per_block=1, + init_blocks=1, + max_blocks=1, + min_blocks=0, + walltime='01:00:00', + ), + ) + ] +) diff --git a/parsl/parsl/tests/configs/ec2_spot.py b/parsl/parsl/tests/configs/ec2_spot.py new file mode 100644 index 00000000..56084a91 --- /dev/null +++ b/parsl/parsl/tests/configs/ec2_spot.py @@ -0,0 +1,33 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.providers import AWSProvider + +# If you are a developer running tests, make sure to update parsl/tests/configs/user_opts.py +# If you are a user copying-and-pasting this as an example, make sure to either +# 1) create a local `user_opts.py`, or +# 2) delete the user_opts import below and replace all appearances of `user_opts` with the literal value +# (i.e., user_opts['swan']['username'] -> 'your_username') +from parsl.tests.configs.user_opts import user_opts + +config = Config( + executors=[ + HighThroughputExecutor( + label='ec2_single_node', + address=user_opts['public_ip'], + encrypted=True, + provider=AWSProvider( + user_opts['ec2']['image_id'], + region=user_opts['ec2']['region'], + key_name=user_opts['ec2']['key_name'], + spot_max_bid='1.0', + profile="default", + state_file='awsproviderstate.json', + nodes_per_block=1, + init_blocks=1, + max_blocks=1, + min_blocks=0, + walltime='01:00:00', + ), + ) + ] +) diff --git a/parsl/parsl/tests/configs/flux_local.py b/parsl/parsl/tests/configs/flux_local.py new file mode 100644 index 00000000..203dd590 --- /dev/null +++ b/parsl/parsl/tests/configs/flux_local.py @@ -0,0 +1,11 @@ +from parsl.config import Config +from parsl.executors import FluxExecutor + + +def fresh_config(): + return Config( + executors=[FluxExecutor()], + ) + + +config = fresh_config() diff --git a/parsl/parsl/tests/configs/frontera.py b/parsl/parsl/tests/configs/frontera.py new file mode 100644 index 00000000..41e2985e --- /dev/null +++ b/parsl/parsl/tests/configs/frontera.py @@ -0,0 +1,42 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SrunLauncher +from parsl.providers import SlurmProvider + +from .user_opts import user_opts + +""" This config assumes that it is used to launch parsl tasks from the login nodes +of Frontera at TACC. Each job submitted to the scheduler will request 2 nodes for 10 minutes. +""" + + +def fresh_config(): + return Config( + executors=[ + HighThroughputExecutor( + label="frontera_htex", + max_workers_per_node=1, + encrypted=True, + provider=SlurmProvider( + cmd_timeout=60, # Add extra time for slow scheduler responses + nodes_per_block=2, + init_blocks=1, + min_blocks=1, + max_blocks=1, + partition='development', # Replace with partition name + scheduler_options=user_opts['frontera']['scheduler_options'], + + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init=user_opts['frontera']['worker_init'], + + # Ideally we set the walltime to the longest supported walltime. + walltime='00:10:00', + launcher=SrunLauncher(), + ), + ) + ], + ) + + +config = fresh_config() diff --git a/parsl/parsl/tests/configs/globus_compute.py b/parsl/parsl/tests/configs/globus_compute.py new file mode 100644 index 00000000..19f90b8c --- /dev/null +++ b/parsl/parsl/tests/configs/globus_compute.py @@ -0,0 +1,20 @@ +import os + +from globus_compute_sdk import Executor + +from parsl.config import Config +from parsl.executors import GlobusComputeExecutor + + +def fresh_config(): + + endpoint_id = os.environ["GLOBUS_COMPUTE_ENDPOINT"] + + return Config( + executors=[ + GlobusComputeExecutor( + executor=Executor(endpoint_id=endpoint_id), + label="globus_compute", + ) + ] + ) diff --git a/parsl/parsl/tests/configs/htcondor_ci.py b/parsl/parsl/tests/configs/htcondor_ci.py new file mode 100644 index 00000000..ef296f82 --- /dev/null +++ b/parsl/parsl/tests/configs/htcondor_ci.py @@ -0,0 +1,22 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SimpleLauncher +from parsl.providers import CondorProvider + + +def fresh_config(): + return Config( + executors=[ + HighThroughputExecutor( + label="docker_htcondor", + provider=CondorProvider( + worker_init=". ~/venv/bin/activate", + nodes_per_block=1, + init_blocks=1, + min_blocks=1, + max_blocks=1, + launcher=SimpleLauncher(), + ), + ) + ], + ) diff --git a/parsl/parsl/tests/configs/htex_local.py b/parsl/parsl/tests/configs/htex_local.py new file mode 100644 index 00000000..712570b6 --- /dev/null +++ b/parsl/parsl/tests/configs/htex_local.py @@ -0,0 +1,24 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SimpleLauncher +from parsl.providers import LocalProvider + + +def fresh_config(): + return Config( + executors=[ + HighThroughputExecutor( + label="htex_local", + loopback_address="::1", + worker_debug=True, + cores_per_worker=1, + encrypted=True, + provider=LocalProvider( + init_blocks=1, + max_blocks=1, + launcher=SimpleLauncher(), + ), + ) + ], + strategy='none', + ) diff --git a/parsl/parsl/tests/configs/htex_local_alternate.py b/parsl/parsl/tests/configs/htex_local_alternate.py new file mode 100644 index 00000000..80d55cef --- /dev/null +++ b/parsl/parsl/tests/configs/htex_local_alternate.py @@ -0,0 +1,73 @@ +""" +The aim of this configuration is to run a local htex +in a similar manner to htex_local.py, but with lots of +options different and more complicated than in that +configuration, so that more code paths are executed +than when testing only with htex_local. + +It does not matter too much *what* is different in this +configuration; what matters is that the differences +cause significantly different pieces of parsl code to be +run - for example, by turning on monitoring, by allowing +blocks to be started by a strategy, by using a different +set of staging providers, by using timing parameters that +will cause substantially different behaviour on whatever +those timing parameters control. +""" + +import os + +from parsl.config import Config +from parsl.data_provider.file_noop import NoOpFileStaging +from parsl.data_provider.ftp import FTPInTaskStaging +from parsl.data_provider.http import HTTPInTaskStaging +from parsl.data_provider.zip import ZipFileStaging +from parsl.dataflow.memoization import BasicMemoizer +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SingleNodeLauncher +from parsl.logconfigs.json import JSONLogging +from parsl.monitoring import MonitoringHub +from parsl.providers import LocalProvider + +working_dir = os.getcwd() + "/" + "test_htex_alternate" + + +def fresh_config(): + return Config( + executors=[ + HighThroughputExecutor( + address="127.0.0.1", + label="htex_Local", + working_dir=working_dir, + storage_access=[ZipFileStaging(), FTPInTaskStaging(), HTTPInTaskStaging(), NoOpFileStaging()], + worker_debug=True, + cores_per_worker=1, + heartbeat_period=2, + heartbeat_threshold=5, + poll_period=100, + encrypted=True, + provider=LocalProvider( + init_blocks=0, + min_blocks=0, + max_blocks=5, + launcher=SingleNodeLauncher(), + ), + block_error_handler=False + ) + ], + strategy='simple', + memoizer=BasicMemoizer(memoize=True, checkpoint_mode='task_exit'), + retries=2, + monitoring=MonitoringHub( + monitoring_debug=False, + resource_monitoring_interval=1, + ), + usage_tracking=3, + project_name="parsl htex_local_alternate test configuration", + + # level 1 is even more debuggy than logging.DEBUG + initialize_logging=JSONLogging(level=1) + ) + + +config = fresh_config() diff --git a/parsl/parsl/tests/configs/htex_local_intask_staging.py b/parsl/parsl/tests/configs/htex_local_intask_staging.py new file mode 100644 index 00000000..69649b2b --- /dev/null +++ b/parsl/parsl/tests/configs/htex_local_intask_staging.py @@ -0,0 +1,25 @@ +from parsl.config import Config +from parsl.data_provider.file_noop import NoOpFileStaging +from parsl.data_provider.ftp import FTPInTaskStaging +from parsl.data_provider.http import HTTPInTaskStaging +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SimpleLauncher +from parsl.providers import LocalProvider + +config = Config( + executors=[ + HighThroughputExecutor( + label="htex_Local", + worker_debug=True, + cores_per_worker=1, + encrypted=True, + provider=LocalProvider( + init_blocks=1, + max_blocks=1, + launcher=SimpleLauncher(), + ), + storage_access=[HTTPInTaskStaging(), FTPInTaskStaging(), NoOpFileStaging()] + ) + ], + strategy='none', +) diff --git a/parsl/parsl/tests/configs/htex_local_rsync_staging.py b/parsl/parsl/tests/configs/htex_local_rsync_staging.py new file mode 100644 index 00000000..d24b1da6 --- /dev/null +++ b/parsl/parsl/tests/configs/htex_local_rsync_staging.py @@ -0,0 +1,26 @@ +from parsl.config import Config +from parsl.data_provider.ftp import FTPInTaskStaging +from parsl.data_provider.http import HTTPInTaskStaging +from parsl.data_provider.rsync import RSyncStaging +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SimpleLauncher +from parsl.providers import LocalProvider + +config = Config( + executors=[ + HighThroughputExecutor( + label="htex_Local", + worker_debug=True, + cores_per_worker=1, + working_dir="./rsync-workdir/", + encrypted=True, + provider=LocalProvider( + init_blocks=1, + max_blocks=1, + launcher=SimpleLauncher(), + ), + storage_access=[HTTPInTaskStaging(), FTPInTaskStaging(), RSyncStaging("fitzroy.cqx.ltd.uk")] + ) + ], + strategy='none', +) diff --git a/parsl/parsl/tests/configs/local_radical.py b/parsl/parsl/tests/configs/local_radical.py new file mode 100644 index 00000000..ec146f50 --- /dev/null +++ b/parsl/parsl/tests/configs/local_radical.py @@ -0,0 +1,17 @@ +import os + +from parsl.config import Config +from parsl.executors.radical import RadicalPilotExecutor, ResourceConfig + +rpex_cfg = ResourceConfig() + + +def fresh_config(): + + return Config( + executors=[ + RadicalPilotExecutor( + label='RPEXBulk', + rpex_cfg=rpex_cfg, + resource='local.localhost', + runtime=30, cores=4)]) diff --git a/parsl/parsl/tests/configs/local_radical_mpi.py b/parsl/parsl/tests/configs/local_radical_mpi.py new file mode 100644 index 00000000..582f406e --- /dev/null +++ b/parsl/parsl/tests/configs/local_radical_mpi.py @@ -0,0 +1,20 @@ +import os + +from parsl.config import Config + + +def fresh_config(): + from parsl.executors.radical import RadicalPilotExecutor, ResourceConfig + + rpex_cfg = ResourceConfig() + rpex_cfg.worker_type = "MPI" + rpex_cfg.worker_cores_per_node = 7 + + return Config( + executors=[ + RadicalPilotExecutor( + label='RPEXMPI', + rpex_cfg=rpex_cfg, + bulk_mode=True, + resource='local.localhost', + runtime=30, cores=8)]) diff --git a/parsl/parsl/tests/configs/local_threads.py b/parsl/parsl/tests/configs/local_threads.py new file mode 100644 index 00000000..4de2a7bd --- /dev/null +++ b/parsl/parsl/tests/configs/local_threads.py @@ -0,0 +1,11 @@ +from parsl.config import Config +from parsl.executors.threads import ThreadPoolExecutor + + +def fresh_config(): + return Config( + executors=[ThreadPoolExecutor()], + ) + + +config = fresh_config() diff --git a/parsl/parsl/tests/configs/local_threads_checkpoint_dfk_exit.py b/parsl/parsl/tests/configs/local_threads_checkpoint_dfk_exit.py new file mode 100644 index 00000000..63cf96b5 --- /dev/null +++ b/parsl/parsl/tests/configs/local_threads_checkpoint_dfk_exit.py @@ -0,0 +1,12 @@ +from parsl.config import Config +from parsl.dataflow.memoization import BasicMemoizer +from parsl.executors.threads import ThreadPoolExecutor + +config = Config( + executors=[ + ThreadPoolExecutor( + label='local_threads_checkpoint_dfk_exit', + ) + ], + memoizer=BasicMemoizer(checkpoint_mode='dfk_exit') +) diff --git a/parsl/parsl/tests/configs/local_threads_checkpoint_task_exit.py b/parsl/parsl/tests/configs/local_threads_checkpoint_task_exit.py new file mode 100644 index 00000000..f5b36e06 --- /dev/null +++ b/parsl/parsl/tests/configs/local_threads_checkpoint_task_exit.py @@ -0,0 +1,12 @@ +from parsl.config import Config +from parsl.dataflow.memoization import BasicMemoizer +from parsl.executors.threads import ThreadPoolExecutor + +config = Config( + executors=[ + ThreadPoolExecutor( + label='local_threads_checkpoint_task_exit', + ) + ], + memoizer=BasicMemoizer(checkpoint_mode='task_exit') +) diff --git a/parsl/parsl/tests/configs/local_threads_ftp_in_task.py b/parsl/parsl/tests/configs/local_threads_ftp_in_task.py new file mode 100644 index 00000000..833465f6 --- /dev/null +++ b/parsl/parsl/tests/configs/local_threads_ftp_in_task.py @@ -0,0 +1,15 @@ +from parsl.config import Config +from parsl.data_provider.file_noop import NoOpFileStaging +from parsl.data_provider.ftp import FTPInTaskStaging +from parsl.executors.threads import ThreadPoolExecutor + + +def fresh_config(): + return Config( + executors=[ + ThreadPoolExecutor( + label='local_threads_http_in_task', + storage_access=[FTPInTaskStaging(), NoOpFileStaging()] + ) + ] + ) diff --git a/parsl/parsl/tests/configs/local_threads_globus.py b/parsl/parsl/tests/configs/local_threads_globus.py new file mode 100644 index 00000000..2a09e9a9 --- /dev/null +++ b/parsl/parsl/tests/configs/local_threads_globus.py @@ -0,0 +1,32 @@ +from parsl.config import Config +from parsl.data_provider.data_manager import default_staging +from parsl.executors.threads import ThreadPoolExecutor + +# If you are a developer running tests, make sure to update parsl/tests/configs/user_opts.py +# If you are a user copying-and-pasting this as an example, make sure to either +# 1) create a local `user_opts.py`, or +# 2) delete the user_opts import below and replace all appearances of `user_opts` with the literal value +# (i.e., user_opts['swan']['username'] -> 'your_username') +from .user_opts import user_opts + + +def fresh_config(): + from parsl.data_provider.globus import GlobusStaging + + storage_access = default_staging + [GlobusStaging( + endpoint_uuid=user_opts['globus']['endpoint'], + endpoint_path=user_opts['globus']['path'] + )] + + return Config( + executors=[ + ThreadPoolExecutor( + label='local_threads_globus', + working_dir=user_opts['globus']['path'], + storage_access=storage_access + ) + ] + ) + + +remote_writeable = user_opts['globus']['remote_writeable'] diff --git a/parsl/parsl/tests/configs/local_threads_http_in_task.py b/parsl/parsl/tests/configs/local_threads_http_in_task.py new file mode 100644 index 00000000..71f718a5 --- /dev/null +++ b/parsl/parsl/tests/configs/local_threads_http_in_task.py @@ -0,0 +1,15 @@ +from parsl.config import Config +from parsl.data_provider.file_noop import NoOpFileStaging +from parsl.data_provider.http import HTTPInTaskStaging +from parsl.executors.threads import ThreadPoolExecutor + + +def fresh_config(): + return Config( + executors=[ + ThreadPoolExecutor( + label='local_threads_http_in_task', + storage_access=[HTTPInTaskStaging(), NoOpFileStaging()] + ) + ] + ) diff --git a/parsl/parsl/tests/configs/midway.py b/parsl/parsl/tests/configs/midway.py new file mode 100644 index 00000000..01cc49fd --- /dev/null +++ b/parsl/parsl/tests/configs/midway.py @@ -0,0 +1,39 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SrunLauncher +from parsl.providers import SlurmProvider + +from .user_opts import user_opts + + +def fresh_config(): + config = Config( + executors=[ + HighThroughputExecutor( + label='Midway_HTEX_multinode', + worker_debug=False, + max_workers_per_node=1, + encrypted=True, + provider=SlurmProvider( + 'broadwl', # Partition name, e.g 'broadwl' + launcher=SrunLauncher(), + nodes_per_block=2, + init_blocks=1, + min_blocks=1, + max_blocks=1, + # string to prepend to #SBATCH blocks in the submit + # script to the scheduler eg: '#SBATCH --constraint=knl,quad,cache' + scheduler_options='', + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init=user_opts['midway']['worker_init'], + walltime='00:30:00', + cmd_timeout=120, + ), + ) + ], + ) + return config + + +config = fresh_config() diff --git a/parsl/parsl/tests/configs/nscc_singapore.py b/parsl/parsl/tests/configs/nscc_singapore.py new file mode 100644 index 00000000..8260fbb6 --- /dev/null +++ b/parsl/parsl/tests/configs/nscc_singapore.py @@ -0,0 +1,43 @@ +from parsl.addresses import address_by_interface +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import MpiRunLauncher +from parsl.providers import PBSProProvider + +from .user_opts import user_opts + + +def fresh_config(): + return Config( + executors=[ + HighThroughputExecutor( + label="htex", + heartbeat_period=15, + heartbeat_threshold=120, + worker_debug=False, + max_workers_per_node=1, + address=address_by_interface('ib0'), + encrypted=True, + provider=PBSProProvider( + launcher=MpiRunLauncher(), + # string to prepend to #PBS blocks in the submit + # script to the scheduler + # E.g., project name + scheduler_options=user_opts['nscc']['scheduler_options'], + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init=user_opts['nscc']['worker_init'], + nodes_per_block=2, + min_blocks=1, + max_blocks=1, + cpus_per_node=24, + walltime="00:20:00", + cmd_timeout=300, + ), + ), + ], + strategy='simple', + ) + + +config = fresh_config() diff --git a/parsl/parsl/tests/configs/osg_htex.py b/parsl/parsl/tests/configs/osg_htex.py new file mode 100644 index 00000000..3e0658b3 --- /dev/null +++ b/parsl/parsl/tests/configs/osg_htex.py @@ -0,0 +1,32 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.providers import CondorProvider + +# If you are a developer running tests, make sure to update parsl/tests/configs/user_opts.py +# If you are a user copying-and-pasting this as an example, make sure to either +# 1) create a local `user_opts.py`, or +# 2) delete the user_opts import below and replace all appearances of `user_opts` with the literal value +# (i.e., user_opts['swan']['username'] -> 'your_username') +from .user_opts import user_opts + +config = Config( + executors=[ + HighThroughputExecutor( + label='OSG_HTEX', + max_workers_per_node=1, + encrypted=True, + provider=CondorProvider( + nodes_per_block=1, + init_blocks=4, + max_blocks=4, + # This scheduler option string ensures that the compute nodes provisioned + # will have modules + scheduler_options='Requirements = OSGVO_OS_STRING == "RHEL 6" && Arch == "X86_64" && HAS_MODULES == True', + # Command to be run before starting a worker, such as: + # 'module load Anaconda; source activate parsl_env'. + worker_init=user_opts['osg']['worker_init'], + walltime="00:20:00", + ), + ) + ] +) diff --git a/parsl/parsl/tests/configs/petrelkube.py b/parsl/parsl/tests/configs/petrelkube.py new file mode 100644 index 00000000..7fe35854 --- /dev/null +++ b/parsl/parsl/tests/configs/petrelkube.py @@ -0,0 +1,54 @@ +import os + +from parsl.addresses import address_by_route +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.providers import KubernetesProvider + +from .user_opts import user_opts + + +def fresh_config(): + worker_init_file = user_opts['petrelkube']['worker_init'] + with open(os.path.expanduser(worker_init_file)) as f: + lines = f.readlines() + worker_init = ';'.join([line.strip() for line in lines]) + + config = Config( + executors=[ + HighThroughputExecutor( + label='kube-htex', + cores_per_worker=1, + max_workers_per_node=1, + worker_logdir_root='.', + + # Address for the pod worker to connect back + address=address_by_route(), + encrypted=True, + provider=KubernetesProvider( + namespace="dlhub-privileged", + + # Docker image url to use for pods + image='python:3.7', + + # Command to be run upon pod start, such as: + # 'module load Anaconda; source activate parsl_env'. + # or 'pip install parsl' + worker_init=worker_init, + + # The secret key to download the image + secret="ryan-kube-secret", + + # Should follow the Kubernetes naming rules + pod_name='parsl-site-test-pod', + + nodes_per_block=1, + init_blocks=1, + # Maximum number of pods to scale up + max_blocks=2, + ), + ), + ] + ) + + return config diff --git a/parsl/parsl/tests/configs/slurm_local.py b/parsl/parsl/tests/configs/slurm_local.py new file mode 100644 index 00000000..c0281cf3 --- /dev/null +++ b/parsl/parsl/tests/configs/slurm_local.py @@ -0,0 +1,24 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SrunLauncher +from parsl.providers import SlurmProvider + + +def fresh_config(): + return Config( + executors=[ + HighThroughputExecutor( + label="docker_slurm", + encrypted=True, + provider=SlurmProvider( + cmd_timeout=60, # Add extra time for slow scheduler responses + nodes_per_block=1, + init_blocks=1, + min_blocks=1, + max_blocks=1, + walltime='00:10:00', + launcher=SrunLauncher(), + ), + ) + ], + ) diff --git a/parsl/parsl/tests/configs/summit.py b/parsl/parsl/tests/configs/summit.py new file mode 100644 index 00000000..69487f78 --- /dev/null +++ b/parsl/parsl/tests/configs/summit.py @@ -0,0 +1,38 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import JsrunLauncher +from parsl.providers import LSFProvider + +from .user_opts import user_opts + +""" This config assumes that it is used to launch parsl tasks from the login nodes +of Frontera at TACC. Each job submitted to the scheduler will request 2 nodes for 10 minutes. +""" + + +def fresh_config(): + return Config( + executors=[ + HighThroughputExecutor( + label='Summit_HTEX', + # On Summit ensure that the working dir is writeable from the compute nodes, + # for eg. paths below /gpfs/alpine/world-shared/ + # working_dir='', + + # address=address_by_interface('ib0'), # This assumes Parsl is running on login node + worker_port_range=(50000, 55000), + max_workers_per_node=1, + encrypted=True, + provider=LSFProvider( + launcher=JsrunLauncher(), + walltime="00:10:00", + nodes_per_block=2, + init_blocks=1, + max_blocks=1, + worker_init=user_opts['summit']['worker_init'], + project=user_opts['summit']['project'], + cmd_timeout=60 + ), + ) + ], + ) diff --git a/parsl/parsl/tests/configs/taskvine_ex.py b/parsl/parsl/tests/configs/taskvine_ex.py new file mode 100644 index 00000000..ee09dc70 --- /dev/null +++ b/parsl/parsl/tests/configs/taskvine_ex.py @@ -0,0 +1,10 @@ +from parsl.config import Config +from parsl.data_provider.file_noop import NoOpFileStaging +from parsl.data_provider.ftp import FTPInTaskStaging +from parsl.data_provider.http import HTTPInTaskStaging +from parsl.executors.taskvine import TaskVineExecutor, TaskVineManagerConfig + + +def fresh_config(): + return Config(executors=[TaskVineExecutor(manager_config=TaskVineManagerConfig(port=9000), + worker_launch_method='provider')]) diff --git a/parsl/parsl/tests/configs/user_opts.py b/parsl/parsl/tests/configs/user_opts.py new file mode 100644 index 00000000..540d13ea --- /dev/null +++ b/parsl/parsl/tests/configs/user_opts.py @@ -0,0 +1,154 @@ +""" +Specification of user-specific configuration options. + +The fields must be configured separately for each user. To disable any associated configurations, comment +out the entry. + +User specific overrides that should not go in version control can be set by creating a +file called local_user_opts.py, which declares a dictionary local_user_opts. Top level +keys in that dictionary will replace entries in the below user opts file, so it should +be safe to cut-and-paste entries from this file into that file. +""" +from typing import Any, Dict + +# PUBLIC_IP = "52.86.208.63" # "128.135.250.229" +# MIDWAY_USERNAME = "yadunand" +# OSG_USERNAME = "yadunand" +# SWAN_USERNAME = "p02509" +# CORI_USERNAME = "yadunand" +# ALCF_USERNAME = "yadunand" +# ALCF_ALLOCATION = "CSC249ADCD01" +# COMET_USERNAME = "yadunand" + +user_opts = { + 'frontera': { + 'worker_init': 'source ~/setup_parsl_test_env.sh;', + }, + 'nscc': { + 'worker_init': 'source ~/setup_parsl_test_env.sh;', + }, + 'theta': { + 'worker_init': 'source ~/setup_parsl_test_env.sh;', + }, + 'summit': { + 'worker_init': 'source ~/setup_parsl_test_env.sh;', + }, + 'bluewaters': { + 'worker_init': 'source ~/setup_parsl_test_env.sh;', + }, + 'midway': { + 'worker_init': 'source ~/setup_parsl_test_env.sh;', + }, + 'petrelkube': { + 'worker_init': '~/setup_parsl_test_env.sh', + }, + # 'comet': { + # 'username': COMET_USERNAME, + # 'script_dir': '/home/{}/parsl_scripts'.format(COMET_USERNAME), + # 'scheduler_options': "", + # 'worker_init': 'export PATH:/home/{}/anaconda3/bin/:$PATH; source activate parsl_0.5.0_py3.6;'.format(COMET_USERNAME), + # }, + # 'midway': { + # 'username': MIDWAY_USERNAME, + # 'script_dir': '/scratch/midway2/{}/parsl_scripts'.format(MIDWAY_USERNAME), + # 'scheduler_options': "", + # 'worker_init': 'cd /scratch/midway2/{}/parsl_scripts; ' + # 'module load Anaconda3/5.1.0; source activate parsl_testing;' + # .format(MIDWAY_USERNAME), + # }, + # 'osg': { + # 'username': OSG_USERNAME, + # 'script_dir': '/home/{}/parsl_scripts'.format(OSG_USERNAME), + # 'scheduler_options': "", + # 'worker_init' : 'module load python/3.5.2; python3 -m venv parsl_env; + # source parsl_env/bin/activate; python3 -m pip install parsl==0.5.2' + # }, + # 'swan': { + # 'username': SWAN_USERNAME, + # 'script_dir' : "/home/users/{}/parsl_scripts".format(SWAN_USERNAME), + # 'scheduler_options': "", + # 'worker_init': "module load cray-python/3.6.1.1; source parsl_env/bin/activate" + # }, + # 'cooley': { + # 'username': ALCF_USERNAME, + # "account": ALCF_ALLOCATION, + # 'scheduler_options': "", + # "worker_init": "source /home/{}/setup_cooley_env.sh".format(ALCF_USERNAME), + # # Once you log onto Cooley, get the ip address of the login machine + # # by running >> ip addr show | grep -o 10.236.1.[0-9]* + # 'public_ip': '10.236.1.193' + # }, + # }, + # 'ec2': { + # "region": "us-east-2", + # "image_id": 'ami-82f4dae7', + # "key_name": "parsl.test", + # # Name of the profile used to identify credentials stored in ~/.aws/config + # "profile_name": "parsl", + # }, + # + # 'azure': { + # + # # Specifies a username/password which can be used to log into Azure VMs + # # These must be specified but are not used by parsl to access the VMs. + # 'admin_username': 'anyuser', + # 'password': 'mypassword1234567!', + # + # # Characteristics of the VMs to be started: + # 'vm_size': 'Standard_D1', + # 'disk_size_gb': '10', + # + # # Details of the image to be started on each VM. + # # Values can be found using, for example, the `az` command line tool: + # # az vm image list --publisher Debian + # 'publisher': 'Debian', + # 'offer': 'debian-10', + # 'sku': '10', + # 'version': 'latest' + # }, + # 'theta': { + # 'username': ALCF_USERNAME, + # "account": ALCF_ALLOCATION, + # 'scheduler_options': "", + # "worker_init": "source /home/{}/setup_theta_env.sh".format(ALCF_USERNAME), + # # Once you log onto theta, get the ip address of the login machine + # # by running >> ip addr show | grep -o 10.236.1.[0-9]* + # 'public_ip': '10.236.1.193' + # }, + # 'beagle': { + # 'username': 'fixme', + # "script_dir": "fixme", + # "scheduler_options": "#SBATCH --constraint=haswell", + # "worker_init": """module load python/3.5-anaconda ; source activate parsl_env_3.5""" + # }, + # 'cc_in2p3': { + # 'script_dir': "~/parsl_scripts", + # 'scheduler_options': "", + # "worker_init": """export PATH=/pbs/throng/lsst/software/anaconda/anaconda3-5.0.1/bin:$PATH; source activate parsl_env_3.5""" + # }, + # 'globus': { + # 'endpoint': 'fixme', + # 'path': 'fixme', + # + # # remote_writeable should specify a directory on a globus endpoint somewhere else, + # # where files can be staged out to via globus during globus staging tests. + # # For example: + # 'remote_writeable': 'globus://af7bda53-6d04-11e5-ba46-22000b92c6ec/home/bzc/' + # }, + # +} # type: Dict[str, Any] + +# This block attempts to import local_user_opts.py, which +# can provide local overrides to the version-controlled +# user_opts. +# Users can add their own overrides into local_user_opts +# in local_user_opts.py, which should not exist in a +# pristine parsl source tree, and which should help avoid +# accidentally committing secrets and other per-user +# config into version control. +try: + from .local_user_opts import local_user_opts + user_opts.update(local_user_opts) + +except ImportError: + pass diff --git a/parsl/parsl/tests/configs/workqueue_ex.py b/parsl/parsl/tests/configs/workqueue_ex.py new file mode 100644 index 00000000..7e9a6967 --- /dev/null +++ b/parsl/parsl/tests/configs/workqueue_ex.py @@ -0,0 +1,10 @@ +from parsl.config import Config +from parsl.data_provider.file_noop import NoOpFileStaging +from parsl.data_provider.ftp import FTPInTaskStaging +from parsl.data_provider.http import HTTPInTaskStaging +from parsl.executors import WorkQueueExecutor + + +def fresh_config(): + return Config(executors=[WorkQueueExecutor(port=9000, + coprocess=True)]) diff --git a/parsl/parsl/tests/conftest.py b/parsl/parsl/tests/conftest.py new file mode 100644 index 00000000..d992bcd9 --- /dev/null +++ b/parsl/parsl/tests/conftest.py @@ -0,0 +1,494 @@ +import importlib.util +import itertools +import logging +import os +import pathlib +import random +import re +import shutil +import signal +import string +import sys +import tempfile +import threading +import time +import traceback +import types +import typing as t +from datetime import datetime +from glob import glob +from itertools import chain + +import _pytest.runner as runner +import pytest + +import parsl +from parsl.dataflow.dflow import DataFlowKernelLoader +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + + +def dumpstacks(sig, frame): + s = '' + try: + thread_names = {thread.ident: thread.name for thread in threading.enumerate()} + tf = sys._current_frames() + for thread_id, frame in tf.items(): + s += '\n\nThread: %s (%d)' % (thread_names[thread_id], thread_id) + s += ''.join(traceback.format_stack(frame)) + except Exception: + s = traceback.format_exc() + with open(os.getenv('HOME') + '/parsl_stack_dump.txt', 'w') as f: + f.write(s) + print(s) + + +def pytest_sessionstart(session): + signal.signal(signal.SIGUSR1, dumpstacks) + + +@pytest.fixture(scope="session") +def tmpd_cwd_session(pytestconfig): + def _chmod(path: pathlib.Path, mode: int): + try: + path.lchmod(mode) # support BSD and derivatives + except NotImplementedError: + path.chmod(mode) + + config = re.sub(r"[^A-z0-9_-]+", "_", pytestconfig.getoption('config')[0]) + cwd = pathlib.Path(os.getcwd()) + pytest_dir = cwd / "pytest-parsl" + pytest_dir.mkdir(mode=0o700, parents=True, exist_ok=True) + + test_dir_prefix = "parsltest-" + link = pytest_dir / f"{test_dir_prefix}current" + link.unlink(missing_ok=True) + n = datetime.now().strftime('%Y%m%d.%H%M%S') + tmpd = tempfile.mkdtemp(dir=pytest_dir, prefix=f"{test_dir_prefix}{n}-{config}-") + tmpd = pathlib.Path(tmpd) + link.symlink_to(tmpd.name) + yield link + + try: + preserve = int(os.getenv("PARSL_TEST_PRESERVE_NUM_RUNS", "3")) + except Exception: + preserve = 3 + + test_runs = sorted( + d for d in pytest_dir.glob(f"{test_dir_prefix}*") + if d.is_dir() and not d.is_symlink() + ) + for run_to_remove in test_runs[:-preserve]: + run_to_remove.chmod(0o700) + for root, subdirnames, fnames in os.walk(run_to_remove): + rpath = pathlib.Path(root) + for d in subdirnames: + _chmod(rpath / d, 0o700) + for f in fnames: + _chmod(rpath / f, 0o600) + shutil.rmtree(run_to_remove) + + +@pytest.fixture +def tmpd_cwd(tmpd_cwd_session, request: pytest.FixtureRequest): + node_name = re.sub(r'[^\w\s-]', '_', request.node.name) + prefix = f"{node_name}-" + tmpd = tempfile.mkdtemp(dir=tmpd_cwd_session, prefix=prefix) + yield pathlib.Path(tmpd) + + +def pytest_addoption(parser): + """Add parsl-specific command-line options to pytest. + """ + parser.addoption( + '--config', + action='store', + metavar='CONFIG', + type=str, + nargs=1, + required=True, + help="run with parsl CONFIG; use 'local' to run locally-defined config" + ) + + parser.addoption( + '--show-filtered-tests', + action='store_true', + default=False, + help="Report filtered tests as skipped instead of deselecting them" + ) + + +def pytest_configure(config): + """Configure help for parsl-specific pytest decorators. + + This help is returned by `pytest --markers`. + """ + config.addinivalue_line( + 'markers', + 'whitelist(config1, config2, ..., reason=None): mark test to run only on named configs. ' + 'Wildcards (*) are accepted. If `reason` is supplied, it will be included in the report.' + ) + config.addinivalue_line( + 'markers', + 'blacklist(config1, config2, ..., reason=None): mark test to skip named configs. ' + 'Wildcards (*) are accepted. If `reason` is supplied, it will be included in the report.' + ) + config.addinivalue_line( + 'markers', + 'local: mark test to only run locally-defined config.' + ) + config.addinivalue_line( + 'markers', + 'local(reason): mark test to only run locally-defined config; report will include supplied reason.' + ) + config.addinivalue_line( + 'markers', + 'cleannet: Enable tests that require a clean network connection (such as for testing FTP)' + ) + config.addinivalue_line( + 'markers', + 'staging_required: Marks tests that require a staging provider, when there is no sharedFS' + ) + config.addinivalue_line( + 'markers', + 'multiple_cores_required: Marks tests that require multiple cores, such as htex affinity' + ) + config.addinivalue_line( + 'markers', + 'unix_filesystem_permissions_required: Marks tests that require unix-level filesystem permission enforcement' + ) + config.addinivalue_line( + 'markers', + 'issue3328: Marks tests broken by issue #3328' + ) + config.addinivalue_line( + 'markers', + 'executor_supports_std_stream_tuples: Marks tests that require tuple support for stdout/stderr' + ) + config.addinivalue_line( + 'markers', + 'shared_fs: Marks tests that require a shared_fs between the workers are the test client' + ) + config.addinivalue_line( + 'markers', + 'issue_3620: Marks tests that do not work correctly on GlobusComputeExecutor (ref: issue 3620)' + ) + config.addinivalue_line( + 'markers', + 'workqueue: Marks local tests that require a working Work Queue installation' + ) + config.addinivalue_line( + 'markers', + 'taskvine: Marks local tests that require a working Task Vine installation' + ) + config.addinivalue_line( + 'markers', + 'globus_compute: tests that require the Globus Compute SDK' + ) + config.addinivalue_line( + 'markers', + 'radical: tests that require the Radical framework' + ) + + +@pytest.fixture(autouse=True, scope='session') +def load_dfk_session(request, pytestconfig, tmpd_cwd_session): + """Load a dfk around entire test suite, except in local mode. + + The special path `local` indicates that configuration will not come + from a pytest managed configuration file; in that case, see + load_dfk_local_module for module-level configuration management. + """ + + RepresentationMixin._validate_repr = True + + config = pytestconfig.getoption('config')[0] + + if config != 'local': + spec = importlib.util.spec_from_file_location('', config) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + + if DataFlowKernelLoader._dfk is not None: + raise RuntimeError("DFK didn't start as None - there was a DFK from somewhere already") + + if hasattr(module, 'config'): + parsl_conf = module.config + elif hasattr(module, 'fresh_config'): + parsl_conf = module.fresh_config() + else: + raise RuntimeError("Config module does not define config or fresh_config") + + if parsl_conf.run_dir == "runinfo": # the default + parsl_conf.run_dir = str(tmpd_cwd_session / parsl_conf.run_dir) + dfk = parsl.load(parsl_conf) + + for ex in dfk.executors.values(): + ex.working_dir = tmpd_cwd_session + + yield + + if parsl.dfk() != dfk: + raise RuntimeError("DFK changed unexpectedly during test") + dfk.cleanup() + assert DataFlowKernelLoader._dfk is None + else: + yield + + +@pytest.fixture(autouse=True, scope='module') +def load_dfk_local_module(request, pytestconfig, tmpd_cwd_session): + """Load the dfk around test modules, in local mode. + + If local_config is specified in the test module, it will be loaded using + parsl.load. It should be a Callable that returns a parsl Config object. + + If local_setup and/or local_teardown are callables (such as functions) in + the test module, they will be invoked before/after the tests. This can + be used to perform more interesting DFK initialisation not possible with + local_config. + """ + RepresentationMixin._validate_repr = True + + config = pytestconfig.getoption('config')[0] + + if config == 'local': + local_setup = getattr(request.module, "local_setup", None) + local_teardown = getattr(request.module, "local_teardown", None) + local_config = getattr(request.module, "local_config", None) + + if local_config: + assert callable(local_config) + c = local_config() + assert isinstance(c, parsl.Config) + + if c.run_dir == "runinfo": # the default + c.run_dir = str(tmpd_cwd_session / c.run_dir) + + dfk = parsl.load(c) + + for ex in dfk.executors.values(): + ex.working_dir = tmpd_cwd_session + + if callable(local_setup): + local_setup() + + yield + + if callable(local_teardown): + local_teardown() + assert DataFlowKernelLoader._dfk is None, "Expected teardown to clear DFK" + + if local_config: + if parsl.dfk() != dfk: + raise RuntimeError("DFK changed unexpectedly during test") + dfk.cleanup() + assert DataFlowKernelLoader._dfk is None + + else: + yield + + +@pytest.hookimpl(hookwrapper=True) +def pytest_collection_modifyitems(items: list[pytest.Item], config): + """Apply whitelist, blacklist, and local markers. + + Ensure that if a whitelist decorator is applied to a test, configs which are + not in the whitelist are skipped. Similarly, configs in a blacklist are skipped, + and configs which are not `local` are skipped if the `local` decorator is applied. + """ + + yield + + show_filtered = config.getoption('--show-filtered-tests') + + parsl_test_config = config.getoption('config')[0] + conf_path = os.path.abspath(parsl_test_config) + + whitelist_reason = f"config '{parsl_test_config}' not in whitelist" + blacklist_reason = f"config '{parsl_test_config}' is in blacklist" + local_reason = "intended for local config" + explicit_reason = "intended for explicit config" + + filtered_tests = [] + i = len(items) + while i > 0: + i -= 1 + t_fn = items[i] + if (m := t_fn.get_closest_marker('whitelist')) is not None: + if conf_path not in chain.from_iterable(glob(x) for x in m.args): + reason = whitelist_reason + if 'reason' in m.kwargs: + reason = m.kwargs['reason'] + filtered_tests.append((i, reason)) + + elif (m := t_fn.get_closest_marker('blacklist')) is not None: + if conf_path in chain.from_iterable(glob(x) for x in m.args): + reason = blacklist_reason + if 'reason' in m.kwargs: + reason = m.kwargs['reason'] + filtered_tests.append((i, reason)) + + elif (m := t_fn.get_closest_marker('local')) is not None: + if parsl_test_config != 'local': + reason = local_reason + if len(m.args) > 0: + reason = str(m.args[0]) + filtered_tests.append((i, reason)) + + elif parsl_test_config == 'local': + filtered_tests.append((i, explicit_reason)) + + for t_idx, reason in filtered_tests: + if show_filtered: + t_fn = items[t_idx] + t_fn.add_marker(pytest.mark.skip(reason=reason)) + else: + del items[t_idx] + + +@pytest.fixture +def setup_data(tmpd_cwd): + data_dir = tmpd_cwd / "data" + data_dir.mkdir() + + (data_dir / "test1.txt").write_text("1\n") + (data_dir / "test2.txt").write_text("2\n") + return data_dir + + +@pytest.fixture(autouse=True, scope='function') +def assert_no_outstanding_tasks(pytestconfig): + """If we're in a config-file based mode, wait for task completion between + each test. This will detect early on (by hanging) if particular test + tasks are not finishing, rather than silently falling off the end of + the test run with tasks still in progress. + In local mode, this fixture does nothing, as there isn't anything + reasonable to assume about DFK behaviour here. + """ + config = pytestconfig.getoption('config')[0] + yield + if config != 'local': + logger.info("Checking no outstanding tasks") + for task_record in parsl.dfk().tasks.values(): + fut = task_record['app_fu'] + assert fut.done(), f"Incomplete task found, task id {task_record['id']}" + logger.info("No outstanding tasks found") + + +def pytest_make_collect_report(collector): + call = runner.CallInfo.from_call(lambda: list(collector.collect()), 'collect') + longrepr = None + if not call.excinfo: + outcome = "passed" + else: + from _pytest.outcomes import Skipped + skip_exceptions = (Skipped,) + if call.excinfo.errisinstance(KeyError): + outcome = "skipped" + r = collector._repr_failure_py(call.excinfo, "line").reprcrash + message = "{} not configured in user_opts.py".format(r.message.split()[-1]) + longrepr = (str(r.path), r.lineno, message) + elif call.excinfo.errisinstance(skip_exceptions): + outcome = "skipped" + r = collector._repr_failure_py(call.excinfo, "line").reprcrash + longrepr = (str(r.path), r.lineno, r.message) + else: + outcome = "failed" + errorinfo = collector.repr_failure(call.excinfo) + if not hasattr(errorinfo, "toterminal"): + errorinfo = runner.CollectErrorRepr(errorinfo) + longrepr = errorinfo + rep = runner.CollectReport(collector.nodeid, outcome, longrepr, getattr(call, 'result', None)) + rep.call = call # see collect_one_node + return rep + + +def pytest_ignore_collect(path): + if 'integration' in path.strpath: + return True + elif 'manual_tests' in path.strpath: + return True + elif 'scaling_tests/test_scale' in path.strpath: + return True + else: + return False + + +def create_traceback(start: int = 0) -> t.Optional[types.TracebackType]: + """ + Dynamically create a traceback. + + Builds a traceback from the top of the stack (the currently executing frame) on + down to the root frame. Optionally, use start to build from an earlier stack + frame. + + N.B. uses `sys._getframe`, which I only know to exist in CPython. + """ + tb = None + for depth in itertools.count(start + 1, 1): + try: + frame = sys._getframe(depth) + tb = types.TracebackType(tb, frame, frame.f_lasti, frame.f_lineno) + except ValueError: + break + return tb + + +@pytest.fixture +def try_assert(): + def _impl( + test_func: t.Callable[[], bool], + fail_msg: str = "", + timeout_ms: float = 5000, + attempts: int = 0, + check_period_ms: int = 20, + factor: float = 2, + ): + tb = create_traceback(start=1) + check_period_s = abs(check_period_ms) / 1000.0 + if attempts > 0: + for _attempt_no in range(attempts): + fraction = random.random() + time.sleep(fraction * check_period_s) # jitter + check_period_s *= factor ** fraction + if test_func(): + return + else: + att_fail = ( + f"\n (Still failing after attempt limit [{attempts}], testing" + f" every {check_period_ms}ms)" + ) + exc = AssertionError(f"{str(fail_msg)}{att_fail}".strip()) + raise exc.with_traceback(tb) + + elif timeout_ms > 0: + timeout_s = abs(timeout_ms) / 1000.0 + end = time.monotonic() + timeout_s + while time.monotonic() < end: + fraction = random.random() + wait_for = fraction * check_period_s # jitter + time.sleep(min(wait_for, end - time.monotonic())) + check_period_s *= factor ** fraction + if test_func(): + return + att_fail = ( + f"\n (Still failing after timeout [{timeout_ms}ms], with attempts " + f"every {check_period_ms}ms)" + ) + exc = AssertionError(f"{str(fail_msg)}{att_fail}".strip()) + raise exc.with_traceback(tb) + + else: + raise AssertionError("Bad assert call: no attempts or timeout period") + + yield _impl + + +@pytest.fixture +def randomstring(): + def func(length=5, alphabet=string.ascii_letters): + return "".join(random.choice(alphabet) for _ in range(length)) + + return func diff --git a/parsl/parsl/tests/integration/__init__.py b/parsl/parsl/tests/integration/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/integration/latency.py b/parsl/parsl/tests/integration/latency.py new file mode 100644 index 00000000..eb76b4bf --- /dev/null +++ b/parsl/parsl/tests/integration/latency.py @@ -0,0 +1,131 @@ +""" +What is this test +================= + +This simple test measures the time taken to launch single tasks and receive results from an executor. + +We will call get the result back after launch as ``latency`` and the combined measure of time to launch +and the time to get the result back as ``roundtrip time``. + + +How do we measure +================= + +1. We start the executor +2. Run 100 tasks and wait for them to prime the executors and the ensure that workers are online +3. Run the actual measurements: + 1) Time to launch the app + 2) Time to receive result. + +4. Take the min, max, and mean of ``latency`` and ``roundtrip``. + + +Preliminary results +================== + +Results from running on Midway with IPP executor. + +Latency | Min:0.005968570709228516 Max:0.011006593704223633 Average:0.0065019774436950685 +Roundtrip | Min:0.00716400146484375 Max:0.012288331985473633 Average:0.007741005420684815 +""" +import time + +import parsl + + +@parsl.python_app +def python_app(): + import platform + return "Hello from {0}".format(platform.uname()) + + +@parsl.python_app +def python_app_slow(duration): + import platform + import time + time.sleep(duration) + return "Hello from {0}".format(platform.uname()) + + +@parsl.python_app +def python_noop(): + return + + +@parsl.bash_app +def bash_app(stdout=None, stderr=None): + return 'echo "Hello from $(uname -a)" ; sleep 2' + + +def test_python_remote(count=2): + """ Run with no delay. + """ + fus = [] + for i in range(0, count): + fu = python_app_slow(0) + fus.extend([fu]) + + for fu in fus: + print(fu.result()) + + +def test_python_remote_slow(count=2): + fus = [] + for i in range(0, count): + fu = python_app_slow(count) + fus.extend([fu]) + + for fu in fus: + print(fu.result()) + + +def average(x): + return sum(x) / len(x) + + +def test_python(count): + results = {} + + print("Priming the system") + items = [] + for i in range(0, 100): + items.extend([python_app()]) + print("Launched primer application") + for i in items: + i.result() + print("Primer done") + + latency = [] + rtt = [] + for i in range(0, 100): + pre = time.time() + results[i] = python_noop() + post = time.time() + results[i].result() + final = time.time() + latency.append(final - post) + rtt.append(final - pre) + + min_latency = min(latency) * 1000 + max_latency = max(latency) * 1000 + avg_latency = average(latency) * 1000 + print("Latency | Min:{0:0.3}ms Max:{1:0.3}ms Average:{2:0.3}ms".format(min_latency, + max_latency, + avg_latency)) + + min_rtt = min(rtt) * 1000 + max_rtt = max(rtt) * 1000 + avg_rtt = average(rtt) * 1000 + + print("Roundtrip | Min:{0:0.3}ms Max:{1:0.3}ms Average:{2:0.3}ms".format(min_rtt, + max_rtt, + avg_rtt)) + + +def test_bash(): + import os + fname = os.path.basename(__file__) + + x = bash_app(stdout="{0}.out".format(fname)) + print("Waiting ....") + print(x.result()) diff --git a/parsl/parsl/tests/integration/test_apps/__init__.py b/parsl/parsl/tests/integration/test_apps/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/integration/test_integration/test_ssh/__init__.py b/parsl/parsl/tests/integration/test_integration/test_ssh/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/integration/test_parsl_load_default_config.py b/parsl/parsl/tests/integration/test_parsl_load_default_config.py new file mode 100644 index 00000000..bb9446c4 --- /dev/null +++ b/parsl/parsl/tests/integration/test_parsl_load_default_config.py @@ -0,0 +1,23 @@ +import parsl +from parsl import python_app + + +@python_app +def cpu_stress(inputs=[], outputs=[]): + s = 0 + for i in range(10**8): + s += i + return s + + +def test_parsl_load_default_config(): + dfk = parsl.load() + a1, b1 = [cpu_stress(), + cpu_stress()] + a1.result() + b1.result() + dfk.cleanup() + + +if __name__ == '__main__': + test_parsl_load_default_config() diff --git a/parsl/parsl/tests/integration/test_providers/ec2/__init__.py b/parsl/parsl/tests/integration/test_providers/ec2/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/integration/test_stress/README.rst b/parsl/parsl/tests/integration/test_stress/README.rst new file mode 100644 index 00000000..fcd4178f --- /dev/null +++ b/parsl/parsl/tests/integration/test_stress/README.rst @@ -0,0 +1,17 @@ +Profiling How-To +---------------- + +There's a heavy performance penalty when using conda vs standard python +distributions. So, these tests are preferably done with python installations +that come with the system. + + +To dump the profiling information +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +> python3 -m cProfile -s cumtime -o -c 1000 + +To view the performance data : +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +> pyprof2calltree -k -i diff --git a/parsl/parsl/tests/integration/test_stress/__init__.py b/parsl/parsl/tests/integration/test_stress/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/integration/test_stress/test_python_simple.py b/parsl/parsl/tests/integration/test_stress/test_python_simple.py new file mode 100644 index 00000000..7d104ba7 --- /dev/null +++ b/parsl/parsl/tests/integration/test_stress/test_python_simple.py @@ -0,0 +1,43 @@ +import argparse +import time + +import parsl +from parsl import python_app +from parsl.configs.htex_local import config + + +@python_app +def increment(x): + return x + 1 + + +def test_stress(count=1000): + """Threaded app RTT stress test""" + + start = time.time() + x = [] + for i in range(count): + f = increment(i) + x.append(f) + end = time.time() + print("Launched {0} tasks in {1:.2f} s".format(count, end - start)) + + [f.result() for f in x] + end = time.time() + print("Completed {0} tasks in {1:.2f} s".format(count, end - start)) + + +if __name__ == '__main__': + parsl.load(config) + + parser = argparse.ArgumentParser() + parser.add_argument("-c", "--count", type=int, default=1000, + help="width of the pipeline") + parser.add_argument("-d", "--debug", action='store_true', + help="Enable stream logging") + args = parser.parse_args() + + if args.debug: + parsl.set_stream_logger() + + test_stress(count=args.count) diff --git a/parsl/parsl/tests/integration/test_stress/test_python_threads.py b/parsl/parsl/tests/integration/test_stress/test_python_threads.py new file mode 100644 index 00000000..30f6ff9b --- /dev/null +++ b/parsl/parsl/tests/integration/test_stress/test_python_threads.py @@ -0,0 +1,40 @@ +''' Testing bash apps +''' +import argparse +import time + +import parsl +from parsl import python_app +from parsl.tests.configs.local_threads import config + +local_config = config + + +@python_app +def increment(x): + return x + 1 + + +def test_stress(count=1000): + """Threaded app launch stress test""" + start = time.time() + x = {} + for i in range(count): + x[i] = increment(i) + end = time.time() + print("Launched {0} tasks in {1} s".format(count, end - start)) + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + parser.add_argument("-c", "--count", default="100", + help="width of the pipeline") + parser.add_argument("-d", "--debug", action='store_true', + help="Count of apps to launch") + args = parser.parse_args() + + if args.debug: + parsl.set_stream_logger() + + test_stress(count=int(args.count)) diff --git a/parsl/parsl/tests/lint-inits.sh b/parsl/parsl/tests/lint-inits.sh new file mode 100755 index 00000000..8e72b17c --- /dev/null +++ b/parsl/parsl/tests/lint-inits.sh @@ -0,0 +1,18 @@ +#!/bin/bash -e + +# Check that each directory with python source in it has a __init__.py +# file in it too. Often code will work in most situations without an +# __init__.py but some corner cases can arise that are awkward to debug; +# so the policy is that every directory must have __init__.py + +find parsl -name \*.py \ + | while read fn; do + dirname $fn + done \ + | sort | uniq \ + | while read dir; do + if [ -f ${dir}/__init__.py ] ; + then true; + else echo FAILURE: $dir does not have an __init__.py ; exit 1; + fi; + done diff --git a/parsl/parsl/tests/manual_tests/__init__.py b/parsl/parsl/tests/manual_tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/manual_tests/htex_local.py b/parsl/parsl/tests/manual_tests/htex_local.py new file mode 100644 index 00000000..e85da878 --- /dev/null +++ b/parsl/parsl/tests/manual_tests/htex_local.py @@ -0,0 +1,25 @@ +from parsl.config import Config +from parsl.executors import HighThroughputExecutor + +# from parsl.launchers import SimpleLauncher +from parsl.launchers import SingleNodeLauncher +from parsl.providers import LocalProvider + +config = Config( + executors=[ + HighThroughputExecutor( + poll_period=1, + label="htex_local", + # worker_debug=True, + cores_per_worker=1, + encrypted=True, + provider=LocalProvider( + init_blocks=1, + max_blocks=1, + # tasks_per_node=1, # For HighThroughputExecutor, this option should in most cases be 1 + launcher=SingleNodeLauncher(), + ), + ) + ], + strategy='none', +) diff --git a/parsl/parsl/tests/manual_tests/site_testing_matrix.rst b/parsl/parsl/tests/manual_tests/site_testing_matrix.rst new file mode 100644 index 00000000..c5b93ea4 --- /dev/null +++ b/parsl/parsl/tests/manual_tests/site_testing_matrix.rst @@ -0,0 +1,54 @@ +Site Testing +============ + +Sites that we will test in this exercise: + +* Local IPP +* Midway RCC at UChicago +* Open Science Grid (OSG) +* Swan at Cray +* Cori at NERSC +* Cooley (ALCF) +* Theta (ALCF) +* CC_in2p3 French grid +* AWS EC2 +* Comet (SDSC) + +Test Progress +============= + ++------------------------+------------+------------+------------+------------+ +|Site |Channel |Provider |Launcher |Test Status | ++========================+============+============+============+============+ +| Local |Local |Local |SingleNode | PASSED | ++------------------------+------------+------------+------------+------------+ +| Midway (RCC) |Ssh |Slurm |SingleNode | PASSED | ++------------------------+------------+------------+------------+------------+ +| Midway (RCC) |Ssh |Slurm |Srun | PASSED | ++------------------------+------------+------------+------------+------------+ +| Open Science Grid(OSG) |Ssh |Condor |SingleNode | PASSED | ++------------------------+------------+------------+------------+------------+ +| Swan (Cray) |Ssh |Torque |Aprun | PASSED | ++------------------------+------------+------------+------------+------------+ +| Cori (NERSC) |Ssh |Slurm |SingleNode | PASSED | ++------------------------+------------+------------+------------+------------+ +| Cori (NERSC) |Ssh |Slurm |Srun | PASSED | ++------------------------+------------+------------+------------+------------+ +| Cooley (ALCF) |SshIL |Cobalt |SingleNode | PASSED | ++------------------------+------------+------------+------------+------------+ +| Theta (ALCF) |Local |Cobalt |Aprun | PASSED | ++------------------------+------------+------------+------------+------------+ +| CC_IN2P3 (French grid) |Local |GridEngine |SingleNode | PASSED | ++------------------------+------------+------------+------------+------------+ +| AWS EC2 |None |AWS |SingleNode | PASSED | ++------------------------+------------+------------+------------+------------+ +| AWS EC2 (spot) |None |AWS |SingleNode | PASSED | ++------------------------+------------+------------+------------+------------+ +| Azure |None |Azure |SingleNode | DEFERRED | ++------------------------+------------+------------+------------+------------+ +| Nova/Jetstream |None |Jetstream |SingleNode | DEFERRED | ++------------------------+------------+------------+------------+------------+ +| Google Cloud |None |GoogleCloud |SingleNode | DEFERRED | ++------------------------+------------+------------+------------+------------+ +| Comet SDSC |Ssh |Slurm |Srun | PASSED | ++------------------------+------------+------------+------------+------------+ diff --git a/parsl/parsl/tests/manual_tests/test_basic.py b/parsl/parsl/tests/manual_tests/test_basic.py new file mode 100644 index 00000000..2a9fe93e --- /dev/null +++ b/parsl/parsl/tests/manual_tests/test_basic.py @@ -0,0 +1,155 @@ +import argparse +import time + +import parsl + +# Tested. Confirmed. Local X Local X SingleNodeLauncher +# from parsl.tests.configs.local_ipp import config + +# Tested. Confirmed. ssh X Slurm X SingleNodeLauncher +# from parsl.tests.configs.midway_ipp import config + +# Tested. Confirmed. ssh X Slurm X SingelNodeLauncher +# from parsl.tests.configs.midway_ipp_multicore import config + +# Tested. Confirmed. ssh X Slurm X SrunLauncher +# from parsl.tests.configs.midway_ipp_multinode import config + +# OSG requires python3.5 for testing. This test is inconsitent, +# breaks often depending on where the test lands +# Tested. Confirmed. ssh X Condor X single_node +# from parsl.tests.configs.osg_ipp_multinode import config + +# Tested. Confirmed. ssh X Torque X AprunLauncher +# from parsl.tests.configs.swan_ipp import config + +# Tested. Confirmed. ssh X Torque X AprunLauncher +# from parsl.tests.configs.swan_ipp_multinode import config + +# from parsl.tests.configs.cooley_ssh_il_single_node import config + +# Tested. Confirmed. local X GridEngine X singleNode +# from parsl.tests.configs.cc_in2p3_local_single_node import config + +# from parsl.tests.configs.comet_ipp_multinode import config + +# from parsl.tests.configs.htex_local import config +parsl.set_stream_logger() + +# from htex_midway import config +# from htex_swan import config + + +from parsl.app.app import python_app # , bash_app + + +@python_app +def double(x): + return x * 2 + + +@python_app +def echo(x, string, stdout=None): + print(string) + return x * 5 + + +@python_app +def import_echo(x, string, stdout=None): + import time + time.sleep(0) + print(string) + return x * 5 + + +@python_app +def platform(sleep=10, stdout=None): + import platform + import time + time.sleep(sleep) + return platform.uname() + + +def test_simple(n=2): + start = time.time() + x = double(n) + print("Result : ", x.result()) + assert x.result() == n * \ + 2, "Expected double to return:{0} instead got:{1}".format( + n * 2, x.result()) + print("Duration : {0}s".format(time.time() - start)) + print("[TEST STATUS] test_parallel_for [SUCCESS]") + return True + + +def test_imports(n=2): + start = time.time() + x = import_echo(n, "hello world") + print("Result : ", x.result()) + assert x.result() == n * \ + 5, "Expected double to return:{0} instead got:{1}".format( + n * 2, x.result()) + print("Duration : {0}s".format(time.time() - start)) + print("[TEST STATUS] test_parallel_for [SUCCESS]") + return True + + +def test_platform(n=2): + # sync + x = platform(sleep=0) + print(x.result()) + + d = [] + for i in range(0, n): + x = platform(sleep=5) + d.append(x) + + print(set([i.result()for i in d])) + + return True + + +def test_parallel_for(n=2): + d = {} + start = time.time() + for i in range(0, n): + d[i] = double(i) + # time.sleep(0.01) + + assert len( + d.keys()) == n, "Only {0}/{1} keys in dict".format(len(d.keys()), n) + + [d[i].result() for i in d] + print("Duration : {0}s".format(time.time() - start)) + print("[TEST STATUS] test_parallel_for [SUCCESS]") + return d + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + parser.add_argument("-s", "--sitespec", default=None) + parser.add_argument("-c", "--count", default="10", + help="Count of apps to launch") + parser.add_argument("-d", "--debug", action='store_true', + help="Count of apps to launch") + args = parser.parse_args() + + if args.sitespec: + c = None + try: + exec("import parsl; from {} import config".format(args.sitespec)) + parsl.load(c) + except Exception: + print("Failed to load the requested config : ", args.sitespec) + exit(0) + + if args.debug: + parsl.set_stream_logger() + + x = test_simple(int(args.count)) + # x = test_imports() + # x = test_parallel_for() + # x = test_parallel_for(int(args.count)) + # x = test_stdout() + x = test_platform() diff --git a/parsl/parsl/tests/manual_tests/test_log_filter.py b/parsl/parsl/tests/manual_tests/test_log_filter.py new file mode 100644 index 00000000..ff620633 --- /dev/null +++ b/parsl/parsl/tests/manual_tests/test_log_filter.py @@ -0,0 +1,79 @@ +import argparse +import logging + +import parsl + +parsl.load() + +from parsl import python_app + + +@python_app +def platform(sleep=10, stdout=None): + import platform + import time + time.sleep(sleep) + return platform.uname() + + +class SkipTasksFilter(logging.Filter): + + def __init__(self, avoid_string): + self.avoid = avoid_string + + def filter(self, record): + return self.avoid not in record.getMessage() + + +def test_platform(n=2): + # sync + logger = logging.getLogger("parsl.dataflow.dflow") + skip_tags = ['Task', 'dependencies'] + for skip in skip_tags: + skip_filter = SkipTasksFilter(skip) + logger.addFilter(skip_filter) + + x = platform(sleep=0) + print(x.result()) + + d = [] + for i in range(0, n): + x = platform(sleep=5) + d.append(x) + + print(set([i.result()for i in d])) + + dfk = parsl.dfk() + dfk.cleanup() + + with open("{}/parsl.log".format(dfk.run_dir)) as f: + + for line in f.readlines(): + if any(skip in line for skip in skip_tags): + raise Exception("Logline {} contains a skip tag".format(line)) + return True + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + parser.add_argument("-s", "--sitespec", default=None) + parser.add_argument("-c", "--count", default="10", + help="Count of apps to launch") + parser.add_argument("-d", "--debug", action='store_true', + help="Count of apps to launch") + args = parser.parse_args() + + if args.sitespec: + c = None + try: + exec("import parsl; from {} import config".format(args.sitespec)) + parsl.load(c) + except Exception: + print("Failed to load the requested config : ", args.sitespec) + exit(0) + + if args.debug: + parsl.set_stream_logger() + + x = test_platform() diff --git a/parsl/parsl/tests/manual_tests/test_memory_limits.py b/parsl/parsl/tests/manual_tests/test_memory_limits.py new file mode 100644 index 00000000..2f923a8b --- /dev/null +++ b/parsl/parsl/tests/manual_tests/test_memory_limits.py @@ -0,0 +1,82 @@ +import argparse +import multiprocessing + +import psutil + +import parsl +from parsl.app.app import python_app # , bash_app +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SingleNodeLauncher +from parsl.providers import LocalProvider + + +@python_app +def double(x): + return x * 2 + + +def test_simple(mem_per_worker): + + config = Config( + executors=[ + HighThroughputExecutor( + poll_period=1, + label="htex_local", + worker_debug=True, + mem_per_worker=mem_per_worker, + cores_per_worker=0.1, + suppress_failure=True, + encrypted=True, + provider=LocalProvider( + init_blocks=1, + max_blocks=1, + launcher=SingleNodeLauncher(), + ), + ) + ], + strategy='none', + ) + parsl.load(config) + + print("Configuration requests:") + print("cores_per_worker: ", config.executors[0].cores_per_worker) + print("mem_per_worker: ", config.executors[0].mem_per_worker) + + available_mem_on_node = round(psutil.virtual_memory().available / (2**30), 1) + expected_workers = multiprocessing.cpu_count() / config.executors[0].cores_per_worker + if mem_per_worker: + expected_workers = int(available_mem_on_node / config.executors[0].mem_per_worker) + + print("Available memory: ", available_mem_on_node) + print("Expected workers: ", expected_workers) + # Prime a worker + double(5).result() + dfk = parsl.dfk() + connected = dfk.executors['htex_local'].connected_workers() + print("Connected : ", connected) + assert expected_workers == connected, "Expected {} workers, instead got {} workers".format(expected_workers, + connected) + parsl.clear() + return True + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + parser.add_argument("-s", "--sleep", default="4") + parser.add_argument("-c", "--count", default="10", + help="Count of apps to launch") + parser.add_argument("-d", "--debug", action='store_true', + help="Count of apps to launch") + args = parser.parse_args() + + if args.debug: + parsl.set_stream_logger() + + available_mem_on_node = round(psutil.virtual_memory().available / (2**30), 1) + + x = test_simple(None) + x = test_simple(0.8) + x = test_simple(1) + x = test_simple(available_mem_on_node - 2) diff --git a/parsl/parsl/tests/manual_tests/test_regression_220.py b/parsl/parsl/tests/manual_tests/test_regression_220.py new file mode 100644 index 00000000..7221c624 --- /dev/null +++ b/parsl/parsl/tests/manual_tests/test_regression_220.py @@ -0,0 +1,32 @@ +import time + +import pytest + +from parsl import DataFlowKernel, set_stream_logger +from parsl.tests.configs.local_threads import config + + +@pytest.mark.skip('meant to be tested by hand') +def test_220(): + """Test async usage_tracking behavior for issue #220 """ + + print("This test assumes /etc/resolv being misconfigured") + with open("/etc/resolv.conf", 'r') as f: + for line in f.readlines(): + line = line.strip() + print("Line: [{}]".format(line)) + if line.startswith("nameserver") and line != "nameserver 2.2.2.2": + assert False, "/etc/resolv.conf should be misconfigured" + + start = time.time() + set_stream_logger() + dfk = DataFlowKernel(config=config) + delta = time.time() - start + print("Time taken : ", delta) + assert delta < 1, "DFK took too much time to start, delta:{}".format(delta) + dfk.cleanup() + + +if __name__ == "__main__": + + test_220() diff --git a/parsl/parsl/tests/manual_tests/test_worker_count.py b/parsl/parsl/tests/manual_tests/test_worker_count.py new file mode 100644 index 00000000..4ff32cc9 --- /dev/null +++ b/parsl/parsl/tests/manual_tests/test_worker_count.py @@ -0,0 +1,75 @@ +import argparse +import math +import multiprocessing +import time + +import parsl + +CORES = multiprocessing.cpu_count() +CORES_PER_WORKER = 1 +EXPECTED_WORKERS = math.floor(CORES / CORES_PER_WORKER) + + +from parsl.executors import HighThroughputExecutor + +# from parsl.tests.configs.htex_local import config +from parsl.tests.manual_tests.htex_local import config + +assert isinstance(config.executors[0], HighThroughputExecutor) +assert config.executors[0].provider is not None, "htex always has a provider" +config.executors[0].cores_per_worker = CORES_PER_WORKER +config.executors[0].provider.init_blocks = 1 + +# from htex_midway import config +# from htex_swan import config + +local_config = config + +from parsl.app.app import python_app # , bash_app + + +@python_app +def slow_pid(sleep=1): + import os + import time + time.sleep(sleep) + return os.getppid(), os.getpid() + + +def test_worker(n=2, sleep=0): + d = {} + start = time.time() + for i in range(0, n): + d[i] = slow_pid(sleep=sleep) + + foo = [d[i].result() for i in d] + manager_ids = set([f[0] for f in foo]) + worker_ids = set([f[1] for f in foo]) + + print("Got workers : {}".format(worker_ids)) + assert len(manager_ids) == 1, "Expected only 1 manager id, got ids : {}".format( + manager_ids) + assert len(worker_ids) == EXPECTED_WORKERS, "Expected {} workers, instead got {}".format(EXPECTED_WORKERS, + len(worker_ids)) + + print("Duration : {0}s".format(time.time() - start)) + print("[TEST STATUS] test_parallel_for [SUCCESS]") + return d + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + parser.add_argument("-s", "--sleep", default="0") + parser.add_argument("-c", "--count", default="10", + help="Count of apps to launch") + parser.add_argument("-d", "--debug", action='store_true', + help="Count of apps to launch") + args = parser.parse_args() + + if args.debug: + parsl.set_stream_logger() + + parsl.load(local_config) + + x = test_worker(n=int(args.count), sleep=float(args.sleep)) diff --git a/parsl/parsl/tests/pytest.ini b/parsl/parsl/tests/pytest.ini new file mode 100644 index 00000000..28856c87 --- /dev/null +++ b/parsl/parsl/tests/pytest.ini @@ -0,0 +1,2 @@ +[pytest] +addopts = -x -rs diff --git a/parsl/parsl/tests/setup_path.sh b/parsl/parsl/tests/setup_path.sh new file mode 100755 index 00000000..854bd896 --- /dev/null +++ b/parsl/parsl/tests/setup_path.sh @@ -0,0 +1,3 @@ +export PATH=$PWD:$PATH +PARSL=$PWD/../../ +export PYTHONPATH=$PARSL:$PYTHONPATH diff --git a/parsl/parsl/tests/site_tests/README.rst b/parsl/parsl/tests/site_tests/README.rst new file mode 100644 index 00000000..85420c3b --- /dev/null +++ b/parsl/parsl/tests/site_tests/README.rst @@ -0,0 +1,73 @@ +Site Testing +============ + +This doc will cover two items: + +1. Running the site tests on a site +2. How to prep a new site for site testing + + +The site tests are setup with the assumption that there is a conda installation +available either through modules available on the system, or from a user's personal +installation. + +* `conda_setup.sh` will create a unique conda env mapped to `parsl/parsl_` and + leave a script `~/setup_parsl_test_env.sh` that will activate this env. + +* `site_config_selector.py` is a facade that checks the submit node's hostname and picks a matching + configuration from `parsl/parsl/tests/configs`. + +* The tests in this folder are designed to use the site_config_selector and runs test against + that config. + + +Running tests +------------- + +Assuming the site tests are supported on your site, here's how you run tests:: + + 1. Checkout parsl into a *shared filesystem* with the right git tag/hash for testing + * If you are running tests on a system where the $HOME is not mounted on compute nodes + checkout to $WORK / $SCRATCH or the equivalent. For eg, Comet and Stampede2 + 2. `cd parsl/tests/site_tests` + 3. `bash conda_setup.sh` + 4. Update parsl/parsl/tests/configs/local_user_opts.py with user specific options. + 5. Now check if the setup script is available at `~/setup_parsl_test_env.sh` + 6. Go back to the parsl root dir + 7. Reinstall parsl, if you've made config changes in step 3 or 4. + >>> pip install . + 8. Run tests with `make site_test` + + +Adding a new site +----------------- + +* We want to pick a python environment that a user is most likely to use on the site. + 1. Specialized python builds for the system (for eg, Summit) + 2. Anaconda available via modules + 3. User's conda installation +* Add a new block to ``conda_setup.sh`` that installs a fresh environment and writes out + the activation commands to ``~/setup_parsl_test_env.sh`` +* Add a site config to ``parsl/tests/configs/`` and add your local user options + to ``parsl/tests/configs/local_user_opts.py``. For example, + `here's mine`_ + Make sure that the site config uses the ``fresh_config`` pattern. + Please ensure that the site config uses: + * ``max_workers_per_node = 1`` + * ``init_blocks = 1`` + * ``min_blocks = 0`` + +* Add this site config to ``parsl/tests/site_tests/site_config_selector.py`` +* Reinstall parsl, using ``pip install .`` +* Test a single test: ``python3 test_site.py -d`` to confirm that the site works correctly. +* Once tests are passing run the whole site_test with ``make site_test`` + + +Shared filesystem option +------------------------ + +There is a new env variable ``SHARED_FS_OPTIONS`` to pass markers to pytest to skip certain tests. + +When there's a shared-FS, the default NoOpStaging works. However, when there's no shared-FS some tests +that uses File objects require a staging provider (eg. rsync). These tests can be turned off with +``-k "not staging_required"`` diff --git a/parsl/parsl/tests/site_tests/__init__.py b/parsl/parsl/tests/site_tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/site_tests/conda_setup.sh b/parsl/parsl/tests/site_tests/conda_setup.sh new file mode 100644 index 00000000..644bda72 --- /dev/null +++ b/parsl/parsl/tests/site_tests/conda_setup.sh @@ -0,0 +1,186 @@ +#!/bin/bash + +SCRIPTPATH="$( cd "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P )" +echo $SCRIPTPATH +PARSL_ROOT=$(dirname $(dirname $(dirname $SCRIPTPATH))) +PARSL_GITHASH=$(git rev-parse --short HEAD) + +CONDA_TARGET=parsl_$PARSL_GITHASH.py3.7 +export CONDA_TARGET + +if [[ "$CONDA_TARGET" == "$CONDA_DEFAULT_ENV" ]] +then + echo "Conda target env $CONDA_TARGET loaded" + exit 0 +fi + + +create_conda() { + pushd . + cd $PARSL_ROOT + + if [[ "$(hostname)" =~ .*thetalogin.* ]] + then + echo "On theta" + module load miniconda-3/latest + conda create -p $CONDA_TARGET --clone $CONDA_PREFIX --yes --force + conda activate $CONDA_TARGET + # Theta is weird, we do explicit install + pip install -r test-requirements.txt + conda install pip psutil --yes + python3 setup.py install + echo "module load miniconda-3/latest;" > ~/setup_parsl_test_env.sh + echo "conda activate $PWD/$CONDA_TARGET" >> ~/setup_parsl_test_env.sh + return + + elif [[ "$(hostname)" =~ .*frontera.* ]] + then + echo "On Frontera" + if [[ -d ~/anaconda3 ]] + then + echo "Loading anaconda3 from ~/anaconda3" + source ~/anaconda3/bin/activate + else + echo "Please install conda to your home dir at ~/anaconda3" + fi + conda create -p $CONDA_TARGET python=3.7 --yes --force + conda activate $CONDA_TARGET + echo "source ~/anaconda3/bin/activate;" > ~/setup_parsl_test_env.sh + echo "conda activate $PWD/$CONDA_TARGET" >> ~/setup_parsl_test_env.sh + + elif [[ "$(hostname)" =~ .*nscc.* ]] + then + echo "On NSCC Singapore" + if [[ -d ~/anaconda3 ]] + then + echo "Loading anaconda3 from ~/anaconda3" + source ~/anaconda3/bin/activate + else + echo "Please install conda to your home dir at ~/anaconda3" + fi + conda create -p $CONDA_TARGET python=3.7 --yes --force + conda activate $CONDA_TARGET/ + echo "source ~/anaconda3/bin/activate;" > ~/setup_parsl_test_env.sh + echo "conda activate $PWD/$CONDA_TARGET/" >> ~/setup_parsl_test_env.sh + echo "module load openmpi/intel/1.10.2;" >> ~/setup_parsl_test_env.sh + + elif [[ "$(hostname -f)" =~ .*summit.* ]] + then + echo "On Summit" + module load ibm-wml-ce + conda create -p $CONDA_TARGET --yes --force + conda activate $PWD/$CONDA_TARGET + conda install paramiko>=2.7.1 pip numpy psutil pandas --yes + # conda install --file requirements.txt --yes + echo "module load ibm-wml-ce" > ~/setup_parsl_test_env.sh + echo "conda activate $PWD/$CONDA_TARGET" >> ~/setup_parsl_test_env.sh + + elif [[ "$(hostname)" =~ .*stampede2.* ]] + then + echo "On Stampede2" + if [[ -d ~/anaconda3 ]] + then + echo "Loading anaconda3 from ~/anaconda3" + source ~/anaconda3/bin/activate + else + echo "Please install conda to your home dir at ~/anaconda3" + fi + conda create -p $CONDA_TARGET python=3.7 --yes --force + conda activate $PWD/$CONDA_TARGET + echo "source ~/anaconda3/bin/activate;" > ~/setup_parsl_test_env.sh + echo "conda activate $PWD/$CONDA_TARGET" >> ~/setup_parsl_test_env.sh + + elif [[ "$(hostname)" =~ .*comet.* ]] + then + echo "On Comet" + if [[ -d ~/anaconda3 ]] + then + echo "Loading anaconda3 from ~/anaconda3" + source ~/anaconda3/bin/activate + else + echo "Please install conda to your home dir at ~/anaconda3" + fi + conda create -p $CONDA_TARGET python=3.7 --yes --force + conda activate $PWD/$CONDA_TARGET + echo "source ~/anaconda3/bin/activate;" > ~/setup_parsl_test_env.sh + echo "conda activate $PWD/$CONDA_TARGET" >> ~/setup_parsl_test_env.sh + + elif [[ "$(hostname)" =~ .*midway.* ]] + then + echo "On Midway" + module unload python + module load Anaconda3/5.3.0 + conda create -p $CONDA_TARGET python=3.7 --yes --force + source /software/Anaconda3-5.3.0-el7-x86_64/bin/activate + conda activate $PWD/$CONDA_TARGET + echo "module unload python" > ~/setup_parsl_test_env.sh + echo "module load Anaconda3/5.3.0" >> ~/setup_parsl_test_env.sh + echo "source /software/Anaconda3-5.3.0-el7-x86_64/bin/activate" >> ~/setup_parsl_test_env.sh + echo "conda activate $PWD/$CONDA_TARGET" >> ~/setup_parsl_test_env.sh + + elif [[ "$(hostname -f)" =~ .*in2p3.* ]] + then + echo "On CC-IN2P3" + export PATH=/pbs/throng/lsst/software/anaconda/anaconda3-5.0.1/bin:$PATH + conda create -p $CONDA_TARGET python=3.7 --yes --force + source activate $PWD/$CONDA_TARGET + conda install pip --yes + cat <<'EOF' > ~/setup_parsl_test_env.sh +export PATH=/pbs/throng/lsst/software/anaconda/anaconda3-5.0.1/bin:$PATH +export SHARED_FS_OPTIONS='-k "not issue363"' +EOF + echo "source activate $PWD/$CONDA_TARGET" >> ~/setup_parsl_test_env.sh + + elif [[ "$(hostname)" =~ .*cooley.* ]] + then + echo "On Cooley, testing Kubernetes on PetrelKube" + if [[ -d ~/anaconda3 ]] + then + echo "Loading anaconda3 from ~/anaconda3" + source ~/anaconda3/bin/activate + else + echo "Please install conda to your home dir at ~/anaconda3" + fi + conda create -p $CONDA_TARGET python=3.7 --yes --force + conda activate $PWD/$CONDA_TARGET + pip install kubernetes + echo "pip install git+https://github.com/Parsl/parsl@$PARSL_GITHASH" > ~/setup_parsl_test_env.sh + echo 'pip install pytest==4.6' >> ~/setup_parsl_test_env.sh + + elif [[ "$(hostname -f)" =~ .*h2ologin.* ]] + then + echo "On Blue Waters" + module load bwpy + python_version=3.6 + export EPYTHON="python$python_version" + mkdir -p parsl_$PARSL_GITHASH.py$python_version + virtualenv --system-site-packages $PWD/parsl_$PARSL_GITHASH.py$python_version + source $PWD/parsl_$PARSL_GITHASH.py$python_version/bin/activate + pip install --upgrade pip + echo "module load bwpy;" > ~/setup_parsl_test_env.sh + echo "cd $PWD;" >> ~/setup_parsl_test_env.sh + echo "source $PWD/parsl_$PARSL_GITHASH.py$python_version/bin/activate;" >> ~/setup_parsl_test_env.sh + + elif [[ "$(hostname -f)" =~ .*bridges.* ]] + then + echo "On bridges" + module load anaconda3 + conda create -p $CONDA_TARGET python=3.7 --yes --force + source activate $PWD/$CONDA_TARGET + echo "module load anaconda3" > ~/setup_parsl_test_env.sh + echo "source activate $PWD/$CONDA_TARGET" >> ~/setup_parsl_test_env.sh + + else + echo "Unknown site" + exit -1 + fi + + echo "Installing parsl from $PARSL_ROOT" + python3 -m pip install . + python3 -m pip install -r test-requirements.txt + + popd + +} + +create_conda diff --git a/parsl/parsl/tests/site_tests/site_config_selector.py b/parsl/parsl/tests/site_tests/site_config_selector.py new file mode 100644 index 00000000..921df197 --- /dev/null +++ b/parsl/parsl/tests/site_tests/site_config_selector.py @@ -0,0 +1,58 @@ +import os +import platform +import socket + + +def fresh_config(): + hostname = os.getenv('PARSL_HOSTNAME', platform.uname().node) + print("Loading config for {}".format(hostname)) + + if 'frontera' in hostname: + print("Loading Frontera config") + from parsl.tests.configs.frontera import fresh_config + config = fresh_config() + + elif 'nscc' in hostname: + print("Loading NSCC Singapore config") + from parsl.tests.configs.nscc_singapore import fresh_config + config = fresh_config() + + elif 'summit' in socket.getfqdn(): + print("Loading Frontera config") + from parsl.tests.configs.summit import fresh_config + config = fresh_config() + + elif 'comet' in hostname: + print("Loading Comet config") + from parsl.tests.configs.comet import fresh_config + config = fresh_config() + + elif 'midway' in hostname: + print("Loading Midway config") + from parsl.tests.configs.midway import fresh_config + config = fresh_config() + + elif 'h2ologin' in hostname: + print("Loading Blue Waters config") + from parsl.tests.configs.bluewaters import fresh_config + config = fresh_config() + + elif 'in2p3' in socket.getfqdn(): + print("Loading CC-IN2P3 config") + from parsl.tests.configs.cc_in2p3 import fresh_config + config = fresh_config() + + elif 'bridges' in socket.getfqdn(): + print("Loading bridges config") + from parsl.tests.configs.bridges import fresh_config + config = fresh_config() + + elif 'cooley' in hostname: + print("on Cooley, loading PetrelKube config") + from parsl.tests.configs.petrelkube import fresh_config + config = fresh_config() + + else: + raise RuntimeError("This site cannot be identified") + + return config diff --git a/parsl/parsl/tests/sites/CC-IN2P3.rst b/parsl/parsl/tests/sites/CC-IN2P3.rst new file mode 100644 index 00000000..5ff3f045 --- /dev/null +++ b/parsl/parsl/tests/sites/CC-IN2P3.rst @@ -0,0 +1,57 @@ +Environment setup on CC-IN2P3 +============================= + +For the DESC folks using CC-IN2P3 resources under the LSST allocation, there's an anaconda installation +on the shared project space that can be used. If you are not under this allocation, please follow +instructions to install anaconda to your $HOME directory from `here `_. + +For DESC users : + +.. code-block:: bash + + # Add this to your $HOME/.bashrc file + export PATH=/pbs/throng/lsst/software/anaconda/anaconda3-5.0.1/bin:$PATH + + # Create your conda env for Parsl, this will by default install to your $HOME/.conda directory + conda create --name parsl_env_3.6 python=3.6 + + # Activate the env + source activate parsl_env_3.6 + + # Install Parsl + pip install parsl + +Running tests +============= + +In order to run tests make sure you have Parsl and the test requirements installed. + +.. code-block:: bash + + # Clone parsl + git clone https://github.com/Parsl/parsl.git + cd parsl + + # Activate the env from the previous section + source activate parsl_env_3.6 + + # Install the test-requirements + python3 -m pip install -r test-requirements.txt + + # Run the CC-IN2P3 tests + cd parsl/tests/test_sites/test_CC-IN2P3/ + nosetests . + +Important Notes +=============== + +The GridEngine is designed for the Grid, a loose collection with cores being +the compute unit which is quite different from HPC systems and Clouds. +As a result resource requests are made in terms of cores, and multinode +mpi-launch capabilities with "mpirun/gerun". + +Please refer `here`_ for +the config option supported by gridEngine. + + + diff --git a/parsl/parsl/tests/sites/__init__.py b/parsl/parsl/tests/sites/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/sites/swan.rst b/parsl/parsl/tests/sites/swan.rst new file mode 100644 index 00000000..c0149949 --- /dev/null +++ b/parsl/parsl/tests/sites/swan.rst @@ -0,0 +1,12 @@ +Swan notes +========== + +Environment +=========== +Swan has a module cray-python/3.6.1.1 which can be used readily to setup and virtual +env:: + + module load cray-python/3.6.1.1 + python3 -m venv parsl_env + source parsl_env/bin/activate + pip install parsl diff --git a/parsl/parsl/tests/sites/test_concurrent.py b/parsl/parsl/tests/sites/test_concurrent.py new file mode 100644 index 00000000..ee91a58d --- /dev/null +++ b/parsl/parsl/tests/sites/test_concurrent.py @@ -0,0 +1,96 @@ +"""Tests of the interfaces to Python's concurrent library""" +from pytest import mark, raises, warns + +from parsl import Config, HighThroughputExecutor, load, python_app +from parsl.concurrent import ParslPoolExecutor + + +def f(x): + return x + 1 + + +def g(x): + return 2 * x + + +@python_app +def is_odd(x): + if x % 2 == 1: + return 1 + else: + return 0 + + +def make_config(): + return Config( + executors=[ + HighThroughputExecutor( + label='test_executor', + address="127.0.0.1", + max_workers_per_node=2, + heartbeat_period=2, + heartbeat_threshold=4, + encrypted=False, + ) + ], + strategy='none', + ) + + +@mark.local +def test_init_errors(): + with load(make_config()) as dfk, raises(ValueError, match='Specify only one of config or dfk'): + ParslPoolExecutor(config=make_config(), dfk=dfk) + + with raises(ValueError, match='Must specify one of config or dfk'): + ParslPoolExecutor() + + +@mark.local +def test_executor(): + my_config = make_config() + + with ParslPoolExecutor(my_config) as exc: + # Test a single submit + future = exc.submit(f, 1) + assert future.result() == 2 + + # Make sure the map works + results = list(exc.map(f, [1, 2, 3])) + assert results == [2, 3, 4] + + # Make sure map works with a timeout + results = list(exc.map(f, [1, 2, 3], timeout=5)) + assert results == [2, 3, 4] + + # Make sure only one function was registered + assert exc.app_count == 1 + + with raises(RuntimeError, match='shut down'): + exc.submit(f, 1) + + with warns(UserWarning): + ParslPoolExecutor(make_config()).shutdown(False, cancel_futures=True) + + +@mark.local +def test_with_dfk(): + config = make_config() + + with load(config) as dfk, ParslPoolExecutor(dfk=dfk, executors=['test_executor']) as exc: + future = exc.submit(f, 1) + assert future.result() == 2 + assert exc.get_app(f).executors == ['test_executor'] + + +@mark.local +def test_chaining(): + """Make sure the executor functions can be chained together""" + config = make_config() + + with ParslPoolExecutor(config) as exc: + future_odd = exc.submit(f, 10) + assert is_odd(future_odd).result() + + future_even = exc.submit(g, future_odd) + assert not is_odd(future_even).result() diff --git a/parsl/parsl/tests/sites/test_dynamic_executor.py b/parsl/parsl/tests/sites/test_dynamic_executor.py new file mode 100644 index 00000000..f1e44f78 --- /dev/null +++ b/parsl/parsl/tests/sites/test_dynamic_executor.py @@ -0,0 +1,98 @@ +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.executors import HighThroughputExecutor +from parsl.executors.threads import ThreadPoolExecutor +from parsl.providers import LocalProvider + + +@python_app(executors=['threads']) +def sleeper(dur=0): + import time + time.sleep(dur) + + +@python_app(executors=['threads2']) +def cpu_stress(dur=0.01): + import time + s = 0 + start = time.time() + for i in range(10**8): + s += i + if time.time() - start >= dur: + break + return s + + +@python_app(executors=['htex_local']) +def add(dur=0.01): + import time + s = 0 + start = time.time() + for i in range(10**8): + s += i + if time.time() - start >= dur: + break + return s + + +@pytest.mark.local +def test_dynamic_executor(caplog): + dfk = parsl.load() + + expected_executors = {('threads', 'parsl.executors.threads.ThreadPoolExecutor')} + + def check_expected_logs(): + seen_executors = {(r.__dict__['parsl.executor.label'], + r.__dict__['parsl.executor.type']) + for r in caplog.records + if 'parsl.executor.label' in r.__dict__ and + 'parsl.executor.type' in r.__dict__} + assert expected_executors <= seen_executors + + check_expected_logs() + + tasks = [sleeper() for i in range(5)] + results = [i.result() for i in tasks] + print("Done with initial test. The results are", results) + + # Here we add a new executor to an active DFK + thread_executors = [ThreadPoolExecutor( + label='threads2', + max_threads=4) + ] + dfk.add_executors(executors=thread_executors) + + expected_executors = {('threads2', 'parsl.executors.threads.ThreadPoolExecutor')} + check_expected_logs() + + tasks = [cpu_stress() for i in range(8)] + results = [i.result() for i in tasks] + print("Successfully added thread executor and ran with it. The results are", results) + + # We add a htex executor to an active DFK + executors = [ + HighThroughputExecutor( + label='htex_local', + cores_per_worker=1, + max_workers_per_node=5, + encrypted=True, + provider=LocalProvider( + init_blocks=1, + max_blocks=1, + ), + ) + ] + dfk.add_executors(executors=executors) + + expected_executors.add(('htex_local', 'parsl.executors.high_throughput.executor.HighThroughputExecutor')) + check_expected_logs() + + tasks = [add() for i in range(10)] + results = [i.result() for i in tasks] + print("Successfully added htex executor and ran with it. The results are", results) + + print("Done testing") + + dfk.cleanup() diff --git a/parsl/parsl/tests/sites/test_ec2.py b/parsl/parsl/tests/sites/test_ec2.py new file mode 100644 index 00000000..7cb089a0 --- /dev/null +++ b/parsl/parsl/tests/sites/test_ec2.py @@ -0,0 +1,79 @@ +import logging + +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.tests.configs.ec2_single_node import config + +logger = logging.getLogger(__name__) + +local_config = config + + +@python_app(executors=['ec2_single_node']) +def python_app_2(): + import os + import threading + import time + time.sleep(1) + return "Hello from PID[{}] TID[{}]".format(os.getpid(), threading.current_thread()) + + +@python_app(executors=['ec2_single_node']) +def python_app_1(): + import os + import threading + import time + time.sleep(1) + return "Hello from PID[{}] TID[{}]".format(os.getpid(), threading.current_thread()) + + +@parsl.bash_app +def bash_app(stdout=None, stderr=None): + return 'echo "Hello from $(uname -a)" ; sleep 2' + + +@pytest.mark.local +def test_python(N=2): + """Testing basic python functionality.""" + + r1 = {} + r2 = {} + for i in range(0, N): + r1[i] = python_app_1() + r2[i] = python_app_2() + print("Waiting ....") + + for x in r1: + print("python_app_1 : ", r1[x].result()) + for x in r2: + print("python_app_2 : ", r2[x].result()) + + return + + +@pytest.mark.local +def test_bash(): + """Testing basic bash functionality.""" + + import os + fname = os.path.basename(__file__) + + x = bash_app(stdout="{0}.out".format(fname)) + print("Waiting ....") + print(x.result()) + + +if __name__ == "__main__": + parsl.set_stream_logger() + + # this config is imported inside the main block for the same + # reason that it has local standup/teardown code at the top + # of this file. For that same reason, set_stream_logger + # is called before this import. + + from parsl.tests.configs.ec2_single_node import config + parsl.load(config) + test_python() + test_bash() diff --git a/parsl/parsl/tests/sites/test_mpi/__init__.py b/parsl/parsl/tests/sites/test_mpi/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/sites/test_mpi/mpi_hello b/parsl/parsl/tests/sites/test_mpi/mpi_hello new file mode 120000 index 00000000..3c9ece2f --- /dev/null +++ b/parsl/parsl/tests/sites/test_mpi/mpi_hello @@ -0,0 +1 @@ +mpi_test/mpi_hello \ No newline at end of file diff --git a/parsl/parsl/tests/sites/test_mpi/mpi_test/Makefile b/parsl/parsl/tests/sites/test_mpi/mpi_test/Makefile new file mode 100644 index 00000000..344f60d7 --- /dev/null +++ b/parsl/parsl/tests/sites/test_mpi/mpi_test/Makefile @@ -0,0 +1,20 @@ +CC=mpicc + +CFLAGS = -Wall -g -lm -fopenmp -DDEBUG + +SRC = $(wildcard *.c) + +DEPS = $(wildcard *.h) + +OBJS = $(SRC:.c=.o) + +EXEC=mpi_hello + +%.o: %.c $(DEPS) + $(CC) -c -o $@ $< $(CFLAGS) + +$(EXEC) : $(OBJS) + $(CC) -o $@ $^ $(CFLAGS) + +clean: + rm -f $(OBJS) $(EXEC) *~ *out *err diff --git a/parsl/parsl/tests/sites/test_mpi/mpi_test/README.md b/parsl/parsl/tests/sites/test_mpi/mpi_test/README.md new file mode 100644 index 00000000..71f64711 --- /dev/null +++ b/parsl/parsl/tests/sites/test_mpi/mpi_test/README.md @@ -0,0 +1,26 @@ +Running the MPI Test +==================== + +This simple MPI test is designed to give you some very basic information: + 1. What are the ranks running on ? + 2. Args passed by a launch mechanism printed as argv[1], and argv[2] + + +Compile +======= + +1. Load the appropriate MPI modules for your system +2. Compile the code with make: + + make clean; make + +Running the app +=============== + +Make sure the right MPI modules is loaded. Run the app as a simple executable: + + ./mpi_hello + +Or launch it with N ranks with an mpi launcher like mpirun: + + mpirun -n 8 mpi_hello \ No newline at end of file diff --git a/parsl/parsl/tests/sites/test_mpi/mpi_test/debug.h b/parsl/parsl/tests/sites/test_mpi/mpi_test/debug.h new file mode 100644 index 00000000..d6be5538 --- /dev/null +++ b/parsl/parsl/tests/sites/test_mpi/mpi_test/debug.h @@ -0,0 +1,14 @@ +#ifndef DEBUG_H +#define DEBUG_H + +extern int GLOB_DEBUG; + +#ifdef DEBUG + #define debug(format, ...) if (GLOB_DEBUG == 1) fprintf(stdout, format , __VA_ARGS__) +#else + #define debug(format, ...) +#endif + +#define error(format, ...) fprintf(stderr, format , __VA_ARGS__) + +#endif diff --git a/parsl/parsl/tests/sites/test_mpi/mpi_test/mpi_hello.c b/parsl/parsl/tests/sites/test_mpi/mpi_test/mpi_hello.c new file mode 100644 index 00000000..815f0bb8 --- /dev/null +++ b/parsl/parsl/tests/sites/test_mpi/mpi_test/mpi_hello.c @@ -0,0 +1,43 @@ +#include +#include +#include +#include +#include + + +void usage (void) +{ + fprintf(stderr, + "Usage : ./mpi_hello [options]" + "-d Debug" + ); +} + +int main(int argc, char *argv[]) +{ + int repeat = 1; + char ch; + int myrank; + int nprocs; + int ticks; + int type = 1; + char processor_name[200]; + int namelen; + int bs = 4; // Default block size + int stat; + MPI_Init(&argc, &argv); + + stat = MPI_Comm_size(MPI_COMM_WORLD, &nprocs); + if ( stat != 0 ) error ("MPI_Comm_size returned an error code : %d", stat); + + stat = MPI_Comm_rank(MPI_COMM_WORLD, &myrank); + if ( stat != 0 ) error ("MPI_Comm_rank returned an error code : %d", stat); + + MPI_Get_processor_name(processor_name, &namelen); + + fprintf(stderr, "[Rank:%d] Processor : %s\n", myrank, processor_name); + fprintf(stdout, "[Rank:%d] %s %s\n", myrank, argv[1], argv[2]); + MPI_Finalize(); + + return 0; +} diff --git a/parsl/parsl/tests/sites/test_mpi/setup.sh b/parsl/parsl/tests/sites/test_mpi/setup.sh new file mode 100644 index 00000000..e165ae45 --- /dev/null +++ b/parsl/parsl/tests/sites/test_mpi/setup.sh @@ -0,0 +1,6 @@ +module load mvapich2 + +cd mpi_test +make clean ; make +cd .. +ln -s mpi_test/mpi_hello . diff --git a/parsl/parsl/tests/slurm-entrypoint.sh b/parsl/parsl/tests/slurm-entrypoint.sh new file mode 100755 index 00000000..418db841 --- /dev/null +++ b/parsl/parsl/tests/slurm-entrypoint.sh @@ -0,0 +1,16 @@ +#!/bin/bash +set -e + +echo "---> Starting the MUNGE Authentication service (munged) ..." +gosu munge /usr/sbin/munged + +echo "---> Starting the slurmctld ..." +exec gosu slurm /usr/sbin/slurmctld -i -Dvvv & + +echo "---> Waiting for slurmctld to become active before starting slurmd..." + +echo "---> Starting the Slurm Node Daemon (slurmd) ..." +exec /usr/sbin/slurmd -Dvvv & + +echo "---> Running user command '${@}'" +exec "$@" diff --git a/parsl/parsl/tests/test-viz.sh b/parsl/parsl/tests/test-viz.sh new file mode 100755 index 00000000..0fdccac6 --- /dev/null +++ b/parsl/parsl/tests/test-viz.sh @@ -0,0 +1,24 @@ +#!/bin/bash -ex + +# pass any argument to make this script generate a fresh monitoring database +# using pytest; otherwise, by default, it will test against an existing +# monitoring.db + +killall --wait parsl-visualize || echo No previous parsl-visualize to kill + +if [ -n "$1" ]; then + rm -f runinfo/monitoring.db + pytest parsl/tests/ -k "not cleannet" --config parsl/tests/configs/htex_local_alternate.py --cov=parsl --cov-append --cov-report= --random-order +fi + +parsl-visualize --debug & + +mkdir -p test-parsl-visualize.tmp +cd test-parsl-visualize.tmp + +# now wait for this to become responsive to connections +wget http://127.0.0.1:8080/ --retry-connrefused --tries 30 --waitretry=1 +# wget will return a failure code if any of the requested URLs don't return an HTTP 200 result +wget http://127.0.0.1:8080/ --recursive --no-verbose --page-requisites --level=inf -e robots=off +killall --wait parsl-visualize + diff --git a/parsl/parsl/tests/test_bash_apps/__init__.py b/parsl/parsl/tests/test_bash_apps/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_bash_apps/test_apptimeout.py b/parsl/parsl/tests/test_bash_apps/test_apptimeout.py new file mode 100644 index 00000000..1c8eebaf --- /dev/null +++ b/parsl/parsl/tests/test_bash_apps/test_apptimeout.py @@ -0,0 +1,24 @@ +import pytest + +import parsl +from parsl.app.app import bash_app +from parsl.app.errors import AppTimeout +from parsl.tests.configs.local_threads import config + + +@bash_app +def echo_to_file(inputs=(), outputs=(), walltime=0.01): + return """echo "sleeping"; sleep 0.05""" + + +def test_walltime(): + """Testing walltime exceeded exception """ + x = echo_to_file() + with pytest.raises(AppTimeout): + x.result() + + +def test_walltime_longer(): + """Test that an app that runs in less than walltime will succeed.""" + y = echo_to_file(walltime=0.2) + y.result() diff --git a/parsl/parsl/tests/test_bash_apps/test_basic.py b/parsl/parsl/tests/test_bash_apps/test_basic.py new file mode 100644 index 00000000..0eea6d4d --- /dev/null +++ b/parsl/parsl/tests/test_bash_apps/test_basic.py @@ -0,0 +1,90 @@ +import logging +import os +import random +import re + +import pytest + +import parsl +from parsl import File +from parsl.app.app import bash_app + + +@bash_app +def echo_to_file(inputs=(), outputs=(), stderr=None, stdout=None): + res = "" + for o in outputs: + for i in inputs: + res += "echo {} >& {}".format(i, o) + return res + + +@bash_app +def foo(x, y, z=10, stdout=None, label=None): + return f"echo {x} {y} {z}" + + +@pytest.mark.shared_fs +def test_command_format_1(tmpd_cwd): + """Testing command format for BashApps""" + + outdir = tmpd_cwd / "outputs" + outdir.mkdir() + stdout = outdir / "foo-std.out" + + foo_future = foo(1, 4, stdout=str(stdout)) + assert foo_future.result() == 0, "BashApp had non-zero exit code" + + so_content = stdout.read_text().strip() + assert so_content == "1 4 10" + + +@pytest.mark.shared_fs +def test_auto_log_filename_format(caplog): + """Testing auto log filename format for BashApps + """ + app_label = "label_test_auto_log_filename_format" + rand_int = random.randint(1000, 1000000000) + + foo_future = foo(1, rand_int, stdout=parsl.AUTO_LOGNAME, label=app_label) + + assert foo_future.result() == 0, "BashApp exited with an error code : {0}".format( + foo_future.result()) + + log_fpath = foo_future.stdout + assert isinstance(log_fpath, str) + + log_pattern = fr".*/task_\d+_foo_{app_label}" + assert re.match(log_pattern, log_fpath), 'Output file "{0}" does not match pattern "{1}"'.format( + log_fpath, log_pattern) + assert os.path.exists(log_fpath), 'Output file does not exist "{0}"'.format(log_fpath) + with open(log_fpath, 'r') as stdout_f: + contents = stdout_f.read() + + assert contents == '1 {0} 10\n'.format(rand_int), \ + 'Output does not match expected string "1 {0} 10", Got: "{1}"'.format(rand_int, contents) + + for record in caplog.records: + assert record.levelno < logging.ERROR + + +@pytest.mark.shared_fs +def test_parallel_for(tmpd_cwd, n=3): + """Testing a simple parallel for loop""" + outdir = tmpd_cwd / "outputs/test_parallel" + outdir.mkdir(parents=True) + futs = [ + echo_to_file( + inputs=[f"Hello World {i}"], + outputs=[File(str(outdir / f"out.{i}.txt"))], + stdout=str(outdir / f"std.{i}.out"), + stderr=str(outdir / f"std.{i}.err"), + ) + for i in range(n) + ] + + for f in futs: + f.result() + + stdout_file_count = len(list(outdir.glob("*.out"))) + assert stdout_file_count == n, sorted(outdir.iterdir()) diff --git a/parsl/parsl/tests/test_bash_apps/test_error_codes.py b/parsl/parsl/tests/test_bash_apps/test_error_codes.py new file mode 100644 index 00000000..96f4140f --- /dev/null +++ b/parsl/parsl/tests/test_bash_apps/test_error_codes.py @@ -0,0 +1,57 @@ +import pytest + +import parsl.app.errors as pe +from parsl.app.app import bash_app +from parsl.tests.configs.local_threads import fresh_config as local_config + + +@pytest.fixture +def test_fn(tmpd_cwd): + def _wrapper(parsl_app): + serr = str(tmpd_cwd / "std.err") + sout = str(tmpd_cwd / "std.out") + return parsl_app(stdout=sout, stderr=serr) + + return _wrapper + + +@bash_app +def command_not_found(stderr='std.err', stdout='std.out'): + cmd_line = 'catdogcat' + return cmd_line + + +@bash_app +def bash_misuse(stderr='std.err', stdout='std.out'): + cmd_line = 'exit 15' + return cmd_line + + +@bash_app +def div_0(stderr='std.err', stdout='std.out'): + cmd_line = '$((5/0))' + return cmd_line + + +@bash_app +def not_executable(stderr='std.err', stdout='std.out'): + cmd_line = '/dev/null' + return cmd_line + + +exit_codes = { + div_0: 1, + bash_misuse: 15, + not_executable: 126, + command_not_found: 127, +} + + +@pytest.mark.shared_fs +@pytest.mark.parametrize("app", (div_0, bash_misuse, not_executable, command_not_found)) +def test_failing_bash_app(test_fn, app): + err_code = exit_codes[app] + with pytest.raises(pe.BashExitFailure) as pyt_e: + test_fn(app).result() + e: pe.BashExitFailure = pyt_e.value + assert e.exitcode == err_code, f"[{e}] {app.__name__} expected {err_code=} but got {e.exitcode}" diff --git a/parsl/parsl/tests/test_bash_apps/test_inputs_default.py b/parsl/parsl/tests/test_bash_apps/test_inputs_default.py new file mode 100644 index 00000000..9b6d7a18 --- /dev/null +++ b/parsl/parsl/tests/test_bash_apps/test_inputs_default.py @@ -0,0 +1,25 @@ +import pytest + +from parsl import AUTO_LOGNAME, Config, bash_app, python_app +from parsl.executors import ThreadPoolExecutor + + +def local_config(): + return Config(executors=[ThreadPoolExecutor()]) + + +@pytest.mark.local +def test_default_inputs(): + @python_app + def identity(inp): + return inp + + @bash_app + def sum_inputs(inputs=[identity(1), identity(2)], stdout=AUTO_LOGNAME): + calc = sum(inputs) + return f"echo {calc}" + + fut = sum_inputs() + fut.result() + with open(fut.stdout, 'r') as f: + assert int(f.read()) == 3 diff --git a/parsl/parsl/tests/test_bash_apps/test_keyword_overlaps.py b/parsl/parsl/tests/test_bash_apps/test_keyword_overlaps.py new file mode 100644 index 00000000..41a9ba74 --- /dev/null +++ b/parsl/parsl/tests/test_bash_apps/test_keyword_overlaps.py @@ -0,0 +1,15 @@ +import parsl + + +@parsl.bash_app +def my_app(cache=7): + assert type(cache) is int + return "true" + + +def test_default_value(): + my_app().result() + + +def test_specified_value(): + my_app(cache=8).result() diff --git a/parsl/parsl/tests/test_bash_apps/test_kwarg_storage.py b/parsl/parsl/tests/test_bash_apps/test_kwarg_storage.py new file mode 100644 index 00000000..e88a4c29 --- /dev/null +++ b/parsl/parsl/tests/test_bash_apps/test_kwarg_storage.py @@ -0,0 +1,33 @@ +import pytest + +from parsl.app.app import bash_app + + +@bash_app +def foo(z=2, stdout=None): + return f"echo {z}" + + +@pytest.mark.shared_fs +def test_command_format_1(tmpd_cwd): + """Testing command format for BashApps + """ + + stdout = tmpd_cwd / "std.out" + for exp_value, z in ( + ("3", 3), + ("4", 4), + ("5", 5), + ): + app_fu = foo(z=z, stdout=str(stdout)) + assert app_fu.result() == 0, "BashApp had non-zero exit" + + so_content = stdout.read_text().strip() + assert so_content == exp_value + stdout.unlink() + + app_fu = foo(stdout=str(stdout)) + assert app_fu.result() == 0, "BashApp had non-zero exit" + + so_content = stdout.read_text().strip() + assert so_content == "2" diff --git a/parsl/parsl/tests/test_bash_apps/test_memoize.py b/parsl/parsl/tests/test_bash_apps/test_memoize.py new file mode 100644 index 00000000..387837f4 --- /dev/null +++ b/parsl/parsl/tests/test_bash_apps/test_memoize.py @@ -0,0 +1,42 @@ +import pytest + +from parsl import File +from parsl.app.app import bash_app + + +@bash_app(cache=True) +def fail_on_presence(outputs=()): + return 'if [ -f {0} ] ; then exit 1 ; else touch {0}; fi'.format(outputs[0]) + + +@pytest.mark.shared_fs +def test_bash_memoization(tmpd_cwd, n=2): + """Testing bash memoization + """ + mpath = tmpd_cwd / "test.memoization.tmp" + temp_file = File(str(mpath)) + fail_on_presence(outputs=[temp_file]).result() + + futs = [fail_on_presence(outputs=[temp_file]) for _ in range(n)] + for f in futs: + assert f.exception() is None + + +@bash_app(cache=True) +def fail_on_presence_kw(outputs=(), foo=None): + return 'if [ -f {0} ] ; then exit 1 ; else touch {0}; fi'.format(outputs[0]) + + +@pytest.mark.shared_fs +def test_bash_memoization_keywords(tmpd_cwd, n=2): + """Testing bash memoization + """ + mpath = tmpd_cwd / "test.memoization.tmp" + temp_file = File(str(mpath)) + + foo = {"a": 1, "b": 2} + fail_on_presence_kw(outputs=[temp_file], foo=foo).result() + + futs = [fail_on_presence_kw(outputs=[temp_file], foo=foo) for _ in range(n)] + for f in futs: + assert f.exception() is None diff --git a/parsl/parsl/tests/test_bash_apps/test_memoize_ignore_args.py b/parsl/parsl/tests/test_bash_apps/test_memoize_ignore_args.py new file mode 100644 index 00000000..16cb919f --- /dev/null +++ b/parsl/parsl/tests/test_bash_apps/test_memoize_ignore_args.py @@ -0,0 +1,43 @@ +import os + +import pytest + +import parsl +from parsl.app.app import bash_app + + +@bash_app(cache=True, ignore_for_cache=['x']) +def oneshot_app(x): + if x != 0: + raise RuntimeError("App was executed when it should not have been") + return "true" + + +def test_memo_same_at_definition(): + oneshot_app(x=0).result() # this should be executed + oneshot_app(x=1).result() # this should be memoized, and will raise a RuntimeError if actually executed + + +@bash_app(cache=True, ignore_for_cache=['stdout']) +def no_checkpoint_stdout_app_ignore_args(stdout=None): + return "echo X" + + +@pytest.mark.shared_fs +def test_memo_stdout(tmpd_cwd): + path_x = tmpd_cwd / "test.memo.stdout.x" + + # this should run and create a file named after path_x + no_checkpoint_stdout_app_ignore_args(stdout=str(path_x)).result() + assert path_x.exists() + + # this should be memoized, so should not get created + path_y = tmpd_cwd / "test.memo.stdout.y" + + no_checkpoint_stdout_app_ignore_args(stdout=path_y).result() + assert not path_y.exists(), "For memoization, expected NO file written" + + # this should also be memoized, so not create an arbitrary name + z_fut = no_checkpoint_stdout_app_ignore_args(stdout=parsl.AUTO_LOGNAME) + z_fut.result() + assert not os.path.exists(z_fut.stdout) diff --git a/parsl/parsl/tests/test_bash_apps/test_memoize_ignore_args_regr.py b/parsl/parsl/tests/test_bash_apps/test_memoize_ignore_args_regr.py new file mode 100644 index 00000000..e1a9465f --- /dev/null +++ b/parsl/parsl/tests/test_bash_apps/test_memoize_ignore_args_regr.py @@ -0,0 +1,48 @@ +import copy +from typing import List + +import pytest + +import parsl +from parsl.app.app import bash_app + +const_list_x: List[str] = [] +const_list_x_arg: List[str] = copy.deepcopy(const_list_x) + +assert const_list_x == const_list_x_arg + + +@bash_app(cache=True, ignore_for_cache=['x']) +def oneshot_app(x): + if x != 0: + raise RuntimeError("App was executed when it should not have been") + return "true" + + +def test_memo_same_at_definition(): + oneshot_app(x=0).result() # this should be executed + oneshot_app(x=1).result() # this should be memoized, and will raise a RuntimeError if actually executed + + +@bash_app(cache=True, ignore_for_cache=const_list_x_arg) +def no_checkpoint_stdout_app(stdout=None): + return "echo X" + + +@pytest.mark.shared_fs +def test_memo_stdout(tmpd_cwd): + assert const_list_x == const_list_x_arg + + path_x = tmpd_cwd / "test.memo.stdout.x" + + # this should run and create a file named after path_x + no_checkpoint_stdout_app(stdout=str(path_x)).result() + path_x.unlink(missing_ok=False) + + no_checkpoint_stdout_app(stdout=str(path_x)).result() + assert not path_x.exists(), "For memoization, expected NO file written" + + # this should also be memoized, so not create an arbitrary name + z_fut = no_checkpoint_stdout_app(stdout=parsl.AUTO_LOGNAME) + z_fut.result() + assert const_list_x == const_list_x_arg diff --git a/parsl/parsl/tests/test_bash_apps/test_multiline.py b/parsl/parsl/tests/test_bash_apps/test_multiline.py new file mode 100644 index 00000000..cfdb674e --- /dev/null +++ b/parsl/parsl/tests/test_bash_apps/test_multiline.py @@ -0,0 +1,37 @@ +import pytest + +from parsl import File +from parsl.app.app import bash_app + + +@bash_app +def multiline(inputs=(), outputs=(), stderr=None, stdout=None): + return """echo {inputs[0]} &> {outputs[0]} + echo {inputs[1]} &> {outputs[1]} + echo {inputs[2]} &> {outputs[2]} + echo "Testing STDOUT" + echo "Testing STDERR" 1>&2 + """.format(inputs=inputs, outputs=outputs) + + +@pytest.mark.shared_fs +def test_multiline(tmpd_cwd): + so, se = tmpd_cwd / "std.out", tmpd_cwd / "std.err" + f = multiline( + inputs=["Hello", "This is", "Cat!"], + outputs=[ + File(str(tmpd_cwd / "hello.txt")), + File(str(tmpd_cwd / "this.txt")), + File(str(tmpd_cwd / "cat.txt")), + ], + stdout=str(so), + stderr=str(se), + ) + f.result() + + flist = list(map(str, (f.name for f in tmpd_cwd.iterdir()))) + assert 'hello.txt' in flist, "hello.txt is missing" + assert 'this.txt' in flist, "this.txt is missing" + assert 'cat.txt' in flist, "cat.txt is missing" + assert "Testing STDOUT" in so.read_text() + assert "Testing STDERR" in se.read_text() diff --git a/parsl/parsl/tests/test_bash_apps/test_pipeline.py b/parsl/parsl/tests/test_bash_apps/test_pipeline.py new file mode 100644 index 00000000..75f16237 --- /dev/null +++ b/parsl/parsl/tests/test_bash_apps/test_pipeline.py @@ -0,0 +1,84 @@ +import pytest + +from parsl.app.app import bash_app +from parsl.app.futures import DataFuture +from parsl.data_provider.files import File + + +@bash_app +def increment(inputs=(), outputs=(), stdout=None, stderr=None): + cmd_line = """ + if ! [ -f {inputs[0]} ] ; then exit 43 ; fi + x=$(cat {inputs[0]}) + echo $(($x+1)) > {outputs[0]} + """.format(inputs=inputs, outputs=outputs) + return cmd_line + + +@bash_app +def slow_increment(dur, inputs=(), outputs=(), stdout=None, stderr=None): + cmd_line = """ + x=$(cat {inputs[0]}) + echo $(($x+1)) > {outputs[0]} + sleep {0} + """.format(dur, inputs=inputs, outputs=outputs) + return cmd_line + + +@pytest.mark.staging_required +def test_increment(tmpd_cwd, depth=5): + """Test simple pipeline A->B...->N + """ + fpath = tmpd_cwd / "test0.txt" + fpath.write_text("0\n") + + prev = [File(str(fpath))] + futs = [] + for i in range(1, depth): + assert isinstance(prev[0], (DataFuture, File)) + output = File(str(tmpd_cwd / f"test{i}.txt")) + f = increment( + inputs=prev, + outputs=[output], + stdout=str(tmpd_cwd / f"incr{i}.out"), + stderr=str(tmpd_cwd / f"incr{i}.err"), + ) + prev = f.outputs + futs.append((i, prev[0])) + assert isinstance(prev[0], DataFuture) + + for key, f in futs: + file = f.result() + expected = str(tmpd_cwd / f"test{key}.txt") + + assert file.local_path is None, "File on local side has overridden local_path, file: {}".format(repr(file)) + assert file.filepath == expected, "Submit side filepath has not been preserved over execution" + data = open(file.filepath).read().strip() + assert data == str(key) + + +@pytest.mark.staging_required +def test_increment_slow(tmpd_cwd, depth=5, dur=0.01): + """Test simple pipeline slow (sleep.5) A->B...->N + """ + + fpath = tmpd_cwd / "test0.txt" + fpath.write_text("0\n") + + prev = [File(str(fpath))] + futs = [] + for i in range(1, depth): + output = File(str(tmpd_cwd / f"test{i}.txt")) + f = slow_increment( + dur, + inputs=prev, + outputs=[output], + stdout=str(tmpd_cwd / f"incr{i}.out"), + stderr=str(tmpd_cwd / f"incr{i}.err"), + ) + prev = f.outputs + futs.append((i, prev[0])) + + for key, f in futs: + data = open(f.result().filepath).read().strip() + assert data == str(key) diff --git a/parsl/parsl/tests/test_bash_apps/test_std_uri.py b/parsl/parsl/tests/test_bash_apps/test_std_uri.py new file mode 100644 index 00000000..3da1fd0d --- /dev/null +++ b/parsl/parsl/tests/test_bash_apps/test_std_uri.py @@ -0,0 +1,123 @@ +import logging +import zipfile +from functools import partial + +import pytest + +import parsl +from parsl.app.futures import DataFuture +from parsl.data_provider.files import File +from parsl.executors import ThreadPoolExecutor + + +@parsl.bash_app +def app_stdout(stdout=parsl.AUTO_LOGNAME): + return "echo hello" + + +def const_str(cpath, task_record, err_or_out): + return cpath + + +def const_with_cpath(autopath_specifier, content_path, caplog): + with parsl.load(parsl.Config(std_autopath=partial(const_str, autopath_specifier))): + fut = app_stdout() + + # we don't have to wait for a result to check this attributes + assert fut.stdout is autopath_specifier + + # there is no DataFuture to wait for in the str case: the model is that + # the stdout will be immediately available on task completion. + fut.result() + + with open(content_path, "r") as file: + assert file.readlines() == ["hello\n"] + + for record in caplog.records: + assert record.levelno < logging.ERROR + + +@pytest.mark.local +def test_std_autopath_const_str(caplog, tmpd_cwd): + """Tests str and tuple mode autopaths with constant autopath, which should + all be passed through unmodified. + """ + cpath = str(tmpd_cwd / "CONST") + const_with_cpath(cpath, cpath, caplog) + + +@pytest.mark.local +def test_std_autopath_const_pathlike(caplog, tmpd_cwd): + cpath = tmpd_cwd / "CONST" + const_with_cpath(cpath, cpath, caplog) + + +@pytest.mark.local +def test_std_autopath_const_tuples(caplog, tmpd_cwd): + file = tmpd_cwd / "CONST" + cpath = (file, "w") + const_with_cpath(cpath, file, caplog) + + +class URIFailError(Exception): + pass + + +def fail_uri(task_record, err_or_out): + raise URIFailError("Deliberate failure in std stream filename generation") + + +@pytest.mark.local +def test_std_autopath_fail(caplog): + with parsl.load(parsl.Config(std_autopath=fail_uri)): + with pytest.raises(URIFailError): + app_stdout() + + +@parsl.bash_app +def app_both(stdout=parsl.AUTO_LOGNAME, stderr=parsl.AUTO_LOGNAME): + return "echo hello; echo goodbye >&2" + + +def zip_uri(base, task_record, err_or_out): + """Should generate Files in base.zip like app_both.0.out or app_both.123.err""" + zip_path = base / "base.zip" + file = f"{task_record['func_name']}.{task_record['id']}.{task_record['try_id']}.{err_or_out}" + return File(f"zip:{zip_path}/{file}") + + +@pytest.mark.local +def test_std_autopath_zip(caplog, tmpd_cwd): + with parsl.load(parsl.Config(run_dir=str(tmpd_cwd), + executors=[ThreadPoolExecutor(working_dir=str(tmpd_cwd))], + std_autopath=partial(zip_uri, tmpd_cwd))): + futs = [] + + for _ in range(10): + fut = app_both() + + # assertions that should hold after submission + assert isinstance(fut.stdout, DataFuture) + assert fut.stdout.file_obj.url.startswith("zip") + + futs.append(fut) + + # Barrier for all the stageouts to complete so that we can + # poke at the zip file. + [(fut.stdout.result(), fut.stderr.result()) for fut in futs] + + with zipfile.ZipFile(tmpd_cwd / "base.zip") as z: + for fut in futs: + + assert fut.done(), "AppFuture should be done if stageout is done" + + stdout_relative_path = f"app_both.{fut.tid}.0.stdout" + with z.open(stdout_relative_path) as f: + assert f.readlines() == [b'hello\n'] + + stderr_relative_path = f"app_both.{fut.tid}.0.stderr" + with z.open(stderr_relative_path) as f: + assert f.readlines()[-1] == b'goodbye\n' + + for record in caplog.records: + assert record.levelno < logging.ERROR diff --git a/parsl/parsl/tests/test_bash_apps/test_stdout.py b/parsl/parsl/tests/test_bash_apps/test_stdout.py new file mode 100644 index 00000000..c935def7 --- /dev/null +++ b/parsl/parsl/tests/test_bash_apps/test_stdout.py @@ -0,0 +1,130 @@ +import logging +import os + +import pytest +import typeguard + +import parsl.app.errors as perror +from parsl.app.app import bash_app + + +@bash_app +def echo_to_streams(msg, stderr=None, stdout=None): + return 'echo "{0}"; echo "{0}" >&2'.format(msg) + + +whitelist = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'configs', '*threads*') + +speclist = ( + ['t3.out', 'w'], + ('t4.out', None), + (42, 'w'), + ('t6.out', 'w', 42), + ('t7.out',), + ('t8.out', 'badmode') +) + +testids = [ + 'list_not_tuple', + 'null_mode', + 'not_a_string', + '3tuple', + '1tuple', + 'bad_mode' +] + + +@pytest.mark.parametrize('spec', speclist, ids=testids) +def test_bad_stdout_specs(spec): + """Testing bad stdout spec cases""" + + fn = echo_to_streams("Hello world", stdout=spec, stderr='t.err') + + try: + fn.result() + except Exception as e: + # This tests for TypeCheckError by string matching on the type name + # because that class does not exist in typeguard 2.x - it is new in + # typeguard 4.x. When typeguard 2.x support is dropped, this test can + # become an isinstance check. + assert "TypeCheckError" in str(type(e)) or isinstance(e, TypeError) or isinstance(e, perror.BadStdStreamFile), "Exception is wrong type" + else: + assert False, "Did not raise expected exception" + + +@pytest.mark.issue3328 +@pytest.mark.unix_filesystem_permissions_required +def test_bad_stdout_file(): + """Testing bad stderr file""" + + o = "/bad/dir/t2.out" + + fn = echo_to_streams("Hello world", stdout=o, stderr='t.err') + + try: + fn.result() + except perror.BadStdStreamFile: + pass + else: + assert False, "Did not raise expected exception BadStdStreamFile" + + return + + +@pytest.mark.issue3328 +@pytest.mark.unix_filesystem_permissions_required +def test_bad_stderr_file(): + """Testing bad stderr file""" + + err = "/bad/dir/t2.err" + + fn = echo_to_streams("Hello world", stderr=err) + + try: + fn.result() + except perror.BadStdStreamFile: + pass + else: + assert False, "Did not raise expected exception BadStdStreamFile" + + return + + +@pytest.mark.executor_supports_std_stream_tuples +@pytest.mark.shared_fs +def test_stdout_truncate(tmpd_cwd, caplog): + """Testing truncation of prior content of stdout""" + + out = (str(tmpd_cwd / 't1.out'), 'w') + err = str(tmpd_cwd / 't1.err') + + echo_to_streams('hi', stdout=out, stderr=err).result() + len1 = len(open(out[0]).readlines()) + + echo_to_streams('hi', stdout=out, stderr=err).result() + len2 = len(open(out[0]).readlines()) + + assert len1 == 1 + assert len1 == len2 + + for record in caplog.records: + assert record.levelno < logging.ERROR + + +@pytest.mark.shared_fs +def test_stdout_append(tmpd_cwd, caplog): + """Testing appending to prior content of stdout (default open() mode)""" + + out = str(tmpd_cwd / 't1.out') + err = str(tmpd_cwd / 't1.err') + + echo_to_streams('hi', stdout=out, stderr=err).result() + len1 = len(open(out).readlines()) + + echo_to_streams('hi', stdout=out, stderr=err).result() + len2 = len(open(out).readlines()) + + assert len1 == 1 and len2 == 2 + + for record in caplog.records: + assert record.levelno < logging.ERROR diff --git a/parsl/parsl/tests/test_callables.py b/parsl/parsl/tests/test_callables.py new file mode 100644 index 00000000..14b533d9 --- /dev/null +++ b/parsl/parsl/tests/test_callables.py @@ -0,0 +1,71 @@ +# this file contains some tests of passing complicated +# callables as parameters to apps, mainly to check that +# they are serialised correctly with serialization based +# executors. + +import importlib +import pathlib +from functools import partial + +import parsl + + +@parsl.python_app +def app(x): + return True + + +def test_check_base_app(): + app(0).result() + + +def somefunc(*args): + pass + + +def test_check_this_module_function(): + app(somefunc).result() + + +def test_check_this_module_function_partial(): + app(partial(somefunc, 1)).result() + + +def test_check_import_module_function(): + from parsl.tests.callables_helper import some_aux_func + app(some_aux_func).result() + + +def test_check_import_module_function_partial(): + from parsl.tests.callables_helper import some_aux_func + app(partial(some_aux_func, 1)).result() + + +def test_check_importlib_file_function(): + helper_path = pathlib.Path(__file__).parent / "callables_helper.py" + spec = importlib.util.spec_from_file_location("dynamically_loaded_module", helper_path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + some_aux_func = module.some_aux_func + app(some_aux_func).result() + + +def test_check_importlib_file_function_partial(): + helper_path = pathlib.Path(__file__).parent / "callables_helper.py" + spec = importlib.util.spec_from_file_location("dynamically_loaded_module", helper_path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + some_aux_func = module.some_aux_func + app(partial(some_aux_func, 1)).result() + + +def test_check_importlib_module_function(): + module = importlib.import_module("parsl.tests.callables_helper") + some_aux_func = module.some_aux_func + app(some_aux_func).result() + + +def test_check_importlib_module_partial(): + module = importlib.import_module("parsl.tests.callables_helper") + some_aux_func = module.some_aux_func + app(partial(some_aux_func, 1)).result() diff --git a/parsl/parsl/tests/test_checkpointing/__init__.py b/parsl/parsl/tests/test_checkpointing/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_checkpointing/test_get_all_checkpoints.py b/parsl/parsl/tests/test_checkpointing/test_get_all_checkpoints.py new file mode 100644 index 00000000..15ea75a9 --- /dev/null +++ b/parsl/parsl/tests/test_checkpointing/test_get_all_checkpoints.py @@ -0,0 +1,44 @@ +import contextlib +import os + +import pytest + +import parsl +from parsl import python_app +from parsl.config import Config +from parsl.dataflow.memoization import BasicMemoizer +from parsl.executors.threads import ThreadPoolExecutor + + +def parsl_configured(run_dir, memoizer): + return parsl.load(Config( + run_dir=str(run_dir), + executors=[ + ThreadPoolExecutor( + label='local_threads_checkpoint', + ) + ], + memoizer=memoizer + )) + + +@python_app(cache=True) +def uuid_app(x): + import uuid + return uuid.uuid4() + + +@pytest.mark.local +def test_loading_checkpoint(tmpd_cwd): + """Tests default loading of all checkpoint files from previous runs. + """ + import uuid + param = str(uuid.uuid4()) + + with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit")): + result = uuid_app(param).result() + + with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit")): + relaunched = uuid_app(param).result() + + assert result == relaunched, "Expected following call to uuid_app to return cached uuid" diff --git a/parsl/parsl/tests/test_checkpointing/test_get_last_checkpoint.py b/parsl/parsl/tests/test_checkpointing/test_get_last_checkpoint.py new file mode 100644 index 00000000..9938cfa3 --- /dev/null +++ b/parsl/parsl/tests/test_checkpointing/test_get_last_checkpoint.py @@ -0,0 +1,107 @@ +import contextlib +import os + +import pytest + +import parsl +from parsl import python_app +from parsl.config import Config +from parsl.dataflow.memoization import BasicMemoizer +from parsl.executors.threads import ThreadPoolExecutor +from parsl.utils import get_last_checkpoint + + +def parsl_configured(run_dir, memoizer): + return parsl.load(Config( + run_dir=str(run_dir), + executors=[ + ThreadPoolExecutor( + label='local_threads_checkpoint', + ) + ], + memoizer=memoizer + )) + + +@python_app(cache=True) +def uuid_app(x): + import uuid + return uuid.uuid4() + + +@pytest.mark.local +def test_loading_checkpoint(tmpd_cwd): + """Tests that get_last_checkpoint finds checkpoints from prior run + """ + import uuid + param = str(uuid.uuid4()) + + with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit", checkpoint_files=[])): + result = uuid_app(param).result() + + with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit", checkpoint_files=get_last_checkpoint(rundir=str(tmpd_cwd)))): + relaunched = uuid_app(param).result() + + assert result == relaunched, "Expected following call to uuid_app to return cached uuid" + + # check that we don't lose the result just because it came from two runs ago, + # by running the same test again. + with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit", checkpoint_files=get_last_checkpoint(rundir=str(tmpd_cwd)))): + relaunched = uuid_app(param).result() + + assert result == relaunched, "Expected following call to uuid_app to return cached uuid" + + +@pytest.mark.local +def test_loading_checkpoint_other_files(tmpd_cwd): + """Tests that get_last_checkpoint ignores other files. + + This is a regression test for a bug where the alphabetically last + per-run directory was being selected, and then ignored if it was + not a checkpoint directory, for example when the monitoring.db + file is also present (and alphabetically after any numbered + run directories). + """ + import uuid + param = str(uuid.uuid4()) + + with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit", checkpoint_files=[])): + result = uuid_app(param).result() + + with open(tmpd_cwd / "X", "w"): + # don't need to write anything, as we only need the directory entry + # which is made by the open call. + pass + + with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit", checkpoint_files=get_last_checkpoint(rundir=str(tmpd_cwd)))): + relaunched = uuid_app(param).result() + + assert result == relaunched, "Expected following call to uuid_app to return cached uuid" + + +@pytest.mark.local +def test_high_rundir(tmpd_cwd): + """Tests that get_last_checkpoint respects rundirs of more than 3 digits. + """ + import uuid + param = str(uuid.uuid4()) + + with open(tmpd_cwd / "998", "w"): + pass + + # this will have rundir "999" + with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit", checkpoint_files=[])): + assert "999" in parsl.dfk().run_dir + result_999 = uuid_app(param).result() + + # this will have rundir "1000", lexically before 999 + with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit", checkpoint_files=[])): + assert "1000" in parsl.dfk().run_dir + result_1000 = uuid_app(param).result() + + assert result_999 != result_1000, "Should have not used previous result" + + with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit", checkpoint_files=get_last_checkpoint(rundir=str(tmpd_cwd)))): + relaunched = uuid_app(param).result() + + assert result_1000 == relaunched, "Expected restore to be from 1000 rundir" diff --git a/parsl/parsl/tests/test_checkpointing/test_manual.py b/parsl/parsl/tests/test_checkpointing/test_manual.py new file mode 100644 index 00000000..a51ee8d0 --- /dev/null +++ b/parsl/parsl/tests/test_checkpointing/test_manual.py @@ -0,0 +1,33 @@ +import os +from pathlib import Path + +import pytest + +import parsl +from parsl import python_app +from parsl.config import Config +from parsl.dataflow.memoization import BasicMemoizer + + +@python_app(cache=True) +def uuid_app(): + import uuid + return uuid.uuid4() + + +@pytest.mark.local +def test_manual_checkpoint() -> None: + """Launch a single app, write manually, check file exists. + """ + memoizer = BasicMemoizer(checkpoint_mode="manual") + + with parsl.load(Config(memoizer=memoizer)): + uuid_app().result() + + cpt_dir = Path(parsl.dfk().run_dir) / 'checkpoint' + + cptpath = cpt_dir / 'tasks.pkl' + + assert not os.path.exists(cptpath), f"Tasks checkpoint should not exist yet: {cptpath}" + memoizer.checkpoint() + assert os.path.exists(cptpath), f"Tasks checkpoint should exist now: {cptpath}" diff --git a/parsl/parsl/tests/test_checkpointing/test_periodic.py b/parsl/parsl/tests/test_checkpointing/test_periodic.py new file mode 100644 index 00000000..9336f7a5 --- /dev/null +++ b/parsl/parsl/tests/test_checkpointing/test_periodic.py @@ -0,0 +1,59 @@ +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.config import Config +from parsl.dataflow.memoization import BasicMemoizer +from parsl.executors.threads import ThreadPoolExecutor + + +def fresh_config(*, config_run_dir): + tpe = ThreadPoolExecutor(label='local_threads_checkpoint_periodic', max_threads=1) + return Config( + run_dir=config_run_dir, + executors=[tpe], + memoizer=BasicMemoizer( + checkpoint_mode='periodic', + checkpoint_period='00:00:02' + ) + ) + + +@python_app(cache=True) +def slow_double(x, sleep_dur=1): + import time + time.sleep(sleep_dur) + return x * 2 + + +def tstamp_to_seconds(line): + f = line.partition(" ")[0] + return float(f) + + +@pytest.mark.local +def test_periodic(tmpd_cwd): + """Test checkpointing with task_periodic behavior + """ + with parsl.load(fresh_config(config_run_dir=str(tmpd_cwd))): + memoizer = parsl.dfk().memoizer + h, m, s = map(int, memoizer.checkpoint_period.split(":")) + assert h == 0, "Verify test setup" + assert m == 0, "Verify test setup" + assert s > 0, "Verify test setup" + sleep_for = s + 1 + futs = [slow_double(sleep_for) for _ in range(4)] + [f.result() for f in futs] + run_dir = parsl.dfk().run_dir + + # Here we will check if the loglines came back with 5 seconds deltas + with open("{}/parsl.log".format(run_dir)) as f: + log_lines = f.readlines() + expected_msg = " Done checkpointing" + expected_msg2 = " No tasks checkpointed in this pass" + + lines = [line for line in log_lines if expected_msg in line or expected_msg2 in line] + assert len(lines) >= 3, "Insufficient checkpoint lines in logfile" + deltas = [tstamp_to_seconds(line) for line in lines] + assert deltas[1] - deltas[0] < 5.5, "Delta between checkpoints exceeded period" + assert deltas[2] - deltas[1] < 5.5, "Delta between checkpoints exceeded period" diff --git a/parsl/parsl/tests/test_checkpointing/test_regression_232.py b/parsl/parsl/tests/test_checkpointing/test_regression_232.py new file mode 100644 index 00000000..c0167df4 --- /dev/null +++ b/parsl/parsl/tests/test_checkpointing/test_regression_232.py @@ -0,0 +1,86 @@ +import os +import pickle +import signal +import subprocess +import time + +import pytest + + +def kill(): + my_pid = os.getpid() + os.kill(my_pid, signal.SIGINT) + print("Killing self") + + +@pytest.mark.local +@pytest.mark.skip('fails on Travis in pytest') +def test_regress_232_task_exit(count=2): + """Recovering from a run that was SIGINT'ed with task_exit checkpointing + """ + cwd = os.path.dirname(os.path.realpath(__file__)) + checkpoint_file = os.path.join(cwd, 'checkpointed.py') + + if os.path.exists("test.txt"): + os.remove("test.txt") + + proc = subprocess.Popen("python3 {} -n {} -m task_exit".format(checkpoint_file, count), + shell=True) + + # Poll for at least 3 seconds + for i in range(30): + if os.path.exists("test.txt"): + break + time.sleep(0.1) + + proc.send_signal(signal.SIGINT) + proc.wait() + # We need to wait after the signal to make sure files were closed and such + last = os.path.abspath( + 'runinfo/{0}/checkpoint'.format(sorted(os.listdir('runinfo/'))[-1])) + checkpoint_file = "{}/tasks.pkl".format(last) + + with open(checkpoint_file, 'rb') as f: + tasks = [] + try: + while f: + tasks.append(pickle.load(f)) + print + except EOFError: + print("Caught error") + pass + print("Tasks from cache : ", tasks) + assert len(tasks) == count, "Expected {} checkpoint events, got {}".format( + 1, len(tasks)) + + +@pytest.mark.local +@pytest.mark.skip('fails on Travis in pytest') +def test_regress_232_dfk_exit(count=2): + """Recovering from a run that was SIGINT'ed with dfk_exit checkpointing + """ + cwd = os.path.dirname(os.path.realpath(__file__)) + checkpoint_file = os.path.join(cwd, 'checkpointed.py') + + proc = subprocess.Popen("python3 {} -n {} -m dkf_exit".format(checkpoint_file, count), + shell=True) + proc.send_signal(signal.SIGINT) + # We need to wait after the signal to make sure files were closed and such + proc.wait() + + last = os.path.abspath( + 'runinfo/{0}/checkpoint'.format(sorted(os.listdir('runinfo/'))[-1])) + checkpoint_file = "{}/tasks.pkl".format(last) + + print(checkpoint_file, "now exists") + with open(checkpoint_file, 'rb') as f: + tasks = [] + try: + while f: + tasks.append(pickle.load(f)) + print + except EOFError: + pass + print("Tasks from cache : ", tasks) + assert len(tasks) == count, "Expected {} checkpoint events, got {}".format( + 1, len(tasks)) diff --git a/parsl/parsl/tests/test_checkpointing/test_regression_233.py b/parsl/parsl/tests/test_checkpointing/test_regression_233.py new file mode 100644 index 00000000..2abd55ab --- /dev/null +++ b/parsl/parsl/tests/test_checkpointing/test_regression_233.py @@ -0,0 +1,75 @@ +import pytest + +from parsl.app.app import python_app +from parsl.config import Config +from parsl.dataflow.dflow import DataFlowKernel +from parsl.dataflow.memoization import BasicMemoizer + + +def run_checkpointed(checkpoints): + config = Config(memoizer=BasicMemoizer(checkpoint_files=checkpoints, checkpoint_mode='task_exit')) + dfk = DataFlowKernel(config=config) + + @python_app(data_flow_kernel=dfk, cache=True) + def cached_rand(x): + import random + return random.randint(0, 10000) + + items = [] + for i in range(0, 5): + x = cached_rand(i) + items.append(x) + + # wait for all of the results + results = [i.result() for i in items] + + dfk.cleanup() + return results, dfk.run_dir + + +def run_race(sleep_dur): + + from parsl.tests.configs.local_threads_checkpoint_dfk_exit import config + dfk = DataFlowKernel(config=config) + + @python_app(data_flow_kernel=dfk, cache=True) + def cached_rand(x, sleep_dur=0): + import random + import time + time.sleep(sleep_dur) + return random.randint(0, 10000) + + items = [] + for i in range(0, 5): + x = cached_rand(i, sleep_dur=sleep_dur) + items.append(x) + + dfk.cleanup() + return [i.result for i in items] + + +@pytest.mark.local +def test_regress_234(): + """Test task_exit checkpointing with fast tasks""" + run_race(0) + + +@pytest.mark.local +def test_slower_apps(): + """Test task_exit tests with slow apps""" + run_race(0.5) + + +@pytest.mark.local +def test_checkpoint_availability(): + import os + + original, run_dir = run_checkpointed([]) + last_checkpoint = os.path.join(run_dir, 'checkpoint') + print(last_checkpoint) + cached, _ = run_checkpointed([last_checkpoint]) + + print(cached) + print(original) + + assert cached == original, "All tasks were not cached" diff --git a/parsl/parsl/tests/test_checkpointing/test_task_exit.py b/parsl/parsl/tests/test_checkpointing/test_task_exit.py new file mode 100644 index 00000000..cc1ffd84 --- /dev/null +++ b/parsl/parsl/tests/test_checkpointing/test_task_exit.py @@ -0,0 +1,65 @@ +import argparse +import pickle + +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.tests.configs.local_threads_checkpoint_task_exit import config +from parsl.utils import time_limited_open + + +def local_setup(): + global dfk + dfk = parsl.load(config) + + +def local_teardown(): + parsl.dfk().cleanup() + + +@python_app(cache=True) +def slow_double(x, sleep_dur=1): + import time + time.sleep(sleep_dur) + return x * 2 + + +@pytest.mark.local +def test_at_task_exit(n=2): + """Test checkpointing at task_exit behavior + """ + + d = {} + + print("Launching: ", n) + for i in range(0, n): + d[i] = slow_double(i) + print("Done launching") + + for i in range(0, n): + d[i].result() + + # There are two potential race conditions here which + # might be useful to be aware of if debugging this test. + + # i) .result() returning does not necessarily mean that + # a checkpoint that has been written: it means that the + # AppFuture has had its result written. In the DFK + # implementation at time of writing, .result() returning + # does not indicate that a checkpoint has been written, + # it seems like. + + # ii) time_limited_open has a specific time limit in it. + # While this limit might seem generous at time of writing, + # it should be remembered that this is still a race. + + with time_limited_open("{}/checkpoint/tasks.pkl".format(dfk.run_dir), 'rb', seconds=5) as f: + tasks = [] + try: + while f: + tasks.append(pickle.load(f)) + except EOFError: + pass + + assert len(tasks) == n, "Expected {} checkpoint events, got {}".format(n, len(tasks)) diff --git a/parsl/parsl/tests/test_checkpointing/test_task_exit_roundtrip.py b/parsl/parsl/tests/test_checkpointing/test_task_exit_roundtrip.py new file mode 100644 index 00000000..e976c6eb --- /dev/null +++ b/parsl/parsl/tests/test_checkpointing/test_task_exit_roundtrip.py @@ -0,0 +1,65 @@ +import contextlib +import os + +import pytest + +import parsl +from parsl import python_app +from parsl.config import Config +from parsl.dataflow.memoization import BasicMemoizer +from parsl.executors.threads import ThreadPoolExecutor + + +def parsl_configured(run_dir, memoizer): + return parsl.load(Config( + run_dir=str(run_dir), + executors=[ + ThreadPoolExecutor( + label='local_threads_checkpoint', + ) + ], + memoizer=memoizer + )) + + +@python_app(cache=True) +def uuid_app(): + import uuid + return uuid.uuid4() + + +@python_app(cache=True) +def uuid_in_exception_app(): + import uuid + raise RuntimeError(uuid.uuid4()) + + +@pytest.mark.local +def test_loading_checkpoint(tmpd_cwd): + """Load memoization table from previous checkpoint + """ + with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit")): + checkpoint_files = [os.path.join(parsl.dfk().run_dir, "checkpoint")] + result = uuid_app().result() + + with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_files=checkpoint_files)): + relaunched = uuid_app().result() + + assert result == relaunched, "Expected following call to uuid_app to return cached uuid" + + +@pytest.mark.local +def test_regression_239(tmpd_cwd): + """Check that a failing app is run again in a subsequent run. + + This doesn't check that the app is not recorded in the checkpoint database, + only that its exception is not re-used at the task execution level. + """ + with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit")): + checkpoint_files = [os.path.join(parsl.dfk().run_dir, "checkpoint")] + result = uuid_in_exception_app().exception() + + with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_files=checkpoint_files)): + relaunched = uuid_in_exception_app().exception() + + assert result.args[0] != relaunched.args[0], "RuntimeError UUIDs should be different" diff --git a/parsl/parsl/tests/test_curvezmq.py b/parsl/parsl/tests/test_curvezmq.py new file mode 100644 index 00000000..bce5ca6f --- /dev/null +++ b/parsl/parsl/tests/test_curvezmq.py @@ -0,0 +1,421 @@ +import os +import pathlib +from typing import Union +from unittest import mock + +import pytest +import zmq +import zmq.auth +from zmq.auth.thread import ThreadAuthenticator + +from parsl import curvezmq + +ADDR = "tcp://127.0.0.1" + + +def get_server_socket(ctx: curvezmq.ServerContext): + sock = ctx.socket(zmq.PULL) + sock.setsockopt(zmq.RCVTIMEO, 200) + sock.setsockopt(zmq.LINGER, 0) + port = sock.bind_to_random_port(ADDR) + return sock, port + + +def get_client_socket(ctx: curvezmq.ClientContext, port: int): + sock = ctx.socket(zmq.PUSH) + sock.setsockopt(zmq.SNDTIMEO, 200) + sock.setsockopt(zmq.LINGER, 0) + sock.connect(f"{ADDR}:{port}") + return sock + + +def get_external_server_socket( + ctx: Union[curvezmq.ServerContext, zmq.Context], secret_key: bytes +): + sock = ctx.socket(zmq.PULL) + sock.setsockopt(zmq.RCVTIMEO, 200) + sock.setsockopt(zmq.LINGER, 0) + sock.setsockopt(zmq.CURVE_SECRETKEY, secret_key) + sock.setsockopt(zmq.CURVE_SERVER, True) + port = sock.bind_to_random_port(ADDR) + return sock, port + + +def get_external_client_socket( + ctx: Union[curvezmq.ClientContext, zmq.Context], + public_key: bytes, + secret_key: bytes, + server_key: bytes, + port: int, +): + sock = ctx.socket(zmq.PUSH) + sock.setsockopt(zmq.LINGER, 0) + sock.setsockopt(zmq.CURVE_PUBLICKEY, public_key) + sock.setsockopt(zmq.CURVE_SECRETKEY, secret_key) + sock.setsockopt(zmq.CURVE_SERVERKEY, server_key) + sock.connect(f"{ADDR}:{port}") + return sock + + +@pytest.fixture +def encrypted(request: pytest.FixtureRequest): + if hasattr(request, "param"): + return request.param + return True + + +@pytest.fixture +def cert_dir(encrypted: bool, tmpd_cwd: pathlib.Path): + if not encrypted: + return None + return curvezmq.create_certificates(tmpd_cwd) + + +@pytest.fixture +def server_ctx(cert_dir: Union[str, None]): + ctx = curvezmq.ServerContext(cert_dir) + yield ctx + ctx.destroy() + + +@pytest.fixture +def client_ctx(cert_dir: Union[str, None]): + ctx = curvezmq.ClientContext(cert_dir) + yield ctx + ctx.destroy() + + +@pytest.fixture +def zmq_ctx(): + ctx = zmq.Context() + yield ctx + ctx.destroy() + + +@pytest.mark.local +@pytest.mark.parametrize("encrypted", (True, False), indirect=True) +def test_client_context_init(cert_dir: Union[str, None]): + ctx = curvezmq.ClientContext(cert_dir=cert_dir) + + assert ctx.cert_dir == cert_dir + if cert_dir is None: + assert not ctx.encrypted + else: + assert ctx.encrypted + + ctx.destroy() + + +@pytest.mark.local +@pytest.mark.parametrize("encrypted", (True, False), indirect=True) +def test_server_context_init(cert_dir: Union[str, None]): + ctx = curvezmq.ServerContext(cert_dir=cert_dir) + + assert ctx.cert_dir == cert_dir + if cert_dir is None: + assert not ctx.encrypted + assert not ctx.auth_thread + else: + assert ctx.encrypted + assert isinstance(ctx.auth_thread, ThreadAuthenticator) + + ctx.destroy() + + +@pytest.mark.local +def test_create_certificates(tmpd_cwd: pathlib.Path): + cert_dir = tmpd_cwd / "certificates" + assert not os.path.exists(cert_dir) + + ret = curvezmq.create_certificates(tmpd_cwd) + + assert str(cert_dir) == ret + assert os.path.exists(cert_dir) + assert os.stat(cert_dir).st_mode & 0o777 == 0o700 + assert len(os.listdir(cert_dir)) == 4 + + +@pytest.mark.local +def test_create_certificates_overwrite(tmpd_cwd: pathlib.Path): + cert_dir = curvezmq.create_certificates(tmpd_cwd) + client_pub_1, client_sec_1 = curvezmq._load_certificate(cert_dir, name="client") + server_pub_1, server_sec_1 = curvezmq._load_certificate(cert_dir, name="server") + + curvezmq.create_certificates(tmpd_cwd) + client_pub_2, client_sec_2 = curvezmq._load_certificate(cert_dir, name="client") + server_pub_2, server_sec_2 = curvezmq._load_certificate(cert_dir, name="server") + + assert client_pub_1 != client_pub_2 + assert client_sec_1 != client_sec_2 + assert server_pub_1 != server_pub_2 + assert server_sec_1 != server_sec_2 + + +@pytest.mark.local +def test_cert_dir_not_private(tmpd_cwd: pathlib.Path): + cert_dir = tmpd_cwd / "certificates" + os.makedirs(cert_dir, mode=0o777) + client_ctx = curvezmq.ClientContext(cert_dir) + server_ctx = curvezmq.ServerContext(cert_dir) + + err_msg = "directory must be private" + + with pytest.raises(OSError) as pyt_e: + client_ctx.socket(zmq.REQ) + assert err_msg in str(pyt_e.value) + + with pytest.raises(OSError) as pyt_e: + server_ctx.socket(zmq.REP) + assert err_msg in str(pyt_e.value) + + client_ctx.destroy() + server_ctx.destroy() + + +@pytest.mark.local +def test_missing_cert_dir(): + cert_dir = "/bad/cert/dir" + client_ctx = curvezmq.ClientContext(cert_dir) + server_ctx = curvezmq.ServerContext(cert_dir) + + err_msg = "No such file or directory" + + with pytest.raises(FileNotFoundError) as pyt_e: + client_ctx.socket(zmq.REQ) + assert err_msg in str(pyt_e.value) + + with pytest.raises(FileNotFoundError) as pyt_e: + server_ctx.socket(zmq.REP) + assert err_msg in str(pyt_e.value) + + client_ctx.destroy() + server_ctx.destroy() + + +@pytest.mark.local +def test_missing_secret_file(tmpd_cwd: pathlib.Path): + cert_dir = tmpd_cwd / "certificates" + os.makedirs(cert_dir, mode=0o700) + + client_ctx = curvezmq.ClientContext(cert_dir) + server_ctx = curvezmq.ServerContext(cert_dir) + + err_msg = "Invalid certificate file" + + with pytest.raises(OSError) as pyt_e: + client_ctx.socket(zmq.REQ) + assert err_msg in str(pyt_e.value) + + with pytest.raises(OSError) as pyt_e: + server_ctx.socket(zmq.REP) + assert err_msg in str(pyt_e.value) + + client_ctx.destroy() + server_ctx.destroy() + + +@pytest.mark.local +def test_bad_secret_file(tmpd_cwd: pathlib.Path): + client_sec = tmpd_cwd / "client.key_secret" + server_sec = tmpd_cwd / "server.key_secret" + client_sec.write_text("bad") + server_sec.write_text("boy") + + client_ctx = curvezmq.ClientContext(tmpd_cwd) + server_ctx = curvezmq.ServerContext(tmpd_cwd) + + err_msg = "No public key found" + + with pytest.raises(ValueError) as pyt_e: + client_ctx.socket(zmq.REQ) + assert err_msg in str(pyt_e.value) + + with pytest.raises(ValueError) as pyt_e: + server_ctx.socket(zmq.REP) + assert err_msg in str(pyt_e.value) + + client_ctx.destroy() + server_ctx.destroy() + + +@pytest.mark.local +@pytest.mark.parametrize("encrypted", (True, False), indirect=True) +def test_client_context_term(client_ctx: curvezmq.ClientContext): + assert not client_ctx.closed + + client_ctx.term() + + assert client_ctx.closed + + +@pytest.mark.local +@pytest.mark.parametrize("encrypted", (True, False), indirect=True) +def test_server_context_term(server_ctx: curvezmq.ServerContext, encrypted: bool): + assert not server_ctx.closed + auth_thread = None + if encrypted: + auth_thread = server_ctx.auth_thread + assert auth_thread + assert auth_thread.pipe + + server_ctx.term() + + assert server_ctx.closed + if encrypted: + assert server_ctx.auth_thread is None + assert auth_thread is not None, "Pulled from ctx before term()" + assert not auth_thread.is_alive() + assert not auth_thread.pipe + + +@pytest.mark.local +@pytest.mark.parametrize("encrypted", (True, False), indirect=True) +def test_client_context_destroy(client_ctx: curvezmq.ClientContext): + sock = client_ctx.socket(zmq.REP) + + assert not client_ctx.closed + + client_ctx.destroy() + + assert sock.closed + assert client_ctx.closed + + +@pytest.mark.local +@pytest.mark.parametrize("encrypted", (True, False), indirect=True) +def test_server_context_destroy(server_ctx: curvezmq.ServerContext, encrypted: bool): + sock = server_ctx.socket(zmq.REP) + + assert not server_ctx.closed + auth_thread = None + if encrypted: + auth_thread = server_ctx.auth_thread + assert auth_thread + assert auth_thread.pipe + + server_ctx.destroy() + + assert sock.closed + assert server_ctx.closed + if encrypted: + assert server_ctx.auth_thread is None + assert auth_thread is not None, "Pulled from ctx before destroy()" + assert not auth_thread.is_alive() + assert not auth_thread.pipe + + +@pytest.mark.local +@pytest.mark.parametrize("encrypted", (True, False), indirect=True) +def test_connection( + server_ctx: curvezmq.ServerContext, client_ctx: curvezmq.ClientContext +): + server_socket, port = get_server_socket(server_ctx) + client_socket = get_client_socket(client_ctx, port) + + msg = b"howdy" + client_socket.send(msg) + recv = server_socket.recv() + + assert recv == msg + + +@pytest.mark.local +@mock.patch.object(curvezmq, "_load_certificate") +def test_invalid_key_format( + mock_load_cert: mock.MagicMock, + server_ctx: curvezmq.ServerContext, + client_ctx: curvezmq.ClientContext, +): + mock_load_cert.return_value = (b"badkey", b"badkey") + + with pytest.raises(ValueError) as e1_info: + server_ctx.socket(zmq.REP) + with pytest.raises(ValueError) as e2_info: + client_ctx.socket(zmq.REQ) + e1, e2 = e1_info.exconly, e2_info.exconly + + assert str(e1) == str(e2) + assert "Invalid CurveZMQ key format" in str(e1) + + +@pytest.mark.local +def test_invalid_client_keys(server_ctx: curvezmq.ServerContext, zmq_ctx: zmq.Context): + server_socket, port = get_server_socket(server_ctx) + + cert_dir = server_ctx.cert_dir + assert cert_dir # For mypy + public_key, secret_key = curvezmq._load_certificate(cert_dir, "client") + server_key, _ = curvezmq._load_certificate(cert_dir, "server") + + BAD_PUB_KEY, BAD_SEC_KEY = zmq.curve_keypair() + msg = b"howdy" + + client_socket = get_external_client_socket( + zmq_ctx, + public_key, + secret_key, + server_key, + port, + ) + client_socket.send(msg) + assert server_socket.recv() == msg + + client_socket = get_external_client_socket( + zmq_ctx, + BAD_PUB_KEY, + BAD_SEC_KEY, + server_key, + port, + ) + client_socket.send(msg) + with pytest.raises(zmq.Again): + server_socket.recv() + + client_socket = get_external_client_socket( + zmq_ctx, + public_key, + secret_key, + BAD_PUB_KEY, + port, + ) + client_socket.send(msg) + with pytest.raises(zmq.Again): + server_socket.recv() + + # Ensure sockets are operational + client_socket = get_external_client_socket( + zmq_ctx, + public_key, + secret_key, + server_key, + port, + ) + client_socket.send(msg) + assert server_socket.recv() == msg + + +@pytest.mark.local +def test_invalid_server_key(client_ctx: curvezmq.ClientContext, zmq_ctx: zmq.Context): + cert_dir = client_ctx.cert_dir + assert cert_dir # For mypy + _, secret_key = curvezmq._load_certificate(cert_dir, "server") + + _, BAD_SEC_KEY = zmq.curve_keypair() + msg = b"howdy" + + server_socket, port = get_external_server_socket(zmq_ctx, secret_key) + client_socket = get_client_socket(client_ctx, port) + client_socket.send(msg) + assert server_socket.recv() == msg + + server_socket, port = get_external_server_socket(zmq_ctx, BAD_SEC_KEY) + client_socket = get_client_socket(client_ctx, port) + client_socket.send(msg) + with pytest.raises(zmq.Again): + server_socket.recv() + + # Ensure sockets are operational + server_socket, port = get_external_server_socket(zmq_ctx, secret_key) + client_socket = get_client_socket(client_ctx, port) + client_socket.send(msg) + assert server_socket.recv() == msg diff --git a/parsl/parsl/tests/test_docs/__init__.py b/parsl/parsl/tests/test_docs/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_docs/test_from_slides.py b/parsl/parsl/tests/test_docs/test_from_slides.py new file mode 100644 index 00000000..b3242e81 --- /dev/null +++ b/parsl/parsl/tests/test_docs/test_from_slides.py @@ -0,0 +1,33 @@ +import os + +import pytest + +from parsl.app.app import bash_app, python_app +from parsl.data_provider.files import File + + +@bash_app +def echo(message, outputs=[]): + return 'echo {m} &> {o}'.format(m=message, o=outputs[0]) + + +@python_app +def cat(inputs=[]): + with open(inputs[0].filepath) as f: + return f.readlines() + + +@pytest.mark.staging_required +def test_slides(): + """Testing code snippet from slides """ + + if os.path.exists('hello1.txt'): + os.remove('hello1.txt') + + hello = echo("Hello World!", outputs=[File('hello1.txt')]) + + message = cat(inputs=[hello.outputs[0]]) + + # Waits. This need not be in the slides. + print(hello.result()) + print(message.result()) diff --git a/parsl/parsl/tests/test_docs/test_kwargs.py b/parsl/parsl/tests/test_docs/test_kwargs.py new file mode 100644 index 00000000..80907ebe --- /dev/null +++ b/parsl/parsl/tests/test_docs/test_kwargs.py @@ -0,0 +1,40 @@ +"""Functions used to explain kwargs""" +from pathlib import Path + +import pytest + +from parsl import File, python_app + + +def test_inputs(): + @python_app() + def map_app(x): + return x * 2 + + @python_app() + def reduce_app(inputs=()): + return sum(inputs) + + map_futures = [map_app(x) for x in range(3)] + reduce_future = reduce_app(inputs=map_futures) + + assert reduce_future.result() == 6 + + +@pytest.mark.shared_fs +def test_outputs(tmpd_cwd): + @python_app() + def write_app(message, outputs=()): + """Write a single message to every file in outputs""" + for path in outputs: + with open(path, 'w') as fp: + print(message, file=fp) + + to_write = [ + File(Path(tmpd_cwd) / 'output-0.txt'), + File(Path(tmpd_cwd) / 'output-1.txt') + ] + write_app('Hello!', outputs=to_write).result() + for path in to_write: + with open(path) as fp: + assert fp.read() == 'Hello!\n' diff --git a/parsl/parsl/tests/test_docs/test_workflow1.py b/parsl/parsl/tests/test_docs/test_workflow1.py new file mode 100644 index 00000000..271baab4 --- /dev/null +++ b/parsl/parsl/tests/test_docs/test_workflow1.py @@ -0,0 +1,41 @@ +import os + +import pytest + +import parsl +from parsl.app.app import bash_app, python_app +from parsl.data_provider.files import File +from parsl.tests.configs.local_threads import config + +# parsl.set_stream_logger() + + +@python_app +def generate(limit): + from random import randint + """Generate a random integer and return it""" + return randint(1, limit) + + +@bash_app +def save(message, outputs=[]): + return 'echo {m} &> {o}'.format(m=message, o=outputs[0]) + + +@pytest.mark.staging_required +def test_procedural(N=2): + """Procedural workflow example from docs on + Composing a workflow + """ + + if os.path.exists('output.txt'): + os.remove('output.txt') + + message = generate(N) + + saved = save(message, outputs=[File('output.txt')]) + + with open(saved.outputs[0].result().filepath, 'r') as f: + item = int(f.read().strip()) + assert item <= N, "Expected file to contain int <= N" + assert item >= 1, "Expected file to contain int >= 1" diff --git a/parsl/parsl/tests/test_docs/test_workflow4.py b/parsl/parsl/tests/test_docs/test_workflow4.py new file mode 100644 index 00000000..05a2a6f8 --- /dev/null +++ b/parsl/parsl/tests/test_docs/test_workflow4.py @@ -0,0 +1,43 @@ +import pytest + +from parsl.app.app import bash_app, python_app +from parsl.data_provider.files import File + + +@bash_app +def generate(outputs=()): + return "echo 1 &> {o}".format(o=outputs[0]) + + +@bash_app +def concat(inputs=(), outputs=(), stdout=None, stderr=None): + return "cat {0} >> {1}".format(" ".join(map(lambda x: x.filepath, inputs)), outputs[0]) + + +@python_app +def total(inputs=()): + with open(inputs[0].filepath, "r") as f: + return sum(int(line) for line in f) + + +@pytest.mark.staging_required +@pytest.mark.parametrize("width", (5, 10, 15)) +def test_parallel_dataflow(tmpd_cwd, width): + """Test parallel dataflow from docs on Composing workflows + """ + + # create 5 files with random numbers + output_files = [ + generate(outputs=[File(str(tmpd_cwd / f"random-{i}.txt"))]) + for i in range(width) + ] + + # concatenate the files into a single file + cc = concat( + inputs=[i.outputs[0] for i in output_files], + outputs=[File(str(tmpd_cwd / "all.txt"))] + ) + + # calculate the average of the random numbers + totals = total(inputs=[cc.outputs[0]]) + assert totals.result() == len(output_files) diff --git a/parsl/parsl/tests/test_error_handling/__init__.py b/parsl/parsl/tests/test_error_handling/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_error_handling/test_fail.py b/parsl/parsl/tests/test_error_handling/test_fail.py new file mode 100644 index 00000000..27d5b005 --- /dev/null +++ b/parsl/parsl/tests/test_error_handling/test_fail.py @@ -0,0 +1,14 @@ +import pytest + +from parsl.app.app import python_app + + +@python_app +def always_fail(): + raise ValueError("This ValueError should propagate to the app caller in fut.result()") + + +def test_simple(): + with pytest.raises(ValueError): + fut = always_fail() + fut.result() diff --git a/parsl/parsl/tests/test_error_handling/test_python_walltime.py b/parsl/parsl/tests/test_error_handling/test_python_walltime.py new file mode 100644 index 00000000..7e570c8c --- /dev/null +++ b/parsl/parsl/tests/test_error_handling/test_python_walltime.py @@ -0,0 +1,42 @@ +import pytest + +import parsl +from parsl.app.errors import AppTimeout + + +@parsl.python_app +def my_app(t=0.0, walltime=0.1): + import time + end = time.monotonic() + t + while time.monotonic() < end: + time.sleep(0.01) # work with timeout(); allow access to thread + return 7 + + +def test_python_walltime(): + f = my_app(2) + with pytest.raises(AppTimeout): + f.result() + + +def test_python_longer_walltime_at_invocation(): + f = my_app(0.01, walltime=6) + assert f.result() == 7 + + +def test_python_walltime_wrapped_names(): + f = my_app(0.01, walltime=6) + assert f.result() == 7 + assert f.task_record['func'].__name__ == "my_app" + assert f.task_record['func_name'] == "my_app" + + +def test_python_bad_decorator_args(): + + with pytest.raises(TypeError): + @pytest.mark.local + @parsl.python_app(walltime=1) + def my_app_2(): + import time + time.sleep(1.2) + return True diff --git a/parsl/parsl/tests/test_error_handling/test_resource_spec.py b/parsl/parsl/tests/test_error_handling/test_resource_spec.py new file mode 100644 index 00000000..5adb27b6 --- /dev/null +++ b/parsl/parsl/tests/test_error_handling/test_resource_spec.py @@ -0,0 +1,46 @@ +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.executors import WorkQueueExecutor +from parsl.executors.errors import InvalidResourceSpecification +from parsl.executors.high_throughput.executor import HighThroughputExecutor +from parsl.executors.threads import ThreadPoolExecutor + + +@python_app +def double(x, parsl_resource_specification={}): + return x * 2 + + +@pytest.mark.issue_3620 +def test_resource(n=2): + executors = parsl.dfk().executors + executor = None + for label in executors: + if label != '_parsl_internal': + executor = executors[label] + break + + # Specify incorrect number of resources + spec = {'cores': 1, 'memory': 1} + fut = double(n, parsl_resource_specification=spec) + try: + fut.result() + except InvalidResourceSpecification: + assert ( + isinstance(executor, HighThroughputExecutor) or + isinstance(executor, WorkQueueExecutor) or + isinstance(executor, ThreadPoolExecutor)) + + # Specify resources with wrong types + # 'cpus' is incorrect, should be 'cores' + spec = {'cpus': 1, 'memory': 1, 'disk': 1} + fut = double(n, parsl_resource_specification=spec) + try: + fut.result() + except InvalidResourceSpecification: + assert ( + isinstance(executor, HighThroughputExecutor) or + isinstance(executor, WorkQueueExecutor) or + isinstance(executor, ThreadPoolExecutor)) diff --git a/parsl/parsl/tests/test_error_handling/test_retries.py b/parsl/parsl/tests/test_error_handling/test_retries.py new file mode 100644 index 00000000..06ae8170 --- /dev/null +++ b/parsl/parsl/tests/test_error_handling/test_retries.py @@ -0,0 +1,101 @@ +import os + +import pytest + +from parsl import bash_app, python_app +from parsl.tests.configs.local_threads import fresh_config + + +def local_config(): + c = fresh_config() + c.retries = 2 + return c + + +@python_app +def sleep_then_fail(inputs=[], sleep_dur=0.1): + import math + import time + time.sleep(sleep_dur) + math.ceil("Trigger TypeError") + return 0 + + +@bash_app +def succeed_on_retry(filename, success_on=1, stdout="succeed.out"): + """If the input file does not exist it creates it. + Then, if the file contains success_on lines it exits with 0 + """ + + return """if [[ ! -e {filename} ]]; then touch {filename}; fi; + tries=`wc -l {filename} | cut -f1 -d' '` + echo $tries >> {filename} + + if [[ "$tries" -eq "{success_on}" ]] + then + echo "Match. Success" + else + echo "Tries != success_on , exiting with error" + exit 5 + fi + """.format(filename=filename, success_on=success_on) + + +@python_app +def sleep(sleep_dur=0.1): + import time + time.sleep(sleep_dur) + return 0 + + +@pytest.mark.local +def test_fail_nowait(numtasks=10): + """Test retries on tasks with no dependencies. + """ + fus = [] + for i in range(0, numtasks): + fu = sleep_then_fail(sleep_dur=0.1) + fus.extend([fu]) + + # wait for all tasks to complete before ending this test + [x.exception() for x in fus] + + try: + [x.result() for x in fus] + except Exception as e: + assert isinstance( + e, TypeError), "Expected a TypeError, got {}".format(e) + + +@pytest.mark.local +def test_fail_delayed(numtasks=10): + """Test retries on tasks with dependencies. + + This is testing retry behavior when AppFutures are created + with no parent. + """ + + x = sleep() + fus = [] + for i in range(0, numtasks): + fu = sleep_then_fail(inputs=[x], sleep_dur=0.5) + fus.extend([fu]) + + # wait for all tasks to complete before ending this test + [x.exception() for x in fus] + + try: + [x.result() for x in fus] + except Exception as e: + assert isinstance( + e, TypeError), "Expected a TypeError, got {}".format(e) + + +@pytest.mark.local +def test_retry(tmpd_cwd): + """Test retries via app that succeeds on the Nth retry. + """ + + fpath = tmpd_cwd / "retry.out" + sout = str(tmpd_cwd / "stdout") + succeed_on_retry(str(fpath), stdout=sout).result() diff --git a/parsl/parsl/tests/test_error_handling/test_retry_handler.py b/parsl/parsl/tests/test_error_handling/test_retry_handler.py new file mode 100644 index 00000000..788c6063 --- /dev/null +++ b/parsl/parsl/tests/test_error_handling/test_retry_handler.py @@ -0,0 +1,65 @@ +import os + +import pytest + +import parsl +from parsl import bash_app +from parsl.tests.configs.local_threads import fresh_config + + +def half_handler(*args): + """Cost 0.5 for each retry, not the default of 1""" + return 0.5 + + +def local_config(): + c = fresh_config() + c.retries = 2 + c.retry_handler = half_handler + return c + + +@bash_app +def succeed_on_retry(filename, success_on=1, stdout="succeed.out"): + """If the input file does not exist it creates it. + Then, if the file contains success_on lines it exits with 0 + """ + + return """if [[ ! -e {filename} ]]; then touch {filename}; fi; + tries=`wc -l {filename} | cut -f1 -d' '` + echo $tries >> {filename} + + if [[ "$tries" -eq "{success_on}" ]] + then + echo "Match. Success" + else + echo "Tries != success_on , exiting with error" + exit 5 + fi + """.format(filename=filename, success_on=success_on) + + +@pytest.mark.local +def test_retry(): + """Test retries via app that succeeds on the Nth retry. + """ + + fname = "retry.out" + try: + os.remove(fname) + except OSError: + pass + fu = succeed_on_retry(fname, success_on=4) + + fu.result() + + try: + os.remove(fname) + except OSError: + pass + fu = succeed_on_retry(fname, success_on=5) + + with pytest.raises(parsl.app.errors.BashExitFailure): + fu.result() + + assert fu.exception().exitcode == 5 diff --git a/parsl/parsl/tests/test_error_handling/test_retry_handler_failure.py b/parsl/parsl/tests/test_error_handling/test_retry_handler_failure.py new file mode 100644 index 00000000..8d5406bb --- /dev/null +++ b/parsl/parsl/tests/test_error_handling/test_retry_handler_failure.py @@ -0,0 +1,24 @@ +import pytest + +import parsl + + +@parsl.python_app +def always_fails(): + raise ValueError("always_fails deliberate exception") + + +def retry_handler_raises(exc, task_record): + raise RuntimeError("retry_handler_raises deliberate exception") + + +def local_config(): + return parsl.config.Config(retry_handler=retry_handler_raises) + + +@pytest.mark.local +def test_retry_handler_exception(): + fut = always_fails() + with pytest.raises(RuntimeError): + fut.result() + assert fut.exception().args[0] == "retry_handler_raises deliberate exception" diff --git a/parsl/parsl/tests/test_error_handling/test_serialization_fail.py b/parsl/parsl/tests/test_error_handling/test_serialization_fail.py new file mode 100644 index 00000000..60b54afb --- /dev/null +++ b/parsl/parsl/tests/test_error_handling/test_serialization_fail.py @@ -0,0 +1,33 @@ +import pytest + +from parsl import python_app +from parsl.serialize.errors import SerializationError +from parsl.tests.configs.htex_local import fresh_config + + +def local_config(): + config = fresh_config() + config.retries = 2 + return config + + +@python_app +def fail_pickling(x): + return next(x) + + +def generator(): + num = 0 + while True: + yield num + num += 1 + + +@pytest.mark.local +def test_serialization_error(): + + gen = generator() + x = fail_pickling(gen) + + assert x.exception() is not None, "Should have got an exception, actually got: " + repr(x.result()) + assert isinstance(x.exception(), SerializationError) diff --git a/parsl/parsl/tests/test_error_handling/test_wrap_with_logs.py b/parsl/parsl/tests/test_error_handling/test_wrap_with_logs.py new file mode 100644 index 00000000..28200b65 --- /dev/null +++ b/parsl/parsl/tests/test_error_handling/test_wrap_with_logs.py @@ -0,0 +1,34 @@ +import logging + +import pytest + +from parsl.process_loggers import wrap_with_logs + + +@wrap_with_logs +def somefunc_ok(): + return 5 + + +@wrap_with_logs +def somefunc_exception(): + raise RuntimeError("Deliberate failure") + + +@pytest.mark.local +def test_wrap_with_logs_ok(caplog): + caplog.set_level(logging.DEBUG) + + # check that return value is passed back through wrap_with_logs + x = somefunc_ok() + assert x == 5 + assert 'Normal ending' in caplog.text + + +@pytest.mark.local +def test_wrap_with_logs_exception(caplog): + caplog.set_level(logging.ERROR) + # check that exception is passed back through wrap_with_logs + with pytest.raises(RuntimeError): + somefunc_exception() + assert 'Exceptional ending' in caplog.text diff --git a/parsl/parsl/tests/test_execute_task.py b/parsl/parsl/tests/test_execute_task.py new file mode 100644 index 00000000..de45d632 --- /dev/null +++ b/parsl/parsl/tests/test_execute_task.py @@ -0,0 +1,20 @@ +import os + +import pytest + +from parsl.executors.execute_task import execute_task +from parsl.serialize.facade import pack_apply_message + + +def addemup(*args: int, name: str = "apples"): + total = sum(args) + return f"{total} {name}" + + +@pytest.mark.local +def test_execute_task(): + args = (1, 2, 3) + kwargs = {"name": "boots"} + buff = pack_apply_message(addemup, args, kwargs) + res = execute_task(buff) + assert res == addemup(*args, **kwargs) diff --git a/parsl/parsl/tests/test_flux.py b/parsl/parsl/tests/test_flux.py new file mode 100644 index 00000000..993d8702 --- /dev/null +++ b/parsl/parsl/tests/test_flux.py @@ -0,0 +1,170 @@ +import concurrent.futures as cf +import os + +import pytest + +from parsl.app.errors import AppException +from parsl.executors.flux.executor import ( + FluxExecutor, + FluxFutureWrapper, + _complete_future, +) + +try: + import flux.job.executor # noqa: F401 +except ImportError: + FLUX_AVAIL = False +else: + FLUX_AVAIL = True + +require_flux = pytest.mark.skipif( + not FLUX_AVAIL, reason="Flux not available, test will fail" +) +ERRMSG = "Some error message" + + +def multiply(x, y): + return x * y + + +def bad_foo(): + raise ValueError(ERRMSG) + + +@require_flux +@pytest.mark.local +def test_multiply(): + with FluxExecutor() as executor: + executor.start() + futures = [executor.submit(multiply, {}, i, 7) for i in range(5)] + for i, future in enumerate(futures): + assert future.result() == i * 7 + assert future.done() + assert future.exception() is None + assert isinstance(future, FluxFutureWrapper) + + +@require_flux +@pytest.mark.local +def test_except(): + with FluxExecutor() as executor: + executor.start() + future = executor.submit(bad_foo, {}) + with pytest.raises(ValueError, match=ERRMSG): + future.result() + assert isinstance(future.exception(), ValueError) + + +@require_flux +@pytest.mark.local +@pytest.mark.skipif( + not hasattr(os, "sched_getaffinity") or len(os.sched_getaffinity(0)) < 2, + reason="Not Linux or too few CPUs", +) +def test_affinity(): + with FluxExecutor() as executor: + executor.start() + future = executor.submit(os.sched_getaffinity, {"cores_per_task": 2}, 0) + assert len(future.result()) > 1 + + +@require_flux +@pytest.mark.local +def test_cancel(): + with FluxExecutor() as executor: + executor.start() + futures = [executor.submit(multiply, {}, i, 9) for i in range(3)] + for i, future in enumerate(futures): + if future.cancel(): + assert future.cancelled() + assert future.done() + with pytest.raises(cf.CancelledError): + future.exception() + with pytest.raises(cf.CancelledError): + future.result() + else: + assert future.running() + assert future.done() + assert not future.cancelled() + assert future.result() == i * 9 + + +@pytest.mark.local +def test_future_cancel(): + underlying_future = cf.Future() + wrapper_future = FluxFutureWrapper() + wrapper_future._flux_future = underlying_future + assert not wrapper_future.done() + assert not wrapper_future.running() + assert not wrapper_future.cancelled() + assert wrapper_future.cancel() # should cancel underlying future + assert wrapper_future.cancel() # try again for good measure + assert wrapper_future.cancelled() + assert wrapper_future.done() + assert underlying_future.cancelled() + assert underlying_future.done() + + +@pytest.mark.local +def test_future_running(): + underlying_future = cf.Future() + wrapper_future = FluxFutureWrapper() + assert not underlying_future.running() + assert underlying_future.set_running_or_notify_cancel() + assert underlying_future.running() + assert not wrapper_future.running() + wrapper_future._flux_future = underlying_future + assert wrapper_future.running() + + +@pytest.mark.local +def test_future_callback_returncode(): + testfile = ".fluxexecutortest.txt" + returncode = 1 + underlying_future = cf.Future() + wrapper_future = FluxFutureWrapper() + wrapper_future._flux_future = underlying_future + underlying_future.add_done_callback( + lambda fut: _complete_future(testfile, wrapper_future, fut) + ) + underlying_future.set_result(returncode) + assert wrapper_future.done() + assert isinstance(wrapper_future.exception(), AppException) + + +@pytest.mark.local +def test_future_callback_nofile(): + testfile = ".fluxexecutortest.txt" + returncode = 0 + if os.path.isfile(testfile): + os.remove(testfile) + underlying_future = cf.Future() + wrapper_future = FluxFutureWrapper() + wrapper_future._flux_future = underlying_future + underlying_future.add_done_callback( + lambda fut: _complete_future(testfile, wrapper_future, fut) + ) + underlying_future.set_result(returncode) + assert wrapper_future.done() + assert isinstance(wrapper_future.exception(), FileNotFoundError) + + +@pytest.mark.local +def test_future_callback_flux_exception(): + underlying_future = cf.Future() + wrapper_future = FluxFutureWrapper() + wrapper_future._flux_future = underlying_future + underlying_future.add_done_callback( + lambda fut: _complete_future(".fluxexecutortest.txt", wrapper_future, fut) + ) + underlying_future.set_exception(ValueError()) + assert wrapper_future.done() + assert isinstance(wrapper_future.exception(), ValueError) + + +@pytest.mark.local +def test_future_cancel_no_underlying_future(): + wrapper_future = FluxFutureWrapper() + assert wrapper_future.cancel() + assert wrapper_future.cancelled() + assert not wrapper_future.running() diff --git a/parsl/parsl/tests/test_htex/__init__.py b/parsl/parsl/tests/test_htex/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_htex/mpi/__init__.py b/parsl/parsl/tests/test_htex/mpi/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_htex/mpi/mocks/pbs_nodefile b/parsl/parsl/tests/test_htex/mpi/mocks/pbs_nodefile new file mode 100644 index 00000000..42e3ab71 --- /dev/null +++ b/parsl/parsl/tests/test_htex/mpi/mocks/pbs_nodefile @@ -0,0 +1,4 @@ +node1 +node2 +node3 +node4 diff --git a/parsl/parsl/tests/test_htex/mpi/mocks/pbs_nodefile.8 b/parsl/parsl/tests/test_htex/mpi/mocks/pbs_nodefile.8 new file mode 100644 index 00000000..643d60f3 --- /dev/null +++ b/parsl/parsl/tests/test_htex/mpi/mocks/pbs_nodefile.8 @@ -0,0 +1,8 @@ +NODE001 +NODE002 +NODE003 +NODE004 +NODE005 +NODE006 +NODE007 +NODE008 diff --git a/parsl/parsl/tests/test_htex/mpi/test_bad_mpi_config.py b/parsl/parsl/tests/test_htex/mpi/test_bad_mpi_config.py new file mode 100644 index 00000000..d7d928f7 --- /dev/null +++ b/parsl/parsl/tests/test_htex/mpi/test_bad_mpi_config.py @@ -0,0 +1,50 @@ +import pytest + +from parsl import Config +from parsl.executors import MPIExecutor +from parsl.launchers import AprunLauncher, SimpleLauncher, SrunLauncher +from parsl.providers import SlurmProvider + + +@pytest.mark.local +def test_bad_launcher(): + """TypeError if a launcher other than SimpleLauncher is supplied""" + + for launcher in [SrunLauncher(), AprunLauncher()]: + with pytest.raises(TypeError): + Config(executors=[ + MPIExecutor( + provider=SlurmProvider(launcher=launcher), + ) + ]) + + +@pytest.mark.local +def test_bad_mpi_launcher(): + """ValueError if an unsupported mpi_launcher is specified""" + + with pytest.raises(ValueError): + Config(executors=[ + MPIExecutor( + mpi_launcher="bad_launcher", + provider=SlurmProvider(launcher=SimpleLauncher()), + ) + ]) + + +@pytest.mark.local +@pytest.mark.parametrize( + "mpi_launcher", + ["srun", "aprun", "mpiexec"] +) +def test_correct_launcher_with_mpi_mode(mpi_launcher: str): + """Confirm that SimpleLauncher works with mpi_mode""" + + executor = MPIExecutor( + mpi_launcher=mpi_launcher, + provider=SlurmProvider(launcher=SimpleLauncher()), + ) + + assert executor.provider is not None, "htex always has a provider" + assert hasattr(executor.provider, 'launcher'), "supplied provider has a launcher attribute" + assert isinstance(executor.provider.launcher, SimpleLauncher) diff --git a/parsl/parsl/tests/test_htex/mpi/test_mpi_mode_enabled.py b/parsl/parsl/tests/test_htex/mpi/test_mpi_mode_enabled.py new file mode 100644 index 00000000..63005a8d --- /dev/null +++ b/parsl/parsl/tests/test_htex/mpi/test_mpi_mode_enabled.py @@ -0,0 +1,188 @@ +import logging +import os +import random +from typing import Dict + +import pytest + +import parsl +from parsl import Config, bash_app, python_app +from parsl.executors import MPIExecutor +from parsl.executors.errors import InvalidResourceSpecification +from parsl.launchers import SimpleLauncher +from parsl.providers import LocalProvider + +EXECUTOR_LABEL = "MPI_TEST" + + +def local_setup(): + + cwd = os.path.abspath(os.path.dirname(__file__)) + pbs_nodefile = os.path.join(cwd, "mocks", "pbs_nodefile") + + config = Config( + executors=[ + MPIExecutor( + label=EXECUTOR_LABEL, + max_workers_per_block=2, + mpi_launcher="mpiexec", + provider=LocalProvider( + worker_init=f"export PBS_NODEFILE={pbs_nodefile}", + launcher=SimpleLauncher() + ) + ) + ]) + + parsl.load(config) + + +def local_teardown(): + parsl.dfk().cleanup() + + +@python_app +def get_env_vars(parsl_resource_specification: Dict = {}) -> Dict: + import os + + parsl_vars = {} + for key in os.environ: + if key.startswith("PARSL_"): + parsl_vars[key] = os.environ[key] + return parsl_vars + + +@pytest.mark.local +def test_only_resource_specs_set(): + """Confirm that resource_spec env vars are set while launch prefixes are not + when enable_mpi_mode = False""" + resource_spec = { + "num_nodes": 2, + "ranks_per_node": 2, + } + + future = get_env_vars(parsl_resource_specification=resource_spec) + + result = future.result() + assert isinstance(result, Dict) + logging.warning(f"Got table: {result}") + assert "PARSL_MPI_PREFIX" in result + assert "PARSL_MPIEXEC_PREFIX" in result + assert result["PARSL_MPI_PREFIX"] == result["PARSL_MPIEXEC_PREFIX"] + assert result["PARSL_NUM_NODES"] == str(resource_spec["num_nodes"]) + assert result["PARSL_RANKS_PER_NODE"] == str(resource_spec["ranks_per_node"]) + assert result["PARSL_NUM_RANKS"] == str(resource_spec["ranks_per_node"] * resource_spec["num_nodes"]) + + +@bash_app +def echo_launch_cmd( + parsl_resource_specification: Dict, + stdout=parsl.AUTO_LOGNAME, + stderr=parsl.AUTO_LOGNAME, +): + return 'echo "$PARSL_MPI_PREFIX hostname"' + + +@pytest.mark.local +def test_bash_default_prefix_set(): + """Confirm that resource_spec env vars are set while launch prefixes are not + when enable_mpi_mode = False""" + resource_spec = { + "num_nodes": 2, + "ranks_per_node": 2, + } + + future = echo_launch_cmd(parsl_resource_specification=resource_spec) + + result = future.result() + assert result == 0 + with open(future.stdout) as f: + output = f.readlines() + assert output[0].startswith("mpiexec") + logging.warning(f"output : {output}") + + +@pytest.mark.local +def test_bash_multiple_set(): + """Confirm that multiple apps can run without blocking each other out + when enable_mpi_mode = False""" + resource_spec = { + "num_nodes": 2, + "num_ranks": 4, + } + futures = [] + for i in range(4): + resource_spec["num_nodes"] = i + 1 + future = echo_launch_cmd(parsl_resource_specification=resource_spec) + futures.append(future) + + for future in futures: + result = future.result() + assert result == 0 + with open(future.stdout) as f: + output = f.readlines() + assert output[0].startswith("mpiexec") + + +@bash_app +def bash_resource_spec(parsl_resource_specification=None, stdout=parsl.AUTO_LOGNAME): + total_ranks = ( + parsl_resource_specification["ranks_per_node"] * parsl_resource_specification["num_nodes"] + ) + return f'echo "{total_ranks}"' + + +@pytest.mark.local +def test_bash_app_using_resource_spec(): + resource_spec = { + "num_nodes": 2, + "ranks_per_node": 2, + } + future = bash_resource_spec(parsl_resource_specification=resource_spec) + assert future.result() == 0 + with open(future.stdout) as f: + output = f.readlines() + total_ranks = resource_spec["num_nodes"] * resource_spec["ranks_per_node"] + assert int(output[0].strip()) == total_ranks + + +@python_app +def mock_app(sleep_dur: float = 0.0, parsl_resource_specification: Dict = {}): + import os + import time + time.sleep(sleep_dur) + + total_ranks = int(os.environ["PARSL_NUM_NODES"]) * int(os.environ["PARSL_RANKS_PER_NODE"]) + nodes = os.environ["PARSL_MPI_NODELIST"].split(',') + + return total_ranks, nodes + + +@pytest.mark.local +def test_simulated_load(rounds: int = 100): + + node_choices = (1, 2, 4) + sleep_choices = (0, 0.01, 0.02, 0.04) + ranks_per_node = (4, 8) + + futures = {} + for i in range(rounds): + resource_spec = { + "num_nodes": random.choice(node_choices), + "ranks_per_node": random.choice(ranks_per_node), + } + future = mock_app(sleep_dur=random.choice(sleep_choices), + parsl_resource_specification=resource_spec) + futures[future] = resource_spec + + for future in futures: + total_ranks, nodes = future.result(timeout=10) + assert len(nodes) == futures[future]["num_nodes"] + assert total_ranks == futures[future]["num_nodes"] * futures[future]["ranks_per_node"] + + +@pytest.mark.local +def test_missing_resource_spec(): + + with pytest.raises(InvalidResourceSpecification): + future = mock_app(sleep_dur=0.4) + future.result(timeout=10) diff --git a/parsl/parsl/tests/test_htex/mpi/test_mpi_prefix.py b/parsl/parsl/tests/test_htex/mpi/test_mpi_prefix.py new file mode 100644 index 00000000..48740950 --- /dev/null +++ b/parsl/parsl/tests/test_htex/mpi/test_mpi_prefix.py @@ -0,0 +1,71 @@ +import logging + +import pytest + +from parsl.executors.high_throughput.mpi_prefix_composer import ( + compose_all, + compose_aprun_launch_cmd, + compose_mpiexec_launch_cmd, + compose_srun_launch_cmd, +) +from parsl.executors.high_throughput.mpi_resource_management import Scheduler + +resource_spec = {"num_nodes": 2, + "num_ranks": 8, + "ranks_per_node": 4} + + +@pytest.mark.local +def test_srun_launch_cmd(): + prefix_name, composed_prefix = compose_srun_launch_cmd( + resource_spec=resource_spec, node_hostnames=["node1", "node2"] + ) + assert prefix_name == "PARSL_SRUN_PREFIX" + logging.warning(composed_prefix) + + assert "None" not in composed_prefix + + +@pytest.mark.local +def test_aprun_launch_cmd(): + prefix_name, composed_prefix = compose_aprun_launch_cmd( + resource_spec=resource_spec, node_hostnames=["node1", "node2"] + ) + logging.warning(composed_prefix) + assert prefix_name == "PARSL_APRUN_PREFIX" + assert "None" not in composed_prefix + + +@pytest.mark.local +def test_mpiexec_launch_cmd(): + prefix_name, composed_prefix = compose_mpiexec_launch_cmd( + resource_spec=resource_spec, node_hostnames=["node1", "node2"] + ) + logging.warning(composed_prefix) + assert prefix_name == "PARSL_MPIEXEC_PREFIX" + assert "None" not in composed_prefix + + +@pytest.mark.local +def test_slurm_launch_cmd(): + table = compose_all( + mpi_launcher="srun", + resource_spec=resource_spec, + node_hostnames=["NODE001", "NODE002"], + ) + + assert "PARSL_MPI_PREFIX" in table + assert "PARSL_SRUN_PREFIX" in table + + +@pytest.mark.local +def test_default_launch_cmd(): + table = compose_all( + mpi_launcher="srun", + resource_spec=resource_spec, + node_hostnames=["NODE001", "NODE002"], + ) + + assert "PARSL_MPI_PREFIX" in table + assert "PARSL_SRUN_PREFIX" in table + assert table["PARSL_MPI_PREFIX"] == table["PARSL_SRUN_PREFIX"] diff --git a/parsl/parsl/tests/test_htex/mpi/test_mpi_scheduler.py b/parsl/parsl/tests/test_htex/mpi/test_mpi_scheduler.py new file mode 100644 index 00000000..38a0420e --- /dev/null +++ b/parsl/parsl/tests/test_htex/mpi/test_mpi_scheduler.py @@ -0,0 +1,163 @@ +import os +import pickle +from unittest import mock + +import pytest + +from parsl.executors.high_throughput.mpi_resource_management import ( + MPITaskScheduler, + TaskScheduler, +) +from parsl.multiprocessing import SpawnContext +from parsl.serialize import pack_apply_message + +mock_task_buffer = pack_apply_message("func", "args", "kwargs") + + +@pytest.fixture(autouse=True) +def set_pbs_nodefile_envvars(): + cwd = os.path.abspath(os.path.dirname(__file__)) + pbs_nodefile = os.path.join(cwd, "mocks", "pbs_nodefile.8") + with mock.patch.dict(os.environ, {"PBS_NODEFILE": pbs_nodefile}): + yield + + +@pytest.mark.local +def test_NoopScheduler(): + task_q, result_q = SpawnContext.Queue(), SpawnContext.Queue() + scheduler = TaskScheduler(task_q, result_q) + + scheduler.put_task("TaskFoo") + assert task_q.get() == "TaskFoo" + + result_q.put("Result1") + assert scheduler.get_result(True, 1) == "Result1" + + +@pytest.mark.local +def test_MPISched_put_task(): + task_q, result_q = SpawnContext.Queue(), SpawnContext.Queue() + scheduler = MPITaskScheduler(task_q, result_q) + + assert scheduler.available_nodes + assert len(scheduler.available_nodes) == 8 + assert scheduler._free_node_counter.value == 8 + + ctxt = {"resource_spec": {"num_nodes": 2, "ranks_per_node": 2}} + task_package = {"task_id": 1, "buffer": mock_task_buffer, "context": ctxt} + scheduler.put_task(task_package) + + assert scheduler._free_node_counter.value == 6 + + +@pytest.mark.local +def test_MPISched_get_result(): + task_q, result_q = SpawnContext.Queue(), SpawnContext.Queue() + scheduler = MPITaskScheduler(task_q, result_q) + + assert scheduler.available_nodes + assert len(scheduler.available_nodes) == 8 + assert scheduler._free_node_counter.value == 8 + + nodes = [scheduler.nodes_q.get() for _ in range(4)] + scheduler._free_node_counter.value = 4 + scheduler._map_tasks_to_nodes[1] = nodes + + result_package = pickle.dumps({"task_id": 1, "type": "result", "buffer": "Foo"}) + result_q.put(result_package) + result_received = scheduler.get_result(block=True, timeout=1) + assert result_received == result_package + + assert scheduler._free_node_counter.value == 8 + + +@pytest.mark.local +def test_MPISched_roundtrip(): + task_q, result_q = SpawnContext.Queue(), SpawnContext.Queue() + scheduler = MPITaskScheduler(task_q, result_q) + + assert scheduler.available_nodes + assert len(scheduler.available_nodes) == 8 + + for trip in range(1, 9): + assert scheduler._free_node_counter.value == 8 + + ctxt = {"resource_spec": {"num_nodes": trip, "ranks_per_node": 2}} + task_package = {"task_id": trip, "buffer": mock_task_buffer, "context": ctxt} + scheduler.put_task(task_package) + + assert scheduler._free_node_counter.value == 8 - trip + + # Pop in a mock result + result_pkl = pickle.dumps({"task_id": trip, "type": "result", "buffer": "RESULT BUF"}) + result_q.put(result_pkl) + + got_result = scheduler.get_result(True, 1) + assert got_result == result_pkl + + +@pytest.mark.local +def test_MPISched_contention(): + """Second task has to wait for the first task due to insufficient resources""" + task_q, result_q = SpawnContext.Queue(), SpawnContext.Queue() + scheduler = MPITaskScheduler(task_q, result_q) + + assert scheduler.available_nodes + assert len(scheduler.available_nodes) == 8 + + assert scheduler._free_node_counter.value == 8 + + ctxt_1 = {"resource_spec": {"num_nodes": 8, "ranks_per_node": 2}} + task_package = {"task_id": 1, "buffer": mock_task_buffer, "context": ctxt_1} + scheduler.put_task(task_package) + + assert scheduler._free_node_counter.value == 0 + assert scheduler._backlog_queue.empty() + + ctxt_2 = {"resource_spec": {"num_nodes": 8, "ranks_per_node": 2}} + task_package = {"task_id": 2, "buffer": mock_task_buffer, "context": ctxt_2} + scheduler.put_task(task_package) + + # Second task should now be in the backlog_queue + assert not scheduler._backlog_queue.empty() + + # Confirm that the first task is available and has all 8 nodes provisioned + task_on_worker_side = task_q.get() + assert task_on_worker_side['task_id'] == 1 + assert len(ctxt_1["resource_spec"]["MPI_NODELIST"].split(",")) == 8 + assert task_q.empty() # Confirm that task 2 is not yet scheduled + + # Simulate worker returning result and the scheduler picking up result + result_pkl = pickle.dumps({"task_id": 1, "type": "result", "buffer": "RESULT BUF"}) + result_q.put(result_pkl) + got_result = scheduler.get_result(True, 1) + assert got_result == result_pkl + + # Now task2 must be scheduled + assert scheduler._backlog_queue.empty() + + # Pop in a mock result + task_on_worker_side = task_q.get() + assert task_on_worker_side['task_id'] == 2 + assert len(ctxt_2["resource_spec"]["MPI_NODELIST"].split(",")) == 8 + + +@pytest.mark.local +def test_hashable_backlog_queue(): + """Run multiple large tasks that to force entry into backlog_queue + where queue.PriorityQueue expects hashability/comparability + """ + + task_q, result_q = SpawnContext.Queue(), SpawnContext.Queue() + scheduler = MPITaskScheduler(task_q, result_q) + + assert scheduler.available_nodes + assert len(scheduler.available_nodes) == 8 + + assert scheduler._free_node_counter.value == 8 + + for i in range(3): + ctxt = {"resource_spec": {"num_nodes": 8, "ranks_per_node": 2}} + task_package = {"task_id": i, "buffer": mock_task_buffer, "context": ctxt} + scheduler.put_task(task_package) + assert scheduler._backlog_queue.qsize() == 2, "Expected 2 backlogged tasks" diff --git a/parsl/parsl/tests/test_htex/mpi/test_mpiex.py b/parsl/parsl/tests/test_htex/mpi/test_mpiex.py new file mode 100644 index 00000000..e52eccd9 --- /dev/null +++ b/parsl/parsl/tests/test_htex/mpi/test_mpiex.py @@ -0,0 +1,64 @@ +"""Tests for the wrapper class""" +from inspect import signature +from pathlib import Path + +import pytest + +from parsl import Config, HighThroughputExecutor +from parsl.executors.high_throughput.mpi_executor import MPIExecutor +from parsl.launchers import SimpleLauncher +from parsl.providers import LocalProvider + +from .test_mpi_mode_enabled import get_env_vars + +cwd = Path(__file__).parent.absolute() +pbs_nodefile = cwd.joinpath("mocks", "pbs_nodefile") + + +def local_config(): + return Config( + executors=[ + MPIExecutor( + max_workers_per_block=1, + provider=LocalProvider( + worker_init=f"export PBS_NODEFILE={pbs_nodefile}", + launcher=SimpleLauncher() + ) + ) + ] + ) + + +@pytest.mark.local +def test_docstring(): + """Ensure the old kwargs are copied over into the new class""" + assert 'label' in MPIExecutor.__doc__ + assert 'max_workers_per_block' in MPIExecutor.__doc__ + assert 'available_accelerators' not in MPIExecutor.__doc__ + + +@pytest.mark.local +def test_init(): + """Ensure all relevant kwargs are copied over from HTEx""" + + new_kwargs = {'max_workers_per_block', 'mpi_launcher'} + excluded_kwargs = {'available_accelerators', 'cores_per_worker', 'max_workers_per_node', + 'mem_per_worker', 'cpu_affinity', 'manager_selector'} + + # Get the kwargs from both HTEx and MPIEx + htex_kwargs = set(signature(HighThroughputExecutor.__init__).parameters) + mpix_kwargs = set(signature(MPIExecutor.__init__).parameters) + + assert mpix_kwargs.difference(htex_kwargs) == new_kwargs + assert len(mpix_kwargs.intersection(excluded_kwargs)) == 0 + assert mpix_kwargs.union(excluded_kwargs).difference(new_kwargs) == htex_kwargs + + +@pytest.mark.local +def test_get_env(): + future = get_env_vars(parsl_resource_specification={ + "num_nodes": 3, + "ranks_per_node": 5, + }) + env_vars = future.result() + assert env_vars['PARSL_NUM_RANKS'] == '15' diff --git a/parsl/parsl/tests/test_htex/mpi/test_resource_spec.py b/parsl/parsl/tests/test_htex/mpi/test_resource_spec.py new file mode 100644 index 00000000..058a7ef4 --- /dev/null +++ b/parsl/parsl/tests/test_htex/mpi/test_resource_spec.py @@ -0,0 +1,138 @@ +import contextlib +import logging +import os +import queue +import typing +import unittest +from typing import Dict +from unittest import mock + +import pytest + +from parsl.app.app import python_app +from parsl.executors.errors import InvalidResourceSpecification +from parsl.executors.high_throughput.executor import HighThroughputExecutor +from parsl.executors.high_throughput.mpi_executor import MPIExecutor +from parsl.executors.high_throughput.mpi_resource_management import ( + get_nodes_in_batchjob, + get_pbs_hosts_list, + get_slurm_hosts_list, + identify_scheduler, +) +from parsl.launchers import SimpleLauncher +from parsl.providers import LocalProvider +from parsl.tests.configs.htex_local import fresh_config + +EXECUTOR_LABEL = "MPI_TEST" + + +def local_config(): + config = fresh_config() + config.executors[0].label = EXECUTOR_LABEL + config.executors[0].max_workers_per_node = 1 + return config + + +@python_app +def double(x, resource_spec=None): + return x * 2 + + +@python_app +def get_env_vars(parsl_resource_specification: Dict = {}) -> Dict: + import os + + parsl_vars = {} + for key in os.environ: + if key.startswith("PARSL_"): + parsl_vars[key] = os.environ[key] + return parsl_vars + + +@pytest.mark.local +@unittest.mock.patch("subprocess.check_output", return_value=b"c203-031\nc203-032\n") +def test_slurm_mocked_mpi_fetch(subprocess_check): + nodeinfo = get_slurm_hosts_list() + assert isinstance(nodeinfo, list) + assert len(nodeinfo) == 2 + + +@contextlib.contextmanager +def add_to_path(path: os.PathLike) -> typing.Generator[None, None, None]: + old_path = os.environ["PATH"] + try: + os.environ["PATH"] += str(path) + yield + finally: + os.environ["PATH"] = old_path + + +@contextlib.contextmanager +def mock_pbs_nodefile(nodefile: str = "pbs_nodefile") -> typing.Generator[None, None, None]: + cwd = os.path.abspath(os.path.dirname(__file__)) + filename = os.path.join(cwd, "mocks", nodefile) + try: + os.environ["PBS_NODEFILE"] = filename + yield + finally: + del os.environ["PBS_NODEFILE"] + + +@pytest.mark.local +def test_get_pbs_hosts_list(): + with mock_pbs_nodefile(): + nodelist = get_pbs_hosts_list() + assert nodelist + assert len(nodelist) == 4 + + +@pytest.mark.local +def test_top_level(): + with mock_pbs_nodefile(): + scheduler = identify_scheduler() + nodelist = get_nodes_in_batchjob(scheduler) + assert len(nodelist) > 0 + + +@pytest.mark.local +@pytest.mark.parametrize( + "resource_spec, exception", + ( + + ({"num_nodes": 2, "ranks_per_node": 1}, None), + ({"launcher_options": "--debug_foo"}, None), + ({"num_nodes": 2, "BAD_OPT": 1}, InvalidResourceSpecification), + ({}, InvalidResourceSpecification), + ) +) +def test_mpi_resource_spec(resource_spec: Dict, exception): + """Test validation of resource_specification in MPIExecutor""" + + mpi_ex = MPIExecutor(provider=LocalProvider(launcher=SimpleLauncher())) + mpi_ex.outgoing_q = mock.Mock(spec=queue.Queue) + + if exception: + with pytest.raises(exception): + mpi_ex.validate_resource_spec(resource_spec) + else: + result = mpi_ex.validate_resource_spec(resource_spec) + assert result is None + + +@pytest.mark.local +@pytest.mark.parametrize( + "resource_spec", + ( + {"num_nodes": 2, "ranks_per_node": 1}, + {"launcher_options": "--debug_foo"}, + {"BAD_OPT": 1}, + ) +) +def test_mpi_resource_spec_passed_to_htex(resource_spec: dict): + """HTEX should reject every resource_spec""" + + htex = HighThroughputExecutor() + htex.outgoing_q = mock.Mock(spec=queue.Queue) + + with pytest.raises(InvalidResourceSpecification): + htex.validate_resource_spec(resource_spec) diff --git a/parsl/parsl/tests/test_htex/test_affinity.py b/parsl/parsl/tests/test_htex/test_affinity.py new file mode 100644 index 00000000..792d490e --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_affinity.py @@ -0,0 +1,51 @@ +"""Tests related to assigning workers to specific compute units""" + +import os + +import pytest + +from parsl import python_app +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.providers import LocalProvider + + +def local_config(): + return Config( + executors=[ + HighThroughputExecutor( + label="htex_Local", + worker_debug=True, + max_workers_per_node=2, + cpu_affinity='block', + available_accelerators=2, + encrypted=True, + provider=LocalProvider( + init_blocks=1, + max_blocks=1, + ), + ) + ], + strategy='none', + ) + + +@python_app +def get_worker_info(): + import os + from time import sleep + rank = int(os.environ['PARSL_WORKER_RANK']) + aff = os.sched_getaffinity(0) + device = os.environ.get('CUDA_VISIBLE_DEVICES') + sleep(1.0) + return rank, (aff, device) + + +@pytest.mark.local +@pytest.mark.skipif('sched_getaffinity' not in dir(os), reason='System does not support sched_setaffinity') +@pytest.mark.skipif(os.cpu_count() == 1, reason='Must have a more than one CPU') +def test_htex(): + worker_info = [get_worker_info() for _ in range(4)] + worker_affinity = dict([r.result() for r in worker_info]) + assert worker_affinity[0] != worker_affinity[1] + assert worker_affinity[0][1] == "0" # Make sure it is pinned to the correct CUDA device diff --git a/parsl/parsl/tests/test_htex/test_basic.py b/parsl/parsl/tests/test_htex/test_basic.py new file mode 100644 index 00000000..feb8404e --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_basic.py @@ -0,0 +1,27 @@ +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.tests.configs.htex_local import fresh_config + + +def local_setup(): + config = fresh_config() + config.executors[0].poll_period = 1 + config.executors[0].max_workers_per_node = 1 + parsl.load(config) + + +def local_teardown(): + parsl.dfk().cleanup() + + +@python_app +def dummy(): + pass + + +@pytest.mark.local +def test_app(): + x = dummy() + assert x.result() is None diff --git a/parsl/parsl/tests/test_htex/test_block_manager_selector_unit.py b/parsl/parsl/tests/test_htex/test_block_manager_selector_unit.py new file mode 100644 index 00000000..46b2bb1e --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_block_manager_selector_unit.py @@ -0,0 +1,20 @@ +import pytest + +from parsl.executors.high_throughput.manager_record import ManagerRecord +from parsl.executors.high_throughput.manager_selector import BlockIdManagerSelector + + +@pytest.mark.local +def test_sort_managers(): + ready_managers = { + b'manager1': {'block_id': 1}, + b'manager2': {'block_id': None}, + b'manager3': {'block_id': 3}, + b'manager4': {'block_id': 2} + } + + manager_list = {b'manager1', b'manager2', b'manager3', b'manager4'} + expected_sorted_list = [b'manager2', b'manager1', b'manager4', b'manager3'] + manager_selector = BlockIdManagerSelector() + sorted_managers = manager_selector.sort_managers(ready_managers, manager_list) + assert sorted_managers == expected_sorted_list diff --git a/parsl/parsl/tests/test_htex/test_command_client_timeout.py b/parsl/parsl/tests/test_htex/test_command_client_timeout.py new file mode 100644 index 00000000..f003950a --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_command_client_timeout.py @@ -0,0 +1,66 @@ +import threading +import time + +import pytest +import zmq + +from parsl import curvezmq +from parsl.executors.high_throughput.errors import ( + CommandClientBadError, + CommandClientTimeoutError, +) +from parsl.executors.high_throughput.zmq_pipes import CommandClient + +# Time constant used for timeout tests: various delays and +# timeouts will be appropriate multiples of this, but the +# value of T itself should not matter too much as long as +# it is big enough for zmq connections to happen successfully. +T = 0.25 + + +@pytest.mark.local +def test_command_not_sent() -> None: + """Tests timeout on command send. + """ + # RFC6335 ephemeral port range + cc = CommandClient("127.0.0.1", (49152, 65535)) + + # cc will now wait for a connection, but we won't do anything to make the + # other side of the connection exist, so any command given to cc should + # timeout. + + with pytest.raises(CommandClientTimeoutError): + cc.run("SOMECOMMAND", timeout_s=T) + + cc.close() + + +@pytest.mark.local +def test_command_ignored() -> None: + """Tests timeout on command response. + Tests that we timeout after a response and that the command client + sets itself into a bad state. + + This only tests sequential access to the command client, even though + htex makes multithreaded use of the command client: see issue #3376 about + that lack of thread safety. + """ + # RFC6335 ephemeral port range + cc = CommandClient("127.0.0.1", (49152, 65535)) + + ic_ctx = curvezmq.ServerContext(None) + ic_channel = ic_ctx.socket(zmq.REP) + ic_channel.connect(f"tcp://127.0.0.1:{cc.port}") + + with pytest.raises(CommandClientTimeoutError): + cc.run("SLOW_COMMAND", timeout_s=T) + + req = ic_channel.recv_pyobj() + assert req == "SLOW_COMMAND", "Should have received command on interchange side" + assert not cc.ok, "CommandClient should have set itself to bad" + + with pytest.raises(CommandClientBadError): + cc.run("ANOTHER_COMMAND") + + cc.close() + ic_channel.close() diff --git a/parsl/parsl/tests/test_htex/test_command_concurrency_regression_1321.py b/parsl/parsl/tests/test_htex/test_command_concurrency_regression_1321.py new file mode 100644 index 00000000..f319789c --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_command_concurrency_regression_1321.py @@ -0,0 +1,53 @@ +import time +from threading import Event, Thread + +import pytest + +import parsl +from parsl.tests.configs.htex_local import fresh_config as local_config + +N_THREADS = 50 +DURATION_S = 10 + + +@pytest.mark.local +def test_concurrency_blast(): + """Blast interchange command channel from many threads. + """ + + cc = parsl.dfk().executors['htex_local'].command_client + + threads = [] + + ok_so_far = True + + for _ in range(N_THREADS): + + # This event will be set if the thread reaches the end of its body. + event = Event() + + thread = Thread(target=blast, args=(cc, event)) + threads.append((thread, event)) + + for thread, event in threads: + thread.start() + + for thread, event in threads: + thread.join() + if not event.is_set(): + ok_so_far = False + + assert ok_so_far, "at least one thread did not exit normally" + + +def blast(cc, e): + target_end = time.monotonic() + DURATION_S + + while time.monotonic() < target_end: + cc.run("MANGERS_PACKAGES") + cc.run("CONNECTED_BLOCKS") + cc.run("WORKER_BINDS") + + # If any of the preceeding cc.run calls raises an exception, the thread + # will not set its successful completion event. + e.set() diff --git a/parsl/parsl/tests/test_htex/test_connected_blocks.py b/parsl/parsl/tests/test_htex/test_connected_blocks.py new file mode 100644 index 00000000..94f36b35 --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_connected_blocks.py @@ -0,0 +1,62 @@ +import pytest + +import parsl +from parsl import Config +from parsl.executors import HighThroughputExecutor +from parsl.providers import LocalProvider + + +def local_config(): + return Config( + executors=[ + HighThroughputExecutor( + label="HTEX", + heartbeat_period=1, + heartbeat_threshold=2, + poll_period=100, + address="127.0.0.1", + max_workers_per_node=1, + provider=LocalProvider( + init_blocks=0, + max_blocks=2, + min_blocks=0, + ), + ) + ], + max_idletime=0.5, + strategy='htex_auto_scale', + ) + + +@parsl.python_app +def double(x): + return x * 2 + + +@pytest.mark.local +def test_get_connected_blocks(): + """Test reporting of connected blocks from HTEX""" + dfk = parsl.dfk() + executor = dfk.executors["HTEX"] + + connected_blocks = executor.connected_blocks() + assert not connected_blocks, "Expected 0 blocks" + + blocking_task = double(5).result() + assert blocking_task == 10 + + connected_blocks = executor.connected_blocks() + assert len(connected_blocks) == 1, "Expected 1 block" + + executor.scale_in(1) + + connected_blocks = executor.connected_blocks() + assert len(connected_blocks) == 1, "Expected 1 block" + + blocking_task = double(5).result() + assert blocking_task == 10 + + # With the first block scaled_in there should be reporting + # 2 blocks including the older one. + connected_blocks = executor.connected_blocks() + assert len(connected_blocks) == 2, "Expected 2 blocks" diff --git a/parsl/parsl/tests/test_htex/test_cpu_affinity_explicit.py b/parsl/parsl/tests/test_htex/test_cpu_affinity_explicit.py new file mode 100644 index 00000000..0dfef432 --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_cpu_affinity_explicit.py @@ -0,0 +1,48 @@ +import logging +import os +import random + +import pytest + +import parsl +from parsl.tests.configs.htex_local import fresh_config + +logger = logging.getLogger(__name__) + + +@parsl.python_app +def my_affinity(): + """an app that returns the affinity of the unix process it is currently in. + """ + import os + return os.sched_getaffinity(0) + + +@pytest.mark.local +@pytest.mark.multiple_cores_required +@pytest.mark.skipif('sched_getaffinity' not in dir(os), reason='System does not support sched_setaffinity') +def test_cpu_affinity_explicit(): + available_cores = os.sched_getaffinity(0) + + logger.debug(f"Got these cores: {available_cores}") + + assert len(available_cores) >= 2, "This test requires multiple cores. Run with '-k not multiple_cores' to skip" + + cores_as_list = list(available_cores) + + single_core = random.choice(cores_as_list) + affinity = f"list:{single_core}" + + logger.debug(f"Will test with affinity for one worker, one core: {affinity}") + + config = fresh_config() + config.executors[0].cpu_affinity = affinity + config.executors[0].max_workers_per_node = 1 + + logger.debug(f"config: {config}") + + with parsl.load(config): + worker_affinity = my_affinity().result() + logger.debug(f"worker reported this affinity: {worker_affinity}") + assert len(worker_affinity) == 1 + assert worker_affinity == set((single_core,)) diff --git a/parsl/parsl/tests/test_htex/test_disconnected_blocks.py b/parsl/parsl/tests/test_htex/test_disconnected_blocks.py new file mode 100644 index 00000000..2fc6c627 --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_disconnected_blocks.py @@ -0,0 +1,65 @@ +import logging + +import pytest + +import parsl +from parsl import Config +from parsl.executors import HighThroughputExecutor +from parsl.executors.errors import BadStateException +from parsl.jobs.states import JobState, JobStatus +from parsl.providers import LocalProvider + + +def local_config(): + """Config to simulate failing blocks without connecting""" + return Config( + executors=[ + HighThroughputExecutor( + label="HTEX", + heartbeat_period=1, + heartbeat_threshold=2, + poll_period=100, + max_workers_per_node=1, + provider=LocalProvider( + worker_init="conda deactivate; export PATH=''; which python; exit 0", + init_blocks=0, + max_blocks=2, + min_blocks=0, + ), + ) + ], + max_idletime=0.5, + strategy='htex_auto_scale', + ) + + +@parsl.python_app +def double(x): + return x * 2 + + +@pytest.mark.local +def test_disconnected_blocks(): + """Test reporting of blocks that fail to connect from HTEX""" + dfk = parsl.dfk() + executor = dfk.executors["HTEX"] + + connected_blocks = executor.connected_blocks() + assert not connected_blocks, "Expected 0 blocks" + + future = double(5) + with pytest.raises(BadStateException): + future.result() + + assert isinstance(future.exception(), BadStateException) + exception_body = str(future.exception()) + assert "EXIT CODE: 0" in exception_body + + status_dict = executor.status() + assert len(status_dict) == 1, "Expected only 1 block" + for status in status_dict.values(): + assert isinstance(status, JobStatus) + assert status.state == JobState.MISSING + + connected_blocks = executor.connected_blocks() + assert not connected_blocks, "Expected 0 blocks" diff --git a/parsl/parsl/tests/test_htex/test_disconnected_blocks_failing_provider.py b/parsl/parsl/tests/test_htex/test_disconnected_blocks_failing_provider.py new file mode 100644 index 00000000..b2fa507a --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_disconnected_blocks_failing_provider.py @@ -0,0 +1,71 @@ +import logging + +import pytest + +import parsl +from parsl import Config +from parsl.executors import HighThroughputExecutor +from parsl.executors.errors import BadStateException +from parsl.jobs.states import JobState, JobStatus +from parsl.providers import LocalProvider + + +class FailingProvider(LocalProvider): + def submit(*args, **kwargs): + raise RuntimeError("Deliberate failure of provider.submit") + + +def local_config(): + """Config to simulate failing blocks without connecting""" + return Config( + executors=[ + HighThroughputExecutor( + label="HTEX", + heartbeat_period=1, + heartbeat_threshold=2, + poll_period=100, + max_workers_per_node=1, + provider=FailingProvider( + init_blocks=0, + max_blocks=2, + min_blocks=0, + ), + ) + ], + max_idletime=0.5, + strategy='htex_auto_scale', + strategy_period=0.1 + # this strategy period needs to be a few times smaller than the + # status_polling_interval of FailingProvider, which is 5s at + # time of writing + ) + + +@parsl.python_app +def double(x): + return x * 2 + + +@pytest.mark.local +def test_disconnected_blocks(): + """Test reporting of blocks that fail to connect from HTEX""" + dfk = parsl.dfk() + executor = dfk.executors["HTEX"] + + connected_blocks = executor.connected_blocks() + assert not connected_blocks, "Expected 0 blocks" + + future = double(5) + with pytest.raises(BadStateException): + future.result() + + assert isinstance(future.exception(), BadStateException) + + status_dict = executor.status() + assert len(status_dict) == 1, "Expected exactly 1 block" + for status in status_dict.values(): + assert isinstance(status, JobStatus) + assert status.state == JobState.MISSING + + connected_blocks = executor.connected_blocks() + assert connected_blocks == [], "Expected exactly 0 connected blocks" diff --git a/parsl/parsl/tests/test_htex/test_drain.py b/parsl/parsl/tests/test_htex/test_drain.py new file mode 100644 index 00000000..663978f4 --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_drain.py @@ -0,0 +1,79 @@ +import time + +import pytest + +import parsl +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SimpleLauncher +from parsl.providers import LocalProvider + +# this constant is used to scale some durations that happen +# based around the expected drain period: the drain period +# is TIME_CONST seconds, and the single executed task will +# last twice that many number of seconds. +TIME_CONST = 4 + +CONNECTED_MANAGERS_POLL_MS = 100 + + +def local_config(): + return Config( + executors=[ + HighThroughputExecutor( + label="htex_local", + drain_period=TIME_CONST, + worker_debug=True, + cores_per_worker=1, + encrypted=True, + provider=LocalProvider( + init_blocks=1, + min_blocks=0, + max_blocks=0, + launcher=SimpleLauncher(), + ), + ) + ], + strategy='none', + strategy_period=0.1 + ) + + +@parsl.python_app +def f(n): + import time + time.sleep(n) + + +@pytest.mark.local +def test_drain(try_assert): + + htex = parsl.dfk().executors['htex_local'] + + # wait till we have a block running... + + try_assert(lambda: len(htex.connected_managers()) == 1, check_period_ms=CONNECTED_MANAGERS_POLL_MS) + + managers = htex.connected_managers() + assert managers[0]['active'], "The manager should be active" + assert not managers[0]['draining'], "The manager should not be draining" + + fut = f(TIME_CONST * 2) + + time.sleep(TIME_CONST) + + # this assert should happen *very fast* after the above delay... + try_assert(lambda: htex.connected_managers()[0]['draining'], timeout_ms=500, check_period_ms=CONNECTED_MANAGERS_POLL_MS) + + # and the test task should still be running... + assert not fut.done(), "The test task should still be running" + + fut.result() + + # and now we should see the manager disappear... + # ... with strategy='none', this should be coming from draining but + # that information isn't immediately obvious from the absence in + # connected managers. + # As with the above draining assert, this should happen very fast after + # the task ends. + try_assert(lambda: len(htex.connected_managers()) == 0, timeout_ms=500, check_period_ms=CONNECTED_MANAGERS_POLL_MS) diff --git a/parsl/parsl/tests/test_htex/test_htex.py b/parsl/parsl/tests/test_htex/test_htex.py new file mode 100644 index 00000000..a8d9e86b --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_htex.py @@ -0,0 +1,185 @@ +import logging +import pathlib +from subprocess import Popen, TimeoutExpired +from typing import Optional, Sequence +from unittest import mock + +import pytest + +from parsl import HighThroughputExecutor, curvezmq +from parsl.serialize.facade import pack_apply_message, unpack_apply_message + +_MOCK_BASE = "parsl.executors.high_throughput.executor" + + +@pytest.fixture +def encrypted(request: pytest.FixtureRequest): + if hasattr(request, "param"): + return request.param + return True + + +@pytest.fixture +def htex(encrypted: bool, tmpd_cwd): + htex = HighThroughputExecutor(encrypted=encrypted) + htex.max_workers_per_node = 1 + htex.run_dir = tmpd_cwd + + assert htex.provider is not None, "htex always has a provider" + htex.provider.script_dir = tmpd_cwd + + yield htex + + if hasattr(htex, "outgoing_q"): + htex.scale_in(blocks=1000) + htex.shutdown() + + +@pytest.mark.local +@pytest.mark.parametrize("encrypted", (True, False), indirect=True) +@pytest.mark.parametrize("cert_dir_provided", (True, False)) +def test_htex_start_encrypted( + encrypted: bool, + cert_dir_provided: bool, + htex: HighThroughputExecutor, + tmpd_cwd: pathlib.Path, +): + htex.run_dir = str(tmpd_cwd) + if cert_dir_provided: + provided_base_dir = tmpd_cwd / "provided" + provided_base_dir.mkdir() + cert_dir = curvezmq.create_certificates(provided_base_dir) + htex.cert_dir = cert_dir + else: + cert_dir = curvezmq.create_certificates(htex.logdir) + + if not encrypted and cert_dir_provided: + with pytest.raises(AttributeError) as pyt_e: + htex.start() + assert "change cert_dir to None" in str(pyt_e.value) + return + + htex.start() + + assert htex.encrypted is encrypted + if encrypted: + assert htex.cert_dir == cert_dir + assert htex.outgoing_q.zmq_context.cert_dir == cert_dir + assert htex.incoming_q.zmq_context.cert_dir == cert_dir + assert htex.command_client.zmq_context.cert_dir == cert_dir + assert isinstance(htex.outgoing_q.zmq_context, curvezmq.ClientContext) + assert isinstance(htex.incoming_q.zmq_context, curvezmq.ClientContext) + assert isinstance(htex.command_client.zmq_context, curvezmq.ClientContext) + else: + assert htex.cert_dir is None + assert htex.outgoing_q.zmq_context.cert_dir is None + assert htex.incoming_q.zmq_context.cert_dir is None + assert htex.command_client.zmq_context.cert_dir is None + + +@pytest.mark.local +@pytest.mark.parametrize("started", (True, False)) +@pytest.mark.parametrize("timeout_expires", (True, False)) +def test_htex_shutdown( + started: bool, + timeout_expires: bool, + htex: HighThroughputExecutor, + caplog +): + mock_ix_proc = mock.Mock(spec=Popen) + + if started: + htex.interchange_proc = mock_ix_proc + + # This will, in the absence of any exit trigger, block forever if + # no timeout is given and if the interchange does not terminate. + # Raise an exception to report that, rather than actually block, + # and hope that nothing is catching that exception. + + # this function implements the behaviour if the interchange has + # not received a termination call + def proc_wait_alive(timeout): + if timeout: + raise TimeoutExpired(cmd="mock-interchange", timeout=timeout) + else: + raise RuntimeError("This wait call would hang forever") + + def proc_wait_terminated(timeout): + return 0 + + mock_ix_proc.wait.side_effect = proc_wait_alive + + if not timeout_expires: + # Simulate termination of the Interchange process + def kill_interchange(*args, **kwargs): + mock_ix_proc.wait.side_effect = proc_wait_terminated + + mock_ix_proc.terminate.side_effect = kill_interchange + + with caplog.at_level(logging.INFO): + htex.shutdown() + + if started: + assert mock_ix_proc.terminate.called + assert mock_ix_proc.wait.called + assert {"timeout": 10} == mock_ix_proc.wait.call_args[1] + if timeout_expires: + assert "Unable to terminate Interchange" in caplog.text + assert mock_ix_proc.kill.called + assert "Attempting HighThroughputExecutor shutdown" in caplog.text + assert "Finished HighThroughputExecutor shutdown" in caplog.text + else: + assert not mock_ix_proc.terminate.called + assert not mock_ix_proc.wait.called + assert "HighThroughputExecutor has not started" in caplog.text + + +@pytest.mark.local +@pytest.mark.parametrize("cmd", (None, "custom-launch-cmd")) +def test_htex_worker_pool_launch_cmd(cmd: Optional[str]): + if cmd: + htex = HighThroughputExecutor(launch_cmd=cmd) + assert htex.launch_cmd == cmd + else: + htex = HighThroughputExecutor() + assert htex.launch_cmd.startswith("process_worker_pool.py") + + +@pytest.mark.local +@pytest.mark.parametrize("cmd", (None, ["custom", "launch", "cmd"])) +def test_htex_interchange_launch_cmd(cmd: Optional[Sequence[str]]): + if cmd: + htex = HighThroughputExecutor(interchange_launch_cmd=cmd) + assert htex.interchange_launch_cmd == cmd + else: + htex = HighThroughputExecutor() + assert htex.interchange_launch_cmd == ["interchange.py"] + + +def dyn_exec(buf, *vec_y): + f, a, _ = unpack_apply_message(buf) + custom_args = [a, vec_y] + return f(*custom_args) + + +@pytest.mark.local +def test_worker_dynamic_import(htex: HighThroughputExecutor): + def _dot_prod(vec_x, vec_y): + return sum(x * y for x, y in zip(vec_x, vec_y)) + + htex.start() + htex.scale_out_facade(1) + + num_array = tuple(range(10)) + + fn_buf = pack_apply_message(_dot_prod, num_array, {}) + ctxt = { + "task_executor": { + "f": f"{dyn_exec.__module__}.{dyn_exec.__name__}", + "a": num_array, # prove "custom" dyn_exec + } + } + val = htex.submit_payload(ctxt, fn_buf).result() + exp_val = _dot_prod(num_array, num_array) + + assert val == exp_val diff --git a/parsl/parsl/tests/test_htex/test_interchange_exit_bad_registration.py b/parsl/parsl/tests/test_htex/test_interchange_exit_bad_registration.py new file mode 100644 index 00000000..196c24ca --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_interchange_exit_bad_registration.py @@ -0,0 +1,241 @@ +import logging +import os +import pickle +import platform +import subprocess +import sys +import time + +import psutil +import pytest +import zmq + +import parsl.executors.high_throughput.zmq_pipes as zmq_pipes +from parsl.executors.high_throughput.executor import DEFAULT_INTERCHANGE_LAUNCH_CMD +from parsl.executors.high_throughput.manager_selector import RandomManagerSelector +from parsl.version import VERSION as PARSL_VERSION + +P_ms = 10 + + +@pytest.mark.local +def test_exit_with_bad_registration(tmpd_cwd, try_assert): + """Test that the interchange exits when it receives a bad registration message. + This complements parsl/tests/test_scaling/test_worker_interchange_bad_messages_3262.py + which tests that the interchange is resistent to other forms of bad message. + """ + + outgoing_q = zmq_pipes.TasksOutgoing( + "127.0.0.1", (49152, 65535), None + ) + incoming_q = zmq_pipes.ResultsIncoming( + "127.0.0.1", (49152, 65535), None + ) + command_client = zmq_pipes.CommandClient( + "127.0.0.1", (49152, 65535), None + ) + + interchange_config = {"client_address": "127.0.0.1", + "client_ports": (outgoing_q.port, + incoming_q.port, + command_client.port), + "interchange_address": "127.0.0.1", + "worker_port": None, + "worker_port_range": (50000, 60000), + "hub_address": None, + "hub_zmq_port": None, + "logdir": tmpd_cwd, + "log_config": None, + "heartbeat_threshold": 120, + "poll_period": P_ms, + "logging_level": logging.DEBUG, + "cert_dir": None, + "manager_selector": RandomManagerSelector(), + "run_id": "test", + "_check_python_mismatch": True, + } + + config_pickle = pickle.dumps(interchange_config) + + interchange_proc = subprocess.Popen(DEFAULT_INTERCHANGE_LAUNCH_CMD, stdin=subprocess.PIPE) + stdin = interchange_proc.stdin + assert stdin is not None, "Popen should have created an IO object (vs default None) because of PIPE mode" + + stdin.write(config_pickle) + stdin.flush() + stdin.close() + + # wait for interchange to be alive, by waiting for the command thread to become + # responsive. if the interchange process didn't start enough to get the command + # thread running, this will time out. + + worker_port = command_client.run("WORKER_BINDS", timeout_s=120) + + # now we'll assume that if the interchange command thread is responding, + # then the worker polling code is also running and that the interchange has + # started successfully. + + # send bad registration message as if from a new worker pool. The badness here + # is that the Python version does not match the real Python version - which + # unlike some other bad interchange messages, should cause the interchange + # to shut down. + + msg = {'type': 'registration', + 'parsl_v': PARSL_VERSION, + 'python_v': "1.1.1", # this is the bad bit + 'worker_count': 1, + 'uid': 'testuid', + 'block_id': 0, + 'start_time': time.time(), + 'drain_time': float('inf'), + 'prefetch_capacity': 0, + 'max_capacity': 1, + 'os': platform.system(), + 'hostname': platform.node(), + 'dir': os.getcwd(), + 'cpu_count': psutil.cpu_count(logical=False), + 'total_memory': psutil.virtual_memory().total, + } + + # connect to worker port and send this message. + + context = zmq.Context() + channel_timeout = 10000 # in milliseconds + task_channel = context.socket(zmq.DEALER) + task_channel.setsockopt(zmq.LINGER, 0) + task_channel.setsockopt(zmq.IDENTITY, b'testid') + + task_channel.set_hwm(0) + task_channel.setsockopt(zmq.SNDTIMEO, channel_timeout) + task_channel.connect(f"tcp://127.0.0.1:{worker_port}") + + task_channel.send(pickle.dumps(msg)) + + # check that the interchange exits within some reasonable time + try_assert(lambda: interchange_proc.poll() is not None, "Interchange did not exit after killing watched client process", timeout_ms=5000) + + # See issue #3697 - ideally the interchange would exit cleanly, but it does not. + # assert interchange_proc.poll() == 0, "Interchange exited with an error code, not 0" + + task_channel.close() + context.term() + + +@pytest.mark.local +@pytest.mark.parametrize( + "worker_version_info", [ + {"parsl_v": PARSL_VERSION, + "python_v": "0.0.0", # python_v mismatch + }, + {"parsl_v": "0.0.0", # parsl_v mismatch + "python_v": "{}.{}.{}".format(sys.version_info.major, + sys.version_info.minor, + sys.version_info.micro) + }, + {"parsl_v": "0.0.0", # parsl_v mismatch and + "python_v": "0.0.0", # python_v mismatch + }] +) +def test_ignore_version_check_at_registration(tmpd_cwd, try_assert, worker_version_info): + """Test that the interchange will ignore version checks + + Uses _check_python_version flag to indicate that version mismatches should + be ignored. + """ + + outgoing_q = zmq_pipes.TasksOutgoing( + "127.0.0.1", (49152, 65535), None + ) + incoming_q = zmq_pipes.ResultsIncoming( + "127.0.0.1", (49152, 65535), None + ) + command_client = zmq_pipes.CommandClient( + "127.0.0.1", (49152, 65535), None + ) + + interchange_config = {"client_address": "127.0.0.1", + "client_ports": (outgoing_q.port, + incoming_q.port, + command_client.port), + "interchange_address": "127.0.0.1", + "worker_port": None, + "worker_port_range": (50000, 60000), + "hub_address": None, + "hub_zmq_port": None, + "logdir": tmpd_cwd, + "log_config": None, + "heartbeat_threshold": 120, + "poll_period": P_ms, + "logging_level": logging.DEBUG, + "cert_dir": None, + "manager_selector": RandomManagerSelector(), + "run_id": "test", + "_check_python_mismatch": False, + } + + config_pickle = pickle.dumps(interchange_config) + + interchange_proc = subprocess.Popen(DEFAULT_INTERCHANGE_LAUNCH_CMD, stdin=subprocess.PIPE) + stdin = interchange_proc.stdin + assert stdin is not None, "Popen should have created an IO object (vs default None) because of PIPE mode" + + stdin.write(config_pickle) + stdin.flush() + stdin.close() + + # wait for interchange to be alive, by waiting for the command thread to become + # responsive. if the interchange process didn't start enough to get the command + # thread running, this will time out. + + worker_port = command_client.run("WORKER_BINDS", timeout_s=120) + + # now we'll assume that if the interchange command thread is responding, + # then the worker polling code is also running and that the interchange has + # started successfully. + + # send bad registration message as if from a new worker pool. The badness here + # is that the Python version does not match the real Python version - which + # unlike some other bad interchange messages, should cause the interchange + # to shut down. + + msg = {'type': 'registration', + 'worker_count': 1, + 'uid': 'testuid', + 'block_id': 0, + 'start_time': time.time(), + 'drain_time': float('inf'), + 'prefetch_capacity': 0, + 'max_capacity': 1, + 'os': platform.system(), + 'hostname': platform.node(), + 'dir': os.getcwd(), + 'cpu_count': psutil.cpu_count(logical=False), + 'total_memory': psutil.virtual_memory().total, + **worker_version_info, + } + + # connect to worker port and send this message. + + context = zmq.Context() + channel_timeout = 10000 # in milliseconds + task_channel = context.socket(zmq.DEALER) + task_channel.setsockopt(zmq.LINGER, 0) + task_channel.setsockopt(zmq.IDENTITY, b'testid') + + task_channel.set_hwm(0) + task_channel.setsockopt(zmq.SNDTIMEO, channel_timeout) + task_channel.connect(f"tcp://127.0.0.1:{worker_port}") + + task_channel.send(pickle.dumps(msg)) + + # Run a command against the interchange to confirm that the interchange has + # not failed, this call will hang if the interchange exits early + try_assert( + lambda: len(command_client.run("MANAGERS", max_retries=0, timeout_s=1)) == 1, + "Interchange did not accept the version-mismatched registration", + timeout_ms=5000, + ) + + interchange_proc.terminate() + task_channel.close() + context.term() diff --git a/parsl/parsl/tests/test_htex/test_log_config.py b/parsl/parsl/tests/test_htex/test_log_config.py new file mode 100644 index 00000000..8a9ac727 --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_log_config.py @@ -0,0 +1,60 @@ +import pathlib + +import pytest + +import parsl +from parsl.executors.high_throughput.executor import HighThroughputExecutor +from parsl.logconfigs.file import FileLogging + + +@parsl.python_app +def noop_app(): + pass + + +@pytest.mark.local +def test_interchange_log_default(tmpd_cwd): + with parsl.load(parsl.Config(run_dir=str(tmpd_cwd), + executors=[HighThroughputExecutor(label="htex")])): + assert (pathlib.Path(parsl.dfk().run_dir) / "htex" / "interchange.log").exists(), \ + "By default, interchange.log should exist in executor directory after startup" + + +@pytest.mark.local +def test_log_fileconfig(tmpd_cwd): + # this arbitrary token should flow via the format_string parameter into the + # resulting text-format log file, and the test will check it arrives there. + token = "XXXXYYYYY" + msg = f"{token} %(message)s" + with parsl.load(parsl.Config(initialize_logging=FileLogging(format_string=msg), + run_dir=str(tmpd_cwd), + executors=[HighThroughputExecutor(label="htex", max_workers_per_node=1)])): + # ensure that enough has started to run a task - so that includes everything + # all the way to a worker, on the task execution path + noop_app().result() + + interchange_logfile = pathlib.Path(parsl.dfk().run_dir) / "htex" / "interchange.log" + + # assume in this configuration that there is only one subdirectory of block-0, + # that it is the directory for a started worker pool, and that the worker that completed + # the above app invocation is worker 0 (the only worker). + pool_dir = next((pathlib.Path(parsl.dfk().run_dir) / "htex" / "block-0").iterdir()) + manager_logfile = pool_dir / "manager.log" + worker_logfile = pool_dir / "worker_0.log" + + assert interchange_logfile.exists(), \ + "interchange.log should exist after startup" + assert manager_logfile.exists(), \ + "a manager log file should exist after startup" + assert worker_logfile.exists(), \ + "a worker log file should exist after startup" + + def check_file(logfile: pathlib.Path): + with open(logfile, "r") as f: + ls = f.readlines() + assert len(ls) >= 1, f"There should be at least one log entry in {logfile}" + assert ls[0].startswith(token), f"Log entry in {logfile} should be formatted with token from format_string parameter" + + check_file(interchange_logfile) + check_file(manager_logfile) + check_file(worker_logfile) diff --git a/parsl/parsl/tests/test_htex/test_manager_failure.py b/parsl/parsl/tests/test_htex/test_manager_failure.py new file mode 100644 index 00000000..b07b62d3 --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_manager_failure.py @@ -0,0 +1,51 @@ +import os +import signal +import time + +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.tests.configs.htex_local import fresh_config + + +@pytest.fixture(autouse=True, scope="function") +def load_config(): + config = fresh_config() + config.executors[0].poll_period = 1 + config.executors[0].max_workers_per_node = 1 + config.executors[0].heartbeat_period = 1 + + parsl.load(config) + yield + + parsl.dfk().cleanup() + + +@python_app +def get_worker_pid(): + import os + return os.getpid() + + +@python_app +def kill_manager(sig: int): + import os + os.kill(os.getppid(), sig) + + +@pytest.mark.local +@pytest.mark.parametrize("sig", [signal.SIGTERM, signal.SIGKILL]) +def test_htex_manager_failure_worker_shutdown(sig: int): + """Ensure that HTEX workers shut down when the Manager process dies.""" + worker_pid = get_worker_pid().result() + + kill_manager(sig) + + with pytest.raises(OSError): + end = time.monotonic() + 5 + while time.monotonic() < end: + # Raises an exception if the process + # does not exist + os.kill(worker_pid, 0) + time.sleep(.1) diff --git a/parsl/parsl/tests/test_htex/test_manager_selector_by_block.py b/parsl/parsl/tests/test_htex/test_manager_selector_by_block.py new file mode 100644 index 00000000..1b2a4ee1 --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_manager_selector_by_block.py @@ -0,0 +1,51 @@ +import time + +import pytest + +import parsl +from parsl.app.app import bash_app, python_app +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.executors.high_throughput.manager_selector import ( + BlockIdManagerSelector, + ManagerSelector, +) +from parsl.launchers import WrappedLauncher +from parsl.providers import LocalProvider +from parsl.usage_tracking.levels import LEVEL_1 + +BLOCK_COUNT = 2 + + +@parsl.python_app +def get_worker_pid(): + import os + return os.environ.get('PARSL_WORKER_BLOCK_ID') + + +@pytest.mark.local +def test_block_id_selection(try_assert): + htex = HighThroughputExecutor( + label="htex_local", + max_workers_per_node=1, + manager_selector=BlockIdManagerSelector(), + provider=LocalProvider( + init_blocks=BLOCK_COUNT, + max_blocks=BLOCK_COUNT, + min_blocks=BLOCK_COUNT, + ), + ) + + config = Config( + executors=[htex], + usage_tracking=LEVEL_1, + ) + + with parsl.load(config): + blockids = [] + try_assert(lambda: len(htex.connected_managers()) == BLOCK_COUNT, timeout_ms=20000) + for i in range(10): + future = get_worker_pid() + blockids.append(future.result()) + + assert all(blockid == "1" for blockid in blockids) diff --git a/parsl/parsl/tests/test_htex/test_managers_command.py b/parsl/parsl/tests/test_htex/test_managers_command.py new file mode 100644 index 00000000..5d60213a --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_managers_command.py @@ -0,0 +1,54 @@ +import sys + +import packaging.version +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.tests.configs.htex_local import fresh_config + + +def local_config(): + config = fresh_config() + config.executors[0].poll_period = 1 + config.executors[0].max_workers_per_node = 1 + return config + + +@python_app +def dummy(): + pass + + +@pytest.mark.local +def test_connected_managers(): + + # Run dummy function to ensure a manager is online + x = dummy() + assert x.result() is None + executor = parsl.dfk().executors['htex_local'] + manager_info_list = executor.connected_managers() + assert len(manager_info_list) == 1 + manager_info = manager_info_list[0] + assert 'python_version' in manager_info + assert 'parsl_version' in manager_info + assert manager_info['parsl_version'] == parsl.__version__ + assert manager_info['python_version'] == f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}" + + +@pytest.mark.local +def test_connected_managers_packages(): + # Run dummy function to ensure a manager is online + f = dummy() + assert f.result() is None + + htex = parsl.dfk().executors['htex_local'] + assert isinstance(htex, parsl.HighThroughputExecutor) + managers_info_list = htex.connected_managers() + managers_packages = htex.connected_managers_packages() + + assert len(managers_packages) == len(managers_info_list) == 1 + manager_id, packages = list(managers_packages.items())[0] + assert manager_id == managers_info_list[0]['manager'] + normalized_version = str(packaging.version.parse(parsl.__version__)) + assert packages['parsl'] == normalized_version diff --git a/parsl/parsl/tests/test_htex/test_maxworker_computation_executor.py b/parsl/parsl/tests/test_htex/test_maxworker_computation_executor.py new file mode 100644 index 00000000..5c9f26e4 --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_maxworker_computation_executor.py @@ -0,0 +1,77 @@ +import random + +import pytest + +import parsl +from parsl.config import Config +from parsl.executors.high_throughput.executor import HighThroughputExecutor +from parsl.providers import LocalProvider + + +@pytest.mark.local +def test_max_workers_per_node(): + + w = random.randint(1, 8) + h = HighThroughputExecutor(max_workers_per_node=w, + provider=LocalProvider(max_blocks=0, init_blocks=0, min_blocks=0) + ) + c = Config(executors=[h]) + + with parsl.load(c): + assert h.max_workers_per_node == w, "max_workers_per_node should be wired all the way through" + assert h.workers_per_node == w, "max_workers_per_node is chosen in the absence of other restrictions" + + +@pytest.mark.local +def test_cores(): + + p = LocalProvider(max_blocks=0, init_blocks=0, min_blocks=0) + p.cores_per_node = 8 + + h = HighThroughputExecutor(max_workers_per_node=16, + provider=p, + cores_per_worker=4 + ) + c = Config(executors=[h]) + + with parsl.load(c): + assert h.cores_per_worker == 4, "cores_per_worker should be wired all the way through" + assert h.workers_per_node == 2, "8 cores per node, 4 cores per worker => 2 workers" + + +@pytest.mark.local +def test_mem(): + + p = LocalProvider(max_blocks=0, init_blocks=0, min_blocks=0) + p.cores_per_node = 8 + p.mem_per_node = 16 + + h = HighThroughputExecutor(max_workers_per_node=16, + provider=p, + cores_per_worker=4, + mem_per_worker=16 + ) + c = Config(executors=[h]) + + with parsl.load(c): + assert h.mem_per_worker == 16, "mem_per_worker should be wired all the way through" + assert h.workers_per_node == 1, "one worker gets all the memory" + + +@pytest.mark.local +def test_accelerators(): + + p = LocalProvider(max_blocks=0, init_blocks=0, min_blocks=0) + p.cores_per_node = 16 + p.mem_per_node = 16 + + h = HighThroughputExecutor(max_workers_per_node=16, + provider=p, + cores_per_worker=4, + mem_per_worker=2, + available_accelerators=3 + ) + c = Config(executors=[h]) + + with parsl.load(c): + assert h.workers_per_node == 3, "number of accelerators is worker constraining limit here" diff --git a/parsl/parsl/tests/test_htex/test_missing_worker.py b/parsl/parsl/tests/test_htex/test_missing_worker.py new file mode 100644 index 00000000..f74ab0c3 --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_missing_worker.py @@ -0,0 +1,33 @@ +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.tests.configs.htex_local import fresh_config + + +def local_config(): + config = fresh_config() + config.executors[0].poll_period = 1 + config.executors[0].max_workers_per_node = 1 + config.executors[0].launch_cmd = "executable_that_hopefully_does_not_exist_1030509.py" + return config + + +@python_app +def dummy(): + pass + + +@pytest.mark.local +def test_that_it_fails(): + x = dummy() + failed = False + try: + x.result() + except Exception as ex: + print(ex) + failed = True + if not failed: + raise Exception("The app somehow ran without a valid worker") + + assert parsl.dfk().config.executors[0]._executor_bad_state.is_set() diff --git a/parsl/parsl/tests/test_htex/test_multiple_disconnected_blocks.py b/parsl/parsl/tests/test_htex/test_multiple_disconnected_blocks.py new file mode 100644 index 00000000..4168f41b --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_multiple_disconnected_blocks.py @@ -0,0 +1,67 @@ +import logging + +import pytest + +import parsl +from parsl import Config +from parsl.executors import HighThroughputExecutor +from parsl.executors.errors import BadStateException +from parsl.jobs.states import JobState, JobStatus +from parsl.providers import LocalProvider + + +def local_config(): + """Config to simulate failing blocks without connecting""" + return Config( + executors=[ + HighThroughputExecutor( + label="HTEX", + heartbeat_period=1, + heartbeat_threshold=2, + poll_period=100, + max_workers_per_node=1, + provider=LocalProvider( + worker_init="exit 0", + init_blocks=2 + ), + ) + ], + run_dir="/tmp/test_htex", + max_idletime=0.5, + strategy='none', + ) + + +@parsl.python_app +def double(x): + return x * 2 + + +@pytest.mark.local +def test_multiple_disconnected_blocks(): + """Test reporting of blocks that fail to connect from HTEX + When init_blocks == N, error handling expects N failures before + the run is cancelled + """ + dfk = parsl.dfk() + executor = dfk.executors["HTEX"] + + connected_blocks = executor.connected_blocks() + assert not connected_blocks, "Expected 0 blocks" + + future = double(5) + with pytest.raises(BadStateException): + future.result() + + assert isinstance(future.exception(), BadStateException) + exception_body = str(future.exception()) + assert "EXIT CODE: 0" in exception_body + + status_dict = executor.status() + assert len(status_dict) == 2, "Expected 2 blocks" + for status in status_dict.values(): + assert isinstance(status, JobStatus) + assert status.state == JobState.MISSING + + connected_blocks = executor.connected_blocks() + assert not connected_blocks, "Expected 0 blocks" diff --git a/parsl/parsl/tests/test_htex/test_priority_queue.py b/parsl/parsl/tests/test_htex/test_priority_queue.py new file mode 100644 index 00000000..91a6d458 --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_priority_queue.py @@ -0,0 +1,93 @@ +from random import randint + +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.executors.high_throughput.manager_selector import RandomManagerSelector +from parsl.providers import LocalProvider +from parsl.usage_tracking.levels import LEVEL_1 + + +@python_app +def fake_task(parsl_resource_specification=None): + import time + return time.time() + + +@pytest.mark.local +def test_priority_queue(try_assert): + provider = LocalProvider( + init_blocks=0, + max_blocks=0, + min_blocks=0, + ) + + htex = HighThroughputExecutor( + label="htex_local", + max_workers_per_node=1, + manager_selector=RandomManagerSelector(), + provider=provider, + worker_debug=True, # needed to instrospect interchange logs + ) + + config = Config( + executors=[htex], + strategy="htex_auto_scale", + usage_tracking=LEVEL_1, + ) + + with parsl.load(config): + futures = {} + + # Submit tasks with mixed priorities + # Test fallback behavior with a guaranteed-unsorted priorities + priorities = [randint(2, 9) for _ in range(randint(1, 10))] + priorities.insert(0, 10) + priorities.extend((1, 10, 1)) + for i, priority in enumerate(priorities): + spec = {'priority': priority} + futures[(priority, i)] = fake_task(parsl_resource_specification=spec) + + # wait for the interchange to have received all tasks + # (which happens asynchronously to the main thread, and is otherwise + # a race condition which can cause this test to fail) + + n = len(priorities) + + def interchange_logs_task_count(): + with open(htex.worker_logdir + "/interchange.log", "r") as f: + lines = f.readlines() + for line in lines: + if f"Put task {n} onto pending_task_queue" in line: + return True + return False + + try_assert(interchange_logs_task_count) + + provider.max_blocks = 1 + htex.scale_out_facade(1) # don't wait for the JSP to catch up + + # Wait for completion + results = { + key: future.result() for key, future in futures.items() + } + + # Sort by finish time + sorted_by_completion = sorted(results.items(), key=lambda item: item[1]) + execution_order = [key for key, _ in sorted_by_completion] + + # check priority queue functionality + priorities_only = [p for (p, i) in execution_order] + assert priorities_only == sorted(priorities_only), "Priority execution order failed" + + # check FIFO fallback + from collections import defaultdict + seen = defaultdict(list) + for (priority, idx) in execution_order: + seen[priority].append(idx) + + for priority, indices in seen.items(): + assert indices == sorted(indices), f"FIFO fallback violated for priority {priority}" diff --git a/parsl/parsl/tests/test_htex/test_resource_spec_validation.py b/parsl/parsl/tests/test_htex/test_resource_spec_validation.py new file mode 100644 index 00000000..4cf717b2 --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_resource_spec_validation.py @@ -0,0 +1,45 @@ +import queue +from unittest import mock + +import pytest + +from parsl.executors import HighThroughputExecutor +from parsl.executors.errors import InvalidResourceSpecification + + +def double(x): + return x * 2 + + +@pytest.mark.local +def test_submit_calls_validate(): + + htex = HighThroughputExecutor() + htex.outgoing_q = mock.Mock(spec=queue.Queue) + htex.validate_resource_spec = mock.Mock(spec=htex.validate_resource_spec) + + res_spec = {} + htex.submit(double, res_spec, (5,), {}) + htex.validate_resource_spec.assert_called() + + +@pytest.mark.local +def test_resource_spec_validation(): + htex = HighThroughputExecutor() + ret_val = htex.validate_resource_spec({}) + assert ret_val is None + + +@pytest.mark.local +def test_resource_spec_validation_bad_keys(): + htex = HighThroughputExecutor() + + with pytest.raises(InvalidResourceSpecification): + htex.validate_resource_spec({"num_nodes": 2}) + + +@pytest.mark.local +def test_resource_spec_validation_one_key(): + htex = HighThroughputExecutor() + ret_val = htex.validate_resource_spec({"priority": 2}) + assert ret_val is None diff --git a/parsl/parsl/tests/test_htex/test_worker_failure.py b/parsl/parsl/tests/test_htex/test_worker_failure.py new file mode 100644 index 00000000..ee24fc7c --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_worker_failure.py @@ -0,0 +1,26 @@ +import pytest + +from parsl.app.app import python_app +from parsl.executors.high_throughput.errors import WorkerLost + + +def local_config(): + from parsl.tests.configs.htex_local import fresh_config + config = fresh_config() + config.executors[0].poll_period = 1 + config.executors[0].max_workers_per_node = 1 + config.executors[0].heartbeat_period = 1 + return config + + +@python_app +def kill_worker(): + raise SystemExit(2) + + +@pytest.mark.local +def test_htex_worker_failures(): + for _ in range(3): + with pytest.raises(WorkerLost): + f = kill_worker() + f.result() diff --git a/parsl/parsl/tests/test_htex/test_worker_info.py b/parsl/parsl/tests/test_htex/test_worker_info.py new file mode 100644 index 00000000..e55064a5 --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_worker_info.py @@ -0,0 +1,44 @@ +"""Tests related to Parsl workers being able to access their worker ID""" + +import pytest + +from parsl import python_app +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.providers import LocalProvider + + +def local_config(): + return Config( + executors=[ + HighThroughputExecutor( + label="htex_Local", + worker_debug=True, + max_workers_per_node=4, + encrypted=True, + provider=LocalProvider( + init_blocks=1, + max_blocks=1, + ), + ) + ], + strategy='none', + ) + + +@python_app +def get_worker_info(): + import os + rank = int(os.environ['PARSL_WORKER_RANK']) + size = int(os.environ['PARSL_WORKER_COUNT']) + pool_id = os.environ['PARSL_WORKER_POOL_ID'] + return rank, size, pool_id + + +@pytest.mark.local +def test_htex(): + worker_info = [get_worker_info() for _ in range(4)] + worker_ids, worker_size, pool_info = zip(*[r.result() for r in worker_info]) + assert len(set(worker_info)) > 1 # Tasks should run on >1 worker + assert len(set(worker_size)) == 1 # All workers have same pool size + assert len(set(pool_info)) == 1 # All from the same pool diff --git a/parsl/parsl/tests/test_htex/test_worker_interchange_bad_messages_3262.py b/parsl/parsl/tests/test_htex/test_worker_interchange_bad_messages_3262.py new file mode 100644 index 00000000..9a613e2c --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_worker_interchange_bad_messages_3262.py @@ -0,0 +1,86 @@ +import pytest +import zmq + +import parsl +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SimpleLauncher +from parsl.providers import LocalProvider + +T_s = 1 + + +def fresh_config(): + htex = HighThroughputExecutor( + heartbeat_period=1 * T_s, + heartbeat_threshold=3 * T_s, + label="htex_local", + worker_debug=True, + cores_per_worker=1, + encrypted=False, + provider=LocalProvider( + init_blocks=0, + min_blocks=0, + max_blocks=0, + launcher=SimpleLauncher(), + ), + ) + c = Config( + executors=[htex], + strategy='none', + strategy_period=0.5, + ) + return c, htex + + +@parsl.python_app +def app(): + return 7 + + +@pytest.mark.local +@pytest.mark.parametrize("msg", + (b'FuzzyByte\rSTREAM', # not JSON + b'{}', # missing fields + b'{"type":"heartbeat"}', # regression test #3262 + ) + ) +def test_bad_messages(try_assert, msg): + """This tests that the interchange is resilient to a few different bad + messages: malformed messages caused by implementation errors, and + heartbeat messages from managers that are not registered. + + The heartbeat test is a regression test for issues #3262, #3632 + """ + + c, htex = fresh_config() + + with parsl.load(c): + + # send a bad message into the interchange on the worker_sock worker + # channel, and then check that the interchange is still alive enough + # that we can scale out a block and run a task. + + worker_port = htex.command_client.run("WORKER_BINDS") + + context = zmq.Context() + channel_timeout = 10000 # in milliseconds + task_channel = context.socket(zmq.DEALER) + task_channel.setsockopt(zmq.LINGER, 0) + task_channel.setsockopt(zmq.IDENTITY, b'testid') + + task_channel.set_hwm(0) + task_channel.setsockopt(zmq.SNDTIMEO, channel_timeout) + task_channel.connect(f"tcp://localhost:{worker_port}") + + task_channel.send(msg) + + # If the interchange exits, it's likely that this test will hang rather + # than raise an error, because the interchange interaction code + # assumes the interchange is always there. + # In the case of issue #3262, an exception message goes to stderr, and + # no error goes to the interchange log file. + htex.scale_out_facade(1) + try_assert(lambda: len(htex.connected_managers()) == 1, timeout_ms=10000) + + assert app().result() == 7 diff --git a/parsl/parsl/tests/test_htex/test_zmq_binding.py b/parsl/parsl/tests/test_htex/test_zmq_binding.py new file mode 100644 index 00000000..562341fd --- /dev/null +++ b/parsl/parsl/tests/test_htex/test_zmq_binding.py @@ -0,0 +1,118 @@ +import logging +import pathlib +from typing import Optional +from unittest import mock + +import psutil +import pytest +import zmq + +from parsl import curvezmq +from parsl.executors.high_throughput.interchange import Interchange +from parsl.executors.high_throughput.manager_selector import RandomManagerSelector + + +def make_interchange(*, + interchange_address: Optional[str], + cert_dir: Optional[str], + worker_port: Optional[int] = None) -> Interchange: + return Interchange(interchange_address=interchange_address, + cert_dir=cert_dir, + client_address="127.0.0.1", + client_ports=(50055, 50056, 50057), + worker_port=worker_port, + worker_port_range=(54000, 55000), + hub_address=None, + hub_zmq_port=None, + heartbeat_threshold=60, + logdir=".", + logging_level=logging.INFO, + log_config=None, + manager_selector=RandomManagerSelector(), + poll_period=10, + run_id="test_run_id", + _check_python_mismatch=True) + + +@pytest.fixture +def encrypted(request: pytest.FixtureRequest): + if hasattr(request, "param"): + return request.param + return True + + +@pytest.fixture +def cert_dir(encrypted: bool, tmpd_cwd: pathlib.Path): + if not encrypted: + return None + return curvezmq.create_certificates(tmpd_cwd) + + +@pytest.mark.local +@pytest.mark.parametrize("encrypted", (True, False), indirect=True) +@mock.patch.object(curvezmq.ServerContext, "socket", return_value=mock.MagicMock()) +def test_interchange_curvezmq_sockets( + mock_socket: mock.MagicMock, cert_dir: Optional[str], encrypted: bool +): + address = "127.0.0.1" + ix = make_interchange(interchange_address=address, cert_dir=cert_dir) + assert isinstance(ix.zmq_context, curvezmq.ServerContext) + assert ix.zmq_context.encrypted is encrypted + assert mock_socket.call_count == 4 + + +@pytest.mark.local +@pytest.mark.parametrize("encrypted", (True, False), indirect=True) +def test_interchange_binding_no_address(cert_dir: Optional[str]): + ix = make_interchange(interchange_address=None, cert_dir=cert_dir) + assert ix.interchange_address == "*" + + +@pytest.mark.local +@pytest.mark.parametrize("encrypted", (True, False), indirect=True) +def test_interchange_binding_with_address(cert_dir: Optional[str]): + # Using loopback address + address = "127.0.0.1" + ix = make_interchange(interchange_address=address, cert_dir=cert_dir) + assert ix.interchange_address == address + + +@pytest.mark.skip("This behaviour is possibly unexpected. See issue #3037") +@pytest.mark.local +@pytest.mark.parametrize("encrypted", (True, False), indirect=True) +def test_interchange_binding_with_non_ipv4_address(cert_dir: Optional[str]): + # Confirm that a ipv4 address is required + address = "localhost" + with pytest.raises(zmq.error.ZMQError): + make_interchange(interchange_address=address, cert_dir=cert_dir) + + +@pytest.mark.local +@pytest.mark.parametrize("encrypted", (True, False), indirect=True) +def test_interchange_binding_bad_address(cert_dir: Optional[str]): + """Confirm that we raise a ZMQError when a bad address is supplied""" + address = "550.0.0.0" + with pytest.raises(ValueError): + make_interchange(interchange_address=address, cert_dir=cert_dir) + + +@pytest.mark.local +@pytest.mark.parametrize("encrypted", (True, False), indirect=True) +def test_limited_interface_binding(cert_dir: Optional[str]): + """When address is specified the worker_port would be bound to it rather than to 0.0.0.0""" + address = "127.0.0.1" + ix = make_interchange(interchange_address=address, cert_dir=cert_dir) + proc = psutil.Process() + conns = proc.connections(kind="tcp") + + matched_conns = [conn for conn in conns if conn.laddr.port == ix.worker_port] + assert len(matched_conns) == 1 + # laddr.ip can return ::ffff:127.0.0.1 when using IPv6 + assert address in matched_conns[0].laddr.ip + + +@pytest.mark.local +@pytest.mark.parametrize("encrypted", (True, False), indirect=True) +def test_fixed_ports(cert_dir: Optional[str]): + ix = make_interchange(interchange_address=None, cert_dir=cert_dir, worker_port=51117) + assert ix.interchange_address == "*" diff --git a/parsl/parsl/tests/test_logging/__init__.py b/parsl/parsl/tests/test_logging/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_logging/test_dfk_executor_binds.py b/parsl/parsl/tests/test_logging/test_dfk_executor_binds.py new file mode 100644 index 00000000..961d22b6 --- /dev/null +++ b/parsl/parsl/tests/test_logging/test_dfk_executor_binds.py @@ -0,0 +1,45 @@ +import pytest + +import parsl + + +@parsl.python_app +def noop(): + pass + + +@pytest.mark.local +def test_executor_binding_threads(caplog): + """Tests executor binding for executor with no task IDs""" + with parsl.load(): + fut = noop() + fut.result() + + bindings = [e for e in caplog.records + if hasattr(e, 'parsl.task') and + hasattr(e, 'parsl.executor') and + e.__dict__['parsl.task'] == fut.tid and + e.__dict__['parsl.executor'] == 'threads' + ] + + assert len(bindings) == 1, "expected exactly one task/executor binding" + + +@pytest.mark.local +def test_executor_binding_htex(caplog): + """Tests executor binding for executor with task IDs""" + LABEL = "htex_log_test" + with parsl.load(parsl.Config(executors=[parsl.HighThroughputExecutor(label=LABEL)])): + fut = noop() + fut.result() + + bindings = [e for e in caplog.records + if hasattr(e, 'parsl.task') and + hasattr(e, 'parsl.executor') and + hasattr(e, 'parsl.executor_task') and + e.__dict__['parsl.task'] == fut.tid and + e.__dict__['parsl.executor'] == LABEL and + e.__dict__['parsl.executor_task'] == 1 # HTEX task IDs start at 1 + ] + + assert len(bindings) == 1, "expected exactly one task/executor binding" diff --git a/parsl/parsl/tests/test_logging/test_dfk_states.py b/parsl/parsl/tests/test_logging/test_dfk_states.py new file mode 100644 index 00000000..8446c631 --- /dev/null +++ b/parsl/parsl/tests/test_logging/test_dfk_states.py @@ -0,0 +1,50 @@ +import pytest + +import parsl + + +@parsl.python_app +def noop(): + pass + + +@parsl.python_app +def fail(): + raise RuntimeError() + + +@pytest.mark.local +def test_state_logs_success(caplog): + with parsl.load(): + noop().result() + + # from the DFK logs, we wouldn't expect to see running or + # running_done states as those exist only in the worker. + + expected_states = {"pending", "launched", "exec_done"} + + seen_states = {r.__dict__['parsl.task_state'] + for r in caplog.records + if 'parsl.task_state' in r.__dict__} + + assert expected_states <= seen_states, "every expected state should have been seen" + + for s in expected_states: + assert s in caplog.text, f"Expected state {s} to be logged as text" + + +@pytest.mark.local +def test_state_logs_fail(caplog): + with parsl.load(): + fail().exception() + + expected_states = {"pending", "launched", "failed"} + + seen_states = {r.__dict__['parsl.task_state'] + for r in caplog.records + if 'parsl.task_state' in r.__dict__} + + assert expected_states <= seen_states, "every expected state should have been seen" + + for s in expected_states: + assert s in caplog.text, f"Expected state {s} to be logged as text" diff --git a/parsl/parsl/tests/test_logging/test_initialize_logging.py b/parsl/parsl/tests/test_logging/test_initialize_logging.py new file mode 100644 index 00000000..64fc2d71 --- /dev/null +++ b/parsl/parsl/tests/test_logging/test_initialize_logging.py @@ -0,0 +1,80 @@ +import json +import logging +import pathlib + +import pytest + +import parsl +from parsl.logconfigs.file import FileLogging +from parsl.logconfigs.json import JSONLogging +from parsl.logconfigs.noop import NoopLogging + + +@pytest.mark.local +def test_parsl_log_exists(tmpd_cwd): + with parsl.load(parsl.Config(run_dir=str(tmpd_cwd))): + assert (pathlib.Path(parsl.dfk().run_dir) / "parsl.log").exists(), \ + "By default, parsl.log should exist in rundir after startup" + + +@pytest.mark.local +def test_parsl_log_not_exists(tmpd_cwd): + with parsl.load(parsl.Config(run_dir=str(tmpd_cwd), initialize_logging=False)): + assert not (pathlib.Path(parsl.dfk().run_dir) / "parsl.log").exists(), \ + "With initialize_logging=False, parsl.log should not exist in rundir after startup" + + +@pytest.mark.local +def test_parsl_log_Noop_not_exists(tmpd_cwd): + with parsl.load(parsl.Config(run_dir=str(tmpd_cwd), initialize_logging=NoopLogging())): + assert not (pathlib.Path(parsl.dfk().run_dir) / "parsl.log").exists(), \ + "With NoopLogging, parsl.log should not exist in rundir after startup" + + +@pytest.mark.local +def test_parsl_log_FileLogging(tmpd_cwd): + with parsl.load(parsl.Config(run_dir=str(tmpd_cwd), initialize_logging=FileLogging())): + assert (pathlib.Path(parsl.dfk().run_dir) / "parsl.log").exists(), \ + "With FileLogging, parsl.log should exist in rundir after startup" + + +@pytest.mark.local +def test_parsl_log_FileLogging_format(tmpd_cwd): + msg = "XXXYYY %(message)s" + with parsl.load(parsl.Config(run_dir=str(tmpd_cwd), initialize_logging=FileLogging(format_string=msg))): + logfile = pathlib.Path(parsl.dfk().run_dir) / "parsl.log" + assert logfile.exists(), \ + "With FileLogging, parsl.log should exist in rundir after startup" + with open(logfile, "r") as f: + ls = f.readlines() + assert len(ls) >= 1, "logfile should contain lines" + assert ls[0].startswith("XXXYYY "), "logged lines should match format string" + + +@pytest.mark.local +def test_parsl_log_JSONLogging(tmpd_cwd): + with parsl.load(parsl.Config(run_dir=str(tmpd_cwd), initialize_logging=JSONLogging())): + logger = logging.getLogger(__name__) + msg1 = "Test without extra" + msg2 = "Test with extra" + + logger.info(msg1) + logger.info("Test with extra", extra={"parsl.somekey": "vv"}) + + logfile = pathlib.Path(parsl.dfk().run_dir) / "parsl.jsonlog" + assert logfile.exists(), \ + "With JSONLogging, parsl.jsonlog should exist in rundir after startup" + + # close down the DFK before attempt to read log file, so that there aren't any partial + # writes. + with open(logfile, "r") as f: + lines = f.readlines() + + # we should parse every line as JSON, without Exceptions + parsed = [json.loads(line) for line in lines] + + assert len([None for p in parsed if p['msg'] == msg1]) == 1, "msg1 should appear once" + + msgs2 = [p for p in parsed if p['msg'] == msg2] + assert len(msgs2) == 1, "msg2 should appear once" + assert msgs2[0]['parsl.somekey'] == "vv", "extra key should have been stored in record" diff --git a/parsl/parsl/tests/test_logging/test_oneshot.py b/parsl/parsl/tests/test_logging/test_oneshot.py new file mode 100644 index 00000000..b6fce295 --- /dev/null +++ b/parsl/parsl/tests/test_logging/test_oneshot.py @@ -0,0 +1,145 @@ +import pickle +import unittest +import uuid +from unittest.mock import Mock + +import pytest + +import parsl.logconfigs.base as b + + +@pytest.mark.local +def test_simple(tmpd_cwd): + lc = Mock(spec=b.LogConfig) + lc.uuid = uuid.uuid4() + + assert b._initialized_log_contexts == {}, "precondition" + + lc.initialize_logging.assert_not_called() + b.oneshot_initialize_logging(log_config=lc, log_dir=tmpd_cwd, log_name="test_simple") + lc.initialize_logging.assert_called() + + cb = b._initialized_log_contexts[lc.uuid].uninit_callback + + cb.assert_not_called() + b.oneshot_uninitialize_logging(log_config=lc) + cb.assert_called() + + assert b._initialized_log_contexts == {}, "back to satisfying the precondition" + + +@pytest.mark.local +def test_nested(tmpd_cwd): + lc = Mock(spec=b.LogConfig) + lc.uuid = uuid.uuid4() + + assert b._initialized_log_contexts == {}, "precondition" + + b.oneshot_initialize_logging(log_config=lc, log_dir=tmpd_cwd, log_name="test_nested_1") + lc.initialize_logging.assert_called_once() + + b.oneshot_initialize_logging(log_config=lc, log_dir=tmpd_cwd, log_name="test_nested_2") + lc.initialize_logging.assert_called_once() + + cb = b._initialized_log_contexts[lc.uuid].uninit_callback + + cb.assert_not_called() + b.oneshot_uninitialize_logging(log_config=lc) + cb.assert_not_called() + b.oneshot_uninitialize_logging(log_config=lc) + cb.assert_called() + + assert b._initialized_log_contexts == {}, "back to satisfying the precondition" + + +@pytest.mark.local +def test_sequential(tmpd_cwd): + """Test that a log context can be released completely, and then reinitialized.""" + lc = Mock(spec=b.LogConfig) + lc.uuid = uuid.uuid4() + + assert b._initialized_log_contexts == {}, "precondition" + + b.oneshot_initialize_logging(log_config=lc, log_dir=tmpd_cwd, log_name="test_nested_1") + lc.initialize_logging.assert_called_once() + cb = b._initialized_log_contexts[lc.uuid].uninit_callback + cb.assert_not_called() + b.oneshot_uninitialize_logging(log_config=lc) + cb.assert_called() + + assert b._initialized_log_contexts == {}, "back to satisfying the precondition, between uses" + + b.oneshot_initialize_logging(log_config=lc, log_dir=tmpd_cwd, log_name="test_nested_2") + assert lc.initialize_logging.call_count == 2, "Should have been initialized once per block" + + cb2 = b._initialized_log_contexts[lc.uuid].uninit_callback + + # There is no requirement that cb2 be a fresh callback (mock) or be re-used. + # When writing this test, cb2 is a reused mock, `cb2 is cb`, but that isn't + # a requirement of the API. + c = cb2.call_count + b.oneshot_uninitialize_logging(log_config=lc) + assert cb2.call_count == c + 1 + + assert b._initialized_log_contexts == {}, "back to satisfying the precondition" + + +class NestedPickleTestConfig(b.LogConfig): + def initialize_logging(self, *args, **kwargs): + def de(): + pass + return de + + +@pytest.mark.local +def test_nested_pickle(tmpd_cwd): + + # this config will never be exposed to the log system except after passing + # through pickle via two different paths, to generate different "remote-like" + # objects. + lc_orig = NestedPickleTestConfig() + + p = pickle.dumps(lc_orig) + + assert lc_orig.uuid not in b._initialized_log_contexts + lc_1 = pickle.loads(p) + b.oneshot_initialize_logging(log_config=lc_1, log_dir=tmpd_cwd, log_name="test_nested_1") + + assert lc_orig.uuid in b._initialized_log_contexts + lc_2 = pickle.loads(p) + b.oneshot_initialize_logging(log_config=lc_2, log_dir=tmpd_cwd, log_name="test_nested_2") + + assert lc_orig.uuid in b._initialized_log_contexts + + b.oneshot_uninitialize_logging(log_config=lc_2) + assert lc_orig.uuid in b._initialized_log_contexts + + b.oneshot_uninitialize_logging(log_config=lc_1) + assert lc_orig.uuid not in b._initialized_log_contexts + + +@pytest.mark.local +def test_overlap_pickle(tmpd_cwd): + + # this config will never be exposed to the log system except after passing + # through pickle via two different paths, to generate different "remote-like" + # objects. + lc_orig = NestedPickleTestConfig() + + p = pickle.dumps(lc_orig) + + assert lc_orig.uuid not in b._initialized_log_contexts + lc_1 = pickle.loads(p) + b.oneshot_initialize_logging(log_config=lc_1, log_dir=tmpd_cwd, log_name="test_nested_1") + + assert lc_orig.uuid in b._initialized_log_contexts + lc_2 = pickle.loads(p) + b.oneshot_initialize_logging(log_config=lc_2, log_dir=tmpd_cwd, log_name="test_nested_2") + + assert lc_orig.uuid in b._initialized_log_contexts + + b.oneshot_uninitialize_logging(log_config=lc_2) + assert lc_orig.uuid in b._initialized_log_contexts + + b.oneshot_uninitialize_logging(log_config=lc_1) + assert lc_orig.uuid not in b._initialized_log_contexts diff --git a/parsl/parsl/tests/test_monitoring/__init__.py b/parsl/parsl/tests/test_monitoring/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_monitoring/test_app_names.py b/parsl/parsl/tests/test_monitoring/test_app_names.py new file mode 100644 index 00000000..fdc08370 --- /dev/null +++ b/parsl/parsl/tests/test_monitoring/test_app_names.py @@ -0,0 +1,86 @@ +"""Tests monitoring records app name under various decoration patterns. +""" + +import os +import time + +import pytest + +import parsl +from parsl.tests.configs.htex_local_alternate import fresh_config + + +@parsl.python_app +def regular_decorated_app(): + return 5 + + +@pytest.mark.local +def get_regular_decorated_app(): + return regular_decorated_app + + +def for_decoration_later(): + return 77 + + +def get_for_decoration_later(): + return parsl.python_app(for_decoration_later) + + +def get_decorated_closure(): + + r = 53 + + @parsl.python_app + def decorated_closure(): + return r + + return decorated_closure + + +@pytest.mark.local +@pytest.mark.parametrize("get_app,expected_name,expected_result", + [(get_regular_decorated_app, "regular_decorated_app", 5), + (get_for_decoration_later, "for_decoration_later", 77), + (get_decorated_closure, "decorated_closure", 53) + ]) +def test_app_name(get_app, expected_name, expected_result, tmpd_cwd): + + # this is imported here rather than at module level because + # it isn't available in a plain parsl install, so this module + # would otherwise fail to import and break even a basic test + # run. + import sqlalchemy + + c = fresh_config() + c.run_dir = str(tmpd_cwd) + c.monitoring.logging_endpoint = f"sqlite:///{tmpd_cwd}/monitoring.db" + parsl.load(c) + + app = get_app() + assert app().result() == expected_result + + parsl.dfk().cleanup() + + engine = sqlalchemy.create_engine(c.monitoring.logging_endpoint) + with engine.begin() as connection: + + def count_rows(table: str): + result = connection.execute(sqlalchemy.text(f"SELECT COUNT(*) FROM {table}")) + (c, ) = result.first() + return c + + # one workflow... + assert count_rows("workflow") == 1 + + # ... with one task ... + assert count_rows("task") == 1 + + # ... that was tried once ... + assert count_rows("try") == 1 + + # ... and has the expected name. + result = connection.execute(sqlalchemy.text("SELECT task_func_name FROM task")) + (c, ) = result.first() + assert c == expected_name diff --git a/parsl/parsl/tests/test_monitoring/test_basic.py b/parsl/parsl/tests/test_monitoring/test_basic.py new file mode 100644 index 00000000..73bceb32 --- /dev/null +++ b/parsl/parsl/tests/test_monitoring/test_basic.py @@ -0,0 +1,169 @@ +import os +import time + +import pytest + +import parsl +from parsl import HighThroughputExecutor, ThreadPoolExecutor +from parsl.config import Config +from parsl.executors.status_handling import BlockProviderExecutor +from parsl.monitoring import MonitoringHub +from parsl.monitoring.radios.filesystem import FilesystemRadio +from parsl.monitoring.radios.htex import HTEXRadio +from parsl.monitoring.radios.udp import UDPRadio + + +@parsl.python_app +def this_app(): + return 5 + + +# The below fresh configs are for use in parametrization, and should return +# a configuration that is suitably configured for monitoring. + +def thread_config(): + c = Config(executors=[ThreadPoolExecutor(remote_monitoring_radio=UDPRadio(address="localhost", atexit_timeout=0))], + monitoring=MonitoringHub(resource_monitoring_interval=0)) + return c + + +def htex_config(): + """This config will use htex's default htex-specific monitoring radio mode""" + from parsl.tests.configs.htex_local_alternate import fresh_config + return fresh_config() + + +def htex_udp_config(): + """This config will force UDP""" + from parsl.tests.configs.htex_local_alternate import fresh_config + c = fresh_config() + assert len(c.executors) == 1 + ex = c.executors[0] + + assert isinstance(ex.remote_monitoring_radio, HTEXRadio), "precondition: htex is configured for the HTEXRadio" + ex.remote_monitoring_radio = UDPRadio(address="localhost", atexit_timeout=0) + + return c + + +def htex_filesystem_config(): + """This config will force filesystem radio""" + from parsl.tests.configs.htex_local_alternate import fresh_config + c = fresh_config() + assert len(c.executors) == 1 + ex = c.executors[0] + + assert isinstance(ex.remote_monitoring_radio, HTEXRadio), "precondition: htex is configured for the HTEXRadio" + ex.remote_monitoring_radio = FilesystemRadio() + + return c + + +def workqueue_config(): + from parsl.tests.configs.workqueue_ex import fresh_config + c = fresh_config() + c.monitoring = MonitoringHub( + resource_monitoring_interval=1) + return c + + +def taskvine_config(): + from parsl.executors.taskvine import TaskVineExecutor, TaskVineManagerConfig + c = Config(executors=[TaskVineExecutor(manager_config=TaskVineManagerConfig(port=9000), + worker_launch_method='provider')], + strategy_period=0.5, + + monitoring=MonitoringHub(resource_monitoring_interval=1)) + return c + + +def row_counts_parametrized(tmpd_cwd, fresh_config): + # this is imported here rather than at module level because + # it isn't available in a plain parsl install, so this module + # would otherwise fail to import and break even a basic test + # run. + import sqlalchemy + from sqlalchemy import text + + db_url = f"sqlite:///{tmpd_cwd}/monitoring.db" + + config = fresh_config() + config.run_dir = str(tmpd_cwd) + config.monitoring.logging_endpoint = db_url + + with parsl.load(config): + assert this_app().result() == 5 + + # at this point, we should find one row in the monitoring database. + + engine = sqlalchemy.create_engine(db_url) + with engine.begin() as connection: + + result = connection.execute(text("SELECT COUNT(*) FROM workflow")) + (c, ) = result.first() + assert c == 1 + + result = connection.execute(text("SELECT COUNT(*) FROM task")) + (c, ) = result.first() + assert c == 1 + + result = connection.execute(text("SELECT COUNT(*) FROM try")) + (c, ) = result.first() + assert c == 1 + + result = connection.execute(text("SELECT COUNT(*) FROM status, try " + "WHERE status.task_id = try.task_id " + "AND status.task_status_name='exec_done' " + "AND task_try_time_running is NULL")) + (c, ) = result.first() + assert c == 0 + + result = connection.execute(text("SELECT COUNT(*) FROM workflow WHERE time_began IS NOT NULL AND time_completed IS NOT NULL")) + (c, ) = result.first() + assert c == 1, "start/end times should be populated" + + if isinstance(config.executors[0], HighThroughputExecutor): + # The node table is specific to the HighThroughputExecutor + # Two entries: one showing manager active, one inactive + result = connection.execute(text("SELECT COUNT(*) FROM node")) + (c, ) = result.first() + assert c == 4 + + if isinstance(config.executors[0], BlockProviderExecutor): + # This case assumes that a BlockProviderExecutor is actually being + # used with blocks. It might not be (for example, Work Queue and + # Task Vine can be configured to launch their own workers; and it + # is a valid (although occasional) use of htex to launch executors + # manually. + # If you just added test cases like that and are wondering why this + # assert is failing, that might be why. + result = connection.execute(text("SELECT COUNT(*) FROM block")) + (c, ) = result.first() + assert c >= 2, "There should be at least two block statuses from a BlockProviderExecutor" + + result = connection.execute(text("SELECT COUNT(*) FROM resource")) + (c, ) = result.first() + if isinstance(config.executors[0], ThreadPoolExecutor): + assert c == 0, "Thread pool executors should not be recording resources" + else: + assert c >= 1, "Task execution should have created some resource records" + + +@pytest.mark.local +@pytest.mark.parametrize("fresh_config", [thread_config, htex_config, htex_filesystem_config, htex_udp_config]) +def test_row_counts_base(tmpd_cwd, fresh_config): + row_counts_parametrized(tmpd_cwd, fresh_config) + + +@pytest.mark.workqueue +@pytest.mark.local +@pytest.mark.parametrize("fresh_config", [workqueue_config]) +def test_row_counts_wq(tmpd_cwd, fresh_config): + row_counts_parametrized(tmpd_cwd, fresh_config) + + +@pytest.mark.taskvine +@pytest.mark.local +@pytest.mark.parametrize("fresh_config", [taskvine_config]) +def test_row_counts_tv(tmpd_cwd, fresh_config): + row_counts_parametrized(tmpd_cwd, fresh_config) diff --git a/parsl/parsl/tests/test_monitoring/test_db_locks.py b/parsl/parsl/tests/test_monitoring/test_db_locks.py new file mode 100644 index 00000000..e1d821a4 --- /dev/null +++ b/parsl/parsl/tests/test_monitoring/test_db_locks.py @@ -0,0 +1,88 @@ +import logging +import os +import time + +import pytest + +import parsl + +logger = logging.getLogger(__name__) + + +@parsl.python_app +def this_app(): + return 5 + + +@pytest.mark.local +def test_row_counts(): + import sqlalchemy + from sqlalchemy import text + + from parsl.tests.configs.htex_local_alternate import fresh_config + if os.path.exists("runinfo/monitoring.db"): + logger.info("Monitoring database already exists - deleting") + os.remove("runinfo/monitoring.db") + + engine = sqlalchemy.create_engine("sqlite:///runinfo/monitoring.db") + + logger.info("loading parsl") + parsl.load(fresh_config()) + + # parsl.load() returns before all initialisation of monitoring + # is complete, which means it isn't safe to take a read lock on + # the database yet. This delay tries to work around that - some + # better async behaviour might be nice, but what? + # + # Taking a read lock before monitoring is initialized will cause + # a failure in the part of monitoring which creates tables, and + # which is not protected against read locks at the time this test + # was written. + time.sleep(10) + + # to get an sqlite3 read lock that is held over a controllable + # long time, create a transaction and perform a SELECT in it. + # The lock will be held until the end of the transaction. + # (see bottom of https://sqlite.org/lockingv3.html) + + logger.info("Getting a read lock on the monitoring database") + with engine.begin() as readlock_connection: + readlock_connection.execute(text("BEGIN TRANSACTION")) + result = readlock_connection.execute(text("SELECT COUNT(*) FROM workflow")) + (c, ) = result.first() + assert c == 1 + # now readlock_connection should have a read lock that will + # stay locked until the transaction is ended, or the with + # block ends. + + logger.info("invoking and waiting for result") + assert this_app().result() == 5 + + # there is going to be some raciness here making sure that + # the database manager actually tries to write while the + # read lock is held. I'm not sure if there is a better way + # to detect this other than a hopefully long-enough sleep. + time.sleep(10) + + logger.info("cleaning up parsl") + parsl.dfk().cleanup() + + # at this point, we should find data consistent with executing one + # task in the database. + + logger.info("checking database content") + with engine.begin() as connection: + + result = connection.execute(text("SELECT COUNT(*) FROM workflow")) + (c, ) = result.first() + assert c == 1 + + result = connection.execute(text("SELECT COUNT(*) FROM task")) + (c, ) = result.first() + assert c == 1 + + result = connection.execute(text("SELECT COUNT(*) FROM try")) + (c, ) = result.first() + assert c == 1 + + logger.info("all done") diff --git a/parsl/parsl/tests/test_monitoring/test_exit_helper.py b/parsl/parsl/tests/test_monitoring/test_exit_helper.py new file mode 100644 index 00000000..0e12e595 --- /dev/null +++ b/parsl/parsl/tests/test_monitoring/test_exit_helper.py @@ -0,0 +1,54 @@ +import multiprocessing +import signal + +import psutil +import pytest + +from parsl.multiprocessing import SpawnEvent, SpawnProcess, join_terminate_close_proc + + +def noop(): + pass + + +@pytest.mark.local +def test_end_process_already_exited(): + p = SpawnProcess(target=noop) + p.start() + p.join() + join_terminate_close_proc(p) + + +def hang(): + while True: + pass + + +@pytest.mark.local +def test_end_hung_process(): + """Test calling against a process that will not exit itself.""" + p = SpawnProcess(target=hang) + p.start() + pid = p.pid + join_terminate_close_proc(p, timeout=1) + assert not psutil.pid_exists(pid), "process should not exist any more" + + +def hang_no_sigint(e): + def s(*args, **kwargs): + e.set() + signal.signal(signal.SIGTERM, s) + while True: + pass + + +@pytest.mark.local +def test_end_hung_process_no_sigint(): + """Test calling against a process that will not exit itself.""" + e = SpawnEvent() + p = SpawnProcess(target=hang_no_sigint, args=(e,)) + p.start() + pid = p.pid + join_terminate_close_proc(p, timeout=2) + assert not psutil.pid_exists(pid), "process should not exist any more" + assert e.is_set(), "hung process should have set event on signal" diff --git a/parsl/parsl/tests/test_monitoring/test_htex_fuzz_zmq.py b/parsl/parsl/tests/test_monitoring/test_htex_fuzz_zmq.py new file mode 100644 index 00000000..e35702ed --- /dev/null +++ b/parsl/parsl/tests/test_monitoring/test_htex_fuzz_zmq.py @@ -0,0 +1,113 @@ +import logging +import os +import socket +import time + +import pytest +import zmq + +import parsl + +logger = logging.getLogger(__name__) + + +@parsl.python_app +def this_app(): + return 5 + + +@pytest.mark.local +def test_fuzz(): + """This test sends fuzz into the ZMQ radio receiver that HTEX starts + for receiving monitoring messages from the interchange, and checks + that monitoring still records things ok. + """ + + import sqlalchemy + from sqlalchemy import text + + from parsl.tests.configs.htex_local_alternate import fresh_config + + if os.path.exists("runinfo/monitoring.db"): + logger.info("Monitoring database already exists - deleting") + os.remove("runinfo/monitoring.db") + + logger.info("loading parsl") + parsl.load(fresh_config()) + + logger.info("invoking apps and waiting for result") + + assert this_app().result() == 5 + assert this_app().result() == 5 + + # now we've run some apps, send fuzz into the monitoring ZMQ + # socket, before trying to run some more tests. + + # there are different kinds of fuzz: + # could send ZMQ messages that are weird + # could send random bytes to the TCP socket + # the latter is what i'm most suspicious of in my present investigation + + # dig out the interchange port... + hub_address = parsl.dfk().executors["htex_Local"].loopback_address + hub_zmq_port = parsl.dfk().executors["htex_Local"].hub_zmq_port + + # this will send a string to a new socket connection + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.connect((hub_address, hub_zmq_port)) + s.sendall(b'fuzzing\r') + + context = zmq.Context() + channel_timeout = 10000 # in milliseconds + hub_channel = context.socket(zmq.DEALER) + hub_channel.setsockopt(zmq.LINGER, 0) + hub_channel.set_hwm(0) + hub_channel.setsockopt(zmq.SNDTIMEO, channel_timeout) + hub_channel.connect("tcp://{}:{}".format(hub_address, hub_zmq_port)) + + # this will send a non-object down the DFK's existing ZMQ connection + hub_channel.send(b'FuzzyByte\rSTREAM') + + # This following attack is commented out, because monitoring is not resilient + # to this. + # In practice, it works some of the time but in some circumstances, + # it would still abandon writing multiple unrelated records to the database, + # causing ongoing monitoring data loss. + + # This will send an unusual python object down the + # DFK's existing ZMQ connection. this doesn't break the router, + # but breaks the db_manager in a way that isn't reported until + # the very end of the run, and database writing is abandoned + # rather than completing, in this case. + # I'm unclear if this is a case we should be trying to handle. + # parsl.dfk().monitoring._dfk_channel.send_pyobj("FUZZ3") + + # hopefully long enough for any breakage to happen + # before attempting to run more tasks + time.sleep(5) + + assert this_app().result() == 5 + assert this_app().result() == 5 + + logger.info("cleaning up parsl") + parsl.dfk().cleanup() + + # at this point, we should find one row in the monitoring database. + + logger.info("checking database content") + engine = sqlalchemy.create_engine("sqlite:///runinfo/monitoring.db") + with engine.begin() as connection: + + result = connection.execute(text("SELECT COUNT(*) FROM workflow")) + (c, ) = result.first() + assert c == 1 + + result = connection.execute(text("SELECT COUNT(*) FROM task")) + (c, ) = result.first() + assert c == 4 + + result = connection.execute(text("SELECT COUNT(*) FROM try")) + (c, ) = result.first() + assert c == 4 + + logger.info("all done") diff --git a/parsl/parsl/tests/test_monitoring/test_htex_init_blocks_vs_monitoring.py b/parsl/parsl/tests/test_monitoring/test_htex_init_blocks_vs_monitoring.py new file mode 100644 index 00000000..73daaaad --- /dev/null +++ b/parsl/parsl/tests/test_monitoring/test_htex_init_blocks_vs_monitoring.py @@ -0,0 +1,79 @@ +import logging +import os +import time + +import pytest + +import parsl +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SimpleLauncher +from parsl.monitoring import MonitoringHub +from parsl.providers import LocalProvider + + +def fresh_config(run_dir, strategy, db_url): + return Config( + run_dir=os.fspath(run_dir), + executors=[ + HighThroughputExecutor( + label="htex_local", + cores_per_worker=1, + encrypted=True, + provider=LocalProvider( + init_blocks=1, + # min and max are set to 0 to ensure that we don't get + # a block from ongoing strategy scaling, only from + # init_blocks + min_blocks=0, + max_blocks=0, + launcher=SimpleLauncher(), + ), + ) + ], + strategy=strategy, + strategy_period=0.1, + monitoring=MonitoringHub( + logging_endpoint=db_url + ) + ) + + +@parsl.python_app +def this_app(): + pass + + +@pytest.mark.local +@pytest.mark.parametrize("strategy", ('none', 'simple', 'htex_auto_scale')) +def test_row_counts(tmpd_cwd, strategy): + # this is imported here rather than at module level because + # it isn't available in a plain parsl install, so this module + # would otherwise fail to import and break even a basic test + # run. + import sqlalchemy + from sqlalchemy import text + + db_url = f"sqlite:///{tmpd_cwd}/monitoring.db" + with parsl.load(fresh_config(tmpd_cwd, strategy, db_url)): + dfk = parsl.dfk() + run_id = dfk.run_id + + this_app().result() + + engine = sqlalchemy.create_engine(db_url) + with engine.begin() as connection: + + binds = {"run_id": run_id} + + result = connection.execute(text("SELECT COUNT(DISTINCT block_id) FROM block WHERE run_id = :run_id"), binds) + (c, ) = result.first() + assert c == 1, "We should see a single block in this database" + + result = connection.execute(text("SELECT COUNT(*) FROM block WHERE block_id = 0 AND status = 'PENDING' AND run_id = :run_id"), binds) + (c, ) = result.first() + assert c == 1, "There should be a single pending status" + + result = connection.execute(text("SELECT COUNT(*) FROM block WHERE block_id = 0 AND status = 'SCALED_IN' AND run_id = :run_id"), binds) + (c, ) = result.first() + assert c == 1, "There should be a single cancelled status" diff --git a/parsl/parsl/tests/test_monitoring/test_incomplete_futures.py b/parsl/parsl/tests/test_monitoring/test_incomplete_futures.py new file mode 100644 index 00000000..237e92a3 --- /dev/null +++ b/parsl/parsl/tests/test_monitoring/test_incomplete_futures.py @@ -0,0 +1,66 @@ +import logging +import os +import random +from concurrent.futures import Future + +import pytest + +import parsl + + +@parsl.python_app +def this_app(inputs=()): + return inputs[0] + + +@pytest.mark.local +def test_future_representation(tmpd_cwd): + import sqlalchemy + from sqlalchemy import text + + from parsl.tests.configs.htex_local_alternate import fresh_config + + monitoring_db = str(tmpd_cwd / "monitoring.db") + monitoring_url = "sqlite:///" + monitoring_db + + c = fresh_config() + c.monitoring.logging_endpoint = monitoring_url + c.run_dir = str(tmpd_cwd) + + parsl.load(c) + + # we're going to pass this TOKEN into an app via a pre-requisite Future, + # and then expect to see it appear in the monitoring database. + TOKEN = random.randint(0, 1000000) + + # make a Future that has no result yet + # invoke a task that depends on it + # inspect and insert something about the monitoring recorded value of that Future + # make the Future complete + # inspect and insert something about the monitoring recorded value of that Future + + f1 = Future() + + f2 = this_app(inputs=[f1]) + + f1.set_result(TOKEN) + + assert f2.result() == TOKEN + + # this cleanup gives a barrier that allows the monitoring code to store + # everything it has in the database - without this, there's a race + # condition that the task will not have arrived in the database yet. + # A different approach for this test might be to poll the DB for a few + # seconds, with the assumption "data will arrive in the DB within + # 30 seconds, but probably much sooner". + parsl.dfk().cleanup() + + engine = sqlalchemy.create_engine(monitoring_url) + with engine.begin() as connection: + result = connection.execute(text("SELECT COUNT(*) FROM task")) + (task_count, ) = result.first() + assert task_count == 1 + + result = connection.execute(text("SELECT task_inputs FROM task")) + (task_inputs, ) = result.first() + assert task_inputs == "[" + repr(TOKEN) + "]" diff --git a/parsl/parsl/tests/test_monitoring/test_log_config.py b/parsl/parsl/tests/test_monitoring/test_log_config.py new file mode 100644 index 00000000..80ec10de --- /dev/null +++ b/parsl/parsl/tests/test_monitoring/test_log_config.py @@ -0,0 +1,36 @@ +import pathlib + +import pytest + +import parsl +from parsl.config import Config +from parsl.executors import ThreadPoolExecutor +from parsl.logconfigs.json import JSONLogging +from parsl.monitoring import MonitoringHub + + +def config(*, run_dir): + c = Config(executors=[ThreadPoolExecutor()], + monitoring=MonitoringHub(), + run_dir=str(run_dir)) + return c + + +def config_log(*, run_dir, log_config): + c = Config(executors=[ThreadPoolExecutor()], + monitoring=MonitoringHub(), + run_dir=str(run_dir), + initialize_logging=log_config) + return c + + +@pytest.mark.local +def test_log_default_config(tmpd_cwd, try_assert): + with parsl.load(config(run_dir=tmpd_cwd)): + try_assert(lambda: (pathlib.Path(parsl.dfk().run_dir) / "database_manager.log").exists()) + + +@pytest.mark.local +def test_log_json_config(tmpd_cwd, try_assert): + with parsl.load(config_log(run_dir=tmpd_cwd, log_config=JSONLogging())): + try_assert(lambda: (pathlib.Path(parsl.dfk().run_dir) / "database_manager.jsonlog").exists()) diff --git a/parsl/parsl/tests/test_monitoring/test_memoization_representation.py b/parsl/parsl/tests/test_monitoring/test_memoization_representation.py new file mode 100644 index 00000000..bb673872 --- /dev/null +++ b/parsl/parsl/tests/test_monitoring/test_memoization_representation.py @@ -0,0 +1,96 @@ + +import logging +import os + +import pytest + +import parsl +from parsl.config import Config +from parsl.executors import ThreadPoolExecutor +from parsl.monitoring import MonitoringHub +from parsl.monitoring.radios.udp import UDPRadio + +logger = logging.getLogger(__name__) + + +@parsl.python_app(cache=True) +def this_app(x): + return x + 1 + + +def fresh_config(*, config_run_dir: str): + c = Config(run_dir=config_run_dir, + executors=[ThreadPoolExecutor(remote_monitoring_radio=UDPRadio(address="localhost", atexit_timeout=0))], + monitoring=MonitoringHub(resource_monitoring_interval=0)) + return c + + +@pytest.mark.local +def test_hashsum(tmpd_cwd): + import sqlalchemy + from sqlalchemy import text + + if os.path.exists("runinfo/monitoring.db"): + logger.info("Monitoring database already exists - deleting") + os.remove("runinfo/monitoring.db") + + logger.info("loading parsl") + parsl.load(fresh_config(config_run_dir=str(tmpd_cwd))) + + logger.info("invoking and waiting for result (1/4)") + f1 = this_app(4) + assert f1.result() == 5 + + logger.info("invoking and waiting for result (2/4)") + f2 = this_app(17) + assert f2.result() == 18 + + logger.info("invoking and waiting for result (3/4)") + f3 = this_app(4) + assert f3.result() == 5 + + logger.info("invoking and waiting for result (4/4)") + f4 = this_app(4) + assert f4.result() == 5 + + assert f1.task_record['hashsum'] == f3.task_record['hashsum'] + assert f1.task_record['hashsum'] == f4.task_record['hashsum'] + assert f1.task_record['hashsum'] != f2.task_record['hashsum'] + + logger.info("cleaning up parsl") + parsl.dfk().cleanup() + + # at this point, we should find one row in the monitoring database. + + logger.info("checking database content") + + monitoring_db = str(tmpd_cwd / "monitoring.db") + monitoring_url = "sqlite:///" + monitoring_db + + engine = sqlalchemy.create_engine(monitoring_url) + with engine.begin() as connection: + + # we should have three tasks, but with only two tries, because the + # memo try should be missing + result = connection.execute(text("SELECT COUNT(*) FROM task")) + (task_count, ) = result.first() + assert task_count == 4 + + # this will check that the number of task rows for each hashsum matches the above app invocations + result = connection.execute(text(f"SELECT COUNT(task_hashsum) FROM task WHERE task_hashsum='{f1.task_record['hashsum']}'")) + (hashsum_count, ) = result.first() + assert hashsum_count == 3 + + result = connection.execute(text(f"SELECT COUNT(task_hashsum) FROM task WHERE task_hashsum='{f2.task_record['hashsum']}'")) + (hashsum_count, ) = result.first() + assert hashsum_count == 1 + + result = connection.execute(text("SELECT COUNT(*) FROM status WHERE task_status_name='exec_done'")) + (memo_count, ) = result.first() + assert memo_count == 2 + + result = connection.execute(text("SELECT COUNT(*) FROM status WHERE task_status_name='memo_done'")) + (memo_count, ) = result.first() + assert memo_count == 2 + + logger.info("all done") diff --git a/parsl/parsl/tests/test_monitoring/test_radio_filesystem.py b/parsl/parsl/tests/test_monitoring/test_radio_filesystem.py new file mode 100644 index 00000000..c9fbdcca --- /dev/null +++ b/parsl/parsl/tests/test_monitoring/test_radio_filesystem.py @@ -0,0 +1,48 @@ +import pytest + +from parsl.monitoring.message_type import MessageType +from parsl.monitoring.radios.filesystem import FilesystemRadio +from parsl.multiprocessing import SpawnQueue + + +@pytest.mark.local +def test_filesystem(tmpd_cwd): + """Test filesystem radio/receiver pair. + This test checks that the pair can be started up locally, that a message + is conveyed from radio to receiver, and that the receiver process goes + away at shutdown. + """ + + resource_msgs = SpawnQueue() + + radio_config = FilesystemRadio() + + # start receiver + receiver = radio_config.create_receiver(run_dir=str(tmpd_cwd), + resource_msgs=resource_msgs) + + # make radio + + radio_sender = radio_config.create_sender() + + # send message into radio + + message = (MessageType.RESOURCE_INFO, {}) + + radio_sender.send(message) + + # verify it comes out of the receiver + + m = resource_msgs.get() + + assert m == message, "The sent message should appear in the queue" + + # shut down router + + receiver.shutdown() + + # we can't inspect the process if it has been closed properly, but + # we can verify that it raises the expected ValueError the closed + # processes raise on access. + with pytest.raises(ValueError): + receiver.process.exitcode diff --git a/parsl/parsl/tests/test_monitoring/test_radio_multiprocessing.py b/parsl/parsl/tests/test_monitoring/test_radio_multiprocessing.py new file mode 100644 index 00000000..fca75783 --- /dev/null +++ b/parsl/parsl/tests/test_monitoring/test_radio_multiprocessing.py @@ -0,0 +1,44 @@ +import pytest + +from parsl.monitoring.message_type import MessageType +from parsl.monitoring.radios.multiprocessing import MultiprocessingQueueRadio +from parsl.multiprocessing import SpawnQueue + + +@pytest.mark.local +def test_radio(tmpd_cwd): + """Test filesystem radio/receiver pair. + This test checks that the pair can be started up locally, that a message + is conveyed from radio to receiver, and that the receiver process goes + away at shutdown. + """ + + resource_msgs = SpawnQueue() + + radio_config = MultiprocessingQueueRadio() + + # start receiver + receiver = radio_config.create_receiver(run_dir=str(tmpd_cwd), + resource_msgs=resource_msgs) + + # make radio + + radio_sender = radio_config.create_sender() + + # send message into radio + + message = (MessageType.RESOURCE_INFO, {}) + + radio_sender.send(message) + + # verify it comes out of the receiver + + m = resource_msgs.get() + + assert m == message, "The sent message should appear in the queue" + + # Shut down router. + # In the multiprocessing radio, nothing happens at shutdown, so this + # validates that the call executes without raising an exception, but + # not much else. + receiver.shutdown() diff --git a/parsl/parsl/tests/test_monitoring/test_radio_udp.py b/parsl/parsl/tests/test_monitoring/test_radio_udp.py new file mode 100644 index 00000000..2c4f8f14 --- /dev/null +++ b/parsl/parsl/tests/test_monitoring/test_radio_udp.py @@ -0,0 +1,204 @@ +import socket +import time + +import pytest + +from parsl.monitoring.message_type import MessageType +from parsl.monitoring.radios.udp import UDPRadio +from parsl.multiprocessing import SpawnQueue + + +@pytest.mark.local +def test_udp(tmpd_cwd): + """Test UDP radio/receiver pair. + This test checks that the pair can be started up locally, that a message + is conveyed from radio to receiver, and that the receiver process goes + away at shutdown. + """ + + resource_msgs = SpawnQueue() + + radio_config = UDPRadio(address="localhost", atexit_timeout=0) + + # start receiver + udp_receiver = radio_config.create_receiver(run_dir=str(tmpd_cwd), + resource_msgs=resource_msgs) + + # check hash properties + + assert len(radio_config.hmac_key) == 64, "With default hash, should expect 64 byte key" + + # make radio + + radio_sender = radio_config.create_sender() + + # send message into radio + + message = (MessageType.RESOURCE_INFO, {}) + + radio_sender.send(message) + + # verify it comes out of the receiver + + m = resource_msgs.get() + + assert m == message, "The sent message should appear in the queue" + + # shut down router + + udp_receiver.shutdown() + + # we can't inspect the process if it has been closed properly, but + # we can verify that it raises the expected ValueError the closed + # processes raise on access. + with pytest.raises(ValueError): + udp_receiver.process.exitcode + + +@pytest.mark.local +def test_bad_hmac(tmpd_cwd, caplog): + """Test when HMAC does not match. + """ + + resource_msgs = SpawnQueue() + + radio_config = UDPRadio(address="localhost", atexit_timeout=0) + + # start receiver + udp_receiver = radio_config.create_receiver(run_dir=str(tmpd_cwd), + resource_msgs=resource_msgs) + + # check the hmac is configured in the right place, + # then change it to something different (by prepending a new byte) + assert radio_config.hmac_key is not None + radio_config.hmac_key += b'x' + + # make radio, after changing the HMAC key + + radio_sender = radio_config.create_sender() + + # send message into radio + + message = (MessageType.RESOURCE_INFO, {}) + + radio_sender.send(message) + + # We should expect no message from the UDP side. That's hard to + # state in this scenario because UDP doesn't have any delivery + # guarantees for the test-failing case. + # So sleep a while to allow that test to misdeliver and fail. + time.sleep(1) + + assert resource_msgs.empty(), "receiving queue should be empty" + assert udp_receiver.process.is_alive(), "UDP router process should still be alive" + + with open(f"{tmpd_cwd}/monitoring_udp_router.log", "r") as logfile: + assert "ERROR" in logfile.read(), "Router log file should contain an error" + + # shut down router + + udp_receiver.shutdown() + + # we can't inspect the process if it has been closed properly, but + # we can verify that it raises the expected ValueError the closed + # processes raise on access. + with pytest.raises(ValueError): + udp_receiver.process.exitcode + + +@pytest.mark.local +def test_wrong_digest(tmpd_cwd, caplog): + """Test when HMAC does not match. + """ + + resource_msgs = SpawnQueue() + + radio_config = UDPRadio(address="localhost", atexit_timeout=0) + + # start receiver + udp_receiver = radio_config.create_receiver(run_dir=str(tmpd_cwd), + resource_msgs=resource_msgs) + + # check the hmac is configured in the right place, + # then change it to a different digest. The choice of different + # digest is arbitrary. + assert radio_config.hmac_digest is not None + radio_config.hmac_digest = "sha3_224" + + # make radio, after changing the HMAC digest + + radio_sender = radio_config.create_sender() + + # send message into radio + + message = (MessageType.RESOURCE_INFO, {}) + + radio_sender.send(message) + + # We should expect no message from the UDP side. That's hard to + # state in this scenario because UDP doesn't have any delivery + # guarantees for the test-failing case. + # So sleep a while to allow that test to misdeliver and fail. + time.sleep(1) + + assert resource_msgs.empty(), "receiving queue should be empty" + assert udp_receiver.process.is_alive(), "UDP router process should still be alive" + + with open(f"{tmpd_cwd}/monitoring_udp_router.log", "r") as logfile: + assert "ERROR" in logfile.read(), "Router log file should contain an error" + + # shut down router + + udp_receiver.shutdown() + + # we can't inspect the process if it has been closed properly, but + # we can verify that it raises the expected ValueError the closed + # processes raise on access. + with pytest.raises(ValueError): + udp_receiver.process.exitcode + + +@pytest.mark.local +def test_short_message(tmpd_cwd, caplog): + """Test when UDP message is so short it can't even be parsed into + HMAC + the rest. + """ + + resource_msgs = SpawnQueue() + + radio_config = UDPRadio(address="localhost", atexit_timeout=0) + + # start receiver + udp_receiver = radio_config.create_receiver(run_dir=str(tmpd_cwd), + resource_msgs=resource_msgs) + + # now send a bad UDP message, rather than using the sender mechanism. + + sock = socket.socket(socket.AF_INET, + socket.SOCK_DGRAM, + socket.IPPROTO_UDP) + + sock.sendto(b'', (radio_config.address, radio_config.port)) + sock.close() + + # We should expect no message from the UDP side. That's hard to + # state in this scenario because UDP doesn't have any delivery + # guarantees for the test-failing case. + # So sleep a while to allow that test to misdeliver and fail. + time.sleep(1) + + assert resource_msgs.empty(), "receiving queue should be empty" + assert udp_receiver.process.is_alive(), "UDP router process should still be alive" + + with open(f"{tmpd_cwd}/monitoring_udp_router.log", "r") as logfile: + assert "ERROR" in logfile.read(), "Router log file should contain an error" + + # shut down router + + udp_receiver.shutdown() + + # we can't inspect the process if it has been closed properly, but + # we can verify that it raises the expected ValueError the closed + # processes raise on access. + with pytest.raises(ValueError): + udp_receiver.process.exitcode diff --git a/parsl/parsl/tests/test_monitoring/test_radio_zmq.py b/parsl/parsl/tests/test_monitoring/test_radio_zmq.py new file mode 100644 index 00000000..92e43c44 --- /dev/null +++ b/parsl/parsl/tests/test_monitoring/test_radio_zmq.py @@ -0,0 +1,36 @@ +import pytest + +import parsl.curvezmq as curvezmq +from parsl.monitoring.radios.zmq import ZMQRadioSender +from parsl.monitoring.radios.zmq_router import start_zmq_receiver +from parsl.multiprocessing import SpawnQueue + + +@pytest.mark.local +@pytest.mark.parametrize("encrypted", (False, True)) +def test_send_recv_message(tmpd_cwd, try_assert, encrypted): + + if encrypted: + cert_dir = curvezmq.create_certificates(tmpd_cwd) + else: + cert_dir = None + + q = SpawnQueue() + loopback = "127.0.0.1" + r = start_zmq_receiver(monitoring_messages=q, + loopback_address=loopback, + port_range=(49152, 65535), + logdir=str(tmpd_cwd), + worker_debug=False, + cert_dir=cert_dir) + + s = ZMQRadioSender(loopback, r.port, cert_dir=cert_dir) + + test_msg = ("test", {}) + s.send(test_msg) + + assert q.get() == test_msg + + assert r.process.is_alive() + r.exit_event.set() + try_assert(lambda: not r.process.is_alive()) diff --git a/parsl/parsl/tests/test_monitoring/test_stdouterr.py b/parsl/parsl/tests/test_monitoring/test_stdouterr.py new file mode 100644 index 00000000..3bb4b195 --- /dev/null +++ b/parsl/parsl/tests/test_monitoring/test_stdouterr.py @@ -0,0 +1,135 @@ +"""Tests monitoring records app name under various decoration patterns. +""" + +import logging +import os +import re +import time +from typing import Union + +import pytest + +import parsl +from parsl.config import Config +from parsl.data_provider.data_manager import default_staging +from parsl.data_provider.files import File +from parsl.data_provider.staging import Staging +from parsl.executors import HighThroughputExecutor +from parsl.monitoring import MonitoringHub +from parsl.providers import LocalProvider + + +def fresh_config(run_dir): + return Config( + run_dir=str(run_dir), + executors=[ + HighThroughputExecutor( + address="127.0.0.1", + label="htex_Local", + provider=LocalProvider( + init_blocks=1, + min_blocks=1, + max_blocks=1, + ) + ) + ], + strategy='simple', + strategy_period=0.1, + monitoring=MonitoringHub(), + ) + + +@parsl.python_app +def stdapp(stdout=None, stderr=None): + pass + + +class ArbitraryPathLike(os.PathLike): + def __init__(self, path: Union[str, bytes]) -> None: + self.path = path + + def __fspath__(self) -> Union[str, bytes]: + return self.path + + +class ArbitraryStaging(Staging): + """This staging provider will not actually do any staging, but will + accept arbitrary: scheme URLs. That's enough for this monitoring test + which doesn't need any actual stage out action to happen. + """ + def can_stage_out(self, file): + return file.scheme == "arbitrary" + + +@pytest.mark.local +@pytest.mark.parametrize('stdx,expected_stdx', + [('hello.txt', 'hello.txt'), + (None, ''), + (('tuple.txt', 'w'), 'tuple.txt'), + (ArbitraryPathLike('pl.txt'), 'pl.txt'), + (ArbitraryPathLike(b'pl2.txt'), 'pl2.txt'), + ((ArbitraryPathLike('pl3.txt'), 'w'), 'pl3.txt'), + ((ArbitraryPathLike(b'pl4.txt'), 'w'), 'pl4.txt'), + (parsl.AUTO_LOGNAME, + lambda p: + isinstance(p, str) and + os.path.isabs(p) and + re.match("^.*/task_0000_stdapp\\.std...$", p)), + (File("arbitrary:abc123"), "arbitrary:abc123"), + (File("file:///tmp/pl5"), "file:///tmp/pl5"), + ]) +@pytest.mark.parametrize('stream', ['stdout', 'stderr']) +def test_stdstream_to_monitoring(stdx, expected_stdx, stream, tmpd_cwd, caplog): + """This tests that various forms of stdout/err specification are + represented in monitoring correctly. The stderr and stdout codepaths + are generally duplicated, rather than factorised, and so this test + runs the same tests on both stdout and stderr. + """ + + # this is imported here rather than at module level because + # it isn't available in a plain parsl install, so this module + # would otherwise fail to import and break even a basic test + # run. + import sqlalchemy + + c = fresh_config(tmpd_cwd) + c.monitoring.logging_endpoint = f"sqlite:///{tmpd_cwd}/monitoring.db" + c.executors[0].storage_access = default_staging + [ArbitraryStaging()] + + with parsl.load(c): + kwargs = {stream: stdx} + stdapp(**kwargs).result() + + for record in caplog.records: + assert record.levelno < logging.ERROR + + engine = sqlalchemy.create_engine(c.monitoring.logging_endpoint) + with engine.begin() as connection: + + def count_rows(table: str): + result = connection.execute(sqlalchemy.text(f"SELECT COUNT(*) FROM {table}")) + (c, ) = result.first() + return c + + # one workflow... + assert count_rows("workflow") == 1 + + # ... with one task ... + assert count_rows("task") == 1 + + # ... that was tried once ... + assert count_rows("try") == 1 + + # ... and has the expected name. + result = connection.execute(sqlalchemy.text(f"SELECT task_{stream} FROM task")) + (c, ) = result.first() + + if isinstance(expected_stdx, str): + assert c == expected_stdx + elif callable(expected_stdx): + assert expected_stdx(c) + else: + raise RuntimeError("Bad expected_stdx value") + + for record in caplog.records: + assert record.levelno < logging.ERROR diff --git a/parsl/parsl/tests/test_monitoring/test_viz_colouring.py b/parsl/parsl/tests/test_monitoring/test_viz_colouring.py new file mode 100644 index 00000000..5d7d82f9 --- /dev/null +++ b/parsl/parsl/tests/test_monitoring/test_viz_colouring.py @@ -0,0 +1,18 @@ +import pytest + +from parsl.dataflow.states import States + + +@pytest.mark.local +def test_all_states_colored() -> None: + """This checks that the coloring tables in parsl-visualize contain + a color for each state defined in the task state enumeration. + """ + + # imports inside test because viz can't be imported in an environment + # with no monitoring installed + import parsl.monitoring.visualization.plots.default.workflow_plots as workflow_plots + + for s in States: + assert s.name in workflow_plots.gantt_colors + assert s.name in workflow_plots.dag_state_colors diff --git a/parsl/parsl/tests/test_providers/__init__.py b/parsl/parsl/tests/test_providers/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_providers/test_kubernetes_provider.py b/parsl/parsl/tests/test_providers/test_kubernetes_provider.py new file mode 100644 index 00000000..453dc574 --- /dev/null +++ b/parsl/parsl/tests/test_providers/test_kubernetes_provider.py @@ -0,0 +1,102 @@ +import re +from unittest import mock + +import pytest + +from parsl.providers.kubernetes.kube import KubernetesProvider +from parsl.tests.test_utils.test_sanitize_dns import DNS_SUBDOMAIN_REGEX + +_MOCK_BASE = "parsl.providers.kubernetes.kube" + + +@pytest.fixture(autouse=True) +def mock_kube_config(): + with mock.patch(f"{_MOCK_BASE}.config") as mock_config: + mock_config.load_kube_config.return_value = None + yield mock_config + + +@pytest.fixture +def mock_kube_client(): + mock_client = mock.MagicMock() + with mock.patch(f"{_MOCK_BASE}.client.CoreV1Api") as mock_api: + mock_api.return_value = mock_client + yield mock_client + + +@pytest.mark.local +def test_submit_happy_path(mock_kube_client: mock.MagicMock): + image = "test-image" + namespace = "test-namespace" + cmd_string = "test-command" + volumes = [("test-volume", "test-mount-path")] + service_account_name = "test-service-account" + annotations = {"test-annotation": "test-value"} + max_cpu = 2 + max_mem = "2Gi" + init_cpu = 1 + init_mem = "1Gi" + provider = KubernetesProvider( + image=image, + persistent_volumes=volumes, + namespace=namespace, + service_account_name=service_account_name, + annotations=annotations, + max_cpu=max_cpu, + max_mem=max_mem, + init_cpu=init_cpu, + init_mem=init_mem, + ) + + job_name = "test.job.name" + job_id = provider.submit(cmd_string=cmd_string, tasks_per_node=1, job_name=job_name) + + assert job_id in provider.resources + assert mock_kube_client.create_namespaced_pod.call_count == 1 + + call_args = mock_kube_client.create_namespaced_pod.call_args[1] + pod = call_args["body"] + container = pod.spec.containers[0] + volume = container.volume_mounts[0] + + assert image == container.image + assert namespace == call_args["namespace"] + assert any(cmd_string in arg for arg in container.args) + assert volumes[0] == (volume.name, volume.mount_path) + assert service_account_name == pod.spec.service_account_name + assert annotations == pod.metadata.annotations + assert str(max_cpu) == container.resources.limits["cpu"] + assert max_mem == container.resources.limits["memory"] + assert str(init_cpu) == container.resources.requests["cpu"] + assert init_mem == container.resources.requests["memory"] + assert job_id == pod.metadata.labels["parsl-job-id"] + assert job_id == container.name + assert f"{job_name}.{job_id}" == pod.metadata.name + + +@pytest.mark.local +@mock.patch(f"{_MOCK_BASE}.KubernetesProvider._create_pod") +@pytest.mark.parametrize("char", (".", "-")) +def test_submit_pod_name_includes_job_id(mock_create_pod: mock.MagicMock, char: str): + provider = KubernetesProvider(image="test-image") + + job_name = "a." * 121 + f"a{char}" + "a" * 9 + assert len(job_name) == 253 # Max length for pod name + job_id = provider.submit(cmd_string="test-command", tasks_per_node=1, job_name=job_name) + + expected_pod_name = job_name[:253 - len(job_id) - 2] + f".{job_id}" + actual_pod_name = mock_create_pod.call_args[1]["pod_name"] + assert re.match(DNS_SUBDOMAIN_REGEX, actual_pod_name) + assert expected_pod_name == actual_pod_name + + +@pytest.mark.local +@mock.patch(f"{_MOCK_BASE}.KubernetesProvider._create_pod") +@mock.patch(f"{_MOCK_BASE}.logger") +@pytest.mark.parametrize("job_name", ("", ".", "-", "a.-.a", "$$$")) +def test_submit_invalid_job_name(mock_logger: mock.MagicMock, mock_create_pod: mock.MagicMock, job_name: str): + provider = KubernetesProvider(image="test-image") + job_id = provider.submit(cmd_string="test-command", tasks_per_node=1, job_name=job_name) + assert mock_logger.warning.call_count == 1 + assert f"Invalid pod name '{job_name}' for job '{job_id}'" in mock_logger.warning.call_args[0][0] + assert f"parsl.kube.{job_id}" == mock_create_pod.call_args[1]["pod_name"] diff --git a/parsl/parsl/tests/test_providers/test_local_provider.py b/parsl/parsl/tests/test_providers/test_local_provider.py new file mode 100644 index 00000000..c7508144 --- /dev/null +++ b/parsl/parsl/tests/test_providers/test_local_provider.py @@ -0,0 +1,80 @@ +import logging +import os +import pathlib +import random +import shutil +import socket +import subprocess +import tempfile +import threading +import time + +import pytest + +from parsl.jobs.states import JobState +from parsl.launchers import SingleNodeLauncher +from parsl.providers import LocalProvider + +logger = logging.getLogger(__name__) + + +def _run_tests(p: LocalProvider): + status = _run(p, '/bin/true') + assert status.message is None + assert status.state == JobState.COMPLETED + assert status.exit_code == 0 + assert status.stdout == '' + assert status.stderr == '' + + status = _run(p, '/bin/true', np=2) + assert status.message is None + assert status.state == JobState.COMPLETED + assert status.exit_code == 0 + assert status.stdout == '' + assert status.stderr == '' + + status = _run(p, '/bin/false') + assert status.state == JobState.FAILED + assert status.exit_code != 0 + assert status.stdout == '' + assert status.stderr == '' + + status = _run(p, '/bin/false', np=2) + assert status.state == JobState.FAILED + assert status.exit_code != 0 + assert status.stdout == '' + assert status.stderr == '' + + status = _run(p, '/bin/echo -n magic') + assert status.state == JobState.COMPLETED + assert status.exit_code == 0 + assert status.stdout == 'magic' + assert status.stderr == '' + + status = _run(p, '/bin/echo -n magic 1>&2') + assert status.state == JobState.COMPLETED + assert status.exit_code == 0 + assert status.stdout == '' + assert status.stderr == 'magic' + + +@pytest.mark.local +def test_local_channel(): + with tempfile.TemporaryDirectory() as script_dir: + script_dir = tempfile.mkdtemp() + p = LocalProvider(launcher=SingleNodeLauncher(debug=False)) + p.script_dir = script_dir + _run_tests(p) + + +def _run(p: LocalProvider, command: str, np: int = 1): + id = p.submit(command, np) + return _wait(p, id) + + +def _wait(p: LocalProvider, id: object): + status = p.status([id])[0] + while not status.terminal: + time.sleep(0.1) + status = p.status([id])[0] + return status diff --git a/parsl/parsl/tests/test_providers/test_pbspro_template.py b/parsl/parsl/tests/test_providers/test_pbspro_template.py new file mode 100644 index 00000000..12647311 --- /dev/null +++ b/parsl/parsl/tests/test_providers/test_pbspro_template.py @@ -0,0 +1,27 @@ +import random +from unittest import mock + +import pytest + +from parsl.providers import PBSProProvider + + +@pytest.mark.local +def test_submit_script_basic(tmp_path): + """Test slurm resources table""" + + provider = PBSProProvider( + queue="debug" + ) + provider.script_dir = tmp_path + job_id = str(random.randint(55000, 59000)) + provider.execute_wait = mock.Mock(spec=PBSProProvider.execute_wait) + provider.execute_wait.return_value = (0, job_id, "") + result_job_id = provider.submit("test", tasks_per_node=1) + assert job_id == result_job_id + provider.execute_wait.assert_called() + assert job_id in provider.resources + + job_info = provider.resources[job_id] + assert "job_stdout_path" in job_info + assert "job_stderr_path" in job_info diff --git a/parsl/parsl/tests/test_providers/test_slurm_instantiate.py b/parsl/parsl/tests/test_providers/test_slurm_instantiate.py new file mode 100644 index 00000000..1fed0f18 --- /dev/null +++ b/parsl/parsl/tests/test_providers/test_slurm_instantiate.py @@ -0,0 +1,16 @@ +import pytest + +from parsl.providers import SlurmProvider + + +@pytest.mark.local +def test_slurm_instantiate_regression_2994(): + """This test checks that repr can be executed on SlurmProvider. + This does not need a SLURM installation around. + + Because of the behaviour of RepresentationMixin, it's a relatively + common problem to break repr when adding new parameters to the + SlurmProvider, and this tests that repr does not raise an exception. + """ + p = SlurmProvider() + repr(p) diff --git a/parsl/parsl/tests/test_providers/test_slurm_template.py b/parsl/parsl/tests/test_providers/test_slurm_template.py new file mode 100644 index 00000000..55074fef --- /dev/null +++ b/parsl/parsl/tests/test_providers/test_slurm_template.py @@ -0,0 +1,28 @@ +import logging +import random +from unittest import mock + +import pytest + +from parsl.providers import SlurmProvider + + +@pytest.mark.local +def test_submit_script_basic(tmp_path): + """Test slurm resources table""" + + provider = SlurmProvider( + partition="debug" + ) + provider.script_dir = tmp_path + job_id = str(random.randint(55000, 59000)) + provider.execute_wait = mock.MagicMock(spec=SlurmProvider.execute_wait) + provider.execute_wait.return_value = (0, f"Submitted batch job {job_id}", "") + result_job_id = provider.submit("test", tasks_per_node=1) + assert job_id == result_job_id + provider.execute_wait.assert_called() + assert job_id in provider.resources + + job_info = provider.resources[job_id] + assert "job_stdout_path" in job_info + assert "job_stderr_path" in job_info diff --git a/parsl/parsl/tests/test_providers/test_submiterror_deprecation.py b/parsl/parsl/tests/test_providers/test_submiterror_deprecation.py new file mode 100644 index 00000000..352e6844 --- /dev/null +++ b/parsl/parsl/tests/test_providers/test_submiterror_deprecation.py @@ -0,0 +1,22 @@ +import random +import string + +import pytest + +from parsl.providers.errors import SubmitException + + +@pytest.mark.local +def test_submit_exception_task_name_deprecation(): + """This tests the deprecation warning of task_name in SubmitException + """ + j = "the_name-" + "".join(random.sample(string.ascii_lowercase, 10)) + + ex = SubmitException(j, "m") + + # the new behaviour + assert ex.job_name == j + + # the old behaviour + with pytest.deprecated_call(): + assert ex.task_name == j diff --git a/parsl/parsl/tests/test_python_apps/__init__.py b/parsl/parsl/tests/test_python_apps/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_python_apps/test_arg_input_types.py b/parsl/parsl/tests/test_python_apps/test_arg_input_types.py new file mode 100644 index 00000000..1149705e --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_arg_input_types.py @@ -0,0 +1,34 @@ +""" +These tests check that inputs[] can be passed from any type, +and that they can be used as regular objects unless they are +Files. +""" +from parsl import python_app + + +@python_app +def take_a_value(inputs=[]): + return str(inputs[0]) + + +@python_app +def add_two_values(inputs=[]): + return inputs[0] + inputs[1] + + +def test_input_str(): + f = take_a_value(inputs=["hi"]) + assert f.result() == "hi" + + +def test_input_num(): + f = take_a_value(inputs=[3.14]) + assert f.result() == "3.14" + + f = add_two_values(inputs=[5, 7, 3]) + assert f.result() == 12 + + +def test_input_list(): + f = take_a_value(inputs=[["foo", 7]]) + assert f.result() == "['foo', 7]" diff --git a/parsl/parsl/tests/test_python_apps/test_basic.py b/parsl/parsl/tests/test_python_apps/test_basic.py new file mode 100644 index 00000000..0e67c3d5 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_basic.py @@ -0,0 +1,46 @@ +import pytest + +from parsl.app.app import python_app + + +@python_app +def double(x): + return x * 2 + + +@python_app +def import_square(x): + import math + return math.pow(x, 2) + + +def test_simple(n=2): + x = double(n) + assert x.result() == n * 2 + + +def test_log(n=2, *, caplog): + x = double(n) + assert x.result() == n * 2 + ks = [e for e in caplog.records + if hasattr(e, "parsl.task") and + e.__dict__["parsl.task"] == x.tid and + hasattr(e, "parsl.app_name") and + e.__dict__["parsl.app_name"] == "double" + ] + assert len(ks) > 0, "should have got at least one binding log record" + + +@pytest.mark.parametrize("n", (-2, -1, 0, 1, 2, 3)) +def test_imports(n): + x = import_square(n) + assert x.result() == n * n + + +@pytest.mark.parametrize("n", (0, 1, 2, 3, 5, 8, 13, 21)) +def test_parallel_for(n): + d = {i: double(i) for i in range(n)} + assert len(d.keys()) == n + + for i in d: + assert d[i].result() == 2 * i diff --git a/parsl/parsl/tests/test_python_apps/test_context_manager.py b/parsl/parsl/tests/test_python_apps/test_context_manager.py new file mode 100644 index 00000000..6d3b020b --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_context_manager.py @@ -0,0 +1,128 @@ +from concurrent.futures import Future +from threading import Event + +import pytest + +import parsl +from parsl.config import Config +from parsl.dataflow.dflow import DataFlowKernel, DataFlowKernelLoader +from parsl.errors import NoDataFlowKernelError +from parsl.tests.configs.local_threads import fresh_config + + +@parsl.python_app +def square(x): + return x * x + + +@parsl.bash_app +def foo(x, stdout='foo.stdout'): + return f"echo {x + 1}" + + +@parsl.python_app +def wait_for_event(ev: Event): + ev.wait() + + +@parsl.python_app +def raise_app(): + raise RuntimeError("raise_app deliberate failure") + + +@pytest.mark.local +def test_within_context_manger(tmpd_cwd): + config = fresh_config() + with parsl.load(config=config) as dfk: + assert isinstance(dfk, DataFlowKernel) + + bash_future = foo(1, stdout=tmpd_cwd / 'foo.stdout') + assert bash_future.result() == 0 + + with open(tmpd_cwd / 'foo.stdout', 'r') as f: + assert f.read() == "2\n" + + with pytest.raises(NoDataFlowKernelError) as excinfo: + square(2).result() + assert str(excinfo.value) == "Must first load config" + + +@pytest.mark.local +def test_exit_skip(): + config = fresh_config() + config.exit_mode = "skip" + + with parsl.load(config) as dfk: + ev = Event() + fut = wait_for_event(ev) + # deliberately don't wait for this to finish, so that the context + # manager can exit + + assert parsl.dfk() is dfk, "global dfk should be left in place by skip mode" + + assert not fut.done(), "wait_for_event should not be done yet" + ev.set() + + # now we can wait for that result... + fut.result() + assert fut.done(), "wait_for_event should complete outside of context manager in 'skip' mode" + + # now cleanup the DFK that the above `with` block + # deliberately avoided doing... + dfk.cleanup() + + +# 'wait' mode has two cases to test: +# 1. that we wait when there is no exception +# 2. that we do not wait when there is an exception +@pytest.mark.local +def test_exit_wait_no_exception(): + config = fresh_config() + config.exit_mode = "wait" + + with parsl.load(config) as dfk: + fut = square(1) + # deliberately don't wait for this to finish, so that the context + # manager can exit + + assert fut.done(), "This future should be marked as done before the context manager exits" + + assert dfk.cleanup_called, "The DFK should have been cleaned up by the context manager" + assert DataFlowKernelLoader._dfk is None, "The global DFK should have been removed" + + +@pytest.mark.local +def test_exit_wait_exception(): + config = fresh_config() + config.exit_mode = "wait" + + with pytest.raises(RuntimeError): + with parsl.load(config) as dfk: + # we'll never fire this future + fut_never = Future() + + fut_raise = raise_app() + + fut_depend = square(fut_never) + + # this should cause an exception, which should cause the context + # manager to exit, without waiting for fut_depend to finish. + fut_raise.result() + + assert dfk.cleanup_called, "The DFK should have been cleaned up by the context manager" + assert DataFlowKernelLoader._dfk is None, "The global DFK should have been removed" + assert fut_raise.exception() is not None, "fut_raise should contain an exception" + assert not fut_depend.done(), "fut_depend should have been left un-done (due to dependency failure)" + + +@pytest.mark.local +def test_exit_wrong_mode(): + + with pytest.raises(Exception) as ex: + Config(exit_mode="wrongmode") + + # with typeguard 4.x this is TypeCheckError, + # with typeguard 2.x this is TypeError + # we can't instantiate TypeCheckError if we're in typeguard 2.x environment + # because it does not exist... so check name using strings. + assert ex.type.__name__ == "TypeCheckError" or ex.type.__name__ == "TypeError" diff --git a/parsl/parsl/tests/test_python_apps/test_dep_standard_futures.py b/parsl/parsl/tests/test_python_apps/test_dep_standard_futures.py new file mode 100644 index 00000000..29bb6b27 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_dep_standard_futures.py @@ -0,0 +1,48 @@ +from concurrent.futures import Future + +import parsl +from parsl.dataflow.errors import DependencyError + + +@parsl.python_app +def copy_app(v): + return v + + +def test_future_result_dependency(): + + plain_fut = Future() + + parsl_fut = copy_app(plain_fut) + + assert not parsl_fut.done() + + message = "Test" + + plain_fut.set_result(message) + + assert parsl_fut.result() == message + + +def test_future_fail_dependency(): + + plain_fut = Future() + + parsl_fut = copy_app(plain_fut) + + assert not parsl_fut.done() + + plain_fut.set_exception(ValueError("Plain failure")) + + ex = parsl_fut.exception() + + # check that what we got is a dependency error... + assert isinstance(ex, DependencyError) + + # and that the dependency error string mentions the dependency + # Future, plain_fut, somewhere in its str + + assert repr(plain_fut) in str(ex) + assert len(ex.dependent_exceptions_tids) == 1 + assert isinstance(ex.dependent_exceptions_tids[0][0], ValueError) + assert ex.dependent_exceptions_tids[0][1].startswith("140 causes Parsl to hang +N = 100 + +# MAX_STACK is the maximum Python stack depth allowed for either +# task submission to an executor or execution of a task. +# With mid-2024 Parsl, 2-3 stack entries will be used per +# recursively launched parsl task. So this should be smaller than +# 2*N, but big enough to allow regular pytest+parsl stuff to +# happen. +MAX_STACK = 50 + + +def local_config(): + return parsl.Config(executors=[ImmediateExecutor()]) + + +class ImmediateExecutor(ParslExecutor): + def start(self): + pass + + def shutdown(self): + pass + + def submit(self, func: Callable, resource_specification: Dict[str, Any], *args: Any, **kwargs: Any) -> Future: + stack_depth = len(inspect.stack()) + assert stack_depth < MAX_STACK, "tasks should not be launched deep in the Python stack" + fut: Future[None] = Future() + res = func(*args, **kwargs) + fut.set_result(res) + return fut + + +@parsl.python_app +def chain(upstream): + stack_depth = len(inspect.stack()) + assert stack_depth < MAX_STACK, "chained dependencies should not be launched deep in the Python stack" + + +@pytest.mark.local +def test_deep_dependency_stack_depth(): + + fut = Future() + here = fut + + for _ in range(N): + here = chain(here) + + fut.set_result(None) + here.result() diff --git a/parsl/parsl/tests/test_python_apps/test_depfail_propagation.py b/parsl/parsl/tests/test_python_apps/test_depfail_propagation.py new file mode 100644 index 00000000..23758a11 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_depfail_propagation.py @@ -0,0 +1,64 @@ +import parsl +from parsl import python_app +from parsl.dataflow.errors import DependencyError +from parsl.dataflow.states import States + + +@python_app +def fails(): + raise ValueError("Deliberate failure") + + +@python_app +def depends(parent): + return 1 + + +def test_depfail_once(): + """Test the simplest dependency failure case""" + start_dep_fail_count = parsl.dfk().task_state_counts[States.dep_fail] + f1 = fails() + f2 = depends(f1) + + assert isinstance(f1.exception(), Exception) + assert not isinstance(f1.exception(), DependencyError) + assert isinstance(f2.exception(), DependencyError) + + # check that the task ID of the failing task is mentioned + # in the DependencyError message + assert ("task " + str(f1.task_record['id'])) in str(f2.exception()) + + assert parsl.dfk().task_state_counts[States.dep_fail] == start_dep_fail_count + 1 + + +def test_depfail_chain(): + """Test that dependency failures chain""" + start_dep_fail_count = parsl.dfk().task_state_counts[States.dep_fail] + f1 = fails() + f2 = depends(f1) + f3 = depends(f2) + f4 = depends(f3) + + assert isinstance(f1.exception(), Exception) + assert not isinstance(f1.exception(), DependencyError) + assert isinstance(f2.exception(), DependencyError) + assert isinstance(f3.exception(), DependencyError) + assert isinstance(f4.exception(), DependencyError) + + assert parsl.dfk().task_state_counts[States.dep_fail] == start_dep_fail_count + 3 + + +def test_depfail_branches(): + """Test that dependency failures propagate in the + presence of multiple downstream tasks.""" + start_dep_fail_count = parsl.dfk().task_state_counts[States.dep_fail] + f1 = fails() + f2 = depends(f1) + f3 = depends(f1) + + assert isinstance(f1.exception(), Exception) + assert not isinstance(f1.exception(), DependencyError) + assert isinstance(f2.exception(), DependencyError) + assert isinstance(f3.exception(), DependencyError) + + assert parsl.dfk().task_state_counts[States.dep_fail] == start_dep_fail_count + 2 diff --git a/parsl/parsl/tests/test_python_apps/test_exception.py b/parsl/parsl/tests/test_python_apps/test_exception.py new file mode 100644 index 00000000..52d4dc74 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_exception.py @@ -0,0 +1,19 @@ +import pytest + +from parsl.app.app import python_app + + +class CustomException(Exception): + pass + + +@python_app +def custom_exception(): + from parsl.tests.test_python_apps.test_exception import CustomException + raise CustomException('foobar') + + +def test_custom_exception(): + x = custom_exception() + with pytest.raises(CustomException): + x.result() diff --git a/parsl/parsl/tests/test_python_apps/test_fail.py b/parsl/parsl/tests/test_python_apps/test_fail.py new file mode 100644 index 00000000..8f6616f1 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_fail.py @@ -0,0 +1,58 @@ +import pytest + +from parsl.app.app import python_app +from parsl.dataflow.errors import DependencyError + + +class ManufacturedTestFailure(Exception): + pass + + +@python_app +def random_fail(fail_prob: float, inputs=()): + import random + if random.random() < fail_prob: + raise ManufacturedTestFailure("App failure") + + +def test_no_deps(): + """Test basic error handling, with no dependent failures + """ + futs = [random_fail(1), random_fail(0), random_fail(0)] + + for f in futs: + try: + f.result() + except ManufacturedTestFailure: + pass + + +def test_fail_sequence_first(): + t1 = random_fail(fail_prob=1) + t2 = random_fail(fail_prob=0, inputs=[t1]) + t_final = random_fail(fail_prob=0, inputs=[t2]) + + with pytest.raises(DependencyError): + t_final.result() + + assert len(t_final.exception().dependent_exceptions_tids) == 1 + assert isinstance(t_final.exception().dependent_exceptions_tids[0][0], DependencyError) + assert t_final.exception().dependent_exceptions_tids[0][1].startswith("task ") + + assert hasattr(t_final.exception(), '__cause__') + assert t_final.exception().__cause__ == t1.exception() + + +def test_fail_sequence_middle(): + t1 = random_fail(fail_prob=0) + t2 = random_fail(fail_prob=1, inputs=[t1]) + t_final = random_fail(fail_prob=0, inputs=[t2]) + + with pytest.raises(DependencyError): + t_final.result() + + assert len(t_final.exception().dependent_exceptions_tids) == 1 + assert isinstance(t_final.exception().dependent_exceptions_tids[0][0], ManufacturedTestFailure) + + assert hasattr(t_final.exception(), '__cause__') + assert t_final.exception().__cause__ == t2.exception() diff --git a/parsl/parsl/tests/test_python_apps/test_fibonacci_iterative.py b/parsl/parsl/tests/test_python_apps/test_fibonacci_iterative.py new file mode 100644 index 00000000..10372a8d --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_fibonacci_iterative.py @@ -0,0 +1,25 @@ +import argparse + +import parsl +from parsl.app.app import python_app +from parsl.tests.configs.local_threads import config + + +@python_app +def get_num(first, second): + return first + second + + +def test_fibonacci(num=3): + counter = 0 + results = [] + results.append(0) + results.append(1) + while counter < num - 2: + counter += 1 + results.append(get_num(results[counter - 1], results[counter])) + for i in range(len(results)): + if isinstance(results[i], int): + print(results[i]) + else: + print(results[i].result()) diff --git a/parsl/parsl/tests/test_python_apps/test_fibonacci_recursive.py b/parsl/parsl/tests/test_python_apps/test_fibonacci_recursive.py new file mode 100644 index 00000000..077cd5ae --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_fibonacci_recursive.py @@ -0,0 +1,28 @@ +from parsl.app.app import join_app, python_app + + +@python_app +def add(*args): + """Add all of the arguments together. If no arguments, then + zero is returned (the neutral element of +) + """ + accumulator = 0 + for v in args: + accumulator += v + return accumulator + + +@join_app +def fibonacci(n): + if n == 0: + return add() + elif n == 1: + return add(1) + else: + return add(fibonacci(n - 1), fibonacci(n - 2)) + + +def test_fibonacci(): + assert fibonacci(0).result() == 0 + assert fibonacci(4).result() == 3 + assert fibonacci(6).result() == 8 diff --git a/parsl/parsl/tests/test_python_apps/test_futures.py b/parsl/parsl/tests/test_python_apps/test_futures.py new file mode 100644 index 00000000..500845f7 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_futures.py @@ -0,0 +1,88 @@ +"""Testing behavior of futures + +We have the following cases for AppFutures: + +1. App launched immmediately on call +2. App launch was delayed (due to unresolved dep(s)) + +Same applies to datafutures, and we need to know the behavior wrt. + +1. result() called on 1, vs 2 +2. done() called on 1, vs 2 + +""" +from os.path import basename + +import pytest + +from parsl.app.app import python_app +from parsl.data_provider.files import File + + +@python_app +def delay_incr(x, delay=0.0, outputs=()): + import time + if outputs: + with open(outputs[0].filepath, 'w') as outs: + outs.write(str(x + 1)) + time.sleep(delay) + return x + 1 + + +def get_contents(filename): + with open(filename, 'r') as f: + return f.read() + + +def test_fut_case_1(): + """Testing the behavior of AppFutures where there are no dependencies + """ + + app_fu = delay_incr(1, delay=0.01) + assert app_fu.result() == 2 + + +@pytest.mark.staging_required +def test_fut_case_2(tmpd_cwd): + """Testing the behavior of DataFutures where there are no dependencies + """ + output_f = tmpd_cwd / 'test_fut_case_2.txt' + app_fu = delay_incr(1, delay=0.01, outputs=[File(str(output_f))]) + data_fu = app_fu.outputs[0] + + result = data_fu.result().filepath + assert basename(result) == output_f.name, "DataFuture did not return filename" + + contents = get_contents(result) + assert contents == '2' + + +def test_fut_case_3(): + """Testing the behavior of AppFutures where there are dependencies + + The first call has a delay, and the second call depends on the first + """ + + app_1 = delay_incr(1, delay=0.01) + app_2 = delay_incr(app_1) + + assert app_2.result() == 3 + + +@pytest.mark.staging_required +def test_fut_case_4(tmpd_cwd): + """Testing the behavior of DataFutures where there are dependencies + + The first call has a delay, and the second call depends on the first + """ + output_f1 = tmpd_cwd / 'test_fut_case_4_f1.txt' + output_f2 = tmpd_cwd / 'test_fut_case_4_f2.txt' + app_1 = delay_incr(1, delay=0.01, outputs=[File(str(output_f1))]) + app_2 = delay_incr(app_1, delay=0.01, outputs=[File(str(output_f2))]) + data_2 = app_2.outputs[0] + + result = data_2.result().filepath + assert basename(result) == output_f2.name, "DataFuture did not return the filename" + + contents = get_contents(result) + assert contents == '3' diff --git a/parsl/parsl/tests/test_python_apps/test_garbage_collect.py b/parsl/parsl/tests/test_python_apps/test_garbage_collect.py new file mode 100644 index 00000000..6dc17e24 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_garbage_collect.py @@ -0,0 +1,30 @@ +import threading +import time + +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.tests.configs.local_threads import fresh_config as local_config + + +@python_app +def slow_double(x, may_continue: threading.Event): + may_continue.wait() + return x * 2 + + +@pytest.mark.local +def test_garbage_collect(): + """ Launches an app with a dependency and waits till it's done and asserts that + the internal refs were wiped + """ + evt = threading.Event() + x = slow_double(10, evt) + x = slow_double(x, evt) + + assert parsl.dfk().tasks[x.tid]['app_fu'] == x, "Tasks table should have app_fu ref before done" + + evt.set() + assert x.result() == 10 * 4 + assert x.tid not in parsl.dfk().tasks, "Task record should be wiped after task completion" diff --git a/parsl/parsl/tests/test_python_apps/test_import_fail.py b/parsl/parsl/tests/test_python_apps/test_import_fail.py new file mode 100644 index 00000000..654247cd --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_import_fail.py @@ -0,0 +1,41 @@ +import parsl +from parsl.app.app import python_app + + +@python_app +def platform_name(): + return platform.platform() + + +def test_name_error(n=2): + """Catch NameError for missing name + """ + + p = platform_name() + + try: + p.result() + except NameError: + print("Caught NameError") + else: + assert False, "Raise the wrong Error" + + +@python_app +def bad_import(): + import non_existent + return non_existent.foo() + + +def test_import_error(n=2): + """Catch ImportError for missing name + """ + + p = bad_import() + + try: + p.result() + except ImportError: + print("Caught ImportError") + else: + assert False, "Raise the wrong Error" diff --git a/parsl/parsl/tests/test_python_apps/test_inputs_default.py b/parsl/parsl/tests/test_python_apps/test_inputs_default.py new file mode 100644 index 00000000..cf77c1a8 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_inputs_default.py @@ -0,0 +1,22 @@ +import pytest + +import parsl +from parsl import python_app +from parsl.executors.threads import ThreadPoolExecutor + + +def local_config(): + return parsl.Config(executors=[ThreadPoolExecutor()]) + + +@pytest.mark.local +def test_default_inputs(): + @python_app + def identity(inp): + return inp + + @python_app + def add_inputs(inputs=[identity(1), identity(2)]): + return sum(inputs) + + assert add_inputs().result() == 3 diff --git a/parsl/parsl/tests/test_python_apps/test_join.py b/parsl/parsl/tests/test_python_apps/test_join.py new file mode 100644 index 00000000..926d90bf --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_join.py @@ -0,0 +1,145 @@ +import pytest + +from parsl import join_app, python_app +from parsl.dataflow.errors import JoinError + +RESULT_CONSTANT = 3 + + +@python_app +def inner_app(): + return RESULT_CONSTANT + + +@join_app +def outer_app(): + fut = inner_app() + return fut + + +@python_app +def add_one(n): + return n + 1 + + +@python_app +def combine(*args): + """Wait for an arbitrary list of futures and return them as a list""" + return list(args) + + +@join_app +def outer_make_a_dag_combine(n): + return combine(*(inner_app() for _ in range(n))) + + +@join_app +def outer_make_a_dag_multi(n): + return [inner_app() for _ in range(n)] + + +def test_result_flow(): + f = outer_app() + assert f.result() == RESULT_CONSTANT + + +@join_app +def join_wrong_type_app(): + return 3 + + +def test_wrong_type(): + f = join_wrong_type_app() + with pytest.raises(TypeError): + f.result() + + +def test_dependency_on_joined(): + g = add_one(outer_app()) + assert g.result() == RESULT_CONSTANT + 1 + + +def test_combine(): + f = outer_make_a_dag_combine(inner_app()) + assert f.result() == [RESULT_CONSTANT] * RESULT_CONSTANT + + +def test_multiple_return(): + f = outer_make_a_dag_multi(inner_app()) + assert f.result() == [RESULT_CONSTANT] * RESULT_CONSTANT + + +class InnerError(RuntimeError): + pass + + +@python_app +def inner_error(s="X"): + raise InnerError("Error " + s) + + +@join_app +def outer_error(): + return inner_error() + + +@join_app +def outer_two_errors(): + return [inner_error("A"), inner_error("B")] + + +@join_app +def outer_one_error_one_result(): + return [inner_error("A"), inner_app()] + + +def test_error(): + f = outer_error() + e = f.exception() + assert isinstance(e, JoinError) + + assert len(e.dependent_exceptions_tids) == 1 + assert isinstance(e.dependent_exceptions_tids[0][0], InnerError) + assert e.dependent_exceptions_tids[0][1].startswith("task ") + + +def test_two_errors(): + f = outer_two_errors() + e = f.exception() + assert isinstance(e, JoinError) + assert len(e.dependent_exceptions_tids) == 2 + + de0 = e.dependent_exceptions_tids[0][0] + assert isinstance(de0, InnerError) + assert de0.args[0] == "Error A" + assert e.dependent_exceptions_tids[0][1].startswith("task ") + + de1 = e.dependent_exceptions_tids[1][0] + assert isinstance(de1, InnerError) + assert de1.args[0] == "Error B" + assert e.dependent_exceptions_tids[1][1].startswith("task ") + + +def test_one_error_one_result(): + f = outer_one_error_one_result() + e = f.exception() + + assert isinstance(e, JoinError) + assert len(e.dependent_exceptions_tids) == 1 + + de0 = e.dependent_exceptions_tids[0][0] + assert isinstance(de0, InnerError) + assert de0.args[0] == "Error A" + assert e.dependent_exceptions_tids[0][1].startswith("task ") + + +@join_app +def app_no_futures(): + return [] + + +def test_no_futures(): + # tests that a list of futures that contains no futures will + # complete - regression test for issue #2792 + f = app_no_futures() + assert f.result() == [] diff --git a/parsl/parsl/tests/test_python_apps/test_lifted.py b/parsl/parsl/tests/test_python_apps/test_lifted.py new file mode 100644 index 00000000..77084ac0 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_lifted.py @@ -0,0 +1,142 @@ +from concurrent.futures import Future +from typing import TypeVar + +import pytest + +from parsl import python_app + +T = TypeVar('T') + + +@python_app +def returns_a_dict() -> dict: + return {"a": "X", "b": "Y"} + + +@python_app +def returns_a_list() -> list: + return ["X", "Y"] + + +@python_app +def returns_a_tuple() -> tuple: + return ("X", "Y") + + +@python_app +def returns_a_class() -> type: + from dataclasses import dataclass + + @dataclass + class MyClass: + a: str = "X" + b: str = "Y" + + return MyClass + + +class MyOuterClass(): + def __init__(self): + self.q = "A" + self.r = "B" + + +@python_app +def returns_a_class_instance() -> object: + return MyOuterClass() + + +def test_returns_a_dict(): + + # precondition that returns_a_dict behaves + # correctly + assert returns_a_dict().result()["a"] == "X" + + # check that the deferred __getitem__ functionality works, + # allowing [] to be used on an AppFuture + assert returns_a_dict()["a"].result() == "X" + + +def test_returns_a_list(): + + # precondition that returns_a_list behaves + # correctly + assert returns_a_list().result()[0] == "X" + + # check that the deferred __getitem__ functionality works, + # allowing [] to be used on an AppFuture + assert returns_a_list()[0].result() == "X" + + +def test_returns_a_tuple(): + + # precondition that returns_a_tuple behaves + # correctly + assert returns_a_tuple().result()[0] == "X" + + # check that the deferred __getitem__ functionality works, + # allowing [] to be used on an AppFuture + assert returns_a_tuple()[0].result() == "X" + + +def test_lifted_getitem_on_dict_bad_key(): + assert isinstance(returns_a_dict()["invalid"].exception(), KeyError) + + +def test_returns_a_class_instance(): + # precondition + assert returns_a_class_instance().result().q == "A" + + # test of commuting . and result() + assert returns_a_class_instance().q.result() == "A" + + +def test_returns_a_class_instance_no_underscores(): + # test that _underscore attribute references are not lifted + f = returns_a_class_instance() + with pytest.raises(AttributeError): + f._nosuchattribute.result() + f.exception() # wait for f to complete before the test ends + + +def test_returns_a_class(): + + # precondition that returns_a_class behaves + # correctly + assert returns_a_class().result().a == "X" + + # check that the deferred __getattr__ functionality works, + # allowing [] to be used on an AppFuture + assert returns_a_class().a.result() == "X" + + # when the result is not indexable, a sensible error should + # appear in the appropriate future + + +@python_app +def passthrough(v: T) -> T: + return v + + +def test_lifted_getitem_ordering(): + # this should test that lifting getitem has the correct execution + # order: that it does not defer the execution of following code + + f_prereq = Future() + + f_top = passthrough(f_prereq) + + f_a = f_top['a'] + + # lifted ['a'] should not defer execution here (so it should not + # implicitly call result() on f_top). If it does, this test will + # hang at this point, waiting for f_top to get a value, which + # will not happen until f_prereq gets a value.. + # which doesn't happen until: + + f_prereq.set_result({"a": "X"}) + + # now at this point it should be safe to wait for f_a to get a result + # while passthrough and lifted getitem run... + + assert f_a.result() == "X" diff --git a/parsl/parsl/tests/test_python_apps/test_mapred.py b/parsl/parsl/tests/test_python_apps/test_mapred.py new file mode 100644 index 00000000..1c22334c --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_mapred.py @@ -0,0 +1,34 @@ +import pytest + +from parsl.app.app import python_app + + +@python_app +def times_two(x): + return x * 2 + + +@python_app +def accumulate(inputs=()): + return sum(inputs) + + +@python_app +def accumulate_t(*args): + return sum(args) + + +@pytest.mark.parametrize("width", (2, 3, 5)) +def test_mapred_type1(width): + """MapReduce test with the reduce stage taking futures in inputs=[]""" + futs = [times_two(i) for i in range(width)] + red = accumulate(inputs=futs) + assert red.result() == 2 * sum(range(width)) + + +@pytest.mark.parametrize("width", (2, 3, 5)) +def test_mapred_type2(width): + """MapReduce test with the reduce stage taking futures on the args""" + futs = [times_two(i) for i in range(width)] + red = accumulate_t(*futs) + assert red.result() == 2 * sum(range(width)) diff --git a/parsl/parsl/tests/test_python_apps/test_memoize_1.py b/parsl/parsl/tests/test_python_apps/test_memoize_1.py new file mode 100644 index 00000000..63076701 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_memoize_1.py @@ -0,0 +1,21 @@ +import argparse + +import parsl +from parsl.app.app import python_app + + +@python_app(cache=True) +def random_uuid(x, cache=True): + import uuid + return str(uuid.uuid4()) + + +def test_python_memoization(n=2): + """Testing python memoization.""" + x = random_uuid(0) + print(x.result()) + + for i in range(0, n): + foo = random_uuid(0) + print(foo.result()) + assert foo.result() == x.result(), "Memoized results were not used" diff --git a/parsl/parsl/tests/test_python_apps/test_memoize_2.py b/parsl/parsl/tests/test_python_apps/test_memoize_2.py new file mode 100644 index 00000000..fe418b1d --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_memoize_2.py @@ -0,0 +1,43 @@ +import argparse + +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.config import Config +from parsl.dataflow.memoization import BasicMemoizer +from parsl.executors.threads import ThreadPoolExecutor + + +def local_config(): + return Config( + executors=[ + ThreadPoolExecutor(max_threads=4), + ], + memoizer=BasicMemoizer(memoize=False) + ) + + +@python_app(cache=True) +def random_uuid(x): + import uuid + return str(uuid.uuid4()) + + +@pytest.mark.local +def test_python_memoization(n=2): + """Testing python memoization disable via DFK call + """ + x = random_uuid(0) + + # Force x to completion before running other tests that will + # potentially re-use (or not-re-use) the result of x. Otherwise, + # results might not be reused because x is not completed, rather + # than because app-caching is disabled. + + x.result() + + for i in range(0, n): + foo = random_uuid(0) + assert foo.result() != x.result( + ), "Memoized results were used when memoization was disabled" diff --git a/parsl/parsl/tests/test_python_apps/test_memoize_4.py b/parsl/parsl/tests/test_python_apps/test_memoize_4.py new file mode 100644 index 00000000..92706ce4 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_memoize_4.py @@ -0,0 +1,26 @@ +import argparse + +import parsl +from parsl.app.app import python_app +from parsl.tests.configs.local_threads import config + + +def test_python_memoization(n=2): + """Testing python memoization when func bodies differ + """ + @python_app + def random_uuid(x): + import uuid + return str(uuid.uuid4()) + + x = random_uuid(0) + print(x.result()) + + @python_app + def random_uuid(x): + import uuid + print("hi") + return str(uuid.uuid4()) + + y = random_uuid(0) + assert x.result() != y.result(), "Memoized results were not used" diff --git a/parsl/parsl/tests/test_python_apps/test_memoize_bad_id_for_memo.py b/parsl/parsl/tests/test_python_apps/test_memoize_bad_id_for_memo.py new file mode 100644 index 00000000..d4235457 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_memoize_bad_id_for_memo.py @@ -0,0 +1,64 @@ +import pytest + +from parsl import python_app +from parsl.dataflow.memoization import id_for_memo + + +# this class should not have a memoizer registered for it +class Unmemoizable: + pass + + +# this class should have a memoizer that always raises an +# exception +class FailingMemoizable: + pass + + +class FailingMemoizerTestError(ValueError): + pass + + +@id_for_memo.register(FailingMemoizable) +def failing_memoizer(v, output_ref=False): + raise FailingMemoizerTestError("Deliberate memoizer failure") + + +@python_app(cache=True) +def noop_app(x, inputs=[], cache=True): + return None + + +@python_app +def some_func(_t): + pass + + +def test_python_unmemoizable(): + """Testing behaviour when an unmemoizable parameter is used + """ + fut = noop_app(Unmemoizable()) + with pytest.raises(ValueError): + fut.result() + + +def test_python_failing_memoizer(): + """Testing behaviour when id_for_memo raises an exception + """ + fut = noop_app(FailingMemoizable()) + with pytest.raises(FailingMemoizerTestError): + fut.result() + + +def test_python_unmemoizable_after_dep(): + memoizable_fut = some_func(1) + fut = noop_app(Unmemoizable(), inputs=[memoizable_fut]) + with pytest.raises(ValueError): + fut.result() + + +def test_python_failing_memoizer_afer_dep(): + memoizable_fut = some_func(1) + fut = noop_app(FailingMemoizable(), inputs=[memoizable_fut]) + with pytest.raises(ValueError): + fut.result() diff --git a/parsl/parsl/tests/test_python_apps/test_memoize_exception.py b/parsl/parsl/tests/test_python_apps/test_memoize_exception.py new file mode 100644 index 00000000..28a53685 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_memoize_exception.py @@ -0,0 +1,55 @@ +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.config import Config +from parsl.dataflow.memoization import BasicMemoizer +from parsl.executors.threads import ThreadPoolExecutor + + +def local_config(): + return Config( + executors=[ThreadPoolExecutor()], + memoizer=BasicMemoizer() + ) + + +@python_app(cache=True) +def raise_exception_cache(x, cache=True): + raise RuntimeError("exception from raise_exception_cache") + + +@python_app(cache=False) +def raise_exception_nocache(x, cache=True): + raise RuntimeError("exception from raise_exception_nocache") + + +@pytest.mark.local +def test_python_memoization(n=2): + """Test BasicMemoizer memoization of exceptions, with cache=True""" + x = raise_exception_cache(0) + + # wait for x to be done + x.exception() + + for i in range(0, n): + fut = raise_exception_cache(0) + + # check that we get back the same exception object, rather than + # a new one from a second invocation of raise_exception(). + assert fut.exception() is x.exception(), "Memoized exception should have been memoized" + + +@pytest.mark.local +def test_python_no_memoization(n=2): + """Test BasicMemoizer non-memoization of exceptions, with cache=False""" + x = raise_exception_nocache(0) + + # wait for x to be done + x.exception() + + for i in range(0, n): + fut = raise_exception_nocache(0) + + # check that we get back a different exception object each time + assert fut.exception() is not x.exception(), "Memoized exception should have been memoized" diff --git a/parsl/parsl/tests/test_python_apps/test_memoize_ignore_args.py b/parsl/parsl/tests/test_python_apps/test_memoize_ignore_args.py new file mode 100644 index 00000000..03ebff57 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_memoize_ignore_args.py @@ -0,0 +1,30 @@ +from parsl.app.app import python_app + + +@python_app(cache=True, ignore_for_cache=[]) +def random_uuid(x): + import uuid + return str(uuid.uuid4()) + + +@python_app(cache=True, ignore_for_cache=['x']) +def random_uuid_def(x): + import uuid + return str(uuid.uuid4()) + + +def test_memo_different(): + # explicitly call result() before launching next app so that + # the executions are serialized and the x result will be in + # memo table + x = random_uuid(x=0).result() + y = random_uuid(x=1).result() + + assert x != y, "Memoized results were used incorrectly" + + +def test_memo_same_at_definition(): + x = random_uuid_def(x=0).result() + y = random_uuid_def(x=1).result() + + assert x == y, "Memoized results were not used" diff --git a/parsl/parsl/tests/test_python_apps/test_memoize_joinapp.py b/parsl/parsl/tests/test_python_apps/test_memoize_joinapp.py new file mode 100644 index 00000000..42c8e043 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_memoize_joinapp.py @@ -0,0 +1,23 @@ +from parsl.app.app import join_app, python_app + + +@join_app(cache=True) +def random_uuid(x): + import uuid + return const(str(uuid.uuid4())) + + +@python_app +def const(x): + return x + + +def test_join_memoization(): + """Testing python memoization disable + """ + x = random_uuid(0) + x.result() + + for i in range(0, 2): + foo = random_uuid(0) + assert foo.result() == x.result(), "Memoized results were not used" diff --git a/parsl/parsl/tests/test_python_apps/test_memoize_plugin.py b/parsl/parsl/tests/test_python_apps/test_memoize_plugin.py new file mode 100644 index 00000000..f983f37d --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_memoize_plugin.py @@ -0,0 +1,49 @@ +import argparse + +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.config import Config +from parsl.dataflow.memoization import BasicMemoizer +from parsl.dataflow.taskrecord import TaskRecord + + +class DontReuseSevenMemoizer(BasicMemoizer): + def check_memo(self, task_record: TaskRecord): + if task_record['args'][0] == 7: + return None # we didn't find a suitable memo record... + else: + return super().check_memo(task_record) + + +def local_config(): + return Config(memoizer=DontReuseSevenMemoizer()) + + +@python_app(cache=True) +def random_uuid(x, cache=True): + import uuid + return str(uuid.uuid4()) + + +@pytest.mark.local +@pytest.mark.parametrize("n", (10,)) +def test_python_memoization(n: int): + """Testing using an alternate memoization plugin. + + The plugin under test memoizes except for functions with the parameter 7, + and that behaviour should be observable in uuid app results. + """ + + x = random_uuid(0).result() + + for i in range(0, n): + foo = random_uuid(0) + assert foo.result() == x, "Memoized results were incorrectly not used" + + y = random_uuid(7).result() + + for i in range(0, n): + foo = random_uuid(7) + assert foo.result() != y, "Memoized results were incorrectly used" diff --git a/parsl/parsl/tests/test_python_apps/test_outputs.py b/parsl/parsl/tests/test_python_apps/test_outputs.py new file mode 100644 index 00000000..b4273cf2 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_outputs.py @@ -0,0 +1,28 @@ +import os +from concurrent.futures import wait + +import pytest + +from parsl import File, python_app + + +@python_app +def double(x, outputs=[]): + with open(outputs[0], 'w') as f: + f.write(x * 5) + return x * 5 + + +whitelist = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'configs', '*threads*') + + +@pytest.mark.shared_fs +def test_launch_apps(tmpd_cwd, n=2): + outdir = tmpd_cwd / "outputs" + outdir.mkdir() + + futs = [double(i, outputs=[File(str(outdir / f"{i}.txt"))]) for i in range(n)] + wait(futs) + + stdout_file_count = len(list(outdir.glob("*.txt"))) + assert stdout_file_count == n, sorted(outdir.glob("*.txt")) diff --git a/parsl/parsl/tests/test_python_apps/test_overview.py b/parsl/parsl/tests/test_python_apps/test_overview.py new file mode 100644 index 00000000..63e205ef --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_overview.py @@ -0,0 +1,23 @@ +from parsl.app.app import python_app + + +@python_app +def app_double(x): + return x * 2 + + +@python_app +def app_sum(inputs=()): + return sum(inputs) + + +def test_1(N=10): + """Testing code snippet from the documentation + """ + + # Create a list of integers, then apply *app* function to each + items = range(N) + mapped_results = list(map(app_double, items)) + + total = app_sum(inputs=mapped_results) + assert total.result() == 2 * sum(items) diff --git a/parsl/parsl/tests/test_python_apps/test_pipeline.py b/parsl/parsl/tests/test_python_apps/test_pipeline.py new file mode 100644 index 00000000..ceb2d11e --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_pipeline.py @@ -0,0 +1,35 @@ +import argparse + +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.tests.configs.local_threads import config + + +@python_app +def increment(x): + return x + 1 + + +@python_app +def slow_increment(x): + import time + time.sleep(0.001) + return x + 1 + + +@pytest.mark.parametrize("depth", (2, 3)) +def test_increment(depth): + """Test simple pipeline A->B...->N""" + futs = [increment(0)] + futs.extend(increment(futs[i - 1]) for i in range(1, depth)) + assert sum(f.result() for f in futs) == sum(range(1, depth + 1)) + + +@pytest.mark.parametrize("depth", (2, 3)) +def test_increment_slow(depth): + """Test simple pipeline A->B...->N with delay""" + futs = [slow_increment(0)] + futs.extend(slow_increment(futs[i - 1]) for i in range(1, depth)) + assert sum(f.result() for f in futs) == sum(range(1, depth + 1)) diff --git a/parsl/parsl/tests/test_python_apps/test_pluggable_future_resolution.py b/parsl/parsl/tests/test_python_apps/test_pluggable_future_resolution.py new file mode 100644 index 00000000..4793c0ac --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_pluggable_future_resolution.py @@ -0,0 +1,161 @@ +from concurrent.futures import Future +from pathlib import Path +from threading import Event +from typing import Sequence + +import pytest + +import parsl +from parsl.config import Config +from parsl.dataflow.dependency_resolvers import DEEP_DEPENDENCY_RESOLVER +from parsl.dataflow.errors import DependencyError + + +def local_config(): + return Config(dependency_resolver=DEEP_DEPENDENCY_RESOLVER) + + +@parsl.python_app +def a(event): + event.wait() + return 7 + + +@parsl.python_app +def b(x: int): + return x + 1 + + +@pytest.mark.local +def test_simple_pos_arg(): + e = Event() + s = a(e) + f_b = b(s) + e.set() + + assert f_b.result() == 8 + + +@parsl.python_app +def b_first(x: Sequence[int]): + return x[0] + 1 + + +@pytest.mark.local +def test_tuple_pos_arg(): + e = Event() + s = (a(e),) + f_b = b_first(s) + e.set() + assert f_b.result() == 8 + + +@pytest.mark.local +def test_list_exception(): + a = Future() + a.set_exception(RuntimeError("artificial error")) + f_b = b([a]) + assert isinstance(f_b.exception(), DependencyError) + + +@parsl.python_app +def make_path(s: str): + return Path(s) + + +@parsl.python_app +def append_paths(iterable, end_str: str = "end"): + type_ = type(iterable) + return type_([Path(s, end_str) for s in iterable]) + + +@pytest.mark.local +@pytest.mark.parametrize( + "type_", + [ + tuple, + list, + set, + ], +) +def test_resolving_iterables(type_): + output1 = make_path("test1") + output2 = make_path("test2") + output3 = append_paths(type_([output1, output2]), end_str="end") + assert output3.result() == type_([Path("test1", "end"), Path("test2", "end")]) + + +@parsl.python_app +def append_paths_dict(iterable: dict, end_str: str = "end"): + return {Path(k, end_str): Path(v, end_str) for k, v in iterable.items()} + + +@pytest.mark.local +def test_resolving_dict(): + output1 = make_path("test1") + output2 = make_path("test2") + output3 = append_paths_dict({output1: output2}, end_str="end") + assert output3.result() == {Path("test1", "end"): Path("test2", "end")} + + +@parsl.python_app +def extract_deep(struct: list): + return struct[0][0][0][0][0] + + +@pytest.mark.local +def test_deeper_list(): + f = Future() + f.set_result(7) + f_b = extract_deep([[[[[f]]]]]) + + assert f_b.result() == 7 + + +@pytest.mark.local +def test_deeper_list_and_tuple(): + f = Future() + f.set_result(7) + f_b = extract_deep([([([f],)],)]) + + assert f_b.result() == 7 + + +@parsl.python_app +def dictionary_checker(d): + assert d["a"] == [30, 10] + assert d["b"] == 20 + + +@pytest.mark.local +def test_dictionary(): + k1 = Future() + k1.set_result("a") + k2 = Future() + k2.set_result("b") + v1 = Future() + v1.set_result(10) + + # this .result() will fail if the asserts fail + dictionary_checker({k1: [30, v1], k2: 20}).result() + + +@pytest.mark.local +def test_dictionary_later(): + k1 = Future() + k2 = Future() + v1 = Future() + + f1 = dictionary_checker({k1: [30, v1], k2: 20}) + + assert not f1.done() + k1.set_result("a") + k2.set_result("b") + v1.set_result(10) + + # having set the results, f1 should fairly rapidly complete (but not + # instantly) so we can't assert on done() here... but we can + # check that f1 does "eventually" complete... meaning that + # none of the assertions inside test_dictionary have failed + + assert f1.result() is None diff --git a/parsl/parsl/tests/test_python_apps/test_simple.py b/parsl/parsl/tests/test_python_apps/test_simple.py new file mode 100644 index 00000000..007db7b9 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_simple.py @@ -0,0 +1,32 @@ +from parsl.app.app import python_app + + +@python_app +def increment(x): + return x + 1 + + +@python_app +def slow_increment(x, dur): + import time + time.sleep(dur) + return x + 1 + + +def test_increment(depth=5): + futs = {0: 0} + for i in range(1, depth): + futs[i] = increment(futs[i - 1]) + + x = sum([futs[i].result() for i in futs if not isinstance(futs[i], int)]) + assert x == sum(range(1, depth)), "[TEST] increment [FAILED]" + + +def test_slow_increment(depth=5): + futs = {0: 0} + for i in range(1, depth): + futs[i] = slow_increment(futs[i - 1], 0.01) + + x = sum([futs[i].result() for i in futs if not isinstance(futs[i], int)]) + + assert x == sum(range(1, depth)), "[TEST] slow_increment [FAILED]" diff --git a/parsl/parsl/tests/test_python_apps/test_timeout.py b/parsl/parsl/tests/test_python_apps/test_timeout.py new file mode 100644 index 00000000..1b051332 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_timeout.py @@ -0,0 +1,40 @@ +import time + +import pytest + +from parsl.app.errors import AppTimeout +from parsl.app.python import timeout + + +class TimeoutTestSpecificException(Exception): + pass + + +def kernel(fail=False): + if fail: + raise TimeoutTestSpecificException() + + +def test_timeout(): + timeout_dur = 0.1 + try: + timeout(kernel, timeout_dur)() # nominally returns "instantly" + except AppTimeout: + pytest.skip("Pre-condition failed: result not received in timely fashion") + assert False + + try: + time.sleep(timeout_dur + 0.01) # this is the verification + except AppTimeout: + assert False, "Timer was not cancelled!" + + +@pytest.mark.parametrize("timeout_dur", (0.005, 0.01)) +def test_timeout_after_exception(timeout_dur): + with pytest.raises(TimeoutTestSpecificException): + timeout(kernel, timeout_dur)(True) + + try: + time.sleep(timeout_dur + 0.01) # this is the verification + except AppTimeout: + assert False, "Timer was not cancelled!" diff --git a/parsl/parsl/tests/test_python_apps/test_type5.py b/parsl/parsl/tests/test_python_apps/test_type5.py new file mode 100644 index 00000000..a58a97f4 --- /dev/null +++ b/parsl/parsl/tests/test_python_apps/test_type5.py @@ -0,0 +1,31 @@ +import pytest + +from parsl.app.app import python_app + + +@python_app +def map_x2(x): + return x * 2 + + +@python_app +def add_xy(x, y): + return x + y + + +def test_func_linked_to_previous_result(depth=2): + futs = (map_x2(i) for i in range(1, depth + 1)) + for _ in range(1, depth - 1): + futs = (map_x2(fut) for fut in futs) + futs = [map_x2(fut) for fut in futs] + + assert sum(f.result() for f in futs) == (2 ** depth) * sum(range(1, depth + 1)) + + +def test_func_linked_to_multiple_previous_results(width=4): + futs = (map_x2(i) for i in range(1, width + 1)) + f_it = iter(futs) + + # Requires that len(futs) % 2 == 0 (even number of items) + futs = [add_xy(f, next(f_it)) for f in f_it] + assert sum(f.result() for f in futs) == 2 * sum(range(1, width + 1)) diff --git a/parsl/parsl/tests/test_radical/__init__.py b/parsl/parsl/tests/test_radical/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_radical/test_mpi_funcs.py b/parsl/parsl/tests/test_radical/test_mpi_funcs.py new file mode 100644 index 00000000..851328da --- /dev/null +++ b/parsl/parsl/tests/test_radical/test_mpi_funcs.py @@ -0,0 +1,34 @@ +import os + +import pytest + +import parsl +from parsl.tests.configs.local_radical_mpi import fresh_config as local_config + + +@parsl.python_app +def some_mpi_func(msg, sleep, comm=None, parsl_resource_specification={}): + import time + msg = 'hello %d/%d: %s' % (comm.rank, comm.size, msg) + time.sleep(sleep) + print(msg) + return comm.size + + +apps = [] + + +@pytest.mark.local +@pytest.mark.radical +@pytest.mark.skipif( + os.environ.get("RADICAL_CI") != "1", + reason="Only runs in Radical CI workflow" +) +def test_radical_mpi(n=7): + # rank size should be > 1 for the + # radical runtime system to run this function in MPI env + for i in range(2, n): + spec = {'ranks': i} + t = some_mpi_func(msg='mpi.func.%06d' % i, sleep=1, comm=None, parsl_resource_specification=spec) + apps.append(t) + assert [len(app.result()) for app in apps] == list(range(2, n)) diff --git a/parsl/parsl/tests/test_regression/__init__.py b/parsl/parsl/tests/test_regression/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_regression/test_1480.py b/parsl/parsl/tests/test_regression/test_1480.py new file mode 100644 index 00000000..a52a1f3e --- /dev/null +++ b/parsl/parsl/tests/test_regression/test_1480.py @@ -0,0 +1,20 @@ +import pytest + +from parsl import python_app +from parsl.tests.configs.htex_local import fresh_config as local_config + + +@python_app +def compute_descript(size=1000): + import numpy as np + x = np.array(list(range(0, size)), dtype=complex).astype(np.float32) + return x + + +@pytest.mark.local +def test_1480(size=10**6): + x = compute_descript(size=size) + + # Before PR#1841 this would have raised a TypeError + # Now, with the threshold increased this should not trigger any error + assert len(x.result()) == size, "Lengths do not match" diff --git a/parsl/parsl/tests/test_regression/test_1606_wait_for_current_tasks.py b/parsl/parsl/tests/test_regression/test_1606_wait_for_current_tasks.py new file mode 100644 index 00000000..5cced409 --- /dev/null +++ b/parsl/parsl/tests/test_regression/test_1606_wait_for_current_tasks.py @@ -0,0 +1,42 @@ +import threading +import time + +import pytest + +import parsl +from parsl.tests.configs.local_threads import fresh_config as local_config + + +@parsl.python_app +def slow_app(evt: threading.Event): + evt.wait() + + +@pytest.mark.local +def test_wait_for_tasks(): + """ + gh#1606 reported that wait_for_current_tasks fails due to tasks being removed + from the DFK tasks dict as they complete; bug introduced in #1543. + """ + def test_kernel(may_wait: threading.Event): + e1, e2 = threading.Event(), threading.Event() + + # app_slow is in *middle* of internal DFK data structure + app_fast1, app_slow, app_fast2 = slow_app(e1), slow_app(e2), slow_app(e1) + + may_wait.set() # initiated wait in outer test + time.sleep(0.01) + + e1.set() + + while not all(f.done() for f in (app_fast1, app_fast2)): + time.sleep(0.01) + + e2.set() + app_slow.result() + + may_continue = threading.Event() + threading.Thread(target=test_kernel, daemon=True, args=(may_continue,)).start() + + may_continue.wait() + parsl.dfk().wait_for_current_tasks() # per sleeps, waits for all 3 tasks diff --git a/parsl/parsl/tests/test_regression/test_1653.py b/parsl/parsl/tests/test_regression/test_1653.py new file mode 100644 index 00000000..61ab1bd4 --- /dev/null +++ b/parsl/parsl/tests/test_regression/test_1653.py @@ -0,0 +1,22 @@ +import pytest + +from parsl import python_app +from parsl.tests.configs.htex_local import fresh_config as local_config + + +@python_app +def compute_descript(size=1000): + import numpy as np + x = np.array(list(range(0, size)), dtype=complex).astype(np.float32) + return x + + +@pytest.mark.local +def test_1653(): + """ Check if #1653 works correctly + """ + x = compute_descript(size=100).result() + assert x.shape == (100,), "Got incorrect numpy shape" + + x = compute_descript(size=1000).result() + assert x.shape == (1000,), "Got incorrect numpy shape" diff --git a/parsl/parsl/tests/test_regression/test_221.py b/parsl/parsl/tests/test_regression/test_221.py new file mode 100644 index 00000000..d9b96b58 --- /dev/null +++ b/parsl/parsl/tests/test_regression/test_221.py @@ -0,0 +1,29 @@ +import pytest + +from parsl.app.app import python_app +from parsl.tests.configs.htex_local import fresh_config as local_config + + +@python_app +def slow_double(x, dur=0.1): + import time + time.sleep(dur) + return x * 5 + + +@pytest.mark.local +def test_cleanup_behavior_221(): + """ A1 A2 A3 -> cleanup + B1 B2 B3 + + """ + + round_1 = [] + for i in range(0, 2): + f = slow_double(i) + round_1.append(f) + + round_2 = [] + for i in round_1: + f = slow_double(i) + round_2.append(f) diff --git a/parsl/parsl/tests/test_regression/test_226.py b/parsl/parsl/tests/test_regression/test_226.py new file mode 100644 index 00000000..8babf4cb --- /dev/null +++ b/parsl/parsl/tests/test_regression/test_226.py @@ -0,0 +1,63 @@ +''' +Regression test for #226. +''' +import os + +import pandas as pd +import pytest + +from parsl.app.app import bash_app, python_app + + +class Foo: + def __init__(self, x): + self.x = x + + def __eq__(self, value): + raise NotImplementedError + + +bar = Foo(1) + + +@python_app +def get_foo_x(a, b=bar, c=None): + return b.x + + +@bash_app +def get_foo_x_bash(a, b=bar, c=None): + return "echo {}".format(b.x) + + +data = pd.DataFrame({'x': [None, 2, [3]]}) + + +@python_app +def get_dataframe(d=data): + return d + + +@bash_app +def echo(msg, postfix='there', stdout='std.out'): + return 'echo {} {}'.format(msg, postfix) + + +def test_no_eq(): + res = get_foo_x('foo').result() + assert res == 1, 'Expected 1, returned {}'.format(res) + + +def test_get_dataframe(): + res = get_dataframe().result() + assert res.equals(data), 'Unexpected dataframe' + + +@pytest.mark.shared_fs +def test_bash_default_arg(): + if os.path.exists('std.out'): + os.remove('std.out') + + echo('hello').result() + with open('std.out', 'r') as f: + assert f.read().strip() == 'hello there', "Output should be 'hello there'" diff --git a/parsl/parsl/tests/test_regression/test_2652.py b/parsl/parsl/tests/test_regression/test_2652.py new file mode 100644 index 00000000..42cdae9c --- /dev/null +++ b/parsl/parsl/tests/test_regression/test_2652.py @@ -0,0 +1,28 @@ +import pytest + +from parsl.jobs.states import JobState + + +@pytest.mark.local +def test_JobStatus_repr(): + # in Python 3.11, the behavior of enums changed a bit, and so repr + # (inherited from a superclass) raised an exception rather than + # returning a string. + + # This test checks that repr returns some string value - it does + # not validate that that string is some expected value. + + j = JobState.COMPLETED + + # this repr call started raising exceptions with python 3.11 + # prior to the #2652 bugfix. + r = repr(j) + assert isinstance(r, str) + + s = str(j) + assert isinstance(s, str) + + # IntEnum changed behaviour from python 3.10 -> 3.10 so as to return + # an integer. This checks that the JobState enum implementation + # returns a name string on all tested platforms. + assert s == "JobState.COMPLETED" diff --git a/parsl/parsl/tests/test_regression/test_3874.py b/parsl/parsl/tests/test_regression/test_3874.py new file mode 100644 index 00000000..00390ed5 --- /dev/null +++ b/parsl/parsl/tests/test_regression/test_3874.py @@ -0,0 +1,47 @@ +import shutil + +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.config import Config +from parsl.executors import HighThroughputExecutor + + +@python_app +def noop(): + pass + + +@pytest.mark.local +def test_regression_3874(tmpd_cwd_session): + # HTEX run 1 + + rundir_1 = str(tmpd_cwd_session / "1") + + config = Config(executors=[HighThroughputExecutor()], strategy_period=0.5) + config.run_dir = rundir_1 + + with parsl.load(config): + noop().result() + + # It is necessary to delete this rundir to exercise the bug. Otherwise, + # the next run will be able to continue looking at this directory - the + # bug manifests when it cannot. + + shutil.rmtree(rundir_1) + + # HTEX run 2 + # In the case of issue 3874, this run hangs (rather than failing) as the + # JobStatusPoller fails to collect status of all of its managed tasks + # every iteration, without converging towards failure. + + rundir_2 = str(tmpd_cwd_session / "2") + + config = Config(executors=[HighThroughputExecutor()], strategy_period=0.5) + config.run_dir = rundir_2 + + with parsl.load(config): + noop().result() + + shutil.rmtree(rundir_2) diff --git a/parsl/parsl/tests/test_regression/test_97_parallelism_0.py b/parsl/parsl/tests/test_regression/test_97_parallelism_0.py new file mode 100644 index 00000000..a83a5b79 --- /dev/null +++ b/parsl/parsl/tests/test_regression/test_97_parallelism_0.py @@ -0,0 +1,54 @@ +import pytest + +import parsl +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SimpleLauncher +from parsl.providers import LocalProvider + + +def local_config() -> Config: + return Config( + executors=[ + HighThroughputExecutor( + label="htex_local", + worker_debug=True, + cores_per_worker=1, + encrypted=True, + provider=LocalProvider( + init_blocks=0, + min_blocks=0, + max_blocks=1, + parallelism=0, + launcher=SimpleLauncher(), + ), + ) + ], + strategy='simple', + ) + + +@parsl.python_app +def python_app(): + return 7 + + +@pytest.mark.local +def test_parallism_0(): + """This is a regression test for parsl issue #97, + https://github.com/Parsl/parsl/issues/97 + + In that issue, if parallelism is set to 0, then no blocks would be + provisioned by the simple scaling strategy, as the number of blocks + to be scaled out would include a parallelism factor of 0 and so would + always be 0. With 0 blocks, no tasks would execute. + + Commit 2828a779e00bf0188ec82454c9aa3004480b5809 adds in an additional + scaling strategy case to make one block be scaled out when there are + active tasks, even if the parallelism-based path says 0 blocks are + needed. + + This test runs a single app to completion. This test would hang in the + case of a regression. + """ + assert python_app().result() == 7 diff --git a/parsl/parsl/tests/test_regression/test_98.py b/parsl/parsl/tests/test_regression/test_98.py new file mode 100644 index 00000000..486e33f1 --- /dev/null +++ b/parsl/parsl/tests/test_regression/test_98.py @@ -0,0 +1,23 @@ +''' +Regression test for issue #98 +''' +import argparse + +import pytest + +import parsl +from parsl.dataflow.dflow import DataFlowKernel +from parsl.tests.configs.local_threads import config + + +@pytest.mark.local +def test_immutable_config(n=2): + """Regression test for immutable config #98 + """ + + original = str(config) + dfk = DataFlowKernel(config=config) + after = str(config) + + dfk.cleanup() + assert original == after, "Config modified" diff --git a/parsl/parsl/tests/test_scaling/__init__.py b/parsl/parsl/tests/test_scaling/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_scaling/test_block_error_handler.py b/parsl/parsl/tests/test_scaling/test_block_error_handler.py new file mode 100644 index 00000000..3a7beb9f --- /dev/null +++ b/parsl/parsl/tests/test_scaling/test_block_error_handler.py @@ -0,0 +1,174 @@ +from functools import partial +from unittest.mock import Mock + +import pytest + +from parsl.executors import HighThroughputExecutor +from parsl.jobs.error_handlers import ( + noop_error_handler, + simple_error_handler, + windowed_error_handler, +) +from parsl.jobs.states import JobState, JobStatus +from parsl.providers import LocalProvider + + +@pytest.mark.local +def test_block_error_handler_false(): + mock = Mock() + htex = HighThroughputExecutor(block_error_handler=False, encrypted=True) + assert htex.block_error_handler is noop_error_handler + htex.set_bad_state_and_fail_all = mock + + bad_jobs = {'1': JobStatus(JobState.FAILED), + '2': JobStatus(JobState.FAILED), + '3': JobStatus(JobState.FAILED), + '4': JobStatus(JobState.FAILED)} + + htex.handle_errors(bad_jobs) + mock.assert_not_called() + + +@pytest.mark.local +def test_block_error_handler_mock(): + handler_mock = Mock() + htex = HighThroughputExecutor(block_error_handler=handler_mock, encrypted=True) + assert htex.block_error_handler is handler_mock + + bad_jobs = {'1': JobStatus(JobState.FAILED), + '2': JobStatus(JobState.FAILED), + '3': JobStatus(JobState.FAILED), + '4': JobStatus(JobState.FAILED)} + + htex.handle_errors(bad_jobs) + handler_mock.assert_called() + handler_mock.assert_called_with(htex, bad_jobs) + + +@pytest.mark.local +def test_simple_error_handler(): + htex = HighThroughputExecutor(block_error_handler=simple_error_handler, + encrypted=True, + provider=LocalProvider(init_blocks=3)) + + assert htex.block_error_handler is simple_error_handler + + bad_state_mock = Mock() + htex.set_bad_state_and_fail_all = bad_state_mock + + bad_jobs = {'1': JobStatus(JobState.FAILED), + '2': JobStatus(JobState.FAILED)} + htex.handle_errors(bad_jobs) + bad_state_mock.assert_not_called() + + # Check the bad behavior where if any job is not failed + # bad state won't be set + bad_jobs = {'1': JobStatus(JobState.COMPLETED), + '2': JobStatus(JobState.FAILED), + '3': JobStatus(JobState.FAILED), + '4': JobStatus(JobState.FAILED)} + + htex.handle_errors(bad_jobs) + bad_state_mock.assert_not_called() + + bad_jobs = {'1': JobStatus(JobState.FAILED), + '2': JobStatus(JobState.FAILED), + '3': JobStatus(JobState.FAILED), + '4': JobStatus(JobState.FAILED)} + + htex.handle_errors(bad_jobs) + bad_state_mock.assert_called() + + +@pytest.mark.local +def test_windowed_error_handler(): + htex = HighThroughputExecutor(block_error_handler=windowed_error_handler, encrypted=True) + assert htex.block_error_handler is windowed_error_handler + + bad_state_mock = Mock() + htex.set_bad_state_and_fail_all = bad_state_mock + + bad_jobs = {'1': JobStatus(JobState.FAILED), + '2': JobStatus(JobState.FAILED)} + htex.handle_errors(bad_jobs) + bad_state_mock.assert_not_called() + + bad_jobs = {'1': JobStatus(JobState.COMPLETED), + '2': JobStatus(JobState.FAILED), + '3': JobStatus(JobState.FAILED)} + htex.handle_errors(bad_jobs) + bad_state_mock.assert_not_called() + + bad_jobs = {'1': JobStatus(JobState.FAILED), + '2': JobStatus(JobState.FAILED), + '3': JobStatus(JobState.COMPLETED), + '4': JobStatus(JobState.FAILED)} + htex.handle_errors(bad_jobs) + bad_state_mock.assert_not_called() + + bad_jobs = {'1': JobStatus(JobState.COMPLETED), + '2': JobStatus(JobState.FAILED), + '3': JobStatus(JobState.FAILED), + '4': JobStatus(JobState.FAILED)} + htex.handle_errors(bad_jobs) + bad_state_mock.assert_called() + + +@pytest.mark.local +def test_windowed_error_handler_sorting(): + htex = HighThroughputExecutor(block_error_handler=windowed_error_handler, encrypted=True) + assert htex.block_error_handler is windowed_error_handler + + bad_state_mock = Mock() + htex.set_bad_state_and_fail_all = bad_state_mock + + bad_jobs = {'8': JobStatus(JobState.FAILED), + '9': JobStatus(JobState.FAILED), + '10': JobStatus(JobState.FAILED), + '11': JobStatus(JobState.COMPLETED), + '12': JobStatus(JobState.COMPLETED)} + htex.handle_errors(bad_jobs) + bad_state_mock.assert_not_called() + + bad_jobs = {'8': JobStatus(JobState.COMPLETED), + '9': JobStatus(JobState.FAILED), + '21': JobStatus(JobState.FAILED), + '22': JobStatus(JobState.FAILED), + '10': JobStatus(JobState.FAILED)} + htex.handle_errors(bad_jobs) + bad_state_mock.assert_called() + + +@pytest.mark.local +def test_windowed_error_handler_with_threshold(): + error_handler = partial(windowed_error_handler, threshold=2) + htex = HighThroughputExecutor(block_error_handler=error_handler, encrypted=True) + assert htex.block_error_handler is error_handler + + bad_state_mock = Mock() + htex.set_bad_state_and_fail_all = bad_state_mock + + bad_jobs = {'1': JobStatus(JobState.COMPLETED), + '2': JobStatus(JobState.FAILED)} + htex.handle_errors(bad_jobs) + bad_state_mock.assert_not_called() + + bad_jobs = {'1': JobStatus(JobState.COMPLETED), + '2': JobStatus(JobState.FAILED), + '3': JobStatus(JobState.COMPLETED)} + htex.handle_errors(bad_jobs) + bad_state_mock.assert_not_called() + + bad_jobs = {'1': JobStatus(JobState.COMPLETED), + '2': JobStatus(JobState.COMPLETED), + '3': JobStatus(JobState.COMPLETED), + '4': JobStatus(JobState.FAILED)} + htex.handle_errors(bad_jobs) + bad_state_mock.assert_not_called() + + bad_jobs = {'1': JobStatus(JobState.COMPLETED), + '2': JobStatus(JobState.COMPLETED), + '3': JobStatus(JobState.FAILED), + '4': JobStatus(JobState.FAILED)} + htex.handle_errors(bad_jobs) + bad_state_mock.assert_called() diff --git a/parsl/parsl/tests/test_scaling/test_regression_1621.py b/parsl/parsl/tests/test_scaling/test_regression_1621.py new file mode 100644 index 00000000..ea7d8f26 --- /dev/null +++ b/parsl/parsl/tests/test_scaling/test_regression_1621.py @@ -0,0 +1,70 @@ +import threading + +import pytest + +import parsl +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SimpleLauncher +from parsl.providers import LocalProvider + +# Timing notes: +# The configured strategy_period must be much smaller than the delay in +# app() so that multiple iterations of the strategy have had a chance +# to (mis)behave. +# The status polling interval in OneShotLocalProvider must be much bigger +# than the above times, so that the job status cached from the provider +# will not be updated while the single invocation of app() runs. + + +@parsl.python_app +def app(): + import time + time.sleep(1) + + +class OneShotLocalProvider(LocalProvider): + def __init__(self, *args, **kwargs): + self.recorded_submits = 0 + super().__init__(*args, **kwargs) + + def submit(self, *args, **kwargs): + self.recorded_submits += 1 + return super().submit(*args, **kwargs) + + status_polling_interval = 600 + + +@pytest.mark.local +def test_one_block(tmpd_cwd): + """ + this test is intended to ensure that only one block is launched when only + one app is invoked. this is a regression test. + """ + oneshot_provider = OneShotLocalProvider( + init_blocks=0, + min_blocks=0, + max_blocks=10, + launcher=SimpleLauncher(), + ) + + config = Config( + executors=[ + HighThroughputExecutor( + label="htex_local", + address="127.0.0.1", + worker_debug=True, + cores_per_worker=1, + encrypted=True, + provider=oneshot_provider, + worker_logdir_root=str(tmpd_cwd) + ) + ], + strategy='simple', + strategy_period=0.1 + ) + + with parsl.load(config): + app().result() + + assert oneshot_provider.recorded_submits == 1 diff --git a/parsl/parsl/tests/test_scaling/test_regression_3568_scaledown_vs_MISSING.py b/parsl/parsl/tests/test_scaling/test_regression_3568_scaledown_vs_MISSING.py new file mode 100644 index 00000000..0c4a474b --- /dev/null +++ b/parsl/parsl/tests/test_scaling/test_regression_3568_scaledown_vs_MISSING.py @@ -0,0 +1,84 @@ +import time + +import pytest + +import parsl +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import WrappedLauncher +from parsl.providers import LocalProvider + + +def local_config(): + # see the comments inside test_regression for reasoning about why each + # of these parameters is set why it is. + return Config( + max_idletime=1, + + strategy='htex_auto_scale', + strategy_period=1, + + executors=[ + HighThroughputExecutor( + label="htex_local", + encrypted=True, + provider=LocalProvider( + init_blocks=1, + min_blocks=0, + max_blocks=1, + launcher=WrappedLauncher(prepend="sleep inf ; "), + ), + ) + ], + ) + + +@parsl.python_app +def task(): + return 7 + + +@pytest.mark.local +def test_regression(try_assert): + # The above config means that we should start scaling out one initial + # block, but then scale it back in after a second or so if the executor + # is kept idle (which this test does using try_assert). + + # Because of 'sleep inf' in the WrappedLaucher, the block will not ever + # register. + + # The bug being tested is about mistreatment of blocks which are scaled in + # before they have a chance to register, and the above forces that to + # happen. + + # After that scaling in has happened, we should see that we have one block + # and it should be in a terminal state. The below try_assert waits for + # that to become true. + + # At that time, we should also see htex reporting no blocks registered - as + # mentioned above, that is a necessary part of the bug being tested here. + + # Give 10 strategy periods for the above to happen: each step of scale up, + # and scale down due to idleness isn't guaranteed to happen in exactly one + # scaling step. + + htex = parsl.dfk().executors['htex_local'] + + try_assert(lambda: len(htex.status_facade) == 1 and htex.status_facade['0'].terminal, + timeout_ms=10000) + + assert htex.connected_blocks() == [], "No block should have connected to interchange" + + # Now we can reconfigure the launcher to let subsequent blocks launch ok, + # and run a trivial task. That trivial task will scale up a new block and + # run the task successfully. + + # Prior to issue #3568, the bug was that the scale in of the first + # block earlier in the test case would have incorrectly been treated as a + # failure, and then the block error handler would have treated that failure + # as a permanent htex failure, and so the task execution below would raise + # a BadStateException rather than attempt to run the task. + + assert htex.provider.launcher.prepend != "", "Pre-req: prepend attribute should exist and be non-empty" + htex.provider.launcher.prepend = "" + assert task().result() == 7 diff --git a/parsl/parsl/tests/test_scaling/test_regression_3696_oscillation.py b/parsl/parsl/tests/test_scaling/test_regression_3696_oscillation.py new file mode 100644 index 00000000..1b221c27 --- /dev/null +++ b/parsl/parsl/tests/test_scaling/test_regression_3696_oscillation.py @@ -0,0 +1,104 @@ +import math +from unittest.mock import MagicMock + +import pytest + +from parsl.executors.high_throughput.executor import HighThroughputExecutor +from parsl.jobs.states import JobState, JobStatus +from parsl.jobs.strategy import Strategy + + +# the parameterize tuple consists of: +# Input: +# * number of tasks to mock the load as +# * number of workers per node +# Expected output: +# * the number of blocks we should expect to be launched +# in this situation +# +# This test will configure an executor, then run strategize +# a few times, asserting that it converges to the correct +# number of blocks without oscillating. +@pytest.mark.local +@pytest.mark.parametrize("ns", [(14, 48, 1), # values from issue #3696 + + (1, 1, 1), # one task needs one block + + (100, 1, 20), # many one-task blocks, hitting hard-coded max blocks + + (47, 48, 1), # some edge cases around #3696 values + (48, 48, 1), # " + (49, 48, 2), # " + (149, 50, 3)]) # " +def test_htex_strategy_does_not_oscillate(ns): + """Check for oscillations in htex scaling. + In issue 3696, with a large number of workers per block + and a smaller number of active tasks, the htex scaling + strategy oscillates between 0 and 1 active block, rather + than converging to 1 active block. + """ + + n_tasks, n_workers, n_blocks = ns + + s = Strategy(strategy='htex_auto_scale', max_idletime=0) + + provider = MagicMock() + executor = MagicMock(spec=HighThroughputExecutor) + + statuses = {} + + executor.provider = provider + executor.outstanding = lambda: n_tasks + executor.status_facade = statuses + executor.workers_per_node = n_workers + executor.bad_state_is_set = False + + provider.parallelism = 1 + provider.init_blocks = 0 + provider.min_blocks = 0 + provider.max_blocks = 20 + provider.nodes_per_block = 1 + + def scale_out(n): + for _ in range(n): + statuses[len(statuses)] = JobStatus(state=JobState.PENDING) + + executor.scale_out_facade.side_effect = scale_out + + def scale_in(n, max_idletime=None): + # find n PENDING jobs and set them to CANCELLED + for k in statuses: + if n == 0: + return + if statuses[k].state == JobState.PENDING: + statuses[k].state = JobState.CANCELLED + n -= 1 + + executor.scale_in_facade.side_effect = scale_in + + s.add_executors([executor]) + + # In issue #3696, this first strategise does initial and load based + # scale outs, because n_tasks > n_workers*0 + s.strategize([executor]) + + executor.scale_out_facade.assert_called() + assert len(statuses) == n_blocks, "Should have launched n_blocks" + assert len([k for k in statuses if statuses[k].state == JobState.PENDING]) == n_blocks + # there might be several calls to scale_out_facade inside strategy, + # but the end effect should be that exactly one block is scaled out. + + executor.scale_in_facade.assert_not_called() + + # In issue #3696, this second strategize does a scale in, because n_tasks < n_workers*1 + s.strategize([executor]) + + # assert that there should still be n_blocks pending blocks + assert len([k for k in statuses if statuses[k].state == JobState.PENDING]) == n_blocks + # this assert fails due to issue #3696 + + # Now check scale in happens with 0 load + executor.outstanding = lambda: 0 + s.strategize([executor]) + executor.scale_in_facade.assert_called() + assert len([k for k in statuses if statuses[k].state == JobState.PENDING]) == 0 diff --git a/parsl/parsl/tests/test_scaling/test_scale_down.py b/parsl/parsl/tests/test_scaling/test_scale_down.py new file mode 100644 index 00000000..057c3e6c --- /dev/null +++ b/parsl/parsl/tests/test_scaling/test_scale_down.py @@ -0,0 +1,97 @@ +import logging +import time + +import pytest + +import parsl +from parsl import File, python_app +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SingleNodeLauncher +from parsl.providers import LocalProvider + +logger = logging.getLogger(__name__) + +_max_blocks = 5 +_min_blocks = 2 + + +def local_config(): + return Config( + executors=[ + HighThroughputExecutor( + heartbeat_period=1, + heartbeat_threshold=2, + poll_period=100, + label="htex_local", + address="127.0.0.1", + max_workers_per_node=1, + encrypted=True, + provider=LocalProvider( + init_blocks=0, + max_blocks=_max_blocks, + min_blocks=_min_blocks, + launcher=SingleNodeLauncher(), + ), + ) + ], + max_idletime=0.5, + strategy='simple', + strategy_period=0.5, + ) + + +@python_app +def waiting_app(ident: int, inputs=(), outputs=()): + import pathlib + import time + + # Approximate an Event by writing to files; the test logic will poll this file + with open(outputs[0], "a") as f: + f.write(f"Ready: {ident}\n") + + # Similarly, use Event approximation (file check!) by polling. + may_finish_file = pathlib.Path(inputs[0]) + while not may_finish_file.exists(): + time.sleep(0.01) + + +# see issue #1885 for details of failures of this test. +# at the time of issue #1885 this test was failing frequently +# in CI. +@pytest.mark.local +def test_scale_out(tmpd_cwd, try_assert): + dfk = parsl.dfk() + + num_managers = len(dfk.executors['htex_local'].connected_managers()) + + assert num_managers == 0, "Expected 0 managers at start" + assert dfk.executors['htex_local'].outstanding() == 0, "Expected 0 tasks at start" + + ntasks = 10 + ready_path = tmpd_cwd / "workers_ready" + finish_path = tmpd_cwd / "workers_may_continue" + ready_path.touch() + inputs = [File(finish_path)] + outputs = [File(ready_path)] + + futs = [waiting_app(i, outputs=outputs, inputs=inputs) for i in range(ntasks)] + + while ready_path.read_text().count("\n") < _max_blocks: + time.sleep(0.5) + + assert len(dfk.executors['htex_local'].connected_managers()) == _max_blocks + + finish_path.touch() # Approximation of Event, via files + [x.result() for x in futs] + + assert dfk.executors['htex_local'].outstanding() == 0 + + def assert_kernel(): + return len(dfk.executors['htex_local'].connected_managers()) == _min_blocks + + try_assert( + assert_kernel, + fail_msg=f"Expected {_min_blocks} managers when no tasks (min_blocks)", + timeout_ms=15000, + ) diff --git a/parsl/parsl/tests/test_scaling/test_scale_down_htex_auto_scale.py b/parsl/parsl/tests/test_scaling/test_scale_down_htex_auto_scale.py new file mode 100644 index 00000000..9ca91be1 --- /dev/null +++ b/parsl/parsl/tests/test_scaling/test_scale_down_htex_auto_scale.py @@ -0,0 +1,147 @@ +from threading import Event + +import pytest + +import parsl +from parsl import File, python_app +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SingleNodeLauncher +from parsl.providers import LocalProvider + +_max_blocks = 5 +_min_blocks = 0 + + +def local_config(): + return Config( + executors=[ + HighThroughputExecutor( + heartbeat_period=1, + heartbeat_threshold=2, + poll_period=100, + label="htex_local", + address="127.0.0.1", + max_workers_per_node=1, + encrypted=True, + provider=LocalProvider( + init_blocks=0, + max_blocks=_max_blocks, + min_blocks=_min_blocks, + launcher=SingleNodeLauncher(), + ), + ) + ], + max_idletime=0.5, + strategy='htex_auto_scale', + strategy_period=0.1 + ) + + +@python_app +def waiting_app(ident: int, outputs=(), inputs=()): + import pathlib + import time + + # Approximate an Event by writing to files; the test logic will poll this file + with open(outputs[0], "a") as f: + f.write(f"Ready: {ident}\n") + + # Similarly, use Event approximation (file check!) by polling. + may_finish_file = pathlib.Path(inputs[0]) + while not may_finish_file.exists(): + time.sleep(0.01) + + +# see issue #1885 for details of failures of this test. +# at the time of issue #1885 this test was failing frequently +# in CI. +@pytest.mark.local +def test_scale_out(tmpd_cwd, try_assert): + dfk = parsl.dfk() + + num_managers = len(dfk.executors['htex_local'].connected_managers()) + + assert num_managers == 0, "Expected 0 managers at start" + assert dfk.executors['htex_local'].outstanding() == 0, "Expected 0 tasks at start" + + ntasks = _max_blocks * 2 + ready_path = tmpd_cwd / "workers_ready" + finish_path = tmpd_cwd / "stage1_workers_may_continue" + ready_path.touch() + inputs = [File(finish_path)] + outputs = [File(ready_path)] + + futs = [waiting_app(i, outputs=outputs, inputs=inputs) for i in range(ntasks)] + + try_assert(lambda: ready_path.read_text().count("\n") == _max_blocks, "Wait for _max_blocks tasks to be running", timeout_ms=15000) + + # This should be true immediately, because the previous try_assert should + # wait until there are max_blocks tasks running, and his test should be + # configured to use 1 worker per block. + assert len(dfk.executors['htex_local'].connected_managers()) == _max_blocks + + finish_path.touch() # Approximation of Event, via files + [x.result() for x in futs] + + assert dfk.executors['htex_local'].outstanding() == 0 + + # now we can launch one "long" task - + # and what should happen is that the connected_managers count "eventually" (?) converges to 1 and stays there. + + finish_path = tmpd_cwd / "stage2_workers_may_continue" + + fut = waiting_app(0, outputs=outputs, inputs=[File(finish_path)]) + + def check_one_block(): + return len(dfk.executors['htex_local'].connected_managers()) == 1 + + try_assert( + check_one_block, + fail_msg="Expected 1 managers during a single long task", + ) + + # the task should not have finished by the time we end up with 1 manager + assert not fut.done() + + # This section wait for the strategy to run again, with the above single + # task outstanding, and check that the strategy has not scaled up or + # down more on those subsequent iterations. + + # It does this by hooking the callback of the job status poller, and + # waiting until it has run. + + old_cb = dfk.job_status_poller.callback + + strategy_iterated = Event() + + def hook_cb(*args, **kwargs): + r = old_cb(*args, **kwargs) + strategy_iterated.set() + return r + + dfk.job_status_poller.callback = hook_cb + + # hack strategies to run more frequently. this allo + # dfk.job_status_poller. + + try_assert( + strategy_iterated.is_set, + fail_msg="Expected strategy to have run within this period", + ) + + assert check_one_block() + + finish_path.touch() # now we can end the single stage-2 task + + fut.result() + + # now we should expect min_blocks scale down + + def check_min_blocks(): + return len(dfk.executors['htex_local'].connected_managers()) == _min_blocks + + try_assert( + check_min_blocks, + fail_msg=f"Expected {_min_blocks} managers when no tasks (min_blocks)", + ) diff --git a/parsl/parsl/tests/test_scaling/test_scale_down_htex_unregistered.py b/parsl/parsl/tests/test_scaling/test_scale_down_htex_unregistered.py new file mode 100644 index 00000000..90a9b9ff --- /dev/null +++ b/parsl/parsl/tests/test_scaling/test_scale_down_htex_unregistered.py @@ -0,0 +1,71 @@ +import logging +import time + +import pytest + +import parsl +from parsl import File, python_app +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.jobs.states import TERMINAL_STATES, JobState +from parsl.launchers import SingleNodeLauncher +from parsl.providers import LocalProvider + +logger = logging.getLogger(__name__) + +_max_blocks = 1 +_min_blocks = 0 + + +def local_config(): + return Config( + executors=[ + HighThroughputExecutor( + heartbeat_period=1, + heartbeat_threshold=2, + poll_period=100, + label="htex_local", + address="127.0.0.1", + max_workers_per_node=1, + encrypted=True, + launch_cmd="sleep inf", + provider=LocalProvider( + init_blocks=1, + max_blocks=_max_blocks, + min_blocks=_min_blocks, + launcher=SingleNodeLauncher(), + ), + ) + ], + max_idletime=0.5, + strategy='htex_auto_scale', + strategy_period=0.1 + ) + + +# see issue #1885 for details of failures of this test. +# at the time of issue #1885 this test was failing frequently +# in CI. +@pytest.mark.local +def test_scaledown_with_register(try_assert): + dfk = parsl.dfk() + htex = dfk.executors['htex_local'] + + num_managers = len(htex.connected_managers()) + assert num_managers == 0, "Expected 0 managers at start" + + try_assert(lambda: len(htex.status()), + fail_msg="Expected 1 block at start") + + s = htex.status() + assert s['0'].state == JobState.RUNNING, "Expected block to be in RUNNING" + + def check_zero_blocks(): + s = htex.status() + return len(s) == 1 and s['0'].state in TERMINAL_STATES + + try_assert( + check_zero_blocks, + fail_msg="Expected 0 blocks after idle scaledown", + timeout_ms=15000, + ) diff --git a/parsl/parsl/tests/test_scaling/test_shutdown_scalein.py b/parsl/parsl/tests/test_scaling/test_shutdown_scalein.py new file mode 100644 index 00000000..c46b770e --- /dev/null +++ b/parsl/parsl/tests/test_scaling/test_shutdown_scalein.py @@ -0,0 +1,76 @@ +import random +import threading + +import pytest + +import parsl +from parsl.config import Config +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SimpleLauncher +from parsl.providers import LocalProvider + +# we need some blocks, but it doesn't matter too much how many, as long +# as they can all start up and get registered within the try_assert +# timeout later on. +BLOCK_COUNT = 3 + +# the try_assert timeout for the above number of blocks to get started +PERMITTED_STARTUP_TIME_S = 30 + + +class AccumulatingLocalProvider(LocalProvider): + def __init__(self, *args, **kwargs): + # Use a list for submitted job IDs because if there are multiple + # submissions returning the same job ID, this test should count + # those... + self.submit_job_ids = [] + + # ... but there's no requirement, I think, that cancel must be called + # only once per job id. What matters here is that each job ID is + # cancelled at least once. + self.cancel_job_ids = set() + + super().__init__(*args, **kwargs) + + def submit(self, *args, **kwargs): + job_id = super().submit(*args, **kwargs) + self.submit_job_ids.append(job_id) + return job_id + + def cancel(self, job_ids): + self.cancel_job_ids.update(job_ids) + return super().cancel(job_ids) + + +@pytest.mark.local +def test_shutdown_scalein_blocks(tmpd_cwd, try_assert): + """ + This test scales up several blocks, and then checks that they are all + scaled in at DFK shutdown. + """ + accumulating_provider = AccumulatingLocalProvider( + init_blocks=BLOCK_COUNT, + min_blocks=0, + max_blocks=0, + launcher=SimpleLauncher(), + ) + + htex = HighThroughputExecutor( + label="htex_local", + cores_per_worker=1, + provider=accumulating_provider + ) + + config = Config( + executors=[htex], + strategy='none', + strategy_period=0.1, + run_dir=str(tmpd_cwd) + ) + + with parsl.load(config): + # this will wait for everything to be scaled out fully + try_assert(lambda: len(htex.connected_managers()) == BLOCK_COUNT, timeout_ms=PERMITTED_STARTUP_TIME_S * 1000) + + assert len(accumulating_provider.submit_job_ids) == BLOCK_COUNT, f"Exactly {BLOCK_COUNT} blocks should have been launched" + assert len(accumulating_provider.cancel_job_ids) == BLOCK_COUNT, f"Exactly {BLOCK_COUNT} blocks should have been scaled in" diff --git a/parsl/parsl/tests/test_serialization/__init__.py b/parsl/parsl/tests/test_serialization/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_serialization/test_2555_caching_deserializer.py b/parsl/parsl/tests/test_serialization/test_2555_caching_deserializer.py new file mode 100644 index 00000000..7a46731e --- /dev/null +++ b/parsl/parsl/tests/test_serialization/test_2555_caching_deserializer.py @@ -0,0 +1,34 @@ +import pytest + +import parsl +from parsl.tests.configs.htex_local import fresh_config as local_config + + +@parsl.python_app +def return_range(x): + return list(range(x)) + + +@pytest.mark.local +def test_range_identities(): + x = 3 + + fut1 = return_range(x) + res1 = fut1.result() + + fut2 = return_range(x) + res2 = fut2.result() + + # Check that the returned futures are different, by both usual + # Python equalities. + # This is not strictly part of the regression test for #2555 + # but will detect related unexpected Future caching. + + assert fut1 != fut2 + assert id(fut1) != id(fut2) + + # check that the two invocations returned the same value... + assert res1 == res2 + + # ... but in two different objects. + assert id(res1) != id(res2) diff --git a/parsl/parsl/tests/test_serialization/test_3495_deserialize_managerlost.py b/parsl/parsl/tests/test_serialization/test_3495_deserialize_managerlost.py new file mode 100644 index 00000000..8a6cebf1 --- /dev/null +++ b/parsl/parsl/tests/test_serialization/test_3495_deserialize_managerlost.py @@ -0,0 +1,50 @@ +import os +import signal + +import pytest + +import parsl +from parsl import Config, HighThroughputExecutor +from parsl.executors.high_throughput.errors import ManagerLost + + +@parsl.python_app +def get_manager_pgid(): + import os + return os.getpgid(os.getpid()) + + +@parsl.python_app +def lose_manager(): + import os + import signal + import time + + manager_pid = os.getppid() + os.kill(manager_pid, signal.SIGSTOP) + + time.sleep(5) # approximate "forever"; if not enough, something's awry + + +@pytest.mark.local +def test_manager_lost_system_failure(tmpd_cwd): + hte = HighThroughputExecutor( + label="htex_local", + address="127.0.0.1", + max_workers_per_node=2, + cores_per_worker=1, + worker_logdir_root=str(tmpd_cwd), + heartbeat_period=1, + heartbeat_threshold=3, + ) + c = Config(executors=[hte], strategy='simple', strategy_period=0.1) + + with parsl.load(c): + manager_pgid = get_manager_pgid().result() + try: + with pytest.raises(ManagerLost): + lose_manager().result() + finally: + # Allow process to clean itself up + os.killpg(manager_pgid, signal.SIGCONT) + os.killpg(manager_pgid, signal.SIGTERM) diff --git a/parsl/parsl/tests/test_serialization/test_basic.py b/parsl/parsl/tests/test_serialization/test_basic.py new file mode 100644 index 00000000..15f506fb --- /dev/null +++ b/parsl/parsl/tests/test_serialization/test_basic.py @@ -0,0 +1,25 @@ +import pytest + +from parsl.serialize import deserialize, serialize +from parsl.serialize.concretes import DillSerializer, PickleSerializer + + +@pytest.mark.local +def test_serialize(): + assert deserialize(serialize(1)) == 1 + + +@pytest.mark.local +def test_pickle_wrapper(): + s = PickleSerializer() + d = s.serialize(1) + assert isinstance(d, bytes) + assert s.deserialize(d) == 1 + + +@pytest.mark.local +def test_dill_wrapper(): + s = DillSerializer() + d = s.serialize(1) + assert isinstance(d, bytes) + assert s.deserialize(d) == 1 diff --git a/parsl/parsl/tests/test_serialization/test_htex_code_cache.py b/parsl/parsl/tests/test_serialization/test_htex_code_cache.py new file mode 100644 index 00000000..65cb7252 --- /dev/null +++ b/parsl/parsl/tests/test_serialization/test_htex_code_cache.py @@ -0,0 +1,56 @@ +from typing import Any + +import pytest + +import parsl +from parsl.serialize.facade import methods_for_code +from parsl.tests.configs.htex_local import fresh_config as local_config + + +@parsl.python_app +def f(x): + return x + 1 + + +@pytest.mark.local +def test_caching() -> None: + # for future serializer devs: if this is failing because you added another + # code serializer, you'll also probably need to re-think what is being tested + # about serialization caching here. + assert len(methods_for_code) == 1 + + serializer = methods_for_code[b'C2'] + + # force type to Any here because a serializer method coming from + # methods_for_code doesn't statically have any cache management + # methods on itself such as cache_clear or cache_info. + serialize_method: Any = serializer.serialize + + serialize_method.cache_clear() + + assert serialize_method.cache_info().hits == 0 + assert serialize_method.cache_info().misses == 0 + assert serialize_method.cache_info().currsize == 0 + + assert f(7).result() == 8 + + # the code serializer cache should now contain only a (probably wrapped) f ... + assert serialize_method.cache_info().currsize == 1 + + # ... which was not already in the cache. + assert serialize_method.cache_info().misses == 1 + assert serialize_method.cache_info().hits == 0 + + assert f(100).result() == 101 + + # this time round, we should have got a single cache hit... + assert serialize_method.cache_info().hits == 1 + assert serialize_method.cache_info().misses == 1 + assert serialize_method.cache_info().currsize == 1 + + assert f(200).result() == 201 + + # this time round, we should have got another single cache hit... + assert serialize_method.cache_info().hits == 2 + assert serialize_method.cache_info().misses == 1 + assert serialize_method.cache_info().currsize == 1 diff --git a/parsl/parsl/tests/test_serialization/test_proxystore_configured.py b/parsl/parsl/tests/test_serialization/test_proxystore_configured.py new file mode 100644 index 00000000..90bea12a --- /dev/null +++ b/parsl/parsl/tests/test_serialization/test_proxystore_configured.py @@ -0,0 +1,86 @@ +import logging +import uuid + +import pytest + +import parsl +from parsl.serialize.facade import ( + additional_methods_for_deserialization, + methods_for_data, + register_method_for_data, +) +from parsl.tests.configs.htex_local import fresh_config + +logger = logging.getLogger(__name__) + + +def local_setup(): + from proxystore.connectors.file import FileConnector + from proxystore.store import Store, register_store + + from parsl.serialize.proxystore import ProxyStoreSerializer + + parsl.load(fresh_config()) + + store = Store(name='parsl_store_' + str(uuid.uuid4()), connector=FileConnector(store_dir="/tmp")) + register_store(store) + + s = ProxyStoreSerializer(store=store, should_proxy=policy_example) + + global previous_methods + previous_methods = methods_for_data.copy() + + # get rid of all data serialization methods, in preparation for using only + # proxystore. put all the old methods as additional methods used only for + # deserialization, because those will be needed to deserialize the results, + # which will be serialized using the default serializer set. + additional_methods_for_deserialization.update(previous_methods) + methods_for_data.clear() + + register_method_for_data(s) + logger.info(f"BENC: methods for data: {methods_for_data}") + + +def local_teardown(): + parsl.dfk().cleanup() + + methods_for_data.clear() + methods_for_data.update(previous_methods) + + additional_methods_for_deserialization.clear() + + +@parsl.python_app +def identity(o): + return o + + +@parsl.python_app +def is_proxy(o) -> bool: + from proxystore.proxy import Proxy + return isinstance(o, Proxy) + + +def policy_example(o): + """Example policy will proxy only lists.""" + return isinstance(o, frozenset) + + +@pytest.mark.local +def test_proxystore_via_apps(): + from proxystore.proxy import Proxy + + # check roundtrip for an int, which should not be proxystored according + # to example_policy() + roundtripped_7 = identity(7).result() + assert roundtripped_7 == 7 + assert not isinstance(roundtripped_7, Proxy) + + # check roundtrip for a list, which should be proxystored according + # to example_policy() + v = frozenset([1, 2, 3]) + + assert is_proxy(v).result() + + roundtripped_v = identity(v).result() + assert roundtripped_v == v diff --git a/parsl/parsl/tests/test_serialization/test_proxystore_impl.py b/parsl/parsl/tests/test_serialization/test_proxystore_impl.py new file mode 100644 index 00000000..5a04af0f --- /dev/null +++ b/parsl/parsl/tests/test_serialization/test_proxystore_impl.py @@ -0,0 +1,40 @@ +import uuid + +import pytest + + +def policy_example(o): + """Example policy will proxy only lists.""" + return isinstance(o, list) + + +@pytest.mark.local +def test_proxystore_nonglobal(): + """Check that values are roundtripped, for both proxied and non-proxied types. + """ + # import in function, because proxystore is not importable in base parsl + # installation. + from proxystore.connectors.file import FileConnector + from proxystore.proxy import Proxy + from proxystore.store import Store, register_store + + from parsl.serialize.proxystore import ProxyStoreSerializer + + store = Store(name='parsl_store_' + str(uuid.uuid4()), connector=FileConnector(store_dir="/tmp")) + register_store(store) + + s = ProxyStoreSerializer(store=store, should_proxy=policy_example) + + # check roundtrip for an int, which will not be proxystored + s_7 = s.serialize(7) + assert isinstance(s_7, bytes) + roundtripped_7 = s.deserialize(s_7) + assert roundtripped_7 == 7 + assert not isinstance(roundtripped_7, Proxy) + + v = [1, 2, 3] + s_v = s.serialize(v) + assert isinstance(s_7, bytes) + roundtripped_v = s.deserialize(s_v) + assert roundtripped_v == v + assert isinstance(roundtripped_v, Proxy) diff --git a/parsl/parsl/tests/test_shutdown/__init__.py b/parsl/parsl/tests/test_shutdown/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_shutdown/test_kill_monitoring.py b/parsl/parsl/tests/test_shutdown/test_kill_monitoring.py new file mode 100644 index 00000000..a7d6ea3e --- /dev/null +++ b/parsl/parsl/tests/test_shutdown/test_kill_monitoring.py @@ -0,0 +1,64 @@ +import os +import signal +import time + +import pytest + +import parsl +from parsl.tests.configs.htex_local_alternate import fresh_config + +# This is a very generous upper bound on expected shutdown time of target +# process after receiving a signal, measured in seconds. +PERMITTED_SHUTDOWN_TIME_S = 60 + + +@parsl.python_app +def simple_app(): + return True + + +@pytest.mark.local +def test_no_kills(): + """This tests that we can create a monitoring-enabled DFK and shut it down.""" + + parsl.load(fresh_config()) + + assert parsl.dfk().monitoring is not None, "This test requires monitoring" + + parsl.dfk().cleanup() + + +@pytest.mark.local +@pytest.mark.parametrize("sig", [signal.SIGINT, signal.SIGTERM, signal.SIGKILL, signal.SIGQUIT]) +@pytest.mark.parametrize("process_attr", ["dbm_proc"]) +def test_kill_monitoring_helper_process(sig, process_attr, try_assert): + """This tests that we can kill a monitoring process and still have successful shutdown. + SIGINT emulates some racy behaviour when ctrl-C is pressed: that + monitoring processes receive a ctrl-C too, and so the other processes + need to be tolerant to monitoring processes arbitrarily exiting. + """ + + parsl.load(fresh_config()) + + dfk = parsl.dfk() + + assert dfk.monitoring is not None, "Monitoring required" + + target_proc = getattr(dfk.monitoring, process_attr) + + assert target_proc is not None, "prereq: target process must exist" + assert target_proc.is_alive(), "prereq: target process must be alive" + + target_pid = target_proc.pid + assert target_pid is not None, "prereq: target process must have a pid" + + os.kill(target_pid, sig) + + try_assert(lambda: not target_proc.is_alive(), timeout_ms=PERMITTED_SHUTDOWN_TIME_S * 1000) + + # now we have broken one piece of the monitoring system, do some app + # execution and then shut down. + + simple_app().result() + + parsl.dfk().cleanup() diff --git a/parsl/parsl/tests/test_staging/README.rst b/parsl/parsl/tests/test_staging/README.rst new file mode 100644 index 00000000..5a360d36 --- /dev/null +++ b/parsl/parsl/tests/test_staging/README.rst @@ -0,0 +1,18 @@ +Setting up globus endpoint +========================== + +Setup a Globus personal endpoint or activate a shared endpoint, following instructions here: +`Globus docs `_ + +Once an endpoint is setup, get the endpoint UUID for the endpoint from `Globus.org `_. + +You can track the file staging event here: `transfer activity `_. + +Running the tests +================= + +1. Ensure that the endpoint is active. +2. Edit the ``user_opts.py`` script with the Globus endpoint. +3. Run the tests + ``pytest test_globus`` + diff --git a/parsl/parsl/tests/test_staging/__init__.py b/parsl/parsl/tests/test_staging/__init__.py new file mode 100644 index 00000000..8da4e9fa --- /dev/null +++ b/parsl/parsl/tests/test_staging/__init__.py @@ -0,0 +1,7 @@ +def read_sort_write(in_path, out_path): + with open(in_path) as u: + strs = u.read().split() + strs.sort() + with open(out_path, 'w') as s: + for e in strs: + print(e, file=s) diff --git a/parsl/parsl/tests/test_staging/staging_provider.py b/parsl/parsl/tests/test_staging/staging_provider.py new file mode 100644 index 00000000..d309e8d3 --- /dev/null +++ b/parsl/parsl/tests/test_staging/staging_provider.py @@ -0,0 +1,89 @@ +# this replaces the no-op file staging provider with a more +# complicated no-op provider that launches (empty) tasks +# for staging, and asserts specific behaviours do/do not happen. + +# the elaborate no-op provider is based around the structure +# of the globus staging provider + +import logging + +from parsl import python_app +from parsl.data_provider.staging import Staging +from parsl.utils import RepresentationMixin + +logger = logging.getLogger(__name__) + + +class NoOpTestingFileStaging(Staging, RepresentationMixin): + + def __init__(self, allow_stage_in=True, allow_stage_out=True): + """The NoOpTestingFileStaging provider can be configured to fail in + specific ways to ensure that certain things are not happening. + + The provider will always claim that it can stage "file" URLs, + but will raise an exception if staging is actually attempted + if allow_stage[in/out] is set to False. + """ + self.allow_stage_in = allow_stage_in + self.allow_stage_out = allow_stage_out + + def can_stage_in(self, file): + logger.info("NoOpTestingFileStaging checking file {} for stage-in".format(repr(file))) + b = file.scheme == 'file' + logger.info("b = {}".format(b)) + return b + + def can_stage_out(self, file): + logger.info("NoOpTestingFileStaging checking file {} for stage-out".format(repr(file))) + b = file.scheme == 'file' + logger.info("b = {}".format(b)) + return b + + def stage_in(self, dm, executor, file, parent_fut): + logger.info("Creating state-in app for file {}".format(file)) + if self.allow_stage_in: + stage_in_app = make_stage_in_app(executor=executor, dfk=dm.dfk) + app_fut = stage_in_app(outputs=[file], _parsl_staging_inhibit=True, parent_fut=parent_fut) + return app_fut._outputs[0] + else: + raise NoOpError("NoOpTestingFileStaging provider configured to prohibit stage in") + + def stage_out(self, dm, executor, file, app_fu): + logger.info("Creating state-out app for file {}".format(file)) + if self.allow_stage_out: + stage_out_app = make_stage_out_app(executor=executor, dfk=dm.dfk) + return stage_out_app(app_fu, inputs=[file], _parsl_staging_inhibit=True) + else: + raise NoOpError("NoOpTestingFileStaging provider configured to prohibit stage out") + + +def make_stage_out_app(executor, dfk): + return python_app(executors=[executor], data_flow_kernel=dfk)(stage_out_noop) + + +def stage_out_noop(app_fu, inputs=[], _parsl_staging_inhibit=True): + import logging + import time + logger = logging.getLogger(__name__) + logger.info("stage_out_noop") + time.sleep(1) + return None + + +def make_stage_in_app(executor, dfk): + return python_app(executors=[executor], data_flow_kernel=dfk)(stage_in_noop) + + +def stage_in_noop(parent_fut=None, outputs=[], _parsl_staging_inhibit=True): + import logging + import time + logger = logging.getLogger(__name__) + logger.info("stage_in_noop") + time.sleep(1) + return None + + +class NoOpError(RuntimeError): + """This should behave the same as a RuntimeError, but is a subclass so + that it can be checked for specifically in test cases.""" + pass diff --git a/parsl/parsl/tests/test_staging/test_1316.py b/parsl/parsl/tests/test_staging/test_1316.py new file mode 100644 index 00000000..9d5b7a33 --- /dev/null +++ b/parsl/parsl/tests/test_staging/test_1316.py @@ -0,0 +1,85 @@ +import time + +import pytest + +import parsl +from parsl import ThreadPoolExecutor, python_app +from parsl.config import Config +from parsl.data_provider.files import File +from parsl.data_provider.staging import Staging + + +@python_app +def observe_input_local_path(f): + """Returns the local_path that is seen by the app, + so that the test suite can also see that path. + """ + return f.local_path + + +@python_app +def wait_and_create(outputs=[]): + # for test purposes, this doesn't actually need to create the output + # file as nothing ever touches file content - the test only deals with + # names and Futures. + time.sleep(10) + + +class SP2(Staging): + def can_stage_in(self, file): + return file.scheme == 'sp2' + + def stage_in(self, dm, executor, file, parent_fut): + file.local_path = "./test1.tmp" + return None # specify no tasks inserted in graph so parsl will preserve stageout dependency + + def can_stage_out(self, file): + return file.scheme == 'sp2' + + def stage_out(self, dm, executor, file, app_fu): + file.local_path = "./test1.tmp" + return None # no tasks inserted so parsl should use app completion for DataFuture completion + + +@pytest.mark.local +def test_1316_local_path_on_execution_side_sp2(): + """This test demonstrates the ability of a StagingProvider to set the + local_path of a File on the execution side, but that the change does not + modify the local_path of the corresponding submit side File, even when + running in a single python process. + """ + + config = Config(executors=[ThreadPoolExecutor(storage_access=[SP2()])]) + + file = File("sp2://test") + + parsl.load(config) + p = observe_input_local_path(file).result() + + assert p == "./test1.tmp", "File object on the execution side gets the local_path set by the staging provider" + + assert not file.local_path, "The local_path on the submit side should not be set" + + parsl.dfk().cleanup() + + +@pytest.mark.local +def test_1316_local_path_setting_preserves_dependency_sp2(): + config = Config(executors=[ThreadPoolExecutor(storage_access=[SP2()])]) + + file = File("sp2://test") + + parsl.load(config) + + wc_app_future = wait_and_create(outputs=[file]) + data_future = wc_app_future.outputs[0] + + p = observe_input_local_path(data_future).result() + + assert wc_app_future.done(), "wait_and_create should finish before observe_input_local_path finishes" + + assert p == "./test1.tmp", "File object on the execution side gets the local_path set by the staging provider" + + assert not file.local_path, "The local_path on the submit side should not be set" + + parsl.dfk().cleanup() diff --git a/parsl/parsl/tests/test_staging/test_docs_1.py b/parsl/parsl/tests/test_staging/test_docs_1.py new file mode 100644 index 00000000..c4f0e3b0 --- /dev/null +++ b/parsl/parsl/tests/test_staging/test_docs_1.py @@ -0,0 +1,25 @@ +import pytest + +from parsl import File, python_app + + +@python_app +def convert(inputs=[], outputs=[]): + with open(inputs[0].filepath, 'r') as inp: + content = inp.read() + with open(outputs[0].filepath, 'w') as out: + out.write(content.upper()) + + +@pytest.mark.cleannet +@pytest.mark.staging_required +def test(): + # create an remote Parsl file + inp = File('ftp://ftp.iana.org/pub/mirror/rirstats/arin/ARIN-STATS-FORMAT-CHANGE.txt') + + # create a local Parsl file + out = File('file:///tmp/ARIN-STATS-FORMAT-CHANGE.txt') + + # call the convert app with the Parsl file + f = convert(inputs=[inp], outputs=[out]) + f.result() diff --git a/parsl/parsl/tests/test_staging/test_docs_2.py b/parsl/parsl/tests/test_staging/test_docs_2.py new file mode 100644 index 00000000..a985872b --- /dev/null +++ b/parsl/parsl/tests/test_staging/test_docs_2.py @@ -0,0 +1,21 @@ +import pytest + +from parsl import File, bash_app +from parsl.tests.configs.local_threads import fresh_config as local_config + + +@bash_app +def cat(inputs=[], stdout='stdout.txt'): + return 'cat %s' % (inputs[0]) + + +@pytest.mark.local +def test(): + # create a test file + open('/tmp/test.txt', 'w').write('Hello\n') + + # create the Parsl file + parsl_file = File('file:///tmp/test.txt') + + # call the cat app with the Parsl file + cat(inputs=[parsl_file]) diff --git a/parsl/parsl/tests/test_staging/test_elaborate_noop_file.py b/parsl/parsl/tests/test_staging/test_elaborate_noop_file.py new file mode 100644 index 00000000..6f525360 --- /dev/null +++ b/parsl/parsl/tests/test_staging/test_elaborate_noop_file.py @@ -0,0 +1,82 @@ +# this replaces the no-op file staging provider with a more +# complicated no-op provider that launches (empty) tasks +# for staging + +# the elaborate no-op provider is based around the structure +# of the globus staging provider + +import logging + +import pytest + +import parsl +from parsl import bash_app, python_app +from parsl.config import Config +from parsl.data_provider.files import File +from parsl.executors.threads import ThreadPoolExecutor +from parsl.tests.test_staging.staging_provider import NoOpError, NoOpTestingFileStaging + +logger = logging.getLogger(__name__) + + +@bash_app +def touch(filename, outputs=()): + return f"touch {filename}" + + +@python_app +def app_test_in(file): + # does not need to do anything as this + # is just for exercising staging + pass + + +@pytest.fixture +def storage_access_parsl(): + def _setup_config(*args, **kwargs): + tpe = ThreadPoolExecutor( + label='local_threads', + storage_access=[NoOpTestingFileStaging(*args, **kwargs)] + ) + config = Config(executors=[tpe]) + parsl.load(config) + + yield _setup_config + + parsl.dfk().cleanup() + + +@pytest.mark.local +def test_regression_stage_out_does_not_stage_in(storage_access_parsl, tmpd_cwd): + storage_access_parsl(allow_stage_in=False) + + # Test that the helper app runs with no staging + touch(str(tmpd_cwd / "test.1"), outputs=[]).result() + + # Test with stage-out, checking that provider stage-in is never + # invoked. If stage-in is invoked, then the NoOpTestingFileStaging + # provider will raise an exception, which should propagate to + # .result() here. + fpath = tmpd_cwd / "test.2" + touch(str(fpath), outputs=[File(fpath)]).result() + + # Test that stage-in exceptions propagate out to user code. + with pytest.raises(NoOpError): + touch("test.3", inputs=[File("test.3")]).result() + + +@pytest.mark.local +def test_regression_stage_in_does_not_stage_out(storage_access_parsl, tmpd_cwd): + storage_access_parsl(allow_stage_out=False) + + fpath = tmpd_cwd / "test.4" + fpath.write_text("test") + + # Test that stage in does not invoke stage out. If stage out is + # attempted, then the NoOpTestingFileStaging provider will raise + # an exception which should propagate here. + app_test_in(File("test.4")).result() + + # Test that stage out exceptions propagate to user code. + with pytest.raises(NoOpError): + touch("test.5", outputs=[File("test.5")]).result() diff --git a/parsl/parsl/tests/test_staging/test_file.py b/parsl/parsl/tests/test_staging/test_file.py new file mode 100644 index 00000000..d7897da1 --- /dev/null +++ b/parsl/parsl/tests/test_staging/test_file.py @@ -0,0 +1,32 @@ +import os + +import pytest + +from parsl.data_provider.files import File + + +@pytest.mark.local +def test_files(): + fp = os.path.abspath('test_file.py') + strings = [{'f': 'file:///test_file.py', 'scheme': 'file', 'path': '/test_file.py'}, + {'f': './test_file.py', 'scheme': 'file', 'path': './test_file.py'}, + {'f': fp, 'scheme': 'file', 'path': fp}, + ] + + for test in strings: + x = File(test['f']) + assert x.scheme == test['scheme'], "[TEST] Scheme error. Expected {0} Got {1}".format( + test['scheme'], x.scheme) + assert x.filepath == test['path'], "[TEST] Path error. Expected {0} Got {1}".format( + test['path'], x.path) + + +@pytest.mark.local +def test_open(tmpd_cwd): + fpath = tmpd_cwd / 'test-open.txt' + fpath.write_text('Hello') + + pfile = File(fpath) + + with open(pfile) as opfile: + assert (opfile.read() == 'Hello') diff --git a/parsl/parsl/tests/test_staging/test_file_apps.py b/parsl/parsl/tests/test_staging/test_file_apps.py new file mode 100644 index 00000000..fa56bb54 --- /dev/null +++ b/parsl/parsl/tests/test_staging/test_file_apps.py @@ -0,0 +1,58 @@ +import pytest + +from parsl.app.app import bash_app +from parsl.data_provider.files import File + + +@bash_app +def cat(inputs=(), outputs=(), stdout=None, stderr=None): + infiles = " ".join(i.filepath for i in inputs) + return f"cat {infiles} &> {outputs[0]}" + + +@pytest.mark.staging_required +def test_files(setup_data): + fs = sorted(setup_data / f for f in setup_data.iterdir()) + fs = list(map(File, fs)) + x = cat( + inputs=fs, + outputs=[File(setup_data / "cat_out.txt")], + stdout=setup_data / "f_app.out", + stderr=setup_data / "f_app.err", + ) + x.result() + d_x = x.outputs[0] + data = open(d_x.filepath).read().strip() + assert "1\n2" == data, "Per setup_data fixture" + + +@bash_app +def increment(inputs=(), outputs=(), stdout=None, stderr=None): + return ( + f"x=$(cat {inputs[0]})\n" + f"echo $(($x+1)) > {outputs[0]}" + ) + + +@pytest.mark.staging_required +def test_increment(tmpd_cwd, depth=5): + """Test simple pipeline A->B...->N""" + # Test setup + first_fpath = tmpd_cwd / "test0.txt" + first_fpath.write_text("0\n") + + prev = [File(first_fpath)] + futs = [] + for i in range(1, depth): + f = increment( + inputs=prev, + outputs=[File(tmpd_cwd / f"test{i}.txt")], + stdout=tmpd_cwd / f"incr{i}.out", + stderr=tmpd_cwd / f"incr{i}.err", + ) + prev = f.outputs + futs.append((i, prev[0])) + + for i, f in futs: + data = open(f.result().filepath).read().strip() + assert data == str(i) diff --git a/parsl/parsl/tests/test_staging/test_file_staging.py b/parsl/parsl/tests/test_staging/test_file_staging.py new file mode 100644 index 00000000..4e3102c5 --- /dev/null +++ b/parsl/parsl/tests/test_staging/test_file_staging.py @@ -0,0 +1,25 @@ +import pytest + +from parsl.app.app import bash_app +from parsl.data_provider.files import File + + +@bash_app +def cat(inputs=(), outputs=(), stdout=None, stderr=None): + infiles = " ".join(i.filepath for i in inputs) + return f"cat {infiles} &> {outputs[0]}" + + +@pytest.mark.staging_required +def test_regression_200(tmpd_cwd): + """Regression test for #200. Pickleablility of Files""" + opath = tmpd_cwd / "test_output.txt" + fpath = tmpd_cwd / "test.txt" + + fpath.write_text("Hello World") + f = cat(inputs=[File(fpath)], outputs=[File(opath)]) + + f.result() + with open(f.outputs[0].filepath) as f: + data = f.read() + assert "Hello World" == data diff --git a/parsl/parsl/tests/test_staging/test_output_chain_filenames.py b/parsl/parsl/tests/test_staging/test_output_chain_filenames.py new file mode 100644 index 00000000..442bd6fd --- /dev/null +++ b/parsl/parsl/tests/test_staging/test_output_chain_filenames.py @@ -0,0 +1,39 @@ +from concurrent.futures import Future + +import pytest + +from parsl import File +from parsl.app.app import bash_app + + +@bash_app +def app1(inputs=(), outputs=(), stdout=None, stderr=None, mock=False): + return f"echo 'test' > {outputs[0]}" + + +@bash_app +def app2(inputs=(), outputs=(), stdout=None, stderr=None, mock=False): + return f"echo '{inputs[0]}' > {outputs[0]}" + + +@pytest.mark.shared_fs +def test_behavior(tmpd_cwd): + expected_path = str(tmpd_cwd / "simple-out.txt") + app1_future = app1( + inputs=[], + outputs=[File(expected_path)] + ) + + o = app1_future.outputs[0] + assert isinstance(o, Future) + + app2_future = app2( + inputs=[o], + outputs=[File(str(tmpd_cwd / "simple-out2.txt"))] + ) + app2_future.result() + + with open(app2_future.outputs[0].filepath, 'r') as f: + name = f.read().strip() + + assert name == expected_path, "Filename mangled due to DataFuture handling" diff --git a/parsl/parsl/tests/test_staging/test_staging_ftp.py b/parsl/parsl/tests/test_staging/test_staging_ftp.py new file mode 100644 index 00000000..a004f5a5 --- /dev/null +++ b/parsl/parsl/tests/test_staging/test_staging_ftp.py @@ -0,0 +1,31 @@ +import pytest + +from parsl.app.app import python_app +from parsl.data_provider.files import File + + +@python_app +def sort_strings(inputs=[], outputs=[]): + with open(inputs[0].filepath, 'r') as u: + strs = u.readlines() + strs.sort() + with open(outputs[0].filepath, 'w') as s: + for e in strs: + s.write(e) + + +@pytest.mark.cleannet +@pytest.mark.staging_required +def test_staging_ftp(): + """Test staging for an ftp file + + Create a remote input file (ftp) that points to file_test_cpt.txt. + """ + + unsorted_file = File('ftp://ftp.iana.org/pub/mirror/rirstats/arin/ARIN-STATS-FORMAT-CHANGE.txt') + + # Create a local file for output data + sorted_file = File('sorted.txt') + + f = sort_strings(inputs=[unsorted_file], outputs=[sorted_file]) + f.result() diff --git a/parsl/parsl/tests/test_staging/test_staging_ftp_in_task.py b/parsl/parsl/tests/test_staging/test_staging_ftp_in_task.py new file mode 100644 index 00000000..da6a508b --- /dev/null +++ b/parsl/parsl/tests/test_staging/test_staging_ftp_in_task.py @@ -0,0 +1,32 @@ +import pytest + +from parsl.app.app import python_app +from parsl.data_provider.files import File +from parsl.tests.configs.local_threads_ftp_in_task import fresh_config as local_config + + +@python_app +def sort_strings(inputs=[], outputs=[]): + with open(inputs[0].filepath, 'r') as u: + strs = u.readlines() + strs.sort() + with open(outputs[0].filepath, 'w') as s: + for e in strs: + s.write(e) + + +@pytest.mark.local +@pytest.mark.cleannet +def test_staging_ftp(): + """Test staging for an ftp file + + Create a remote input file (ftp) that points to file_test_cpt.txt. + """ + + unsorted_file = File('ftp://ftp.iana.org/pub/mirror/rirstats/arin/ARIN-STATS-FORMAT-CHANGE.txt') + + # Create a local file for output data + sorted_file = File('sorted.txt') + + f = sort_strings(inputs=[unsorted_file], outputs=[sorted_file]) + f.result() diff --git a/parsl/parsl/tests/test_staging/test_staging_globus.py b/parsl/parsl/tests/test_staging/test_staging_globus.py new file mode 100644 index 00000000..9bdb76c0 --- /dev/null +++ b/parsl/parsl/tests/test_staging/test_staging_globus.py @@ -0,0 +1,71 @@ +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.data_provider.files import File +from parsl.tests.configs.local_threads_globus import fresh_config, remote_writeable + +local_config = fresh_config + + +@python_app +def sort_strings(inputs=[], outputs=[]): + with open(inputs[0].filepath, 'r') as u: + strs = u.readlines() + strs.sort() + with open(outputs[0].filepath, 'w') as s: + for e in strs: + s.write(e) + + +@pytest.mark.local +def test_stage_in_globus(): + """Test stage-in for a file coming from a remote Globus endpoint + + Prerequisite: + unsorted.txt must already exist at the specified endpoint + """ + + unsorted_file = File('globus://03d7d06a-cb6b-11e8-8c6a-0a1d4c5c824a/unsorted.txt') + + # Create a local file for output data + sorted_file = File('sorted.txt') + + f = sort_strings(inputs=[unsorted_file], outputs=[sorted_file]) + + f.result() + + +@pytest.mark.local +def test_stage_in_out_globus(): + """Test stage-in then stage-out to/from Globus + + Prerequisite: + unsorted.txt must already exist at the specified endpoint + the specified output endpoint must be writeable + """ + + unsorted_file = File('globus://03d7d06a-cb6b-11e8-8c6a-0a1d4c5c824a/unsorted.txt') + + # Create a local file for output data + sorted_file = File(remote_writeable + "/sorted.txt") + + f = sort_strings(inputs=[unsorted_file], outputs=[sorted_file]) + + # wait for both the app to complete, and the stageout DataFuture to complete. + # It isn't clearly defined whether we need to wait for both, or whether + # waiting for one is sufficient, but at time of writing this test, + # neither is sufficient (!) - see issue #778 - and instead this test will + # sometimes pass even though stageout is not working. + + f.result() + + result_file = f.outputs[0].result() + + assert unsorted_file.local_path is None, "Input file on local side has overridden local_path, file: {}".format(repr(unsorted_file)) + + # use 'is' rather than '==' because specifically want to check + # object identity rather than value equality + assert sorted_file is result_file, "Result file is not the specified input-output file" + + assert result_file.local_path is None, "Result file on local side has overridden local_path, file: {}".format(repr(result_file)) diff --git a/parsl/parsl/tests/test_staging/test_staging_https.py b/parsl/parsl/tests/test_staging/test_staging_https.py new file mode 100644 index 00000000..c9774722 --- /dev/null +++ b/parsl/parsl/tests/test_staging/test_staging_https.py @@ -0,0 +1,106 @@ +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.data_provider.files import File + +# This config is for the local test which will adding an executor. +# Most tests in this file should be non-local and use the configuration +# specificed with --config, not this one. +from parsl.tests.configs.htex_local import fresh_config as local_config + +""" +Test staging for an http file + +Create a remote input file (https) that points to unsorted.txt, then +a local file for output data (sorted.txt). +""" + +_unsorted_url = ( + 'https://gist.githubusercontent.com/yadudoc/7f21dd15e64a421990a46766bfa5359c/' + 'raw/7fe04978ea44f807088c349f6ecb0f6ee350ec49/unsorted.txt' +) +_exp_sorted = sorted(f"{i}\n" for i in range(1, 101))[:20] + + +@python_app +def sort_strings(inputs=(), outputs=()): + from parsl.tests.test_staging import read_sort_write + read_sort_write(inputs[0].filepath, outputs[0].filepath) + + +@python_app +def sort_strings_kw(*, x=None, outputs=()): + from parsl.tests.test_staging import read_sort_write + read_sort_write(x.filepath, outputs[0].filepath) + + +@python_app +def sort_strings_arg(x, /, outputs=()): + from parsl.tests.test_staging import read_sort_write + read_sort_write(x.filepath, outputs[0].filepath) + + +@python_app(executors=['other']) +def sort_strings_additional_executor(inputs=(), outputs=()): + from parsl.tests.test_staging import read_sort_write + read_sort_write(inputs[0].filepath, outputs[0].filepath) + + +@pytest.mark.cleannet +@pytest.mark.staging_required +def test_staging_https_cleannet(tmpd_cwd): + unsorted_file = File(_unsorted_url) + sorted_file = File(tmpd_cwd / 'sorted.txt') + + sort_strings(inputs=[unsorted_file], outputs=[sorted_file]).result() + with open(sorted_file) as f: + assert all(a == b for a, b in zip(f.readlines(), _exp_sorted)) + + +@pytest.mark.local +def test_staging_https_local(tmpd_cwd): + unsorted_file = File(_unsorted_url) + sorted_file = File(tmpd_cwd / 'sorted.txt') + + sort_strings(inputs=[unsorted_file], outputs=[sorted_file]).result() + with open(sorted_file) as f: + assert all(a == b for a, b in zip(f.readlines(), _exp_sorted)) + + +@pytest.mark.cleannet +@pytest.mark.staging_required +def test_staging_https_kwargs(tmpd_cwd): + unsorted_file = File(_unsorted_url) + sorted_file = File(tmpd_cwd / 'sorted.txt') + + sort_strings_kw(x=unsorted_file, outputs=[sorted_file]).result() + with open(sorted_file) as f: + assert all(a == b for a, b in zip(f.readlines(), _exp_sorted)) + + +@pytest.mark.cleannet +@pytest.mark.staging_required +def test_staging_https_args(tmpd_cwd): + unsorted_file = File(_unsorted_url) + sorted_file = File(tmpd_cwd / 'sorted.txt') + + sort_strings_arg(unsorted_file, outputs=[sorted_file]).result() + with open(sorted_file) as f: + assert all(a == b for a, b in zip(f.readlines(), _exp_sorted)) + + +@pytest.mark.cleannet +@pytest.mark.local +def test_staging_https_additional_executor(tmpd_cwd): + unsorted_file = File(_unsorted_url) + sorted_file = File(tmpd_cwd / 'sorted.txt') + + other_executor = parsl.ThreadPoolExecutor(label='other') + other_executor.working_dir = tmpd_cwd + + parsl.dfk().add_executors([other_executor]) + + sort_strings_additional_executor(inputs=[unsorted_file], outputs=[sorted_file]).result() + with open(sorted_file) as f: + assert all(a == b for a, b in zip(f.readlines(), _exp_sorted)) diff --git a/parsl/parsl/tests/test_staging/test_staging_stdout.py b/parsl/parsl/tests/test_staging/test_staging_stdout.py new file mode 100644 index 00000000..0eae6df7 --- /dev/null +++ b/parsl/parsl/tests/test_staging/test_staging_stdout.py @@ -0,0 +1,64 @@ +import logging +import os +import zipfile + +import pytest + +import parsl +from parsl.app.futures import DataFuture +from parsl.data_provider.files import File +from parsl.tests.configs.htex_local import fresh_config as local_config + + +@parsl.bash_app +def output_to_stds(*, stdout=parsl.AUTO_LOGNAME, stderr=parsl.AUTO_LOGNAME): + return "echo hello ; echo goodbye >&2" + + +@pytest.mark.staging_required +def test_stdout_staging_file(tmpd_cwd, caplog): + basename = str(tmpd_cwd) + "/stdout.txt" + stdout_file = File("file://" + basename) + + app_future = output_to_stds(stdout=stdout_file) + + assert isinstance(app_future.stdout, DataFuture) + app_future.stdout.result() + + assert os.path.exists(basename) + + for record in caplog.records: + assert record.levelno < logging.ERROR + + +@pytest.mark.staging_required +def test_stdout_stderr_staging_zip(tmpd_cwd, caplog): + zipfile_name = str(tmpd_cwd) + "/staging.zip" + stdout_relative_path = "somewhere/test-out.txt" + stdout_file = File("zip:" + zipfile_name + "/" + stdout_relative_path) + + stderr_relative_path = "somewhere/test-error.txt" + stderr_file = File("zip:" + zipfile_name + "/" + stderr_relative_path) + + app_future = output_to_stds(stdout=stdout_file, stderr=stderr_file) + + assert isinstance(app_future.stdout, DataFuture) + app_future.stdout.result() + + # check the file exists as soon as possible + assert os.path.exists(zipfile_name) + with zipfile.ZipFile(zipfile_name) as z: + with z.open(stdout_relative_path) as f: + assert f.readlines() == [b'hello\n'] + + assert isinstance(app_future.stderr, DataFuture) + app_future.stderr.result() + with zipfile.ZipFile(zipfile_name) as z: + with z.open(stderr_relative_path) as f: + # The last line of stderr should be goodbye, but Parsl will write + # other Parsl-specific into to stderr before that, so only assert + # the behaviour of the final line. + assert f.readlines()[-1] == b'goodbye\n' + + for record in caplog.records: + assert record.levelno < logging.ERROR diff --git a/parsl/parsl/tests/test_staging/test_zip_in.py b/parsl/parsl/tests/test_staging/test_zip_in.py new file mode 100644 index 00000000..9d43a4ab --- /dev/null +++ b/parsl/parsl/tests/test_staging/test_zip_in.py @@ -0,0 +1,39 @@ +import random +import zipfile + +import pytest + +import parsl +from parsl.config import Config +from parsl.data_provider.files import File +from parsl.data_provider.zip import ZipAuthorityError, ZipFileStaging +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SimpleLauncher +from parsl.providers import LocalProvider +from parsl.tests.configs.htex_local import fresh_config as local_config + + +@parsl.python_app +def count_lines(file): + with open(file, "r") as f: + return len(f.readlines()) + + +@pytest.mark.local +def test_zip_in(tmpd_cwd): + # basic test of zip file stage-in + zip_path = tmpd_cwd / "container.zip" + file_base = "data.txt" + zip_file = File(f"zip:{zip_path / file_base}") + + # create a zip file containing one file with some abitrary number of lines + n_lines = random.randint(0, 1000) + + with zipfile.ZipFile(zip_path, mode='w') as z: + with z.open(file_base, mode='w') as f: + for _ in range(n_lines): + f.write(b'someline\n') + + app_future = count_lines(zip_file) + + assert app_future.result() == n_lines diff --git a/parsl/parsl/tests/test_staging/test_zip_out.py b/parsl/parsl/tests/test_staging/test_zip_out.py new file mode 100644 index 00000000..79fbb504 --- /dev/null +++ b/parsl/parsl/tests/test_staging/test_zip_out.py @@ -0,0 +1,110 @@ +import zipfile + +import pytest + +import parsl +from parsl.config import Config +from parsl.data_provider.data_manager import default_staging +from parsl.data_provider.files import File +from parsl.data_provider.zip import ZipAuthorityError, ZipFileStaging +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SimpleLauncher +from parsl.providers import LocalProvider +from parsl.tests.configs.htex_local import fresh_config as local_config + + +@pytest.mark.local +def test_zip_path_split(): + from parsl.data_provider.zip import zip_path_split + assert zip_path_split("/tmp/foo/this.zip/inside/here.txt") == ("/tmp/foo/this.zip", "inside/here.txt") + + +@parsl.bash_app +def output_something(outputs=()): + """This should output something into every specified output file: + the position in the output sequence will be written into the + corresponding output file. + """ + cmds = [] + for n in range(len(outputs)): + cmds.append(f"echo {n} > {outputs[n]}") + + return "; ".join(cmds) + + +@pytest.mark.local +def test_zip_out(tmpd_cwd): + # basic test of zip file stage-out + zip_path = tmpd_cwd / "container.zip" + file_base = "data.txt" + of = File(f"zip:{zip_path / file_base}") + + app_future = output_something(outputs=[of]) + output_file_future = app_future.outputs[0] + + app_future.result() + output_file_future.result() + + assert zipfile.is_zipfile(zip_path) + + with zipfile.ZipFile(zip_path) as z: + assert file_base in z.namelist() + assert len(z.namelist()) == 1 + with z.open(file_base) as f: + assert f.readlines() == [b'0\n'] + + +@pytest.mark.local +def test_zip_out_multi(tmpd_cwd): + # tests multiple files, multiple zip files and multiple + # sub-paths + + zip_path_1 = tmpd_cwd / "container1.zip" + zip_path_2 = tmpd_cwd / "container2.zip" + + relative_file_path_1 = "a/b/c/data.txt" + relative_file_path_2 = "something.txt" + relative_file_path_3 = "a/d/other.txt" + of1 = File(f"zip:{zip_path_1 / relative_file_path_1}") + of2 = File(f"zip:{zip_path_1 / relative_file_path_2}") + of3 = File(f"zip:{zip_path_2 / relative_file_path_3}") + + app_future = output_something(outputs=[of1, of2, of3]) + + for f in app_future.outputs: + f.result() + + app_future.result() + + assert zipfile.is_zipfile(zip_path_1) + + with zipfile.ZipFile(zip_path_1) as z: + assert relative_file_path_1 in z.namelist() + assert relative_file_path_2 in z.namelist() + assert len(z.namelist()) == 2 + with z.open(relative_file_path_1) as f: + assert f.readlines() == [b'0\n'] + with z.open(relative_file_path_2) as f: + assert f.readlines() == [b'1\n'] + + assert zipfile.is_zipfile(zip_path_2) + + with zipfile.ZipFile(zip_path_2) as z: + assert relative_file_path_3 in z.namelist() + assert len(z.namelist()) == 1 + with z.open(relative_file_path_3) as f: + assert f.readlines() == [b'2\n'] + + +@pytest.mark.local +def test_zip_bad_authority(tmpd_cwd): + # tests that there's an exception when staging a ZIP url with an authority + # section specified, rather than silently ignoring it. This simulates a + # user who misunderstands what that piece of what a zip: URL means. + + zip_path = tmpd_cwd / "container.zip" + file_base = "data.txt" + of = File(f"zip://someauthority/{zip_path / file_base}") + + with pytest.raises(ZipAuthorityError): + output_something(outputs=[of]) diff --git a/parsl/parsl/tests/test_staging/test_zip_to_zip.py b/parsl/parsl/tests/test_staging/test_zip_to_zip.py new file mode 100644 index 00000000..3fea4216 --- /dev/null +++ b/parsl/parsl/tests/test_staging/test_zip_to_zip.py @@ -0,0 +1,41 @@ +import random +import zipfile + +import pytest + +import parsl +from parsl.config import Config +from parsl.data_provider.files import File +from parsl.data_provider.zip import ZipAuthorityError, ZipFileStaging +from parsl.executors import HighThroughputExecutor +from parsl.launchers import SimpleLauncher +from parsl.providers import LocalProvider +from parsl.tests.configs.htex_local import fresh_config as local_config + + +@parsl.python_app +def generate_lines(n: int, *, outputs): + with open(outputs[0], "w") as f: + for x in range(n): + # write numbered lines + f.write(str(x) + "\n") + + +@parsl.python_app +def count_lines(file): + with open(file, "r") as f: + return len(f.readlines()) + + +@pytest.mark.local +def test_zip_pipeline(tmpd_cwd): + # basic test of zip file stage-in + zip_path = tmpd_cwd / "container.zip" + file_base = "data.txt" + zip_file = File(f"zip:{zip_path / file_base}") + + n_lines = random.randint(0, 1000) + generate_fut = generate_lines(n_lines, outputs=[zip_file]) + n_lines_out = count_lines(generate_fut.outputs[0]).result() + + assert n_lines == n_lines_out diff --git a/parsl/parsl/tests/test_summary.py b/parsl/parsl/tests/test_summary.py new file mode 100644 index 00000000..e98f3939 --- /dev/null +++ b/parsl/parsl/tests/test_summary.py @@ -0,0 +1,29 @@ +import pytest + +import parsl +from parsl.tests.configs.local_threads import fresh_config + + +@parsl.python_app +def succeed(): + pass + + +@parsl.python_app +def fail(): + raise RuntimeError("Deliberate failure in fail() app") + + +@pytest.mark.local +def test_summary(caplog): + + parsl.load(fresh_config()) + + succeed().result() + fail().exception() + + parsl.dfk().cleanup() + + assert "Summary of tasks in DFK:" in caplog.text + assert "Tasks in state States.exec_done: 1" in caplog.text + assert "Tasks in state States.failed: 1" in caplog.text diff --git a/parsl/parsl/tests/test_threads/__init__.py b/parsl/parsl/tests/test_threads/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_threads/test_configs.py b/parsl/parsl/tests/test_threads/test_configs.py new file mode 100644 index 00000000..022f8825 --- /dev/null +++ b/parsl/parsl/tests/test_threads/test_configs.py @@ -0,0 +1,33 @@ +import pytest + +import parsl +from parsl.app.app import python_app +from parsl.tests.configs.local_threads import fresh_config + + +@python_app +def worker_identify(x, sleep_dur=0.2): + import os + import threading + import time + time.sleep(sleep_dur) + return {"pid": os.getpid(), + "tid": threading.current_thread()} + + +@pytest.mark.local +def test_parallel_for(): + config = fresh_config() + dfk = parsl.load(config) + d = [] + for i in range(0, config.executors[0].max_threads): + d.extend([worker_identify(i)]) + + [item.result() for item in d] + + thread_count = len(set([item.result()['tid'] for item in d])) + process_count = len(set([item.result()['pid'] for item in d])) + assert thread_count <= config.executors[0].max_threads, "More threads than allowed" + assert process_count == 1, "More processes than allowed" + dfk.cleanup() + return d diff --git a/parsl/parsl/tests/test_threads/test_lazy_errors.py b/parsl/parsl/tests/test_threads/test_lazy_errors.py new file mode 100644 index 00000000..a070487e --- /dev/null +++ b/parsl/parsl/tests/test_threads/test_lazy_errors.py @@ -0,0 +1,28 @@ +import pytest + +import parsl +from parsl import python_app +from parsl.tests.configs.local_threads import fresh_config + + +@pytest.mark.local +def test_lazy_behavior(): + """Testing that lazy errors work""" + + config = fresh_config() + parsl.load(config) + + @python_app + def divide(a, b): + return a / b + + futures = [] + for i in range(0, 10): + futures.append(divide(10, 0)) + + for f in futures: + assert isinstance(f.exception(), ZeroDivisionError) + assert f.done() + + parsl.dfk().cleanup() + return diff --git a/parsl/parsl/tests/test_utils/__init__.py b/parsl/parsl/tests/test_utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/test_utils/test_execute_wait.py b/parsl/parsl/tests/test_utils/test_execute_wait.py new file mode 100644 index 00000000..44488c23 --- /dev/null +++ b/parsl/parsl/tests/test_utils/test_execute_wait.py @@ -0,0 +1,35 @@ +import pytest + +from parsl.utils import execute_wait + + +@pytest.mark.local +def test_env(): + ''' Regression testing for issue #27 + ''' + + rc, stdout, stderr = execute_wait("env", 1) + + stdout = stdout.split('\n') + x = [s for s in stdout if s.startswith("PATH=")] + assert x, "PATH not found" + + x = [s for s in stdout if s.startswith("HOME=")] + assert x, "HOME not found" + + +@pytest.mark.local +def test_large_output_2210(): + """Regression test for #2210. + execute_wait was hanging if the specified command gave too + much output, due to a race condition between process exiting and + pipes filling up. + """ + + # this will output 128kb of stdout + execute_wait("yes | dd count=128 bs=1024", walltime=60) + + # if this test fails, execute_wait should raise a timeout + # exception. + + # The contents out the output is not verified by this test diff --git a/parsl/parsl/tests/test_utils/test_logutils.py b/parsl/parsl/tests/test_utils/test_logutils.py new file mode 100644 index 00000000..e0aac778 --- /dev/null +++ b/parsl/parsl/tests/test_utils/test_logutils.py @@ -0,0 +1,70 @@ +import io +import logging + +import pytest + +from parsl.log_utils import set_file_logger, set_stream_logger + + +@pytest.mark.local +def test_stream_close(): + """Tests that set_stream_logger callback detaches log handler. + """ + + logger = logging.getLogger("parsl") + + s1 = io.StringIO() + close_callback_1 = set_stream_logger(stream=s1) + logger.info("AAA") + close_callback_1() + + s2 = io.StringIO() + close_callback_2 = set_stream_logger(stream=s2) + logger.info("BBB") + close_callback_2() + + logger.info("CCC") + + assert "AAA" in s1.getvalue() + assert "AAA" not in s2.getvalue() + + assert "BBB" not in s1.getvalue() + assert "BBB" in s2.getvalue() + + assert "CCC" not in s1.getvalue() + assert "CCC" not in s2.getvalue() + + +@pytest.mark.local +def test_file_close(tmpd_cwd): + """Tests that set_file_Logger callback detaches log handler. + """ + + logger = logging.getLogger("parsl") + + f1 = str(tmpd_cwd / "log1") + close_callback_1 = set_file_logger(filename=f1) + logger.info("AAA") + close_callback_1() + + f2 = str(tmpd_cwd / "log2") + close_callback_2 = set_file_logger(filename=f2) + logger.info("BBB") + close_callback_2() + + logger.info("CCC") + + with open(f1, "r") as f: + s1 = f.read() + + with open(f2, "r") as f: + s2 = f.read() + + assert "AAA" in s1 + assert "AAA" not in s2 + + assert "BBB" not in s1 + assert "BBB" in s2 + + assert "CCC" not in s1 + assert "CCC" not in s2 diff --git a/parsl/parsl/tests/test_utils/test_representation_mixin.py b/parsl/parsl/tests/test_utils/test_representation_mixin.py new file mode 100644 index 00000000..0f08a0b3 --- /dev/null +++ b/parsl/parsl/tests/test_utils/test_representation_mixin.py @@ -0,0 +1,121 @@ +import pytest + +from parsl.utils import RepresentationMixin + + +class GoodRepr(RepresentationMixin): + def __init__(self, x, y): + self.x = x + self.y = y + + +class GoodReprDefaults(RepresentationMixin): + def __init__(self, x, y="default 2"): + self.x = x + self.y = y + + +class GoodReprKeywordOnly(RepresentationMixin): + def __init__(self, *, x, y): + self.x = x + self.y = y + + +class BadRepr(RepresentationMixin): + """This class incorrectly subclasses RepresentationMixin. + It does not store the parameter x on self. + """ + def __init__(self, x, y): + self.y = y + + +@pytest.mark.local +def test_repr_good(): + p1 = "parameter 1" + p2 = "the second parameter" + + # repr should not raise an exception + r = repr(GoodRepr(p1, p2)) + + # representation should contain both values supplied + # at object creation. + assert p1 in r + assert p2 in r + + +@pytest.mark.local +def test_repr_good_defaults_overridden(): + p1 = "parameter 1" + p2 = "the second parameter" + + # repr should not raise an exception + r = repr(GoodReprDefaults(p1, p2)) + + # representation should contain both values supplied + # at object creation. + assert p1 in r + assert p2 in r + + +@pytest.mark.local +def test_repr_good_defaults_defaulted(): + p1 = "parameter 1" + + # repr should not raise an exception + r = repr(GoodReprDefaults(p1)) + + # representation should contain one value supplied + # at object creation, and the other defaulted. + assert p1 in r + assert "default 2" in r + + +@pytest.mark.local +def test_repr_good_keyword_only(): + p1 = "parameter 1" + p2 = "the second parameter" + + # repr should not raise an exception + r = repr(GoodReprKeywordOnly(x=p1, y=p2)) + + # representation should contain both values supplied + # at object creation. + assert p1 in r + assert p2 in r + + +@pytest.mark.local +def test_repr_bad(): + p1 = "parameter 1" + p2 = "the second parameter" + + # repr should raise an exception + with pytest.raises(AttributeError): + repr(BadRepr(p1, p2)) + + +class NonValidatingRepresentationMixin(RepresentationMixin): + """This will override the process level RepresentationMixin which can + be set to validating mode by pytest fixtures""" + _validate_repr = False + + +class BadReprNonValidating(NonValidatingRepresentationMixin): + """This class incorrectly subclasses RepresentationMixin. + It does not store the parameter x on self. + """ + def __init__(self, x, y): + self.y = y + + +@pytest.mark.local +def test_repr_bad_unvalidated(): + p1 = "parameter 1" + p2 = "the second parameter" + + # repr should not raise an exception + r = repr(BadReprNonValidating(p1, p2)) + # parameter 2 should be found in the representation, but not + # parameter 1 + assert p1 not in r + assert p2 in r diff --git a/parsl/parsl/tests/test_utils/test_sanitize_dns.py b/parsl/parsl/tests/test_utils/test_sanitize_dns.py new file mode 100644 index 00000000..17b80133 --- /dev/null +++ b/parsl/parsl/tests/test_utils/test_sanitize_dns.py @@ -0,0 +1,76 @@ +import random +import re + +import pytest + +from parsl.utils import sanitize_dns_label_rfc1123, sanitize_dns_subdomain_rfc1123 + +# Ref: https://datatracker.ietf.org/doc/html/rfc1123 +DNS_LABEL_REGEX = r'^[a-z0-9]([-a-z0-9]{0,61}[a-z0-9])?$' +DNS_SUBDOMAIN_REGEX = r'^[a-z0-9]([-a-z0-9]{0,61}[a-z0-9])?(\.[a-z0-9]([-a-z0-9]{0,61}[a-z0-9])?)*$' + +test_labels = [ + "example-label-123", # Valid label + "EXAMPLE", # Case sensitivity + "!@#example*", # Remove invalid characters + "--leading-and-trailing--", # Leading and trailing hyphens + "..leading.and.trailing..", # Leading and tailing dots + "multiple..dots", # Consecutive dots + "valid--label", # Consecutive hyphens + "a" * random.randint(64, 70), # Longer than 63 characters + f"{'a' * 62}-a", # Trailing hyphen at max length +] + + +def _generate_test_subdomains(num_subdomains: int): + subdomains = [] + for _ in range(num_subdomains): + num_labels = random.randint(1, 5) + labels = [test_labels[random.randint(0, num_labels - 1)] for _ in range(num_labels)] + subdomain = ".".join(labels) + subdomains.append(subdomain) + return subdomains + + +@pytest.mark.local +@pytest.mark.parametrize("raw_string", test_labels) +def test_sanitize_dns_label_rfc1123(raw_string: str): + print(sanitize_dns_label_rfc1123(raw_string)) + assert re.match(DNS_LABEL_REGEX, sanitize_dns_label_rfc1123(raw_string)) + + +@pytest.mark.local +@pytest.mark.parametrize("raw_string", ("", "-", "@", "$$$")) +def test_sanitize_dns_label_rfc1123_empty(raw_string: str): + with pytest.raises(ValueError) as e_info: + sanitize_dns_label_rfc1123(raw_string) + assert str(e_info.value) == f"Sanitized DNS label is empty for input '{raw_string}'" + + +@pytest.mark.local +@pytest.mark.parametrize("raw_string", _generate_test_subdomains(10)) +def test_sanitize_dns_subdomain_rfc1123(raw_string: str): + assert re.match(DNS_SUBDOMAIN_REGEX, sanitize_dns_subdomain_rfc1123(raw_string)) + + +@pytest.mark.local +@pytest.mark.parametrize("char", ("-", ".")) +def test_sanitize_dns_subdomain_rfc1123_trailing_non_alphanumeric_at_max_length(char: str): + raw_string = (f"{'a' * 61}." * 4) + f".aaaa{char}a" + assert re.match(DNS_SUBDOMAIN_REGEX, sanitize_dns_subdomain_rfc1123(raw_string)) + + +@pytest.mark.local +@pytest.mark.parametrize("raw_string", ("", ".", "...")) +def test_sanitize_dns_subdomain_rfc1123_empty(raw_string: str): + with pytest.raises(ValueError) as e_info: + sanitize_dns_subdomain_rfc1123(raw_string) + assert str(e_info.value) == f"Sanitized DNS subdomain is empty for input '{raw_string}'" + + +@pytest.mark.local +@pytest.mark.parametrize( + "raw_string", ("a" * 253, "a" * random.randint(254, 300)), ids=("254 chars", ">253 chars") +) +def test_sanitize_dns_subdomain_rfc1123_max_length(raw_string: str): + assert len(sanitize_dns_subdomain_rfc1123(raw_string)) <= 253 diff --git a/parsl/parsl/tests/unit/__init__.py b/parsl/parsl/tests/unit/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/unit/executors/high_throughput/__init__.py b/parsl/parsl/tests/unit/executors/high_throughput/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/tests/unit/executors/high_throughput/test_htex.py b/parsl/parsl/tests/unit/executors/high_throughput/test_htex.py new file mode 100644 index 00000000..e1359163 --- /dev/null +++ b/parsl/parsl/tests/unit/executors/high_throughput/test_htex.py @@ -0,0 +1,24 @@ +from unittest import mock + +import pytest + +from parsl import HighThroughputExecutor +from parsl.executors.high_throughput import zmq_pipes + + +@pytest.mark.local +def test_submit_payload(): + htex = HighThroughputExecutor() + htex.outgoing_q = mock.Mock(spec=zmq_pipes.TasksOutgoing) + ctxt = {"some": "context"} + buf = b'some buffer (function) payload' + for task_num in range(1, 20): + htex.outgoing_q.reset_mock() + fut = htex.submit_payload(ctxt, buf) + (msg,), _ = htex.outgoing_q.put.call_args + + assert htex.tasks[fut.parsl_executor_task_id] is fut + assert fut.parsl_executor_task_id == task_num, "Expect monotonic increase" + assert msg["task_id"] == fut.parsl_executor_task_id + assert msg["context"] == ctxt, "Expect no modification" + assert msg["buffer"] == buf, "Expect no modification" diff --git a/parsl/parsl/tests/unit/executors/high_throughput/test_interchange.py b/parsl/parsl/tests/unit/executors/high_throughput/test_interchange.py new file mode 100644 index 00000000..964aed60 --- /dev/null +++ b/parsl/parsl/tests/unit/executors/high_throughput/test_interchange.py @@ -0,0 +1,133 @@ +import logging +import pickle +import threading +import time +import uuid +from typing import cast +from unittest import mock + +import pytest +import zmq + +from parsl import curvezmq +from parsl.executors.high_throughput.interchange import ( + PKL_SHUTDOWN_CODE, + Interchange, +) +from parsl.executors.high_throughput.manager_selector import RandomManagerSelector +from parsl.version import VERSION as PARSL_VERSION + + +@pytest.fixture +def ix(tmpd_cwd): + with mock.patch.object(curvezmq.ServerContext, "socket", side_effect=lambda *a, **kw: mock.MagicMock()): + yield Interchange(interchange_address="127.0.0.1", + cert_dir=None, + client_address="127.0.0.1", + client_ports=(50055, 50056, 50057), + worker_port=None, + worker_port_range=(54000, 55000), + hub_address=None, + hub_zmq_port=None, + heartbeat_threshold=60, + logdir=str(tmpd_cwd), + logging_level=logging.INFO, + log_config=None, + manager_selector=RandomManagerSelector(), + poll_period=10, + run_id="test_run_id", + _check_python_mismatch=True) + + +@pytest.fixture +def manager_id() -> bytes: + return f"manager-{uuid.uuid4()}".encode() + + +def send_from_manager(ix: Interchange, manager_id: bytes, msg: dict) -> None: + ix.socks = {ix.manager_sock: zmq.POLLIN} + mock_sock = cast(mock.MagicMock, ix.manager_sock) + mock_sock.recv_multipart.return_value = [manager_id, pickle.dumps(msg)] + ix.process_manager_socket_message(set(), None, threading.Event()) + + +def registration_msg(ix: Interchange, **kw) -> dict: + return {'type': 'registration', + 'parsl_v': PARSL_VERSION, + 'python_v': ix.current_platform['python_v'], + 'worker_count': 1, + 'uid': 'testuid', + 'block_id': '0', + 'start_time': time.time(), + 'prefetch_capacity': 0, + 'max_capacity': 1, + **kw} + + +def shutdown_sent(ix: Interchange, manager_id: bytes) -> bool: + mock_sock = cast(mock.MagicMock, ix.manager_sock) + return mock.call([manager_id, PKL_SHUTDOWN_CODE]) in mock_sock.send_multipart.call_args_list + + +@pytest.mark.local +def test_no_drain_before_drain_time(ix, manager_id): + reg_msg = registration_msg(ix, drain_time=time.time() + 3600) + send_from_manager(ix, manager_id, reg_msg) + assert manager_id in ix._ready_managers, "manager should have been registered" + + ix.expire_drained_managers(set(), None) + + assert not ix._ready_managers[manager_id]['draining'] + assert not shutdown_sent(ix, manager_id) + + +@pytest.mark.local +@pytest.mark.parametrize("is_interesting", (True, False)) +def test_idle_manager_shut_down_at_drain_time(ix, manager_id, is_interesting): + reg_msg = registration_msg(ix, drain_time=time.time()) + send_from_manager(ix, manager_id, reg_msg) + assert manager_id in ix._ready_managers, "manager should have been registered" + + interesting_managers = set([manager_id] if is_interesting else []) + ix.expire_drained_managers(interesting_managers, None) + + assert shutdown_sent(ix, manager_id) + assert manager_id not in ix._ready_managers + assert manager_id not in interesting_managers + + +@pytest.mark.local +def test_draining_manager_shut_down_after_last_result(ix, manager_id): + reg_msg = registration_msg(ix, drain_time=time.time()) + send_from_manager(ix, manager_id, reg_msg) + assert manager_id in ix._ready_managers, "manager should have been registered" + + ix._ready_managers[manager_id]['tasks'].append(1) + ix.expire_drained_managers(set(), None) + + assert ix._ready_managers[manager_id]['draining'] + assert not shutdown_sent(ix, manager_id) + + send_from_manager(ix, manager_id, {'type': 'result', 'task_id': 1}) + ix.expire_drained_managers(set(), None) + + assert shutdown_sent(ix, manager_id) + assert manager_id not in ix._ready_managers + + +@pytest.mark.local +def test_legacy_manager_without_drain_time(ix, manager_id): + # ensure new/post-drain_time interchanges work with old/pre-drain_time managers + reg_msg = registration_msg(ix) + assert 'drain_time' not in reg_msg, "test prerequisite" + send_from_manager(ix, manager_id, reg_msg) + assert manager_id in ix._ready_managers, "manager should have been registered" + + ix.expire_drained_managers(set(), None) + assert not shutdown_sent(ix, manager_id), "ix should wait for legacy manager to request shutdown" + + send_from_manager(ix, manager_id, {'type': 'drain'}) + ix.expire_drained_managers(set(), None) + + assert shutdown_sent(ix, manager_id) + assert manager_id not in ix._ready_managers diff --git a/parsl/parsl/tests/unit/executors/high_throughput/test_process_worker_pool.py b/parsl/parsl/tests/unit/executors/high_throughput/test_process_worker_pool.py new file mode 100644 index 00000000..672b29f1 --- /dev/null +++ b/parsl/parsl/tests/unit/executors/high_throughput/test_process_worker_pool.py @@ -0,0 +1,195 @@ +import os +import pickle +import sys +from argparse import ArgumentError +from typing import Any, Dict +from unittest import mock + +import pytest + +from parsl.app.errors import RemoteExceptionWrapper +from parsl.executors.high_throughput import process_worker_pool +from parsl.executors.high_throughput.process_worker_pool import worker +from parsl.multiprocessing import SpawnContext +from parsl.serialize.facade import deserialize + +if sys.version_info < (3, 12): + # exit_on_error bug; see https://github.com/python/cpython/issues/121018 + # "argparse.ArgumentParser.parses_args does not honor exit_on_error=False when + # given unrecognized arguments" + pytest.skip(allow_module_level=True, reason="exit_on_error argparse bug") + +# due to above pytest.skip, mypy on < Py312 thinks this is unreachable. :facepalm: +_known_required = ( # type: ignore[unreachable, unused-ignore] + "--cert_dir", + "--cpu-affinity", + "--port", + "--addresses", +) + + +@pytest.mark.local +def test_arg_parser_exits_on_error(): + p = process_worker_pool.get_arg_parser() + assert p.exit_on_error + + +@pytest.mark.local +def test_arg_parser_known_required(): + p = process_worker_pool.get_arg_parser() + reqd = [a for a in p._actions if a.required] + for a in reqd: + assert a.option_strings[-1] in _known_required, "Update _known_required?" + + +@pytest.mark.local +@pytest.mark.parametrize("req", _known_required) +def test_arg_parser_required(req): + p = process_worker_pool.get_arg_parser() + p.exit_on_error = False + with pytest.raises(ArgumentError) as pyt_exc: + p.parse_args([]) + + e_msg = pyt_exc.value.args[1] + assert req in e_msg + + +@pytest.mark.local +@pytest.mark.parametrize("valid,val", ( + (True, "NoNe"), + (True, "none"), + (True, "block"), + (True, "alternating"), + (True, "block-reverse"), + (True, "list"), + (False, "asdf"), + (False, ""), +)) +def test_arg_parser_validates_cpu_affinity(valid, val): + reqd_args = [] + reqd_args.extend(("--cert_dir", "/some/path")) + reqd_args.extend(("--port", "123")) + reqd_args.extend(("--addresses", "asdf")) + reqd_args.extend(("--cpu-affinity", val)) + + p = process_worker_pool.get_arg_parser() + p.exit_on_error = False + if valid: + p.parse_args(reqd_args) + else: + with pytest.raises(ArgumentError) as pyt_exc: + p.parse_args(reqd_args) + assert "must be one of" in pyt_exc.value.args[1] + + +def _always_raise(*a, **k): + raise ArithmeticError(f"{a=}\n{k=}") + + +@pytest.mark.local +def test_worker_dynamic_import_happy_path(tmpd_cwd) -> None: + import_str = f"{_always_raise.__module__}.{_always_raise.__name__}" + task_exec: Dict[str, Any] = { + "f": import_str, + "a": (1, 2), + "k": {"a": "b"}, + } + req = { + "task_id": 15, + "context": {"task_executor": task_exec}, + "buffer": b"some serialized value" + } + + try: + task_args = [req["buffer"]] + task_args.extend(task_exec["a"]) + _always_raise(*task_args, **task_exec["k"]) + except Exception as e: + exp_exc = e + else: + raise RuntimeError("Test failure; this branch should not run") + + q = mock.Mock(side_effect=(req, MemoryError("intentional test error"))) + q.get = q + + block_id = "bid" + worker_id = 1 + pool = 1 + (tmpd_cwd / f"block-{block_id}/{worker_id}").mkdir(parents=True) + with pytest.raises(MemoryError): + worker( + worker_id, + pool_id=str(pool), + pool_size=pool, + task_queue=q, + result_queue=q, + monitoring_queue=None, + ready_worker_count=SpawnContext.Value("i", 0), + tasks_in_progress={}, + cpu_affinity="none", + accelerator=None, + block_id=block_id, + task_queue_timeout=0, + manager_pid=os.getpid(), + logdir=str(tmpd_cwd), + log_config=None, + debug=True, + mpi_launcher="", + ) + (result_pkl,), _ = q.put.call_args + r = pickle.loads(result_pkl) + assert "exception" in r + wrapped_exc: RemoteExceptionWrapper = deserialize(r["exception"]) + exc = wrapped_exc.get_exception() + assert isinstance(exc, type(exp_exc)), "Approximate equality" + assert str(exp_exc) == str(exc), "Approximate equality; all args, kwargs conveyed" + + +@pytest.mark.local +def test_worker_bad_dynamic_import(tmpd_cwd) -> None: + req = { + "task_id": 15, + "context": { + "task_executor": { + "f": "parsl.some.not_existing.module.__nope", + "a": (1, 2), + "k": {"a": "b"}, + }, + }, + "buffer": b"some serialized value" + } + + q = mock.Mock(side_effect=(req, MemoryError("intentional test error"))) + q.get = q + + block_id = "bid" + worker_id = 1 + pool = 1 + (tmpd_cwd / f"block-{block_id}/{worker_id}").mkdir(parents=True) + with pytest.raises(MemoryError): + worker( + worker_id, + pool_id=str(pool), + pool_size=pool, + task_queue=q, + result_queue=q, + monitoring_queue=None, + ready_worker_count=SpawnContext.Value("i", 0), + tasks_in_progress={}, + cpu_affinity="none", + accelerator=None, + block_id=block_id, + task_queue_timeout=0, + manager_pid=os.getpid(), + logdir=str(tmpd_cwd), + log_config=None, + debug=True, + mpi_launcher="", + ) + (result_pkl,), _ = q.put.call_args + r = pickle.loads(result_pkl) + assert "exception" in r + wrapped_exc: RemoteExceptionWrapper = deserialize(r["exception"]) + exc = wrapped_exc.get_exception() + assert isinstance(exc, ModuleNotFoundError) + assert "No module named" in str(exc) diff --git a/parsl/parsl/tests/unit/test_address.py b/parsl/parsl/tests/unit/test_address.py new file mode 100644 index 00000000..b579baa7 --- /dev/null +++ b/parsl/parsl/tests/unit/test_address.py @@ -0,0 +1,21 @@ +import pytest + +from parsl.addresses import tcp_url + + +@pytest.mark.local +@pytest.mark.parametrize("address, port,expected", [ + ("127.0.0.1", 55001, "tcp://127.0.0.1:55001"), + ("127.0.0.1", "55001", "tcp://127.0.0.1:55001"), + ("127.0.0.1", None, "tcp://127.0.0.1"), + ("::1", "55001", "tcp://[::1]:55001"), + ("::ffff:127.0.0.1", 55001, "tcp://[::ffff:127.0.0.1]:55001"), + ("::ffff:127.0.0.1", None, "tcp://::ffff:127.0.0.1"), + ("::ffff:127.0.0.1", None, "tcp://::ffff:127.0.0.1"), + ("*", None, "tcp://*"), + ("*", 55001, "tcp://*:55001") +]) +def test_tcp_url(address, port, expected): + """Confirm valid address generation""" + result = tcp_url(address, port) + assert result == expected diff --git a/parsl/parsl/tests/unit/test_file.py b/parsl/parsl/tests/unit/test_file.py new file mode 100644 index 00000000..87b3746c --- /dev/null +++ b/parsl/parsl/tests/unit/test_file.py @@ -0,0 +1,99 @@ +import os +from unittest import mock + +import pytest + +from parsl import File + +_MOCK_BASE = "parsl.data_provider.files." + + +@pytest.mark.local +@pytest.mark.parametrize("scheme", ("http", "https", "ftp", "ftps", "asdfasdf")) +def test_file_init_scheme(scheme): + basename = "some_base_name" + path = f"/some/path/1/2/3/{basename}" + fqdn = "some.fqdn.example.com" + exp_url = f"{scheme}://{fqdn}{path}" + f = File(exp_url) + assert f.url == exp_url, "Expected given url to be stored" + assert f.scheme == scheme + assert f.netloc == fqdn + assert f.path == path + assert f.filename == basename + assert f.local_path is None, "Expect only set by API consumer, not constructor" + + +@pytest.mark.local +@pytest.mark.parametrize("url", ("some weird :// url", "", "a")) +def test_file_init_file_url_fallback(url): + exp_url = "some weird :// url" + f = File(exp_url) + assert f.url == exp_url + assert not f.netloc, "invalid host, should be no netloc" + assert f.path == exp_url, "Should fail to fully parse, so path is whole url" + assert f.filename == exp_url.rsplit("/", 1)[-1] + + assert f.scheme == "file" + + +@pytest.mark.local +def test_file_proxies_for_filepath(randomstring): + # verify (current) expected internal hookup + exp_filepath = randomstring() + with mock.patch( + f"{_MOCK_BASE}File.filepath", new_callable=mock.PropertyMock + ) as mock_fpath: + mock_fpath.return_value = exp_filepath + f = File("") + assert str(f) == exp_filepath + assert os.fspath(f) == exp_filepath + + +@pytest.mark.local +@pytest.mark.parametrize("scheme", ("file://", "")) +def test_file_filepath_local_path_is_priority(scheme, randomstring): + exp_path = "/some/local/path" + url = f"{scheme}{exp_path}" + f = File(url) + + f.local_path = randomstring() + assert f.filepath == f.local_path + + f.local_path = None + assert f.filepath == exp_path + + +@pytest.mark.local +def test_file_filepath_requires_local_accessible_path(): + with pytest.raises(ValueError) as pyt_exc: + _ = File("http://").filepath + + assert "No local_path" in str(pyt_exc.value), "Expected reason in exception" + + +@pytest.mark.local +@pytest.mark.parametrize("scheme", ("https", "ftps", "", "file", "asdfasdf")) +def test_file_repr(scheme): + netloc = "some.netloc" + filename = "some_file_name" + path = f"/some/path/{filename}" + if scheme: + url = f"{scheme}://{netloc}{path}" + else: + scheme = "file" + url = path + + f = File(url) + r = repr(f) + assert r.startswith("<") + assert r.endswith(">") + assert f"<{type(f).__name__} " in r + assert f" at 0x{id(f):x}" in r + assert f" url={url}" in r + assert f" scheme={scheme}" in r + assert f" path={path}" in r + assert f" filename={filename}" in r + + if scheme != "file": + assert f" netloc={netloc}" in r diff --git a/parsl/parsl/tests/unit/test_globus_compute_executor.py b/parsl/parsl/tests/unit/test_globus_compute_executor.py new file mode 100644 index 00000000..21e1da00 --- /dev/null +++ b/parsl/parsl/tests/unit/test_globus_compute_executor.py @@ -0,0 +1,113 @@ +import random +from unittest import mock + +import pytest + +from parsl.executors import GlobusComputeExecutor + + +@pytest.fixture +def mock_ex(): + # Not Parsl's job to test GC's Executor, although it + # still needs to be importable for these test cases. + from globus_compute_sdk import Executor + + yield mock.Mock(spec=Executor) + + +@pytest.mark.local +@pytest.mark.globus_compute +def test_gc_executor_mock_spec(mock_ex): + # a test of tests -- make sure we're using spec= in the mock + with pytest.raises(AttributeError): + mock_ex.aasdf() + + +@pytest.mark.local +@pytest.mark.globus_compute +def test_gc_executor_label_default(mock_ex): + gce = GlobusComputeExecutor(mock_ex) + assert gce.label == type(gce).__name__, "Expect reasonable default label" + + +@pytest.mark.local +@pytest.mark.globus_compute +def test_gc_executor_label(mock_ex, randomstring): + exp_label = randomstring() + gce = GlobusComputeExecutor(mock_ex, label=exp_label) + assert gce.label == exp_label + + +@pytest.mark.local +@pytest.mark.globus_compute +def test_gc_executor_resets_spec_after_submit(mock_ex, randomstring): + submit_res = {randomstring(): "some submit res"} + res = {"some": randomstring(), "spec": randomstring()} + mock_ex.resource_specification = res + mock_ex.user_endpoint_config = None + gce = GlobusComputeExecutor(mock_ex) + + fn = mock.Mock() + orig_res = mock_ex.resource_specification + orig_uep = mock_ex.user_endpoint_config + + def mock_submit(*a, **k): + assert mock_ex.resource_specification == submit_res, "Expect set for submission" + assert mock_ex.user_endpoint_config is None + mock_ex.submit.side_effect = mock_submit + + gce.submit(fn, resource_specification=submit_res) + + assert mock_ex.resource_specification == orig_res + assert mock_ex.user_endpoint_config is orig_uep + + +@pytest.mark.local +@pytest.mark.globus_compute +def test_gc_executor_resets_uep_after_submit(mock_ex, randomstring): + uep_conf = randomstring() + res = {"some": randomstring()} + gce = GlobusComputeExecutor(mock_ex) + + fn = mock.Mock() + orig_res = mock_ex.resource_specification + orig_uep = mock_ex.user_endpoint_config + + def mock_submit(*a, **k): + + assert mock_ex.resource_specification == res, "Expect set for submission" + assert mock_ex.user_endpoint_config == uep_conf, "Expect set for submission" + mock_ex.submit.side_effect = mock_submit + + gce.submit(fn, resource_specification={"user_endpoint_config": uep_conf, **res}) + + assert mock_ex.resource_specification == orig_res + assert mock_ex.user_endpoint_config is orig_uep + + +@pytest.mark.local +@pytest.mark.globus_compute +def test_gc_executor_happy_path(mock_ex, randomstring): + mock_fn = mock.Mock() + args = tuple(randomstring() for _ in range(random.randint(0, 3))) + kwargs = {randomstring(): randomstring() for _ in range(random.randint(0, 3))} + + gce = GlobusComputeExecutor(mock_ex) + gce.submit(mock_fn, {}, *args, **kwargs) + + assert mock_ex.submit.called, "Expect proxying of args to underlying executor" + found_a, found_k = mock_ex.submit.call_args + assert found_a[0] is mock_fn + assert found_a[1:] == args + assert found_k == kwargs + + +@pytest.mark.local +@pytest.mark.globus_compute +def test_gc_executor_shuts_down_asynchronously(mock_ex): + gce = GlobusComputeExecutor(mock_ex) + gce.shutdown() + assert mock_ex.shutdown.called + a, k = mock_ex.shutdown.call_args + assert k["wait"] is False + assert k["cancel_futures"] is True diff --git a/parsl/parsl/tests/unit/test_launchers.py b/parsl/parsl/tests/unit/test_launchers.py new file mode 100644 index 00000000..9f184479 --- /dev/null +++ b/parsl/parsl/tests/unit/test_launchers.py @@ -0,0 +1,360 @@ +import os +import subprocess +import tempfile +from pathlib import Path + +import pytest + +from parsl.launchers import ( + AprunLauncher, + GnuParallelLauncher, + JsrunLauncher, + MpiExecLauncher, + MpiRunLauncher, + SimpleLauncher, + SingleNodeLauncher, + SrunLauncher, + SrunMPILauncher, + WrappedLauncher, +) + + +def test_wrapped_launcher(caplog): + w = WrappedLauncher('docker') + assert w('test', 1, 1) == 'docker test' + + # Make sure it complains if you have > 1 node or task per node + w('test', 2, 2) + assert 'tasks per node' in caplog.text + assert 'nodes per block' in caplog.text + + +def create_mock_script(script_content: str, mock_dir: Path) -> str: + """Prepend mock function definitions to the launcher script.""" + + # Mock functions for HPC commands + mocks = f""" +# Mock HPC commands for testing +export PATH="{mock_dir}:$PATH" + +srun() {{ + echo "MOCK_SRUN called with: $@" >&2 + # Look for bash and execute the script that follows + while [[ $# -gt 0 ]]; do + if [[ "$1" == "bash" ]]; then + shift + bash "$@" + return $? + fi + shift + done + echo "ERROR: srun mock did not find bash command to execute" >&2 + return 1 +}} + +mpiexec() {{ + echo "MOCK_MPIEXEC called with: $@" >&2 + # Look for bash and execute the script that follows + while [[ $# -gt 0 ]]; do + if [[ "$1" == "bash" ]] || [[ "$1" == "/usr/bin/sh" ]]; then + shift + bash "$@" + return $? + fi + shift + done + echo "ERROR: mpiexec mock did not find bash command to execute" >&2 + return 1 +}} + +mpirun() {{ + echo "MOCK_MPIRUN called with: $@" >&2 + # Look for bash and execute the script that follows + while [[ $# -gt 0 ]]; do + if [[ "$1" == "bash" ]] || [[ "$1" == *"/bash" ]]; then + shift + bash "$@" + return $? + fi + shift + done + echo "ERROR: mpirun mock did not find bash command to execute" >&2 + return 1 +}} + +aprun() {{ + echo "MOCK_APRUN called with: $@" >&2 + # Look for bash and execute the script that follows + while [[ $# -gt 0 ]]; do + if [[ "$1" == "/bin/bash" ]] || [[ "$1" == "bash" ]]; then + shift + bash "$@" + return $? + fi + shift + done + echo "ERROR: aprun mock did not find bash command to execute" >&2 + return 1 +}} + +jsrun() {{ + echo "MOCK_JSRUN called with: $@" >&2 + # Look for bash and execute the script that follows + while [[ $# -gt 0 ]]; do + if [[ "$1" == "/bin/bash" ]] || [[ "$1" == "bash" ]]; then + shift + bash "$@" + return $? + fi + shift + done + echo "ERROR: jsrun mock did not find bash command to execute" >&2 + return 1 +}} + +parallel() {{ + echo "MOCK_PARALLEL called with: $@" >&2 + # For gnu parallel, read commands from stdin and execute them + while IFS= read -r line; do + eval "$line" + done +}} + +# Original script content follows +{script_content} +""" + return mocks + + +@pytest.mark.local +@pytest.mark.parametrize("launcher_class,creates_cmd_script", [ + (SimpleLauncher, False), + (SingleNodeLauncher, False), + (GnuParallelLauncher, True), + (MpiExecLauncher, True), + (MpiRunLauncher, True), + (SrunLauncher, True), + (SrunMPILauncher, True), + (AprunLauncher, True), + (JsrunLauncher, True), +]) +def test_launcher_script_execution(launcher_class, creates_cmd_script): + """Test that launcher scripts can be generated and executed with mocked commands.""" + + test_command = "echo 'Hello from worker'" + launcher = launcher_class() + name = launcher_class.__name__ + + with tempfile.TemporaryDirectory() as tmpdir: + tmppath = Path(tmpdir) + mock_bin = tmppath / "mock_bin" + mock_bin.mkdir() + + # Generate the launcher script + script_dir = tmppath / f"scripts_{name}" + script_dir.mkdir(exist_ok=True) + + # Call the launcher + launcher_script = launcher( + command=test_command, + tasks_per_node=2, + nodes_per_block=1, + script_dir=str(script_dir) + ) + + # Verify we got a string back + assert isinstance(launcher_script, str), f"{name} should return a string" + assert len(launcher_script) > 0, f"{name} should return non-empty script" + + # Create a test script with mocks + test_script = tmppath / f"test_{name}.sh" + full_script = create_mock_script(launcher_script, mock_bin) + test_script.write_text(full_script) + test_script.chmod(0o755) + + # Set environment variables that scripts might expect + env = os.environ.copy() + env['JOBNAME'] = f'test_job_{name}' + env['SLURM_JOB_NAME'] = f'test_job_{name}' + env['SLURM_CPUS_ON_NODE'] = '4' # Mock CPU count for SLURM launchers + env['SLURM_JOB_NUM_NODES'] = '2' # Mock node count for SLURM launchers + env['PBS_NODEFILE'] = '/dev/null' + + # Execute the script + result = subprocess.run( + ['bash', str(test_script)], + capture_output=True, + text=True, + timeout=10, + env=env, + cwd=tmpdir + ) + + # Check that script executed successfully + assert result.returncode == 0, ( + f"{name} script failed with returncode {result.returncode}\n" + f"STDOUT: {result.stdout}\n" + f"STDERR: {result.stderr}\n" + f"SCRIPT:\n{full_script}" + ) + + # Verify the command was actually executed by checking output + assert "Hello from worker" in result.stdout, ( + f"{name} command did not produce expected output\n" + f"Expected 'Hello from worker' in stdout\n" + f"STDOUT: {result.stdout}\n" + f"STDERR: {result.stderr}" + ) + + # Verify command script was created if launcher creates one + if creates_cmd_script: + cmd_scripts = list(script_dir.glob("cmd_*.sh")) + assert len(cmd_scripts) > 0, f"{name} should create a cmd_*.sh file in {script_dir}" + + +@pytest.mark.local +@pytest.mark.parametrize("launcher_class,uses_script_dir", [ + (GnuParallelLauncher, True), + (MpiExecLauncher, True), + (MpiRunLauncher, True), + (SrunLauncher, True), + (SrunMPILauncher, True), + (AprunLauncher, True), + (JsrunLauncher, True), +]) +def test_launcher_script_dir_parameter(launcher_class, uses_script_dir): + """Test that script_dir parameter is properly set in generated scripts.""" + + with tempfile.TemporaryDirectory() as tmpdir: + script_dir = Path(tmpdir) / "my_custom_scripts" + script_dir.mkdir() + + launcher = launcher_class() + script = launcher( + command="echo test", + tasks_per_node=1, + nodes_per_block=1, + script_dir=str(script_dir) + ) + + # Verify the exact SCRIPT_DIR assignment appears in the script + expected_line = f'SCRIPT_DIR="{script_dir}"' + assert expected_line in script, ( + f"{launcher_class.__name__} should set SCRIPT_DIR to the provided path\n" + f"Expected line: {expected_line}\n" + f"Generated script:\n{script}" + ) + + # Verify the directory check logic is present + assert "[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR=\"\"" in script, ( + f"{launcher_class.__name__} should include directory existence check" + ) + + # Verify the script uses the conditional expansion pattern + assert "${SCRIPT_DIR:+$SCRIPT_DIR/}" in script, ( + f"{launcher_class.__name__} should use bash parameter expansion for script_dir" + ) + + +@pytest.mark.local +@pytest.mark.parametrize("launcher_class,uses_script_dir", [ + (SimpleLauncher, False), + (SingleNodeLauncher, False), + (GnuParallelLauncher, True), + (MpiExecLauncher, True), + (MpiRunLauncher, True), + (SrunLauncher, True), + (SrunMPILauncher, True), + (AprunLauncher, True), + (JsrunLauncher, True), +]) +def test_launcher_with_empty_script_dir(launcher_class, uses_script_dir): + """Test that launchers handle empty script_dir gracefully.""" + + launcher = launcher_class() + script = launcher( + command="echo test", + tasks_per_node=1, + nodes_per_block=1, + script_dir="" + ) + + # Should still generate valid script + assert isinstance(script, str) + assert len(script) > 0 + + # Launchers that create command scripts should properly handle empty script_dir + if uses_script_dir: + # Verify SCRIPT_DIR is set to empty string + assert 'SCRIPT_DIR=""' in script, ( + f"{launcher_class.__name__} should set SCRIPT_DIR to empty string when script_dir is empty\n" + f"Generated script:\n{script}" + ) + + # Verify the directory check logic is present + assert "[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR=\"\"" in script, ( + f"{launcher_class.__name__} should include directory existence check" + ) + + # Verify the parameter expansion pattern is used + assert "${SCRIPT_DIR:+$SCRIPT_DIR/}" in script, ( + f"{launcher_class.__name__} should use bash parameter expansion for script_dir" + ) + + +@pytest.mark.local +@pytest.mark.parametrize("launcher_class", [ + SimpleLauncher, + SingleNodeLauncher, + GnuParallelLauncher, + MpiExecLauncher, + MpiRunLauncher, + SrunLauncher, + SrunMPILauncher, + AprunLauncher, + JsrunLauncher, +]) +def test_launcher_bash_syntax(launcher_class): + """Test that generated launcher scripts have valid bash syntax.""" + + test_command = "echo test" + launcher = launcher_class() + script = launcher(test_command, 1, 1, "") + + # Check bash syntax without executing + result = subprocess.run( + ['bash', '-n'], # -n flag checks syntax only + input=script, + capture_output=True, + text=True + ) + + launcher_name = launcher_class.__name__ + assert result.returncode == 0, ( + f"Syntax error in {launcher_name}\n" + f"Error: {result.stderr}\n" + f"Script:\n{script}" + ) + + +@pytest.mark.local +def test_wrapped_launcher_bash_syntax(): + """Test that WrappedLauncher generates valid bash syntax.""" + + test_command = "echo test" + launcher = WrappedLauncher('echo') + script = launcher(test_command, 1, 1, "") + + # Check bash syntax without executing + result = subprocess.run( + ['bash', '-n'], # -n flag checks syntax only + input=script, + capture_output=True, + text=True + ) + + assert result.returncode == 0, ( + f"Syntax error in WrappedLauncher\n" + f"Error: {result.stderr}\n" + f"Script:\n{script}" + ) diff --git a/parsl/parsl/tests/unit/test_usage_tracking.py b/parsl/parsl/tests/unit/test_usage_tracking.py new file mode 100644 index 00000000..1581249c --- /dev/null +++ b/parsl/parsl/tests/unit/test_usage_tracking.py @@ -0,0 +1,66 @@ +"""Test usage_tracking values.""" + +import pytest + +import parsl +from parsl.config import Config +from parsl.errors import ConfigurationError + + +@pytest.mark.local +def test_config_load(): + """Test loading a config with usage tracking.""" + with parsl.load(Config(usage_tracking=3)): + pass + parsl.clear() + + +@pytest.mark.local +@pytest.mark.parametrize("level", (0, 1, 2, 3, False, True)) +def test_valid(level): + """Test valid usage_tracking values.""" + Config(usage_tracking=level) + assert Config(usage_tracking=level).usage_tracking == level + + +@pytest.mark.local +@pytest.mark.parametrize("level", (12, 1000, -1)) +def test_invalid_values(level): + """Test invalid usage_tracking values.""" + with pytest.raises(ConfigurationError): + Config(usage_tracking=level) + + +@pytest.mark.local +@pytest.mark.parametrize("level", ("abcd", None, bytes(1), 1.0, 1j, object())) +def test_invalid_types(level): + """Test invalid usage_tracking types.""" + with pytest.raises(Exception) as ex: + Config(usage_tracking=level) + + # with typeguard 4.x this is TypeCheckError, + # with typeguard 2.x this is TypeError + # we can't instantiate TypeCheckError if we're in typeguard 2.x environment + # because it does not exist... so check name using strings. + assert ex.type.__name__ in ["TypeCheckError", "TypeError"] + + +@pytest.mark.local +def test_valid_project_name(): + """Test valid project_name.""" + assert ( + Config( + usage_tracking=3, + project_name="unit-test", + ).project_name == "unit-test" + ) + + +@pytest.mark.local +@pytest.mark.parametrize("name", (1, 1.0, True, object())) +def test_invalid_project_name(name): + """Test invalid project_name.""" + with pytest.raises(Exception) as ex: + Config(usage_tracking=3, project_name=name) + + assert ex.type.__name__ in ["TypeCheckError", "TypeError"] diff --git a/parsl/parsl/tests/utils.py b/parsl/parsl/tests/utils.py new file mode 100644 index 00000000..110758c2 --- /dev/null +++ b/parsl/parsl/tests/utils.py @@ -0,0 +1,5 @@ +import os + + +def get_config(filename): + return os.path.join(os.path.dirname(__file__), 'configs', filename) diff --git a/parsl/parsl/usage_tracking/__init__.py b/parsl/parsl/usage_tracking/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/parsl/parsl/usage_tracking/api.py b/parsl/parsl/usage_tracking/api.py new file mode 100644 index 00000000..2d8b0811 --- /dev/null +++ b/parsl/parsl/usage_tracking/api.py @@ -0,0 +1,65 @@ +import inspect +from abc import abstractmethod +from functools import singledispatch +from typing import Any, List, Sequence + +from parsl.utils import RepresentationMixin + +# Traverse the configuration hierarchy, returning a JSON component +# for each one. Configuration components which implement +# RepresentationMixin will be in the right form for inspecting +# object attributes. Configuration components which are lists or tuples +# are traversed in sequence. Other types default to reporting no +# usage information. + + +@singledispatch +def get_parsl_usage(obj) -> List[Any]: + return [] + + +@get_parsl_usage.register +def get_parsl_usage_representation_mixin(obj: RepresentationMixin) -> List[Any]: + t = type(obj) + qualified_name = t.__module__ + "." + t.__name__ + + # me can contain anything that can be rendered as JSON + me: List[Any] = [] + + if isinstance(obj, UsageInformation): + # report rich usage information for this component + attrs = {'c': qualified_name} + attrs.update(obj.get_usage_information()) + me = [attrs] + else: + # report the class name of this component + me = [qualified_name] + + # unwrap typeguard-style unwrapping + init: Any = type(obj).__init__ + if hasattr(init, '__wrapped__'): + init = init.__wrapped__ + + argspec = inspect.getfullargspec(init) + + for arg in argspec.args[1:]: # skip first arg, self + arg_value = getattr(obj, arg) + d = get_parsl_usage(arg_value) + me += d + + return me + + +@get_parsl_usage.register(list) +@get_parsl_usage.register(tuple) +def get_parsl_usage_sequence(obj: Sequence) -> List[Any]: + result = [] + for v in obj: + result += get_parsl_usage(v) + return result + + +class UsageInformation: + @abstractmethod + def get_usage_information(self) -> dict: + pass diff --git a/parsl/parsl/usage_tracking/levels.py b/parsl/parsl/usage_tracking/levels.py new file mode 100644 index 00000000..a772220c --- /dev/null +++ b/parsl/parsl/usage_tracking/levels.py @@ -0,0 +1,6 @@ +"""Module for defining the usage tracking levels.""" + +DISABLED = 0 # Tracking is disabled +LEVEL_1 = 1 # Share info about Parsl version, Python version, platform +LEVEL_2 = 2 # Share info about config + level 1 +LEVEL_3 = 3 # Share info about app count, app fails, execution time + level 2 diff --git a/parsl/parsl/usage_tracking/usage.py b/parsl/parsl/usage_tracking/usage.py new file mode 100644 index 00000000..a916ed54 --- /dev/null +++ b/parsl/parsl/usage_tracking/usage.py @@ -0,0 +1,245 @@ +import json +import logging +import platform +import socket +import sys +import time +import uuid + +from parsl.dataflow.states import States +from parsl.errors import ConfigurationError +from parsl.multiprocessing import SpawnProcess +from parsl.usage_tracking.api import get_parsl_usage +from parsl.usage_tracking.levels import DISABLED as USAGE_TRACKING_DISABLED +from parsl.usage_tracking.levels import LEVEL_3 as USAGE_TRACKING_LEVEL_3 +from parsl.utils import setproctitle +from parsl.version import VERSION as PARSL_VERSION + +logger = logging.getLogger(__name__) + +from typing import Callable + +from typing_extensions import ParamSpec + +# protocol version byte: when (for example) compression parameters are changed +# that cannot be inferred from the compressed message itself, this version +# ID needs to imply those parameters. + +# Earlier protocol versions: b'{' - the original pure-JSON protocol pre-March 2024 +PROTOCOL_VERSION = b'1' + +P = ParamSpec("P") + + +def async_process(fn: Callable[P, None]) -> Callable[P, None]: + """ Decorator function to launch a function as a separate process """ + + def run(*args, **kwargs): + proc = SpawnProcess(target=fn, args=args, kwargs=kwargs, name="Usage-Tracking") + proc.start() + return proc + + return run + + +@async_process +def udp_messenger(domain_name: str, UDP_PORT: int, sock_timeout: int, message: bytes) -> None: + """Send UDP messages to usage tracker asynchronously + + This multiprocessing based messenger was written to overcome the limitations + of signalling/terminating a thread that is blocked on a system call. + + Args: + - domain_name (str) : Domain name string + - UDP_PORT (int) : UDP port to send out on + - sock_timeout (int) : Socket timeout + """ + setproctitle("parsl: Usage tracking") + + try: + UDP_IP = socket.gethostbyname(domain_name) + + sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # UDP + sock.settimeout(sock_timeout) + sock.sendto(message, (UDP_IP, UDP_PORT)) + sock.close() + + except socket.timeout: + logger.debug("Failed to send usage tracking data: socket timeout") + except OSError as e: + logger.debug("Failed to send usage tracking data: OSError: {}".format(e)) + except Exception as e: + logger.debug("Failed to send usage tracking data: Exception: {}".format(e)) + + +class UsageTracker: + """Usage Tracking for Parsl. + + The server for this is here: https://github.com/Parsl/parsl_tracking + This issue captures the discussion that went into functionality + implemented here: https://github.com/Parsl/parsl/issues/34 + + """ + + def __init__(self, dfk, port=50077, + domain_name='tracking.parsl-project.org'): + """Initialize usage tracking unless the user has opted-out. + + We will try to resolve the hostname specified in kwarg:domain_name + and if that fails attempt to use the kwarg:ip. Determining the + IP and sending message happens in an asynchronous processs to avoid + slowing down DFK initialization. + + Tracks usage stats by inspecting the internal state of the dfk. + + Args: + - dfk (DFK object) : Data Flow Kernel object + + KWargs: + - port (int) : Port number, Default:50077 + - domain_name (string) : Domain name, will override IP + Default: tracking.parsl-project.org + """ + + self.domain_name = domain_name + # The sock timeout will only apply to UDP send and not domain resolution + self.sock_timeout = 5 + self.UDP_PORT = port + self.procs = [] + self.dfk = dfk + self.config = self.dfk.config + self.correlator_uuid = str(uuid.uuid4()) + self.parsl_version = PARSL_VERSION + self.python_version = "{}.{}.{}".format(sys.version_info.major, + sys.version_info.minor, + sys.version_info.micro) + self.tracking_level = self.check_tracking_level() + self.project_name = self.config.project_name + self.start_time = None + logger.debug("Tracking level: {}".format(self.tracking_level)) + + def check_tracking_level(self) -> int: + """Check if tracking is enabled and return level. + + Checks usage_tracking in Config + - Possible values: [True, False, 0, 1, 2, 3] + + True/False values are treated as Level 1/Level 0 respectively. + + Returns: int + - 0 : Tracking is disabled + - 1 : Tracking is enabled with level 1 + Share info about Parsl version, Python version, platform + - 2 : Tracking is enabled with level 2 + Share info about config + level 1 + - 3 : Tracking is enabled with level 3 + Share info about app count, app fails, execution time + level 2 + """ + if not USAGE_TRACKING_DISABLED <= self.config.usage_tracking <= USAGE_TRACKING_LEVEL_3: + raise ConfigurationError( + f"Usage Tracking values must be 0, 1, 2, or 3 and not {self.config.usage_tracking}" + ) + + return self.config.usage_tracking + + def construct_start_message(self) -> bytes: + """Collect preliminary run info at the start of the DFK. + + Returns : + - Message dict dumped as json string, ready for UDP + """ + message = {'correlator': self.correlator_uuid, + 'parsl_v': self.parsl_version, + 'python_v': self.python_version, + 'platform.system': platform.system(), + 'tracking_level': int(self.tracking_level)} + + if self.project_name: + message['project_name'] = self.project_name + + if self.tracking_level >= 2: + message['components'] = get_parsl_usage(self.dfk._config) + + if self.tracking_level == 3: + self.start_time = int(time.time()) + message['start'] = self.start_time + + logger.debug(f"Usage tracking start message: {message}") + + return self.encode_message(message) + + def construct_end_message(self) -> bytes: + """Collect the final run information at the time of DFK cleanup. + This is only called if tracking level is 3. + + Returns: + - Message dict dumped as json string, ready for UDP + """ + end_time = int(time.time()) + + app_count = self.dfk.task_count + + app_fails = self.dfk.task_state_counts[States.failed] + self.dfk.task_state_counts[States.dep_fail] + + # the DFK is tangled into this code as a god-object, so it is + # handled separately from the usual traversal code, but presenting + # the same protocol-level report. + dfk_component = {'c': type(self.dfk).__module__ + "." + type(self.dfk).__name__, + 'app_count': app_count, + 'app_fails': app_fails} + + message = {'correlator': self.correlator_uuid, + 'end': end_time, + 'execution_time': end_time - self.start_time, + 'components': [dfk_component] + get_parsl_usage(self.dfk._config)} + + if self.project_name: + message['project_name'] = self.project_name + + logger.debug(f"Usage tracking end message (unencoded): {message}") + + return self.encode_message(message) + + def encode_message(self, obj): + return PROTOCOL_VERSION + json.dumps(obj).encode() + + def send_UDP_message(self, message: bytes) -> None: + """Send UDP message.""" + try: + proc = udp_messenger(self.domain_name, self.UDP_PORT, self.sock_timeout, message) + self.procs.append(proc) + except Exception as e: + logger.debug("Usage tracking failed: {}".format(e)) + + def send_start_message(self) -> None: + if self.tracking_level: + logger.info("Sending start message for usage tracking") + self.start_time = time.time() + message = self.construct_start_message() + self.send_UDP_message(message) + + def send_end_message(self) -> None: + if self.tracking_level == 3: + logger.info("Sending end message for usage tracking") + message = self.construct_end_message() + self.send_UDP_message(message) + + def close(self, timeout: float = 10.0) -> None: + """First give each process one timeout period to finish what it is + doing, then kill it (SIGKILL). There's no softer SIGTERM step, + because that adds one join period of delay for what is almost + definitely either: going to behave broadly the same as to SIGKILL, + or won't respond to SIGTERM. + """ + if self.tracking_level: + logger.info("Closing usage tracking") + + for proc in self.procs: + logger.debug("Joining usage tracking process %s", proc) + proc.join(timeout=timeout) + if proc.is_alive(): + logger.warning("Usage tracking process did not end itself; sending SIGKILL") + proc.kill() + proc.join(timeout=timeout) + + proc.close() diff --git a/parsl/parsl/utils.py b/parsl/parsl/utils.py new file mode 100644 index 00000000..0b727c19 --- /dev/null +++ b/parsl/parsl/utils.py @@ -0,0 +1,522 @@ +import inspect +import logging +import os +import re +import shlex +import subprocess +import threading +import time +from contextlib import contextmanager +import platform +from types import TracebackType +from typing import ( + IO, + Any, + Callable, + Dict, + Generator, + List, + Optional, + Sequence, + Tuple, + Union, +) + +import typeguard +from typing_extensions import Type + +import parsl +from parsl.app.errors import BadStdStreamFile +from parsl.version import VERSION + +try: + import setproctitle as setproctitle_module +except ImportError: + _setproctitle_enabled = False +else: + _setproctitle_enabled = True + + +logger = logging.getLogger(__name__) + + +@typeguard.typechecked +def get_version() -> str: + version = parsl.__version__ # type: str + work_tree = os.path.dirname(os.path.dirname(__file__)) + git_dir = os.path.join(work_tree, '.git') + if os.path.exists(git_dir): + env = {'GIT_WORK_TREE': work_tree, 'GIT_DIR': git_dir} + try: + cmd = shlex.split('git rev-parse --short HEAD') + head = subprocess.check_output(cmd, env=env).strip().decode('utf-8') + diff = subprocess.check_output(shlex.split('git diff HEAD'), env=env) + status = 'dirty' if diff else 'clean' + version = f'{VERSION}-{head}-{status}' + except Exception: + pass + + return version + + +@typeguard.typechecked +def get_all_checkpoints(rundir: str = "runinfo") -> Sequence[str]: + """Finds the checkpoints from all runs in the rundir. + + Kwargs: + - rundir(str) : Path to the runinfo directory + + Returns: + - a list suitable for the checkpoint_files parameter of `Config` + + """ + + if not os.path.isdir(rundir): + return [] + + dirs = sorted(os.listdir(rundir)) + + checkpoints = [] + + for runid in dirs: + + checkpoint = os.path.abspath(f'{rundir}/{runid}/checkpoint') + + if os.path.isdir(checkpoint): + checkpoints.append(checkpoint) + + return checkpoints + + +@typeguard.typechecked +def get_last_checkpoint(rundir: str = "runinfo") -> Sequence[str]: + """Finds the checkpoint from the last run, if one exists. + + Note that checkpoints are incremental, and this helper will not find + previous checkpoints from earlier than the most recent run. If you + want that behaviour, see `get_all_checkpoints`. + + Kwargs: + - rundir(str) : Path to the runinfo directory + + Returns: + - a list suitable for the checkpoint_files parameter of `Config`, + with 0 or 1 elements + + """ + if not os.path.isdir(rundir): + return [] + + dirs = os.listdir(rundir) + + logger.debug("Rundir candidates: %s", dirs) + + # dirs_parseable is a list of number-like directory names, + # which is the rundir subdirectories, excluding non-numerical + # files such as the monitoring database. + dirs_parseable = [d for d in dirs if d.isdigit()] + logger.debug("Parseable candidates: %s", dirs_parseable) + + # dirs_sorted will be a list containing the largest numbered directories + # first (parsed as an int, not lexically sorted, to accomodate different + # length run directory names that can happen after 1000 runs) + dirs_sorted = sorted(dirs_parseable, key=int, reverse=True) + + # use a generator so that the resulting co-routine will only do filesystem + # operations on paths until it finds the first one with a checkpoint - which + # is likely to be the first one. + last_path = next(([path] for d in dirs_sorted + if os.path.isdir(path := os.path.abspath(f'{rundir}/{d}/checkpoint'))), + []) + + logger.debug("Selected path: %s", last_path) + return last_path + + +@typeguard.typechecked +def get_std_fname_mode( + fdname: str, + stdfspec: Union[os.PathLike, str, Tuple[str, str], Tuple[os.PathLike, str]] +) -> Tuple[str, str]: + import parsl.app.errors as pe + if isinstance(stdfspec, (str, os.PathLike)): + fname = stdfspec + mode = 'a+' + elif isinstance(stdfspec, tuple): + if len(stdfspec) != 2: + # this is annotated as unreachable because the type annotation says + # it cannot be reached. Earlier versions of typeguard did not enforce + # that type annotation at runtime, though, and the parameters to this + # function come from the user. + # When typeguard lower bound is raised to around version 4, this + # unreachable can be removed. + msg = (f"std descriptor {fdname} has incorrect tuple length " # type: ignore[unreachable] + f"{len(stdfspec)}") + raise pe.BadStdStreamFile(msg) + fname, mode = stdfspec + + path = os.fspath(fname) + + if isinstance(path, str): + return path, mode + elif isinstance(path, bytes): + return path.decode(), mode + else: + raise BadStdStreamFile(f"fname has invalid type {type(path)}") + + +@contextmanager +def wait_for_file(path: str, seconds: int = 10) -> Generator[None, None, None]: + for _ in range(0, int(seconds * 100)): + time.sleep(seconds / 100.) + if os.path.exists(path): + break + yield + + +@contextmanager +def time_limited_open(path: str, mode: str, seconds: int = 1) -> Generator[IO, None, None]: + with wait_for_file(path, seconds): + logger.debug("wait_for_file yielded") + f = open(path, mode) + yield f + f.close() + + +def wtime_to_minutes(time_string: str) -> int: + ''' wtime_to_minutes + + Convert standard wallclock time string to minutes. + + Args: + - Time_string in HH:MM:SS format + + Returns: + (int) minutes + + ''' + hours, mins, seconds = time_string.split(':') + total_mins = int(hours) * 60 + int(mins) + if total_mins < 1: + msg = (f"Time string '{time_string}' parsed to {total_mins} minutes, " + f"less than 1") + logger.warning(msg) + return total_mins + + +class RepresentationMixin: + """A mixin class for adding a __repr__ method. + + The __repr__ method will return a string equivalent to the code used to instantiate + the child class, with any defaults included explicitly. The __max_width__ class variable + controls the maximum width of the representation string. If this width is exceeded, + the representation string will be split up, with one argument or keyword argument per line. + + Any arguments or keyword arguments in the constructor must be defined as attributes, or + an AttributeError will be raised. + + Examples + -------- + >>> from parsl.utils import RepresentationMixin + >>> class Foo(RepresentationMixin): + def __init__(self, first, second, third='three', fourth='fourth'): + self.first = first + self.second = second + self.third = third + self.fourth = fourth + >>> bar = Foo(1, 'two', fourth='baz') + >>> bar + Foo(1, 'two', third='three', fourth='baz') + """ + __max_width__ = 80 + + _validate_repr = False + + def __repr__(self) -> str: + init = self.__init__ # type: ignore[misc] + + # This test looks for a single layer of wrapping performed by + # functools.update_wrapper, commonly used in decorators. This will + # allow RepresentationMixin to see through a single such decorator + # applied to the __init__ method of a class, and find the underlying + # arguments. It will not see through multiple layers of such + # decorators, or cope with other decorators which do not use + # functools.update_wrapper. + + if hasattr(init, '__wrapped__'): + init = init.__wrapped__ + + argspec = inspect.getfullargspec(init) + if len(argspec.args) > 1 and argspec.defaults is not None: + defaults = dict(zip(reversed(argspec.args), reversed(argspec.defaults))) + else: + defaults = {} + + if self._validate_repr: + for arg in argspec.args[1:]: + if not hasattr(self, arg): + template = (f'class {self.__class__.__name__} uses {arg} in the' + f' constructor, but does not define it as an ' + f'attribute') + raise AttributeError(template) + + default = "" + + if len(defaults) != 0: + args = [getattr(self, a, default) for a in argspec.args[1:-len(defaults)]] + else: + args = [getattr(self, a, default) for a in argspec.args[1:]] + kwargs = {key: getattr(self, key, default) for key in defaults} + + kwonlyargs = {key: getattr(self, key, default) for key in argspec.kwonlyargs} + kwargs.update(kwonlyargs) + + def assemble_multiline(args: List[str], kwargs: Dict[str, object]) -> str: + def indent(text: str) -> str: + lines = text.splitlines() + if len(lines) <= 1: + return text + return "\n".join(" " + line for line in lines).strip() + args = [f"\n {indent(repr(a))}," for a in args] + kwargsl = [f"\n {k}={indent(repr(v))}" for k, v in + sorted(kwargs.items())] + + info = "".join(args) + ", ".join(kwargsl) + return self.__class__.__name__ + f"({info}\n)" + + def assemble_line(args: List[str], kwargs: Dict[str, object]) -> str: + kwargsl = [f'{k}={repr(v)}' for k, v in sorted(kwargs.items())] + + info = ", ".join([repr(a) for a in args] + kwargsl) + return self.__class__.__name__ + f"({info})" + + if len(assemble_line(args, kwargs)) <= self.__class__.__max_width__: + return assemble_line(args, kwargs) + else: + return assemble_multiline(args, kwargs) + + +class AtomicIDCounter: + """A class to allocate counter-style IDs, in a thread-safe way. + """ + + def __init__(self) -> None: + self.count = 0 + self.lock = threading.Lock() + + def get_id(self) -> int: + with self.lock: + new_id = self.count + self.count += 1 + return new_id + + +def setproctitle(title: str) -> None: + if _setproctitle_enabled: + setproctitle_module.setproctitle(title) + else: + logger.warn(f"setproctitle not enabled for process {title}") + + +class Timer: + """This class will make a callback periodically, with a period + specified by the interval parameter. + + This is based on the following logic : + + .. code-block:: none + + + BEGIN (INTERVAL, THRESHOLD, callback) : + start = current_time() + + while (current_time()-start < INTERVAL) : + wait() + break + + callback() + + """ + + def __init__(self, callback: Callable, *args: Any, interval: Union[float, int] = 5, name: Optional[str] = None) -> None: + """Initialize the Timer object. + We start the timer thread here + + KWargs: + - interval (int or float) : number of seconds between callback events + - name (str) : a base name to use when naming the started thread + """ + + self.interval = max(0, interval) + self.cb_args = args + self.callback = callback + + self._kill_event = threading.Event() + tname = f"Timer-Thread-{id(self)}" + if name: + tname = f"{name}-{tname}" + self._thread = threading.Thread( + target=self._wake_up_timer, name=tname, daemon=True + ) + self._thread.start() + + def _wake_up_timer(self) -> None: + self.make_callback() + + while not self._kill_event.wait(self.interval): + self.make_callback() + + def make_callback(self) -> None: + """Makes the callback and resets the timer. + """ + try: + self.callback(*self.cb_args) + except Exception: + logger.error("Callback threw an exception - logging and proceeding anyway", exc_info=True) + + def close(self, timeout: Optional[float] = None) -> None: + """Merge the threads and terminate. + """ + self._kill_event.set() + self._thread.join(timeout=timeout) + + +class AutoCancelTimer(threading.Timer): + """ + Extend threading.Timer for use as a context manager + + Example: + + with AutoCancelTimer(delay, your_callback): + some_func() + + If `some_func()` returns before the delay is up, the timer will + be cancelled. + """ + def __enter__(self) -> "AutoCancelTimer": + self.start() + return self + + def __exit__( + self, + exc_type: Optional[Type[BaseException]], + exc_val: Optional[BaseException], + exc_tb: Optional[TracebackType] + ) -> None: + self.cancel() + + +def sanitize_dns_label_rfc1123(raw_string: str) -> str: + """Convert input string to a valid RFC 1123 DNS label. + + Parameters + ---------- + raw_string : str + String to sanitize. + + Returns + ------- + str + Sanitized string. + + Raises + ------ + ValueError + If the string is empty after sanitization. + """ + # Convert to lowercase and replace non-alphanumeric characters with hyphen + sanitized = re.sub(r'[^a-z0-9]', '-', raw_string.lower()) + + # Remove consecutive hyphens + sanitized = re.sub(r'-+', '-', sanitized) + + # DNS label cannot exceed 63 characters + sanitized = sanitized[:63] + + # Strip after trimming to avoid trailing hyphens + sanitized = sanitized.strip("-") + + if not sanitized: + raise ValueError(f"Sanitized DNS label is empty for input '{raw_string}'") + + return sanitized + + +def sanitize_dns_subdomain_rfc1123(raw_string: str) -> str: + """Convert input string to a valid RFC 1123 DNS subdomain. + + Parameters + ---------- + raw_string : str + String to sanitize. + + Returns + ------- + str + Sanitized string. + + Raises + ------ + ValueError + If the string is empty after sanitization. + """ + segments = raw_string.split('.') + + sanitized_segments = [] + for segment in segments: + if not segment: + continue + sanitized_segment = sanitize_dns_label_rfc1123(segment) + sanitized_segments.append(sanitized_segment) + + sanitized = '.'.join(sanitized_segments) + + # DNS subdomain cannot exceed 253 characters + sanitized = sanitized[:253] + + # Strip after trimming to avoid trailing dots or hyphens + sanitized = sanitized.strip(".-") + + if not sanitized: + raise ValueError(f"Sanitized DNS subdomain is empty for input '{raw_string}'") + + return sanitized + + +def execute_wait(cmd: str, walltime: Optional[int] = None) -> Tuple[int, str, str]: + ''' Synchronously execute a commandline string on the shell. + + Args: + - cmd (string) : Commandline string to execute + - walltime (int) : walltime in seconds + + Returns: + - retcode : Return code from the execution + - stdout : stdout string + - stderr : stderr string + ''' + try: + logger.debug("Creating process with command '%s'", cmd) + if platform.system() == "Windows": + kwargs = dict(creationflags=subprocess.CREATE_NEW_PROCESS_GROUP) + else: + kwargs = dict(preexec_fn=os.setpgrp) + proc = subprocess.Popen( + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + shell=True, + **kwargs + ) + logger.debug("Created process with pid %s. Performing communicate", proc.pid) + (stdout, stderr) = proc.communicate(timeout=walltime) + retcode = proc.returncode + logger.debug("Process %s returned %s", proc.pid, proc.returncode) + + except Exception: + logger.exception(f"Execution of command failed:\n{cmd}") + raise + else: + logger.debug("Execution of command in process %s completed normally", proc.pid) + + return (retcode, stdout.decode("utf-8"), stderr.decode("utf-8")) diff --git a/parsl/parsl/version.py b/parsl/parsl/version.py new file mode 100644 index 00000000..0ba4a3c9 --- /dev/null +++ b/parsl/parsl/version.py @@ -0,0 +1,3 @@ +"""Set module version. +""" +VERSION = '1.3.0-dev' diff --git a/parsl/requirements.txt b/parsl/requirements.txt new file mode 100644 index 00000000..8d3832ed --- /dev/null +++ b/parsl/requirements.txt @@ -0,0 +1,17 @@ +pyzmq>=17.1.2 + +# typeguard uses semantic versioning. +# major v3 was a shortlived version that was rapidly replaced by major v4, +# so this specification excludes it. +# v2 and v4 are both seen in the wild with users in complex deployments. +typeguard>=2.10,!=3.*,<5 + + +typing-extensions>=4.6,<5 +dill +tblib +requests +sortedcontainers +psutil>=5.5.1 +setproctitle +filelock>=3.13,<4 diff --git a/parsl/setup.py b/parsl/setup.py new file mode 100755 index 00000000..6c7eb06e --- /dev/null +++ b/parsl/setup.py @@ -0,0 +1,99 @@ +from setuptools import find_packages, setup + +with open('parsl/version.py') as f: + exec(f.read()) + +with open('requirements.txt') as f: + install_requires = f.readlines() + +extras_require = { + 'monitoring' : [ + # sqlalchemy does not use semantic versioning. + # see https://github.com/sqlalchemy/sqlalchemy/discussions/11391#discussioncomment-9472033 + 'sqlalchemy>=2,<2.1' + ], + 'visualization' : [ + # this pydot bound is copied from networkx's pyproject.toml, + # version 3.2 (aa2de1adecea09f7b86ff6093b212ca86f22b3ef), + # because networkx[extra] installs quite a lot of extra stuff + # that needs more OS dependencies in addition to pydot. + 'pydot>=1.4.2', + + 'networkx>=3.2,<3.3', + 'Flask>=1.0.2', + 'flask_sqlalchemy', + + # pandas uses "loose semantic versioning" + # https://pandas.pydata.org/docs/development/policies.html#version-policy + 'pandas<3,>=2.2', + + # plotly doesn't explicitly use semantic versioning, but their + # breakage behaviour is as if they do + 'plotly>=5.19,<7', + + 'python-daemon' + ], + 'aws' : ['boto3'], + 'kubernetes' : ['kubernetes'], + 'docs' : [ + 'ipython<=8.6.0', + 'nbsphinx', + 'sphinx>=7.4,<8', + 'sphinx_rtd_theme' + ], + 'google_cloud' : ['google-auth', 'google-api-python-client'], + 'gssapi' : ['python-gssapi'], + 'azure' : ['azure<=4', 'msrestazure'], + 'workqueue': ['work_queue'], + 'flux': ['pyyaml', 'cffi', 'jsonschema'], + 'proxystore': ['proxystore'], + 'radical-pilot': ['radical.pilot==1.90', 'radical.utils==1.90'], + 'globus_compute': ['globus_compute_sdk>=2.34.0'], + 'globus_transfer': ['globus-sdk'], + # Disabling psi-j since github direct links are not allowed by pypi + # 'psij': ['psi-j-parsl@git+https://github.com/ExaWorks/psi-j-parsl'] +} +extras_require['all'] = sum(extras_require.values(), []) + +setup( + name='parsl', + version=VERSION, + description='Simple data dependent workflows in Python', + long_description='Simple parallel workflows system for Python', + url='https://github.com/Parsl/parsl', + author='The Parsl Team', + author_email='parsl@googlegroups.com', + license='Apache 2.0', + download_url='https://github.com/Parsl/parsl/archive/{}.tar.gz'.format(VERSION), + include_package_data=True, + package_data={'parsl': ['py.typed']}, + packages=find_packages(), + python_requires=">=3.10.0", + install_requires=install_requires, + scripts = ['parsl/executors/high_throughput/process_worker_pool.py', + 'parsl/executors/high_throughput/interchange.py', + 'parsl/executors/workqueue/exec_parsl_function.py', + 'parsl/executors/workqueue/parsl_coprocess.py', + ], + + extras_require=extras_require, + classifiers=[ + # Maturity + 'Development Status :: 5 - Production/Stable', + # Intended audience + 'Intended Audience :: Developers', + # Licence, must match with licence above + 'License :: OSI Approved :: Apache Software License', + # Python versions supported + 'Programming Language :: Python :: 3.10', + 'Programming Language :: Python :: 3.11', + 'Programming Language :: Python :: 3.12', + ], + keywords=['Workflows', 'Scientific computing'], + entry_points={'console_scripts': + [ + 'parsl-globus-auth=parsl.data_provider.globus:cli_run', + 'parsl-visualize=parsl.monitoring.visualization.app:cli_run', + 'parsl-perf=parsl.benchmark.perf:cli_run', + ]} +) diff --git a/parsl/tag_and_release.sh b/parsl/tag_and_release.sh new file mode 100755 index 00000000..8fdfb34b --- /dev/null +++ b/parsl/tag_and_release.sh @@ -0,0 +1,67 @@ +#!/bin/bash +set -euf -o pipefail + +create_tag () { + if [ -z "$1" ] + then + VERSION="unknown" + else + VERSION=$1 + fi + + PARSL_VERSION=$(python3 -c "import parsl; print(parsl.__version__)") + + if [[ $PARSL_VERSION == "$VERSION" ]] + then + echo "Version requested matches package version: $VERSION" + else + echo "[ERROR] Version mismatch. User request: '$VERSION' while package version is: '$PARSL_VERSION'" + exit 1 + fi + + echo "Creating tag" + git tag -a "$VERSION" -m "Parsl $VERSION" + + echo "Pushing tag" + git push origin --tags + +} + +package() { + + rm -f dist/* + + echo "=======================================================================" + echo "Starting clean builds" + echo "=======================================================================" + python3 setup.py sdist + python3 setup.py bdist_wheel + + echo "=======================================================================" + echo "Done with builds" + echo "=======================================================================" + + +} + +release () { + echo "=======================================================================" + echo "Push to PyPi. This will require your username and password" + echo "=======================================================================" + twine upload dist/* +} + +update_version () { + target_version=$VERSION + echo "Target version = $target_version" + cat << EOF > parsl/version.py +"""Set module version. + +Year.Month.Day[alpha/beta/..] +Alphas will be numbered like this -> 2024.12.10a0 +""" +VERSION = '$target_version' +EOF +} + +"$@" diff --git a/parsl/test-requirements.txt b/parsl/test-requirements.txt new file mode 100644 index 00000000..8fff0425 --- /dev/null +++ b/parsl/test-requirements.txt @@ -0,0 +1,24 @@ +flake8==6.1.0 +pandas +pytest>=7.4.0,<8 +pytest-cov +pytest-random-order +nbsphinx +sphinx_rtd_theme +mypy==1.18.2 +types-mock +types-python-dateutil +types-requests +mpi4py + +# sqlalchemy is needed for typechecking, so it's here +# as well as at runtime for optional monitoring execution +# (where it's specified in setup.py) +sqlalchemy>=2,<2.1 +sortedcontainers-stubs + +Sphinx==4.5.0 +twine +wheel +isort + diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 0c6bb7a2..675c8b14 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -20,9 +20,7 @@ from importlib.metadata import PackageNotFoundError from importlib.metadata import version -from taskgraph import _parsl_multiprocessing_override -sys.modules["parsl.multiprocessing"] = _parsl_multiprocessing_override - +import parsl from parsl.app.app import python_app from parsl.concurrent import ParslPoolExecutor from parsl.configs.local_threads import config @@ -31,7 +29,7 @@ from parsl.dataflow.memoization import BasicMemoizer, id_for_memo from parsl.dataflow.memoization import CheckpointCommand from parsl.executors import HighThroughputExecutor -import parsl.executors.high_throughput +from parsl.executors.high_throughput import interchange from parsl.executors import ThreadPoolExecutor from parsl.log_utils import set_file_logger from parsl.log_utils import DEFAULT_FORMAT @@ -829,7 +827,7 @@ def __init__( encrypted=False, interchange_launch_cmd=[ sys.executable, - pathlib.Path(__file__).parent / "_parsl_interchange.py"]) + interchange.__file__]) elif n_workers == 0: self.executor_label = "threaded" executor = ThreadPoolExecutor( From fe82a67324e7f915c79f2081987c873f76530925 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 14:30:30 -0700 Subject: [PATCH 038/112] debugging --- pyproject.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 7337ea4e..79af46d3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,6 +40,9 @@ requires = [ ] build-backend = "setuptools.build_meta" +[tool.setuptools] +packages = ["taskgraph"] + [tool.setuptools_scm] version_scheme = "post-release" local_scheme = "node-and-date" From f7f773ca7a66b798125ceae2f8a34eb85d5a5234 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 14:32:27 -0700 Subject: [PATCH 039/112] debugging --- parsl/parsl/multiprocessing.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/parsl/parsl/multiprocessing.py b/parsl/parsl/multiprocessing.py index 6f3ec7fc..d055f9da 100644 --- a/parsl/parsl/multiprocessing.py +++ b/parsl/parsl/multiprocessing.py @@ -14,6 +14,9 @@ from multiprocessing.context import ForkProcess as ForkProcessType ForkContext = multiprocessing.get_context("fork") ForkProcess: Callable[..., ForkProcessType] = ForkContext.Process +else: + ForkContext = None + ForkProcess = None SpawnContext = multiprocessing.get_context("spawn") SpawnProcess: Callable[..., SpawnProcessType] = SpawnContext.Process From e93ee59fba0a98f3bf9ca0fa0df0485aa76cba88 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 14:42:36 -0700 Subject: [PATCH 040/112] debugging --- parsl/parsl/providers/local/local.py | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index 259885fb..2650d299 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -1,5 +1,6 @@ import logging import os +import platform import time from parsl.jobs.states import JobState, JobStatus @@ -221,8 +222,19 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): # We need to do the >/dev/null 2>&1 so that bash closes stdout, otherwise # execute_wait hangs reading the process stdout until all the # background commands complete. - cmd = '/bin/bash -c \'echo - >{0}.ec && {{ {{ bash {0} 1>{0}.out 2>{0}.err ; ' \ - 'echo $? > {0}.ec ; }} >/dev/null 2>&1 & echo "PID:$!" ; }}\''.format(script_path) + if platform.system() == 'Windows': + bash_executable = 'powershell.exe' + else: + bash_executable = '/bin/bash' + cmd = '{1} -c \'echo - >{0}.ec && {{ {{ bash {0} 1>{0}.out 2>{0}.err ; ' \ + 'echo $? > {0}.ec ; }} >/dev/null 2>&1 & echo "PID:$!" ; }}\''.format( + script_path, bash_executable) + print(cmd) + print('script path exists?', os.path.exists(script_path)) + print('script content:') + with open(script_path) as f: + print(f.read()) + retcode, stdout, stderr = execute_wait(cmd, self.cmd_timeout) print(retcode) print('stdout:', stdout) From 4eb9ea331d37db9f5c9f2bfb7aa252f5c2c8a85c Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 15:20:54 -0700 Subject: [PATCH 041/112] create powershell submit script for windows --- parsl/parsl/launchers/launchers.py | 122 ++++++++++++++++++++--------- 1 file changed, 84 insertions(+), 38 deletions(-) diff --git a/parsl/parsl/launchers/launchers.py b/parsl/parsl/launchers/launchers.py index 75312754..c9d78298 100644 --- a/parsl/parsl/launchers/launchers.py +++ b/parsl/parsl/launchers/launchers.py @@ -1,4 +1,6 @@ import logging +import platform +import textwrap from parsl.launchers.base import Launcher @@ -70,44 +72,88 @@ def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, scri fail_on_any_num = int(self.fail_on_any) debug_num = int(self.debug) - x = '''set -e -export CORES=$(getconf _NPROCESSORS_ONLN) -[[ "{debug}" == "1" ]] && echo "Found cores : $CORES" -WORKERCOUNT={task_blocks} -FAILONANY={fail_on_any} -PIDS="" - -CMD() {{ -{command} -}} -for COUNT in $(seq 1 1 $WORKERCOUNT); do - [[ "{debug}" == "1" ]] && echo "Launching worker: $COUNT" - CMD $COUNT & - PIDS="$PIDS $!" -done - -ALLFAILED=1 -ANYFAILED=0 -for PID in $PIDS ; do - wait $PID - if [ "$?" != "0" ]; then - ANYFAILED=1 - else - ALLFAILED=0 - fi -done - -[[ "{debug}" == "1" ]] && echo "All workers done" -if [ "$FAILONANY" == "1" ]; then - exit $ANYFAILED -else - exit $ALLFAILED -fi -'''.format(command=command, - task_blocks=task_blocks, - debug=debug_num, - fail_on_any=fail_on_any_num) - return x + if platform.system() == 'Windows': + x = textwrap.dedent(''' + $ErrorActionPreference = "Stop" + + $env:JOBNAME = "parsl.htex.block-0.1792532640.30171" + $env:CORES = [Environment]::ProcessorCount + + if ("{debug}" -eq "1") { Write-Host "Found cores : $env:CORES" } + + $WORKERCOUNT = {task_blocks} + $FAILONANY = {fail_on_any} + $processes = @() + + for ($COUNT = 1; $COUNT -le $WORKERCOUNT; $COUNT++) { + if ("{debug}" -eq "1") { Write-Host "Launching worker: $COUNT" } + $proc = Start-Process -FilePath "python" -ArgumentList "{command} $COUNT" -NoNewWindow -PassThru + $processes += $proc + } + + $ALLFAILED = 1 + $ANYFAILED = 0 + + foreach ($proc in $processes) { + $proc.WaitForExit() + if ($proc.ExitCode -ne 0) { + $ANYFAILED = 1 + } else { + $ALLFAILED = 0 + } + } + + if ("{debug}" -eq "1") { Write-Host "All workers done" } + + if ($FAILONANY -eq 1) { + exit $ANYFAILED + } else { + exit $ALLFAILED + } + ''').format(command=command, + task_blocks=task_blocks, + debug=debug_num, + fail_on_any=fail_on_any_num) + else: + x = textwrap.dedent(''' + set -e + export CORES=$(getconf _NPROCESSORS_ONLN) + [[ "{debug}" == "1" ]] && echo "Found cores : $CORES" + WORKERCOUNT={task_blocks} + FAILONANY={fail_on_any} + PIDS="" + + CMD() {{ + {command} + }} + for COUNT in $(seq 1 1 $WORKERCOUNT); do + [[ "{debug}" == "1" ]] && echo "Launching worker: $COUNT" + CMD $COUNT & + PIDS="$PIDS $!" + done + + ALLFAILED=1 + ANYFAILED=0 + for PID in $PIDS ; do + wait $PID + if [ "$?" != "0" ]; then + ANYFAILED=1 + else + ALLFAILED=0 + fi + done + + [[ "{debug}" == "1" ]] && echo "All workers done" + if [ "$FAILONANY" == "1" ]; then + exit $ANYFAILED + else + exit $ALLFAILED + fi + ''').format(command=command, + task_blocks=task_blocks, + debug=debug_num, + fail_on_any=fail_on_any_num) + return x class GnuParallelLauncher(Launcher): From 1f62a768a70e637376dff60e3a20aeded6f065bf Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 15:31:41 -0700 Subject: [PATCH 042/112] debugging --- parsl/parsl/launchers/launchers.py | 3 ++- parsl/parsl/providers/local/local.py | 13 ++++++------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/parsl/parsl/launchers/launchers.py b/parsl/parsl/launchers/launchers.py index c9d78298..1e35a3df 100644 --- a/parsl/parsl/launchers/launchers.py +++ b/parsl/parsl/launchers/launchers.py @@ -76,7 +76,6 @@ def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, scri x = textwrap.dedent(''' $ErrorActionPreference = "Stop" - $env:JOBNAME = "parsl.htex.block-0.1792532640.30171" $env:CORES = [Environment]::ProcessorCount if ("{debug}" -eq "1") { Write-Host "Found cores : $env:CORES" } @@ -153,6 +152,8 @@ def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, scri task_blocks=task_blocks, debug=debug_num, fail_on_any=fail_on_any_num) + + print('script:', x) return x diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index 2650d299..1283ac01 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -191,7 +191,7 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): - job_id: (string) Identifier for the job ''' - + print('submit command', command) job_name = "{0}.{1}".format(job_name, time.time()) # Set script path @@ -200,9 +200,12 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): wrap_command = (self.worker_init + f'\nexport JOBNAME={job_name}\n' + self.launcher(command, tasks_per_node, self.nodes_per_block, self.script_dir)) - + print('wrap command:', wrap_command) self._write_submit_script(wrap_command, script_path) - + print('script path exists?', os.path.exists(script_path)) + print('script content:') + with open(script_path) as f: + print(f.read()) job_id = None remote_pid = None @@ -230,10 +233,6 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): 'echo $? > {0}.ec ; }} >/dev/null 2>&1 & echo "PID:$!" ; }}\''.format( script_path, bash_executable) print(cmd) - print('script path exists?', os.path.exists(script_path)) - print('script content:') - with open(script_path) as f: - print(f.read()) retcode, stdout, stderr = execute_wait(cmd, self.cmd_timeout) print(retcode) From 4418a680612e19be45e08fe6f5c1ec1022cb95fd Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 15:41:41 -0700 Subject: [PATCH 043/112] debugging --- parsl/parsl/launchers/launchers.py | 54 +++++++++++++----------------- 1 file changed, 24 insertions(+), 30 deletions(-) diff --git a/parsl/parsl/launchers/launchers.py b/parsl/parsl/launchers/launchers.py index 1e35a3df..57996237 100644 --- a/parsl/parsl/launchers/launchers.py +++ b/parsl/parsl/launchers/launchers.py @@ -68,65 +68,63 @@ def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, scri - script_dir (str): Path to the submit script directory """ + print('calling launcher') task_blocks = tasks_per_node * nodes_per_block fail_on_any_num = int(self.fail_on_any) debug_num = int(self.debug) if platform.system() == 'Windows': - x = textwrap.dedent(''' + x = textwrap.dedent(f''' $ErrorActionPreference = "Stop" $env:CORES = [Environment]::ProcessorCount - if ("{debug}" -eq "1") { Write-Host "Found cores : $env:CORES" } - + if ("{debug_num}" -eq "1") {{ Write-Host "Found cores : $env:CORES" }} +f $WORKERCOUNT = {task_blocks} - $FAILONANY = {fail_on_any} + $FAILONANY = {fail_on_any_num} $processes = @() - for ($COUNT = 1; $COUNT -le $WORKERCOUNT; $COUNT++) { - if ("{debug}" -eq "1") { Write-Host "Launching worker: $COUNT" } + for ($COUNT = 1; $COUNT -le $WORKERCOUNT; $COUNT++) {{ + if ("{debug_num}" -eq "1") {{ Write-Host "Launching worker: $COUNT" }} $proc = Start-Process -FilePath "python" -ArgumentList "{command} $COUNT" -NoNewWindow -PassThru $processes += $proc - } + }} $ALLFAILED = 1 $ANYFAILED = 0 - foreach ($proc in $processes) { + foreach ($proc in $processes) {{ $proc.WaitForExit() - if ($proc.ExitCode -ne 0) { + if ($proc.ExitCode -ne 0) {{ $ANYFAILED = 1 - } else { + }} else {{ $ALLFAILED = 0 - } - } + }} + }} - if ("{debug}" -eq "1") { Write-Host "All workers done" } + if ("{debug_num}" -eq "1") {{ Write-Host "All workers done" }} - if ($FAILONANY -eq 1) { + if ($FAILONANY -eq 1) {{ exit $ANYFAILED - } else { + }} else {{ exit $ALLFAILED - } - ''').format(command=command, - task_blocks=task_blocks, - debug=debug_num, - fail_on_any=fail_on_any_num) + }} + ''') else: - x = textwrap.dedent(''' + x = textwrap.dedent(f''' set -e export CORES=$(getconf _NPROCESSORS_ONLN) - [[ "{debug}" == "1" ]] && echo "Found cores : $CORES" + [[ "{debug_num}" == "1" ]] && echo "Found cores : $CORES" WORKERCOUNT={task_blocks} - FAILONANY={fail_on_any} + FAILONANY={fail_on_any_num} PIDS="" CMD() {{ {command} }} for COUNT in $(seq 1 1 $WORKERCOUNT); do - [[ "{debug}" == "1" ]] && echo "Launching worker: $COUNT" + [[ "{debug_num}" == "1" ]] && echo "Launching worker: $COUNT" CMD $COUNT & PIDS="$PIDS $!" done @@ -142,17 +140,13 @@ def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, scri fi done - [[ "{debug}" == "1" ]] && echo "All workers done" + [[ "{debug_num}" == "1" ]] && echo "All workers done" if [ "$FAILONANY" == "1" ]; then exit $ANYFAILED else exit $ALLFAILED fi - ''').format(command=command, - task_blocks=task_blocks, - debug=debug_num, - fail_on_any=fail_on_any_num) - + ''') print('script:', x) return x From 1136bdc33dae9f61d7b1e5eac956f8de62bd86e7 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 15:57:38 -0700 Subject: [PATCH 044/112] debugging --- parsl/parsl/launchers/launchers.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/parsl/parsl/launchers/launchers.py b/parsl/parsl/launchers/launchers.py index 57996237..7e0eda4d 100644 --- a/parsl/parsl/launchers/launchers.py +++ b/parsl/parsl/launchers/launchers.py @@ -147,8 +147,8 @@ def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, scri exit $ALLFAILED fi ''') - print('script:', x) - return x + print('script:', x) + return x class GnuParallelLauncher(Launcher): From 23ba038998c48baebf6f94a6ebac6adc105e1ab5 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 16:02:48 -0700 Subject: [PATCH 045/112] fix typo --- parsl/parsl/launchers/launchers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/parsl/parsl/launchers/launchers.py b/parsl/parsl/launchers/launchers.py index 7e0eda4d..629ef99d 100644 --- a/parsl/parsl/launchers/launchers.py +++ b/parsl/parsl/launchers/launchers.py @@ -80,7 +80,7 @@ def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, scri $env:CORES = [Environment]::ProcessorCount if ("{debug_num}" -eq "1") {{ Write-Host "Found cores : $env:CORES" }} -f + $WORKERCOUNT = {task_blocks} $FAILONANY = {fail_on_any_num} $processes = @() From bcef789e64e3596fb8dbe79c671a93673a78a49f Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 20 Oct 2026 16:17:30 -0700 Subject: [PATCH 046/112] debugging --- parsl/parsl/providers/local/local.py | 24 +++++++++++++++++------- 1 file changed, 17 insertions(+), 7 deletions(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index 1283ac01..a6eaedfe 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -198,7 +198,11 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): script_path = "{0}/{1}.sh".format(self.script_dir, job_name) script_path = os.path.abspath(script_path) - wrap_command = (self.worker_init + f'\nexport JOBNAME={job_name}\n' + + if platform.system() == 'Windows': + jobname_cmd = f'\n$env:JOBNAME = "{job_name}"\n' + else: + jobname_cmd = f'\nexport JOBNAME={job_name}\n' + wrap_command = (self.worker_init + jobname_cmd + self.launcher(command, tasks_per_node, self.nodes_per_block, self.script_dir)) print('wrap command:', wrap_command) self._write_submit_script(wrap_command, script_path) @@ -226,13 +230,19 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): # execute_wait hangs reading the process stdout until all the # background commands complete. if platform.system() == 'Windows': - bash_executable = 'powershell.exe' + cmd = ( + 'powershell.exe -ExecutionPolicy Bypass -Command "' + "Set-Content -Path '{0}.ec' -Value '-'; " + "$p = Start-Process powershell.exe -ArgumentList '-ExecutionPolicy', 'Bypass', '-Command', " + "'$proc = Start-Process powershell.exe -ArgumentList \"-ExecutionPolicy\", \"Bypass\", \"-File\", \"{0}\" " + "-RedirectStandardOutput \"{0}.out\" -RedirectStandardError \"{0}.err\" -PassThru -Wait; " + "Set-Content -Path \"{0}.ec\" -Value $proc.ExitCode' -PassThru; " + 'Write-Host "PID:$($p.Id)"' + '"' + ).format(script_path) else: - bash_executable = '/bin/bash' - cmd = '{1} -c \'echo - >{0}.ec && {{ {{ bash {0} 1>{0}.out 2>{0}.err ; ' \ - 'echo $? > {0}.ec ; }} >/dev/null 2>&1 & echo "PID:$!" ; }}\''.format( - script_path, bash_executable) - print(cmd) + cmd = '/bin/bash -c \'echo - >{0}.ec && {{ {{ bash {0} 1>{0}.out 2>{0}.err ; ' \ + 'echo $? > {0}.ec ; }} >/dev/null 2>&1 & echo "PID:$!" ; }}\''.format(script_path) retcode, stdout, stderr = execute_wait(cmd, self.cmd_timeout) print(retcode) From 01e39fd4ce219416f26062ff79b95b465e89c552 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 09:24:49 -0700 Subject: [PATCH 047/112] all tests passing locally --- taskgraph/Task.py | 14 ++++---------- tests/test_task.py | 21 +++++++++------------ 2 files changed, 13 insertions(+), 22 deletions(-) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 675c8b14..fd951a3d 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -235,7 +235,8 @@ def get_task_hash_info(func, args, kwargs, target_path_list): } -def get_task_hash(hash_info): +def get_task_hash(func, args, kwargs, target_path_list): + hash_info = get_task_hash_info(func, args, kwargs, target_path_list) argument_hash_string = ':'.join([ repr(hash_info[key]) for key in sorted(hash_info.keys())]) @@ -773,11 +774,6 @@ def __init__( # to the taskgraph during `add_task` self._task_hash_map = dict() - # use this to keep track of all the tasks added to the graph by their - # task names. Used to map a unique task name to the task object it - # represents - self._task_name_map = dict() - # used to remember if task_graph has been closed self._closed = False @@ -990,8 +986,7 @@ def add_task( "Objects passed to dependent task list that are not " "tasks: %s", dependent_task_list) - hash = get_task_hash(get_task_hash_info( - func, args, kwargs, target_path_list)) + hash = get_task_hash(func, args, kwargs, target_path_list) # it may be this task was already created in an earlier call, # use that object in its place @@ -1042,7 +1037,6 @@ def add_task( checkpoint_dir=self._taskgraph_cache_dir_path, executor_label=self.executor_label) - self._task_name_map[new_task.task_name] = new_task self._task_hash_map[hash] = new_task if self._n_workers < 0: # call directly if single threaded @@ -1146,7 +1140,6 @@ def join(self, timeout=None): LOGGER.debug("joining taskgraph") try: LOGGER.debug("attempting to join threads") - for task in self._task_hash_map.values(): try: task.join(timeout=timeout) @@ -1346,6 +1339,7 @@ def join(self, timeout=None): """Block until task is complete, raise exception if runtime failed.""" LOGGER.debug(f'joining task {self.task_name}') self._call(timeout=timeout) + return True def get(self, timeout=None): """Return the result of the ``func`` once it is ready. diff --git a/tests/test_task.py b/tests/test_task.py index 4ae8cbe8..49028440 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -961,13 +961,13 @@ def test_kwargs_hashed(self): """TaskGraph: ensure kwargs are considered in determining id hash.""" task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) - task_a = task_graph.add_task( + task_graph.add_task( func=_test_fixtures._noop_function, kwargs={ 'content': ['this value: a']}, task_name='noop a') - task_b = task_graph.add_task( + task_graph.add_task( func=_test_fixtures._noop_function, kwargs={ 'content': ['this value b']}, @@ -975,11 +975,10 @@ def test_kwargs_hashed(self): task_graph.close() task_graph.join() - del task_graph - self.assertNotEqual( - task_a._task_id_hash, task_b._task_id_hash, - "task ids should be different since the kwargs are different") + # taskgraph should have registered two different tasks + # because the kwarg values are different + self.assertEqual(len(task_graph._task_hash_map), 2) def test_same_timestamp_and_value(self): """TaskGraph: ensure identical files but filename are noticed.""" @@ -996,13 +995,13 @@ def test_same_timestamp_and_value(self): os.utime(file_a_path, (0, 0)) os.utime(file_b_path, (0, 0)) - task_a = task_graph.add_task( + task_graph.add_task( func=_test_fixtures._noop_function, kwargs={ 'path': file_a_path}, task_name='noop a') - task_b = task_graph.add_task( + task_graph.add_task( func=_test_fixtures._noop_function, kwargs={ 'path': file_b_path}, @@ -1010,11 +1009,9 @@ def test_same_timestamp_and_value(self): task_graph.close() task_graph.join() - del task_graph - self.assertNotEqual( - task_a._task_id_hash, task_b._task_id_hash, - "task ids should be different since the filenames are different") + # taskgraph should have registered two different tasks + self.assertEqual(len(task_graph._task_hash_map), 2) def test_different_hash_different_file(self): """TaskGraph: ensure identical files but filename are noticed.""" From c19a4753e0dd191e0cee6f13f71f8310cbfb25a9 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 11:58:21 -0700 Subject: [PATCH 048/112] debugging --- parsl/parsl/providers/local/local.py | 8 ++------ tests/test_task.py | 5 +++-- 2 files changed, 5 insertions(+), 8 deletions(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index a6eaedfe..2605c275 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -191,7 +191,6 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): - job_id: (string) Identifier for the job ''' - print('submit command', command) job_name = "{0}.{1}".format(job_name, time.time()) # Set script path @@ -204,12 +203,7 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): jobname_cmd = f'\nexport JOBNAME={job_name}\n' wrap_command = (self.worker_init + jobname_cmd + self.launcher(command, tasks_per_node, self.nodes_per_block, self.script_dir)) - print('wrap command:', wrap_command) self._write_submit_script(wrap_command, script_path) - print('script path exists?', os.path.exists(script_path)) - print('script content:') - with open(script_path) as f: - print(f.read()) job_id = None remote_pid = None @@ -262,6 +256,8 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): self.resources[job_id] = {'job_id': job_id, 'status': JobStatus(JobState.RUNNING), 'remote_pid': remote_pid, 'script_path': script_path} + from pudb import set_trace; set_trace() + print('returning job id', job_id) return job_id def cancel(self, job_ids): diff --git a/tests/test_task.py b/tests/test_task.py index 49028440..05ececd3 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -446,8 +446,9 @@ def test_closed_graph(self): def test_single_task_multiprocessing(self): """TaskGraph: Test a single task with multiprocessing.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, 1) - target_path = os.path.join(self.workspace_dir, '1000.dat') + workspace_dir = '/Users/emily/taskgraph/workdir' + task_graph = taskgraph.TaskGraph(workspace_dir, 1) + target_path = os.path.join(workspace_dir, '1000.dat') value = 5 list_len = 1000 _ = task_graph.add_task( From f0b06bab4220b0b3a0ee5c7367c6c60eabd8c3bc Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 12:11:25 -0700 Subject: [PATCH 049/112] use psutil for cross-platform pid check --- parsl/parsl/providers/local/local.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index 2605c275..6237dcf0 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -3,6 +3,8 @@ import platform import time +import psutil + from parsl.jobs.states import JobState, JobStatus from parsl.launchers import SingleNodeLauncher from parsl.providers.base import ExecutionProvider @@ -117,13 +119,7 @@ def status(self, job_ids): @staticmethod def _is_alive(job_dict) -> bool: - try: - os.kill(job_dict['remote_pid'], 0) - except ProcessLookupError: - return False - except PermissionError: - pass # exists; just no permissions to send signal - return True + return psutil.pid_exists(job_dict['remote_pid']) def _job_file_path(self, script_path: str, suffix: str) -> str: path = '{0}{1}'.format(script_path, suffix) From 3562b1d1382326fffb275b013736593af75013fa Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 12:18:17 -0700 Subject: [PATCH 050/112] debugging --- parsl/parsl/providers/local/local.py | 5 ----- tests/test_task.py | 3 +-- 2 files changed, 1 insertion(+), 7 deletions(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index 6237dcf0..8dee46a5 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -235,9 +235,6 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): 'echo $? > {0}.ec ; }} >/dev/null 2>&1 & echo "PID:$!" ; }}\''.format(script_path) retcode, stdout, stderr = execute_wait(cmd, self.cmd_timeout) - print(retcode) - print('stdout:', stdout) - print('stderr:', stderr) if retcode != 0: raise SubmitException(job_name, "Launch command exited with code {0}".format(retcode), stdout, stderr) @@ -252,8 +249,6 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): self.resources[job_id] = {'job_id': job_id, 'status': JobStatus(JobState.RUNNING), 'remote_pid': remote_pid, 'script_path': script_path} - from pudb import set_trace; set_trace() - print('returning job id', job_id) return job_id def cancel(self, job_ids): diff --git a/tests/test_task.py b/tests/test_task.py index 05ececd3..b8d88170 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -61,7 +61,6 @@ def test_version_loaded(self): def test_single_task(self): """TaskGraph: Test a single task.""" - # from pudb import set_trace; set_trace() task_graph = taskgraph.TaskGraph(self.workspace_dir, 0, 0.1) # forcing this one to be unicode since there shouldn't be a problem # with that at all... @@ -446,7 +445,7 @@ def test_closed_graph(self): def test_single_task_multiprocessing(self): """TaskGraph: Test a single task with multiprocessing.""" - workspace_dir = '/Users/emily/taskgraph/workdir' + workspace_dir = 'workdir' task_graph = taskgraph.TaskGraph(workspace_dir, 1) target_path = os.path.join(workspace_dir, '1000.dat') value = 5 From e1d667afcf6bc974456b97f4be6fd8f4ed1f968d Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 12:29:31 -0700 Subject: [PATCH 051/112] debugging --- parsl/parsl/executors/high_throughput/executor.py | 1 + parsl/parsl/utils.py | 1 + 2 files changed, 2 insertions(+) diff --git a/parsl/parsl/executors/high_throughput/executor.py b/parsl/parsl/executors/high_throughput/executor.py index c05ca83b..64608dab 100644 --- a/parsl/parsl/executors/high_throughput/executor.py +++ b/parsl/parsl/executors/high_throughput/executor.py @@ -600,6 +600,7 @@ def _start_local_interchange_process(self) -> None: config_pickle = pickle.dumps(interchange_config) + from pudb import set_trace; set_trace() self.interchange_proc = subprocess.Popen(self.interchange_launch_cmd, stdin=subprocess.PIPE) stdin = self.interchange_proc.stdin assert stdin is not None, "Popen should have created an IO object (vs default None) because of PIPE mode" diff --git a/parsl/parsl/utils.py b/parsl/parsl/utils.py index 0b727c19..a886ac5e 100644 --- a/parsl/parsl/utils.py +++ b/parsl/parsl/utils.py @@ -501,6 +501,7 @@ def execute_wait(cmd: str, walltime: Optional[int] = None) -> Tuple[int, str, st kwargs = dict(creationflags=subprocess.CREATE_NEW_PROCESS_GROUP) else: kwargs = dict(preexec_fn=os.setpgrp) + from pudb import set_trace; set_trace() proc = subprocess.Popen( cmd, stdout=subprocess.PIPE, From 13358fd0a00759960def945b43b7a016a3888085 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 12:32:22 -0700 Subject: [PATCH 052/112] debugging --- taskgraph/Task.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index fd951a3d..188c97b2 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -35,6 +35,8 @@ from parsl.log_utils import DEFAULT_FORMAT from parsl.logconfigs.base import LogConfig +from pudb import set_trace; set_trace() + LOGGER = logging.getLogger(__name__) # LOGGING_MANAGER = multiprocessing.Manager() @@ -809,7 +811,9 @@ def __init__( config.app_cache = True config.memoizer = TaskGraphMemoizer(checkpoint_mode='task_exit') config.initialize_logging = FileLogging() + from pudb import set_trace; set_trace() _DFK = parsl.load(config) + from pudb import set_trace; set_trace() _DFK.config.memoizer.add_checkpoint_dir(self._taskgraph_cache_dir_path) if n_workers > 0: @@ -824,6 +828,7 @@ def __init__( interchange_launch_cmd=[ sys.executable, interchange.__file__]) + from pudb import set_trace; set_trace() elif n_workers == 0: self.executor_label = "threaded" executor = ThreadPoolExecutor( From 83bba0fb4764904568576231c3d51a78acfe16f1 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 12:44:59 -0700 Subject: [PATCH 053/112] debugging --- taskgraph/Task.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 188c97b2..72d9d594 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -35,8 +35,6 @@ from parsl.log_utils import DEFAULT_FORMAT from parsl.logconfigs.base import LogConfig -from pudb import set_trace; set_trace() - LOGGER = logging.getLogger(__name__) # LOGGING_MANAGER = multiprocessing.Manager() @@ -811,7 +809,6 @@ def __init__( config.app_cache = True config.memoizer = TaskGraphMemoizer(checkpoint_mode='task_exit') config.initialize_logging = FileLogging() - from pudb import set_trace; set_trace() _DFK = parsl.load(config) from pudb import set_trace; set_trace() From a63333cbf2ae733d077bda5d3b3b87c0465f7e07 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 12:46:17 -0700 Subject: [PATCH 054/112] debugging --- taskgraph/Task.py | 1 + tests/test_task.py | 28 ++++++++++++++-------------- 2 files changed, 15 insertions(+), 14 deletions(-) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 72d9d594..f0fa50ff 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -809,6 +809,7 @@ def __init__( config.app_cache = True config.memoizer = TaskGraphMemoizer(checkpoint_mode='task_exit') config.initialize_logging = FileLogging() + from pudb import set_trace; set_trace() _DFK = parsl.load(config) from pudb import set_trace; set_trace() diff --git a/tests/test_task.py b/tests/test_task.py index b8d88170..2df1776a 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -447,20 +447,20 @@ def test_single_task_multiprocessing(self): """TaskGraph: Test a single task with multiprocessing.""" workspace_dir = 'workdir' task_graph = taskgraph.TaskGraph(workspace_dir, 1) - target_path = os.path.join(workspace_dir, '1000.dat') - value = 5 - list_len = 1000 - _ = task_graph.add_task( - func=_test_fixtures._create_list_on_disk, - args=(value, list_len), - kwargs={ - 'target_path': target_path, - }, - target_path_list=[target_path]) - task_graph.close() - task_graph.join() - result = pickle.load(open(target_path, 'rb')) - self.assertEqual(result, [value]*list_len) + # target_path = os.path.join(workspace_dir, '1000.dat') + # value = 5 + # list_len = 1000 + # _ = task_graph.add_task( + # func=_test_fixtures._create_list_on_disk, + # args=(value, list_len), + # kwargs={ + # 'target_path': target_path, + # }, + # target_path_list=[target_path]) + # task_graph.close() + # task_graph.join() + # result = pickle.load(open(target_path, 'rb')) + # self.assertEqual(result, [value]*list_len) def test_get_file_stats(self): """TaskGraph: Test _get_file_stats subroutine.""" From c2ebb2deb526e2aa7fc454d2a0ca09839b63d52f Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 13:24:26 -0700 Subject: [PATCH 055/112] debugging --- parsl/parsl/dataflow/dflow.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index a3aa151c..958129a8 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -167,6 +167,7 @@ def __init__(self, config: Config) -> None: if self.monitoring is not None and self.monitoring.workflow_version is not None: self.workflow_version = self.monitoring.workflow_version + from pudb import set_trace; set_trace() workflow_info = { 'python_version': "{}.{}.{}".format(sys.version_info.major, sys.version_info.minor, @@ -212,6 +213,7 @@ def __init__(self, config: Config) -> None: self._task_launch_pool = cf.ThreadPoolExecutor(max_workers=1, thread_name_prefix="Task-Launch") + from pudb import set_trace; set_trace() self.dependency_resolver = self.config.dependency_resolver if self.config.dependency_resolver is not None \ else SHALLOW_DEPENDENCY_RESOLVER From 57464439c0a13a9507582d633f07b538d3328637 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 13:26:17 -0700 Subject: [PATCH 056/112] debugging --- parsl/parsl/dataflow/dflow.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index 958129a8..b28f0b72 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -123,7 +123,7 @@ def __init__(self, config: Config) -> None: logger.info("Starting DataFlowKernel with config\n%r", config) logger.info("Parsl version: %s", get_version()) - + from pudb import set_trace; set_trace() self.usage_tracker = UsageTracker(self) self.usage_tracker.send_start_message() @@ -145,7 +145,7 @@ def __init__(self, config: Config) -> None: time_began = datetime.datetime.now() logger.info("Run id is: " + self.run_id) - + from pudb import set_trace; set_trace() self.workflow_name = None if self.monitoring is not None and self.monitoring.workflow_name is not None: self.workflow_name = self.monitoring.workflow_name From abed70b90811d2af336331c4e64f2c7daf328832 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 13:27:25 -0700 Subject: [PATCH 057/112] debugging --- parsl/parsl/dataflow/dflow.py | 4 ++-- taskgraph/Task.py | 1 - 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index b28f0b72..1a203d12 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -123,7 +123,6 @@ def __init__(self, config: Config) -> None: logger.info("Starting DataFlowKernel with config\n%r", config) logger.info("Parsl version: %s", get_version()) - from pudb import set_trace; set_trace() self.usage_tracker = UsageTracker(self) self.usage_tracker.send_start_message() @@ -138,6 +137,7 @@ def __init__(self, config: Config) -> None: self.monitoring_radio = None + from pudb import set_trace; set_trace() if self.monitoring: self.monitoring.start(self.run_dir, self.config.run_dir, self.log_config) self.monitoring_radio = MultiprocessingQueueRadioSender(self.monitoring.resource_msgs) @@ -145,7 +145,7 @@ def __init__(self, config: Config) -> None: time_began = datetime.datetime.now() logger.info("Run id is: " + self.run_id) - from pudb import set_trace; set_trace() + self.workflow_name = None if self.monitoring is not None and self.monitoring.workflow_name is not None: self.workflow_name = self.monitoring.workflow_name diff --git a/taskgraph/Task.py b/taskgraph/Task.py index f0fa50ff..72d9d594 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -809,7 +809,6 @@ def __init__( config.app_cache = True config.memoizer = TaskGraphMemoizer(checkpoint_mode='task_exit') config.initialize_logging = FileLogging() - from pudb import set_trace; set_trace() _DFK = parsl.load(config) from pudb import set_trace; set_trace() From 6f1e05d72d7d8817a3af3fccf69ffd1dcb182fd4 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 13:28:28 -0700 Subject: [PATCH 058/112] debugging --- parsl/parsl/dataflow/dflow.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index 1a203d12..fe26a8b5 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -123,6 +123,7 @@ def __init__(self, config: Config) -> None: logger.info("Starting DataFlowKernel with config\n%r", config) logger.info("Parsl version: %s", get_version()) + from pudb import set_trace; set_trace() self.usage_tracker = UsageTracker(self) self.usage_tracker.send_start_message() @@ -137,7 +138,6 @@ def __init__(self, config: Config) -> None: self.monitoring_radio = None - from pudb import set_trace; set_trace() if self.monitoring: self.monitoring.start(self.run_dir, self.config.run_dir, self.log_config) self.monitoring_radio = MultiprocessingQueueRadioSender(self.monitoring.resource_msgs) From 7af11da0bc5f0c8d01c4ee278f516ce3cb358426 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 13:29:29 -0700 Subject: [PATCH 059/112] debugging --- parsl/parsl/dataflow/dflow.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index fe26a8b5..30fb2e3d 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -123,8 +123,8 @@ def __init__(self, config: Config) -> None: logger.info("Starting DataFlowKernel with config\n%r", config) logger.info("Parsl version: %s", get_version()) - from pudb import set_trace; set_trace() self.usage_tracker = UsageTracker(self) + from pudb import set_trace; set_trace() self.usage_tracker.send_start_message() self.task_state_counts_lock = threading.Lock() From c806c82c7d0d6dcfb2a0eedf58955c895a8604c6 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 13:30:11 -0700 Subject: [PATCH 060/112] debugging --- parsl/parsl/dataflow/dflow.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index 30fb2e3d..f5589a2c 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -124,10 +124,10 @@ def __init__(self, config: Config) -> None: logger.info("Parsl version: %s", get_version()) self.usage_tracker = UsageTracker(self) - from pudb import set_trace; set_trace() self.usage_tracker.send_start_message() self.task_state_counts_lock = threading.Lock() + from pudb import set_trace; set_trace() self.task_state_counts = {state: 0 for state in States} # Monitoring From 13b98e0aa34ed2f75d1b2a57f96319a4f7558966 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 13:32:02 -0700 Subject: [PATCH 061/112] debugging --- parsl/parsl/dataflow/dflow.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index f5589a2c..d5313568 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -126,8 +126,8 @@ def __init__(self, config: Config) -> None: self.usage_tracker = UsageTracker(self) self.usage_tracker.send_start_message() - self.task_state_counts_lock = threading.Lock() from pudb import set_trace; set_trace() + self.task_state_counts_lock = threading.Lock() self.task_state_counts = {state: 0 for state in States} # Monitoring From 93064c836a1999770fd780ac8c2f9711d5621872 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 13:34:02 -0700 Subject: [PATCH 062/112] debugging --- parsl/parsl/dataflow/dflow.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index d5313568..30fb2e3d 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -124,9 +124,9 @@ def __init__(self, config: Config) -> None: logger.info("Parsl version: %s", get_version()) self.usage_tracker = UsageTracker(self) + from pudb import set_trace; set_trace() self.usage_tracker.send_start_message() - from pudb import set_trace; set_trace() self.task_state_counts_lock = threading.Lock() self.task_state_counts = {state: 0 for state in States} From 3ce129c19dd8c2ad9fb46529b8e8f2aa6c39b8fc Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 13:36:08 -0700 Subject: [PATCH 063/112] debugging --- parsl/parsl/dataflow/dflow.py | 1 - parsl/parsl/usage_tracking/usage.py | 1 + 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index 30fb2e3d..bfaf96b8 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -124,7 +124,6 @@ def __init__(self, config: Config) -> None: logger.info("Parsl version: %s", get_version()) self.usage_tracker = UsageTracker(self) - from pudb import set_trace; set_trace() self.usage_tracker.send_start_message() self.task_state_counts_lock = threading.Lock() diff --git a/parsl/parsl/usage_tracking/usage.py b/parsl/parsl/usage_tracking/usage.py index a916ed54..1ef0deae 100644 --- a/parsl/parsl/usage_tracking/usage.py +++ b/parsl/parsl/usage_tracking/usage.py @@ -36,6 +36,7 @@ def async_process(fn: Callable[P, None]) -> Callable[P, None]: def run(*args, **kwargs): proc = SpawnProcess(target=fn, args=args, kwargs=kwargs, name="Usage-Tracking") + from pudb import set_trace; set_trace() proc.start() return proc From 27cd5c34b712bdd97d97c684842b6edb6fcca67d Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 13:53:04 -0700 Subject: [PATCH 064/112] debugging --- parsl/parsl/usage_tracking/usage.py | 18 ++++++------------ 1 file changed, 6 insertions(+), 12 deletions(-) diff --git a/parsl/parsl/usage_tracking/usage.py b/parsl/parsl/usage_tracking/usage.py index 1ef0deae..5bf79268 100644 --- a/parsl/parsl/usage_tracking/usage.py +++ b/parsl/parsl/usage_tracking/usage.py @@ -31,19 +31,12 @@ P = ParamSpec("P") -def async_process(fn: Callable[P, None]) -> Callable[P, None]: - """ Decorator function to launch a function as a separate process """ +def start_udp_messenger_process(*args, **kwargs): + proc = SpawnProcess(target=udp_messenger, args=args, kwargs=kwargs, name="Usage-Tracking") + proc.start() + return proc - def run(*args, **kwargs): - proc = SpawnProcess(target=fn, args=args, kwargs=kwargs, name="Usage-Tracking") - from pudb import set_trace; set_trace() - proc.start() - return proc - return run - - -@async_process def udp_messenger(domain_name: str, UDP_PORT: int, sock_timeout: int, message: bytes) -> None: """Send UDP messages to usage tracker asynchronously @@ -207,7 +200,8 @@ def encode_message(self, obj): def send_UDP_message(self, message: bytes) -> None: """Send UDP message.""" try: - proc = udp_messenger(self.domain_name, self.UDP_PORT, self.sock_timeout, message) + proc = start_udp_messenger_process( + self.domain_name, self.UDP_PORT, self.sock_timeout, message) self.procs.append(proc) except Exception as e: logger.debug("Usage tracking failed: {}".format(e)) From 11f40ef3b85b3e15253e4bdc63708c329ea760d9 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 13:54:33 -0700 Subject: [PATCH 065/112] debugging --- parsl/parsl/dataflow/dflow.py | 2 -- .../executors/high_throughput/executor.py | 1 - parsl/parsl/utils.py | 1 - taskgraph/Task.py | 2 -- tests/test_task.py | 28 +++++++++---------- 5 files changed, 14 insertions(+), 20 deletions(-) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index bfaf96b8..3e2a5202 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -166,7 +166,6 @@ def __init__(self, config: Config) -> None: if self.monitoring is not None and self.monitoring.workflow_version is not None: self.workflow_version = self.monitoring.workflow_version - from pudb import set_trace; set_trace() workflow_info = { 'python_version': "{}.{}.{}".format(sys.version_info.major, sys.version_info.minor, @@ -212,7 +211,6 @@ def __init__(self, config: Config) -> None: self._task_launch_pool = cf.ThreadPoolExecutor(max_workers=1, thread_name_prefix="Task-Launch") - from pudb import set_trace; set_trace() self.dependency_resolver = self.config.dependency_resolver if self.config.dependency_resolver is not None \ else SHALLOW_DEPENDENCY_RESOLVER diff --git a/parsl/parsl/executors/high_throughput/executor.py b/parsl/parsl/executors/high_throughput/executor.py index 64608dab..c05ca83b 100644 --- a/parsl/parsl/executors/high_throughput/executor.py +++ b/parsl/parsl/executors/high_throughput/executor.py @@ -600,7 +600,6 @@ def _start_local_interchange_process(self) -> None: config_pickle = pickle.dumps(interchange_config) - from pudb import set_trace; set_trace() self.interchange_proc = subprocess.Popen(self.interchange_launch_cmd, stdin=subprocess.PIPE) stdin = self.interchange_proc.stdin assert stdin is not None, "Popen should have created an IO object (vs default None) because of PIPE mode" diff --git a/parsl/parsl/utils.py b/parsl/parsl/utils.py index a886ac5e..0b727c19 100644 --- a/parsl/parsl/utils.py +++ b/parsl/parsl/utils.py @@ -501,7 +501,6 @@ def execute_wait(cmd: str, walltime: Optional[int] = None) -> Tuple[int, str, st kwargs = dict(creationflags=subprocess.CREATE_NEW_PROCESS_GROUP) else: kwargs = dict(preexec_fn=os.setpgrp) - from pudb import set_trace; set_trace() proc = subprocess.Popen( cmd, stdout=subprocess.PIPE, diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 72d9d594..fd951a3d 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -810,7 +810,6 @@ def __init__( config.memoizer = TaskGraphMemoizer(checkpoint_mode='task_exit') config.initialize_logging = FileLogging() _DFK = parsl.load(config) - from pudb import set_trace; set_trace() _DFK.config.memoizer.add_checkpoint_dir(self._taskgraph_cache_dir_path) if n_workers > 0: @@ -825,7 +824,6 @@ def __init__( interchange_launch_cmd=[ sys.executable, interchange.__file__]) - from pudb import set_trace; set_trace() elif n_workers == 0: self.executor_label = "threaded" executor = ThreadPoolExecutor( diff --git a/tests/test_task.py b/tests/test_task.py index 2df1776a..b8d88170 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -447,20 +447,20 @@ def test_single_task_multiprocessing(self): """TaskGraph: Test a single task with multiprocessing.""" workspace_dir = 'workdir' task_graph = taskgraph.TaskGraph(workspace_dir, 1) - # target_path = os.path.join(workspace_dir, '1000.dat') - # value = 5 - # list_len = 1000 - # _ = task_graph.add_task( - # func=_test_fixtures._create_list_on_disk, - # args=(value, list_len), - # kwargs={ - # 'target_path': target_path, - # }, - # target_path_list=[target_path]) - # task_graph.close() - # task_graph.join() - # result = pickle.load(open(target_path, 'rb')) - # self.assertEqual(result, [value]*list_len) + target_path = os.path.join(workspace_dir, '1000.dat') + value = 5 + list_len = 1000 + _ = task_graph.add_task( + func=_test_fixtures._create_list_on_disk, + args=(value, list_len), + kwargs={ + 'target_path': target_path, + }, + target_path_list=[target_path]) + task_graph.close() + task_graph.join() + result = pickle.load(open(target_path, 'rb')) + self.assertEqual(result, [value]*list_len) def test_get_file_stats(self): """TaskGraph: Test _get_file_stats subroutine.""" From 158a55cb24930e0f77a782cf5fb21c60ab3c8d3d Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 14:05:43 -0700 Subject: [PATCH 066/112] debugging --- parsl/parsl/executors/high_throughput/executor.py | 1 + parsl/parsl/executors/high_throughput/process_worker_pool.py | 2 ++ tests/test_task.py | 1 + 3 files changed, 4 insertions(+) diff --git a/parsl/parsl/executors/high_throughput/executor.py b/parsl/parsl/executors/high_throughput/executor.py index c05ca83b..423e9621 100644 --- a/parsl/parsl/executors/high_throughput/executor.py +++ b/parsl/parsl/executors/high_throughput/executor.py @@ -524,6 +524,7 @@ def _result_queue_worker(self): else: msg = pickle.loads(serialized_msg) + print('loaded message:', msg) if msg['type'] == 'result': try: diff --git a/parsl/parsl/executors/high_throughput/process_worker_pool.py b/parsl/parsl/executors/high_throughput/process_worker_pool.py index 31af7a79..cfb904eb 100755 --- a/parsl/parsl/executors/high_throughput/process_worker_pool.py +++ b/parsl/parsl/executors/high_throughput/process_worker_pool.py @@ -986,6 +986,8 @@ def strategyorlist(s: str) -> Union[str, list]: f"\n mpi_launcher: {args.mpi_launcher}" ) try: + from pudb.remote import set_trace + set_trace(term_size=(80, 24)) manager = Manager(port=args.port, addresses=args.addresses, address_probe_timeout=int(args.address_probe_timeout), diff --git a/tests/test_task.py b/tests/test_task.py index b8d88170..f07c9ff5 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -445,6 +445,7 @@ def test_closed_graph(self): def test_single_task_multiprocessing(self): """TaskGraph: Test a single task with multiprocessing.""" + from pudb.remote import set_trace workspace_dir = 'workdir' task_graph = taskgraph.TaskGraph(workspace_dir, 1) target_path = os.path.join(workspace_dir, '1000.dat') From 9ec16ec45bb65da55358739a36c1cbe2758153a5 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 14:07:30 -0700 Subject: [PATCH 067/112] debugging --- parsl/parsl/executors/high_throughput/process_worker_pool.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/parsl/parsl/executors/high_throughput/process_worker_pool.py b/parsl/parsl/executors/high_throughput/process_worker_pool.py index cfb904eb..d37b5406 100755 --- a/parsl/parsl/executors/high_throughput/process_worker_pool.py +++ b/parsl/parsl/executors/high_throughput/process_worker_pool.py @@ -941,6 +941,8 @@ def strategyorlist(s: str) -> Union[str, list]: if __name__ == "__main__": + from pudb.remote import set_trace + set_trace(term_size=(80, 24)) parser = get_arg_parser() args = parser.parse_args() @@ -986,8 +988,6 @@ def strategyorlist(s: str) -> Union[str, list]: f"\n mpi_launcher: {args.mpi_launcher}" ) try: - from pudb.remote import set_trace - set_trace(term_size=(80, 24)) manager = Manager(port=args.port, addresses=args.addresses, address_probe_timeout=int(args.address_probe_timeout), From 68c30b2df54c2605436ac9e61d69a9bbb93e46cc Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 14:11:39 -0700 Subject: [PATCH 068/112] debugging --- parsl/parsl/providers/local/local.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index 8dee46a5..5f1375ed 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -71,7 +71,7 @@ def status(self, job_ids): - List of status codes. ''' - + from pudb import set_trace; set_trace() for job_id in self.resources: # This job dict should really be a class on its own job_dict = self.resources[job_id] From 1d018f54c57632f0e3e48a40d890f9f7465b5d6e Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 15:11:46 -0700 Subject: [PATCH 069/112] debugging --- parsl/parsl/providers/local/local.py | 30 ++++++++++++++++------------ 1 file changed, 17 insertions(+), 13 deletions(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index 5f1375ed..1ca86d39 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -1,6 +1,7 @@ import logging import os import platform +import textwrap import time import psutil @@ -71,7 +72,6 @@ def status(self, job_ids): - List of status codes. ''' - from pudb import set_trace; set_trace() for job_id in self.resources: # This job dict should really be a class on its own job_dict = self.resources[job_id] @@ -221,19 +221,23 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): # background commands complete. if platform.system() == 'Windows': cmd = ( - 'powershell.exe -ExecutionPolicy Bypass -Command "' - "Set-Content -Path '{0}.ec' -Value '-'; " - "$p = Start-Process powershell.exe -ArgumentList '-ExecutionPolicy', 'Bypass', '-Command', " - "'$proc = Start-Process powershell.exe -ArgumentList \"-ExecutionPolicy\", \"Bypass\", \"-File\", \"{0}\" " - "-RedirectStandardOutput \"{0}.out\" -RedirectStandardError \"{0}.err\" -PassThru -Wait; " - "Set-Content -Path \"{0}.ec\" -Value $proc.ExitCode' -PassThru; " - 'Write-Host "PID:$($p.Id)"' - '"' - ).format(script_path) + f"powershell.exe -ExecutionPolicy Bypass -Command \"" + f"Set-Content -Path '{script_path}.ec' -Value '-'; " + f"$p = Start-Process powershell.exe -ArgumentList '-ExecutionPolicy', 'Bypass', '-File', '{script_path}' " + f"-RedirectStandardOutput '{script_path}.out' -RedirectStandardError '{script_path}.err' -PassThru -Wait; " + f"Set-Content -Path '{script_path}.ec' -Value $p.ExitCode' -PassThru; " + f"Write-Host \"PID:$($p.Id)\"" + ) else: - cmd = '/bin/bash -c \'echo - >{0}.ec && {{ {{ bash {0} 1>{0}.out 2>{0}.err ; ' \ - 'echo $? > {0}.ec ; }} >/dev/null 2>&1 & echo "PID:$!" ; }}\''.format(script_path) - + cmd = textwrap.dedent( + f''' + /bin/bash -c 'echo - >{script_path}.ec && + {{ {{ + bash {script_path} 1>{script_path}.out 2>{script_path}.err ; + echo $? > {script_path}.ec ; + }} >/dev/null 2>&1 & echo "PID:$!" ; }}' + ''') + print('cmd', cmd) retcode, stdout, stderr = execute_wait(cmd, self.cmd_timeout) if retcode != 0: raise SubmitException(job_name, "Launch command exited with code {0}".format(retcode), From 92053dcda84b8637c5ccb75608e865ae108c7b49 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 15:29:06 -0700 Subject: [PATCH 070/112] debugging --- parsl/parsl/providers/local/local.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index 1ca86d39..1ed05d79 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -219,14 +219,14 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): # We need to do the >/dev/null 2>&1 so that bash closes stdout, otherwise # execute_wait hangs reading the process stdout until all the # background commands complete. - if platform.system() == 'Windows': + if platform.system() != 'Windows': cmd = ( f"powershell.exe -ExecutionPolicy Bypass -Command \"" f"Set-Content -Path '{script_path}.ec' -Value '-'; " - f"$p = Start-Process powershell.exe -ArgumentList '-ExecutionPolicy', 'Bypass', '-File', '{script_path}' " - f"-RedirectStandardOutput '{script_path}.out' -RedirectStandardError '{script_path}.err' -PassThru -Wait; " - f"Set-Content -Path '{script_path}.ec' -Value $p.ExitCode' -PassThru; " - f"Write-Host \"PID:$($p.Id)\"" + f"$p = Start-Process powershell.exe -ArgumentList '-ExecutionPolicy', 'Bypass', '-File', '{script_path}', " + f"'-RedirectStandardOutput', '{script_path}.out', '-RedirectStandardError', '{script_path}.err', '-PassThru', '-Wait'; " + f"Set-Content -Path '{script_path}.ec' -Value $p.ExitCode -PassThru; " + f"Write-Host `\"PID:$($p.Id)`\"\"" ) else: cmd = textwrap.dedent( From 315e04ff0fcf4cd1977b6c91a76d668cde36558d Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 16:14:52 -0700 Subject: [PATCH 071/112] debugging --- parsl/parsl/providers/local/local.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index 1ed05d79..69993455 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -219,7 +219,7 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): # We need to do the >/dev/null 2>&1 so that bash closes stdout, otherwise # execute_wait hangs reading the process stdout until all the # background commands complete. - if platform.system() != 'Windows': + if platform.system() == 'Windows': cmd = ( f"powershell.exe -ExecutionPolicy Bypass -Command \"" f"Set-Content -Path '{script_path}.ec' -Value '-'; " From 883e1b6a2deb076a2c5b10cb8fbc310925f6c628 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 16:25:13 -0700 Subject: [PATCH 072/112] debugging --- parsl/parsl/providers/local/local.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index 69993455..284666ae 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -223,10 +223,10 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): cmd = ( f"powershell.exe -ExecutionPolicy Bypass -Command \"" f"Set-Content -Path '{script_path}.ec' -Value '-'; " - f"$p = Start-Process powershell.exe -ArgumentList '-ExecutionPolicy', 'Bypass', '-File', '{script_path}', " + f"`$p = Start-Process powershell.exe -ArgumentList '-ExecutionPolicy', 'Bypass', '-File', '{script_path}', " f"'-RedirectStandardOutput', '{script_path}.out', '-RedirectStandardError', '{script_path}.err', '-PassThru', '-Wait'; " - f"Set-Content -Path '{script_path}.ec' -Value $p.ExitCode -PassThru; " - f"Write-Host `\"PID:$($p.Id)`\"\"" + f"Set-Content -Path '{script_path}.ec' -Value `$p.ExitCode -PassThru; " + f"Write-Host `\"PID:`$(`$p.Id)`\"\"" ) else: cmd = textwrap.dedent( From f481b240ca44caa41b909c753266e1a8c062591e Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 16:29:58 -0700 Subject: [PATCH 073/112] debugging --- parsl/parsl/providers/local/local.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index 284666ae..aeb9c366 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -189,14 +189,13 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): ''' job_name = "{0}.{1}".format(job_name, time.time()) - # Set script path - script_path = "{0}/{1}.sh".format(self.script_dir, job_name) - script_path = os.path.abspath(script_path) - if platform.system() == 'Windows': + script_path = "{0}/{1}.sh".format(self.script_dir, job_name) jobname_cmd = f'\n$env:JOBNAME = "{job_name}"\n' else: + script_path = "{0}/{1}.ps1".format(self.script_dir, job_name) jobname_cmd = f'\nexport JOBNAME={job_name}\n' + script_path = os.path.abspath(script_path) wrap_command = (self.worker_init + jobname_cmd + self.launcher(command, tasks_per_node, self.nodes_per_block, self.script_dir)) self._write_submit_script(wrap_command, script_path) From 644dff36f2a492326cc195c46e9ee51d42bc1cfe Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 16:31:22 -0700 Subject: [PATCH 074/112] debugging --- parsl/parsl/providers/local/local.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index aeb9c366..21862fbf 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -190,10 +190,10 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): job_name = "{0}.{1}".format(job_name, time.time()) if platform.system() == 'Windows': - script_path = "{0}/{1}.sh".format(self.script_dir, job_name) + script_path = "{0}/{1}.ps1".format(self.script_dir, job_name) jobname_cmd = f'\n$env:JOBNAME = "{job_name}"\n' else: - script_path = "{0}/{1}.ps1".format(self.script_dir, job_name) + script_path = "{0}/{1}.sh".format(self.script_dir, job_name) jobname_cmd = f'\nexport JOBNAME={job_name}\n' script_path = os.path.abspath(script_path) wrap_command = (self.worker_init + jobname_cmd + From 0a20559062b556d6005cb05de6ff39ba0d3d448d Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 21 Oct 2026 17:23:01 -0700 Subject: [PATCH 075/112] debugging --- parsl/parsl/launchers/launchers.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/parsl/parsl/launchers/launchers.py b/parsl/parsl/launchers/launchers.py index 629ef99d..439593a1 100644 --- a/parsl/parsl/launchers/launchers.py +++ b/parsl/parsl/launchers/launchers.py @@ -1,5 +1,6 @@ import logging import platform +import sys import textwrap from parsl.launchers.base import Launcher @@ -87,7 +88,7 @@ def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, scri for ($COUNT = 1; $COUNT -le $WORKERCOUNT; $COUNT++) {{ if ("{debug_num}" -eq "1") {{ Write-Host "Launching worker: $COUNT" }} - $proc = Start-Process -FilePath "python" -ArgumentList "{command} $COUNT" -NoNewWindow -PassThru + $proc = Start-Process -FilePath "{sys.executable}" -ArgumentList "{command} $COUNT" -NoNewWindow -PassThru $processes += $proc }} From 16218ffa8d017ce2f68b05966dcad0f9dd9cefaa Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 11:20:50 -0700 Subject: [PATCH 076/112] remove debugger breakpoint --- parsl/parsl/executors/high_throughput/process_worker_pool.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/parsl/parsl/executors/high_throughput/process_worker_pool.py b/parsl/parsl/executors/high_throughput/process_worker_pool.py index d37b5406..31af7a79 100755 --- a/parsl/parsl/executors/high_throughput/process_worker_pool.py +++ b/parsl/parsl/executors/high_throughput/process_worker_pool.py @@ -941,8 +941,6 @@ def strategyorlist(s: str) -> Union[str, list]: if __name__ == "__main__": - from pudb.remote import set_trace - set_trace(term_size=(80, 24)) parser = get_arg_parser() args = parser.parse_args() From 63473b454e5fbb16df69b77068e03ed5114dbe65 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 11:21:09 -0700 Subject: [PATCH 077/112] point to full path of worker pool script --- parsl/parsl/executors/high_throughput/executor.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/parsl/parsl/executors/high_throughput/executor.py b/parsl/parsl/executors/high_throughput/executor.py index 423e9621..e98c01c6 100644 --- a/parsl/parsl/executors/high_throughput/executor.py +++ b/parsl/parsl/executors/high_throughput/executor.py @@ -3,12 +3,14 @@ import os import pickle import subprocess +import sys import threading import typing import warnings from collections import defaultdict from concurrent.futures import Future from dataclasses import dataclass +from pathlib import Path from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union import typeguard @@ -43,7 +45,8 @@ logger = logging.getLogger(__name__) -DEFAULT_LAUNCH_CMD = ("process_worker_pool.py {debug} {max_workers_per_node} " +DEFAULT_LAUNCH_CMD = (f"{Path(sys.executable).parent / 'process_worker_pool.py'} " + " {debug} {max_workers_per_node} " "-a {addresses} " "-p {prefetch_capacity} " "-c {cores_per_worker} " From 0224a9d50220a8dca6f36d5112b1119d6bf4a450 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 11:27:19 -0700 Subject: [PATCH 078/112] platform-specifc script paths --- parsl/parsl/executors/high_throughput/executor.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/parsl/parsl/executors/high_throughput/executor.py b/parsl/parsl/executors/high_throughput/executor.py index e98c01c6..9d034ff1 100644 --- a/parsl/parsl/executors/high_throughput/executor.py +++ b/parsl/parsl/executors/high_throughput/executor.py @@ -2,6 +2,7 @@ import math import os import pickle +import platform import subprocess import sys import threading @@ -45,8 +46,13 @@ logger = logging.getLogger(__name__) -DEFAULT_LAUNCH_CMD = (f"{Path(sys.executable).parent / 'process_worker_pool.py'} " - " {debug} {max_workers_per_node} " +if platform.system() == 'Windows': + process_worker_pool_script = Path( + sys.executable).parent / 'Scripts' / 'process_worker_pool.py' +else: + process_worker_pool_script = 'process_worker_pool.py' +DEFAULT_LAUNCH_CMD = (f"{process_worker_pool_script} " + "{debug} {max_workers_per_node} " "-a {addresses} " "-p {prefetch_capacity} " "-c {cores_per_worker} " From 59c4494e91690877cc069cada78e6dcec0335ae7 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 11:57:53 -0700 Subject: [PATCH 079/112] fix string formatting --- parsl/parsl/executors/high_throughput/process_worker_pool.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/parsl/parsl/executors/high_throughput/process_worker_pool.py b/parsl/parsl/executors/high_throughput/process_worker_pool.py index 31af7a79..b747db96 100755 --- a/parsl/parsl/executors/high_throughput/process_worker_pool.py +++ b/parsl/parsl/executors/high_throughput/process_worker_pool.py @@ -347,9 +347,7 @@ def interchange_communicator(self, pair_setup: threading.Event) -> None: pending_task_count = f"pending task count is not available on {platform.system()}" # type: ignore[assignment] logger.debug( - 'ready workers: %d, pending tasks: %d', - self.ready_worker_count.value, - pending_task_count, + f'ready workers: {self.ready_worker_count.value}, pending tasks: {pending_task_count}' ) if time.time() >= last_beat + self.heartbeat_period: From fa92106d64c288019be943b3e7a7bcb84d806fdd Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 11:58:02 -0700 Subject: [PATCH 080/112] debugging --- parsl/parsl/launchers/launchers.py | 1 - parsl/parsl/providers/local/local.py | 1 + 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/parsl/parsl/launchers/launchers.py b/parsl/parsl/launchers/launchers.py index 439593a1..bae75b4d 100644 --- a/parsl/parsl/launchers/launchers.py +++ b/parsl/parsl/launchers/launchers.py @@ -148,7 +148,6 @@ def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, scri exit $ALLFAILED fi ''') - print('script:', x) return x diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index 21862fbf..0cb6bd4c 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -245,6 +245,7 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): if line.startswith("PID:"): job_id = line.split("PID:")[1].strip() remote_pid = int(job_id) + print(f'pid {remote_pid} exists?', psutil.pid_exists(remote_pid)) break if job_id is None: raise SubmitException(job_name, "Channel failed to start remote command/retrieve PID") From 418c045d96cc271edcfe80f8576b9183ab19858a Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 12:02:22 -0700 Subject: [PATCH 081/112] fix powershell command syntax --- parsl/parsl/providers/local/local.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index 0cb6bd4c..2ba57af2 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -223,7 +223,7 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): f"powershell.exe -ExecutionPolicy Bypass -Command \"" f"Set-Content -Path '{script_path}.ec' -Value '-'; " f"`$p = Start-Process powershell.exe -ArgumentList '-ExecutionPolicy', 'Bypass', '-File', '{script_path}', " - f"'-RedirectStandardOutput', '{script_path}.out', '-RedirectStandardError', '{script_path}.err', '-PassThru', '-Wait'; " + f"'-RedirectStandardOutput', '{script_path}.out', '-RedirectStandardError', '{script_path}.err' -PassThru -Wait; " f"Set-Content -Path '{script_path}.ec' -Value `$p.ExitCode -PassThru; " f"Write-Host `\"PID:`$(`$p.Id)`\"\"" ) From 21a30d096ba2cba795e7f26f0d1238840ac9abb1 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 12:10:05 -0700 Subject: [PATCH 082/112] debugging --- parsl/parsl/providers/local/local.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index 2ba57af2..ca1c4c32 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -220,12 +220,14 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): # background commands complete. if platform.system() == 'Windows': cmd = ( - f"powershell.exe -ExecutionPolicy Bypass -Command \"" + f'powershell.exe -ExecutionPolicy Bypass -Command "' f"Set-Content -Path '{script_path}.ec' -Value '-'; " - f"`$p = Start-Process powershell.exe -ArgumentList '-ExecutionPolicy', 'Bypass', '-File', '{script_path}', " - f"'-RedirectStandardOutput', '{script_path}.out', '-RedirectStandardError', '{script_path}.err' -PassThru -Wait; " - f"Set-Content -Path '{script_path}.ec' -Value `$p.ExitCode -PassThru; " - f"Write-Host `\"PID:`$(`$p.Id)`\"\"" + f"$p = Start-Process powershell.exe -ArgumentList '-ExecutionPolicy', 'Bypass', '-Command', " + f"'`$proc = Start-Process powershell.exe -ArgumentList ''-ExecutionPolicy'', ''Bypass'', ''-File'', ''{script_path}'' " + f"-RedirectStandardOutput ''{script_path}.out'' -RedirectStandardError ''{script_path}.err'' -PassThru -Wait; " + f"Set-Content -Path ''{script_path}.ec'' -Value `$proc.ExitCode' -PassThru; " + f"Write-Host ('PID:' + $p.Id)" + f'"' ) else: cmd = textwrap.dedent( From c29dc4110f00f24fa5c15893185750d15a9d41b5 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 12:11:50 -0700 Subject: [PATCH 083/112] debugging --- parsl/parsl/providers/local/local.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index ca1c4c32..8e204fc9 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -225,7 +225,7 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): f"$p = Start-Process powershell.exe -ArgumentList '-ExecutionPolicy', 'Bypass', '-Command', " f"'`$proc = Start-Process powershell.exe -ArgumentList ''-ExecutionPolicy'', ''Bypass'', ''-File'', ''{script_path}'' " f"-RedirectStandardOutput ''{script_path}.out'' -RedirectStandardError ''{script_path}.err'' -PassThru -Wait; " - f"Set-Content -Path ''{script_path}.ec'' -Value `$proc.ExitCode' -PassThru; " + f"Set-Content -Path ''{script_path}.ec'' -Value $proc.ExitCode' -PassThru; " f"Write-Host ('PID:' + $p.Id)" f'"' ) From 1c4caf429031220b402bd9dbd14bdc2a25a4c703 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 12:15:18 -0700 Subject: [PATCH 084/112] debugging --- parsl/parsl/providers/local/local.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index 8e204fc9..ba6b054c 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -223,7 +223,7 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): f'powershell.exe -ExecutionPolicy Bypass -Command "' f"Set-Content -Path '{script_path}.ec' -Value '-'; " f"$p = Start-Process powershell.exe -ArgumentList '-ExecutionPolicy', 'Bypass', '-Command', " - f"'`$proc = Start-Process powershell.exe -ArgumentList ''-ExecutionPolicy'', ''Bypass'', ''-File'', ''{script_path}'' " + f"'$proc = Start-Process powershell.exe -ArgumentList ''-ExecutionPolicy'', ''Bypass'', ''-File'', ''{script_path}'' " f"-RedirectStandardOutput ''{script_path}.out'' -RedirectStandardError ''{script_path}.err'' -PassThru -Wait; " f"Set-Content -Path ''{script_path}.ec'' -Value $proc.ExitCode' -PassThru; " f"Write-Host ('PID:' + $p.Id)" From d54be1aae54561847409c2edd83845e25f77c7d4 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 12:17:33 -0700 Subject: [PATCH 085/112] debugging --- parsl/parsl/providers/local/local.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index ba6b054c..7fc41431 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -222,8 +222,8 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): cmd = ( f'powershell.exe -ExecutionPolicy Bypass -Command "' f"Set-Content -Path '{script_path}.ec' -Value '-'; " - f"$p = Start-Process powershell.exe -ArgumentList '-ExecutionPolicy', 'Bypass', '-Command', " - f"'$proc = Start-Process powershell.exe -ArgumentList ''-ExecutionPolicy'', ''Bypass'', ''-File'', ''{script_path}'' " + f"$p = Start-Process powershell.exe -WindowStyle Hidden -ArgumentList '-ExecutionPolicy', 'Bypass', '-Command', " + f"'$proc = Start-Process powershell.exe -WindowStyle Hidden -ArgumentList ''-ExecutionPolicy'', ''Bypass'', ''-File'', ''{script_path}'' " f"-RedirectStandardOutput ''{script_path}.out'' -RedirectStandardError ''{script_path}.err'' -PassThru -Wait; " f"Set-Content -Path ''{script_path}.ec'' -Value $proc.ExitCode' -PassThru; " f"Write-Host ('PID:' + $p.Id)" From 56080520d92e2af72cfbd7fd199f384f2081ad04 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 12:43:11 -0700 Subject: [PATCH 086/112] debugging --- parsl/parsl/launchers/launchers.py | 1 - tests/test_task.py | 4 ++-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/parsl/parsl/launchers/launchers.py b/parsl/parsl/launchers/launchers.py index bae75b4d..cbef89d9 100644 --- a/parsl/parsl/launchers/launchers.py +++ b/parsl/parsl/launchers/launchers.py @@ -69,7 +69,6 @@ def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, scri - script_dir (str): Path to the submit script directory """ - print('calling launcher') task_blocks = tasks_per_node * nodes_per_block fail_on_any_num = int(self.fail_on_any) debug_num = int(self.debug) diff --git a/tests/test_task.py b/tests/test_task.py index f07c9ff5..8ebf34eb 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -406,9 +406,9 @@ def test_broken_task_chain(self): task_name='create list on disk after zero div%d' % task_id) task_graph.close() - with self.assertRaises(parsl.dataflow.errors.DependencyError) as err: + task_graph.join() + with self.assertRaises(ZeroDivisionError): task_graph.join() - assert isinstance(err.__cause__, ZeroDivisionError) def test_empty_task(self): """TaskGraph: Test an empty task.""" From d955525349058b2b0c817de6d38d833e6ceb6d09 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 12:58:34 -0700 Subject: [PATCH 087/112] debugging --- parsl/parsl/dataflow/dflow.py | 6 +++++- tests/test_task.py | 4 ++-- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index 3e2a5202..6637e7bf 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -335,7 +335,7 @@ def handle_exec_update(self, task_record: TaskRecord, future: Future) -> None: future (Future) : The future object corresponding to the task which makes this callback """ - + print('handle_exec_update', task_record) task_id = task_record['id'] task_record['try_time_returned'] = datetime.datetime.now() @@ -344,9 +344,12 @@ def handle_exec_update(self, task_record: TaskRecord, future: Future) -> None: raise InternalConsistencyError("done callback called, despite future not reporting itself as done") try: + print('get result') res = self._unwrap_remote_exception_wrapper(future) + print('result:', res) except Exception as e: + print('got exception', e) logger.info(f"Task {task_id} try {task_record['try_id']} failed with exception of type {type(e).__name__}") # We keep the history separately, since the future itself could be # tossed. @@ -435,6 +438,7 @@ def handle_exec_update(self, task_record: TaskRecord, future: Future) -> None: self.launch_if_ready(task_record) def handle_join_update(self, task_record: TaskRecord, inner_app_future: Optional[AppFuture]) -> None: + print('handle_join_update') with task_record['join_lock']: # inner_app_future has completed, which is one (potentially of many) # futures the outer task is joining on. diff --git a/tests/test_task.py b/tests/test_task.py index 8ebf34eb..f07c9ff5 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -406,9 +406,9 @@ def test_broken_task_chain(self): task_name='create list on disk after zero div%d' % task_id) task_graph.close() - task_graph.join() - with self.assertRaises(ZeroDivisionError): + with self.assertRaises(parsl.dataflow.errors.DependencyError) as err: task_graph.join() + assert isinstance(err.__cause__, ZeroDivisionError) def test_empty_task(self): """TaskGraph: Test an empty task.""" From bed79820bf5890d1d6483e9cd435e3d6ec08c7a4 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 13:00:47 -0700 Subject: [PATCH 088/112] debugging --- parsl/parsl/dataflow/dflow.py | 1 + 1 file changed, 1 insertion(+) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index 6637e7bf..6c75d476 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -577,6 +577,7 @@ def _update_task_state(self, task_record: TaskRecord, new_state: States) -> None @staticmethod def _unwrap_remote_exception_wrapper(future: Future) -> Any: result = future.result() + print('result:', result) if isinstance(result, RemoteExceptionWrapper): result.reraise() return result From b5caf5c9e4861c1d7a43eb8c0fbb81de2aa6c4d1 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 13:05:04 -0700 Subject: [PATCH 089/112] debugging --- parsl/parsl/dataflow/dflow.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index 6c75d476..7fbbb68e 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -374,6 +374,7 @@ def handle_exec_update(self, task_record: TaskRecord, future: Future) -> None: task_record['fail_cost'] += 1 if isinstance(e, DependencyError): + print('is dependencyerror') logger.info("Task %s failed due to dependency failure so skipping retries", task_id) self._complete_task_exception(task_record, States.dep_fail, e) @@ -537,6 +538,7 @@ def _complete_task_result(self, task_record: TaskRecord, new_state: States, resu def _complete_task_exception(self, task_record: TaskRecord, new_state: States, exception: BaseException) -> None: """Set a task into a failure state """ + print('_complete_task_exception') assert new_state in FINAL_STATES assert new_state in FINAL_FAILURE_STATES @@ -549,6 +551,7 @@ def _complete_task_exception(self, task_record: TaskRecord, new_state: States, e self.wipe_task(task_record['id']) with task_record['app_fu']._update_lock: + print('set exception', exception) task_record['app_fu'].set_exception(exception) def _update_task_state(self, task_record: TaskRecord, new_state: States) -> None: From 9082fa85edc271a056bf0e3c505f63d476b0ede6 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 13:06:27 -0700 Subject: [PATCH 090/112] debugging --- parsl/parsl/dataflow/dflow.py | 1 + 1 file changed, 1 insertion(+) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index 7fbbb68e..5485ab61 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -539,6 +539,7 @@ def _complete_task_exception(self, task_record: TaskRecord, new_state: States, e """Set a task into a failure state """ print('_complete_task_exception') + from pudb import set_trace; set_trace() assert new_state in FINAL_STATES assert new_state in FINAL_FAILURE_STATES From 924bf045f124c9eeef03e299c5930240da4657e2 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 13:27:05 -0700 Subject: [PATCH 091/112] debugging --- parsl/parsl/dataflow/dflow.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index 5485ab61..01ac56ac 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -539,7 +539,6 @@ def _complete_task_exception(self, task_record: TaskRecord, new_state: States, e """Set a task into a failure state """ print('_complete_task_exception') - from pudb import set_trace; set_trace() assert new_state in FINAL_STATES assert new_state in FINAL_FAILURE_STATES @@ -675,6 +674,7 @@ def _launch_if_ready_async(self, task_record: TaskRecord) -> None: logger.error("add_done_callback got an exception which will be ignored", exc_info=True) task_record['exec_fu'] = exec_fu + from pudb import set_trace; set_trace() def launch_task(self, task_record: TaskRecord) -> Future: """Handle the actual submission of the task to the executor layer. From 15c78f7dbca57e9c9331276459c1994bc0d532d2 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 13:33:50 -0700 Subject: [PATCH 092/112] debugging --- parsl/parsl/executors/threads.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/parsl/parsl/executors/threads.py b/parsl/parsl/executors/threads.py index 3b53a7f7..4ec3c4cb 100644 --- a/parsl/parsl/executors/threads.py +++ b/parsl/parsl/executors/threads.py @@ -67,7 +67,9 @@ def submit(self, func, resource_specification, *args, **kwargs): raise InvalidResourceSpecification(set(resource_specification.keys()), "Parsl resource specification is not supported in ThreadPool Executor.") - return self.executor.submit(func, *args, **kwargs) + fu = self.executor.submit(func, *args, **kwargs) + print('future exception:', fu.exception()) + return fu def shutdown(self, block=True): """Shutdown the ThreadPool. The underlying concurrent.futures thread pool From 5de5cac62208cf335377e102cd7649b230ed0e9d Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Mon, 26 Oct 2026 13:48:11 -0700 Subject: [PATCH 093/112] debugging --- parsl/parsl/dataflow/dflow.py | 1 + parsl/parsl/executors/high_throughput/executor.py | 5 ++++- tests/test_task.py | 7 ++++--- 3 files changed, 9 insertions(+), 4 deletions(-) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index 01ac56ac..40e5130e 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -579,6 +579,7 @@ def _update_task_state(self, task_record: TaskRecord, new_state: States) -> None @staticmethod def _unwrap_remote_exception_wrapper(future: Future) -> Any: + print('calling result on future', future, future._exception) result = future.result() print('result:', result) if isinstance(result, RemoteExceptionWrapper): diff --git a/parsl/parsl/executors/high_throughput/executor.py b/parsl/parsl/executors/high_throughput/executor.py index 9d034ff1..9fd6cfc1 100644 --- a/parsl/parsl/executors/high_throughput/executor.py +++ b/parsl/parsl/executors/high_throughput/executor.py @@ -725,6 +725,7 @@ def submit(self, func: Callable, resource_specification: dict, *args, **kwargs) try: fn_buf = pack_apply_message(func, args, kwargs, buffer_threshold=1 << 20) + print('submitting', func, args) except TypeError: raise SerializationError(func.__name__) @@ -732,7 +733,9 @@ def submit(self, func: Callable, resource_specification: dict, *args, **kwargs) if resource_specification: context["resource_spec"] = resource_specification - return self.submit_payload(context, fn_buf) + fu = self.submit_payload(context, fn_buf) + print('future exception', fu.exception()) + return fu def submit_payload(self, context: dict, buffer: bytes) -> HTEXFuture: """ diff --git a/tests/test_task.py b/tests/test_task.py index f07c9ff5..a9b4ab05 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -406,9 +406,10 @@ def test_broken_task_chain(self): task_name='create list on disk after zero div%d' % task_id) task_graph.close() - with self.assertRaises(parsl.dataflow.errors.DependencyError) as err: - task_graph.join() - assert isinstance(err.__cause__, ZeroDivisionError) + task_graph.join() + # with self.assertRaises(parsl.dataflow.errors.DependencyError) as err: + # task_graph.join() + # assert isinstance(err.__cause__, ZeroDivisionError) def test_empty_task(self): """TaskGraph: Test an empty task.""" From d77ec6fb07944096fec00e2d0c9dc439cdf20cb8 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 27 Oct 2026 11:54:15 -0700 Subject: [PATCH 094/112] debugging --- parsl/parsl/dataflow/dflow.py | 21 ++++++++++--- taskgraph/Task.py | 3 ++ tests/test_task.py | 55 +++++++++++++++++++++-------------- 3 files changed, 53 insertions(+), 26 deletions(-) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index 40e5130e..3dca2c0b 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -231,12 +231,15 @@ def __exit__(self, exc_type, exc_value, traceback) -> None: if exc_type is None: logger.info("Waiting for all tasks to complete") self.wait_for_current_tasks() + print('clean up') self.cleanup() + print('after cleanup') else: logger.info("There was an exception - cleaning up without waiting for task completion") self.cleanup() else: raise InternalConsistencyError(f"Exit case for {mode} should be unreachable, validated by typeguard on Config()") + print('finished __exit__') def _send_task_info(self, task_record: TaskRecord) -> None: if self.monitoring_radio: @@ -579,7 +582,8 @@ def _update_task_state(self, task_record: TaskRecord, new_state: States) -> None @staticmethod def _unwrap_remote_exception_wrapper(future: Future) -> Any: - print('calling result on future', future, future._exception) + print('calling result on future', future) + print(future.exception()) result = future.result() print('result:', result) if isinstance(result, RemoteExceptionWrapper): @@ -611,7 +615,12 @@ def launch_if_ready(self, task_record: TaskRecord) -> None: launch_if_ready is thread safe, so may be called from any thread or callback. """ - self._task_launch_pool.submit(self._launch_if_ready_async, task_record) + launch_future = self._task_launch_pool.submit(self._launch_if_ready_async, task_record) + try: + r = launch_future.result() + print('launch future result:', r) + except Exception as e: + print('caught', e) @wrap_with_logs def _launch_if_ready_async(self, task_record: TaskRecord) -> None: @@ -675,7 +684,6 @@ def _launch_if_ready_async(self, task_record: TaskRecord) -> None: logger.error("add_done_callback got an exception which will be ignored", exc_info=True) task_record['exec_fu'] = exec_fu - from pudb import set_trace; set_trace() def launch_task(self, task_record: TaskRecord) -> Future: """Handle the actual submission of the task to the executor layer. @@ -1147,6 +1155,8 @@ def wait_for_current_tasks(self) -> None: AppFuture to be completed. This method will not necessarily wait for any tasks added after cleanup has started (such as data stageout?) """ + current_thread = threading.current_thread() + print('wait for current tasks', current_thread.name) logger.info("Waiting for all remaining tasks to complete") @@ -1159,12 +1169,14 @@ def wait_for_current_tasks(self) -> None: # waiting for completion than .result() fut = task_record['app_fu'] if not fut.done(): - fut.exception() + ex = fut.exception() + print('ex', ex) # now app future is done, poll until DFK state is final: a # DFK state being final and the app future being done do not imply each other. while task_record['status'] not in FINAL_STATES: time.sleep(0.1) + print('all tasks completed') logger.info("All remaining tasks completed") @wrap_with_logs @@ -1195,6 +1207,7 @@ def cleanup(self) -> None: logger.info("Shutting down executors") + print('shutting down executor') for executor in self.executors.values(): logger.info(f"Shutting down executor {executor.label}") executor.shutdown() diff --git a/taskgraph/Task.py b/taskgraph/Task.py index fd951a3d..d9465b12 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -830,6 +830,7 @@ def __init__( label=self.executor_label, max_threads=2) else: + print('single threaded') self.executor_label = "threaded" executor = ThreadPoolExecutor( label=self.executor_label, @@ -1137,10 +1138,12 @@ def join(self, timeout=None): True if successful join, False if timed out. """ + print('join taskgraph') LOGGER.debug("joining taskgraph") try: LOGGER.debug("attempting to join threads") for task in self._task_hash_map.values(): + print('joining task', task.task_name) try: task.join(timeout=timeout) except TimeoutError: diff --git a/tests/test_task.py b/tests/test_task.py index a9b4ab05..d15364c0 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -158,6 +158,17 @@ def test_timeout_task(self): # this should timeout since function runs for 5 seconds self.assertTrue(timedout) + def test_windows_exception_handling(self): + """TaskGraph: Test timeout functionality.""" + task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) + _ = task_graph.add_task( + func=_test_fixtures._long_running_function, + args=(5,)) + task_graph.close() + timedout = not task_graph.join(0.5) + # this should timeout since function runs for 5 seconds + self.assertTrue(timedout) + def test_precomputed_task(self): """TaskGraph: Test that a task reuses old results.""" task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) @@ -382,28 +393,28 @@ def test_broken_task_chain(self): target_path = os.path.join(self.workspace_dir, '1000.dat') value = 5 list_len = 1000 - for task_id in range(1): - target_path = os.path.join( - self.workspace_dir, '1000_%d.dat' % task_id) - normal_task = task_graph.add_task( - func=_test_fixtures._create_list_on_disk, - args=(value, list_len), - kwargs={'target_path': target_path}, - target_path_list=[target_path], - task_name='create list on disk %d' % task_id) - zero_div_task = task_graph.add_task( - func=_test_fixtures._div_by_zero, - dependent_task_list=[normal_task], - task_name='test_broken_task_chain_%d' % task_id) - target_path = os.path.join( - self.workspace_dir, 'after_zerodiv_1000_%d.dat' % task_id) - _ = task_graph.add_task( - func=_test_fixtures._create_list_on_disk, - args=(value, list_len), - kwargs={'target_path': target_path}, - dependent_task_list=[zero_div_task], - target_path_list=[target_path], - task_name='create list on disk after zero div%d' % task_id) + task_id = 0 + target_path = os.path.join( + self.workspace_dir, '1000_%d.dat' % task_id) + normal_task = task_graph.add_task( + func=_test_fixtures._create_list_on_disk, + args=(value, list_len), + kwargs={'target_path': target_path}, + target_path_list=[target_path], + task_name='create list on disk %d' % task_id) + zero_div_task = task_graph.add_task( + func=_test_fixtures._div_by_zero, + dependent_task_list=[normal_task], + task_name='test_broken_task_chain_%d' % task_id) + target_path = os.path.join( + self.workspace_dir, 'after_zerodiv_1000_%d.dat' % task_id) + _ = task_graph.add_task( + func=_test_fixtures._create_list_on_disk, + args=(value, list_len), + kwargs={'target_path': target_path}, + dependent_task_list=[zero_div_task], + target_path_list=[target_path], + task_name='create list on disk after zero div%d' % task_id) task_graph.close() task_graph.join() From 5f4c81e285c9042e31fec84ea2615724ddc9bcc2 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 27 Oct 2026 12:04:50 -0700 Subject: [PATCH 095/112] debugging --- parsl/parsl/dataflow/dflow.py | 27 +------------------ .../executors/high_throughput/executor.py | 3 --- parsl/parsl/providers/local/local.py | 2 -- taskgraph/Task.py | 10 +++---- 4 files changed, 5 insertions(+), 37 deletions(-) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py index 3dca2c0b..f1a120d7 100644 --- a/parsl/parsl/dataflow/dflow.py +++ b/parsl/parsl/dataflow/dflow.py @@ -231,15 +231,12 @@ def __exit__(self, exc_type, exc_value, traceback) -> None: if exc_type is None: logger.info("Waiting for all tasks to complete") self.wait_for_current_tasks() - print('clean up') self.cleanup() - print('after cleanup') else: logger.info("There was an exception - cleaning up without waiting for task completion") self.cleanup() else: raise InternalConsistencyError(f"Exit case for {mode} should be unreachable, validated by typeguard on Config()") - print('finished __exit__') def _send_task_info(self, task_record: TaskRecord) -> None: if self.monitoring_radio: @@ -338,7 +335,6 @@ def handle_exec_update(self, task_record: TaskRecord, future: Future) -> None: future (Future) : The future object corresponding to the task which makes this callback """ - print('handle_exec_update', task_record) task_id = task_record['id'] task_record['try_time_returned'] = datetime.datetime.now() @@ -347,12 +343,9 @@ def handle_exec_update(self, task_record: TaskRecord, future: Future) -> None: raise InternalConsistencyError("done callback called, despite future not reporting itself as done") try: - print('get result') res = self._unwrap_remote_exception_wrapper(future) - print('result:', res) except Exception as e: - print('got exception', e) logger.info(f"Task {task_id} try {task_record['try_id']} failed with exception of type {type(e).__name__}") # We keep the history separately, since the future itself could be # tossed. @@ -377,7 +370,6 @@ def handle_exec_update(self, task_record: TaskRecord, future: Future) -> None: task_record['fail_cost'] += 1 if isinstance(e, DependencyError): - print('is dependencyerror') logger.info("Task %s failed due to dependency failure so skipping retries", task_id) self._complete_task_exception(task_record, States.dep_fail, e) @@ -442,7 +434,6 @@ def handle_exec_update(self, task_record: TaskRecord, future: Future) -> None: self.launch_if_ready(task_record) def handle_join_update(self, task_record: TaskRecord, inner_app_future: Optional[AppFuture]) -> None: - print('handle_join_update') with task_record['join_lock']: # inner_app_future has completed, which is one (potentially of many) # futures the outer task is joining on. @@ -541,7 +532,6 @@ def _complete_task_result(self, task_record: TaskRecord, new_state: States, resu def _complete_task_exception(self, task_record: TaskRecord, new_state: States, exception: BaseException) -> None: """Set a task into a failure state """ - print('_complete_task_exception') assert new_state in FINAL_STATES assert new_state in FINAL_FAILURE_STATES @@ -554,7 +544,6 @@ def _complete_task_exception(self, task_record: TaskRecord, new_state: States, e self.wipe_task(task_record['id']) with task_record['app_fu']._update_lock: - print('set exception', exception) task_record['app_fu'].set_exception(exception) def _update_task_state(self, task_record: TaskRecord, new_state: States) -> None: @@ -582,10 +571,7 @@ def _update_task_state(self, task_record: TaskRecord, new_state: States) -> None @staticmethod def _unwrap_remote_exception_wrapper(future: Future) -> Any: - print('calling result on future', future) - print(future.exception()) result = future.result() - print('result:', result) if isinstance(result, RemoteExceptionWrapper): result.reraise() return result @@ -615,12 +601,7 @@ def launch_if_ready(self, task_record: TaskRecord) -> None: launch_if_ready is thread safe, so may be called from any thread or callback. """ - launch_future = self._task_launch_pool.submit(self._launch_if_ready_async, task_record) - try: - r = launch_future.result() - print('launch future result:', r) - except Exception as e: - print('caught', e) + return self._task_launch_pool.submit(self._launch_if_ready_async, task_record) @wrap_with_logs def _launch_if_ready_async(self, task_record: TaskRecord) -> None: @@ -1155,9 +1136,6 @@ def wait_for_current_tasks(self) -> None: AppFuture to be completed. This method will not necessarily wait for any tasks added after cleanup has started (such as data stageout?) """ - current_thread = threading.current_thread() - print('wait for current tasks', current_thread.name) - logger.info("Waiting for all remaining tasks to complete") # .values is made into a list immediately to reduce (although not @@ -1170,13 +1148,11 @@ def wait_for_current_tasks(self) -> None: fut = task_record['app_fu'] if not fut.done(): ex = fut.exception() - print('ex', ex) # now app future is done, poll until DFK state is final: a # DFK state being final and the app future being done do not imply each other. while task_record['status'] not in FINAL_STATES: time.sleep(0.1) - print('all tasks completed') logger.info("All remaining tasks completed") @wrap_with_logs @@ -1207,7 +1183,6 @@ def cleanup(self) -> None: logger.info("Shutting down executors") - print('shutting down executor') for executor in self.executors.values(): logger.info(f"Shutting down executor {executor.label}") executor.shutdown() diff --git a/parsl/parsl/executors/high_throughput/executor.py b/parsl/parsl/executors/high_throughput/executor.py index 9fd6cfc1..aff0f735 100644 --- a/parsl/parsl/executors/high_throughput/executor.py +++ b/parsl/parsl/executors/high_throughput/executor.py @@ -533,7 +533,6 @@ def _result_queue_worker(self): else: msg = pickle.loads(serialized_msg) - print('loaded message:', msg) if msg['type'] == 'result': try: @@ -725,7 +724,6 @@ def submit(self, func: Callable, resource_specification: dict, *args, **kwargs) try: fn_buf = pack_apply_message(func, args, kwargs, buffer_threshold=1 << 20) - print('submitting', func, args) except TypeError: raise SerializationError(func.__name__) @@ -734,7 +732,6 @@ def submit(self, func: Callable, resource_specification: dict, *args, **kwargs) context["resource_spec"] = resource_specification fu = self.submit_payload(context, fn_buf) - print('future exception', fu.exception()) return fu def submit_payload(self, context: dict, buffer: bytes) -> HTEXFuture: diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py index 7fc41431..eaab4087 100644 --- a/parsl/parsl/providers/local/local.py +++ b/parsl/parsl/providers/local/local.py @@ -238,7 +238,6 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): echo $? > {script_path}.ec ; }} >/dev/null 2>&1 & echo "PID:$!" ; }}' ''') - print('cmd', cmd) retcode, stdout, stderr = execute_wait(cmd, self.cmd_timeout) if retcode != 0: raise SubmitException(job_name, "Launch command exited with code {0}".format(retcode), @@ -247,7 +246,6 @@ def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): if line.startswith("PID:"): job_id = line.split("PID:")[1].strip() remote_pid = int(job_id) - print(f'pid {remote_pid} exists?', psutil.pid_exists(remote_pid)) break if job_id is None: raise SubmitException(job_name, "Channel failed to start remote command/retrieve PID") diff --git a/taskgraph/Task.py b/taskgraph/Task.py index d9465b12..3c36fa64 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -1038,7 +1038,9 @@ def add_task( checkpoint_dir=self._taskgraph_cache_dir_path, executor_label=self.executor_label) + print('adding task to map') self._task_hash_map[hash] = new_task + pprint.pprint(self._task_hash_map) if self._n_workers < 0: # call directly if single threaded new_task._call() @@ -1139,6 +1141,7 @@ def join(self, timeout=None): """ print('join taskgraph') + print(self._task_hash_map) LOGGER.debug("joining taskgraph") try: LOGGER.debug("attempting to join threads") @@ -1302,12 +1305,7 @@ def inner_wrap_func(func, *args, _taskgraph_task_info=None, **kwargs): def __repr__(self): """Create a string representation of a Task.""" - return "Task object %s:\n\n" % (id(self)) + pprint.pformat( - { - "task_name": self.task_name, - "target_path_list": self._target_path_list, - "self._result": self._result, - }) + return f"Task object {id(self)} [{self.task_name}]" def _call(self, timeout=None): """Invoke this method to execute task. From e6debf7f0517069e3126bd9e505dcc8281f55c67 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 27 Oct 2026 13:40:20 -0700 Subject: [PATCH 096/112] debugging --- taskgraph/Task.py | 1 + 1 file changed, 1 insertion(+) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 3c36fa64..05ab03b1 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -237,6 +237,7 @@ def get_task_hash_info(func, args, kwargs, target_path_list): def get_task_hash(func, args, kwargs, target_path_list): hash_info = get_task_hash_info(func, args, kwargs, target_path_list) + print('hash info:', hash_info) argument_hash_string = ':'.join([ repr(hash_info[key]) for key in sorted(hash_info.keys())]) From 2e9b2f4320c7d8581865cfaf4815bfb3428e0c12 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 27 Oct 2026 13:45:07 -0700 Subject: [PATCH 097/112] debugging --- taskgraph/Task.py | 1 + 1 file changed, 1 insertion(+) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 05ab03b1..10e3d580 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -1545,6 +1545,7 @@ def _scrub_task_args(base_value, target_path_list): return type(base_value)(result_list) elif isinstance(base_value, _VALID_PATH_TYPES): normalized_path = _normalize_path(base_value) + print('compare paths', normalized_path, target_path_list) if normalized_path in target_path_list: return 'in_target_path_list' else: From 41ac4df830d2e0f4160d3367e874c355c677a5a1 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 27 Oct 2026 13:47:40 -0700 Subject: [PATCH 098/112] debugging --- taskgraph/Task.py | 1 + 1 file changed, 1 insertion(+) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 10e3d580..3baedc8a 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -988,6 +988,7 @@ def add_task( "Objects passed to dependent task list that are not " "tasks: %s", dependent_task_list) + target_path_list = [_normalize_path(path) for path in target_path_list] hash = get_task_hash(func, args, kwargs, target_path_list) # it may be this task was already created in an earlier call, From 7770a57fcf8fa5121e5a5ecfce23ecd7d5892070 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 27 Oct 2026 13:57:05 -0700 Subject: [PATCH 099/112] debugging --- taskgraph/Task.py | 17 ++++++++++++----- tests/test_task.py | 8 ++------ 2 files changed, 14 insertions(+), 11 deletions(-) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 3baedc8a..7ebbcecf 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -988,6 +988,13 @@ def add_task( "Objects passed to dependent task list that are not " "tasks: %s", dependent_task_list) + # it is a common error to accidentally pass a non string as to the + # target path list, this terminates early if so + if any([not (isinstance(path, _VALID_PATH_TYPES)) + for path in target_path_list]): + raise ValueError( + "Values passed to target_path_list are not strings: %s", + target_path_list) target_path_list = [_normalize_path(path) for path in target_path_list] hash = get_task_hash(func, args, kwargs, target_path_list) @@ -1546,11 +1553,11 @@ def _scrub_task_args(base_value, target_path_list): return type(base_value)(result_list) elif isinstance(base_value, _VALID_PATH_TYPES): normalized_path = _normalize_path(base_value) - print('compare paths', normalized_path, target_path_list) - if normalized_path in target_path_list: - return 'in_target_path_list' - else: - return normalized_path + return normalized_path + # if normalized_path in target_path_list: + # return 'in_target_path_list' + # else: + # return normalized_path else: return base_value diff --git a/tests/test_task.py b/tests/test_task.py index d15364c0..6f988d0e 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -390,7 +390,6 @@ def test_broken_task_chain(self): """TaskGraph: test dependent tasks fail on ancestor fail.""" task_graph = taskgraph.TaskGraph(self.workspace_dir, 4) - target_path = os.path.join(self.workspace_dir, '1000.dat') value = 5 list_len = 1000 task_id = 0 @@ -416,11 +415,8 @@ def test_broken_task_chain(self): target_path_list=[target_path], task_name='create list on disk after zero div%d' % task_id) - task_graph.close() - task_graph.join() - # with self.assertRaises(parsl.dataflow.errors.DependencyError) as err: - # task_graph.join() - # assert isinstance(err.__cause__, ZeroDivisionError) + with self.assertRaises(ZeroDivisionError): + task_graph.join() def test_empty_task(self): """TaskGraph: Test an empty task.""" From be86799c19324fa99cd6d8704550614739beffe7 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 27 Oct 2026 14:21:17 -0700 Subject: [PATCH 100/112] removing unused code --- taskgraph/Task.py | 56 +--------------------------------------------- tests/test_task.py | 1 + 2 files changed, 2 insertions(+), 55 deletions(-) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 7ebbcecf..8a3d5ca9 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -784,13 +784,6 @@ def __init__( # keeps track of the tasks currently being processed for logging. self._active_task_list = [] - # maps a list of task names that need to be executed before the key - # task can - self._task_dependent_map = collections.defaultdict(set) - - # maps a list of task names that are dependent to a task - self._dependent_task_map = collections.defaultdict(set) - # tasks that complete are added to this set self._completed_task_names = set() @@ -1015,21 +1008,9 @@ def add_task( disjoint_target_set = ( new_task_target_set.symmetric_difference( duplicate_task_target_set)) - if len(disjoint_target_set) == ( + if len(disjoint_target_set) != ( len(new_task_target_set) + len(duplicate_task_target_set)): - if duplicate_task not in dependent_task_list: - LOGGER.info( - "A task was created that had an identical " - "args signature sans target paths, but a " - "different target_path_list of the same " - "length. To avoid recomputation, dynamically " - "adding previous Task (%s) as a dependent " - "task to this one (%s).", - duplicate_task.task_name, task_name) - dependent_task_list = ( - dependent_task_list + [duplicate_task]) - else: raise RuntimeError( "A task was created that has the same arguments " "as another task, but only partially different " @@ -1050,33 +1031,6 @@ def add_task( print('adding task to map') self._task_hash_map[hash] = new_task pprint.pprint(self._task_hash_map) - if self._n_workers < 0: - # call directly if single threaded - new_task._call() - else: - # determine if task is ready or is dependent on other - # tasks - LOGGER.debug( - "multithreaded: %s sending to new task queue.", - task_name) - outstanding_dep_task_name_list = [ - dep_task.task_name for dep_task in dependent_task_list - if dep_task.task_name - not in self._completed_task_names] - if not outstanding_dep_task_name_list: - LOGGER.debug( - "sending task %s right away", new_task.task_name) - else: - # there are unresolved tasks that the waiting - # process scheduler has not been notified of. - # Record dependencies. - for dep_task_name in outstanding_dep_task_name_list: - # record tasks that are dependent on dep_task_name - self._task_dependent_map[dep_task_name].add( - new_task.task_name) - # record tasks that new_task depends on - self._dependent_task_map[new_task.task_name].add( - dep_task_name) return new_task except Exception: @@ -1087,14 +1041,6 @@ def add_task( self._terminate() raise - except Exception: - # something went wrong, shut down the taskgraph - LOGGER.exception( - "Something went wrong when adding task %s, " - "terminating taskgraph.", task_name) - self._terminate() - raise - def _execution_monitor(self, monitor_wait_event): """Log state of taskgraph every ``self._reporting_interval`` seconds. diff --git a/tests/test_task.py b/tests/test_task.py index 6f988d0e..de3ae1b6 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -940,6 +940,7 @@ def test_target_path_missing_file(self): args=('test', 1, target_path), target_path_list=[not_target_path], task_name='_create_list_on_disk') + task_graph.join() expected_message = "Missing expected target path results" actual_message = str(cm.exception) self.assertTrue(expected_message in actual_message, actual_message) From 24b775af2a76e45d2631cce4932d968adb8dcb4c Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 27 Oct 2026 14:36:55 -0700 Subject: [PATCH 101/112] fix invalid reporting interval --- taskgraph/Task.py | 4 ++-- tests/test_task.py | 16 ++++++++-------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 8a3d5ca9..1e119ba3 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -841,6 +841,8 @@ def __init__( # start concurrent reporting of taskgraph if reporting interval is set self._reporting_interval = reporting_interval if reporting_interval is not None: + if reporting_interval <= 0: + raise ValueError('reporting_interval must be greater than 0') self._execution_monitor_wait_event = threading.Event() self._execution_monitor_thread = threading.Thread( target=self._execution_monitor, @@ -1028,9 +1030,7 @@ def add_task( checkpoint_dir=self._taskgraph_cache_dir_path, executor_label=self.executor_label) - print('adding task to map') self._task_hash_map[hash] = new_task - pprint.pprint(self._task_hash_map) return new_task except Exception: diff --git a/tests/test_task.py b/tests/test_task.py index de3ae1b6..e195b2fc 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -947,7 +947,7 @@ def test_target_path_missing_file(self): def test_expected_path_list(self): """TaskGraph: test expected path list matches actual path list.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) # note it is important this is a relative path that does not # contain the drive letter on Windows. absolute_target_file_path = os.path.join( @@ -969,7 +969,7 @@ def test_expected_path_list(self): def test_kwargs_hashed(self): """TaskGraph: ensure kwargs are considered in determining id hash.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) task_graph.add_task( func=_test_fixtures._noop_function, @@ -992,7 +992,7 @@ def test_kwargs_hashed(self): def test_same_timestamp_and_value(self): """TaskGraph: ensure identical files but filename are noticed.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) file_a_path = os.path.join(self.workspace_dir, 'file_a.txt') file_b_path = os.path.join(self.workspace_dir, 'file_b.txt') @@ -1025,7 +1025,7 @@ def test_same_timestamp_and_value(self): def test_different_hash_different_file(self): """TaskGraph: ensure identical files but filename are noticed.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) target_file_path = os.path.join(self.workspace_dir, 'target.txt') _ = task_graph.add_task( func=_test_fixtures._create_file, @@ -1042,7 +1042,7 @@ def test_different_hash_different_file(self): with open(target_file_path, 'w') as target_file: target_file.write('overwritten') - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) _ = task_graph.add_task( func=_test_fixtures._create_file, args=(target_file_path, 'content'), @@ -1056,7 +1056,7 @@ def test_different_hash_different_file(self): with open(target_file_path, 'r') as target_file: self.assertEqual(target_file.read(), 'overwritten') - task_graph = taskgraph.TaskGraph(self.workspace_dir, -1, 0) + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) _ = task_graph.add_task( func=_test_fixtures._create_file, args=(target_file_path, 'content'), @@ -1075,7 +1075,7 @@ def test_return_value(self): if hasattr(_test_fixtures._return_value_once, 'executed'): del _test_fixtures._return_value_once.executed n_iterations = 3 - task_graph = taskgraph.TaskGraph(self.workspace_dir, 0, 0) + task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) for iteration_id in range(n_iterations): transient_run = iteration_id == n_iterations-1 LOGGER.debug(iteration_id) @@ -1095,7 +1095,7 @@ def test_return_value(self): del _test_fixtures._return_value_once.executed for iteration_id in range(n_iterations): LOGGER.debug(iteration_id) - task_graph = taskgraph.TaskGraph(self.workspace_dir, 0, 0) + task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) expected_value = 'transient run' if iteration_id == 0: value_task = task_graph.add_task( From c1aeef4c8beb277097a52b8c1e5595468849a3ae Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 27 Oct 2026 14:37:52 -0700 Subject: [PATCH 102/112] remove debugger breakpoint --- tests/test_task.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_task.py b/tests/test_task.py index e195b2fc..b4ae248a 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -453,7 +453,6 @@ def test_closed_graph(self): def test_single_task_multiprocessing(self): """TaskGraph: Test a single task with multiprocessing.""" - from pudb.remote import set_trace workspace_dir = 'workdir' task_graph = taskgraph.TaskGraph(workspace_dir, 1) target_path = os.path.join(workspace_dir, '1000.dat') From 349511035628648515b32ed89301a99b3dbdf76a Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Tue, 27 Oct 2026 14:42:43 -0700 Subject: [PATCH 103/112] use concurrent.futures TimeoutError --- taskgraph/Task.py | 3 ++- tests/test_task.py | 11 ----------- 2 files changed, 2 insertions(+), 12 deletions(-) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 1e119ba3..58ee888e 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -1,5 +1,6 @@ """Task graph framework.""" import collections +import concurrent.futures from concurrent.futures import Future from functools import lru_cache import hashlib @@ -1104,7 +1105,7 @@ def join(self, timeout=None): print('joining task', task.task_name) try: task.join(timeout=timeout) - except TimeoutError: + except concurrent.futures._base.TimeoutError: LOGGER.info( "task %s timed out in graph join", task.task_name) return False diff --git a/tests/test_task.py b/tests/test_task.py index b4ae248a..10be7866 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -158,17 +158,6 @@ def test_timeout_task(self): # this should timeout since function runs for 5 seconds self.assertTrue(timedout) - def test_windows_exception_handling(self): - """TaskGraph: Test timeout functionality.""" - task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) - _ = task_graph.add_task( - func=_test_fixtures._long_running_function, - args=(5,)) - task_graph.close() - timedout = not task_graph.join(0.5) - # this should timeout since function runs for 5 seconds - self.assertTrue(timedout) - def test_precomputed_task(self): """TaskGraph: Test that a task reuses old results.""" task_graph = taskgraph.TaskGraph(self.workspace_dir, 0) From 7a906ab821c6954f6521e6d406a57bc245af1e22 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 28 Oct 2026 10:55:24 -0700 Subject: [PATCH 104/112] install parsl from my fork --- parsl/.all-contributorsrc | 36 - parsl/.coveragerc | 3 - parsl/.flake8 | 24 - parsl/.github/ISSUE_TEMPLATE/bug_report.md | 30 - .../.github/ISSUE_TEMPLATE/feature_request.md | 20 - parsl/.github/PULL_REQUEST_TEMPLATE.md | 21 - parsl/.github/workflows/ci.yaml | 111 -- parsl/.github/workflows/parsl+cctools.yaml | 50 - parsl/.github/workflows/parsl+flux.yaml | 43 - parsl/.github/workflows/parsl+htcondor.yaml | 56 - parsl/.github/workflows/parsl+slurm.yaml | 41 - .../workflows/python-publish-to-testpypi.yml | 82 - parsl/.gitignore | 123 -- parsl/.gitmodules | 0 parsl/.isort.cfg | 2 - parsl/.readthedocs.yaml | 28 - parsl/.wci.yml | 46 - parsl/CITATION.cff | 120 -- parsl/CITATION.md | 25 - parsl/CONTRIBUTING.rst | 155 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parsl/parsl/utils.py delete mode 100644 parsl/parsl/version.py delete mode 100644 parsl/requirements.txt delete mode 100755 parsl/setup.py delete mode 100755 parsl/tag_and_release.sh delete mode 100644 parsl/test-requirements.txt diff --git a/parsl/.all-contributorsrc b/parsl/.all-contributorsrc deleted file mode 100644 index b311bff5..00000000 --- a/parsl/.all-contributorsrc +++ /dev/null @@ -1,36 +0,0 @@ -{ - "projectName": "parsl", - "projectOwner": "Parsl", - "files": [ - "CONTRIBUTORS.md" - ], - "commitType": "docs", - "commitConvention": "angular", - "contributorsPerLine": 7, - "contributors": [ - { - "login": "sophie-bui", - "name": "Sophie Bui", - "avatar_url": "https://avatars.githubusercontent.com/u/23347055?v=4", - "profile": "https://www.linkedin.com/in/buisophie/", - "contributions": [ - "projectManagement", - "design", - "eventOrganizing", - "content", - "promotion", - "mentoring" - ] - }, - { - "login": "danielskatz", - "name": "Daniel S. Katz", - "avatar_url": "https://avatars.githubusercontent.com/u/2913845?v=4", - "profile": "https://danielskatz.org", - "contributions": [ - "code", - "content" - ] - } - ] -} diff --git a/parsl/.coveragerc b/parsl/.coveragerc deleted file mode 100644 index a6eb0361..00000000 --- a/parsl/.coveragerc +++ /dev/null @@ -1,3 +0,0 @@ -[run] -concurrency=multiprocessing,thread -parallel=True diff --git a/parsl/.flake8 b/parsl/.flake8 deleted file mode 100644 index d12dc8ef..00000000 --- a/parsl/.flake8 +++ /dev/null @@ -1,24 +0,0 @@ -[flake8] -# E126: continuation line over-indented for hanging indent -# This one is bad. Sometimes ordering matters, conditional imports -# setting env vars necessary etc. -# E402: module level import not at top of file -# E129: Visual indent to not match indent as next line, counter eg here: -# https://github.com/PyCQA/pycodestyle/issues/386 -# W504: line break after binary operator -# (Raised by flake8 even when it is followed) -ignore = E126, E402, E129, W504 -max-line-length = 145 -exclude = test_import_fail.py, - parsl/executors/workqueue/parsl_coprocess.py -# E741 disallows ambiguous single letter names which look like numbers -# We disable it in visualization code because plotly uses 'l' as -# a keyword arg -# F821: undefined name -per-file-ignores = parsl/monitoring/visualization/*:E741, - # test_ssh_errors.py really is broken - - # tests often import fresh_config into their namespace as local_config - # but then do not use it directly, because tests/conftests.py - # looks for it instead. - parsl/tests/**:F401 diff --git a/parsl/.github/ISSUE_TEMPLATE/bug_report.md b/parsl/.github/ISSUE_TEMPLATE/bug_report.md deleted file mode 100644 index 2ad9580f..00000000 --- a/parsl/.github/ISSUE_TEMPLATE/bug_report.md +++ /dev/null @@ -1,30 +0,0 @@ ---- -name: Bug report -about: Create a report to help us improve -title: '' -labels: bug -assignees: '' - ---- - -**Describe the bug** -A clear and concise description of what the bug is. - -**To Reproduce** -Steps to reproduce the behavior, for e.g: -1. Setup Parsl 0.8.0 with Python 3.6 on cluster -2. Run a test script -3. Wait 5 mins -4. See error - -**Expected behavior** -A clear and concise description of what you expected to happen. - -**Environment** - - OS: [e.g. ubuntu, centos, MacOS, windows] - - Python version - - Parsl version - -**Distributed Environment** -- Where are you running the Parsl script from ? [e.g. Laptop/Workstation, Login node, Compute node] -- Where do you need the workers to run ? [e.g. Same as Parsl script, Compute nodes, Cloud nodes] diff --git a/parsl/.github/ISSUE_TEMPLATE/feature_request.md b/parsl/.github/ISSUE_TEMPLATE/feature_request.md deleted file mode 100644 index 647e2b58..00000000 --- a/parsl/.github/ISSUE_TEMPLATE/feature_request.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -name: Feature request -about: Suggest an idea for this project -title: '' -labels: enhancement -assignees: '' - ---- - -**Is your feature request related to a problem? Please describe.** -A clear and concise description of what the problem is. E.g. I'm always frustrated when [...] - -**Describe the solution you'd like** -A clear and concise description of what you want to happen. - -**Describe alternatives you've considered** -A clear and concise description of any alternative solutions or features you've considered. - -**Additional context** -Add any other context about the feature request here. diff --git a/parsl/.github/PULL_REQUEST_TEMPLATE.md b/parsl/.github/PULL_REQUEST_TEMPLATE.md deleted file mode 100644 index 39591aed..00000000 --- a/parsl/.github/PULL_REQUEST_TEMPLATE.md +++ /dev/null @@ -1,21 +0,0 @@ -# Description - -Please include a summary of the change and (optionally) which issue is fixed. Please also include -relevant motivation and context. - -# Changed Behaviour - -Which existing user workflows or functionality will behave differently after this PR? - -# Fixes - -Fixes # (issue) - -## Type of change - -Choose which options apply, and delete the ones which do not apply. - -- Bug fix -- New feature -- Update to human readable text: Documentation/error messages/comments -- Code maintenance/cleanup diff --git a/parsl/.github/workflows/ci.yaml b/parsl/.github/workflows/ci.yaml deleted file mode 100644 index cfab7014..00000000 --- a/parsl/.github/workflows/ci.yaml +++ /dev/null @@ -1,111 +0,0 @@ -name: Parsl - -on: - pull_request: - types: - - opened - - synchronize - merge_group: - -jobs: - main-test-suite: - strategy: - matrix: - python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"] - runs-on: ubuntu-24.04 - timeout-minutes: 60 - - steps: - - uses: actions/checkout@master - - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - - - name: Collect Job Information - id: job-info - run: | - echo "Python Version: ${{ matrix.python-version }}" >> ci_job_info.txt - echo "CI Triggering Event: ${{ github.event_name }}" >> ci_job_info.txt - echo "Triggering Git Ref: ${{ github.ref }}" >> ci_job_info.txt - echo "Triggering Git SHA: ${{ github.sha }}" >> ci_job_info.txt - echo "Workflow Run: ${{ github.run_number }}" >> ci_job_info.txt - echo "Workflow Attempt: ${{ github.run_attempt }}" >> ci_job_info.txt - as_ascii="$(echo "${{ github.ref_name }}" | perl -pe "s/[^A-z0-9-]+/-/g; s/^-+|-+\$//g; s/--+/-/g;")" - echo "as-ascii=$as_ascii" >> $GITHUB_OUTPUT - - - name: Non-requirements based install - run: | - # libpython3.5: make workqueue binary installer happy - # mpich: required by radical executor - sudo apt-get update -q - sudo apt-get install -qy libpython3.5 mpich - - - name: setup virtual env - run: | - make virtualenv - source .venv/bin/activate - - - name: make deps clean_coverage - run: | - source .venv/bin/activate - make deps - make clean_coverage - - - name: make test - run: | - source .venv/bin/activate - - # temporary; until test-matrixification - export PARSL_TEST_PRESERVE_NUM_RUNS=7 - - make test - ln -s pytest-parsl/parsltest-current test_runinfo - - - name: Documentation checks - run: | - source .venv/bin/activate - pip install .[docs] - sudo apt-get install -y pandoc - cd docs - - test ! -e stubs - - # check we can build the docs without warnings - PYTHONPATH=/tmp/cctools/lib/python3.8/site-packages make SPHINXOPTS=-W html - - cd .. - - # assert that none of the runs in this test have put an ERROR message into a - # database manager log file or monitoring router log file. It would be better if - # the tests themselves failed immediately when there was a monitoring error, but - # in the absence of that, this is a dirty way to check. - bash -c '! grep ERROR pytest-parsl/parsltest-current/runinfo*/*/database_manager.log' - bash -c '! grep ERROR pytest-parsl/parsltest-current/runinfo*/*/monitoring_router.log' - - # temporary; until test-matrixification - rm -f pytest-parsl/parsltest-current test_runinfo - - - name: Checking parsl-visualize - run: | - source .venv/bin/activate - sudo apt-get install -y graphviz - pip install .[monitoring,visualization] - parsl/tests/test-viz.sh - - - name: make coverage - run: | - source .venv/bin/activate - make coverage - - - name: Archive runinfo logs - if: ${{ always() }} - uses: actions/upload-artifact@v4 - with: - name: runinfo-${{ matrix.python-version }}-${{ steps.job-info.outputs.as-ascii }}-${{ github.sha }} - path: | - runinfo/ - pytest-parsl/ - ci_job_info.txt - compression-level: 9 diff --git a/parsl/.github/workflows/parsl+cctools.yaml b/parsl/.github/workflows/parsl+cctools.yaml deleted file mode 100644 index 7d0c3b30..00000000 --- a/parsl/.github/workflows/parsl+cctools.yaml +++ /dev/null @@ -1,50 +0,0 @@ -name: WorkQueue and TaskVine tests -on: - pull_request: - types: - - opened - - synchronize - -jobs: - build-linux: - runs-on: ubuntu-24.04 - timeout-minutes: 60 - strategy: - max-parallel: 5 - - steps: - - uses: actions/checkout@v4 - - - name: Set up Python 3.11 - uses: actions/setup-python@v3 - with: - python-version: '3.11' - - - name: Add conda to system path - run: | - # $CONDA is an environment variable pointing to the root of the miniconda directory - echo $CONDA/bin >> $GITHUB_PATH - echo "CONDA BIN: $CONDA/bin" - - - name: Install parsl and dependencies - run: | - conda install --channel=conda-forge ndcctools mpich mpi4py openssl - make deps - pip install .[workqueue,monitoring] - export PATH=/usr/share/miniconda/bin/:$PATH - - - name: Sanity check - run: | - echo "PATH: $PATH" - echo "Python: "; which python3 - python3 -c "import ndcctools; print(ndcctools.__file__)" - python3 -c "from ndcctools import work_queue; print(work_queue.__version__)" - python3 -c "import parsl; print(parsl.__version__)" - which parsl_coprocess.py - which python3 - find /usr/share/miniconda | grep libssl - - - name: Run pytest suite for WorkQueue and TaskVine - run: | - make wqex_local_test - make vineex_local_test diff --git a/parsl/.github/workflows/parsl+flux.yaml b/parsl/.github/workflows/parsl+flux.yaml deleted file mode 100644 index 0143241d..00000000 --- a/parsl/.github/workflows/parsl+flux.yaml +++ /dev/null @@ -1,43 +0,0 @@ -name: Test Flux Scheduler -on: - pull_request: - merge_group: - -jobs: - build: - runs-on: ubuntu-22.04 - permissions: - packages: read - strategy: - fail-fast: false - matrix: - container: ['fluxrm/flux-sched:jammy'] - timeout-minutes: 30 - - container: - image: ${{ matrix.container }} - options: "--platform=linux/amd64 --user root -it --init" - - name: ${{ matrix.container }} - steps: - - name: Checkout - uses: actions/checkout@v3 - - - name: Install Dependencies and Parsl - run: | - apt-get update && apt-get install -y python3-pip curl - pip3 install . -r test-requirements.txt - - - name: Verify Parsl Installation - run: | - pytest parsl/tests/ -k "not cleannet and not unix_filesystem_permissions_required" --config parsl/tests/configs/local_threads.py --random-order --durations 10 - - - name: Test Parsl with Flux - run: | - pytest parsl/tests/test_flux.py --config local --random-order - - - name: Test Parsl with Flux Config - run: | - pytest parsl/tests/ -k "not cleannet and not unix_filesystem_permissions_required" --config parsl/tests/configs/flux_local.py --random-order --durations 10 - - diff --git a/parsl/.github/workflows/parsl+htcondor.yaml b/parsl/.github/workflows/parsl+htcondor.yaml deleted file mode 100644 index 7bff997f..00000000 --- a/parsl/.github/workflows/parsl+htcondor.yaml +++ /dev/null @@ -1,56 +0,0 @@ -name: Test HTCondor Scheduler -on: - pull_request: - merge_group: - -jobs: - build: - runs-on: ubuntu-22.04 - permissions: - packages: read - strategy: - fail-fast: true - matrix: - container: ["htcondor/mini:el9"] - timeout-minutes: 30 - - container: - image: ${{ matrix.container }} - options: "--platform=linux/amd64 --rm -h node01" - - name: ${{ matrix.container }} - steps: - - name: Checkout - uses: actions/checkout@v3 - - - name: Install Dependencies and Parsl - run: | - dnf install -y python3.12 python3-virtualenv - # the container cannot run condor jobs as its default user, - # but supplies a `submituser` user to do so. All the parsl - # testing should run as that user. - - # Copy the checkout from the default location to the submit - # user, ready for installation by that user. - su - submituser -c bash -c "cp -av ${GITHUB_WORKSPACE}/* ./" - - # Build a virtual environment with a recent Python version. - # The htcondor/mini container uses Enterprise Linux 9 which - # uses Python 3.9 by default, which is unsupported by Python. - # It does supply more recent Python versions, though. - su - submituser -c bash -c "virtualenv ./venv --python=3.12" - su - submituser -c bash -c ". ./venv/bin/activate ; pip install . -r test-requirements.txt" - - - name: Verify Parsl Installation - run: | - su - submituser -c bash -c '. venv/bin/activate ; pytest parsl/tests/ -k "not cleannet and not unix_filesystem_permissions_required" --config parsl/tests/configs/local_threads.py --random-order --durations 10' - - - name: Test Parsl with HTCondor Config - # start.sh is supplied by the container image, to start up - # HTCondor enough that jobs can be submitted to it. - # The test configuration does not provide shared filesystem - # access in the way that the Parsl tests expect, so those - # are disabled in the following test by `not shared_fs` and - # `not staging_required`. - run: | - (/start.sh &) && su - submituser -c bash -c '. venv/bin/activate ; sleep 20s && pytest parsl/tests/ -k "not cleannet and not unix_filesystem_permissions_required and not staging_required and not shared_fs" --config parsl/tests/configs/htcondor_ci.py --random-order --durations 10' diff --git a/parsl/.github/workflows/parsl+slurm.yaml b/parsl/.github/workflows/parsl+slurm.yaml deleted file mode 100644 index 64dc1a52..00000000 --- a/parsl/.github/workflows/parsl+slurm.yaml +++ /dev/null @@ -1,41 +0,0 @@ -name: Test Slurm Scheduler -on: - pull_request: - merge_group: - -jobs: - build: - runs-on: ubuntu-22.04 - permissions: - packages: read - strategy: - fail-fast: false - matrix: - container: ["ghcr.io/tylern4/slurm-standalone:slurm-24-05-0-1"] - timeout-minutes: 30 - - container: - image: ${{ matrix.container }} - options: "--platform=linux/amd64 --rm -h node01" - - name: ${{ matrix.container }} - steps: - - name: Checkout - uses: actions/checkout@v3 - - - name: Install Dependencies and Parsl - run: | - apt update && apt install -y python3.13-venv - python3 -m venv ./venv - . ./venv/bin/activate - CC=/usr/lib64/openmpi/bin/mpicc pip3 install . -r test-requirements.txt - - - name: Verify Parsl Installation - run: | - . venv/bin/activate - pytest parsl/tests/ -k "not cleannet and not unix_filesystem_permissions_required" --config parsl/tests/configs/local_threads.py --random-order --durations 10 - - - name: Test Parsl with Slurm Config - run: | - . venv/bin/activate - ./parsl/tests/slurm-entrypoint.sh pytest parsl/tests/ -k "not cleannet and not unix_filesystem_permissions_required" --config parsl/tests/configs/slurm_local.py --random-order --durations 10 diff --git a/parsl/.github/workflows/python-publish-to-testpypi.yml b/parsl/.github/workflows/python-publish-to-testpypi.yml deleted file mode 100644 index c7343f82..00000000 --- a/parsl/.github/workflows/python-publish-to-testpypi.yml +++ /dev/null @@ -1,82 +0,0 @@ -# This workflow will upload a Python Package using Twine when a release is created -# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries - -# This workflow uses actions that are not certified by GitHub. -# They are provided by a third-party and are governed by -# separate terms of service, privacy policy, and support -# documentation. - -name: Upload Python Package - -on: - schedule: - - cron: '42 22 * * MON' # Run every Monday at 22:42 - - workflow_dispatch: - inputs: - suffix: - description: 'Release Suffix to append to version info. For eg. devN, a0' - required: false - default: '' - -permissions: - contents: write - -jobs: - - deploy: - # This action is intended to be invoked manually for debugging purposes - if : github.actor == 'yadudoc' || github.actor == 'benclifford' - - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v3 - with: - fetch-tags: true - fetch-depth: 0 - - - name: Check if this commit is already released - id: already_released - run: | - if git tag --contains HEAD | grep -e '^[0-9]\{4\}\.[0-9]\{2\}\.[0-9]\{2\}$' ; then exit 1 ; fi - - - name: Set up Python - uses: actions/setup-python@v4 - with: - # The release process needs distutils - see Parsl issue #2934 - # which was removed from Python 3.12 - python-version: '3.11' - - - name: Set version info - id: version_setter - run: echo "VERSION=$(date +'%Y.%m.%d')$SUFFIX" >> $GITHUB_OUTPUT - env: - SUFFIX: ${{ inputs.suffix }} - - - name: Install dependencies - run: | - python -m pip install --upgrade pip wheel - pip install build - - name: Build package - run: | - ./tag_and_release.sh update_version - ./tag_and_release.sh package - env: - VERSION: ${{ steps.version_setter.outputs.VERSION }} - - - name: Publish package - uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29 - with: - user: __token__ - # Set the following to publish to TestPypi instead - # password: ${{ secrets.TESTPYPI_API_TOKEN }} - # repository_url: https://test.pypi.org/legacy/ - - password: ${{ secrets.PYPI_API_TOKEN }} - - - name: Mint a tag - uses: rickstaa/action-create-tag@v1 - with: - tag: ${{ steps.version_setter.outputs.VERSION }} - message: "Release version: ${{ steps.version_setter.outputs.VERSION }}" diff --git a/parsl/.gitignore b/parsl/.gitignore deleted file mode 100644 index 8811016b..00000000 --- a/parsl/.gitignore +++ /dev/null @@ -1,123 +0,0 @@ -*~ -*.pyc -parsl.egg-info/* -.scripts -.*out -.*err -*log -.ipynb_checkpoints -tutorials/* -venv/* -web/* -api/* -.idea/* - -# local user configuration which should never be -# version controlled -./parsl/tests/configs/local_user_opts.py - -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -env/ -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -*.egg-info/ -.installed.cfg -*.egg - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.coverage -.coverage.* -.cache -coverage.xml -*.cover -.hypothesis/ -/.pytest/ -/pytest-parsl/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# pyenv -.python-version - -# celery beat schedule file -celerybeat-schedule - -# SageMath parsed files -*.sage.py - -# dotenv -.env - -# virtualenv -.venv -venv/ -ENV/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ - -# emacs buffers -\#* diff --git a/parsl/.gitmodules b/parsl/.gitmodules deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/.isort.cfg b/parsl/.isort.cfg deleted file mode 100644 index f238bf7e..00000000 --- a/parsl/.isort.cfg +++ /dev/null @@ -1,2 +0,0 @@ -[settings] -profile = black diff --git a/parsl/.readthedocs.yaml b/parsl/.readthedocs.yaml deleted file mode 100644 index a276d2f2..00000000 --- a/parsl/.readthedocs.yaml +++ /dev/null @@ -1,28 +0,0 @@ -# .readthedocs.yaml -# Read the Docs configuration file -# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details - -# Required -version: 2 - -formats: - - pdf - -# Set the version of Python and other tools you might need -build: - os: ubuntu-22.04 - tools: - python: "3.11" - -# Build documentation in the docs/ directory with Sphinx -sphinx: - configuration: docs/conf.py - -# We recommend specifying your dependencies to enable reproducible builds: -# https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html -python: - install: - - method: pip - path: "." - extra_requirements: - - docs diff --git a/parsl/.wci.yml b/parsl/.wci.yml deleted file mode 100644 index ccea1300..00000000 --- a/parsl/.wci.yml +++ /dev/null @@ -1,46 +0,0 @@ -name: Parsl -icon: https://parsl-project.org/images/logos/parsl-logo.png -headline: Productive parallel programming in Python -description: | - Parsl is a flexible and scalable parallel programming library for Python. - Parsl augments Python with simple constructs for encoding parallelism. - Developers annotate Python functions to specify opportunities for - concurrent execution. These annotated functions, called apps, may represent - pure Python functions or calls to external applications. Parsl further - allows invocations of these apps, called tasks, to be connected by shared - input/output data (e.g., Python objects or files) via which Parsl - constructs a dynamic dependency graph of tasks to manage concurrent task - execution where possible. - -language: Python - -release: - version: 1.2.0 - date: 2022-01-13 - -documentation: - general: https://parsl.readthedocs.io/en/stable/ - installation: https://parsl.readthedocs.io/en/stable/quickstart.html - tutorial: https://parsl-project.org/binder - -social: - twitter: ParslProject - -execution_environment: - interfaces: - - Python API - resource_managers: - - Slurm - - LSF - - PBS - - Flux - - GridEngine - - HTCondor - - AWS - - Google Cloud - - Kubernetes - transfer_protocols: - - Globus - - HTTP - - FTP - - rsync diff --git a/parsl/CITATION.cff b/parsl/CITATION.cff deleted file mode 100644 index fef63d5c..00000000 --- a/parsl/CITATION.cff +++ /dev/null @@ -1,120 +0,0 @@ -cff-version: 1.2.0 -message: "If you use this software in your work, please cite it using the following metadata." -authors: -- family-names: "Nand Babuji" - given-names: "Yadu" - orcid: https://orcid.org/0000-0002-9162-6003 -- family-names: "Clifford" - given-names: "Ben" - orcid: https://orcid.org/0000-0001-6397-7239 -- family-names: "Woodard" - given-names: "Anna" - orcid: https://orcid.org/0000-0002-8640-5417 -- family-names: "Li" - given-names: "Zhuozhao" - orcid: https://orcid.org/0000-0003-1903-6428 -- family-names: "Chard" - given-names: "Kyle" - orcid: https://orcid.org/0000-0002-7370-4805 -- family-names: "Katz" - given-names: "Daniel S." - orcid: https://orcid.org/0000-0001-5934-7525 -- family-names: "Wilde" - given-names: "Michael" - orcid: https://orcid.org/0000-0001-6660-6206 -- family-names: "Wozniak" - given-names: "Justin" - orcid: https://orcid.org/0000-0002-2441-2048 -- family-names: "Foster" - given-names: "Ian" - orcid: https://orcid.org/0000-0003-2129-5269 -- family-names: "Glick" - given-names: "Benjamin" - orcid: https://orcid.org/0000-0002-0762-2684 -- family-names: "Rowland" - given-names: "Kelly L." - orcid: https://orcid.org/0000-0001-5147-0051 -- family-names: "Pigg" - given-names: "Connor" -- family-names: "Lacinski" - given-names: "Lukasz" -- family-names: "Garrido" - given-names: "Juan David" -- family-names: "Hategan" - given-names: "Mihael" -- family-names: "Tovar" - given-names: "Benjamin" -- family-names: "Gray" - given-names: "Lindsey" -- family-names: "Shaffer" - given-names: "Tim" -- family-names: "Ward" - given-names: "Logan" -- family-names: "Heise" - given-names: "David" -- family-names: "Chard" - given-names: "Ryan" -- family-names: "Summer" - given-names: "Ted" -- family-names: "Dasso" - given-names: "TJ" -- family-names: "Hayhurst" - given-names: "Lyle" -- family-names: "Moon" - given-names: "Joseph" -- family-names: "Perera" - given-names: "Akila Ravihansa" -- family-names: "Lentner" - given-names: "Geoffrey" -- family-names: "Nagaitsev" - given-names: "Kir" -- family-names: "Corbett" - given-names: "James" -- family-names: "Lewis" - given-names: "Monica D." -- family-names: "Thain" - given-names: "Douglas" -- family-names: "Khodygo" - given-names: "Vladimir" -- family-names: "Hayes" - given-names: "Alex" -- family-names: "Litteken" - given-names: "Andrew" -- family-names: "Schwarting" - given-names: "Marcus" -- family-names: "Skluzacek" - given-names: "Tyler J." -- family-names: "Kowalik" - given-names: "Kacper" -- family-names: "Baker" - given-names: "Joseph L." -- family-names: "Wayne" - given-names: "Mitchell R." -- family-names: "Griffith" - given-names: "Joel" -- family-names: "Hoven" - given-names: "John" -- family-names: "Fialho" - given-names: "Raphael" -- family-names: "Lindberg" - given-names: "Gerrick" -- family-names: "Rao" - given-names: "Yongyan" -- family-names: "Hurtado" - given-names: "Kenyi" -- family-names: "Miglani" - given-names: "Sohit" -- family-names: "Arslan" - given-names: "Engin" -- family-names: "Govoni" - given-names: "Darren" -- family-names: "Galewsky" - given-names: "Ben" -- family-names: "Bilke" - given-names: "Lars" -title: "parsl" -identifiers: - - description: Conference proceedings paper - type: doi - value: "10.1145/3307681.3325400" - diff --git a/parsl/CITATION.md b/parsl/CITATION.md deleted file mode 100644 index 6f90c8c1..00000000 --- a/parsl/CITATION.md +++ /dev/null @@ -1,25 +0,0 @@ -Babuji, Y., Woodard, A., Li, Z., Katz, D. S., Clifford, B., Kumar, R., Lacinski, L., Chard, R., Wozniak, J., Foster, I., Wilde, M., and Chard, K., Parsl: Pervasive Parallel Programming in Python. 28th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC). 2019. - -or - -```{tex} -@inproceedings{babuji19parsl, - author = {Babuji, Yadu and - Woodard, Anna and - Li, Zhuozhao and - Katz, Daniel S. and - Clifford, Ben and - Kumar, Rohan and - Lacinski, Lukasz and - Chard, Ryan and - Wozniak, Justin and - Foster, Ian and - Wilde, Mike and - Chard, Kyle}, - title = {Parsl: Pervasive Parallel Programming in Python}, - booktitle = {28th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC)}, - doi = {10.1145/3307681.3325400}, - year = {2019}, - url = {https://doi.org/10.1145/3307681.3325400} -} -``` diff --git a/parsl/CONTRIBUTING.rst b/parsl/CONTRIBUTING.rst deleted file mode 100644 index ec49a59d..00000000 --- a/parsl/CONTRIBUTING.rst +++ /dev/null @@ -1,155 +0,0 @@ -Where to Start --------------- - -We eagerly welcome contributions of any type (e.g., bug fixes, new features, reporting issues, documentation, etc). If you're looking for a good place to start, we recommend perusing our current Git issues (those marked with `help wanted `_ are a good place to start). - -Please be aware of `Parsl's Code of Conduct `_. - -If you are unfamiliar with GitHub pull requests, the primary mechanism for contributing changes to our code, there is `documentation available `_. - -The best places to ask questions or discuss development activities are: - -* in our Slack's `#parsl-hackers channel `_. You can `join our Slack here `_. - -* using `GitHub issues `_. - - -Coding Conventions ------------------- - -Formatting Conventions -====================== - -Parsl code should adhere to Python `PEP-8 `_. This is enforced in CI (with some exceptions). You can also run this test yourself using ``make flake8``. - -Naming Conventions -================== - -The following conventions should be followed: ClassName, ExceptionName, GLOBAL_CONSTANT_NAME, and lowercase_with_underscores for everything else. - -Version Increments -================== - -Parsl follows the `calendar versioning scheme `_ with ``YYYY.MM.DD`` numbering scheme for versions. -This scheme was chosen after switching from ad-hoc versioning and manual release processes to an automated weekly process. -Releases are pushed from GitHub actions to PyPI and will be picked up automatically by Conda. -Manual packaging instructions are included in the -`packaging docs `_ - -Documentation -================== - -Classes should be documented following the `NumPy/SciPy `_ -style. A concise summary is available `here `_. User and developer documentation is auto-generated and made available on -`ReadTheDocs `_. - -Testing -======= - -Parsl uses ``pytest`` to run most tests. All tests should be placed in -the ``parsl/tests`` directory. - -There are two broad groups of tests: those which must run with a -specific configuration, and those that should work with any -configuration. - -Tests that should run with any configuration live under -themed directories ``parsl/tests/test*/`` and should be named ``test*.py``. -They can be run with any configuration by specifying ``--config CONFIGPATH`` -where CONFIGPATH is a path to a ``.py`` file exporting a parsl configuration -object named ``config``. The Parsl-specific test fixtures will ensure -a suitable DFK is loaded with that configuration for each test. - -Tests which require their own specially configured DFK, or no DFK at all, -should be labelled with ``@pytest.mark.local`` and can be run with -``--config local``. -Provide the special configuration creating a ``local_config`` function -that returns the required configuration in that test file. -Or, provide both a ``local_setup`` function that loads the proper configuration -and ``local_teardown`` that stops parsl. - -There is more fine-grained enabling and disabling of tests within the -above categories: - -A pytest marker of ``cleannet`` (for clean network) can be used to select -or deselect tests that need a very clean network (for example, for tests -making FTP transfers). When the test environment (GitHub actions) does not -provide a sufficiently clean network, run all tests with ``-k "not cleannet"`` to -disable those tests. - -Some other markers are available but unused in testing; -see ``pytest --markers parsl/tests/`` for more details. - -A specific test in a specific file can be run like this::: - - $ pytest test_python_apps/test_basic.py::test_simple - -A timeout can be added to test runs using a pytest parameter such as -``--timeout=60`` - -Many tests are marked with ``@pytest.mark.skip`` for reasons usually -specified directly in the annotation - generally because they are broken -in one way or another. - - -Coverage Testing -================ - -There is also some coverage testing available. The CI by default records -coverage for most of the tests it runs and outputs a brief report -at the end of each CI run. This is purely informational and a Lack of -coverage won't produce a CI failure. - -It is possible to produce a more detailed coverage report on your -development machine: make sure you have no `.coverage` file, run the -test commands as shown in `.github/workflows/ci.yaml`, and then run -`coverage report` to produce the summary as seen in CI, or run -`coverage html` to produce annotated source code in the `htmlcov/` -subdirectory. This will show, line by line, if each line of parsl -source code was executed during the coverage test. - -Development Process -------------------- - -If you are a contributor to Parsl at large, we recommend forking the repository and submitting pull requests from your fork. -The `Parsl development team `_ has the additional privilege of creating development branches on the repository. -Parsl development follows a common pull request-based workflow similar to `GitHub flow `_. That is: - -* every development activity (except very minor changes, which can be discussed in the PR) should have a related GitHub issue -* all development occurs in branches (named with a short descriptive name, for example, `add-globus-transfer-#1`) -* the master branch is always stable -* development branches should include tests for added features -* development branches should be tested after being brought up-to-date with the master (in this way, what is being tested is what is actually going into the code; otherwise, unexpected issues from merging may come up) -* branches that have been successfully tested are merged via pull requests (PRs) -* PRs should be used for review and discussion -* PRs should be reviewed in a timely manner to reduce effort in keeping them synced with other changes happening on the master branch - -Git commit messages should include a single summary sentence followed by a more explanatory paragraph. Note: all commit messages should reference the GitHub issue to which they relate. A nice discussion on the topic can be found `here `_. -:: - Implemented Globus data staging support - - Added the ability to reference and automatically transfer Globus-accessible - files. References are represented using the Parsl file format - “globus://endpoint/path/file.” If Globus endpoints are known for source and - destination, Parsl will use the Globus transfer service to move data to the - compute host. Fixes #-1. - -Git Hooks ---------- - -Developers may find it useful to set up a pre-commit git hook to automatically lint and run tests. This is a script that is run before each commit. For example:: - - $ cat ~/parsl/.git/hooks/pre-commit - #!/bin/sh - - make lint flake8 mypy local_thread_test - -Project Documentation ---------------------- - -All project documentation is written in reStructuredText. `Sphinx `_ is used to generate the HTML documentation from the rst documentation and structured docstrings in Parsl code. Project documentation is built automatically and added to the `Parsl documentation `_. - -Credit and Contributions ------------------------- - -Parsl wants to ensure that all contributors receive credit for their contributions. When you make your first contribution, you should update the codemeta.json file to include yourself as a contributor to the project. diff --git a/parsl/LICENSE b/parsl/LICENSE deleted file mode 100644 index 8dada3ed..00000000 --- a/parsl/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright {yyyy} {name of copyright owner} - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. diff --git a/parsl/MANIFEST.in b/parsl/MANIFEST.in deleted file mode 100644 index 9ab9c1c3..00000000 --- a/parsl/MANIFEST.in +++ /dev/null @@ -1,4 +0,0 @@ -include LICENSE -include parsl/monitoring/visualization/templates/* -include parsl/monitoring/visualization/static/* -include requirements.txt diff --git a/parsl/Makefile b/parsl/Makefile deleted file mode 100644 index f8eba682..00000000 --- a/parsl/Makefile +++ /dev/null @@ -1,137 +0,0 @@ -PYTHON := $(shell which python3 || echo ".python_is_missing") -SHELL := $(shell which bash) # Use bash instead of bin/sh as shell -GIT := $(shell which git || echo ".git_is_missing") -CWD := $(shell pwd) -DEPS := .deps -MPICH=mpich -OPENMPI=openmpi -export HYDRA_LAUNCHER=fork -export OMPI_MCA_rmaps_base_oversubscribe=yes -MPI=$(MPICH) - -.PHONY: help -help: ## me - @grep -E '^[0-9a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}' - -VENV = .venv -.PHONY: virtualenv -virtualenv: ## create an activate a virtual env - test -f $(VENV)/bin/activate || $(PYTHON) -m venv $(VENV) - echo "Run 'source $(VENV)/bin/activate' to activate the virtual environment" - - -$(DEPS): test-requirements.txt requirements.txt - pip3 install --upgrade pip - pip3 install -r test-requirements.txt -r requirements.txt - touch $(DEPS) - -.PHONY: deps -deps: $(DEPS) ## install the dependencies - -.PHONY: lint -lint: ## run linter script - parsl/tests/lint-inits.sh - -.PHONY: isort -isort: ## run isort on all files - isort --check parsl/ - -.PHONY: flake8 -flake8: ## run flake - flake8 parsl/ - -.PHONY: clean_coverage -clean_coverage: ## clear the coverage file (.coverage) - rm -f .coverage - -.PHONY: mypy -mypy: ## run mypy checks - MYPYPATH=$(CWD)/mypy-stubs mypy parsl/ - -.PHONY: gce_test -gce_test: ## Run tests with GlobusComputeExecutor (--config .../globus_compute.py) - pytest -v -k "not shared_fs and not issue_3620 and not staging_required" --config parsl/tests/configs/globus_compute.py parsl/tests/ --random-order --durations 10 - pytest -v -k "not shared_fs and not issue_3620 and not staging_required and globus_compute" --config local --random-order --durations 10 - -.PHONY: local_thread_test -local_thread_test: ## run all tests with local_thread config (--config .../local_threads.py) - pytest parsl/tests/ -k "not cleannet" --config parsl/tests/configs/local_threads.py --random-order --durations 10 - -.PHONY: htex_local_test -htex_local_test: ## run all tests with htex_local config (--config .../htex_local.py) - pip3 install . - pytest parsl/tests/ -k "not cleannet" --config parsl/tests/configs/htex_local.py --random-order --durations 10 - -.PHONY: htex_local_alternate_test -htex_local_alternate_test: ## run all tests with htex_local_alternate config (--config .../htex_local_alternate.py) - pip3 install ".[monitoring]" - pytest parsl/tests/ -k "not cleannet" --config parsl/tests/configs/htex_local_alternate.py --random-order --durations 10 - -.PHONY: vineex_local_test -vineex_local_test: ## Run the VineExecutor local tests (-k taskvine --config local) - pytest parsl/tests/ -k "not cleannet" --config parsl/tests/configs/taskvine_ex.py --random-order --durations 10 - pytest parsl/tests/ -k "not cleannet and taskvine" --config local --random-order --durations 10 - -.PHONY: wqex_local_test -wqex_local_test: ## Run the WorkQueueExecutor local tests (-k workqueue --config local) - pytest parsl/tests/ -k "not cleannet" --config parsl/tests/configs/workqueue_ex.py --random-order --durations 10 - pytest parsl/tests/ -k "not cleannet and workqueue" --config local --random-order --durations 10 - -.PHONY: radical_local_test -radical_local_test: ## Run the Radical local tests (-m radical --config local) - pip3 install ".[radical-pilot]" - mkdir -p ~/.radical/pilot/configs && echo '{"localhost": {"virtenv_mode": "local"}}' > ~/.radical/pilot/configs/resource_local.json - pytest parsl/tests/ -k "not cleannet and not issue3328 and not executor_supports_std_stream_tuples" --config parsl/tests/configs/local_radical.py --random-order --durations 10 - pytest parsl/tests/ -m "radical" --config local --random-order --durations 10 - -.PHONY: config_local_test -config_local_test: ## run the config-local tests (--config local) - pip3 install ".[monitoring,visualization,proxystore,kubernetes]" - pytest parsl/tests/ -k "not cleannet and not workqueue and not taskvine and not globus_compute" --config local --random-order --durations 10 - -.PHONY: site_test -site_test: ## Run the site tests - pytest parsl/tests/ -k "not cleannet" ${SHARED_FS_OPTIONS} --config parsl/tests/site_tests/site_config_selector.py --random-order - pytest parsl/tests/site_tests/ ${SHARED_FS_OPTIONS} --config local - -.PHONY: perf_test -perf_test: ## Run `parsl-perf` (--config .../local_threads.py) - parsl-perf --time 5 --config parsl/tests/configs/local_threads.py - parsl-perf --iterate 1,2,3 --config parsl/tests/configs/local_threads.py - -.PHONY: test ## run all tests with all config types -test: clean_coverage isort lint flake8 mypy local_thread_test htex_local_test htex_local_alternate_test config_local_test perf_test ## run all tests - -.PHONY: tag -tag: ## create a tag in git. to run, do a 'make VERSION="version string" tag - ./tag_and_release.sh create_tag $(VERSION) - -.PHONY: package -package: ## package up a distribution. - ./tag_and_release.sh package - -.PHONY: deploy -deploy: ## deploy the distribution - ./tag_and_release.sh release - -# THIS IS MEANT TO BE INVOKED BY GITHUB ACTIONS **ONLY** -.PHONY: update_version -update_version: ## Update version - ./tag_and_release.sh update_version - -.PHONY: release -release: deps tag package deploy ## create a release. To run, do a 'make VERSION="version string" release' - -.PHONY: coverage -coverage: ## show the coverage report - # coverage report - echo no-op coverage report - -.PHONY: clean -clean: ## clean up the environment by deleting the .venv, dist, eggs, mypy caches, coverage info, etc - rm -rf .venv $(DEPS) dist *.egg-info .mypy_cache build .pytest_cache .coverage runinfo $(WORKQUEUE_INSTALL) - -.PHONY: flux_local_test -flux_local_test: ## Test Parsl with Flux Executor - pip3 install . - pytest parsl/tests/ -k "not cleannet" --config parsl/tests/configs/flux_local.py --random-order --durations 10 diff --git a/parsl/README.rst b/parsl/README.rst deleted file mode 100644 index 08b6f877..00000000 --- a/parsl/README.rst +++ /dev/null @@ -1,136 +0,0 @@ -Parsl - Parallel Scripting Library -================================== -|licence| |docs| |NSF-1550588| |NSF-1550476| |NSF-1550562| |NSF-1550528| |NumFOCUS| |CZI-EOSS| |paper| - -Parsl extends parallelism in Python beyond a single computer. - -You can use Parsl -`just like Python's parallel executors `_ -but across *multiple cores and nodes*. -However, the real power of Parsl is in expressing multi-step workflows of functions. -Parsl lets you chain functions together and will launch each function as inputs and computing resources are available. - -.. code-block:: python - - import parsl - from parsl import python_app - - - # Make functions parallel by decorating them - @python_app - def f(x): - return x + 1 - - @python_app - def g(x, y): - return x + y - - # Start Parsl on a single computer - with parsl.load(): - # These functions now return Futures - future = f(1) - assert future.result() == 2 - - # Functions run concurrently, can be chained - f_a, f_b = f(2), f(3) - future = g(f_a, f_b) - assert future.result() == 7 - - -Start with the `configuration quickstart `_ to learn how to tell Parsl how to use your computing resource, -then explore the `parallel computing patterns `_ to determine how to use parallelism best in your application. - -.. |licence| image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg - :target: https://github.com/Parsl/parsl/blob/master/LICENSE - :alt: Apache Licence V2.0 -.. |docs| image:: https://readthedocs.org/projects/parsl/badge/?version=stable - :target: http://parsl.readthedocs.io/en/stable/?badge=stable - :alt: Documentation Status -.. |NSF-1550588| image:: https://img.shields.io/badge/NSF-1550588-blue.svg - :target: https://nsf.gov/awardsearch/showAward?AWD_ID=1550588 - :alt: NSF award info -.. |NSF-1550476| image:: https://img.shields.io/badge/NSF-1550476-blue.svg - :target: https://nsf.gov/awardsearch/showAward?AWD_ID=1550476 - :alt: NSF award info -.. |NSF-1550562| image:: https://img.shields.io/badge/NSF-1550562-blue.svg - :target: https://nsf.gov/awardsearch/showAward?AWD_ID=1550562 - :alt: NSF award info -.. |NSF-1550528| image:: https://img.shields.io/badge/NSF-1550528-blue.svg - :target: https://nsf.gov/awardsearch/showAward?AWD_ID=1550528 - :alt: NSF award info -.. |NSF-1550475| image:: https://img.shields.io/badge/NSF-1550475-blue.svg - :target: https://nsf.gov/awardsearch/showAward?AWD_ID=1550475 - :alt: NSF award info -.. |CZI-EOSS| image:: https://chanzuckerberg.github.io/open-science/badges/CZI-EOSS.svg - :target: https://czi.co/EOSS - :alt: CZI's Essential Open Source Software for Science -.. |NumFOCUS| image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A - :target: https://numfocus.org - :alt: Powered by NumFOCUS -.. |paper| image:: https://img.shields.io/badge/Software_DOI-10.1145/3307681.3325400-blue - :target: https://doi.org/10.1145/3307681.3325400 - :alt: Conference proceedings paper - - -Quickstart -========== - -Install Parsl using pip:: - - $ pip3 install parsl - -To run the Parsl tutorial notebooks you will need to install Jupyter:: - - $ pip3 install jupyter - -Detailed information about setting up Jupyter with Python is available `here `_ - -Note: Parsl uses an opt-in model to collect usage statistics for reporting and improvement purposes. To understand what stats are collected and enable collection please refer to the `usage tracking guide `__ - -Documentation -============= - -The complete parsl documentation is hosted `here `_. - -The Parsl tutorial is hosted on live Jupyter notebooks `here `_ - - -For Developers --------------- - -1. Download Parsl:: - - $ git clone https://github.com/Parsl/parsl - - -2. Build and Test:: - - $ cd parsl # navigate to the root directory of the project - $ make # show all available makefile targets - $ make virtualenv # create a virtual environment - $ source .venv/bin/activate # activate the virtual environment - $ make deps # install python dependencies from test-requirements.txt - $ make test # make (all) tests. Run "make config_local_test" for a faster, smaller test set. - $ make clean # remove virtualenv and all test and build artifacts - -3. Install:: - - $ cd parsl # only if you didn't enter the top-level directory in step 2 above - $ python3 setup.py install - -4. Use Parsl! - -Requirements -============ - -Parsl is supported in Python 3.10+. Requirements can be found `here `_. Requirements for running tests can be found `here `_. - -Code of Conduct -=============== - -Parsl seeks to foster an open and welcoming environment - Please see the `Parsl Code of Conduct `_ for more details. - -Contributing -============ - -We welcome contributions from the community. Please see our `contributing guide `_. diff --git a/parsl/codemeta.json b/parsl/codemeta.json deleted file mode 100644 index 71ed1f90..00000000 --- a/parsl/codemeta.json +++ /dev/null @@ -1,198 +0,0 @@ -{ - "@context": [ - "https://doi.org/10.5063/schema/codemeta-2.0", - "http://schema.org" - ], - "@type": "SoftwareSourceCode", - "identifier": "parsl", - "name": "parsl", - "version": "0.6.1", - "description": "Simple data dependent workflows in Python", - "license": "OSI Approved :: Apache Software License", - "author": [ - { - "@id":"http://orcid.org/0000-0002-9162-6003", - "@type": "Person", - "givenName": "Yadu", - "familyName": "Nand Babuji", - "email": "yadu@uchicago.edu" - }, - { - "@id":"http://orcid.org/0000-0002-8640-5417", - "@type": "Person", - "givenName": "Anna", - "familyName": "Woodard", - "email": "annawoodard@uchicago.edu" - }, - { - "@id":"http://orcid.org/0000-0002-7370-4805", - "@type": "Person", - "givenName": "Kyle", - "familyName": "Chard", - "email": "chard@uchicago.edu" - }, - { - "@id":"http://orcid.org/0000-0001-5934-7525", - "@type": "Person", - "givenName": "Daniel S.", - "familyName": "Katz", - "email": "d.katz@ieee.org" - }, - { - "@id":"http://orcid.org/0000-0000-0000-0000", - "@type": "Person", - "givenName": "Michael", - "familyName": "Wilde", - "email": "wilde@parallelworks.com" - }, - { - "@id":"http://orcid.org/0000-0000-0000-0000", - "@type": "Person", - "givenName": "Justin", - "familyName": "Wozniak", - "email": "woz@anl.gov" - }, - { - "@id":"http://orcid.org/0000-0000-0000-0000", - "@type": "Person", - "givenName": "Ian", - "familyName": "Foster", - "email": "foster@anl.gov" - }, - { - "@id":"http://orcid.org/0000-0002-0762-2684", - "@type": "Person", - "givenName": "Ben", - "familyName": "Glick", - "email": "glick@lclark.edu" - }, - { - "@id":"https://orcid.org/0000-0001-5147-0051", - "@type": "Person", - "givenName": "Kelly L.", - "familyName": "Rowland", - "email": "kellyrowland@lbl.gov" - }, - { - "@id":"https://orcid.org/0000-0001-7774-2246", - "@type": "Person", - "givenName": "Matthew R.", - "familyName": "Becker", - "email": "becker.mr@gmail.com" - } - ], - "softwareRequirements": [ - { - "@type": "SoftwareApplication", - "identifier": "cmreshandler", - "name": "CMRESHandler", - "provider": { - "@id": "https://pypi.org", - "@type": "Organization", - "name": "The Python Package Index", - "url": "https://pypi.org" - }, - "runtimePlatform": "Python 3.6.4" - }, - { - "@type": "SoftwareApplication", - "identifier": "ipykernel", - "name": "ipykernel", - "provider": { - "@id": "https://pypi.org", - "@type": "Organization", - "name": "The Python Package Index", - "url": "https://pypi.org" - }, - "runtimePlatform": "Python 3.6.4" - }, - { - "@type": "SoftwareApplication", - "identifier": "psutil", - "name": "psutil", - "provider": { - "@id": "https://pypi.org", - "@type": "Organization", - "name": "The Python Package Index", - "url": "https://pypi.org" - }, - "runtimePlatform": "Python 3.6.4" - }, - { - "@type": "SoftwareApplication", - "identifier": "dill", - "name": "dill", - "provider": { - "@id": "https://pypi.org", - "@type": "Organization", - "name": "The Python Package Index", - "url": "https://pypi.org" - }, - "runtimePlatform": "Python 3.6.4" - }, - { - "@type": "SoftwareApplication", - "identifier": "globus-sdk", - "name": "globus-sdk", - "provider": { - "@id": "https://pypi.org", - "@type": "Organization", - "name": "The Python Package Index", - "url": "https://pypi.org" - }, - "runtimePlatform": "Python 3.6.4" - }, - { - "@type": "SoftwareApplication", - "identifier": "requests", - "name": "requests", - "provider": { - "@id": "https://pypi.org", - "@type": "Organization", - "name": "The Python Package Index", - "url": "https://pypi.org" - }, - "runtimePlatform": "Python 3.6.4" - }, - { - "@type": "SoftwareApplication", - "identifier": "tblib", - "name": "tblib", - "provider": { - "@id": "https://pypi.org", - "@type": "Organization", - "name": "The Python Package Index", - "url": "https://pypi.org" - }, - "runtimePlatform": "Python 3.6.4" - }, - { - "@type": "SoftwareApplication", - "identifier": "nbsphinx", - "name": "nbsphinx", - "provider": { - "@id": "https://pypi.org", - "@type": "Organization", - "name": "The Python Package Index", - "url": "https://pypi.org" - }, - "runtimePlatform": "Python 3.6.4" - } - ], - "audience": [ - { - "@type": "Audience", - "audienceType": "Developers" - } - ], - "provider": { - "@id": "https://pypi.org", - "@type": "Organization", - "name": "The Python Package Index", - "url": "https://pypi.org" - }, - "runtimePlatform": "Python 3.10", - "url": "https://github.com/Parsl/parsl", - "developmentStatus": "active", - "programmingLanguage": "Python :: 3.10" -} diff --git a/parsl/docs/Makefile b/parsl/docs/Makefile deleted file mode 100644 index 6e79b169..00000000 --- a/parsl/docs/Makefile +++ /dev/null @@ -1,224 +0,0 @@ -# Makefile for Sphinx documentation - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = python3 -m sphinx -PAPER = -BUILDDIR = _build - -# Internal variables. -PAPEROPT_a4 = -D latex_paper_size=a4 -PAPEROPT_letter = -D latex_paper_size=letter -ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . -# the i18n builder cannot share the environment and doctrees with the others -I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . - -.PHONY: help -help: - @echo "Please use \`make ' where is one of" - @echo " html to make standalone HTML files" - @echo " dirhtml to make HTML files named index.html in directories" - @echo " singlehtml to make a single large HTML file" - @echo " pickle to make pickle files" - @echo " json to make JSON files" - @echo " htmlhelp to make HTML files and a HTML help project" - @echo " qthelp to make HTML files and a qthelp project" - @echo " applehelp to make an Apple Help Book" - @echo " devhelp to make HTML files and a Devhelp project" - @echo " epub to make an epub" - @echo " epub3 to make an epub3" - @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" - @echo " latexpdf to make LaTeX files and run them through pdflatex" - @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" - @echo " text to make text files" - @echo " man to make manual pages" - @echo " texinfo to make Texinfo files" - @echo " info to make Texinfo files and run them through makeinfo" - @echo " gettext to make PO message catalogs" - @echo " changes to make an overview of all changed/added/deprecated items" - @echo " xml to make Docutils-native XML files" - @echo " pseudoxml to make pseudoxml-XML files for display purposes" - @echo " linkcheck to check all external links for integrity" - @echo " doctest to run all doctests embedded in the documentation (if enabled)" - @echo " coverage to run coverage check of the documentation (if enabled)" - @echo " dummy to check syntax errors of document sources" - -.PHONY: clean -clean: - rm -rf $(BUILDDIR)/* - -.PHONY: html -html: - $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html - @echo - @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." - -.PHONY: dirhtml -dirhtml: - $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml - @echo - @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." - -.PHONY: singlehtml -singlehtml: - $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml - @echo - @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." - -.PHONY: pickle -pickle: - $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle - @echo - @echo "Build finished; now you can process the pickle files." - -.PHONY: json -json: - $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json - @echo - @echo "Build finished; now you can process the JSON files." - -.PHONY: htmlhelp -htmlhelp: - $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp - @echo - @echo "Build finished; now you can run HTML Help Workshop with the" \ - ".hhp project file in $(BUILDDIR)/htmlhelp." - -.PHONY: qthelp -qthelp: - $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp - @echo - @echo "Build finished; now you can run "qcollectiongenerator" with the" \ - ".qhcp project file in $(BUILDDIR)/qthelp, like this:" - @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/Parsl.qhcp" - @echo "To view the help file:" - @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/Parsl.qhc" - -.PHONY: applehelp -applehelp: - $(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp - @echo - @echo "Build finished. The help book is in $(BUILDDIR)/applehelp." - @echo "N.B. You won't be able to view it unless you put it in" \ - "~/Library/Documentation/Help or install it in your application" \ - "bundle." - -.PHONY: devhelp -devhelp: - $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp - @echo - @echo "Build finished." - @echo "To view the help file:" - @echo "# mkdir -p $$HOME/.local/share/devhelp/Parsl" - @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/Parsl" - @echo "# devhelp" - -.PHONY: epub -epub: - $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub - @echo - @echo "Build finished. The epub file is in $(BUILDDIR)/epub." - -.PHONY: epub3 -epub3: - $(SPHINXBUILD) -b epub3 $(ALLSPHINXOPTS) $(BUILDDIR)/epub3 - @echo - @echo "Build finished. The epub3 file is in $(BUILDDIR)/epub3." - -.PHONY: latex -latex: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo - @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." - @echo "Run \`make' in that directory to run these through (pdf)latex" \ - "(use \`make latexpdf' here to do that automatically)." - -.PHONY: latexpdf -latexpdf: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo "Running LaTeX files through pdflatex..." - $(MAKE) -C $(BUILDDIR)/latex all-pdf - @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." - -.PHONY: latexpdfja -latexpdfja: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo "Running LaTeX files through platex and dvipdfmx..." - $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja - @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." - -.PHONY: text -text: - $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text - @echo - @echo "Build finished. The text files are in $(BUILDDIR)/text." - -.PHONY: man -man: - $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man - @echo - @echo "Build finished. The manual pages are in $(BUILDDIR)/man." - -.PHONY: texinfo -texinfo: - $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo - @echo - @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." - @echo "Run \`make' in that directory to run these through makeinfo" \ - "(use \`make info' here to do that automatically)." - -.PHONY: info -info: - $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo - @echo "Running Texinfo files through makeinfo..." - make -C $(BUILDDIR)/texinfo info - @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." - -.PHONY: gettext -gettext: - $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale - @echo - @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." - -.PHONY: changes -changes: - $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes - @echo - @echo "The overview file is in $(BUILDDIR)/changes." - -.PHONY: linkcheck -linkcheck: - $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck - @echo - @echo "Link check complete; look for any errors in the above output " \ - "or in $(BUILDDIR)/linkcheck/output.txt." - -.PHONY: doctest -doctest: - $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest - @echo "Testing of doctests in the sources finished, look at the " \ - "results in $(BUILDDIR)/doctest/output.txt." - -.PHONY: coverage -coverage: - $(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage - @echo "Testing of coverage in the sources finished, look at the " \ - "results in $(BUILDDIR)/coverage/python.txt." - -.PHONY: xml -xml: - $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml - @echo - @echo "Build finished. The XML files are in $(BUILDDIR)/xml." - -.PHONY: pseudoxml -pseudoxml: - $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml - @echo - @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." - -.PHONY: dummy -dummy: - $(SPHINXBUILD) -b dummy $(ALLSPHINXOPTS) $(BUILDDIR)/dummy - @echo - @echo "Build finished. Dummy builder generates no files." diff --git a/parsl/docs/README.rst b/parsl/docs/README.rst deleted file mode 100644 index f50c03f3..00000000 --- a/parsl/docs/README.rst +++ /dev/null @@ -1,40 +0,0 @@ -Doc Docs -======== - -Documentation location ----------------------- - -Documentation is maintained in Python docstrings throughout the code. These are imported via the -`autodoc `_ Sphinx extension in -``docs/reference.rst``. Individual stubs for user-facing classes (located in ``stubs``) are -generated automatically via sphinx-autogen. Parsl modules, classes, and methods can be -cross-referenced from a docstring by enclosing it in backticks (\`). - -Remote builds -------------- - -Builds are automatically performed by readthedocs.io and published to parsl.readthedocs.io -upon git commits. - -Local builds ------------- - -To build the documentation locally, use:: - - $ make clean html - -To view the freshly rebuilt docs, use:: - - $ cd _build/html - $ python3 -m http.server 8080 - -Once the python http server is launched, point your browser to `http://localhost:8080 `_ - - -Regenerate module stubs --------------------------- - -If necessary, docstring stubs can be regenerated using:: - - $ sphinx-autogen reference.rst - diff --git a/parsl/docs/conf.py b/parsl/docs/conf.py deleted file mode 100644 index 3c448bd3..00000000 --- a/parsl/docs/conf.py +++ /dev/null @@ -1,377 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -# -# Parsl documentation build configuration file, created by -# sphinx-quickstart on Mon Feb 20 16:35:17 2017. -# -# This file is execfile()d with the current directory set to its -# containing dir. -# -# Note that not all possible configuration values are present in this -# autogenerated file. -# -# All configuration values have a default; values that are commented out -# serve to show the default. - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -import os -import sys - -import requests - -sys.path.insert(0, os.path.abspath('../')) -import parsl - -# -- General configuration ------------------------------------------------ - -# If your documentation needs a minimal Sphinx version, state it here. -# -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - 'nbsphinx', - 'sphinx.ext.autodoc', - 'sphinx.ext.autosummary', - 'sphinx.ext.intersphinx', - 'sphinx.ext.linkcode', - 'sphinx.ext.napoleon' -] - -url = 'https://raw.githubusercontent.com/Parsl/parsl-tutorial/master/1-parsl-introduction.ipynb' -r = requests.get(url) -with open(os.path.join(os.path.dirname(__file__), '1-parsl-introduction.ipynb'), 'wb') as f: - f.write(r.content) - -nbsphinx_execute = 'never' - -def linkcode_resolve(domain, info): - if domain != 'py': - return None - if not info['module']: - return None - filename = info['module'].replace('.', '/') - return "http://github.com/Parsl/parsl/blob/master/{}.py".format(filename) - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -intersphinx_mapping = { - 'python': ('https://docs.python.org/3', None), -} -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# -# source_suffix = ['.rst', '.md'] -source_suffix = '.rst' - -# The encoding of source files. -# -# source_encoding = 'utf-8-sig' - -# The master toctree document. -master_doc = 'index' - -# General information about the project. -project = 'Parsl' -copyright = '2018--2020, Parsl Project' -author = 'Parsl Project' - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. -version = parsl.__version__.rsplit('.', 1)[0] -# The full version, including alpha/beta/rc tags. -release = parsl.__version__ - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = 'en' - -# There are two options for replacing |today|: either, you set today to some -# non-false value, then it is used: -# -# today = '' -# -# Else, today_fmt is used as the format for a strftime call. -# -# today_fmt = '%B %d, %Y' - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This patterns also effect to html_static_path and html_extra_path -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] - -# The reST default role (used for this markup: `text`) to use for all -# documents. -# -default_role = 'any' - -# If true, '()' will be appended to :func: etc. cross-reference text. -# -# add_function_parentheses = True - -# If true, the current module name will be prepended to all description -# unit titles (such as .. function::). -# -# add_module_names = True - -# If true, sectionauthor and moduleauthor directives will be shown in the -# output. They are ignored by default. -# -# show_authors = False - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - -# A list of ignored prefixes for module index sorting. -# modindex_common_prefix = [] - -# If true, keep warnings as "system message" paragraphs in the built documents. -# keep_warnings = False - -# If true, `todo` and `todoList` produce output, else they produce nothing. -todo_include_todos = False - - -# -- Options for HTML output ---------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -#html_theme = 'alabaster' -html_theme = 'sphinx_rtd_theme' - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# -# html_theme_options = {} - -# Add any paths that contain custom themes here, relative to this directory. -# html_theme_path = [] - -# The name for this set of Sphinx documents. -# " v documentation" by default. -# -# html_title = 'Parsl v0.1' - -# A shorter title for the navigation bar. Default is the same as html_title. -# -# html_short_title = None - -# The name of an image file (relative to this directory) to place at the top -# of the sidebar. -# -# html_logo = None - -# The name of an image file (relative to this directory) to use as a favicon of -# the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 -# pixels large. -# -# html_favicon = None - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = [] - -# Add any extra paths that contain custom files (such as robots.txt or -# .htaccess) here, relative to this directory. These files are copied -# directly to the root of the documentation. -# -# html_extra_path = [] - -# If not None, a 'Last updated on:' timestamp is inserted at every page -# bottom, using the given strftime format. -# The empty string is equivalent to '%b %d, %Y'. -# -# html_last_updated_fmt = None - -# If true, SmartyPants will be used to convert quotes and dashes to -# typographically correct entities. -# -# html_use_smartypants = True - -# Custom sidebar templates, maps document names to template names. -# -# html_sidebars = {} - -# Additional templates that should be rendered to pages, maps page names to -# template names. -# -# html_additional_pages = {} - -# If false, no module index is generated. -# -# html_domain_indices = True - -# If false, no index is generated. -# -# html_use_index = True - -# If true, the index is split into individual pages for each letter. -# -# html_split_index = False - -# If true, links to the reST sources are added to the pages. -# -# html_show_sourcelink = True - -# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -# -# html_show_sphinx = True - -# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -# -# html_show_copyright = True - -# If true, an OpenSearch description file will be output, and all pages will -# contain a tag referring to it. The value of this option must be the -# base URL from which the finished HTML is served. -# -# html_use_opensearch = '' - -# This is the file name suffix for HTML files (e.g. ".xhtml"). -# html_file_suffix = None - -# Language to be used for generating the HTML full-text search index. -# Sphinx supports the following languages: -# 'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja' -# 'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr', 'zh' -# -# html_search_language = 'en' - -# A dictionary with options for the search language support, empty by default. -# 'ja' uses this config value. -# 'zh' user can custom change `jieba` dictionary path. -# -# html_search_options = {'type': 'default'} - -# The name of a javascript file (relative to the configuration directory) that -# implements a search results scorer. If empty, the default will be used. -# -# html_search_scorer = 'scorer.js' - -# Output file base name for HTML help builder. -htmlhelp_basename = 'Parsldoc' - -# -- Options for LaTeX output --------------------------------------------- - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - # - # 'papersize': 'letterpaper', - - # The font size ('10pt', '11pt' or '12pt'). - # - # 'pointsize': '10pt', - - # Additional stuff for the LaTeX preamble. - # - # 'preamble': '', - - # Latex figure (float) alignment - # - # 'figure_align': 'htbp', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - (master_doc, 'Parsl.tex', 'Parsl Documentation', - 'The Parsl Team', 'manual'), -] - -# The name of an image file (relative to this directory) to place at the top of -# the title page. -# -# latex_logo = None - -# For "manual" documents, if this is true, then toplevel headings are parts, -# not chapters. -# -# latex_use_parts = False - -# If true, show page references after internal links. -# -# latex_show_pagerefs = False - -# If true, show URL addresses after external links. -# -# latex_show_urls = False - -# Documents to append as an appendix to all manuals. -# -# latex_appendices = [] - -# It false, will not define \strong, \code, itleref, \crossref ... but only -# \sphinxstrong, ..., \sphinxtitleref, ... To help avoid clash with user added -# packages. -# -# latex_keep_old_macro_names = True - -# If false, no module index is generated. -# -# latex_domain_indices = True - - -# -- Options for manual page output --------------------------------------- - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, 'parsl', 'Parsl Documentation', - [author], 1) -] - -# If true, show URL addresses after external links. -# -# man_show_urls = False - - -# -- Options for Texinfo output ------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - (master_doc, 'Parsl', 'Parsl Documentation', - author, 'Parsl', 'One line description of project.', - 'Miscellaneous'), -] - -# Documents to append as an appendix to all manuals. -# -# texinfo_appendices = [] - -# If false, no module index is generated. -# -# texinfo_domain_indices = True - -# How to display URL addresses: 'footnote', 'no', or 'inline'. -# -# texinfo_show_urls = 'footnote' - -# If true, do not generate a @detailmenu in the "Top" node's menu. -# -# texinfo_no_detailmenu = False - -autosummary_generate = True - -autodoc_default_options = { - 'members': True, - 'undoc-members': True -} -autodoc_preserve_defaults = True diff --git a/parsl/docs/devguide/contributing.rst b/parsl/docs/devguide/contributing.rst deleted file mode 100644 index 638677d3..00000000 --- a/parsl/docs/devguide/contributing.rst +++ /dev/null @@ -1,6 +0,0 @@ -Contributing -==================== - -Parsl is an open source project that welcomes contributions from the community. - -If you're interested in contributing, please review our `contributing guide `_. diff --git a/parsl/docs/devguide/index.rst b/parsl/docs/devguide/index.rst deleted file mode 100644 index ec99d421..00000000 --- a/parsl/docs/devguide/index.rst +++ /dev/null @@ -1,10 +0,0 @@ -Developer documentation -======================= - -.. toctree:: - :maxdepth: 3 - - contributing - roadmap - packaging - ../README diff --git a/parsl/docs/devguide/packaging.rst b/parsl/docs/devguide/packaging.rst deleted file mode 100644 index 9cd37c0a..00000000 --- a/parsl/docs/devguide/packaging.rst +++ /dev/null @@ -1,46 +0,0 @@ -Packaging ---------- - -Currently packaging is managed by @annawoodard and @yadudoc. - -Steps to release - -1. Update the version number in ``parsl/parsl/version.py`` -2. Check the following files to confirm new release information - * ``parsl/setup.py`` - * ``requirements.txt`` - * ``parsl/docs/devguide/changelog.rst`` - * ``parsl/README.rst`` - -3. Commit and push the changes to github -4. Run the ``tag_and_release.sh`` script. This script will verify that - version number matches the version specified. - - .. code:: bash - - ./tag_and_release.sh - - -Here are the steps that is taken by the ``tag_and_release.sh`` script: - -.. code:: bash - - # Create a new git tag : - git tag .. - # Push tag to github : - git push origin - - # Depending on permission all of the following might have to be run as root. - sudo su - - # Make sure to have twine installed - pip3 install twine - - # Create a source distribution - python3 setup.py sdist - - # Create a wheel package, which is a prebuilt package - python3 setup.py bdist_wheel - - # Upload the package with twine - twine upload dist/* diff --git a/parsl/docs/devguide/roadmap.rst b/parsl/docs/devguide/roadmap.rst deleted file mode 100644 index ccb5abce..00000000 --- a/parsl/docs/devguide/roadmap.rst +++ /dev/null @@ -1,51 +0,0 @@ -Roadmap -======= - -**OVERVIEW** - -While we follow best practices in software development processes (e.g., CI, flake8, code review), there are opportunities to make our code more maintainable and accessible. This roadmap, written in the fall of 2023, covers our major activities planned through 2025 to increase efficiency, productivity, user experience, and community building. - -Features and improvements are documented via GitHub -`issues `_ and `pull requests `_. - - -Code Maintenance ----------------- - -* **Type Annotations and Static Type Checking**: Add static type annotations throughout the codebase and add typeguard checks. -* **Release Process**: `Improve the overall release process `_ to synchronize docs and code releases, automatically produce changelog documentation. -* **Components Maturity Model**: Defines the `component maturity model `_ and tags components with their appropriate maturity level. -* **Define and Document Interfaces**: Identify and document interfaces via which `external components `_ can augment the Parsl ecosystem. -* **Distributed Testing Process**: All tests should be run against all possible schedulers, using different executors, on a variety of remote systems. Explore the use of containerized schedulers and remote testing on real systems. - -New Features and Integrations ------------------------------ - -* **Enhanced MPI Support**: Extend Parsl’s MPI model with MPI apps and runtime support capable of running MPI apps in different environments (MPI flavor and launcher). -* **Serialization Configuration**: Enable users to select what serialization methods are used and enable users to supply their own serializer. -* **PSI/J integration**: Integrate PSI/J as a common interface for schedulers. -* **Internal Concurrency Model**: Revisit and rearchitect the concurrency model to reduce areas that are not well understood and reduce the likelihood of errors. -* **Common Model for Errors**: Make Parsl errors self-describing and understandable by users. -* **Plug-in Model for External Components**: Extend Parsl to implement interfaces defined above. -* **User Configuration Validation Tool**: Provide tooling to help users configure Parsl and diagnose and resolve errors. -* **Anonymized Usage Tracking**: Usage tracking is crucial for our data-oriented approach to understand the adoption of Parsl, which components are used, and where errors occur. This allows us to prioritize investment in components, progress components through the maturity levels, and identify bugs. Revisit prior usage tracking and develop a service that enables users to control tracking information. -* **Support for Globus Compute**: Enable execution of Parsl tasks using Globus Compute as an executor. -* **Update Globus Data Management**: Update Globus integration to use the new Globus Connect v5 model (i.e., needing specific scopes for individual endpoints). -* **Performance Measurement**: Improve ability to measure performance metrics and report to users. -* **Enhanced Debugging**: Application-level `logging `_ to understand app execution. - -Tutorials, Training, and User Support -------------------------------------- - -* **Configuration and Debugging**: Tutorials showing how to configure Parsl for different resources and debug execution. -* **Functional Serialization 101**: Tutorial describing how serialization works and how you can integrate custom serializers. -* **ProxyStore Data Management**: Tutorial showing how you can use ProxyStore to manage data for both inter and intra-site scenarios. -* **Open Dev Calls on Zoom**: The internal core team holds an open dev call/office hours every other Thursday to help users troubleshoot issues, present and share their work, connect with each other, and provide community updates. -* **Project Documentation**: is maintained and updated in `Read the Docs `_. - -Longer-term Objectives ----------------------- - -* **Globus Compute Integration**: Once Globus Compute supports multi-tenancy, Parsl will be able to use it to run remote tasks on initially one and then later multiple resources. -* **Multi-System Optimization**: Once Globus Compute integration is complete, it is best to use multiple systems for multiple tasks as part of a single workflow. -* **HPC Checkpointing and Job Migration**: As new resources become available, HPC tasks will be able to be checkpointed and moved to the system with more resources. diff --git a/parsl/docs/faq.rst b/parsl/docs/faq.rst deleted file mode 100644 index ea498861..00000000 --- a/parsl/docs/faq.rst +++ /dev/null @@ -1,380 +0,0 @@ -FAQ ---- - -How can I debug a Parsl script? -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Parsl interfaces with the Python logger and automatically logs Parsl-related messages a ``runinfo`` directory. -The ``runinfo`` directory will be created in the folder from which you run the Parsl script -and it will contain a series of subfolders for each time you run the code. -Your latest run will be the largest number. - -Alternatively, you can configure the file logger to write to an output file. - -.. code-block:: python - - import logging - import parsl - - # Emit log lines to the screen - parsl.set_stream_logger() - - # Write log to file, specify level of detail for logs - parsl.set_file_logger(FILENAME, level=logging.DEBUG) - -.. note:: - Parsl's logging will not capture STDOUT/STDERR from the apps themselves. - Follow instructions below for application logs. - - -How can I view outputs and errors from apps? -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Parsl apps include keyword arguments for capturing stderr and stdout in files. - -.. code-block:: python - - @bash_app - def hello(msg, stdout=None): - return 'echo {}'.format(msg) - - # When hello() runs the STDOUT will be written to 'hello.txt' - hello('Hello world', stdout='hello.txt') - -How can I make an App dependent on multiple inputs? -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -You can pass any number of futures in to a single App either as positional arguments -or as a list of futures via the special keyword ``inputs=()``. -The App will wait for all inputs to be satisfied before execution. - - -Can I pass any Python object between apps? -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -This depends on the executor in use. The :py:class:`parsl.executors.threads.ThreadPoolExecutor` -can receive and return any Python object. Other executors will serialize their -parameters and return values, so only objects which Parsl knows how to -serialize can be passed. - -Parsl knows how to serialize objects using the Pickle and Dill -libraries. - -Pickle provides a list of objects that it knows how to serialize: -`What can be pickled and unpickled? `_. - -Dill can serialize much more than Pickle, documented in the -`dill documentation `_. - -For objects that can't be pickled, use object specific methods -to write the object into a file and use files to communicate between apps. - -How do I specify where apps should be run? -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Parsl's multi-executor support allows you to define the executor (including local threads) -on which an App should be executed. For example: - -.. code-block:: python - - @python_app(executors=['SuperComputer1']) - def BigSimulation(...): - ... - - @python_app(executors=['GPUMachine']) - def Visualize (...) - ... - -Workers do not connect back to Parsl -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -If you are running via ssh to a remote system from your local machine, or from the -login node of a cluster/supercomputer, it is necessary to have a public IP to which -the workers can connect back. While our remote execution systems can identify the -IP address automatically in certain cases, it is safer to specify the address explicitly. -Parsl provides a few heuristic based address resolution methods that could be useful, -however with complex networks some trial and error might be necessary to find the -right address or network interface to use. - - - -For `parsl.executors.HighThroughputExecutor` the address is specified in the :class:`~parsl.config.Config` -as shown below : - -.. code-block:: python - - # THIS IS A CONFIG FRAGMENT FOR ILLUSTRATION - from parsl.config import Config - from parsl.executors import HighThroughputExecutor - from parsl.addresses import address_by_route, address_by_query, address_by_hostname - config = Config( - executors=[ - HighThroughputExecutor( - label='ALCF_theta_local', - address='' # specify public ip here - # address=address_by_route() # Alternatively you can try this - # address=address_by_query() # Alternatively you can try this - # address=address_by_hostname() # Alternatively you can try this - ) - ], - ) - - -.. note:: - Another possibility that can cause workers not to connect back to Parsl is an incompatibility between - the system and the pre-compiled bindings used for pyzmq. As a last resort, you can try: - ``pip install --upgrade --no-binary pyzmq pyzmq``, which forces re-compilation. - -For the `parsl.executors.HighThroughputExecutor`, ``address`` is a keyword argument -taken at initialization. Here is an example for the `parsl.executors.HighThroughputExecutor`: - -.. code-block:: python - - # THIS IS A CONFIG FRAGMENT FOR ILLUSTRATION - from parsl.config import Config - from parsl.executors import HighThroughputExecutor - from parsl.addresses import address_by_route, address_by_query, address_by_hostname - - config = Config( - executors=[ - HighThroughputExecutor( - label='NERSC_Cori', - address='' # specify public ip here - # address=address_by_route() # Alternatively you can try this - # address=address_by_query() # Alternatively you can try this - # address=address_by_hostname() # Alternatively you can try this - ) - ], - ) - - -.. note:: - On certain systems such as the Midway RCC cluster at UChicago, some network interfaces have an active - intrusion detection system that drops connections that persist beyond a specific duration (~20s). - If you get repeated ``ManagerLost`` exceptions, it would warrant taking a closer look at networking. - -.. _pyversion: - -parsl.errors.ConfigurationError -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The Parsl configuration model underwent a major and non-backward compatible change in the transition to v0.6.0. -Prior to v0.6.0 the configuration object was a python dictionary with nested dictionaries and lists. -The switch to a class based configuration allowed for well-defined options for each specific component being -configured as well as transparency on configuration defaults. The following traceback indicates that the old -style configuration was passed to Parsl v0.6.0+ and requires an upgrade to the configuration. - -.. code-block:: - - File "/home/yadu/src/parsl/parsl/dataflow/dflow.py", line 70, in __init__ - 'Expected `Config` class, received dictionary. For help, ' - parsl.errors.ConfigurationError: Expected `Config` class, received dictionary. For help, - see http://parsl.readthedocs.io/en/stable/stubs/parsl.config.Config.html - -For more information on how to update your configuration script, please refer to: -:ref:`configuration-section`. - - -Remote execution fails with SystemError(unknown opcode) -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -When running with Ipyparallel workers, it is important to ensure that the Python version -on the client side matches that on the side of the workers. If there's a mismatch, -the apps sent to the workers will fail with the following error: -``ipyparallel.error.RemoteError: SystemError(unknown opcode)`` - -.. caution:: - It is **required** that both the parsl script and all workers are set to use python - with the same Major.Minor version numbers. For example, use Python3.5.X on both local - and worker side. - -Parsl complains about missing packages -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -If ``parsl`` is cloned from a Github repository and added to the ``PYTHONPATH``, it is -possible to miss the installation of some dependent libraries. In this configuration, -``parsl`` will raise errors such as: - -``ModuleNotFoundError: No module named 'ipyparallel'`` - -You should usually install parsl using a package managment tool such as ``pip`` or -``conda``, ideally in a restricted environment such a virtualenv or a conda -environment. - -For instance, with conda, follow this `cheatsheet `_ to create a virtual environment: - -.. code-block:: bash - - # Activate an environmentconda install - source activate - - # Install packages: - conda install - - -How do I run code that uses Python2.X? -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Modules or code that require Python2.X cannot be run as python apps, -however they may be run via bash apps. The primary limitation with -python apps is that all the inputs and outputs including the function -would be mangled when being transmitted between python interpreters with -different version numbers (also see :ref:`pyversion`) - -Here is an example of running a python2.7 code as a bash application: - -.. code-block:: python - - @bash_app - def python_27_app (arg1, arg2 ...): - return '''conda activate py2.7_env # Use conda to ensure right env - python2.7 my_python_app.py -arg {0} -d {1} - '''.format(arg1, arg2) - -Parsl hangs -^^^^^^^^^^^ - -There are a few common situations in which a Parsl script might hang: - -1. Circular Dependency in code: - If an ``app`` takes a list as an ``input`` argument and the future returned - is added to that list, it creates a circular dependency that cannot be resolved. - This situation is described in `issue 59 `_ in more detail. - -2. Workers requested are unable to contact the Parsl client due to one or - more issues listed below: - - * Parsl client does not have a public IP (e.g. laptop on wifi). - If your network does not provide public IPs, the simple solution is to - ssh over to a machine that is public facing. Machines provisioned from - cloud-vendors setup with public IPs are another option. - - * Parsl hasn't autodetected the public IP. See `Workers do not connect back to Parsl`_ for more details. - - * Firewall restrictions that block certain port ranges. - If there is a certain port range that is **not** blocked, you may specify - that via configuration: - - .. code-block:: python - - from parsl.config import Config - from parsl.providers import SlurmProvider - from parsl.executors import HighThroughputExecutor - - config = Config( - executors=[ - HighThroughputExecutor( - label='htex', - provider=SlurmProvider(), - worer_port_range=('50000,55000'), - interchange_port_range=('50000,55000') - ) - ], - ) - - -How can I start a Jupyter notebook over SSH? -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Run - -.. code-block:: bash - - jupyter notebook --no-browser --ip=`/sbin/ip route get 8.8.8.8 | awk '{print $NF;exit}'` - -for a Jupyter notebook, or - -.. code-block:: bash - - jupyter lab --no-browser --ip=`/sbin/ip route get 8.8.8.8 | awk '{print $NF;exit}'` - -for Jupyter lab (recommended). If that doesn't work, see `these instructions `_. - -How can I sync my conda environment and Jupyter environment? -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Run:: - - conda install nb_conda - -Now all available conda environments (for example, one created by following the instructions `in the quickstart guide `_) will automatically be added to the list of kernels. - -.. _label_serialization_error: - -Addressing SerializationError -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -As of v1.0.0, Parsl will raise a `SerializationError` when it encounters an object that Parsl cannot serialize. -This applies to objects passed as arguments to an app, as well as objects returned from the app. - -Parsl uses dill and pickle to serialize Python objects -to/from functions. Therefore, Python apps can only use input and output objects that can be serialized by -dill or pickle. For example the following data types are known to have issues with serializability : - -* Closures -* Objects of complex classes with no ``__dict__`` or ``__getstate__`` methods defined -* System objects such as file descriptors, sockets and locks (e.g threading.Lock) - -If Parsl raises a `SerializationError`, first identify what objects are problematic with a quick test: - -.. code-block:: python - - import pickle - # If non-serializable you will get a TypeError - pickle.dumps(YOUR_DATA_OBJECT) - -If the data object simply is complex, please refer `here `_ for more details -on adding custom mechanisms for supporting serialization. - - - -How do I cite Parsl? -^^^^^^^^^^^^^^^^^^^^ - -To cite Parsl in publications, please use the following: - -Babuji, Y., Woodard, A., Li, Z., Katz, D. S., Clifford, B., Kumar, R., Lacinski, L., Chard, R., Wozniak, J., Foster, I., Wilde, M., and Chard, K., Parsl: Pervasive Parallel Programming in Python. 28th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC). 2019. https://doi.org/10.1145/3307681.3325400 - -or - -.. code-block:: latex - - @inproceedings{babuji19parsl, - author = {Babuji, Yadu and - Woodard, Anna and - Li, Zhuozhao and - Katz, Daniel S. and - Clifford, Ben and - Kumar, Rohan and - Lacinski, Lukasz and - Chard, Ryan and - Wozniak, Justin and - Foster, Ian and - Wilde, Mike and - Chard, Kyle}, - title = {Parsl: Pervasive Parallel Programming in Python}, - booktitle = {28th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC)}, - doi = {10.1145/3307681.3325400}, - year = {2019}, - url = {https://doi.org/10.1145/3307681.3325400} - } - - -How can my tasks survive ``WorkerLost`` and ``ManagerLost`` at the end of a batch job? -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -When a batch job ends, pilot workers will be terminated by the batch system, -and any tasks running there will fail. With `HighThroughputExecutor`, -this failure will be reported as a `parsl.executors.high_throughput.errors.WorkerLost` or -`parsl.executors.high_throughput.errors.ManagerLost` in the task future. - -To mitigate against this: - -* use retries by setting ``retries=`` in `parsl.config.Config`. -* if you only want to retry on certain errors such as `WorkerLost` and `ManagerLost`, - use ``retry_handler`` in `parsl.config.Config` to implement that policy. -* avoid sending tasks to batch jobs that will expire soon. With `HighThroughputExecutor`, - set drain_period to a little longer than you expect your tasks to take. - With `WorkQueueExecutor`, you can configure individual expected task duration using - a ``parsl_resource_specification`` and specify a worker ``--wall-time`` using the - ``worker_options`` parameter to the `WorkQueueExecutor`. diff --git a/parsl/docs/historical/changelog.rst b/parsl/docs/historical/changelog.rst deleted file mode 100644 index 931998f9..00000000 --- a/parsl/docs/historical/changelog.rst +++ /dev/null @@ -1,1299 +0,0 @@ -Historical: Changelog -===================== - - -.. note:: - After Parsl 1.2.0, releases moved to a lighter weight automated model. - This manual changelog is no longer updated and is now marked as - historical. - - Change information is delivered as commit messages on individual pull - requests, which can be see using any relevant git history browser - - for example, on the web at https://github.com/Parsl/parsl/commits/master/ - or on the commandline using using git log. - - -Parsl 1.2.0 ------------ - -Release date: January 13th, 2022. - -Parsl v1.2.0 includes 99 pull requests with contributions from: - -Ben Clifford @benclifford, Daniel S. Katz @danielskatz, Douglas Thain @dthain, James Corbett @jameshcorbett, Jonas Rübenach @jrueb, Logan Ward @WardLT, Matthew R. Becker @beckermr, Vladimir @vkhodygo, Yadu Nand Babuji @yadudoc, Yo Yehudi @yochannah, Zhuozhao Li @ZhuozhaoLi, yongyanrao @yongyanrao, Tim Jenness @timj, Darko Marinov @darko-marinov, Quentin Le Boulc'h - - -High Throughput Executor -^^^^^^^^^^^^^^^^^^^^^^^^ - -* Remove htex self.tasks race condition that shows under high load (#2034) -* Fix htex scale down breakage due to overly aggressive result heartbeat (#2119) [ TODO: this fixes a bug introduced since 1.1.0 so note that? #2104 ] -* Send heartbeats via results connection (#2104) - - -Work Queue Executor -^^^^^^^^^^^^^^^^^^^ - -* Allow use of WorkQueue running_time_min resource constraint (#2113) - WQ recently introduced an additional resource constraint: workers can be aware of their remaining wall time, and tasks can be constrained to only go to workers with sufficient remaining time. - -* Implement priority as a Work Queue resource specification (#2067) - The allows a workflow script to influence the order in which queued tasks are executed using Work Queue's existing priority mechanism. - - -* Disable WQ-level retries with an option to re-enable (#2059) - Previously by default, Work Queue will retry tasks that fail at the WQ level (for example, because of worker failure) an infinite number of times, inside the same parsl-level execution try. That hides the repeated tries from parsl (so monitoring does not report start/end times as might naively be expected for a try, and parsl retry counting does not count). - -* Document WorkqueueExecutor project_name remote reporting better (#2089) -* wq executor should show itself using representation mixin (#2064) -* Make WorkQueue worker command configurable (#2036) - - - -Flux Executor -^^^^^^^^^^^^^ - -The new FluxExecutor class uses the Flux resource manager -(github: flux-framework/flux-core) to launch tasks. Each -task is a Flux job. - - -Condor Provider -^^^^^^^^^^^^^^^ - -* Fix bug in condor provider for unknown jobs (#2161) - -LSF Provider -^^^^^^^^^^^^ - -* Update LSF provider to make it more friendly for different LSF-based computers (#2149) - -SLURM Provider -^^^^^^^^^^^^^^ - -* Improve docs and defaults for slurm partition and account parameters. (#2126) - -Grid Engine Provider -^^^^^^^^^^^^^^^^^^^^ - -* missing queue from self - causes config serialisation failure (#2042) - - -Monitoring -^^^^^^^^^^ - -* Index task_hashsum to give cross-run query speedup (#2085) -* Fix monitoring "db locked" errors occuring at scale (#1917) -* Fix worker efficiency plot when tasks are still in progress (#2048) -* Fix use of previously removed reg_time monitoring field (#2020) -* Reorder debug message so it happens when the message is received, without necessarily blocking on the resource_msgs queue put (#2093) - - -General new features -^^^^^^^^^^^^^^^^^^^^ - -* Workflow-pluggable retry scoring (#2068) - When a task fails, instead of causing a retry "cost" of 1 (the previous behaviour), this PR allows that cost to be determined by a user specified function which is given some context about the failure. - -General bug fixes -^^^^^^^^^^^^^^^^^ - -* Fix type error when job status output is large. (#2129) -* Fix a race condition in the local channel (#2115) -* Fix incorrect order of manager and interchange versions in error text (#2108) -* Fix to macos multiprocessing spawn and context issues (#2076) -* Tidy tasks_per_node in strategy (#2030) -* Fix and test wrong type handling for joinapp returns (#2063) -* FIX: os independent path (#2043) - -Platform and packaging -^^^^^^^^^^^^^^^^^^^^^^ - -* Improve support for Windows (#2107) -* Reflect python 3.9 support in setup.py metadata (#2023) -* Remove python <3.6 handling from threadpoolexecutor (#2083) -* Remove breaking .[all] install target (#2069) - -Internal tidying -^^^^^^^^^^^^^^^^ - -* Remove ipp logging hack in PR #204 (#2170) -* Remove BadRegistration exception definition which has been unused since PR #1671 (#2142) -* Remove AppFuture.__repr__, because superclass Future repr is sufficient (#2143) -* Make monitoring hub exit condition more explicit (#2131) -* Replace parsl's logging NullHandler with python's own NullHandler (#2114) -* Remove a commented out line of dead code in htex (#2116) -* Abstract more block handling from HighThroughputExecutor and share with WorkQueue (#2071) -* Regularise monitoring RESOURCE_INFO messages (#2117) -* Pull os x multiprocessing code into a single module (#2099) -* Describe monitoring protocols better (#2029) -* Remove task_id param from memo functions, as whole task record is available (#2080) -* remove irrelevant __main__ stub of local provider (#2026) -* remove unused weakref_cb (#2022) -* Remove unneeded task_id param from sanitize_and_wrap (#2081) -* Remove outdated IPP related comment in memoization (#2058) -* Remove unused AppBase status field (#2053) -* Do not unwrap joinapp future exceptions unnecessarily (#2084) -* Eliminate self.tasks[id] calls from joinapp callback (#2015) -* Looking at eliminating passing of task IDs and passing task records instead (#2016) -* Eliminate self.tasks[id] from launch_if_ready -* Eliminate self.tasks[id] calls from launch_task (#2061) -* Eliminate self.tasks[id] from app done callback (#2017) -* Make process_worker_pool pass mypy (#2052) -* Remove unused walltime from LocalProvider (#2057) -* Tidy human readable text/variable names around DependencyError (#2037) -* Replace old string formatting with f-strings in utils.py (#2055) - -Documentation, error messages and human-readable text -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -* Add a documentation chapter summarizing plugin points (#2066) -* Correct docstring for set_file_logger (#2156) -* Fix typo in two db error messages and make consistent with each other (#2152) -* Update slack join links to currently unexpired link (#2146) -* small typo fix in doc (#2134) -* Update CONTRIBUTING.rst (#2144) -* trying to fix broken link in GitHub (#2133) -* Add CITATION.cff file (#2100) -* Refresh the sanitize_and_wrap docstring (#2086) -* Rephrase ad-hoc config doc now that AdHocProvider (PR #1297) is implemented (#2096) -* Add research notice to readme (#2097) -* Remove untrue claim that parsl_resource_specification keys are case insensitive (#2095) -* Use zsh compatible install syntax (#2009) -* Remove documentation that interchange is walltime aware (#2082) -* Configure sphinx to put in full documentation for each method (#2094) -* autogenerate sphinx stubs rather than requiring manual update each PR (#2087) -* Update docstring for handle_app_update (#2079) -* fix a typo (#2024) -* Switch doc verb from invocated to invoked (#2088) -* Add documentation on meanings of states (#2075) -* Fix summary sentence of ScaleOutException (#2021) -* clarify that max workers is per node (#2056) -* Tidy up slurm state comment (#2035) -* Add nscc singapore example config (#2003) -* better formatting (#2039) -* Add missing f for an f-string (#2062) -* Rework __repr__ and __str__ for OptionalModuleMissing (#2025) -* Make executor bad state exception log use the exception (#2155) - -CI/testing -^^^^^^^^^^ - -* Make changes for CI reliability (#2118) -* Make missing worker test cleanup DFK at end (#2153) -* Tidy bash error codes tests. (#2130) -* Upgrade CI to use recent ubuntu, as old version was deprecated (#2111) -* Remove travis config, replaced by GitHub Actions in PR #2078 (#2112) -* Fix CI broken by dependency package changes (#2105) -* Adding github actions for CI (#2078) -* Test combine() pattern in joinapps (#2054) -* Assert that there should be no doc stubs in version control (#2092) -* Add monitoring dependency to local tests (#2074) -* Put viz test in a script (#2019) -* Reduce the size of recursive fibonacci joinapp testing (#2110) -* Remove disabled midway test (#2028) - - -Parsl 1.1.0 ------------ - -Released on April 26th, 2021. - -Parsl v1.1.0 includes 59 closed issues and 243 pull requests with contributions (code, tests, reviews and reports) from: - -Akila Ravihansa Perera @ravihansa3000, Anna Woodard @annawoodard, @bakerjl, Ben Clifford @benclifford, -Daniel S. Katz @danielskatz, Douglas Thain @dthain, @gerrick, @JG-Quarknet, Joseph Moon @jmoon1506, -Kelly L. Rowland @kellyrowland, Lars Bilke @bilke, Logan Ward @WardLT, Kirill Nagaitsev @Loonride, -Marcus Schwarting @meschw04, Matt Baughman @mattebaughman, Mihael Hategan @hategan, @radiantone, -Rohan Kumar @rohankumar42, Sohit Miglani @sohitmiglani, Tim Shaffer @trshaffer, -Tyler J. Skluzacek @tskluzac, Yadu Nand Babuji @yadudoc, and Zhuozhao Li @ZhuozhaoLi - -Deprecated and Removed features -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -* Python 3.5 is no longer supported. -* Almost definitely broken Jetstream provider removed (#1821) - -New Functionality -^^^^^^^^^^^^^^^^^ - -* Allow HTEX to set CPU affinity (#1853) - -* New serialization system to replace IPP serialization (#1806) - -* Support for Python 3.9 - -* @join_apps are a variation of @python_apps where an app can launch - more apps and then complete only after the launched apps are also - completed. - - These are described more fully in docs/userguide/joins.rst - -* Monitoring: - - * hub.log is now named monitoring_router.log - * Remove denormalised workflow duration from monitoring db (#1774) - * Remove hostname from status table (#1847) - * Clarify distinction between tasks and tries to run tasks (#1808) - * Replace 'done' state with 'exec_done' and 'memo_done' (#1848) - * Use repr instead of str for monitoring fail history (#1966) - -* Monitoring visualization: - - * Make task list appear under .../task/ not under .../app/ (#1762) - * Test that parsl-visualize does not return HTTP errors (#1700) - * Generate Gantt chart from status table rather than task table timestamps (#1767) - * Hyperlinks for app page to task pages should be on the task ID, not the app name (#1776) - * Use real final state to color DAG visualization (#1812) - -* Make task record garbage collection optional. (#1909) - -* Make checkpoint_files = get_all_checkpoints() by default (#1918) - - -Parsl 1.0.0 ------------ - -Released on June 11th, 2020 - -Parsl v1.0.0 includes 59 closed issues and 243 pull requests with contributions (code, tests, reviews and reports) from: - -Akila Ravihansa Perera @ravihansa3000, Aymen Alsaadi @AymenFJA, Anna Woodard @annawoodard, -Ben Clifford @benclifford, Ben Glick @benhg, Benjamin Tovar @btovar, Daniel S. Katz @danielskatz, -Daniel Smith @dgasmith, Douglas Thain @dthain, Eric Jonas @ericmjonas, Geoffrey Lentner @glentner, -Ian Foster @ianfoster, Kalpani Ranasinghe @kalpanibhagya, Kyle Chard @kylechard, Lindsey Gray @lgray, -Logan Ward @WardLT, Lyle Hayhurst @lhayhurst, Mihael Hategan @hategan, Rajini Wijayawardana @rajiniw95, -@saktar-unr, Tim Shaffer @trshaffer, Tom Glanzman @TomGlanzman, Yadu Nand Babuji @yadudoc and, -Zhuozhao Li @ZhuozhaoLi - -Deprecated and Removed features -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -* **Python3.5** is now marked for deprecation, and will not be supported after this release. - Python3.6 will be the earliest Python3 version supported in the next release. - -* **App** decorator deprecated in 0.8 is now removed `issue#1539 `_ - `bash_app` and `python_app` are the only supported App decorators in this release. - -* **IPyParallelExecutor** is no longer a supported executor `issue#1565 `_ - - -New Functionality -^^^^^^^^^^^^^^^^^ - -* `parsl.executors.WorkQueueExecutor` introduced in v0.9.0 is now in beta. `parsl.executors.WorkQueueExecutor` is designed as a drop-in replacement for `parsl.executors.HighThroughputExecutor`. Here are some key features: - - * Support for packaging the python environment and shipping it to the worker side. This mechanism addresses propagating python environments in grid-like systems that lack shared-filesystems or cloud environments. - * `parsl.executors.WorkQueueExecutor` supports resource function tagging and resource specification - * Support for resource specification kwarg `issue#1675 `_ - - -* Limited type-checking in Parsl internal components (as part of an ongoing effort) - - -* Improvements to caching mechanism including ability to mark certain arguments to be not counted for memoization. - - * Normalize known types for memoization, and reject unknown types (#1291). This means that previous unreliable - behaviour for some complex types such as dicts will become more reliable; and that other previous unreliable - behaviour for other unknown complex types will now cause an error. Handling can be added for those types using - parsl.memoization.id_for_memo. - * Add ability to label some arguments in an app invocation as not memoized using the ignore_for_cache app keyword (PR 1568) - -* Special keyword args: 'inputs', 'outputs' that are used to specify files no longer support strings - and now require `File` objects. For example, the following snippet is no longer supported in v1.0.0: - - .. code-block:: python - - @bash_app - def cat(inputs=(), outputs=()): - return 'cat {} > {}'.format(inputs[0], outputs[0]) - - concat = cat(inputs=['hello-0.txt'], - outputs=['hello-1.txt']) - - This is the new syntax: - - .. code-block:: python - - from parsl import File - - @bash_app - def cat(inputs=(), outputs=()): - return 'cat {} > {}'.format(inputs[0].filepath, outputs[0].filepath) - - concat = cat(inputs=[File('hello-0.txt')], - outputs=[File('hello-1.txt')]) - - Since filenames are no longer passed to apps as strings, and the string filepath is required, it can - be accessed from the File object using the ``filepath`` attribute. - - .. code-block:: python - - from parsl import File - - @bash_app - def cat(inputs=(), outputs=()): - return 'cat {} > {}'.format(inputs[0].filepath, outputs[0].filepath) - - -* New launcher: `parsl.launchers.WrappedLauncher` for launching tasks inside containers. - -* ``parsl.channels.SSHChannel`` now supports a ``key_filename`` kwarg `issue#1639 `_ - -* Newly added Makefile wraps several frequent developer operations such as: - - * Run the test-suite: ``make test`` - - * Install parsl: ``make install`` - - * Create a virtualenv: ``make virtualenv`` - - * Tag release and push to release channels: ``make deploy`` - -* Several updates to the `parsl.executors.HighThroughputExecutor`: - - * By default, the `parsl.executors.HighThroughputExecutor` will now use heuristics to detect and try all addresses - when the workers connect back to the parsl master. An address can be configured manually using the - ``HighThroughputExecutor(address=)`` kwarg option. - - * Support for Mac OS. (`pull#1469 `_, `pull#1738 `_) - - * Cleaner reporting of version mismatches and automatic suppression of non-critical errors. - - * Separate worker log directories by block id `issue#1508 `_ - -* Support for garbage collection to limit memory consumption in long-lived scripts. - -* All cluster providers now use ``max_blocks=1`` by default `issue#1730 `_ to avoid over-provisioning. - -* New ``JobStatus`` class for better monitoring of Jobs submitted to batch schedulers. - -Bug Fixes -^^^^^^^^^ - -* Ignore AUTO_LOGNAME for caching `issue#1642 `_ -* Add batch jobs to PBS/torque job status table `issue#1650 `_ -* Use higher default buffer threshold for serialization `issue#1654 `_ -* Do not pass mutable default to ignore_for_cache `issue#1656 `_ -* Several improvements and fixes to Monitoring -* Fix sites/test_ec2 failure when aws user opts specified `issue#1375 `_ -* Fix LocalProvider to kill the right processes, rather than all processes owned by user `issue#1447 `_ -* Exit htex probe loop with first working address `issue#1479 `_ -* Allow slurm partition to be optional `issue#1501 `_ -* Fix race condition with wait_for_tasks vs task completion `issue#1607 `_ -* Fix Torque job_id truncation `issue#1583 `_ -* Cleaner reporting for Serialization Errors `issue#1355 `_ -* Results from zombie managers do not crash the system, but will be ignored `issue#1665 `_ -* Guarantee monitoring will send out at least one message `issue#1446 `_ -* Fix monitoring ctrlc hang `issue#1670 `_ - - -Parsl 0.9.0 ------------ - -Released on October 25th, 2019 - -Parsl v0.9.0 includes 199 closed issues and pull requests with contributions (code, tests, reviews and reports) from: - -Andrew Litteken @AndrewLitteken, Anna Woodard @annawoodard, Ben Clifford @benclifford, -Ben Glick @benhg, Daniel S. Katz @danielskatz, Daniel Smith @dgasmith, -Engin Arslan @earslan58, Geoffrey Lentner @glentner, John Hover @jhover -Kyle Chard @kylechard, TJ Dasso @tjdasso, Ted Summer @macintoshpie, -Tom Glanzman @TomGlanzman, Levi Naden @LNaden, Logan Ward @WardLT, Matthew Welborn @mattwelborn, -@MatthewBM, Raphael Fialho @rapguit, Yadu Nand Babuji @yadudoc, and Zhuozhao Li @ZhuozhaoLi - - -New Functionality -^^^^^^^^^^^^^^^^^ - -* Parsl will no longer do automatic keyword substitution in ``@bash_app`` in favor of deferring to Python's `format method `_ - and newer `f-strings `_. For example, - - .. code-block:: python - - # The following example worked until v0.8.0 - @bash_app - def cat(inputs=(), outputs=()): - return 'cat {inputs[0]} > {outputs[0]}' # <-- Relies on Parsl auto formatting the string - - # Following are two mechanisms that will work going forward from v0.9.0 - @bash_app - def cat(inputs=(), outputs=()): - return 'cat {} > {}'.format(inputs[0], outputs[0]) # <-- Use str.format method - - @bash_app - def cat(inputs=(), outputs=()): - return f'cat {inputs[0]} > {outputs[0]}' # <-- OR use f-strings introduced in Python3.6 - - -* ``@python_app`` now takes a ``walltime`` kwarg to limit the task execution time. -* New file staging API `parsl.data_provider.staging.Staging` to support pluggable - file staging methods. The methods implemented in 0.8.0 (HTTP(S), FTP and - Globus) are still present, along with two new methods which perform HTTP(S) - and FTP staging on worker nodes to support non-shared-filesystem executors - such as clouds. -* Behaviour change for storage_access parameter. In 0.8.0, this was used to - specify Globus staging configuration. In 0.9.0, if this parameter is - specified it must specify all desired staging providers. To keep the same - staging providers as in 0.8.0, specify: - - .. code-block:: python - - from parsl.data_provider.data_manager import default_staging - storage_access = default_staging + [GlobusStaging(...)] - - ``GlobusScheme`` in 0.8.0 has been renamed `GlobusStaging` and moved to a new - module, parsl.data_provider.globus - -* `parsl.executors.WorkQueueExecutor`: a new executor that integrates functionality from `Work Queue `_ is now available. -* New provider to support for Ad-Hoc clusters ``parsl.providers.AdHocProvider`` -* New provider added to support LSF on Summit `parsl.providers.LSFProvider` -* Support for CPU and Memory resource hints to providers `(github) `_. -* The ``logging_level=logging.INFO`` in `parsl.monitoring.MonitoringHub` is replaced with ``monitoring_debug=False``: - - .. code-block:: python - - monitoring=MonitoringHub( - hub_address=address_by_hostname(), - hub_port=55055, - monitoring_debug=False, - resource_monitoring_interval=10, - ), - -* Managers now have a worker watchdog thread to report task failures that crash a worker. -* Maximum idletime after which idle blocks can be relinquished can now be configured as follows: - - .. code-block:: python - - config=Config( - max_idletime=120.0 , # float, unit=seconds - strategy='simple' - ) - -* Several test-suite improvements that have dramatically reduced test duration. -* Several improvements to the Monitoring interface. -* Configurable port on ``parsl.channels.SSHChannel``. -* ``suppress_failure`` now defaults to True. -* `parsl.executors.HighThroughputExecutor` is the recommended executor, and ``IPyParallelExecutor`` is deprecated. -* `parsl.executors.HighThroughputExecutor` will expose worker information via environment variables: ``PARSL_WORKER_RANK`` and ``PARSL_WORKER_COUNT`` - -Bug Fixes -^^^^^^^^^ - -* ZMQError: Operation cannot be accomplished in current state bug `issue#1146 `_ -* Fix event loop error with monitoring enabled `issue#532 `_ -* Tasks per app graph appears as a sawtooth, not as rectangles `issue#1032 `_. -* Globus status processing failure `issue#1317 `_. -* Sporadic globus staging error `issue#1170 `_. -* RepresentationMixin breaks on classes with no default parameters `issue#1124 `_. -* File ``localpath`` staging conflict `issue#1197 `_. -* Fix IndexError when using CondorProvider with strategy enabled `issue#1298 `_. -* Improper dependency error handling causes hang `issue#1285 `_. -* Memoization/checkpointing fixes for bash apps `issue#1269 `_. -* CPU User Time plot is strangely cumulative `issue#1033 `_. -* Issue requesting resources on non-exclusive nodes `issue#1246 `_. -* parsl + htex + slurm hangs if slurm command times out, without making further progress `issue#1241 `_. -* Fix strategy overallocations `issue#704 `_. -* max_blocks not respected in SlurmProvider `issue#868 `_. -* globus staging does not work with a non-default ``workdir`` `issue#784 `_. -* Cumulative CPU time loses time when subprocesses end `issue#1108 `_. -* Interchange KeyError due to too many heartbeat missed `issue#1128 `_. - - - -Parsl 0.8.0 ------------ - -Released on June 13th, 2019 - -Parsl v0.8.0 includes 58 closed issues and pull requests with contributions (code, tests, reviews and reports) - -from: Andrew Litteken @AndrewLitteken, Anna Woodard @annawoodard, Antonio Villarreal @villarrealas, -Ben Clifford @benc, Daniel S. Katz @danielskatz, Eric Tatara @etatara, Juan David Garrido @garri1105, -Kyle Chard @@kylechard, Lindsey Gray @lgray, Tim Armstrong @timarmstrong, Tom Glanzman @TomGlanzman, -Yadu Nand Babuji @yadudoc, and Zhuozhao Li @ZhuozhaoLi - - -New Functionality -^^^^^^^^^^^^^^^^^ - -* Monitoring is now integrated into parsl as default functionality. -* ``parsl.AUTO_LOGNAME``: Support for a special ``AUTO_LOGNAME`` option to auto generate ``stdout`` and ``stderr`` file paths. -* `File` no longer behaves as a string. This means that operations in apps that treated a `File` as a string - will break. For example the following snippet will have to be updated: - - .. code-block:: python - - # Old style: " ".join(inputs) is legal since inputs will behave like a list of strings - @bash_app - def concat(inputs=(), outputs=(), stdout="stdout.txt", stderr='stderr.txt'): - return "cat {0} > {1}".format(" ".join(inputs), outputs[0]) - - # New style: - @bash_app - def concat(inputs=(), outputs=(), stdout="stdout.txt", stderr='stderr.txt'): - return "cat {0} > {1}".format(" ".join(list(map(str,inputs))), outputs[0]) - -* Cleaner user app file log management. -* Updated configurations using `parsl.executors.HighThroughputExecutor` in the configuration section of the userguide. -* Support for OAuth based SSH with ``parsl.channels.OAuthSSHChannel``. - -Bug Fixes -^^^^^^^^^ - -* Monitoring resource usage bug `issue#975 `_ -* Bash apps fail due to missing dir paths `issue#1001 `_ -* Viz server explicit binding fix `issue#1023 `_ -* Fix sqlalchemy version warning `issue#997 `_ -* All workflows are called typeguard `issue#973 `_ -* Fix ``ModuleNotFoundError: No module named 'monitoring'`` `issue#971 `_ -* Fix sqlite3 integrity error `issue#920 `_ -* HTEX interchange check python version mismatch to the micro level `issue#857 `_ -* Clarify warning message when a manager goes missing `issue#698 `_ -* Apps without a specified DFK should use the global DFK in scope at call time, not at other times. `issue#697 `_ - - -Parsl 0.7.2 ------------ - -Released on Mar 14th, 2019 - -New Functionality -^^^^^^^^^^^^^^^^^ - -* Monitoring: Support for reporting monitoring data to a local sqlite database is now available. -* Parsl is switching to an opt-in model for anonymous usage tracking. Read more here: :ref:`label-usage-tracking`. -* `bash_app` now supports specification of write modes for ``stdout`` and ``stderr``. -* Persistent volume support added to `parsl.providers.KubernetesProvider`. -* Scaling recommendations from study on Bluewaters is now available in the userguide. - - -Parsl 0.7.1 ------------ - -Released on Jan 18th, 2019 - -New Functionality -^^^^^^^^^^^^^^^^^ - -* parsl.executors.LowLatencyExecutor: a new executor designed to address use-cases with tight latency requirements - such as model serving (Machine Learning), function serving and interactive analyses is now available. -* New options in `parsl.executors.HighThroughputExecutor`: - * ``suppress_failure``: Enable suppression of worker rejoin errors. - * ``max_workers``: Limit workers spawned by manager -* Late binding of DFK, allows apps to pick DFK dynamically at call time. This functionality adds safety - to cases where a new config is loaded and a new DFK is created. - -Bug fixes -^^^^^^^^^ - -* A critical bug in `parsl.executors.HighThroughputExecutor` that led to debug logs overflowing channels and terminating - blocks of resource is fixed `issue#738 `_ - - -Parsl 0.7.0 ------------ - -Released on Dec 20st, 2018 - -Parsl v0.7.0 includes 110 closed issues with contributions (code, tests, reviews and reports) -from: Alex Hays @ahayschi, Anna Woodard @annawoodard, Ben Clifford @benc, Connor Pigg @ConnorPigg, -David Heise @daheise, Daniel S. Katz @danielskatz, Dominic Fitzgerald @djf604, Francois Lanusse @EiffL, -Juan David Garrido @garri1105, Gordon Watts @gordonwatts, Justin Wozniak @jmjwozniak, -Joseph Moon @jmoon1506, Kenyi Hurtado @khurtado, Kyle Chard @kylechard, Lukasz Lacinski @lukaszlacinski, -Ravi Madduri @madduri, Marco Govoni @mgovoni-devel, Reid McIlroy-Young @reidmcy, Ryan Chard @ryanchard, -@sdustrud, Yadu Nand Babuji @yadudoc, and Zhuozhao Li @ZhuozhaoLi - -New functionality -^^^^^^^^^^^^^^^^^ - - -* `parsl.executors.HighThroughputExecutor`: a new executor intended to replace the ``IPyParallelExecutor`` is now available. - This new executor addresses several limitations of ``IPyParallelExecutor`` such as: - - * Scale beyond the ~300 worker limitation of IPP. - * Multi-processing manager supports execution on all cores of a single node. - * Improved worker side reporting of version, system and status info. - * Supports failure detection and cleaner manager shutdown. - - Here's a sample configuration for using this executor locally: - - .. code-block:: python - - from parsl.providers import LocalProvider - from parsl.channels import LocalChannel - - from parsl.config import Config - from parsl.executors import HighThroughputExecutor - - config = Config( - executors=[ - HighThroughputExecutor( - label="htex_local", - cores_per_worker=1, - provider=LocalProvider( - channel=LocalChannel(), - init_blocks=1, - max_blocks=1, - ), - ) - ], - ) - - More information on configuring is available in the :ref:`configuration-section` section. - -* ExtremeScaleExecutor - a new executor targeting supercomputer scale (>1000 nodes) workflows is now available. - - Here's a sample configuration for using this executor locally: - - .. code-block:: python - - from parsl.providers import LocalProvider - from parsl.channels import LocalChannel - from parsl.launchers import SimpleLauncher - - from parsl.config import Config - from parsl.executors import ExtremeScaleExecutor - - config = Config( - executors=[ - ExtremeScaleExecutor( - label="extreme_local", - ranks_per_node=4, - provider=LocalProvider( - channel=LocalChannel(), - init_blocks=0, - max_blocks=1, - launcher=SimpleLauncher(), - ) - ) - ], - strategy=None, - ) - - More information on configuring is available in the :ref:`configuration-section` section. - - -* The libsubmit repository has been merged with Parsl to reduce overheads on maintenance with respect to documentation, - testing, and release synchronization. Since the merge, the API has undergone several updates to support - the growing collection of executors, and as a result Parsl 0.7.0+ will not be backwards compatible with - the standalone libsubmit repos. The major components of libsubmit are now available through Parsl, and - require the following changes to import lines to migrate scripts to 0.7.0: - - * ``from libsubmit.providers import `` is now ``from parsl.providers import `` - * ``from libsubmit.channels import `` is now ``from parsl.channels import `` - * ``from libsubmit.launchers import `` is now ``from parsl.launchers import `` - - - .. warning:: - This is a breaking change from Parsl v0.6.0 - -* To support resource-based requests for workers and to maintain uniformity across interfaces, ``tasks_per_node`` is - no longer a **provider** option. Instead, the notion of ``tasks_per_node`` is defined via executor specific options, - for eg: - - * ``IPyParallelExecutor`` provides ``workers_per_node`` - * `parsl.executors.HighThroughputExecutor` provides ``cores_per_worker`` to allow for worker launches to be determined based on - the number of cores on the compute node. - * ExtremeScaleExecutor uses ``ranks_per_node`` to specify the ranks to launch per node. - - .. warning:: - This is a breaking change from Parsl v0.6.0 - - -* Major upgrades to the monitoring infrastructure. - * Monitoring information can now be written to a SQLite database, created on the fly by Parsl - * Web-based monitoring to track workflow progress - - -* Determining the correct IP address/interface given network firewall rules is often a nuisance. - To simplify this, three new methods are now supported: - - * ``parsl.addresses.address_by_route`` - * ``parsl.addresses.address_by_query`` - * ``parsl.addresses.address_by_hostname`` - -* `parsl.launchers.AprunLauncher` now supports ``overrides`` option that allows arbitrary strings to be added - to the aprun launcher call. - -* `DataFlowKernel` has a new method ``wait_for_current_tasks()`` - -* `DataFlowKernel` now uses per-task locks and an improved mechanism to handle task completions - improving performance for workflows with large number of tasks. - - -Bug fixes (highlights) -^^^^^^^^^^^^^^^^^^^^^^ - - -* Ctlr+C should cause fast DFK cleanup `issue#641 `_ -* Fix to avoid padding in ``wtime_to_minutes()`` `issue#522 `_ -* Updates to block semantics `issue#557 `_ -* Updates ``public_ip`` to ``address`` for clarity `issue#557 `_ -* Improvements to launcher docs `issue#424 `_ -* Fixes for inconsistencies between stream_logger and file_logger `issue#629 `_ -* Fixes to DFK discarding some un-executed tasks at end of workflow `issue#222 `_ -* Implement per-task locks to avoid deadlocks `issue#591 `_ -* Fixes to internal consistency errors `issue#604 `_ -* Removed unnecessary provider labels `issue#440 `_ -* Fixes to `parsl.providers.TorqueProvider` to work on NSCC `issue#489 `_ -* Several fixes and updates to monitoring subsystem `issue#471 `_ -* DataManager calls wrong DFK `issue#412 `_ -* Config isn't reloading properly in notebooks `issue#549 `_ -* Cobalt provider ``partition`` should be ``queue`` `issue#353 `_ -* bash AppFailure exceptions contain useful but un-displayed information `issue#384 `_ -* Do not CD to engine_dir `issue#543 `_ -* Parsl install fails without kubernetes config file `issue#527 `_ -* Fix import error `issue#533 `_ -* Change Local Database Strategy from Many Writers to a Single Writer `issue#472 `_ -* All run-related working files should go in the rundir unless otherwise configured `issue#457 `_ -* Fix concurrency issue with many engines accessing the same IPP config `issue#469 `_ -* Ensure we are not caching failed tasks `issue#368 `_ -* File staging of unknown schemes fails silently `issue#382 `_ -* Inform user checkpointed results are being used `issue#494 `_ -* Fix IPP + python 3.5 failure `issue#490 `_ -* File creation fails if no executor has been loaded `issue#482 `_ -* Make sure tasks in ``dep_fail`` state are retried `issue#473 `_ -* Hard requirement for CMRESHandler `issue#422 `_ -* Log error Globus events to stderr `issue#436 `_ -* Take 'slots' out of logging `issue#411 `_ -* Remove redundant logging `issue#267 `_ -* Zombie ipcontroller processes - Process cleanup in case of interruption `issue#460 `_ -* IPyparallel failure when submitting several apps in parallel threads `issue#451 `_ -* `parsl.providers.SlurmProvider` + `parsl.launchers.SingleNodeLauncher` starts all engines on a single core `issue#454 `_ -* IPP ``engine_dir`` has no effect if indicated dir does not exist `issue#446 `_ -* Clarify AppBadFormatting error `issue#433 `_ -* confusing error message with simple configs `issue#379 `_ -* Error due to missing kubernetes config file `issue#432 `_ -* ``parsl.configs`` and ``parsl.tests.configs`` missing init files `issue#409 `_ -* Error when Python versions differ `issue#62 `_ -* Fixing ManagerLost error in HTEX/EXEX `issue#577 `_ -* Write all debug logs to rundir by default in HTEX/EXEX `issue#574 `_ -* Write one log per HTEX worker `issue#572 `_ -* Fixing ManagerLost error in HTEX/EXEX `issue#577 `_ - - -Parsl 0.6.1 ------------ - -Released on July 23rd, 2018. - -This point release contains fixes for `issue#409 `_ - - -Parsl 0.6.0 ------------ - -Released July 23rd, 2018. - -New functionality -^^^^^^^^^^^^^^^^^ - -* Switch to class based configuration `issue#133 `_ - - Here's a the config for using threads for local execution - - .. code-block:: python - - from parsl.config import Config - from parsl.executors.threads import ThreadPoolExecutor - - config = Config(executors=[ThreadPoolExecutor()]) - - Here's a more complex config that uses SSH to run on a Slurm based cluster - - .. code-block:: python - - from libsubmit.channels import SSHChannel - from libsubmit.providers import SlurmProvider - - from parsl.config import Config - from parsl.executors.ipp import IPyParallelExecutor - from parsl.executors.ipp_controller import Controller - - config = Config( - executors=[ - IPyParallelExecutor( - provider=SlurmProvider( - 'westmere', - channel=SSHChannel( - hostname='swift.rcc.uchicago.edu', - username=, - script_dir= - ), - init_blocks=1, - min_blocks=1, - max_blocks=2, - nodes_per_block=1, - tasks_per_node=4, - parallelism=0.5, - overrides= - ), - label='midway_ipp', - controller=Controller(public_ip=), - ) - ] - ) - -* Implicit Data Staging `issue#281 `_ - - .. code-block:: python - - # create an remote Parsl file - inp = File('ftp://www.iana.org/pub/mirror/rirstats/arin/ARIN-STATS-FORMAT-CHANGE.txt') - - # create a local Parsl file - out = File('file:///tmp/ARIN-STATS-FORMAT-CHANGE.txt') - - # call the convert app with the Parsl file - f = convert(inputs=[inp], outputs=[out]) - f.result() - -* Support for application profiling `issue#5 `_ - -* Real-time usage tracking via external systems `issue#248 `_, `issue#251 `_ - -* Several fixes and upgrades to tests and testing infrastructure `issue#157 `_, `issue#159 `_, - `issue#128 `_, `issue#192 `_, - `issue#196 `_ - -* Better state reporting in logs `issue#242 `_ - -* Hide DFK `issue#50 `_ - - * Instead of passing a config dictionary to the DataFlowKernel, now you can call ``parsl.load(Config)`` - * Instead of having to specify the ``dfk`` at the time of ``App`` declaration, the DFK is a singleton loaded - at call time : - - .. code-block:: python - - import parsl - from parsl.tests.configs.local_ipp import config - parsl.load(config) - - @App('python') - def double(x): - return x * 2 - - fut = double(5) - fut.result() - -* Support for better reporting of remote side exceptions `issue#110 `_ - - -Bug Fixes -^^^^^^^^^ - -* Making naming conventions consistent `issue#109 `_ - -* Globus staging returns unclear error bug `issue#178 `_ - -* Duplicate log-lines when using IPP `issue#204 `_ - -* Usage tracking with certain missing network causes 20s startup delay. `issue#220 `_ - -* ``task_exit`` checkpointing repeatedly truncates checkpoint file during run bug `issue#230 `_ - -* Checkpoints will not reload from a run that was Ctrl-C'ed `issue#232 `_ - -* Race condition in task checkpointing `issue#234 `_ - -* Failures not to be checkpointed `issue#239 `_ - -* Naming inconsitencies with ``maxThreads``, ``max_threads``, ``max_workers`` are now resolved `issue#303 `_ - -* Fatal not a git repository alerts `issue#326 `_ - -* Default ``kwargs`` in bash apps unavailable at command-line string format time `issue#349 `_ - -* Fix launcher class inconsistencies `issue#360 `_ - -* Several fixes to AWS provider `issue#362 `_ - * Fixes faulty status updates - * Faulty termination of instance at cleanup, leaving zombie nodes. - - -Parsl 0.5.1 ------------ - -Released. May 15th, 2018. - -New functionality -^^^^^^^^^^^^^^^^^ - - -* Better code state description in logging `issue#242 `_ - -* String like behavior for Files `issue#174 `_ - -* Globus path mapping in config `issue#165 `_ - - -Bug Fixes -^^^^^^^^^ - -* Usage tracking with certain missing network causes 20s startup delay. `issue#220 `_ - -* Checkpoints will not reload from a run that was Ctrl-C'ed `issue#232 `_ - -* Race condition in task checkpointing `issue#234 `_ - -* ``task_exit`` checkpointing repeatedly truncates checkpoint file during run `issue#230 `_ - -* Make ``dfk.cleanup()`` not cause kernel to restart with Jupyter on Mac `issue#212 `_ - -* Fix automatic IPP controller creation on OS X `issue#206 `_ - -* Passing Files breaks over IPP `issue#200 `_ - -* `repr` call after `AppException` instantiation raises `AttributeError` `issue#197 `_ - -* Allow `DataFuture` to be initialized with a `str` file object `issue#185 `_ - -* Error for globus transfer failure `issue#162 `_ - - -Parsl 0.5.2 ------------ - -Released. June 21st, 2018. -This is an emergency release addressing `issue#347 `_ - -Bug Fixes -^^^^^^^^^ - -* Parsl version conflict with libsubmit 0.4.1 `issue#347 `_ - - -Parsl 0.5.0 ------------ - -Released. Apr 16th, 2018. - -New functionality -^^^^^^^^^^^^^^^^^ - -* Support for Globus file transfers `issue#71 `_ - - .. caution:: - This feature is available from Parsl ``v0.5.0`` in an ``experimental`` state. - -* PathLike behavior for Files `issue#174 `_ - * Files behave like strings here : - - .. code-block:: python - - myfile = File("hello.txt") - f = open(myfile, 'r') - - -* Automatic checkpointing modes `issue#106 `_ - - .. code-block:: python - - config = { - "globals": { - "lazyErrors": True, - "memoize": True, - "checkpointMode": "dfk_exit" - } - } - -* Support for containers with docker `issue#45 `_ - - .. code-block:: python - - localDockerIPP = { - "sites": [ - {"site": "Local_IPP", - "auth": {"channel": None}, - "execution": { - "executor": "ipp", - "container": { - "type": "docker", # <----- Specify Docker - "image": "app1_v0.1", # <------Specify docker image - }, - "provider": "local", - "block": { - "initBlocks": 2, # Start with 4 workers - }, - } - }], - "globals": {"lazyErrors": True} } - - .. caution:: - This feature is available from Parsl ``v0.5.0`` in an ``experimental`` state. - -* Cleaner logging `issue#85 `_ - * Logs are now written by default to ``runinfo/RUN_ID/parsl.log``. - * ``INFO`` log lines are more readable and compact - -* Local configs are now packaged `issue#96 `_ - - .. code-block:: python - - from parsl.configs.local import localThreads - from parsl.configs.local import localIPP - - -Bug Fixes -^^^^^^^^^ -* Passing Files over IPP broken `issue#200 `_ - -* Fix ``DataFuture.__repr__`` for default instantiation `issue#164 `_ - -* Results added to appCache before retries exhausted `issue#130 `_ - -* Missing documentation added for Multisite and Error handling `issue#116 `_ - -* TypeError raised when a bad stdout/stderr path is provided. `issue#104 `_ - -* Race condition in DFK `issue#102 `_ - -* Cobalt provider broken on Cooley.alfc `issue#101 `_ - -* No blocks provisioned if parallelism/blocks = 0 `issue#97 `_ - -* Checkpoint restart assumes rundir `issue#95 `_ - -* Logger continues after cleanup is called `issue#93 `_ - - -Parsl 0.4.1 ------------ - -Released. Feb 23rd, 2018. - - -New functionality -^^^^^^^^^^^^^^^^^ - -* GoogleCloud provider support via libsubmit -* GridEngine provider support via libsubmit - - -Bug Fixes -^^^^^^^^^ -* Cobalt provider issues with job state `issue#101 `_ -* Parsl updates config inadvertently `issue#98 `_ -* No blocks provisioned if parallelism/blocks = 0 `issue#97 `_ -* Checkpoint restart assumes rundir bug `issue#95 `_ -* Logger continues after cleanup called enhancement `issue#93 `_ -* Error checkpointing when no cache enabled `issue#92 `_ -* Several fixes to libsubmit. - - -Parsl 0.4.0 ------------ - -Here are the major changes included in the Parsl 0.4.0 release. - -New functionality -^^^^^^^^^^^^^^^^^ - -* Elastic scaling in response to workflow pressure. `issue#46 `_ - Options ``minBlocks``, ``maxBlocks``, and ``parallelism`` now work and controls workflow execution. - - Documented in: :ref:`label-elasticity` - -* Multisite support, enables targetting apps within a single workflow to different - sites `issue#48 `_ - - .. code-block:: python - - @App('python', dfk, sites=['SITE1', 'SITE2']) - def my_app(...): - ... - -* Anonymized usage tracking added. `issue#34 `_ - - Documented in: :ref:`label-usage-tracking` - -* AppCaching and Checkpointing `issue#43 `_ - - .. code-block:: python - - # Set cache=True to enable appCaching - @App('python', dfk, cache=True) - def my_app(...): - ... - - - # To checkpoint a workflow: - dfk.checkpoint() - - Documented in: :ref:`label-checkpointing`, :ref:`label-appcaching` - -* Parsl now creates a new directory under ``./runinfo/`` with an incrementing number per workflow - invocation - -* Troubleshooting guide and more documentation - -* PEP8 conformance tests added to travis testing `issue#72 `_ - - -Bug Fixes -^^^^^^^^^ - -* Missing documentation from libsubmit was added back - `issue#41 `_ - -* Fixes for ``script_dir`` | ``scriptDir`` inconsistencies `issue#64 `_ - * We now use ``scriptDir`` exclusively. - -* Fix for caching not working on jupyter notebooks `issue#90 `_ - -* Config defaults module failure when part of the option set is provided `issue#74 `_ - -* Fixes for network errors with usage_tracking `issue#70 `_ - -* PEP8 conformance of code and tests with limited exclusions `issue#72 `_ - -* Doc bug in recommending ``max_workers`` instead of ``maxThreads`` `issue#73 `_ - - - - -Parsl 0.3.1 ------------ - -This is a point release with mostly minor features and several bug fixes - -* Fixes for remote side handling -* Support for specifying IPythonDir for IPP controllers -* Several tests added that test provider launcher functionality from libsubmit -* This upgrade will also push the libsubmit requirement from 0.2.4 -> 0.2.5. - - -Several critical fixes from libsubmit are brought in: - -* Several fixes and improvements to Condor from @annawoodard. -* Support for Torque scheduler -* Provider script output paths are fixed -* Increased walltimes to deal with slow scheduler system -* Srun launcher for slurm systems -* SSH channels now support file_pull() method - While files are not automatically staged, the channels provide support for bi-directional file transport. - -Parsl 0.3.0 ------------ - -Here are the major changes that are included in the Parsl 0.3.0 release. - - -New functionality -^^^^^^^^^^^^^^^^^ - -* Arguments to DFK has changed: - - # Old - dfk(executor_obj) - - # New, pass a list of executors - dfk(executors=[list_of_executors]) - - # Alternatively, pass the config from which the DFK will - #instantiate resources - dfk(config=config_dict) - -* Execution providers have been restructured to a separate repo: `libsubmit `_ - -* Bash app styles have changes to return the commandline string rather than be assigned to the special keyword ``cmd_line``. - Please refer to `RFC #37 `_ for more details. This is a **non-backward** compatible change. - -* Output files from apps are now made available as an attribute of the AppFuture. - Please refer `#26 `_ for more details. This is a **non-backward** compatible change :: - - # This is the pre 0.3.0 style - app_fu, [file1, file2] = make_files(x, y, outputs=['f1.txt', 'f2.txt']) - - #This is the style that will be followed going forward. - app_fu = make_files(x, y, outputs=['f1.txt', 'f2.txt']) - [file1, file2] = app_fu.outputs - -* DFK init now supports auto-start of IPP controllers - -* Support for channels via libsubmit. Channels enable execution of commands from execution providers either - locally, or remotely via ssh. - -* Bash apps now support timeouts. - -* Support for cobalt execution provider. - - -Bug fixes -^^^^^^^^^ -* Futures have inconsistent behavior in bash app fn body `#35 `_ -* Parsl dflow structure missing dependency information `#30 `_ - - -Parsl 0.2.0 ------------ - -Here are the major changes that are included in the Parsl 0.2.0 release. - -New functionality -^^^^^^^^^^^^^^^^^ - -* Support for execution via IPythonParallel executor enabling distributed execution. -* Generic executors - -Parsl 0.1.0 ------------ - -Here are the major changes that are included in the Parsl 0.1.0 release. - -New functionality -^^^^^^^^^^^^^^^^^ - -* Support for Bash and Python apps -* Support for chaining of apps via futures handled by the DataFlowKernel. -* Support for execution over threads. -* Arbitrary DAGs can be constructed and executed asynchronously. - -Bug Fixes -^^^^^^^^^ - -* Initial release, no listed bugs. - - -Historical: Libsubmit Changelog -=============================== - -.. note:: - As of Parsl 0.7.0 the libsubmit repository has been merged into Parsl - and nothing more will appear in this changelog. - -Libsubmit 0.4.1 ---------------- - -Released. June 18th, 2018. -This release folds in massive contributions from @annawoodard. - -New functionality -^^^^^^^^^^^^^^^^^ - -* Several code cleanups, doc improvements, and consistent naming - -* All providers have the initialization and actual start of resources decoupled. - - - -Libsubmit 0.4.0 ---------------- - -Released. May 15th, 2018. -This release folds in contributions from @ahayschi, @annawoodard, @yadudoc - -New functionality -^^^^^^^^^^^^^^^^^ - -* Several enhancements and fixes to the AWS cloud provider (#44, #45, #50) - -* Added support for python3.4 - - -Bug Fixes -^^^^^^^^^ - -* Condor jobs left in queue with X state at end of completion `issue#26 `_ - -* Worker launches on Cori seem to fail from broken ENV `issue#27 `_ - -* EC2 provider throwing an exception at initial run `issue#46 `_ diff --git a/parsl/docs/historical/design.rst b/parsl/docs/historical/design.rst deleted file mode 100644 index f32e0c92..00000000 --- a/parsl/docs/historical/design.rst +++ /dev/null @@ -1,151 +0,0 @@ -Historical: Swift vs Parsl --------------------------- - -.. note:: - This section describes comparisons between Parsl and an earlier workflow - system, Swift, as part of a justification for the early prototyping stages - of Parsl development. It is no longer relevant for modern Parsl users, but - remains of historical interest. - -The following text is not well structured, and is mostly a brain dump that needs to be organized. -Moving from Swift to an established language (python) came with its own tradeoffs. We get the backing -of a rich and very well known language to handle the language aspects as well as the libraries. -However, we lose the parallel evaluation of every statement in a script. The thesis is that what we -lose is minimal and will not affect 95% of our workflows. This is not yet substantiated. - -Please note that there are two Swift languages: `Swift/K `_ -and `Swift/T `_ . These have diverged in syntax and behavior. -Swift/K is designed for grids and clusters runs the java based -`Karajan `_ (hence, /K) execution framework. -Swift/T is a completely new implementation of Swift/K for high-performance computing. Swift/T uses -Turbine(hence, /T) and and -`ADLB `_ runtime libraries for -highly scalable dataflow processing over MPI, -without single-node bottlenecks. - - -Parallel Evaluation -^^^^^^^^^^^^^^^^^^^ - -In Swift (K&T), every statement is evaluated in parallel. - -.. code-block:: c - - y = f(x); - z = g(x); - -We see that y and z are assigned values in different order when we run Swift multiple times. Swift -evaluates both statements in parallel and the order in which they complete is mostly random. - -We will *not* have this behavior in Python. Each statement is evaluated in order. - -.. code-block:: c - - int[] array; - foreach v,i in [1:5] { - array[i] = 2*v; - } - - foreach v in array { - trace(v) - } - -Another consequence is that in Swift, a foreach loop that consumes results in an array need -not wait for the foreach loop that fill the array. In the above example, the second foreach -loop makes progress along with the first foreach loop as it fills the array. - -In parsl, a for loop that **launches** tasks has to complete launches before the control may -proceed to the next statement. The first for loop has to simply finish iterating, and launching -jobs, which should take ~length_of_iterable/1000 (items/task_launch_rate). - -.. code-block:: python - - futures = {}; - - for i in range(0,10): - futures[i] = app_double(i); - - for i in fut_array: - print(i, futures[i]) - -The first for loop first fills the futures dict before control can proceed to the second for -loop that consumes the contents. - -The main conclusion here is that, if the iteration space is sufficiently large (or the app -launches are throttled), then it is possible that tasks that are further down the control -flow have to wait regardless of their dependencies being resolved. - - -Mappers -^^^^^^^ - -In Swift/K, a mapper is a mechanism to map files to variables. Swift need's to know files -on disk so that it could move them to remote sites for execution or as inputs to applications. -Mapped file variables also indicate to swift that, when files are created on remote sites, they -need to be staged back. Swift/K provides several mappers which makes it convenient to map files on -disk to file variables. - -There are two choices here : - -1. Have the user define the mappers and data objects -2. Have the data objects be created only by Apps. - - -In Swift, the user defines file mappings like this : - -.. code-block:: c - - # Mapping a single file - file f <"f.txt">; - - # Array of files - file texts[] ; - -The files mapped to an array could be either inputs or outputs to be created. Which is the case is -inferred from whether they are on the left-hand side or right-hand side of an assignment. Variables on -the left-hand side are inferred to be outputs that have future-like behavior. To avoid conflicting -values being assigned to the same variable, Swift variables are all immutable. - -For instance, the following would be a major concern *if* variables were not immutable: - -.. code-block:: c - - x = 0; - x = 1; - trace(x); - -The results that trace would print would be non-deterministic, if x were mutable. In Swift, the above -code would raise an error. However this is perfectly legal in python, and the x would take the last -value it was assigned. - -Remote-Execution -^^^^^^^^^^^^^^^^ - -In Swift/K, remote execution is handled by `coasters `_. -This is a pilot mechanism that supports dynamic resource provisioning from cluster managers such as PBS, -Slurm, Condor and handles data transport from the client to the workers. Swift/T on the other hand is -designed to run as an MPI job on a single HPC resource. Swift/T utilized shared-filesystems that almost -every HPC resource has. - -To be useful, Parsl will need to support remote execution and file transfers. Here we will discuss just -the remote-execution aspect. - -Here is a set of features that should be implemented or borrowed : - -* [Done] New remote execution system must have the `executor interface `_. -* [Done] Executors must be memory efficient wrt to holding jobs in memory. -* [Done] Continue to support both BashApps and PythonApps. -* [Done] Capable of using templates to submit jobs to Cluster resource managers. -* [Done] Dynamically launch and shutdown workers. - -.. note:: - Since the current roadmap to remote execution is through ipython-parallel, we will limit support - to Python3.5+ to avoid library naming issues. - - -====== -Design -====== - -Under construction. - diff --git a/parsl/docs/historical/index.rst b/parsl/docs/historical/index.rst deleted file mode 100644 index 5575a355..00000000 --- a/parsl/docs/historical/index.rst +++ /dev/null @@ -1,9 +0,0 @@ -Historical Documents -==================== - -.. toctree:: - :maxdepth: 2 - - changelog - design - performance diff --git a/parsl/docs/historical/performance.rst b/parsl/docs/historical/performance.rst deleted file mode 100644 index 4f9f8bad..00000000 --- a/parsl/docs/historical/performance.rst +++ /dev/null @@ -1,92 +0,0 @@ -.. _label-performance: - -Historical: Performance and Scalability -======================================= - -.. note:: - This scalability review summarises results in a paper, Parsl: Pervasive - Parallel Programming in Python, which was published in 2019. The results - have not been updated since then. For that reason, this section is marked - as historical. - -Parsl is designed to scale from small to large systems . - - -Scalability ------------ -We studied strong and weak scaling on the Blue Waters supercomputer. -In strong scaling, the total problem size is fixed; in weak scaling, the problem -size per CPU core is fixed. In both cases, we measure completion -time as a function of number of CPU cores. An ideal framework -should scale linearly, which for strong scaling means that speedup -scales with the number of cores, and for weak scaling means that -completion time remains constant as the number of cores increases. - -To measure the strong and weak scaling of Parsl executors, we -created Parsl programs to run tasks with different durations, ranging from a -"no-op"--a Python function that exits immediately---to -tasks that sleep for 10, 100, and 1,000 ms. For each executor we -deployed a worker per core on each node. - -While we compare here with IPP, Fireworks, and Dask Distributed, -we note that these systems are not necessarily designed for -Parsl-like workloads or scale. - -Further results are presented in our -`HPDC paper `_. - -Strong scaling -^^^^^^^^^^^^^^ -The figures below show the strong scaling results for 5,000 1-second -sleep tasks. HTEX -provides good performance in all cases, slightly exceeding what is -possible with EXEX, while EXEX scales to significantly more workers -than the other executors and frameworks. Both -HTEX and EXEX remain nearly constant, indicating that they likely -will continue to perform well at larger scales. - -.. image:: ../images/performance/strong-scaling.png - - -Weak scaling -^^^^^^^^^^^^ -Here, we launched 10 tasks per worker, while -increasing the number of workers. (We limited experiments to 10 -tasks per worker, as on 3,125 nodes, that represents 3,125 -nodes × 32 workers/node × 10 tasks/worker, or 1M tasks.) The -figure below shows our results. We observe that HTEX -and EXEX outperform other executors and frameworks with more -than 4,096 workers (128 nodes). All frameworks exhibit similar -trends, with completion time remaining close to constant initially -and increasing rapidly as the number of workers increases. - -.. image:: ../images/performance/weak-scaling.png - - -Throughput ----------- -We measured the maximum throughput of all the Parsl executors, -on the UChicago Research Computing Center's Midway Cluster. -To do so, we ran 50,000 “no-op" tasks on a varying number of -workers and recorded the completion times. The throughout is -computed as the number of tasks divided by the completion time. -HTEX, and EXEX achieved maximum throughputs of 1,181 and 1,176 -tasks/s, respectively. - -Summary -------- - -The table below summarizes the scale at which we have tested Parsl executors. -The maximum number of nodes and workers for HTEX and EXEX is limited -by the size of allocation available during testing on Blue Waters. -The throughput results are collected on Midway. - -+-----------+------------------+-------------+------------------+ -| Executor | Max # workers | Max # nodes | Max tasks/second | -+===========+==================+=============+==================+ -| IPP | 2,048 | 64 | 330 | -+-----------+------------------+-------------+------------------+ -| HTEX | 65,536 | 2,048 | 1,181 | -+-----------+------------------+-------------+------------------+ -| EXEX | 262,144 | 8,192 | 1,176 | -+-----------+------------------+-------------+------------------+ diff --git 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expressing multi-step workflows of functions. -Parsl lets you chain functions together and will launch each function as inputs and computing resources are available. - -.. code-block:: python - - import parsl - from parsl import python_app - - # Start Parsl on a single computer - parsl.load() - - # Make functions parallel by decorating them - @python_app - def f(x): - return x + 1 - - @python_app - def g(x, y): - return x + y - - # These functions now return Futures, and can be chained - future = f(1) - assert future.result() == 2 - - # Functions run concurrently, can be chained - f_a, f_b = f(2), f(3) - future = g(f_a, f_b) - assert future.result() == 7 - - -Start with the `configuration quickstart `_ to learn how to tell Parsl how to use your computing resource, -see if `a template configuration for your supercomputer `_ is already available, -then explore the `parallel computing patterns `_ to determine how to use parallelism best in your application. - -Parsl is an open-source code, and available on GitHub: https://github.com/parsl/parsl/ - -Why use Parsl? -============== - -Parsl is Python ---------------- - -*Everything about a Parsl program is written in Python.* -Parsl follows Python's native parallelization approach and functions, -how they combine into workflows, and where they run are all described in Python. - -Parsl works everywhere ----------------------- - -*Parsl can run parallel functions on a laptop and the world's fastest supercomputers.* -Scaling from laptop to supercomputer is often as simple as changing the resource configuration. -Parsl is tested `on many of the top supercomputers `_. - -Parsl is flexible ------------------ - -.. - Is this redundant with 'everywhere'? - -*Parsl supports many kinds of applications.* -Functions can be pure Python or invoke external codes, be single- or multi-threaded or GPUs. - - -Parsl handles data ------------------- - -*Parsl has first-class support for workflows involving files.* -Data will be automatically moved between workers, even if they reside on different filesystems. - -Parsl is fast -------------- - -*Parsl was built for speed.* -Workflows can manage tens of thousands of parallel tasks and process thousands of tasks per second. - - -Parsl is a community --------------------- - -*Parsl is part of a large, experienced community.* - -The Parsl Project was launched by researchers with decades of experience in workflows -as part of a National Science Foundation project to create sustainable research software. - -The Parsl team is guided by the community through its GitHub, -conversations on `Slack `_, -Bi-Weekly developer calls, -and engagement with the `Workflows Community Initiative `_. - -.. - I was going to work in how we integrate with other tools, but that seemed too-detailed in this draft - -Table of Contents -+++++++++++++++++ - -.. toctree:: - :maxdepth: 2 - - quickstart - 1-parsl-introduction.ipynb - userguide/index - userguide/glossary - faq - reference - devguide/index - historical/index - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/parsl/docs/libsubmit_art/README.txt b/parsl/docs/libsubmit_art/README.txt deleted file mode 100644 index c3611b89..00000000 --- a/parsl/docs/libsubmit_art/README.txt +++ /dev/null @@ -1,3 +0,0 @@ -Edittable diagrams embedded in this google doc: -https://docs.google.com/document/d/193LBq7H-dtxrYUER7oZqs0ZlOcLa2fGFPGLOHRS1c5U/edit?usp=sharing - diff --git a/parsl/docs/libsubmit_art/multi_node.png b/parsl/docs/libsubmit_art/multi_node.png deleted file mode 100644 index 058457b6abd213dfe25ae794ac1712a757a5dc8c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 25318 zcmeFZcUY6zw=W*Vkr@%NgVa$NK~WH;S35?UAd1q$fQa-?fDnQ@ihzKE5TuS&1q6{E zAi+Wrgh-HHLXi?mLJ0&ClHB(V;xKd0xzG3B-#O?0p8MSS$1v~StL?S+UVE+2`-WXH z)aBbQyd468@Ifz~H-SKY0)BGW{=5zN$Ep8o00d(D8hZYWsekXx0Pi!RFLDAE)o~6T 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z_zfh%|Gz&bZ{QAO8{hxqJUBxE+@}8d{e!?i2>gS - - - diff --git a/parsl/docs/quickstart.rst b/parsl/docs/quickstart.rst deleted file mode 100644 index d7caf888..00000000 --- a/parsl/docs/quickstart.rst +++ /dev/null @@ -1,270 +0,0 @@ -Quickstart -========== - -To try Parsl now (without installing any code locally), experiment with our -`hosted tutorial notebooks on Binder `_. - - -Installation ------------- - -Parsl is available on `PyPI `_ and `conda-forge `_. - -Parsl requires Python3.10+ and has been tested on Linux. - - -Installation using Pip -^^^^^^^^^^^^^^^^^^^^^^ - -While ``pip`` can be used to install Parsl, we suggest the following approach -for reliable installation when many Python environments are available. - -1. Install Parsl:: - - $ python3 -m pip install parsl - -To update a previously installed parsl to a newer version, use: ``python3 -m pip install -U parsl`` - - -Installation using Conda -^^^^^^^^^^^^^^^^^^^^^^^^ - -1. Create and activate a new conda environment:: - - $ conda create --name parsl_py310 python=3.10 - $ source activate parsl_py310 - -2. Install Parsl:: - - $ python3 -m pip install parsl - - or - - $ conda config --add channels conda-forge - $ conda install parsl - - -The conda documentation provides `instructions `_ for installing conda on macOS and Linux. - -Getting started ---------------- - -.. image:: images/high-level.png - :alt: Relationship between Parsl's main components - :align: center - - -Parsl has much in common with Python's native concurrency library, -but unlocking Parsl's potential requires understanding a few major concepts. - -A Parsl program submits tasks to run on Workers distributed across remote computers. -The instructions for these tasks are contained within `"apps" <#application-types>`_ -that users define using Python functions. -Each remote computer (e.g., a node on a supercomputer) has a single `"Executor" <#executors>`_ -which manages the workers. -Remote resources available to Parsl are acquired by a `"Provider" <#execution-providers>`_, -which places the executor on a system with a `"Launcher" <#launchers>`_. -Task execution is brokered by a `"Data Flow Kernel" <#benefits-of-a-data-flow-kernel>`_ that runs on your local system. - -We describe these components briefly here, and link to more details in the `User Guide `_. - -.. note:: - - Parsl's documentation includes `templates for many supercomputers `_. - Even though you may not need to write a configuration from a blank slate, - understanding the basic terminology below will be very useful. - - -Application Types -^^^^^^^^^^^^^^^^^ - -Parsl enables concurrent execution of Python functions (``python_app``) -or external applications (``bash_app``). -The logic for both are described by Python functions marked with Parsl decorators. -When decorated functions are invoked, they run asynchronously on other resources. -The result of a call to a Parsl app is an :class:`~parsl.app.futures.AppFuture`, -which behaves like a Python Future. - -The following example shows how to write a simple Parsl program -with hello world Python and Bash apps. - -.. code-block:: python - - import parsl - from parsl import python_app, bash_app - - @python_app - def hello_python (message): - return 'Hello %s' % message - - @bash_app - def hello_bash(message, stdout='hello-stdout'): - return 'echo "Hello %s"' % message - - - with parsl.load(): - # invoke the Python app and print the result - print(hello_python('World (Python)').result()) - - # invoke the Bash app and read the result from a file - hello_bash('World (Bash)').result() - - with open('hello-stdout', 'r') as f: - print(f.read()) - -Learn more about the types of Apps and their options `here `__. - -Executors -^^^^^^^^^ - -Executors define how Parsl deploys work on a computer. -Many types are available, each with different advantages. - -The :class:`~parsl.executors.high_throughput.executor.HighThroughputExecutor`, -like Python's ``ProcessPoolExecutor``, creates workers that are separate Python processes. -However, you have much more control over how the work is deployed. -You can dynamically set the number of workers based on available memory and -pin each worker to specific GPUs or CPU cores -among other powerful features. - -Learn more about Executors `here `__. - -Execution Providers -^^^^^^^^^^^^^^^^^^^ - -Resource providers allow Parsl to gain access to computing power. -For supercomputers, gaining resources often requires requesting them from a scheduler (e.g., Slurm). -Parsl Providers write the requests to requisition **"Blocks"** (e.g., supercomputer nodes) on your behalf. -Parsl comes pre-packaged with Providers compatible with most supercomputers and some cloud computing services. - -Another key role of Providers is defining how to start an Executor on a remote computer. -Often, this simply involves specifying the correct Python environment and -(described below) how to launch the Executor on each acquired computers. - -Learn more about Providers `here `__. - -Launchers -^^^^^^^^^ - -The Launcher defines how to spread workers across all nodes available in a Block. -A common example is an :class:`~parsl.launchers.launchers.MPILauncher`, which uses MPI's mechanism -for starting a single program on multiple computing nodes. -Like Providers, Parsl comes packaged with Launchers for most supercomputers and clouds. - -Learn more about Launchers `here `__. - - -Benefits of a Data-Flow Kernel -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The Data-Flow Kernel (DFK) is the behind-the-scenes engine behind Parsl. -The DFK determines when tasks can be started and sends them to open resources, -receives results, restarts failed tasks, propagates errors to dependent tasks, -and performs the many other functions needed to execute complex workflows. -The flexibility and performance of the DFK enables applications with -intricate dependencies between tasks to execute on thousands of parallel workers. - -Start with the Tutorial or the `parallel patterns `_ -to see the complex types of workflows you can make with Parsl. - -Starting Parsl -^^^^^^^^^^^^^^ - -A Parsl script must contain the function definitions, resource configuration, and a call to ``parsl.load`` -before launching tasks. -This script runs on a system that must stay on-line until all of your tasks complete but need not have -much computing power, such as the login node for a supercomputer. - -The :class:`~parsl.config.Config` object holds definitions of Executors and the Providers and Launchers they rely on. -An example which launches 4 workers on 1 node of the Polaris supercomputer looks like - -.. code-block:: python - - from parsl import Config - from parsl.executors import HighThroughputExecutor - from parsl.providers import PBSProProvider - from parsl.launchers import MpiExecLauncher - - config = Config( - retries=1, # Restart task if they fail once - executors=[ - HighThroughputExecutor( - available_accelerators=4, # Maps one worker per GPU - address=address_by_hostname(), - cpu_affinity="alternating", # Prevents thread contention - provider=PBSProProvider( - account="example", - worker_init="module load conda; conda activate parsl", - walltime="1:00:00", - queue="debug", - scheduler_options="#PBS -l filesystems=home:eagle", # Change if data on other filesystem - launcher=MpiExecLauncher( - bind_cmd="--cpu-bind", overrides="--depth=64 --ppn 1" - ), # Ensures 1 manger per node and allows it to divide work to all 64 cores - select_options="ngpus=4", - nodes_per_block=1, - cpus_per_node=64, - ), - ), - ] - ) - - -The documentation has examples for other supercomputers `here `_. - -The next step is to load the configuration - -.. code-block:: python - - parsl.load(config) - -You are then ready to use 10 PFLOPS of computing power through Python! - -Tutorial --------- - -The best way to learn more about Parsl is by reviewing the Parsl tutorials. -There are several options for following the tutorial: - -1. Use `Binder `_ to follow the tutorial online without installing or writing any code locally. -2. Clone the `Parsl tutorial repository `_ using a local Parsl installation. -3. Read through the online `tutorial documentation <1-parsl-introduction.html>`_. - - -Usage Tracking --------------- - -To help support the Parsl project, we ask that users opt-in to anonymized usage tracking -whenever possible. Usage tracking allows us to measure usage, identify bugs, and improve -usability, reliability, and performance. Only aggregate usage statistics will be used -for reporting purposes. - -As an NSF-funded project, our ability to track usage metrics is important for continued funding. - -You can opt-in by setting ``usage_tracking=3`` in the configuration object (`parsl.config.Config`). - -To read more about what information is collected and how it is used see :ref:`label-usage-tracking`. - - -For Developers --------------- - -Parsl is an open source community that encourages contributions from users -and developers. A guide for `contributing `_ -to Parsl is available in the `Parsl GitHub repository `_. - -The following instructions outline how to set up Parsl from source. - -1. Download Parsl:: - - $ git clone https://github.com/Parsl/parsl - -2. Install:: - - $ cd parsl - $ pip install . - ( To install specific extra options from the source :) - $ pip install '.[...]' - -3. Use Parsl! - diff --git a/parsl/docs/reference.rst b/parsl/docs/reference.rst deleted file mode 100644 index 979139c4..00000000 --- a/parsl/docs/reference.rst +++ /dev/null @@ -1,244 +0,0 @@ -API Reference guide -******************* - -Core -==== - -.. autosummary:: - :toctree: stubs - :nosignatures: - - parsl.app.app.python_app - parsl.app.app.bash_app - parsl.app.app.join_app - parsl.dataflow.futures.AppFuture - parsl.dataflow.dflow.DataFlowKernelLoader - parsl.concurrent.ParslPoolExecutor - parsl.dataflow.dependency_resolvers.DependencyResolver - parsl.dataflow.dependency_resolvers.DEEP_DEPENDENCY_RESOLVER - parsl.dataflow.dependency_resolvers.SHALLOW_DEPENDENCY_RESOLVER - -Configuration -============= - -.. autosummary:: - :toctree: stubs - :nosignatures: - - parsl.config.Config - parsl.set_stream_logger - parsl.set_file_logger - parsl.addresses.address_by_hostname - parsl.addresses.address_by_interface - parsl.addresses.address_by_query - parsl.addresses.address_by_route - parsl.addresses.get_all_addresses - parsl.addresses.get_any_address - parsl.utils.get_all_checkpoints - parsl.utils.get_last_checkpoint - -Channels -======== - -Channels are deprecated in Parsl. See -`issue 3515 `_ -for further discussion. - -Data management -=============== - -.. autosummary:: - :toctree: stubs - :nosignatures: - - parsl.app.futures.DataFuture - parsl.data_provider.data_manager.DataManager - parsl.data_provider.staging.Staging - parsl.data_provider.files.File - parsl.data_provider.ftp.FTPSeparateTaskStaging - parsl.data_provider.ftp.FTPInTaskStaging - parsl.data_provider.file_noop.NoOpFileStaging - parsl.data_provider.globus.GlobusStaging - parsl.data_provider.http.HTTPSeparateTaskStaging - parsl.data_provider.http.HTTPInTaskStaging - parsl.data_provider.rsync.RSyncStaging - -Executors -========= - -.. autosummary:: - :toctree: stubs - :nosignatures: - - parsl.executors.base.ParslExecutor - parsl.executors.status_handling.BlockProviderExecutor - parsl.executors.ThreadPoolExecutor - parsl.executors.HighThroughputExecutor - parsl.executors.MPIExecutor - parsl.executors.WorkQueueExecutor - parsl.executors.taskvine.TaskVineExecutor - parsl.executors.FluxExecutor - parsl.executors.radical.RadicalPilotExecutor - parsl.executors.GlobusComputeExecutor - -Manager Selectors -================= - -.. autosummary:: - :toctree: stubs - :nosignatures: - - parsl.executors.high_throughput.manager_selector.RandomManagerSelector - parsl.executors.high_throughput.manager_selector.BlockIdManagerSelector - -Launchers -========= - -.. autosummary:: - :toctree: stubs - :nosignatures: - - parsl.launchers.base.Launcher - parsl.launchers.SimpleLauncher - parsl.launchers.SingleNodeLauncher - parsl.launchers.SrunLauncher - parsl.launchers.AprunLauncher - parsl.launchers.SrunMPILauncher - parsl.launchers.GnuParallelLauncher - parsl.launchers.MpiExecLauncher - parsl.launchers.MpiRunLauncher - parsl.launchers.JsrunLauncher - parsl.launchers.WrappedLauncher - -Providers -========= - -.. autosummary:: - :toctree: stubs - :nosignatures: - - parsl.providers.AWSProvider - parsl.providers.CondorProvider - parsl.providers.GoogleCloudProvider - parsl.providers.GridEngineProvider - parsl.providers.LocalProvider - parsl.providers.LSFProvider - parsl.providers.SlurmProvider - parsl.providers.TorqueProvider - parsl.providers.KubernetesProvider - parsl.providers.PBSProProvider - parsl.providers.base.ExecutionProvider - parsl.providers.cluster_provider.ClusterProvider - -Batch jobs -========== - -.. autosummary:: - :toctree: stubs - :nosignatures: - - parsl.jobs.states.JobState - parsl.jobs.states.JobStatus - parsl.jobs.error_handlers.noop_error_handler - parsl.jobs.error_handlers.simple_error_handler - parsl.jobs.error_handlers.windowed_error_handler - -Checkpointing and Memoization -============================= - -.. autosummary:: - :toctree: stubs - :nosignatures: - - parsl.dataflow.memoization.Memoizer - parsl.dataflow.memoization.BasicMemoizer - -Exceptions -========== - -.. autosummary:: - :toctree: stubs - :nosignatures: - - parsl.app.errors.AppBadFormatting - parsl.app.errors.AppException - parsl.app.errors.AppTimeout - parsl.app.errors.BadStdStreamFile - parsl.app.errors.BashAppNoReturn - parsl.app.errors.BashExitFailure - parsl.app.errors.MissingOutputs - parsl.app.errors.ParslError - parsl.errors.ConfigurationError - parsl.errors.OptionalModuleMissing - parsl.executors.errors.ExecutorError - parsl.executors.errors.ScalingFailed - parsl.executors.errors.BadMessage - parsl.dataflow.errors.DataFlowException - parsl.dataflow.errors.BadCheckpoint - parsl.dataflow.errors.DependencyError - parsl.dataflow.errors.JoinError - parsl.launchers.errors.BadLauncher - parsl.providers.errors.ExecutionProviderException - parsl.providers.errors.ScaleOutFailed - parsl.providers.errors.SchedulerMissingArgs - parsl.providers.errors.ScriptPathError - parsl.executors.high_throughput.errors.WorkerLost - parsl.executors.high_throughput.errors.ManagerLost - parsl.serialize.errors.DeserializationError - parsl.serialize.errors.SerializationError - -Logging -======= - -.. autosummary:: - :toctree: stubs - :nosignatures: - - parsl.logconfigs.base.LogConfig - parsl.logconfigs.noop.NoopLogging - -Monitoring -========== - -.. autosummary:: - :toctree: stubs - :nosignatures: - - parsl.monitoring.MonitoringHub - parsl.monitoring.radios.base.MonitoringRadioReceiver - parsl.monitoring.radios.base.MonitoringRadioSender - parsl.monitoring.radios.base.RadioConfig - parsl.monitoring.radios.filesystem.FilesystemRadio - parsl.monitoring.radios.htex.HTEXRadio - parsl.monitoring.radios.udp.UDPRadio - parsl.monitoring.radios.multiprocessing.MultiprocessingQueueRadio - - -Internal -======== - -.. autosummary:: - :toctree: stubs - :nosignatures: - - parsl.app.app.AppBase - parsl.app.bash.BashApp - parsl.app.python.PythonApp - parsl.dataflow.dflow.DataFlowKernel - parsl.dataflow.memoization.id_for_memo - parsl.dataflow.states.FINAL_STATES - parsl.dataflow.states.States - parsl.dataflow.taskrecord.TaskRecord - parsl.jobs.job_status_poller.JobStatusPoller - parsl.jobs.strategy.Strategy - parsl.utils.Timer - -Task Vine configuration -======================= - -.. autosummary:: - :toctree: stubs - :nosignatures: - - parsl.executors.taskvine.TaskVineManagerConfig - parsl.executors.taskvine.TaskVineFactoryConfig diff --git a/parsl/docs/teaching_scripts/README.md b/parsl/docs/teaching_scripts/README.md deleted file mode 100644 index 67f80899..00000000 --- a/parsl/docs/teaching_scripts/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# Example Scripts - -Scripts which illustrate example from the documentation that do not run well as part of the pytest diff --git a/parsl/docs/teaching_scripts/test_apps.py b/parsl/docs/teaching_scripts/test_apps.py deleted file mode 100644 index f05a5f7f..00000000 --- a/parsl/docs/teaching_scripts/test_apps.py +++ /dev/null @@ -1,83 +0,0 @@ -"""Tests documentation related to building apps. Must reside outside the Parsl library to be effective""" -from typing import List, Union - -import numpy as np - -import parsl -from parsl import Config, HighThroughputExecutor, python_app - -parsl.load(Config(executors=[HighThroughputExecutor(label='htex_spawn', max_workers_per_node=1, address='127.0.0.1')])) - - -# Part 1: Explain imports -# BAD: Assumes library has been imported -@python_app(executors=['htex_spawn']) -def bad_imports(x: Union[List[float], np.ndarray], m: float, b: float): - return np.multiply(x, m) + b - - -# GOOD: Imports libraries itself -@python_app(executors=['htex_spawn']) -def good_imports(x: Union[List[float], 'np.ndarray'], m: float, b: float): - import numpy as np - return np.multiply(x, m) + b - - -future = bad_imports([1.], 1, 0) - -try: - future.result() - raise ValueError() -except NameError as e: - print('Failed, as expected. Error:', e) - -future = good_imports([1.], 1, 0) -print(f'Passed, as expected: {future.result()}') - -# Part 2: Test other types of globals -# BAD: Uses global variables -global_var = {'a': 0} - - -@python_app -def bad_global(string: str, character: str = 'a'): - global_var[character] += string.count(character) # `global_var` will not be accessible - - -# GOOD -@python_app -def good_global(string: str, character: str = 'a'): - return {character: string.count(character)} - - -try: - bad_global('parsl').result() -except NameError as e: - print(f'Failed, as expected: {e}') - -for ch, co in good_global('parsl', 'a').result().items(): - global_var[ch] += co - - -# Part 3: Mutable args - -# BAD: Assumes changes to inputs will be communicated -@python_app -def append_to_list(input_list: list, new_val): - input_list.append(new_val) - - -mutable_arg = [] -append_to_list(mutable_arg, 1).result() -assert mutable_arg == [], 'The list _was_changed' - - -# GOOD: Changes to inputs are returned -@python_app -def append_to_list(input_list: list, new_val) -> list: - input_list.append(new_val) - return input_list - - -mutable_arg = append_to_list(mutable_arg, 1).result() -assert mutable_arg == [1] diff --git a/parsl/docs/userguide/advanced/data.rst b/parsl/docs/userguide/advanced/data.rst deleted file mode 100644 index 7fc89966..00000000 --- a/parsl/docs/userguide/advanced/data.rst +++ /dev/null @@ -1,416 +0,0 @@ -.. _label-data: - -Staging data files -================== - -Parsl apps can take and return data files. A file may be passed as an input -argument to an app, or returned from an app after execution. Parsl -provides support to automatically transfer (stage) files between -the main Parsl program, worker nodes, and external data storage systems. - -Input files can be passed as regular arguments, or a list of them may be -specified in the special ``inputs`` keyword argument to an app invocation. - -Inside an app, the ``filepath`` attribute of a `File` can be read to determine -where on the execution-side file system the input file has been placed. - -Output `File` objects must also be passed in at app invocation, through the -outputs parameter. In this case, the `File` object specifies where Parsl -should place output after execution. - -Inside an app, the ``filepath`` attribute of an output -`File` provides the path at which the corresponding output file should be -placed so that Parsl can find it after execution. - -If the output from an app is to be used as the input to a subsequent app, -then a `DataFuture` that represents whether the output file has been created -must be extracted from the first app's AppFuture, and that must be passed -to the second app. This causes app -executions to be properly ordered, in the same way that passing AppFutures -to subsequent apps causes execution ordering based on an app returning. - -In a Parsl program, file handling is split into two pieces: files are named in an -execution-location independent manner using :py:class:`~parsl.data_provider.files.File` -objects, and executors are configured to stage those files in to and out of -execution locations using instances of the :py:class:`~parsl.data_provider.staging.Staging` -interface. - - -Parsl files ------------ - -Parsl uses a custom :py:class:`~parsl.data_provider.files.File` to provide a -location-independent way of referencing and accessing files. -Parsl files are defined by specifying the URL *scheme* and a path to the file. -Thus a file may represent an absolute path on the submit-side file system -or a URL to an external file. - -The scheme defines the protocol via which the file may be accessed. -Parsl supports the following schemes: file, ftp, http, https, and globus. -If no scheme is specified Parsl will default to the file scheme. - -The following example shows creation of two files with different -schemes: a locally-accessible data.txt file and an HTTPS-accessible -README file. - -.. code-block:: python - - File('file://home/parsl/data.txt') - File('https://github.com/Parsl/parsl/blob/master/README.rst') - - -Parsl automatically translates the file's location relative to the -environment in which it is accessed (e.g., the Parsl program or an app). -The following example shows how a file can be accessed in the app -irrespective of where that app executes. - -.. code-block:: python - - @python_app - def print_file(inputs=()): - with open(inputs[0].filepath, 'r') as inp: - content = inp.read() - return(content) - - # create an remote Parsl file - f = File('https://github.com/Parsl/parsl/blob/master/README.rst') - - # call the print_file app with the Parsl file - r = print_file(inputs=[f]) - r.result() - -As described below, the method by which this files are transferred -depends on the scheme and the staging providers specified in the Parsl -configuration. - -Staging providers -+++++++++++++++++ - -Parsl is able to transparently stage files between at-rest locations and -execution locations by specifying a list of -:py:class:`~parsl.data_provider.staging.Staging` instances for an executor. -These staging instances define how to transfer files in and out of an execution -location. This list should be supplied as the ``storage_access`` -parameter to an executor when it is constructed. - -Parsl includes several staging providers for moving files using the -schemes defined above. By default, Parsl executors are created with -three common staging providers: -the NoOpFileStaging provider for local and shared file systems -and the HTTP(S) and FTP staging providers for transferring -files to and from remote storage locations. The following -example shows how to explicitly set the default staging providers. - -.. code-block:: python - - from parsl.config import Config - from parsl.executors import HighThroughputExecutor - from parsl.data_provider.data_manager import default_staging - - config = Config( - executors=[ - HighThroughputExecutor( - storage_access=default_staging, - # equivalent to the following - # storage_access=[NoOpFileStaging(), FTPSeparateTaskStaging(), HTTPSeparateTaskStaging()], - ) - ] - ) - - -Parsl further differentiates when staging occurs relative to -the app invocation that requires or produces files. -Staging either occurs with the executing task (*in-task staging*) -or as a separate task (*separate task staging*) before app execution. -In-task staging -uses a wrapper that is executed around the Parsl task and thus -occurs on the resource on which the task is executed. Separate -task staging inserts a new Parsl task in the graph and associates -a dependency between the staging task and the task that depends -on that file. Separate task staging may occur on either the submit-side -(e.g., when using Globus) or on the execution-side (e.g., HTTPS, FTP). - - -NoOpFileStaging for Local/Shared File Systems -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The NoOpFileStaging provider assumes that files specified either -with a path or with the ``file`` URL scheme are available both -on the submit and execution side. This occurs, for example, when there is a -shared file system. In this case, files will not moved, and the -File object simply presents the same file path to the Parsl program -and any executing tasks. - -Files defined as follows will be handled by the NoOpFileStaging provider. - -.. code-block:: python - - File('file://home/parsl/data.txt') - File('/home/parsl/data.txt') - - -The NoOpFileStaging provider is enabled by default on all -executors. It can be explicitly set as the only -staging provider as follows. - -.. code-block:: python - - from parsl.config import Config - from parsl.executors import HighThroughputExecutor - from parsl.data_provider.file_noop import NoOpFileStaging - - config = Config( - executors=[ - HighThroughputExecutor( - storage_access=[NoOpFileStaging()] - ) - ] - ) - - -FTP, HTTP, HTTPS: separate task staging -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Files named with the ``ftp``, ``http`` or ``https`` URL scheme will be -staged in using HTTP GET or anonymous FTP commands. These commands -will be executed as a separate -Parsl task that will complete before the corresponding app -executes. These providers cannot be used to stage out output files. - -The following example defines a file accessible on a remote FTP server. - -.. code-block:: python - - File('ftp://www.iana.org/pub/mirror/rirstats/arin/ARIN-STATS-FORMAT-CHANGE.txt') - -When such a file object is passed as an input to an app, Parsl will download the file to whatever location is selected for the app to execute. -The following example illustrates how the remote file is implicitly downloaded from an FTP server and then converted. Note that the app does not need to know the location of the downloaded file on the remote computer, as Parsl abstracts this translation. - -.. code-block:: python - - @python_app - def convert(inputs=(), outputs=()): - with open(inputs[0].filepath, 'r') as inp: - content = inp.read() - with open(outputs[0].filepath, 'w') as out: - out.write(content.upper()) - - # create an remote Parsl file - inp = File('ftp://www.iana.org/pub/mirror/rirstats/arin/ARIN-STATS-FORMAT-CHANGE.txt') - - # create a local Parsl file - out = File('file:///tmp/ARIN-STATS-FORMAT-CHANGE.txt') - - # call the convert app with the Parsl file - f = convert(inputs=[inp], outputs=[out]) - f.result() - -HTTP and FTP separate task staging providers can be configured as follows. - -.. code-block:: python - - from parsl.config import Config - from parsl.executors import HighThroughputExecutor - from parsl.data_provider.http import HTTPSeparateTaskStaging - from parsl.data_provider.ftp import FTPSeparateTaskStaging - - config = Config( - executors=[ - HighThroughputExecutor( - storage_access=[HTTPSeparateTaskStaging(), FTPSeparateTaskStaging()] - ) - ] - ) - -FTP, HTTP, HTTPS: in-task staging -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -These staging providers are intended for use on executors that do not have -a file system shared between each executor node. - -These providers will use the same HTTP GET/anonymous FTP as the separate -task staging providers described above, but will do so in a wrapper around -individual app invocations, which guarantees that they will stage files to -a file system visible to the app. - -A downside of this staging approach is that the staging tasks are less visible -to Parsl, as they are not performed as separate Parsl tasks. - -In-task staging providers can be configured as follows. - -.. code-block:: python - - from parsl.config import Config - from parsl.executors import HighThroughputExecutor - from parsl.data_provider.http import HTTPInTaskStaging - from parsl.data_provider.ftp import FTPInTaskStaging - - config = Config( - executors=[ - HighThroughputExecutor( - storage_access=[HTTPInTaskStaging(), FTPInTaskStaging()] - ) - ] - ) - - -Globus -^^^^^^ - -The ``Globus`` staging provider is used to transfer files that can be accessed -using Globus. A guide to using Globus is available `here -`_). - -A file using the Globus scheme must specify the UUID of the Globus -endpoint and a path to the file on the endpoint, for example: - -.. code-block:: python - - File('globus://037f054a-15cf-11e8-b611-0ac6873fc732/unsorted.txt') - -Note: a Globus endpoint's UUID can be found in the Globus `Manage Endpoints `_ page. - -There must also be a Globus endpoint available with access to a -execute-side file system, because Globus file transfers happen -between two Globus endpoints. - -Globus Configuration -"""""""""""""""""""" - -In order to manage where files are staged, users must configure the default ``working_dir`` on a remote location. This information is specified in the :class:`~parsl.executors.base.ParslExecutor` via the ``working_dir`` parameter in the :class:`~parsl.config.Config` instance. For example: - -.. code-block:: python - - from parsl.config import Config - from parsl.executors import HighThroughputExecutor - - config = Config( - executors=[ - HighThroughputExecutor( - working_dir="/home/user/data" - ) - ] - ) - -Parsl requires knowledge of the Globus endpoint that is associated with an executor. This is done by specifying the ``endpoint_name`` (the UUID of the Globus endpoint that is associated with the system) in the configuration. - -In some cases, for example when using a Globus `shared endpoint `_ or when a Globus endpoint is mounted on a supercomputer, the path seen by Globus is not the same as the local path seen by Parsl. In this case the configuration may optionally specify a mapping between the ``endpoint_path`` (the common root path seen in Globus), and the ``local_path`` (the common root path on the local file system), as in the following. In most cases, ``endpoint_path`` and ``local_path`` are the same and do not need to be specified. - -.. code-block:: python - - from parsl.config import Config - from parsl.executors import HighThroughputExecutor - from parsl.data_provider.globus import GlobusStaging - from parsl.data_provider.data_manager import default_staging - - config = Config( - executors=[ - HighThroughputExecutor( - working_dir="/home/user/parsl_script", - storage_access=default_staging + [GlobusStaging( - endpoint_uuid="7d2dc622-2edb-11e8-b8be-0ac6873fc732", - endpoint_path="/", - local_path="/home/user" - )] - ) - ] - ) - - -Globus Authorization -"""""""""""""""""""" - -In order to transfer files with Globus, the user must first authenticate. -The first time that Globus is used with Parsl on a computer, the program -will prompt the user to follow an authentication and authorization -procedure involving a web browser. Users can authorize out of band by -running the parsl-globus-auth utility. This is useful, for example, -when running a Parsl program in a batch system where it will be unattended. - -.. code-block:: bash - - $ parsl-globus-auth - Parsl Globus command-line authorizer - If authorization to Globus is necessary, the library will prompt you now. - Otherwise it will do nothing - Authorization complete - -rsync -^^^^^ - -The ``rsync`` utility can be used to transfer files in the ``file`` scheme in configurations where -workers cannot access the submit-side file system directly, such as when executing -on an AWS EC2 instance or on a cluster without a shared file system. -However, the submit-side file system must be exposed using rsync. - -rsync Configuration -""""""""""""""""""" - -``rsync`` must be installed on both the submit and worker side. It can usually be installed -by using the operating system package manager: for example, by ``apt-get install rsync``. - -An `RSyncStaging` option must then be added to the Parsl configuration file, as in the following. -The parameter to RSyncStaging should describe the prefix to be passed to each rsync -command to connect from workers to the submit-side host. This will often be the username -and public IP address of the submitting system. - -.. code-block:: python - - from parsl.data_provider.rsync import RSyncStaging - - config = Config( - executors=[ - HighThroughputExecutor( - storage_access=[HTTPInTaskStaging(), FTPInTaskStaging(), RSyncStaging("benc@" + public_ip)], - ... - ) - ) - -rsync Authorization -""""""""""""""""""" - -The rsync staging provider delegates all authentication and authorization to the -underlying ``rsync`` command. This command must be correctly authorized to connect back to -the submit-side system. The form of this authorization will depend on the systems in -question. - -The following example installs an ssh key from the submit-side file system and turns off host key -checking, in the ``worker_init`` initialization of an EC2 instance. The ssh key must have -sufficient privileges to run ``rsync`` over ssh on the submit-side system. - -.. code-block:: python - - with open("rsync-callback-ssh", "r") as f: - private_key = f.read() - - ssh_init = """ - mkdir .ssh - chmod go-rwx .ssh - - cat > .ssh/id_rsa < .ssh/config < List[Tuple[bool, ...]]: - """Convert a list of nonnegative integers to binary""" - return [convert_to_binary(x) for x in xs] diff --git a/parsl/docs/userguide/advanced/examples/library/config.py b/parsl/docs/userguide/advanced/examples/library/config.py deleted file mode 100644 index 4b2301fb..00000000 --- a/parsl/docs/userguide/advanced/examples/library/config.py +++ /dev/null @@ -1,23 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.providers import LocalProvider - - -def make_local_config(cores_per_worker: int = 1) -> Config: - """Generate a configuration which runs all tasks on the local system - - Args: - cores_per_worker: Number of cores to dedicate for each task - Returns: - Configuration object with the requested settings - """ - return Config( - executors=[ - HighThroughputExecutor( - label="htex_local", - cores_per_worker=cores_per_worker, - cpu_affinity='block', - provider=LocalProvider(), - ) - ], - ) diff --git a/parsl/docs/userguide/advanced/examples/library/logic.py b/parsl/docs/userguide/advanced/examples/library/logic.py deleted file mode 100644 index 769e6158..00000000 --- a/parsl/docs/userguide/advanced/examples/library/logic.py +++ /dev/null @@ -1,15 +0,0 @@ -from typing import Tuple - - -def convert_to_binary(x: int) -> Tuple[bool, ...]: - """Convert a nonnegative integer into a binary - - Args: - x: Number to be converted - Returns: - The binary number represented as list of booleans - """ - if x < 0: - raise ValueError('`x` must be nonnegative') - bin_as_string = bin(x) - return tuple(i == '1' for i in bin_as_string[2:]) diff --git a/parsl/docs/userguide/advanced/examples/pyproject.toml b/parsl/docs/userguide/advanced/examples/pyproject.toml deleted file mode 100644 index 4c1639bf..00000000 --- a/parsl/docs/userguide/advanced/examples/pyproject.toml +++ /dev/null @@ -1,7 +0,0 @@ -[project] -name = "library" -version = '0.0.0' -description = 'Example library for Parsl documentation' - -[tool.setuptools.packages.find] -include = ['library*'] diff --git a/parsl/docs/userguide/advanced/examples/run.py b/parsl/docs/userguide/advanced/examples/run.py deleted file mode 100644 index 9ca5b402..00000000 --- a/parsl/docs/userguide/advanced/examples/run.py +++ /dev/null @@ -1,35 +0,0 @@ -from argparse import ArgumentParser - -import parsl - -from library.config import make_local_config -from library.app import convert_many_to_binary -from parsl.app.python import PythonApp - -# Protect the script from running twice. -# See "Safe importing of main module" in Python multiprocessing docs -# https://docs.python.org/3/library/multiprocessing.html#multiprocessing-programming -if __name__ == "__main__": - # Get user instructions - parser = ArgumentParser() - parser.add_argument('--numbers-per-batch', default=8, type=int) - parser.add_argument('numbers', nargs='+', type=int) - args = parser.parse_args() - - # Prepare the workflow functions - convert_app = PythonApp(convert_many_to_binary, cache=False) - - # Load the configuration - # As a context manager so resources are shutdown on exit - with parsl.load(make_local_config()): - - # Spawn tasks - futures = [ - convert_app(args.numbers[start:start + args.numbers_per_batch]) - for start in range(0, len(args.numbers), args.numbers_per_batch) - ] - - # Retrieve task results - for future in futures: - for x, b in zip(future.task_record['args'][0], future.result()): - print(f'{x} -> {"".join("1" if i else "0" for i in b)}') diff --git a/parsl/docs/userguide/advanced/index.rst b/parsl/docs/userguide/advanced/index.rst deleted file mode 100644 index 758928c2..00000000 --- a/parsl/docs/userguide/advanced/index.rst +++ /dev/null @@ -1,14 +0,0 @@ -Advanced Topics -=============== - -More to learn about Parsl after starting a project. - -.. toctree:: - :maxdepth: 2 - - modularizing - usage_tracking - monitoring - data - parsl_perf - plugins diff --git a/parsl/docs/userguide/advanced/modularizing.rst b/parsl/docs/userguide/advanced/modularizing.rst deleted file mode 100644 index 143a4ebc..00000000 --- a/parsl/docs/userguide/advanced/modularizing.rst +++ /dev/null @@ -1,109 +0,0 @@ -.. _codebases: - -Structuring Parsl programs --------------------------- - -While convenient to build simple Parsl programs as a single Python file, -splitting a Parsl programs into multiple files and a Python module -has significant benefits, including: - - 1. Better readability - 2. Logical separation of components (e.g., apps, config, and control logic) - 3. Ease of reuse of components - -Large applications that use Parsl often divide into several core components: - -.. contents:: - :local: - :depth: 2 - -The following sections use an example where each component is in a separate file: - -.. code-block:: - - examples/logic.py - examples/app.py - examples/config.py - examples/__init__.py - run.py - pyproject.toml - -Run the application by first installing the Python library and then executing the "run.py" script. - -.. code-block:: bash - - pip install . # Install module so it can be imported by workers - python run.py - - -Core application logic -====================== - -The core application logic should be developed without any deference to Parsl. -Implement capabilities, write unit tests, and prepare documentation -in which ever way works best for the problem at hand. - -Parallelization with Parsl will be easy if the software already follows best practices. - -The example defines a function to convert a single integer into binary. - -.. literalinclude:: examples/library/logic.py - :caption: library/logic.py - -Workflow functions -================== - -Tasks within a workflow may require unique combinations of core functions. -Functions to be run in parallel must also meet :ref:`specific requirements ` -that may complicate writing the core logic effectively. -As such, separating functions to be used as Apps is often beneficial. - -The example includes a function to convert many integers into binary. - -Key points to note: - -- It is not necessary to have import statements inside the function. - Parsl will serialize this function by reference, as described in :ref:`functions-from-modules`. - -- The function is not yet marked as a Parsl PythonApp. - Keeping Parsl out of the function definitions simplifies testing - because you will not need to run Parsl when testing the code. - -- *Advanced*: Consider including Parsl decorators in the library if using complex workflow patterns, - such as :ref:`join apps ` or functions which take :ref:`special arguments `. - -.. literalinclude:: examples/library/app.py - :caption: library/app.py - - -Parsl configuration functions -============================= - -Create Parsl configurations specific to your application needs as functions. -While not necessary, including the Parsl configuration functions inside the module -ensures they can be imported into other scripts easily. - -Generating Parsl :class:`~parsl.config.Config` objects from a function -makes it possible to change the configuration without editing the module. - -The example function provides a configuration suited for a single node. - -.. literalinclude:: examples/library/config.py - :caption: library/config.py - -Orchestration Scripts -===================== - -The last file defines the workflow itself. - -Such orchestration scripts, at minimum, perform at least four tasks: - -1. *Load execution options* using a tool like :mod:`argparse`. -2. *Prepare workflow functions for execution* by creating :class:`~parsl.app.python.PythonApp` wrappers over each function. -3. *Create configuration then start Parsl* with the :meth:`parsl.load` function. -4. *Launch tasks and retrieve results* depending on the needs of the application. - -An example run script is as follows - -.. literalinclude:: examples/run.py - :caption: run.py diff --git a/parsl/docs/userguide/advanced/monitoring.rst b/parsl/docs/userguide/advanced/monitoring.rst deleted file mode 100644 index cdf38f14..00000000 --- a/parsl/docs/userguide/advanced/monitoring.rst +++ /dev/null @@ -1,121 +0,0 @@ -.. _label-monitoring: - -Monitoring -========== - -Parsl includes a monitoring system to capture task state as well as resource -usage over time. The Parsl monitoring system aims to provide detailed -information and diagnostic capabilities to help track the state of your -programs, down to the individual apps that are executed on remote machines. - -The monitoring system records information to an SQLite database while a -workflow runs. This information can then be visualised in a web dashboard -using the ``parsl-visualize`` tool, or queried using SQL using regular -SQLite tools. - - -Monitoring configuration ------------------------- - -The following example shows how to enable monitoring in the Parsl -configuration. Here the `parsl.monitoring.MonitoringHub` is specified to use port -55055 to receive monitoring messages from workers every 10 seconds. - -.. code-block:: python - - import parsl - from parsl.monitoring.monitoring import MonitoringHub - from parsl.config import Config - from parsl.executors import HighThroughputExecutor - from parsl.addresses import address_by_hostname - - import logging - - config = Config( - executors=[ - HighThroughputExecutor( - label="local_htex", - cores_per_worker=1, - max_workers_per_node=4, - address=address_by_hostname(), - ) - ], - monitoring=MonitoringHub( - hub_address=address_by_hostname(), - monitoring_debug=False, - resource_monitoring_interval=10, - ), - strategy='none' - ) - - -Visualization -------------- - -To run the web dashboard utility ``parsl-visualize`` you first need to install -its dependencies: - - $ pip install 'parsl[monitoring,visualization]' - -To view the web dashboard while or after a Parsl program has executed, run -the ``parsl-visualize`` utility:: - - $ parsl-visualize - -By default, this command expects that the default ``monitoring.db`` database is used -in the runinfo directory. Other databases can be loaded by passing -the database URI on the command line. For example, if the full path -to the database is ``/tmp/my_monitoring.db``, run:: - - $ parsl-visualize sqlite:////tmp/my_monitoring.db - -By default, the visualization web server listens on ``127.0.0.1:8080``. If the web server is deployed on a machine with a web browser, the dashboard can be accessed in the browser at ``127.0.0.1:8080``. If the web server is deployed on a remote machine, such as the login node of a cluster, you will need to use an ssh tunnel from your local machine to the cluster:: - - $ ssh -L 50000:127.0.0.1:8080 username@cluster_address - -This command will bind your local machine's port 50000 to the remote cluster's port 8080. -The dashboard can then be accessed via the local machine's browser at ``127.0.0.1:50000``. - -.. warning:: Alternatively you can deploy the visualization server on a public interface. However, first check that this is allowed by the cluster's security policy. The following example shows how to deploy the web server on a public port (i.e., open to Internet via ``public_IP:55555``):: - - $ parsl-visualize --listen 0.0.0.0 --port 55555 - - -Workflows Page -^^^^^^^^^^^^^^ - -The workflows page lists all Parsl workflows that have been executed with monitoring enabled -with the selected database. -It provides a high level summary of workflow state as shown below: - -.. image:: ../../images/mon_workflows_page.png - -Throughout the dashboard, all blue elements are clickable. For example, clicking a specific worklow -name from the table takes you to the Workflow Summary page described in the next section. - -Workflow Summary -^^^^^^^^^^^^^^^^ - -The workflow summary page captures the run level details of a workflow, including start and end times -as well as task summary statistics. The workflow summary section is followed by the *App Summary* that lists -the various apps and invocation count for each. - -.. image:: ../../images/mon_workflow_summary.png - - -The workflow summary also presents three different views of the workflow: - -* Workflow DAG - with apps differentiated by colors: This visualization is useful to visually inspect the dependency - structure of the workflow. Hovering over the nodes in the DAG shows a tooltip for the app represented by the node and it's task ID. - -.. image:: ../../images/mon_task_app_grouping.png - -* Workflow DAG - with task states differentiated by colors: This visualization is useful to identify what tasks have been completed, failed, or are currently pending. - -.. image:: ../../images/mon_task_state_grouping.png - -* Workflow resource usage: This visualization provides resource usage information at the workflow level. - For example, cumulative CPU/Memory utilization across workers over time. - -.. image:: ../../images/mon_resource_summary.png - diff --git a/parsl/docs/userguide/advanced/parsl_perf.rst b/parsl/docs/userguide/advanced/parsl_perf.rst deleted file mode 100644 index c2ecd51f..00000000 --- a/parsl/docs/userguide/advanced/parsl_perf.rst +++ /dev/null @@ -1,75 +0,0 @@ -.. _label-parsl-perf: - -Measuring performance with parsl-perf -===================================== - -``parsl-perf`` is tool for making basic performance measurements of Parsl -configurations. - -It runs repeated batches of no-op apps, giving a measurement of tasks per -second. By default, ``parsl-perf`` will attempt to estimate a batch size -that will take 2 minutes to execute. - -This can give a basic measurement of some of the overheads in task -execution. - -``parsl-perf`` must be invoked with a configuration file, which is a Python -file containing a variable ``config`` which contains a `Config` object, or -a function ``fresh_config`` which returns a `Config` object. The -``fresh_config`` format is the same as used with the pytest test suite. - -To specify a ``parsl_resource_specification`` for tasks, add a ``--resources`` -argument. - -To change the target runtime from the default of 120 seconds, add a -``--time`` parameter. - -To change the iteration algorithm used to pick the next batch size and to -decide when to stop, use the ``--iterate`` parameter: - - * ``--iterate=estimate`` (the default) will run a small batch, then - repeatedly re-estimate a new batch size until it finds one that takes at least - 75% of the target run time. This will rapidly find a batch size of around - the target size. - - * ``--iterate=exponential`` will start with a small batch, then double the size - in each subsequent batch until reaching the target run time. This is useful - for understanding how a configuration scales down as batch sizes increase, - which is sometimes sub-linear, and to get more consistent batch sizes across - runs of ``parsl-perf``. - - * ``--iterate=10,10000,10000`` (or any other sequence of batch sizes) will run a - fixed sequence of batches: in this example, first a batch of 10 tasks, then - two batches of 10000 tasks. This is useful for making tests that compare - fixed batch sizes along with warmup steps between different configurations, - without the additional batches of ``estimate`` and ``exponential`` to discover - time-sized (rather than count-sized) batch sizes. - -For example: - -.. code-block:: bash - - - $ python -m parsl.benchmark.perf --config parsl/tests/configs/workqueue_ex.py --resources '{"cores":1, "memory":0, "disk":0}' - ==== Iteration 1 ==== - Will run 10 tasks to target 120 seconds runtime - Submitting tasks / invoking apps - warning: using plain-text when communicating with workers. - warning: use encryption with a key and cert when creating the manager. - All 10 tasks submitted ... waiting for completion - Submission took 0.008 seconds = 1248.676 tasks/second - Runtime: actual 3.668s vs target 120s - Tasks per second: 2.726 - - [...] - - ==== Iteration 4 ==== - Will run 57640 tasks to target 120 seconds runtime - Submitting tasks / invoking apps - All 57640 tasks submitted ... waiting for completion - Submission took 34.839 seconds = 1654.487 tasks/second - Runtime: actual 364.387s vs target 120s - Tasks per second: 158.184 - Cleaning up DFK - The end - diff --git a/parsl/docs/userguide/advanced/plugins.rst b/parsl/docs/userguide/advanced/plugins.rst deleted file mode 100644 index 75a81541..00000000 --- a/parsl/docs/userguide/advanced/plugins.rst +++ /dev/null @@ -1,118 +0,0 @@ -Plugins -======= - -Parsl has several places where code can be plugged in. Parsl usually provides -several implementations that use each plugin point. - -This page gives a brief summary of those places and why you might want -to use them, with links to the API guide. - -Executors ---------- -When the parsl dataflow kernel is ready for a task to run, it passes that -task to an `ParslExecutor`. The executor is then responsible for running the task's -Python code and returning the result. This is the abstraction that allows one -executor to run code on the local submitting host, while another executor can -run the same code on a large supercomputer. - - -Providers and Launchers ------------------------ -Some executors are based on blocks of workers (for example the -`parsl.executors.HighThroughputExecutor`: the submit side requires a -batch system (eg slurm, kubernetes) to start worker processes, which then -execute tasks. - -The particular way in which a system makes those workers start is implemented -by providers and launchers. - -An `ExecutionProvider` allows a command line to be submitted as a request to the -underlying batch system to be run inside an allocation of nodes. - -A `Launcher` modifies that command line when run inside the allocation to -add on any wrappers that are needed to launch the command (eg srun inside -slurm). Providers and launchers are usually paired together for a particular -system type. - -File staging ------------- -Parsl can copy input files from an arbitrary URL into a task's working -environment, and copy output files from a task's working environment to -an arbitrary URL. A small set of data staging providers is installed by default, -for ``file://`` ``http://`` and ``ftp://`` URLs. More data staging providers can -be added in the workflow configuration, in the ``storage`` parameter of the -relevant `ParslExecutor`. Each provider should subclass the `Staging` class. - - -Default stdout/stderr name generation -------------------------------------- -Parsl can choose names for your bash apps stdout and stderr streams -automatically, with the parsl.AUTO_LOGNAME parameter. The choice of path is -made by a function which can be configured with the ``std_autopath`` -parameter of Parsl `Config`. By default, ``DataFlowKernel.default_std_autopath`` -will be used. - - -Memoization/checkpointing -------------------------- - -The default checkpoint/memoization system can be replaced by supplying an -instance of the `Memoizer` class. This instance is queried before an app is -launched, and is given details of completed apps. - -When parsl memoizes/checkpoints an app parameter, it does so by computing a -hash of that parameter that should be the same if that parameter is the same -on subsequent invocations. This isn't straightforward to do for arbitrary -objects, so parsl implements a checkpointing hash function for a few common -types, and raises an exception on unknown types: - -.. code-block:: - - ValueError("unknown type for memoization ...") - -You can plug in your own type-specific hash code for additional types that -you need and understand using `id_for_memo`. - - -Invoking other asynchronous components --------------------------------------- - -Parsl code can invoke other asynchronous components which return Futures, and -integrate those Futures into the task graph: Parsl apps can be given any -`concurrent.futures.Future` as a dependency, even if those futures do not come -from invoking a Parsl app. This includes as the return value of a -``join_app``. - -An specific example of this is integrating Globus Compute tasks into a Parsl -task graph. See :ref:`label-join-globus-compute` - -Dependency resolution ---------------------- - -When Parsl examines the arguments to an app, it uses a `DependencyResolver`. -The default `DependencyResolver` will cause Parsl to wait for -``concurrent.futures.Future`` instances (including `AppFuture` and -`DataFuture`), and pass through other arguments without waiting. - -This behaviour is pluggable: Parsl comes with another dependency resolver, -`DEEP_DEPENDENCY_RESOLVER` which knows about futures contained with structures -such as tuples, lists, sets and dicts. - -This plugin interface might be used to interface other task-like or future-like -objects to the Parsl dependency mechanism, by describing how they can be -interpreted as a Future. - -Log configuration ------------------ - -The `LogConfig` class provides a place for log configuration, which is planned -to extend across the various Python processes involved in a workflow -execution. Right now, it is only used by the ``initialize_logging`` parameter of -the `DataFlowKernel`. - -Removed interfaces ------------------- - -Parsl had a deprecated ``Channel`` abstraction. See -`issue 3515 `_ -for further discussion on its removal. diff --git a/parsl/docs/userguide/advanced/usage_tracking.rst b/parsl/docs/userguide/advanced/usage_tracking.rst deleted file mode 100644 index da8ac9b7..00000000 --- a/parsl/docs/userguide/advanced/usage_tracking.rst +++ /dev/null @@ -1,171 +0,0 @@ -.. _label-usage-tracking: - -Usage Statistics Collection -=========================== - -Parsl uses an **Opt-in** model for usage tracking, allowing users to decide if they wish to participate. Usage statistics are crucial for improving software reliability and help focus development and maintenance efforts on the most used components of Parsl. The collected data is used solely for enhancements and reporting and is not shared in its raw form outside of the Parsl team. - -Why are we doing this? ----------------------- - -The Parsl development team relies on funding from government agencies. To sustain this funding and advocate for continued support, it is essential to show that the research community benefits from these investments. - -By opting in to share usage data, you actively support the ongoing development and maintenance of Parsl. (See:ref:`What is sent? ` below). - -Opt-In Model ------------- - -We use an **opt-in model** for usage tracking to respect user privacy and provide full control over shared information. We hope that developers and researchers will choose to send us this information. The reason is that we need this data - it is a requirement for funding. - -Choose the data you share with Usage Tracking Levels. - -**Usage Tracking Levels:** - -* **Level 1:** Only basic information such as Python version, Parsl version, and platform name (Linux, MacOS, etc.) -* **Level 2:** Level 1 information and configuration information including provider, executor, and launcher names. -* **Level 3:** Level 2 information and workflow execution details, including the number of applications run, failures, and execution time. - -By enabling usage tracking, you support Parsl's development. - -**To opt-in, set** ``usage_tracking`` **to the desired level (1, 2, or 3) in the configuration object** (``parsl.config.Config``) **.** - -Example: - -.. code-block:: python3 - - config = Config( - executors=[ - HighThroughputExecutor( - ... - ) - ], - usage_tracking=3 - ) - -.. _what-is-sent: - -What is sent? -------------- - -The data collected depends on the tracking level selected: - -* **Level 1:** Only basic information such as Python version, Parsl version, and platform name (Linux, MacOS, etc.) -* **Level 2:** Level 1 information and configuration information including provider, executor, and launcher names. -* **Level 3:** Level 2 information and workflow execution details, including the number of applications run, failures, and execution time. - -**Example Messages:** - -- At launch: - - .. code-block:: json - - { - "correlator":"6bc7484e-5693-48b2-b6c0-5889a73f7f4e", - "parsl_v":"1.3.0-dev", - "python_v":"3.12.2", - "platform.system":"Darwin", - "tracking_level":3, - "components":[ - { - "c":"parsl.config.Config", - "executors_len":1, - "dependency_resolver":false - }, - "parsl.executors.threads.ThreadPoolExecutor" - ], - "start":1727156153 - } - -- On closure (Tracking Level 3 only): - - .. code-block:: json - - { - "correlator":"6bc7484e-5693-48b2-b6c0-5889a73f7f4e", - "execution_time":31, - "components":[ - { - "c":"parsl.dataflow.dflow.DataFlowKernel", - "app_count":3, - "app_fails":0 - }, - { - "c":"parsl.config.Config", - "executors_len":1, - "dependency_resolver":false - }, - "parsl.executors.threads.ThreadPoolExecutor" - ], - "end":1727156156 - } - -**All messages sent are logged in the** ``parsl.log`` **file, ensuring complete transparency.** - -How is the data sent? ---------------------- - -Data is sent using **UDP** to minimize the impact on workflow performance. While this may result in some data loss, it significantly reduces the chances of usage tracking affecting the software's operation. - -The data is processed through AWS CloudWatch to generate a monitoring dashboard, providing valuable insights into usage patterns. - -When is the data sent? ----------------------- - -Data is sent twice per run: - -1. At the start of the script. -2. Upon script completion (for Tracking Level 3). - -What will the data be used for? -------------------------------- - -The data will help the Parsl team understand Parsl usage and make development and maintenance decisions, including: - -* Focus development and maintenance on the most-used components of Parsl. -* Determine which Python versions to continue supporting. -* Track the age of Parsl installations. -* Assess how long it takes for most users to adopt new changes. -* Track usage statistics to report to funders. - -Usage Statistics Dashboard --------------------------- - -The collected data is aggregated and displayed on a publicly accessible dashboard. This dashboard provides an overview of how Parsl is being used across different environments and includes metrics such as: - -* Total workflows executed over time -* Most-used Python and Parsl versions -* Most common platforms and executors and more - -`Find the dashboard here `_ - -Leaderboard ------------ - -**Opting in to usage tracking also allows you to participate in the Parsl Leaderboard. -To participate in the leaderboard, you can deanonymize yourself using the** ``project_name`` **parameter in the parsl configuration object** (``parsl.config.Config``) **.** - -`Find the Parsl Leaderboard here `_ - -Example: - -.. code-block:: python3 - - config = Config( - executors=[ - HighThroughputExecutor( - ... - ) - ], - usage_tracking=3, - project_name="my-test-project" - ) - -Every run of parsl with usage tracking **Level 1** or **Level 2** earns you **1 point**. And every run with usage tracking **Level 3**, earns you **2 points**. - -Feedback --------- - -Please send us your feedback at parsl@googlegroups.com. Feedback from our user communities will be -useful in determining our path forward with usage tracking in the future. - -**Please consider turning on usage tracking to support the continued development of Parsl.** diff --git a/parsl/docs/userguide/app.rst b/parsl/docs/userguide/app.rst deleted file mode 100644 index 5e58276c..00000000 --- a/parsl/docs/userguide/app.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. meta:: - :content http-equiv="refresh": 0;url=apps/index.html - -Redirect --------- - -This page has been `moved `_ diff --git a/parsl/docs/userguide/apps/bash.rst b/parsl/docs/userguide/apps/bash.rst deleted file mode 100644 index 9f99eb4d..00000000 --- a/parsl/docs/userguide/apps/bash.rst +++ /dev/null @@ -1,66 +0,0 @@ - -Bash Apps ---------- - -.. code-block:: python - - @bash_app - def echo( - name: str, - stdout=parsl.AUTO_LOGNAME # Requests Parsl to return the stdout - ): - return f'echo "Hello, {name}!"' - - future = echo('user') - future.result() # block until task has completed - - with open(future.stdout, 'r') as f: - print(f.read()) - - -A Parsl Bash app executes an external application by making a command-line execution. -Parsl will execute the string returned by the function as a command-line script on a remote worker. - -Rules for Function Contents -^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Bash Apps follow the same rules :ref:`as Python Apps `. -For example, imports may need to be inside functions and global variables will be inaccessible. - -Inputs and Outputs -^^^^^^^^^^^^^^^^^^ - -Bash Apps can use the same kinds of inputs as Python Apps, but only communicate results with Files. - -Bash Apps, unlike Python Apps, can also return the content printed to the Standard Output and Error. - -Special Keywords Arguments -++++++++++++++++++++++++++ - -In addition to the ``inputs``, ``outputs``, and ``walltime`` keyword arguments -described above, a Bash app can accept the following keywords: - -1. stdout: (string, tuple or ``parsl.AUTO_LOGNAME``) The path to a file to which standard output should be redirected. If set to ``parsl.AUTO_LOGNAME``, the log will be automatically named according to task id and saved under ``task_logs`` in the run directory. If set to a tuple ``(filename, mode)``, standard output will be redirected to the named file, opened with the specified mode as used by the Python `open `_ function. -2. stderr: (string or ``parsl.AUTO_LOGNAME``) Like stdout, but for the standard error stream. -3. label: (string) If the app is invoked with ``stdout=parsl.AUTO_LOGNAME`` or ``stderr=parsl.AUTO_LOGNAME``, this argument will be appended to the log name. - -Outputs -+++++++ - -If the Bash app exits with Unix exit code 0, then the AppFuture will complete. If the Bash app -exits with any other code, Parsl will treat this as a failure, and the AppFuture will instead -contain an `BashExitFailure` exception. The Unix exit code can be accessed through the -``exitcode`` attribute of that `BashExitFailure`. - - -Execution Options -^^^^^^^^^^^^^^^^^ - -Bash Apps have the same execution options (e.g., pinning to specific sites) as the Python Apps. - -MPI Apps -^^^^^^^^ - -Applications which employ MPI to span multiple nodes are a special case of Bash apps, -and require special modification of Parsl's `execution environment <../configuration/execution.html>`_ to function. -Support for MPI applications is described `in a later section `_. diff --git a/parsl/docs/userguide/apps/index.rst b/parsl/docs/userguide/apps/index.rst deleted file mode 100644 index be46d4be..00000000 --- a/parsl/docs/userguide/apps/index.rst +++ /dev/null @@ -1,26 +0,0 @@ -.. _apps: - -Writing Parsl Apps -================== - -An **App** defines a computation that will be executed asynchronously by Parsl. -Apps are Python functions marked with a decorator which -designates that the function will run asynchronously and cause it to return -a :class:`~concurrent.futures.Future` instead of the result. - -Apps can be one of three types of functions, each with their own type of decorator - -- ``@python_app``: Most Python functions -- ``@bash_app``: A Python function which returns a command line program to execute -- ``@join_app``: A function which launches one or more new Apps - -Start by learning how to write Python Apps, which define most of the rules needed to write -other types of Apps. - -.. toctree:: - :maxdepth: 1 - - python - bash - mpi_apps - joins diff --git a/parsl/docs/userguide/apps/joins.rst b/parsl/docs/userguide/apps/joins.rst deleted file mode 100644 index 1222027a..00000000 --- a/parsl/docs/userguide/apps/joins.rst +++ /dev/null @@ -1,257 +0,0 @@ -.. _label-joinapp: - -Join Apps -========= - -Join apps, defined with the ``@join_app`` decorator, are a form of app that can -launch other pieces of a workflow: for example a Parsl sub-workflow, or a task -that runs in some other system. - -Parsl sub-workflows -------------------- - -One reason for launching Parsl apps from inside a join app, rather than -directly in the main workflow code, is because the definitions of those tasks -are not known well enough at the start of the workflow. - -For example, a workflow might run an expensive step to detect some objects -in an image, and then on each object, run a further expensive step. Because -the number of objects is not known at the start of the workflow, but instead -only after an expensive step has completed, the subsequent tasks cannot be -defined until after that step has completed. - -In simple cases, the main workflow script can be stopped using -``Future.result()`` and join apps are not necessary, but in more complicated -cases, that approach can severely limit concurrency. - -Join apps allow more naunced dependencies to be expressed that can help with: - -* increased concurrency - helping with strong scaling -* more focused error propagation - allowing more of an ultimately failing workflow to complete -* more useful monitoring information - -Using Futures from other components ------------------------------------ - -Sometimes, a workflow might need to incorporate tasks from other systems that -run asynchronously but do not need a Parsl worker allocated for their entire -run. An example of this is delegating some work into Globus Compute: work can -be given to Globus Compute, but Parsl does not need to keep a worker allocated -to that task while it runs. Instead, Parsl can be told to wait for the ``Future`` -returned by Globus Compute to complete. - -Usage ------ - -A `join_app` looks quite like a `python_app`, but should return a ``Future``, -or a list of ``Future`` objects, rather than a value. Once the Python code has -run, the app will wait for those Futures to complete without occupying a Parsl -worker, and when those Futures complete, their contents will be the return -value of the `join_app`. - -For example: - -.. code-block:: python - - @python_app - def some_app(): - return 3 - - @join_app - def example(): - x: Future = some_app() - return x # note that x is a Future, not a value - - assert example.result() == 3 - -Example of a Parsl sub-workflow -------------------------------- - -This example workflow shows a preprocessing step, followed by -a middle stage that is chosen by the result of the pre-processing step -(either option 1 or option 2) followed by a know post-processing step. - -.. code-block:: python - - @python_app - def pre_process(): - return 3 - - @python_app - def option_one(x): - return x*2 - - @python_app - def option_two(x): - return (-x) * 2 - - @join_app - def process(x): - if x > 0: - return option_one(x) - else: - return option_two(x) - - @python_app - def post_process(x): - return str(x) - - assert post_process(process(pre_process()))).result() == "6" - -* Why can't process be a regular python function? - -``process`` needs to inspect the value of ``x`` to make a decision about -what app to launch. So it needs to defer execution until after the -pre-processing stage has completed. In Parsl, the way to defer that is -using apps: even though ``process`` is invoked at the start of the workflow, -it will execute later on, when the Future returned by ``pre_process`` has a -value. - -* Why can't process be a @python_app? - -A Python app, if run in a `parsl.executors.ThreadPoolExecutor`, can launch -more parsl apps; so a ``python_app`` implementation of process() would be able -to inspect x and choose and invoke the appropriate ``option_{one, two}``. - -From launching the ``option_{one, two}`` app, the app body python code would -get a ``Future[int]`` - a ``Future`` that will eventually contain ``int``. - -But, we want to invoke ``post_process`` at submission time near the start of -workflow so that Parsl knows about as many tasks as possible. But we don't -want it to execute until the value of the chosen ``option_{one, two}`` app -is known. - -If we don't have join apps, how can we do this? - -We could make process wait for ``option_{one, two}`` to complete, before -returning, like this: - -.. code-block:: python - - @python_app - def process(x): - if x > 0: - f = option_one(x) - else: - f = option_two(x) - return f.result() - -but this will block the worker running ``process`` until ``option_{one, two}`` -has completed. If there aren't enough workers to run ``option_{one, two}`` this -can even deadlock. (principle: apps should not wait on completion of other -apps and should always allow parsl to handle this through dependencies) - -We could make process return the ``Future`` to the main workflow thread: - -.. code-block:: python - - @python_app - def process(x): - if x > 0: - f = option_one(x) - else: - f = option_two(x) - return f # f is a Future[int] - - # process(3) is a Future[Future[int]] - - -What comes out of invoking ``process(x)`` now is a nested ``Future[Future[int]]`` -- it's a promise that eventually process will give you a promise (from -``option_one, two}``) that will eventually give you an int. - -We can't pass that future into post_process... because post_process wants the -final int, and that future will complete before the int is ready, and that -(outer) future will have as its value the inner future (which won't be complete yet). - -So we could wait for the result in the main workflow thread: - -.. code-block:: python - - f_outer = process(pre_process()) # Future[Future[int]] - f_inner = f_outer.result # Future[int] - result = post_process(f_inner) - # result == "6" - -But this now blocks the main workflow thread. If we really only need to run -these three lines, that's fine, but what about if we are in a for loop that -sets up 1000 parametrised iterations: - -.. code-block:: python - - for x in [1..1000]: - f_outer = process(pre_process(x)) # Future[Future[int]] - f_inner = f_outer.result() # Future[int] - result = post_process(f_inner) - -The ``for`` loop can only iterate after pre_processing is done for each -iteration - it is unnecessarily serialised by the ``.result()`` call, -so that pre_processing cannot run in parallel. - -So, the rule about not calling ``.result()`` applies in the main workflow thread -too. - -What join apps add is the ability for parsl to unwrap that Future[Future[int]] into a -Future[int] in a "sensible" way (eg it doesn't need to block a worker). - - -.. _label-join-globus-compute: - -Example of invoking a Futures-driven task from another system -------------------------------------------------------------- - - -This example shows launching some activity in another system, without -occupying a Parsl worker while that activity happens: in this example, work is -delegated to Globus Compute, which performs the work elsewhere. When the work -is completed, Globus Compute will put the result into the future that it -returns, and then (because the Parsl app is a ``@join_app``), that result will -be used as the result of the Parsl app. - -As above, the motivation for doing this inside an app, rather than in the -top level is that sufficient information to launch the Globus Compute task -might not be available at start of the workflow. - -This workflow will run a first stage, ``const_five``, on a Parsl worker, -then using the result of that stage, pass the result as a parameter to a -Globus Compute task, getting a ``Future`` from that submission. Then, the -results of the Globus Compute task will be passed onto a second Parsl -local task, ``times_two``. - -.. code-block:: python - - import parsl - from globus_compute_sdk import Executor - - tutorial_endpoint_uuid = '4b116d3c-1703-4f8f-9f6f-39921e5864df' - gce = Executor(endpoint_id=tutorial_endpoint_uuid) - - def increment_in_funcx(n): - return n+1 - - @parsl.join_app - def increment_in_parsl(n): - future = gce.submit(increment_in_funcx, n) - return future - - @parsl.python_app - def times_two(n): - return n*2 - - @parsl.python_app - def const_five(): - return 5 - - parsl.load() - - workflow = times_two(increment_in_parsl(const_five())) - - r = workflow.result() - - assert r == (5+1)*2 - - -Terminology ------------ - -The term "join" comes from use of monads in functional programming, especially Haskell. diff --git a/parsl/docs/userguide/apps/mpi_apps.rst b/parsl/docs/userguide/apps/mpi_apps.rst deleted file mode 100644 index 82123123..00000000 --- a/parsl/docs/userguide/apps/mpi_apps.rst +++ /dev/null @@ -1,153 +0,0 @@ -MPI and Multi-node Apps -======================= - -The :class:`~parsl.executors.MPIExecutor` supports running MPI applications or other computations which can -run on multiple compute nodes. - -Background ----------- - -MPI applications run multiple copies of a program that complete a single task by -coordinating using messages passed within or across nodes. - -Starting MPI application requires invoking a "launcher" code (e.g., ``mpiexec``) -with options that define how the copies of a program should be distributed. - -The launcher includes options that control how copies of the program are distributed -across the nodes (e.g., how many copies per node) and -how each copy is configured (e.g., which CPU cores it can use). - -The options for launchers vary between MPI implementations and compute clusters. - -Configuring ``MPIExecutor`` ---------------------------- - -The :class:`~parsl.executors.MPIExecutor` is a wrapper over -:class:`~parsl.executors.high_throughput.executor.HighThroughputExecutor` -which eliminates options that are irrelevant for MPI applications. - -Define a configuration for :class:`~parsl.executors.MPIExecutor` by - -1. Setting ``max_workers_per_block`` to the maximum number of tasks to run per block of compute nodes. - This value is typically the number of nodes per block divided by the number of nodes per task. -2. Setting ``mpi_launcher`` to the launcher used for your application. -3. Specifying a provider that matches your cluster and use the :class:`~parsl.launchers.SimpleLauncher`, - which will ensure that no Parsl processes are placed on the compute nodes. - -An example for ALCF's Polaris supercomputer that will run 3 MPI tasks of 2 nodes each at the same time: - -.. code-block:: python - - config = Config( - executors=[ - MPIExecutor( - address=address_by_interface('bond0'), - max_workers_per_block=3, # Assuming 2 nodes per task - provider=PBSProProvider( - account="parsl", - worker_init=f"""module load miniconda; source activate /lus/eagle/projects/parsl/env""", - walltime="1:00:00", - queue="debug", - scheduler_options="#PBS -l filesystems=home:eagle:grand", - launcher=SimpleLauncher(), - select_options="ngpus=4", - nodes_per_block=6, - max_blocks=1, - cpus_per_node=64, - ), - ), - ] - ) - - -.. warning:: - Please note that ``Provider`` options that specify per-task or per-node resources, for example, - ``SlurmProvider(cores_per_node=N, ...)`` should not be used with :class:`~parsl.executors.high_throughput.MPIExecutor`. - Parsl primarily uses a pilot job model and assumptions from that context do not translate to the MPI context. For - more info refer to : - `github issue #3006 `_ - -Writing an MPI App ------------------- - -:class:`~parsl.executors.high_throughput.MPIExecutor` can execute both Python or Bash Apps which invoke an MPI application. - -Create the app by first defining a function which includes ``parsl_resource_specification`` keyword argument. -The resource specification is a dictionary which defines the number of nodes and ranks used by the application: - -.. code-block:: python - - resource_specification = { - 'num_nodes': , # Number of nodes required for the application instance - 'ranks_per_node': , # Number of ranks / application elements to be launched per node - 'num_ranks': , # Number of ranks in total - } - -Then, replace the call to the MPI launcher with ``$PARSL_MPI_PREFIX``. -``$PARSL_MPI_PREFIX`` references an environmental variable which will be replaced with -the correct MPI launcher configured for the resource list provided when calling the function -and with options that map the task to nodes which Parsl knows to be available. - -The function can be a Bash app - -.. code-block:: python - - @bash_app - def lammps_mpi_application(infile: File, parsl_resource_specification: Dict): - # PARSL_MPI_PREFIX will resolve to `mpiexec -n 4 -ppn 2 -hosts NODE001,NODE002` - return f"$PARSL_MPI_PREFIX lmp_mpi -in {infile.filepath}" - - -or a Python app: - -.. code-block:: python - - @python_app - def lammps_mpi_application(infile: File, parsl_resource_specification: Dict): - from subprocess import run - with open('stdout.lmp', 'w') as fp, open('stderr.lmp', 'w') as fe: - proc = run(['$PARSL_MPI_PREFIX', '-i', 'in.lmp'], stdout=fp, stderr=fe) - return proc.returncode - - -Run either App by calling with its arguments and a resource specification which defines how to execute it - -.. code-block:: python - - # Resources in terms of nodes and how ranks are to be distributed are set on a per app - # basis via the resource_spec dictionary. - resource_spec = { - "num_nodes": 2, - "ranks_per_node": 2, - "num_ranks": 4, - } - future = lammps_mpi_application(File('in.file'), parsl_resource_specification=resource_spec) - -Advanced: More Environment Variables -++++++++++++++++++++++++++++++++++++ - -Parsl Apps which run using :class:`~parsl.executors.high_throughput.MPIExecutor` -can make their own MPI invocation using other environment variables. - -These other variables include versions of the launch command for different launchers - -- ``PARSL_MPIEXEC_PREFIX``: mpiexec launch command which works for a large number of batch systems especially PBS systems -- ``PARSL_SRUN_PREFIX``: srun launch command for Slurm based clusters -- ``PARSL_APRUN_PREFIX``: aprun launch command prefix for some Cray machines - -And the information used by Parsl when assembling the launcher commands: - -- ``PARSL_NUM_RANKS``: Total number of ranks to use for the MPI application -- ``PARSL_NUM_NODES``: Number of nodes to use for the calculation -- ``PARSL_MPI_NODELIST``: List of assigned nodes separated by commas (Eg, NODE1,NODE2) -- ``PARSL_RANKS_PER_NODE``: Number of ranks per node - -Limitations -+++++++++++ - -Support for MPI tasks in HTEX is limited. It is designed for running many multi-node MPI applications within a single -batch job. - -#. MPI tasks may not span across nodes from more than one block. -#. Parsl does not correctly determine the number of execution slots per block (`Issue #1647 `_) -#. The executor uses a Python process per task, which can use a lot of memory (`Issue #2264 `_) \ No newline at end of file diff --git a/parsl/docs/userguide/apps/python.rst b/parsl/docs/userguide/apps/python.rst deleted file mode 100644 index 07c4e974..00000000 --- a/parsl/docs/userguide/apps/python.rst +++ /dev/null @@ -1,266 +0,0 @@ -Python Apps ------------ - -.. code-block:: python - - @python_app - def hello_world(name: str) -> str: - return f'Hello, {name}!' - - print(hello_world('user').result()) - - -Python Apps run Python functions. The code inside a function marked by ``@python_app`` is what will -be executed either locally or on a remote system. - -Most functions can run without modification. -Limitations on the content of the functions and their inputs/outputs are described below. - - -.. _function-rules: - -Rules for Function Contents -^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Parsl apps have access to less information from the script that defined them -than functions run via Python's native multiprocessing libraries. -The reason is that functions are executed on workers that -lack access to the global variables in the script that defined them. -Practically, this means - -1. *Functions may need to re-import libraries.* - Place the import statements that define functions or classes inside the function. - Type annotations should not use libraries defined in the function. - - - .. code-block:: python - - import numpy as np - - # BAD: Assumes library has been imported - @python_app - def linear_model(x: list[float] | np.ndarray, m: float, b: float): - return np.multiply(x, m) + b - - # GOOD: Function imports libraries on remote worker - @python_app - def linear_model(x: list[float] | 'np.ndarray', m: float, b: float): - import numpy as np - return np.multiply(x, m) + b - - -2. *Global variables are inaccessible*. - Functions should not use variables defined outside the function. - Likewise, do not assume that variables created inside the function are visible elsewhere. - - -.. code-block:: python - - # BAD: Uses global variables - global_var = {'a': 0} - - @python_app - def counter_func(string: str, character: str = 'a'): - global_var[character] += string.count(character) # `global_var` will not be accessible - - - # GOOD - @python_app - def counter_func(string: str, character: str = 'a'): - return {character: string.count(character)} - - for ch, co in good_global('parsl', 'a').result().items(): - global_var[ch] += co - - - -3. *Outputs are only available through return statements*. - Parsl does not support generator functions (i.e., those which use ``yield`` statements) and - any changes to input arguments will not be communicated. - -.. code-block:: python - - # BAD: Assumes changes to inputs will be communicated - @python_app - def append_to_list(input_list: list, new_val): - input_list.append(new_val) - - - # GOOD: Changes to inputs are returned - @python_app - def append_to_list(input_list: list, new_val) -> list: - input_list.append(new_val) - return input_list - - -.. _functions-from-modules: - -Functions from Modules -++++++++++++++++++++++ - -The above rules assume that the user is running the example code from a standalone script or Jupyter Notebook. -Functions that are defined in an installed Python module do not need to abide by these guidelines, -as they are sent to workers differently than functions defined locally within a script. - -Directly convert a function from a library to a Python App by passing it as an argument to ``python_app``: - -.. code-block:: python - - from module import function - function_app = python_app(function) - -``function_app`` will act as Parsl App function of ``function``. - -It is also possible to create wrapped versions of functions, such as ones with pinned arguments. -Parsl just requires first calling :meth:`~functools.update_wrapped` with the wrapped function -to include attributes from the original function (e.g., its name). - -.. code-block:: python - - from functools import partial, update_wrapped - import numpy as np - my_max = partial(np.max, axis=0, keepdims=True) - my_max = update_wrapper(my_max, max) # Copy over the names - my_max_app = python_app(my_max) - -The above example is equivalent to creating a new function (as below) - -.. code-block:: python - - @python_app - def my_max_app(*args, **kwargs): - import numpy as np - return np.max(*args, keepdims=True, axis=0, **kwargs) - -Inputs and Outputs -^^^^^^^^^^^^^^^^^^ - -Python apps may be passed any Python type as an input and return any Python type, with a few exceptions. -There are several classes of allowed types, each with different rules. - -- *Python Objects*: Any Python object that can be saved with - `pickle `_ or `dill `_ - can be used as an import or output. - All primitive types (e.g., floats, strings) are valid as are many complex types (e.g., numpy arrays). -- *Files*: Pass files as inputs as a :py:class:`~parsl.data_provider.files.File` object. - Parsl can transfer them to a remote system and update the ``File`` object with a new path. - Access the new path with ``File.filepath`` attribute. - - .. code-block:: python - - @python_app - def read_first_line(x: File): - with open(x.filepath, 'r') as fp: - return fp.readline() - - Files can also be outputs of a function, but only through the ``outputs`` kwargs (described below). -- *Parsl Futures*. Functions can receive results from other Apps as Parsl ``Future`` objects. - Parsl will establish a dependency on the App(s) which created the Future(s) - and start executing as soon as the preceding ones complete. - - .. code-block:: python - - @python_app - def capitalize(x: str): - return x.upper() - - input_file = File('text.txt') - first_line_future = read_first_line(input_file) - capital_future = capitalize(first_line_future) - print(capital_future.result()) - - See the section on `Futures <../workflows/futures.html>`_ for more details. - - -Learn more about the types of data allowed in `the data section <../configuration/data.html>`_. - -.. note:: - - Any changes to mutable input arguments will be ignored. - -.. _special-kwargs: - -Special Keyword Arguments -+++++++++++++++++++++++++ - -Some keyword arguments to the Python function are treated differently by Parsl - -1. inputs: (list) This keyword argument defines a list of input :ref:`label-futures` or files. - Parsl will wait for the results of any listed :ref:`label-futures` to be resolved before executing the app. - The ``inputs`` argument is useful both for passing files as arguments - and when one wishes to pass in an arbitrary number of futures at call time. - -.. code-block:: python - - @python_app() - def map_app(x): - return x * 2 - - @python_app() - def reduce_app(inputs = ()): - return sum(inputs) - - map_futures = [map_app(x) for x in range(3)] - reduce_future = reduce_app(inputs=map_futures) - - print(reduce_future.result()) # 0 + 1 * 2 + 2 * 2 = 6 - -2. outputs: (list) This keyword argument defines a list of files that - will be produced by the app. For each file thus listed, Parsl will create a future, - track the file, and ensure that it is correctly created. The future - can then be passed to other apps as an input argument. - -.. code-block:: python - - @python_app() - def write_app(message, outputs=()): - """Write a single message to every file in outputs""" - for path in outputs: - with open(path, 'w') as fp: - print(message, file=fp) - - to_write = [ - File(Path(tmpdir) / 'output-0.txt'), - File(Path(tmpdir) / 'output-1.txt') - ] - write_app('Hello!', outputs=to_write).result() - for path in to_write: - with open(path) as fp: - assert fp.read() == 'Hello!\n' - -3. walltime: (int) This keyword argument places a limit on the app's - runtime in seconds. If the walltime is exceed, Parsl will raise an `parsl.app.errors.AppTimeout` exception. - -Outputs -+++++++ - -A Python app returns an AppFuture (see :ref:`label-futures`) as a proxy for the results that will be returned by the -app once it is executed. This future can be inspected to obtain task status; -and it can be used to wait for the result, and when complete, present the output Python object(s) returned by the app. -In case of an error or app failure, the future holds the exception raised by the app. - -Options for Python Apps -^^^^^^^^^^^^^^^^^^^^^^^ - -The :meth:`~parsl.app.app.python_app` decorator has a few options which controls how Parsl executes all tasks -run with that application. -For example, you can ensure that Parsl caches the results of the function and executes tasks on specific sites. - -.. code-block:: python - - @python_app(cache=True, executors=['gpu']) - def expensive_gpu_function(): - # ... - return - -See the Parsl documentation for full details. - -Limitations -^^^^^^^^^^^ - -To summarize, any Python function can be made a Python App with a few restrictions - -1. Functions should act only on defined input arguments. That is, they should not use script-level or global variables. -2. Functions must explicitly import any required modules if they are defined in script which starts Parsl. -3. Parsl uses dill and pickle to serialize Python objects to/from apps. Therefore, Parsl require that all input and output objects can be serialized by dill or pickle. See :ref:`label_serialization_error`. -4. STDOUT and STDERR produced by Python apps remotely are not captured. diff --git a/parsl/docs/userguide/checkpoints.rst b/parsl/docs/userguide/checkpoints.rst deleted file mode 100644 index 23af844c..00000000 --- a/parsl/docs/userguide/checkpoints.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. meta:: - :content http-equiv="refresh": 0;url=workflows/checkpoints.html - -Redirect --------- - -This page has been `moved `_ diff --git a/parsl/docs/userguide/configuration/config.rst b/parsl/docs/userguide/configuration/config.rst deleted file mode 100644 index 59ab4e90..00000000 --- a/parsl/docs/userguide/configuration/config.rst +++ /dev/null @@ -1,133 +0,0 @@ -Config Objects -============== - -A :class:`~parsl.config.Config` object defines and implements how Parsl connects -to compute resources used to execute tasks. - -The main part of the ``Config`` is a list of -**executors** which each define a type of worker. - -Consider the following example, a configuration to run 7168 parallel workers on -the Frontera supercomputer at TACC: - -.. code-block:: python - - from parsl.config import Config - from parsl.providers import SlurmProvider - from parsl.executors import HighThroughputExecutor - from parsl.launchers import SrunLauncher - from parsl.addresses import address_by_interface - - config = Config( - executors=[ - HighThroughputExecutor( - label="frontera_htex", - address=address_by_interface('ib0'), - max_workers_per_node=56, - provider=SlurmProvider( - partition='normal', - nodes_per_block=128, - init_blocks=1, - launcher=SrunLauncher(), - ), - ) - ], - ) - - -The worker options are those for :class:`~parsl.executors.HighThroughputExecutor`, -the baseline Executor available in Parsl. -The options include a name for the workers of this type (``label``), -how the worker connects to the main Parsl process (``address``), -and how many workers to place on each compute node (``max_workers_per_node``). - -The provider options define the queue used for submission (``partition``), -and tells Parsl request one job (``init_blocks``) of 128 nodes (``nodes_per_block``). - -The launcher uses the default mechanism for starting programs on Frontera compute nodes, ``srun``. - -Using a Config Object ---------------------- - -Use the ``Config`` object to start Parsl's data flow kernel with the ``parsl.load`` method: - -.. code-block:: python - - from parsl.configs.htex_local import config - import parsl - - with parsl.load(config) as dfk: - future = app(x) - future.wait() - -The ``.load()`` function creates a DataFlowKernel ("DFK") object that maintains the workflows state. -The DFK acquires the resources used by Parsl and should be closed to release the resources. -While the DFK can be closed manually (``dfk.cleanup()``), the preferred and Pythonic route is use it as a context manager (the ``with`` statement) -Using a context manager avoids unnecessary code and ensures cleanup occurs even if the workflow fails. - -The ``load`` statement can happen after Apps are defined but must occur before tasks are started. -Loading the Config object within context manager like ``with`` is recommended -for implicit cleaning of DFK on exiting the context manager. - -The :class:`~parsl.config.Config` object may not be used again once loaded. -Consider a configuration function if the application will shut down and re-launch the DFK. - -.. code-block:: python - - from parsl.config import Config - import parsl - - def make_config() -> Config: - return Config(...) - - with parsl.load(make_config()): - # Your workflow here - - # Section which does not require Parsl - - with parsl.load(make_config()): - # Another workflow here - - -Config Options --------------- - -Options for the :class:`~parsl.config.Config` object apply to Parsl's general behavior -and affect all executors. -Common options include: - -- ``run_dir`` for setting where Parsl writes log files -- ``retries`` to restart failed tasks -- ``usage_tracking`` to help Parsl `by reporting how you use it <../advanced/usage_tracking.html>`_ - -Consult the :py:class:`API documentation for Config ` -or the `advanced documentation <../advanced/index.html>`_ to learn about options. - -.. _config-multiple: - -Multiple Executors ------------------- - -A single application can configure multiple executors. - -All executors define a ``label`` field that is used -route to specific workers. -All types of apps include a ``executors`` option which takes -a list of executor labels. -For example, tasks from the following App will only run on an executor labelled "frontera_htex". - -.. code-block:: python - - @python_app(executors=['frontera_htex']) - def single_threaded_task(x: int): - return x * 2 + 1 - - -Consider using multiple executors in the following cases: - -- *Different resource requirements between tasks*, such as a workflow - with a simulation stage that runs on the CPU nodes of an HPC system - followed by an analysis and visualization stage that runs on GPU nodes. -- *Different scales between workflow stages*, such as a workflow - with a "fan-out" stage of many long running running on a cluster - and quick "fan-in" computations which can run on fewer nodes. diff --git a/parsl/docs/userguide/configuration/examples.rst b/parsl/docs/userguide/configuration/examples.rst deleted file mode 100644 index 35c9b1f9..00000000 --- a/parsl/docs/userguide/configuration/examples.rst +++ /dev/null @@ -1,376 +0,0 @@ -Example configurations -====================== - -.. note:: - All configuration examples below must be customized for the user's - allocation, Python environment, file system, etc. - - -The configuration specifies what, and how, resources are to be used for executing -the Parsl program and its apps. -It is important to carefully consider the needs of the Parsl program and its apps, -and the characteristics of the compute resources, to determine an ideal configuration. -Aspects to consider include: -1) where the Parsl apps will execute; -2) how many nodes will be used to execute the apps, and how long the apps will run; -3) should Parsl request multiple nodes in an individual scheduler job; and -4) where will the main Parsl program run and how will it communicate with the apps. - -Stepping through the following question should help formulate a suitable configuration object. - -1. Where should apps be executed? - -+---------------------+-----------------------------------------------+----------------------------------------+ -| Target | Executor | Provider | -+=====================+===============================================+========================================+ -| Laptop/Workstation | * `parsl.executors.HighThroughputExecutor` | `parsl.providers.LocalProvider` | -| | * `parsl.executors.ThreadPoolExecutor` | | -| | * `parsl.executors.WorkQueueExecutor` | | -| | * `parsl.executors.taskvine.TaskVineExecutor` | | -+---------------------+-----------------------------------------------+----------------------------------------+ -| Amazon Web Services | * `parsl.executors.HighThroughputExecutor` | `parsl.providers.AWSProvider` | -+---------------------+-----------------------------------------------+----------------------------------------+ -| Google Cloud | * `parsl.executors.HighThroughputExecutor` | `parsl.providers.GoogleCloudProvider` | -+---------------------+-----------------------------------------------+----------------------------------------+ -| Slurm based system | * `parsl.executors.HighThroughputExecutor` | `parsl.providers.SlurmProvider` | -| | * `parsl.executors.WorkQueueExecutor` | | -| | * `parsl.executors.taskvine.TaskVineExecutor` | | -+---------------------+-----------------------------------------------+----------------------------------------+ -| Torque/PBS based | * `parsl.executors.HighThroughputExecutor` | `parsl.providers.TorqueProvider` | -| system | * `parsl.executors.WorkQueueExecutor` | | -+---------------------+-----------------------------------------------+----------------------------------------+ -| GridEngine based | * `parsl.executors.HighThroughputExecutor` | `parsl.providers.GridEngineProvider` | -| system | * `parsl.executors.WorkQueueExecutor` | | -+---------------------+-----------------------------------------------+----------------------------------------+ -| Condor based | * `parsl.executors.HighThroughputExecutor` | `parsl.providers.CondorProvider` | -| cluster or grid | * `parsl.executors.WorkQueueExecutor` | | -| | * `parsl.executors.taskvine.TaskVineExecutor` | | -+---------------------+-----------------------------------------------+----------------------------------------+ -| Kubernetes cluster | * `parsl.executors.HighThroughputExecutor` | `parsl.providers.KubernetesProvider` | -+---------------------+-----------------------------------------------+----------------------------------------+ - - -2. How many nodes will be used to execute the apps? What task durations are necessary to achieve good performance? - - -+--------------------------------------------+----------------------+-------------------------------------+ -| Executor | Number of Nodes [*]_ | Task duration for good performance | -+============================================+======================+=====================================+ -| `parsl.executors.ThreadPoolExecutor` | 1 (Only local) | Any | -+--------------------------------------------+----------------------+-------------------------------------+ -| `parsl.executors.HighThroughputExecutor` | <=2000 | Task duration(s)/#nodes >= 0.01 | -| | | longer tasks needed at higher scale | -+--------------------------------------------+----------------------+-------------------------------------+ -| `parsl.executors.WorkQueueExecutor` | <=1000 [*]_ | 10s+ | -+--------------------------------------------+----------------------+-------------------------------------+ -| `parsl.executors.taskvine.TaskVineExecutor`| <=1000 [*]_ | 10s+ | -+--------------------------------------------+----------------------+-------------------------------------+ - - -.. [*] Assuming 32 workers per node. If there are fewer workers launched - per node, a larger number of nodes could be supported. - -.. [*] The maximum number of nodes tested for the `parsl.executors.WorkQueueExecutor` is 10,000 GPU cores and - 20,000 CPU cores. - -.. [*] The maximum number of nodes tested for the `parsl.executors.taskvine.TaskVineExecutor` is - 10,000 GPU cores and 20,000 CPU cores. - -3. Should Parsl request multiple nodes in an individual scheduler job? -(Here the term block is equivalent to a single scheduler job.) - -+--------------------------------------------------------------------------------------------+ -| ``nodes_per_block = 1`` | -+---------------------+--------------------------+-------------------------------------------+ -| Provider | Executor choice | Suitable Launchers | -+=====================+==========================+===========================================+ -| Systems that don't | Any | * `parsl.launchers.SingleNodeLauncher` | -| use Aprun | | * `parsl.launchers.SimpleLauncher` | -+---------------------+--------------------------+-------------------------------------------+ -| Aprun based systems | Any | * `parsl.launchers.AprunLauncher` | -+---------------------+--------------------------+-------------------------------------------+ - -+---------------------------------------------------------------------------------------------------------------------+ -| ``nodes_per_block > 1`` | -+-------------------------------------+--------------------------+----------------------------------------------------+ -| Provider | Executor choice | Suitable Launchers | -+=====================================+==========================+====================================================+ -| `parsl.providers.TorqueProvider` | Any | * `parsl.launchers.AprunLauncher` | -| | | * `parsl.launchers.MpiExecLauncher` | -+-------------------------------------+--------------------------+----------------------------------------------------+ -| `parsl.providers.SlurmProvider` | Any | * `parsl.launchers.SrunLauncher` if native slurm | -| | | * `parsl.launchers.AprunLauncher`, otherwise | -+-------------------------------------+--------------------------+----------------------------------------------------+ - -.. note:: If using a Cray system, you most likely need to use the `parsl.launchers.AprunLauncher` to launch workers unless you - are on a **native Slurm** system like :ref:`configuring_nersc_cori` - -Ad-Hoc Clusters ---------------- - -Parsl's support of ad-hoc clusters of compute nodes without a scheduler -is deprecated. - -See -`issue #3515 `_ -for further discussion. - -Amazon Web Services -------------------- - -.. image:: img/aws_image.png - -.. note:: - To use AWS with Parsl, install Parsl with AWS dependencies via ``python3 -m pip install 'parsl[aws]'`` - -Amazon Web Services is a commercial cloud service which allows users to rent a range of computers and other computing services. -The following snippet shows how Parsl can be configured to provision nodes from the Elastic Compute Cloud (EC2) service. -The first time this configuration is used, Parsl will configure a Virtual Private Cloud and other networking and security infrastructure that will be -re-used in subsequent executions. The configuration uses the `parsl.providers.AWSProvider` to connect to AWS. - -.. literalinclude:: ../../../parsl/configs/ec2.py - -Anvil (Purdue) --------------- - -.. image:: https://www.rcac.purdue.edu/storage/resources/anvil/resource.jpg - -Anvil is hosted at Purdue University, and is a powerful new supercomputer that provides advanced computing capabilities to support a wide range of computational and data-intensive research spanning from traditional high-performance computing to modern artificial intelligence applications. -The configuration below provides access to a single GPU on a single node, and assumes that the user is running on a login node and uses the `parsl.providers.SlurmProvider` to interface with the scheduler, and uses the `parsl.launchers.SrunLauncher` to launch workers. - -.. literalinclude:: ../../../parsl/configs/anvil.py - -ASPIRE 1 (NSCC) ---------------- - -.. image:: https://www.nscc.sg/wp-content/uploads/2017/04/ASPIRE1Img.png - -The following snippet shows an example configuration for accessing NSCC's **ASPIRE 1** supercomputer. This example uses the `parsl.executors.HighThroughputExecutor` executor and connects to ASPIRE1's PBSPro scheduler. It also shows how ``scheduler_options`` parameter could be used for scheduling array jobs in PBSPro. - -.. literalinclude:: ../../../parsl/configs/ASPIRE1.py - - - - -Illinois Campus Cluster (UIUC) ------------------------------- - -.. image:: https://campuscluster.illinois.edu/wp-content/uploads/2018/02/ND2_3633-sm.jpg - -The following snippet shows an example configuration for executing on the Illinois Campus Cluster. -The configuration assumes the user is running on a login node and uses the `parsl.providers.SlurmProvider` to interface -with the scheduler, and uses the `parsl.launchers.SrunLauncher` to launch workers. - -.. literalinclude:: ../../../parsl/configs/illinoiscluster.py - -Bridges (PSC) -------------- - -.. image:: https://insidehpc.com/wp-content/uploads/2016/08/Bridges_FB1b.jpg - -The following snippet shows an example configuration for executing on the Bridges supercomputer at the Pittsburgh Supercomputing Center. -The configuration assumes the user is running on a login node and uses the `parsl.providers.SlurmProvider` to interface -with the scheduler, and uses the `parsl.launchers.SrunLauncher` to launch workers. - -.. literalinclude:: ../../../parsl/configs/bridges.py - - - -CC-IN2P3 --------- - -.. image:: https://cc.in2p3.fr/wp-content/uploads/2017/03/bandeau_accueil.jpg - -The snippet below shows an example configuration for executing from a login node on IN2P3's Computing Centre. -The configuration uses the `parsl.providers.LocalProvider` to run on a login node primarily to avoid GSISSH, which Parsl does not support. -This system uses Grid Engine which Parsl interfaces with using the `parsl.providers.GridEngineProvider`. - -.. literalinclude:: ../../../parsl/configs/cc_in2p3.py - - -CCL (Notre Dame, TaskVine) --------------------------- - -.. image:: https://ccl.cse.nd.edu/software/taskvine/taskvine-logo.png - -To utilize TaskVine with Parsl, please install the full CCTools software package within an appropriate Anaconda or Miniconda environment -(instructions for installing Miniconda can be found `in the Conda install guide `_): - -.. code-block:: bash - - $ conda create -y --name python= conda-pack - $ conda activate - $ conda install -y -c conda-forge ndcctools parsl - -This creates a Conda environment on your machine with all the necessary tools and setup needed to utilize TaskVine with the Parsl library. - -The following snippet shows an example configuration for using the Parsl/TaskVine executor to run applications on the local machine. -This examples uses the `parsl.executors.taskvine.TaskVineExecutor` to schedule tasks, and a local worker will be started automatically. -For more information on using TaskVine, including configurations for remote execution, visit the -`TaskVine/Parsl documentation online `_. - -.. literalinclude:: ../../../parsl/configs/vineex_local.py - -TaskVine's predecessor, WorkQueue, may continue to be used with Parsl. -For more information on using WorkQueue visit the `CCTools documentation online `_. - -Delta (NCSA) --------------- - -.. image:: https://docs.ncsa.illinois.edu/systems/delta/en/latest/_images/delta_front_1.png - -Delta is a dedicated, ACCESS-allocated resource designed by HPE and NCSA, delivering a highly capable GPU-focused compute environment for GPU and CPU workloads. -The configuration below provides access to a single GPU on a single node, and assumes that the user is running on a login node and uses the `parsl.providers.SlurmProvider` to interface with the scheduler, and uses the `parsl.launchers.SrunLauncher` to launch workers. - -.. literalinclude:: ../../../parsl/configs/delta.py - -Expanse (SDSC) --------------- - -.. image:: https://www.hpcwire.com/wp-content/uploads/2019/07/SDSC-Expanse-graphic-cropped.jpg - -The following snippet shows an example configuration for executing remotely on San Diego Supercomputer -Center's **Expanse** supercomputer. The example is designed to be executed on the login nodes, using the -`parsl.providers.SlurmProvider` to interface with the Slurm scheduler used by Comet and the `parsl.launchers.SrunLauncher` to launch workers. - -.. literalinclude:: ../../../parsl/configs/expanse.py - - -Improv (Argonne LCRC) ---------------------- - -.. image:: https://www.lcrc.anl.gov/sites/default/files/styles/965_wide/public/2023-12/20231214_114057.jpg?itok=A-Rz5pP9 - -**Improv** is a PBS Pro based supercomputer at Argonne's Laboratory Computing Resource -Center (LCRC). The following snippet is an example configuration that uses `parsl.providers.PBSProProvider` -and `parsl.launchers.MpiRunLauncher` to run on multinode jobs. - -.. literalinclude:: ../../../parsl/configs/improv.py - - -.. _configuring_nersc_cori: - -Perlmutter (NERSC) ------------------- - -NERSC provides documentation on `how to use Parsl on Perlmutter `_. -Perlmutter is a Slurm based HPC system and parsl uses `parsl.providers.SlurmProvider` with `parsl.launchers.SrunLauncher` -to launch tasks onto this machine. - - -Frontera (TACC) ---------------- - -.. image:: https://frontera-portal.tacc.utexas.edu/media/filer_public/2c/fb/2cfbf6ab-818d-42c8-b4d5-9b39eb9d0a05/frontera-banner-home.jpg - -Deployed in June 2019, Frontera is the 5th most powerful supercomputer in the world. Frontera replaces the NSF Blue Waters system at NCSA -and is the first deployment in the National Science Foundation's petascale computing program. The configuration below assumes that the user is -running on a login node and uses the `parsl.providers.SlurmProvider` to interface with the scheduler, and uses the `parsl.launchers.SrunLauncher` to launch workers. - -.. literalinclude:: ../../../parsl/configs/frontera.py - -Globus Compute (Multisite) --------------------------- - -Globus Compute is a distributed Function as a Service (FaaS) platform that enables secure -execution of functions on heterogeneous remote computers, from laptops to campus clusters, clouds, and supercomputers. -Functions are executed on `Globus Compute Endpoints `_ -that can be `configured `_ -for most clusters/HPC systems. The example configuration below allows task submission -to Globus Compute's hosted tutorial endpoint. - -.. literalinclude:: ../../../parsl/configs/gc_tutorial.py - -.. note:: The Globus Compute tutorial endpoint runs Python 3.11. We recommend - using the same Python environment to avoid potential serialization errors - caused by environment mismatches. Globus Compute will raise a warning if any - environment version mismatches are detected although minor version differences - may not cause faults (eg, Python3.11.7 vs Python3.11.8) - -The configuration below specifies two remote endpoints, one at `SDSC's Expanse Supercomputer `_ -and the other at `NERSC's Perlmutter Supercomputer `_. - -.. literalinclude:: ../../../parsl/configs/gc_multisite.py - - - -Kubernetes Clusters -------------------- - -.. image:: https://d1.awsstatic.com/PAC/kuberneteslogo.eabc6359f48c8e30b7a138c18177f3fd39338e05.png - -Kubernetes is an open-source system for container management, such as automating deployment and scaling of containers. -The snippet below shows an example configuration for deploying pods as workers on a Kubernetes cluster. -The KubernetesProvider exploits the Python Kubernetes API, which assumes that you have kube config in ``~/.kube/config``. - -.. literalinclude:: ../../../parsl/configs/kubernetes.py - - -Midway (RCC, UChicago) ----------------------- - -.. image:: https://rcc.uchicago.edu/sites/rcc.uchicago.edu/files/styles/slideshow-image/public/uploads/images/slideshows/20140430_RCC_8978.jpg?itok=BmRuJ-wq - -This Midway cluster is a campus cluster hosted by the Research Computing Center at the University of Chicago. -The snippet below shows an example configuration for executing remotely on Midway. -The configuration assumes the user is running on a login node and uses the `parsl.providers.SlurmProvider` to interface -with the scheduler, and uses the `parsl.launchers.SrunLauncher` to launch workers. - -.. literalinclude:: ../../../parsl/configs/midway.py - - -Open Science Grid ------------------ - -.. image:: https://www.renci.org/wp-content/uploads/2008/10/osg_logo.png - -The Open Science Grid (OSG) is a national, distributed computing Grid spanning over 100 individual sites to provide tens of thousands of CPU cores. -The snippet below shows an example configuration for executing remotely on OSG. You will need to have a valid project name on the OSG. -The configuration uses the `parsl.providers.CondorProvider` to interface with the scheduler. - -.. literalinclude:: ../../../parsl/configs/osg.py - - -Polaris (ALCF) --------------- - -.. image:: https://www.alcf.anl.gov/sites/default/files/styles/965x543/public/2022-07/33181D_086_ALCF%20Polaris%20Crop.jpg?itok=HVAHsZtt - :width: 75% - -ALCF provides documentation on `how to use Parsl on Polaris `_. -Polaris uses `parsl.providers.PBSProProvider` and `parsl.launchers.MpiExecLauncher` to launch tasks onto the HPC system. - - - -Stampede2 (TACC) ----------------- - -.. image:: https://www.tacc.utexas.edu/documents/1084364/1413880/stampede2-0717.jpg/ - -The following snippet shows an example configuration for accessing TACC's **Stampede2** supercomputer. This example uses theHighThroughput executor and connects to Stampede2's Slurm scheduler. - -.. literalinclude:: ../../../parsl/configs/stampede2.py - - -Summit (ORNL) -------------- - -.. image:: https://www.olcf.ornl.gov/wp-content/uploads/2018/06/Summit_Exaop-1500x844.jpg - -The following snippet shows an example configuration for executing from the login node on Summit, the leadership class supercomputer hosted at the Oak Ridge National Laboratory. -The example uses the :class:`parsl.providers.LSFProvider` to provision compute nodes from the LSF cluster scheduler and the `parsl.launchers.JsrunLauncher` to launch workers across the compute nodes. - -.. literalinclude:: ../../../parsl/configs/summit.py - - -TOSS3 (LLNL) ------------- - -.. image:: https://hpc.llnl.gov/sites/default/files/Magma--2020-LLNL.jpg - -The following snippet shows an example configuration for executing on one of LLNL's **TOSS3** -machines, such as Quartz, Ruby, Topaz, Jade, or Magma. This example uses the `parsl.executors.FluxExecutor` -and connects to Slurm using the `parsl.providers.SlurmProvider`. This configuration assumes that the script -is being executed on the login nodes of one of the machines. - -.. literalinclude:: ../../../parsl/configs/toss3_llnl.py diff --git a/parsl/docs/userguide/configuration/executors/advanced.rst b/parsl/docs/userguide/configuration/executors/advanced.rst deleted file mode 100644 index 41fce3ab..00000000 --- a/parsl/docs/userguide/configuration/executors/advanced.rst +++ /dev/null @@ -1,54 +0,0 @@ -Advanced Configuration -====================== - -Executors are designed to function adequately with the default options, -but there are several cases where the executors needed to be tuned for specific systems. - - -.. contents:: - :local: - :depth: 1 - -.. _label-networking: - -Connecting Parsl to Workers ---------------------------- - -.. note:: - - Skip this section if: - - 1. Using the ``ThreadPoolExecutor`` and ``GlobusComputeExecutor``. - 2. Plan to run all tasks on the same computer as the Parsl script. - -Parsl workers launched on remote nodes need to know the address of the node running the Parsl script. -Parsl will detect all addresses associated with the node running Parsl, and this auto-configuration works -for many users. - -However, some computing systems disallow applications from attempting to access external networks -(which Parsl may break during auto-configuration) or -auto-detection will result in selecting a lower-performance network. -In such cases, determine the proper IP address for the main node using the tools in :mod:`parsl.addresses`. - -The most common case is to use :meth:`~parsl.addresses.address_by_interface` to determine the -address on the network associated with a certain network interface. -For example, the compute nodes on the Polaris supercomputer connect to the login nodes over the -``bond0`` interface. -Configure a Parsl application where the main process runs on the login node by -acquiring the address through that interface: - -.. code-block:: python - - config = Config( - executors=[ - HighThroughputExecutor( - address=address_by_interface("bond0"), - provider=PBSProProvider( - launcher=MpiExecLauncher(bind_cmd="--cpu-bind", overrides="--depth=64 --ppn 1"), - account='ACCT', - queue='debug', - select_options="ngpus=4", - ), - ), - ], - ) diff --git a/parsl/docs/userguide/configuration/executors/htex.rst b/parsl/docs/userguide/configuration/executors/htex.rst deleted file mode 100644 index 3fd8478f..00000000 --- a/parsl/docs/userguide/configuration/executors/htex.rst +++ /dev/null @@ -1,192 +0,0 @@ -.. _label-htex: - -The High-Throughput Executor -============================ - -The :class:`~parsl.executors.HighThroughputExecutor` (HTEx) is the standard Executor provided with Parsl. -The following sections detail the most-used configuration options of HTEx. - -.. contents:: - :local: - :depth: 1 - -Defining Workers Per Node -------------------------- - -HTEx determines how many workers to run on each node individually. -The number of nodes is the minimum of the ``max_workers_per_node``, -the number of workers determined from ``cores_per_worker``, -and the number of workers defined by ``mem_per_worker``. - -Resource pinning ----------------- - -Resource pinning reduces contention between multiple workers using the same CPU cores or accelerators. - -Multi-Threaded Applications -+++++++++++++++++++++++++++ - -Workflows which launch multiple workers on a single node which perform multi-threaded tasks (e.g., NumPy, Tensorflow operations) may run into thread contention issues. -Each worker may try to use the same hardware threads, which leads to performance penalties. -Use ``cpu_affinity`` to assign workers to specific threads either by -defining an allocation strategy method or an explicit list. - -The strategy methods will auto assign all detected hardware threads to workers. -Allowed strategies are ``block``, ``block-reverse``, and ``alternating``. -The ``block`` method pins threads to workers in sequential order (ex: 4 threads are grouped (0, 1) and (2, 3) on two workers); -``block-reverse`` pins threads in reverse sequential order (ex: (3, 2) and (1, 0)); and ``alternating`` alternates threads among workers (ex: (0, 2) and (1, 3)). - -Select the best blocking strategy based on the node's CPU cache hierarchy (query the cache hierarchy using ``lscpu``). - -.. code-block:: python - - local_config = Config( - executors=[ - HighThroughputExecutor( - label="htex_Local", - worker_debug=True, - cpu_affinity='alternating', - provider=LocalProvider( - init_blocks=1, - max_blocks=1, - ), - ) - ], - strategy='none', - ) - -Users can also use ``cpu_affinity`` to assign explicitly threads to workers with a string that has the format of -``cpu_affinity="list:::"``. - -Each worker's threads can be specified as a comma separated list or a hyphenated range: -``thread1,thread2,thread3`` -or -``thread_start-thread_end``. - -An example for 12 workers on a node with 208 threads is: - -.. code-block:: python - - cpu_affinity="list:0-7,104-111:8-15,112-119:16-23,120-127:24-31,128-135:32-39,136-143:40-47,144-151:52-59,156-163:60-67,164-171:68-75,172-179:76-83,180-187:84-91,188-195:92-99,196-203" - -This example assigns 16 threads each to 12 workers. Note that in this example there are threads that are skipped. -If a thread is not explicitly assigned to a worker, it will be left idle. -The number of thread "ranks" (colon separated thread lists/ranges) must match the total number of workers on the node; otherwise an exception will be raised. - -Thread affinity is accomplished in two ways. -Each worker first sets the affinity for the Python process using `the affinity mask `_, -which may not be available on all operating systems. -It then sets environment variables to control -`OpenMP thread affinity `_ -so that any subprocesses launched by a worker which use OpenMP know which processors are valid. -These include ``OMP_NUM_THREADS``, ``GOMP_COMP_AFFINITY``, and ``KMP_THREAD_AFFINITY``. - -Accelerators -++++++++++++ - -Many modern clusters provide multiple accelerators per compute node, yet many applications are best suited to using a -single accelerator per task. Parsl supports pinning each worker to different accelerators using -``available_accelerators`` option of the :class:`~parsl.executors.HighThroughputExecutor`. Provide either the number of -executors (Parsl will assume they are named in integers starting from zero) or a list of the names of the accelerators -available on the node. Parsl will limit the number of workers it launches to the number of accelerators specified, -in other words, you cannot have more workers per node than there are accelerators. By default, Parsl will launch -as many workers as the accelerators specified via ``available_accelerators``. - -.. code-block:: python - - local_config = Config( - executors=[ - HighThroughputExecutor( - label="htex_Local", - worker_debug=True, - available_accelerators=2, - provider=LocalProvider( - init_blocks=1, - max_blocks=1, - ), - ) - ], - strategy='none', - ) - -It is possible to bind multiple/specific accelerators to each worker by specifying a list of comma separated strings, -each specifying accelerators. - -Here's an example: - -.. code-block:: python - - # The following config is trimmed for clarity - local_config = Config( - executors=[ - HighThroughputExecutor( - # Starts 2 workers per node, each bound to 2 GPUs - available_accelerators=["0,1", "2,3"], - - # Start a single worker bound to all 4 GPUs - # available_accelerators=["0,1,2,3"] - ) - ], - ) - -Binding is achieved by setting ``CUDA_VISIBLE_DEVICES`` (specific to NVIDIA GPUs), -``ROCR_VISIBLE_DEVICES`` (AMD GPUs), -and ``ZE_AFFINITY_MASK`` (Intel GPUs) to the appropriate accelerator names. - -GPU Oversubscription -^^^^^^^^^^^^^^^^^^^^ - -For hardware that uses NVIDIA devices, Parsl allows for the oversubscription of workers to GPUs. This is intended to -make use of NVIDIA's `Multi-Process Service (MPS) `_ available on many of their -GPUs that allows users to run multiple concurrent processes on a single GPU. The user needs to set the -``worker_init`` command of the Provider to start MPS on every node in the block (this is machine dependent). The -``available_accelerators`` option should then be set to the total number of GPU partitions run on a single node in the -block. For example, for a node with 4 NVIDIA GPUs, to create 8 workers per GPU, set ``available_accelerators=32``. -GPUs will be assigned to workers in ascending order in contiguous blocks. In the example, workers 0-7 will be placed -on GPU 0, workers 8-15 on GPU 1, workers 16-23 on GPU 2, and workers 24-31 on GPU 3. - -Encryption ----------- - -Users can encrypt traffic between the Parsl DFK and ``HighThroughputExecutor`` instances by setting its ``encrypted`` -initialization argument to ``True``. - -For example, - -.. code-block:: python - - from parsl.config import Config - from parsl.executors import HighThroughputExecutor - - config = Config( - executors=[ - HighThroughputExecutor( - encrypted=True - ) - ] - ) - -Under the hood, we use `CurveZMQ `_ to encrypt all communication channels -between the executor and related nodes. - -Encryption performance -++++++++++++++++++++++ - -CurveZMQ depends on `libzmq `_ and `libsodium `_, -which `pyzmq `_ (a Parsl dependency) includes as part of its -installation via ``pip``. This installation path should work on most systems, but users have -reported significant performance degradation as a result. - -If you experience a significant performance hit after enabling encryption, we recommend installing -``pyzmq`` with conda: - -.. code-block:: bash - - conda install conda-forge::pyzmq - -Alternatively, you can `install libsodium `_, then -`install libzmq `_, then build ``pyzmq`` from source: - -.. code-block:: bash - - pip3 install parsl --no-binary pyzmq diff --git a/parsl/docs/userguide/configuration/executors/index.rst b/parsl/docs/userguide/configuration/executors/index.rst deleted file mode 100644 index 702e6e18..00000000 --- a/parsl/docs/userguide/configuration/executors/index.rst +++ /dev/null @@ -1,49 +0,0 @@ -Executors ---------- - -Parsl provides many Executors to handle a wide variety of workloads. - -Workloads that execute a small number of tasks which run locally -benefit from the minimal configuration required for :class:`~parsl.executors.ThreadPoolExecutor`, -whereas those which require thousands of nodes should use the -:class:`~parsl.executors.HighThroughputExecutor`. -These executors include: - -#. :class:`~parsl.executors.HighThroughputExecutor` (HTEx): The best starting choice for Parsl applications. - HTEx deploys many identical Python workers as separate processes across multiple compute nodes. - Implements a hierarchical scheduling strategy and can scale to thousands of workers in many applications.. - -#. :class:`~parsl.executors.taskvine.TaskVineExecutor` (TVEx): An executor which supports performance features including: - resizing the resources pinned to workers, - smart caching and sharing of large files between tasks and compute nodes, - and automatically sharing Python environments between tasks. - Scales to tens of thousands of cores. - Built using `TaskVine `_. - -#. :class:`~parsl.executors.WorkQueueExecutor` (WQEx): An Executor which supports dynamically adjusting the resources - allocated to each worker, built using `Work Queue `_. - WQEx scales to tens of thousands of cores. - - -#. :class:`~parsl.executors.GlobusComputeExecutor`: Employs `Globus Compute `_, - to manage execution of tasks through a cloud service. - Functions are securely executed on `Globus Compute Endpoints `_ - running on resources that need not be accessible from the computer running the Parsl script. - Endpoints can use any of Parsl's other Executors to execute the tasks. - -#. :class:`~parsl.executors.MPIExecutor` (MPEx): A specialized wrapper over HTEx designed to manage tasks which span multiple compute nodes. - See `MPI and Multi-node Apps <../apps/mpi_apps.rst>`_. - -#. :class:`~parsl.executors.ThreadPoolExecutor`: A lightweight executor that runs on the same computer as the Parsl script. - Tasks running on the ``ThreadPoolExecutor`` can start other Parsl tasks. - -Start by following the documentation for the chosen executor. -Continue by specifying how to request compute nodes in the `Provider <../providers/index.html>`_ documentation. - -.. toctree:: - :maxdepth: 2 - :caption: Table of Contents - - htex - wqex - advanced diff --git a/parsl/docs/userguide/configuration/executors/wqex.rst b/parsl/docs/userguide/configuration/executors/wqex.rst deleted file mode 100644 index f2335cb9..00000000 --- a/parsl/docs/userguide/configuration/executors/wqex.rst +++ /dev/null @@ -1,117 +0,0 @@ -The WorkQueue Executor -====================== - -The :class:`parsl.executors.WorkQueueExecutor` (WQEx) executes Parsl tasks -using the `Work Queue, a framework from the Cooperative Computing Lab `_. -WQEx dispatches Parsl tasks to Work Queue's "managers" and places -the managers on to compute nodes via Parsl's `Providers <./providers/index.html>`_. - -This part of the documentation details the unique capabilities available through Work Queue. - -.. contents:: - :local: - :depth: 1 - - -Heterogeneous Resources ------------------------ - -The :class:`parsl.executors.WorkQueueExecutor` provides several features to work with heterogeneous resources. -By default, Parsl only runs one app at a time on each worker node. -However, it is possible to specify the requirements for a particular app, -and Work Queue will automatically run as many parallel instances as possible on each node. -Work Queue automatically detects the amount of cores, memory, and other resources available on each execution node. -To activate this feature, add a resource specification to your apps. A resource specification is a dictionary with -the following three keys: ``cores`` (an integer corresponding to the number of cores required by the task), -``memory`` (an integer corresponding to the task's memory requirement in MB), and ``disk`` (an integer corresponding to -the task's disk requirement in MB), passed to an app via the special keyword argument ``parsl_resource_specification``. The specification can be set for all app invocations via a default, for example: - - .. code-block:: python - - @python_app - def compute(x, parsl_resource_specification={'cores': 1, 'memory': 1000, 'disk': 1000}): - return x*2 - - -or updated when the app is invoked: - - .. code-block:: python - - spec = {'cores': 1, 'memory': 500, 'disk': 500} - future = compute(x, parsl_resource_specification=spec) - -This ``parsl_resource_specification`` special keyword argument will inform Work Queue about the resources this app requires. -When placing instances of ``compute(x)``, Work Queue will run as many parallel instances as possible based on each worker node's available resources. - -If an app's resource requirements are not known in advance, -Work Queue has an auto-labeling feature that measures the actual resource usage of your apps and automatically chooses resource labels for you. -With auto-labeling, it is not necessary to provide ``parsl_resource_specification``; -Work Queue collects stats in the background and updates resource labels as your workflow runs. -To activate this feature, add the following flags to your executor config: - - .. code-block:: python - - config = Config( - executors=[ - WorkQueueExecutor( - # ...other options go here - autolabel=True, - autocategory=True - ) - ] - ) - -The ``autolabel`` flag tells Work Queue to automatically generate resource labels. -By default, these labels are shared across all apps in your workflow. -The ``autocategory`` flag puts each app into a different category, -so that Work Queue will choose separate resource requirements for each app. -This is important if e.g. some of your apps use a single core and some apps require multiple cores. -Unless you know that all apps have uniform resource requirements, -you should turn on ``autocategory`` when using ``autolabel``. - -Software Environment Detection ------------------------------- - -The Work Queue executor can also help deal with sites that have non-uniform software environments across nodes. -Parsl assumes that the Parsl program and the compute nodes all use the same Python version. -In addition, any packages your apps import must be available on compute nodes. -If no shared filesystem is available or if node configuration varies, -this can lead to difficult-to-trace execution problems. - -If your Parsl program is running in a Conda environment, -the Work Queue executor can automatically scan the imports in your apps, -create a self-contained software package, -transfer the software package to worker nodes, -and run your code inside the packaged and uniform environment. -First, make sure that the Conda environment is active and you have the required packages installed (via either ``pip`` or ``conda``): - -- ``python`` -- ``parsl`` -- ``ndcctools`` -- ``conda-pack`` - -Then add the following to your config: - - .. code-block:: python - - config = Config( - executors=[ - WorkQueueExecutor( - # ...other options go here - pack=True - ) - ] - ) - -.. note:: - There will be a noticeable delay the first time Work Queue sees an app; - it is creating and packaging a complete Python environment. - This packaged environment is cached, so subsequent app invocations should be much faster. - -Using this approach, it is possible to run Parsl applications on nodes that don't have Python available at all. -The packaged environment includes a Python interpreter, -and Work Queue does not require Python to run. - -.. note:: - The automatic packaging feature only supports packages installed via ``pip`` or ``conda``. - Importing from other locations (e.g. via ``$PYTHONPATH``) or importing other modules in the same directory is not supported. diff --git a/parsl/docs/userguide/configuration/img/aws_image.png b/parsl/docs/userguide/configuration/img/aws_image.png deleted file mode 100644 index a4d74c79fb1f496316921288a0be63d97be5d5fa..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 852370 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      %2D8jL^v|N9Tv;RJSm|Na4W{DA)bdiDRC8}t9)+>!Br<23jG z(;H3xR~730FUZmPpOB&RKQ3M8e^ja#5NrOAOaXRyLCc$g9p20w{r^RIhW~4e4F0#4 z8U0_{Ve@~}WS9Rh4(0!UeXjNYm$!$3>ERMEz91(${|37GH_}x`SP!>??Z-vw_~N>c z7=rPK3;K0N==UYR2d3SRU<~T`{sMMBlhzKwpev1ld|9BlIgxX{%`?F>y=sNs|JC8R=f8SU&wrEpng1=C z=KS}avhsiEq^19hd#3$wZ)*L&rXb`0?g+pCryNcH@6gl!zaH4hU8$n{f4Pd%|AmT5 z|7R&E{4bQ0`0M=yzW@LLAxT6*R39lK_+Orl`M)4D6QX?q#>`kSa-G0{gyHqWcL0f7 VH#~TrS)Tv^002ovPDHLkV1nb3@p}LO diff --git a/parsl/docs/userguide/configuration/index.rst b/parsl/docs/userguide/configuration/index.rst deleted file mode 100644 index 0b4d6e50..00000000 --- a/parsl/docs/userguide/configuration/index.rst +++ /dev/null @@ -1,51 +0,0 @@ -.. _configuration-section: - -Configuring Parsl -================= - -.. note:: - - This part of the guide describes the how to configure Parsl from the start. - Consider browsing the `example configurations `_ to - see if there is already an example which matches your needs. - -Contemporary computing environments offer a wide range of computational platforms -or **execution providers**, from laptops and PCs to various clusters, supercomputers, and cloud computing platforms. -Different execution providers use of different **execution models**, such as threads (for efficient parallel execution on a multicore processor), processes, and pilot jobs for running many small tasks on a large parallel system. -Parsl is designed to abstract these low-level details so that an identical Parsl program can run unchanged on different platforms or across multiple platforms. -The Parsl configuration object (:class:`~parsl.config.Config`) specifies -which execution provider(s) and execution model(s) to use. - -:class:`~parsl.config.Config` objects are expressed in Python -so that software can -introspect permissible options, validate settings, and retrieve/edit -configurations dynamically during execution. - -The :class:`~parsl.config.Config` object, in short, is a list of **Executors**. -Each executor represents a different type of **worker** that will run Parsl tasks -and is defined by at least three components: - -- *Worker Definition* which controls how workers are deployed on a resource. - Options typically include how many workers per node and - how many resources (e.g., CPU cores) each worker is a allowed to use. -- *Provider Description* which specifies how Parsl gains access to resources, - such as by requesting nodes from a cluster's queuing system. - Many types of providers require the name of a queue, how many nodes to request from the queue, - and how long to request the nodes for. -- *Launcher Specification* which defines how the workers which execute Parsl tasks - are placed on to compute nodes. - -Build a :class:`~parsl.config.Config` by first learning about -the `configuration object `_ then -the types of `Executors `_ available in Parsl, which -each may require using a `resource provider and launcher `_. - -Alternatively, find an `example configuration `_ which is similar to what you need. - -.. toctree:: - :maxdepth: 2 - - config - executors/index - providers/index - examples diff --git a/parsl/docs/userguide/configuration/providers/elasticity.rst b/parsl/docs/userguide/configuration/providers/elasticity.rst deleted file mode 100644 index 40707d85..00000000 --- a/parsl/docs/userguide/configuration/providers/elasticity.rst +++ /dev/null @@ -1,86 +0,0 @@ -.. _label-elasticity: - -Elasticity -========== - -Parsl can change the amount of resources available as workloads vary over time. -Allow the amount of workers used by Parsl to change elastically through two mechanisms: - -1. Specifying the range of number of "blocks" allowed -2. Controlling the target ratio of number of tasks to workers. - -Controlling Number of Blocks ----------------------------- - -.. image:: img/parsl_scaling.gif - -All `Providers `_ request resources in "blocks" of nodes. -The default for most Providers is to request a single block of workers -at the beginning and only request another block if the current one expires. - -Allow Parsl to vary the amount of blocks through the following options: - -1. ``min_blocks``: Minimum number of blocks to maintain. -2. ``init_blocks``: Initial number of blocks to provision at initialization of workflow. -3. ``max_blocks``: Maximum number of blocks that can be active. - - -Parallelism ------------ - -The ``parallelism`` option of a Provider controls how many blocks are requested as a function of demand. -Parallelism is the ratio of the number of workers available to the number of tasks running or ready to run, -and should be a value between 0 and 1. - -For example: - -- (default) ``parallelism=1``: Request as many workers as tasks until ``max_blocks`` is met. - -.. code-block:: python - - blocks = min(max_blocks, ceil(num_tasks / (workers_per_node * nodes_per_block)) - - -- ``parallelism=0``: Use the fewest resources possible. Request - -.. code:: python - - blocks = min_blocks if active_tasks == 0 else max(min_blocks, 1) - -- ``parallelism=0.5``: Maintain one worker for every two tasks in queue. - - -Example Configuration ----------------------- - -The following configuration shows a ``HighThroughputExecutor`` -which will run between 1 and 2 blocks depending on the number of tasks. -Each block is one node in size and each node will host 2 workers (2 workers per block). -Parallelism of 0.5 means that a new block will be requested for every 4 tasks (2 workers per block / 0.5 workers per task). - -.. code:: python - - from parsl.config import Config - from parsl.providers import SlurmProvider - from parsl.executors import HighThroughputExecutor - - config = Config( - executors=[ - HighThroughputExecutor( - max_workers_per_node=2, - provider=SlurmProvider( - min_blocks=1, - init_blocks=1, - max_blocks=2, - nodes_per_block=1, - parallelism=0.5 - ) - ) - ] - ) - -The animation below shows how the number of blocks 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zx}hA}p&t67AR3|~I-(?6q9%HxD4L=wx}q%FqAvQPFdCy!GCHF)TBA04qd1zQI=Z7g z+M_=Dqd*#@LOP^GTBJsLq)3{iO1h*>+N4hUq)-~AQaYtnTBTNcrC6G!TDqlN+NECl RrC=JSVmhW|T8{w%06VpMB&`4d diff --git a/parsl/docs/userguide/configuration/providers/index.rst b/parsl/docs/userguide/configuration/providers/index.rst deleted file mode 100644 index 1933e421..00000000 --- a/parsl/docs/userguide/configuration/providers/index.rst +++ /dev/null @@ -1,20 +0,0 @@ -.. _label-execution: - -Providers -========= - -The **Provider** defines how to acquire then start Parsl workers on remote resources. -Providers may, for example, interface with queuing system of a computer cluster, -provision virtual machines from a cloud computing vendor, -or manage containers via Kubernetes. - -Define a Provider by first selecting the `appropriate interface `_, -specifying how to `launch workers on newly-acquired resources `_ -then configuring how Parsl will adjust `amount of resources according to demand `_. - -.. toctree:: - :maxdepth: 2 - - providers - launchers - elasticity diff --git a/parsl/docs/userguide/configuration/providers/launchers.rst b/parsl/docs/userguide/configuration/providers/launchers.rst deleted file mode 100644 index 940e38a8..00000000 --- a/parsl/docs/userguide/configuration/providers/launchers.rst +++ /dev/null @@ -1,92 +0,0 @@ -Starting Workers -================ - -.. note:: - - `Select a provider `_ if you have not already. - -The next step in defining a **Provider** is configuring how to start Parsl on -newly-acquired resources. -Starting Parsl occurs in two steps, each with their own Provider option: - -1. Defining how to connect to each new compute node (``launcher``) -2. Defining how to initialize the computational information (``worker_init``) - -Selecting a Launcher --------------------- - -Parsl uses **Launchers** to start workers on every node acquired by a Provider. -Launchers are required when requesting more than one node per batch job from a -`cluster scheduler `_. - -Parsl will use the same mechanism employed for launching MPI tasks across compute nodes. -Consult the documentation or example scripts for a compute cluster to find how to launch MPI tasks, -and then find the `launcher script which matches <../../reference.html#launchers>`_. - -No options are required in most cases. -Simply import the Launcher class and provide an instance of the launcher to the ``launcher`` option of the Provider. - -.. code-block:: python - - config = Config( - executors=[ - ... - provider=SlurmProvider( - ... - launcher=SrunLauncher(), - ... - ), - ) - ], - ) - -For example, `the Polaris supercomputer at ALCF `_ -suggests to use ``mpiexec`` to start jobs -and Parsl will use the :class:`~parsl.launchers.MpiExecLauncher`. - - -Advanced: Configuring a Launcher -++++++++++++++++++++++++++++++++ - -Common cases for configuring a launcher include: - -1. Defining binding between the Parsl processes and available cores of the computer, - as when `using Parsl on the Aurora supercomputer `_. -2. Launching Parsl inside of Shifter containers by prepending the command to start - Parsl with the :class:`~parsl.launchers.WrappedLauncher`. - -.. _label-worker-init: - -Worker Initialization ---------------------- - -The ``worker_init`` argument of all Providers accepts a script for readying a newly-acquired node to run Parsl. -Worker initialization commands typically perform tasks such as: - -1. Activating the Python virtual environment which contains Parsl -2. Loading modules and setting environment variables of codes invoked by Parsl Apps -3. Launching services required for proper execution (e.g., port forwarding, NVIDIA MPS Daemons) - -Most ``worker_init`` scripts activate a virtual environment then check that the proper -version of Python is available. - -.. code-block:: python - - config = Config( - executors=[ - ... - provider=SlurmProvider( - ... - worker_init="conda activate /path/to/conda/env; which python" - ... - ), - ) - ], - ) - -.. note:: - - Consider including commands which print information about a node (e.g., ``hostname``) - or the computational environment (e.g., ``echo $PATH``) as part of the worker init - to aid determining where Parsl is running or whether the init function created the - desired environment. diff --git a/parsl/docs/userguide/configuration/providers/providers.rst b/parsl/docs/userguide/configuration/providers/providers.rst deleted file mode 100644 index c6083720..00000000 --- a/parsl/docs/userguide/configuration/providers/providers.rst +++ /dev/null @@ -1,72 +0,0 @@ -Execution Providers -=================== - -Parsl presents a uniform interface to managing compute resources -from a single computer with a single user up to -supercomputers managed through batch schedulers. -Each provider exposes three core actions: submit a -job for execution (e.g., sbatch for the Slurm resource manager), -retrieve the status of an allocation (e.g., squeue), -and cancel a running job (e.g., scancel). - -All Providers manage resources in terms of **blocks**. -Blocks are collections of one or more **nodes** that each -run at least one worker. [#mpi]_ -Control the size of a block with the ``nodes_per_block`` option -and the number of blocks with `elasticity settings `_. - -Start by selecting a scheduler from following classes, -then configuring the details required. - -.. contents:: - :local: - :depth: 1 - -Local Provider --------------- - -The :class:`~parsl.providers.LocalProvider` starts workers on the same -computer running the Parsl main script. -It is primarily used when testing Parsl programs -before deploying them to distributed resources. - -Cluster Schedulers ------------------- - -Schedulers are used at facilities where a single compute cluster is shared by many teams. -Users request individual "batch jobs" that will run once resources are available. -Parsl will create and manage its own batch jobs. - -Configuring a cluster scheduler requires: - -1. Account information (e.g., where to charge hours) -2. The size of each Job (e.g., node count, duration) - -Available options include: - -1. :class:`~parsl.providers.SlurmProvider` -2. :class:`~parsl.providers.CondorProvider` -3. :class:`~parsl.providers.GridEngineProvider` -4. :class:`~parsl.providers.TorqueProvider` -5. :class:`~parsl.providers.PBSProvider` -6. :class:`~parsl.providers.LSFProvider` - -Kubernetes ----------- - -**Provider**: :class:`~parsl.providers.KubernetesProvider` - -Kubernetes manages many small services running together on a single set of compute nodes. -Users define services then request Kubernetes to start or stop copies as appropriate. -Kubernetes creates each service inside an isolated "container," -decides where each copy runs, and manages connecting them to networks or storage. -Parsl can use Kubernetes to launch workers as services. - -Configure :class:`~parsl.providers.KubernetesProvider` by providing: - -1. Authentication details (e.g., user name, secrets) -2. The type of each container (e.g., base image, RAM, CPU) -3. Disk availability (e.g., which volumes containers can access) - - -.. [#mpi] Except for Providers running `multi-node Apps <../../apps/mpi_apps.html>`_ diff --git a/parsl/docs/userguide/configuring.rst b/parsl/docs/userguide/configuring.rst deleted file mode 100644 index 1b0f2be0..00000000 --- a/parsl/docs/userguide/configuring.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. meta:: - :content http-equiv="refresh": 0;url=configuration/index.html - -Redirect --------- - -This page has been `moved `_ diff --git a/parsl/docs/userguide/data.rst b/parsl/docs/userguide/data.rst deleted file mode 100644 index 4626f0ed..00000000 --- a/parsl/docs/userguide/data.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. meta:: - :content http-equiv="refresh": 0;url=configuration/data.html - -Redirect --------- - -This page has been `moved `_ diff --git a/parsl/docs/userguide/exceptions.rst b/parsl/docs/userguide/exceptions.rst deleted file mode 100644 index 71af9d52..00000000 --- a/parsl/docs/userguide/exceptions.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. meta:: - :content http-equiv="refresh": 0;url=workflows/exceptions.html - -Redirect --------- - -This page has been `moved `_ diff --git a/parsl/docs/userguide/execution.rst b/parsl/docs/userguide/execution.rst deleted file mode 100644 index 20346bfe..00000000 --- a/parsl/docs/userguide/execution.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. meta:: - :content http-equiv="refresh": 0;url=configuration/execution.html - -Redirect --------- - -This page has been `moved `_ diff --git a/parsl/docs/userguide/futures.rst b/parsl/docs/userguide/futures.rst deleted file mode 100644 index 1a4f40e7..00000000 --- a/parsl/docs/userguide/futures.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. meta:: - :content http-equiv="refresh": 0;url=execution/futures.html - -Redirect --------- - -This page has been `moved `_ diff --git a/parsl/docs/userguide/glossary.rst b/parsl/docs/userguide/glossary.rst deleted file mode 100644 index 62a735ff..00000000 --- a/parsl/docs/userguide/glossary.rst +++ /dev/null @@ -1,218 +0,0 @@ -Glossary -======== - -This glossary defines terms based on their usage within Parsl. By defining our terminology, we hope to create understanding across our community and reduce confusion. When asking for or providing support to fellow Parsl users, please use these terms as defined. - -Our glossary is organized alphabetically in English. Feel free to contribute terms and definitions to this list that will benefit Parsl users. - -.. _glossary: - -.. _appglossary: - -App ---- - -In Parsl, an app is a small, self-contained program that does a specific job. It's a piece of code, such as a Python function or a Bash script, that can run separately from your main program. Think of it as a mini-tool within your larger toolbox. - -.. _appfutureglossary: - -AppFuture ---------- - -An AppFuture is a placeholder for the result of an app that runs in the background. It's like a ticket you get when you order food at a restaurant – you get the ticket right away, but you have to wait for the food to be ready. Similarly, you get an AppFuture immediately when you start an app, but you have to wait for the app to finish before you can see the results. - -.. _bashappglossary: - -Bash App --------- - -A Bash app is a special kind of app in Parsl that lets you run commands from your computer's terminal (like the ones you type in the command prompt or shell). It's a way to use Parsl to automate tasks that you would normally do manually in the terminal. - -.. _blockglossary: - -Block ------ - -A block is a group of resources, such as nodes or computational units, allocated for executing tasks. Parsl manages the distribution of work across these resources to expedite task completion. - -.. _checkpointingglossary: - -Checkpointing -------------- - -Checkpointing is like saving your progress in a video game. If something goes wrong, you can restart from the last saved point instead of starting over. In Parsl, checkpointing saves the state of your work so you can resume it later if interrupted. - -.. _concurrencyglossary: - -Concurrency ------------ - -Concurrency means doing multiple things at the same time. In Parsl, it enables your apps to run in parallel across different resources, significantly speeding up program execution. It's like a chef preparing multiple dishes in a single kitchen, switching between all of them quickly. - -.. _configurationglossary: - -Configuration -------------- - -Configuration sets up the rules for how Parsl should work. It's like adjusting the settings on your phone – you can choose how you want things to look and behave. In Parsl, you can configure things like how many resources to use, where to store data, and how to handle errors. - -.. _datafutureglossary: - -DataFuture ----------- - -A DataFuture is a placeholder for a file that an app is creating. It's like a receipt for a package you're expecting – you get the receipt right away, but you have to wait for the package to arrive. Similarly, you get a DataFuture immediately when an app starts creating a file, but you have to wait for the file to be finished before you can use it. - -.. _dfkglossary: - -DataFlowKernel (DFK) --------------------- - -The DataFlowKernel is like the brain of Parsl. It's the part that controls how your apps run and how they share information. It's like the conductor of an orchestra, making sure that all the musicians play together in harmony. - -.. _elasticityglossary: - -Elasticity ----------- - -Elasticity refers to the ability to scale resources up or down as needed. In Parsl, it allows you to add or remove blocks of computational resources based on workload demands. - -.. _executionproviderglossary: - -Execution Provider ------------------- - -An execution provider acts as a bridge between Parsl and the resources you want to use, such as your laptop, a cluster, or a cloud service. It handles communication with these resources to execute tasks. - -.. _executorglossary: - -Executor --------- - -An executor is a manager that determines which app runs on which resource and when. It directs the flow of apps to ensure efficient task execution. It's like a traffic controller, directing the flow of apps to make sure they all get where they need to go. - -.. _futureglossary: - -Future ------- - -A future is a placeholder for the result of a task that hasn't finished yet. Both AppFuture and DataFuture are types of Futures. You can use the ``.result()`` method to get the actual result when it's ready. - -.. _jobglossary: - -Job ---- - -A job in Parsl is a unit of work submitted to an execution environment (such as a cluster or cloud) for processing. It can consist of one or more apps executed on computational resources. - -.. _launcherglossary: - -Launcher --------- - -A launcher in Parsl is responsible for placing the workers onto each computer, preparing them to run the apps. It’s like a bus driver who brings the players to the stadium, ensuring they are ready to start, but not directly involved in telling them what to do once they arrive. - -.. _managerglossary: - -Manager -------- - -A manager in Parsl is responsible for overseeing the execution of tasks on specific compute resources. It's like a supervisor who ensures that all workers (or workers within a block) are carrying out their tasks correctly and efficiently. - -.. _memoizationglossary: - -Memoization ------------ - -Memoization is like remembering something so you don't have to do it again. In Parsl, if you are using memoization and you run an app with the same inputs multiple times, Parsl will remember the result from the first time and give it to you again instead of running the app again. This can save a lot of time. - -.. _mpiappglossary: - -MPI App -------- - -An MPI app is a specialized app that uses the Message Passing Interface (MPI) for communication, which can occur both across nodes and within a single node. MPI enables different parts of the app to communicate and coordinate their activities, similar to how a walkie-talkie allows different teams to stay in sync. - -.. _nodeglossary: - -Node ----- - -A node in Parsl is like a workstation in a factory. It's a physical or virtual machine that provides the computational power needed to run tasks. Each node can host several workers that execute tasks. - -.. _parallelismglossary: - -Parallelism ------------ - -Parallelism means doing multiple things at the same time but not necessarily in the same location or using the same resources. In Parsl, it involves running apps simultaneously across different nodes or computational resources, accelerating program execution. Unlike concurrency which is like a chef preparing multiple dishes in a single kitchen, parallelism is like multiple chefs preparing different dishes in separate kitchens, at the same time. - -.. _parslscriptglossary: - -Parsl Script ------------- - -A Parsl script is a Python program that uses the Parsl library to define and run apps in parallel. It's like a recipe that tells you what ingredients to use and how to combine them. - -.. _pluginglossary: - -Plugin ------- - -A plugin is an add-on for Parsl. It's a piece of code that you can add to Parsl to give it new features or change how it works. It's like an extra tool that you can add to your toolbox. - -.. _pythonappglossary: - -Python App ----------- - -A Python app is a special kind of app in Parsl that's written as a Python function. It's a way to use Parsl to run your Python code in parallel. - -.. _resourceglossary: - -Resource --------- - -A resource in Parsl refers to any computational asset that can be used to execute tasks, such as CPU cores, memory, or entire nodes. It's like the tools and materials you need to get a job done. Resources, often grouped in nodes or clusters, are essential for processing workloads. - -.. _serializationglossary: - -Serialization -------------- - -Serialization is like packing your belongings into a suitcase so you can take them on a trip. In Parsl, it means converting your data into a format that can be sent over a network to another computer. - -.. _stagingglossary: - -Staging -------- - -Staging in Parsl involves moving data to the appropriate location before an app starts running and can also include moving data back after the app finishes. This process ensures that all necessary data is available where it needs to be for the app to execute properly and that the output data is returned to a specified location once the execution is complete. - -.. _taskglossary: - -Task ----- - -A task in Parsl is the execution of an app, it is the smallest unit of work that can be executed. It's like a single step in a larger process, where each task is part of a broader workflow or job. - -.. _threadglossary: - -Thread ------- - -A thread is like a smaller part of a program that can run independently. It's like a worker in a factory who can do their job at the same time as other workers. Threads are commonly used for parallelism within a single node. - -.. _workerglossary: - -Worker ------- - -A worker in Parsl is an independent process that runs on a node to execute tasks. Unlike threads, which share resources within a single process, workers operate as separate entities, each potentially handling different tasks on the same or different nodes. - -.. _workflowglossary: - -Workflow --------- - -A workflow is like a series of steps that you follow to complete a task. In Parsl, it's a way to describe how your apps should run and how they depend on each other, like a flowchart that shows you the order in which things need to happen. A workflow is typically expressed in a Parsl script, which is a Python program that leverages the Parsl library to orchestrate these tasks in a structured manner. diff --git a/parsl/docs/userguide/index.rst b/parsl/docs/userguide/index.rst deleted file mode 100644 index a626b42a..00000000 --- a/parsl/docs/userguide/index.rst +++ /dev/null @@ -1,17 +0,0 @@ -User guide -========== - -Parsl applications are composed of **Apps** that define tasks to be executed, -**Configuration** objects which define resources available for executing tasks, -and **Workflow** scripts that weave tasks together into parallel workflows. - -Start with an `overview of Parsl `_ before learning about each component. - -.. toctree:: - :maxdepth: 2 - - overview - apps/index - configuration/index - workflows/index - advanced/index diff --git a/parsl/docs/userguide/joins.rst b/parsl/docs/userguide/joins.rst deleted file mode 100644 index 4cc741f9..00000000 --- a/parsl/docs/userguide/joins.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. meta:: - :content http-equiv="refresh": 0;url=apps/joins.html - -Redirect --------- - -This page has been `moved `_ diff --git a/parsl/docs/userguide/lifted_ops.rst b/parsl/docs/userguide/lifted_ops.rst deleted file mode 100644 index ed28f55d..00000000 --- a/parsl/docs/userguide/lifted_ops.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. meta:: - :content http-equiv="refresh": 0;url=workflows/lifted_ops.html - -Redirect --------- - -This page has been `moved `_ diff --git a/parsl/docs/userguide/modularizing.rst b/parsl/docs/userguide/modularizing.rst deleted file mode 100644 index 69ed8248..00000000 --- a/parsl/docs/userguide/modularizing.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. meta:: - :content http-equiv="refresh": 0;url=advanced/modularizing.html - -Redirect --------- - -This page has been `moved `_ diff --git a/parsl/docs/userguide/monitoring.rst b/parsl/docs/userguide/monitoring.rst deleted file mode 100644 index 138127ec..00000000 --- a/parsl/docs/userguide/monitoring.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. meta:: - :content http-equiv="refresh": 0;url=advanced/monitoring.html - -Redirect --------- - -This page has been `moved `_ diff --git a/parsl/docs/userguide/mpi_apps.rst b/parsl/docs/userguide/mpi_apps.rst deleted file mode 100644 index 3f758190..00000000 --- a/parsl/docs/userguide/mpi_apps.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. meta:: - :content http-equiv="refresh": 0;url=apps/mpi_apps.html - -Redirect --------- - -This page has been `moved `_ diff --git a/parsl/docs/userguide/overview.rst b/parsl/docs/userguide/overview.rst deleted file mode 100644 index a83d9875..00000000 --- a/parsl/docs/userguide/overview.rst +++ /dev/null @@ -1,320 +0,0 @@ -Overview -======== - -Parsl is designed to enable straightforward parallelism and orchestration of asynchronous -tasks into dataflow-based workflows, in Python. Parsl manages the concurrent execution of -these tasks across various computation resources, from laptops to supercomputers, -scheduling each task only when its dependencies (e.g., input data dependencies) are met. - -Developing a Parsl program is a two-step process: - -1. Define Parsl apps by annotating Python functions to indicate that they can be executed concurrently. -2. Use standard Python code to invoke Parsl apps, creating asynchronous tasks and adhering to dependencies defined between apps. - -We aim in this section to provide a mental model of how Parsl programs behave. -We discuss how Parsl programs create concurrent tasks, how tasks communicate, -and the nature of the environment on which Parsl programs can perform -operations. In each case, we compare and contrast the behavior of Python -programs that use Parsl constructs with those of conventional Python -programs. - -.. note:: - The behavior of a Parsl program can vary in minor respects depending on the - Executor used (see :ref:`label-execution`). We focus here on the behavior seen when - using the recommended `parsl.executors.HighThroughputExecutor` (HTEX). - -Parsl and Concurrency ---------------------- -Any call to a Parsl app creates a new task that executes concurrently with the -main program and any other task(s) that are currently executing. Different -tasks may execute on the same nodes or on different nodes, and on the same or -different computers. - -The Parsl execution model thus differs from the Python native execution model, -which is inherently sequential. A Python program that does not contain Parsl -constructs, or make use of other concurrency mechanisms, executes statements -one at a time, in the order that they appear in the program. This behavior is -illustrated in the following figure, which shows a Python program on the left -and, on the right, the statements executed over time when that program is run, -from top to bottom. Each time that the program calls a function, control passes -from the main program (in black) to the function (in red). Execution of the -main program resumes only after the function returns. - -.. image:: ../images/overview/python-concurrency.png - :scale: 70 - :align: center - -In contrast, the Parsl execution model is inherently concurrent. Whenever a -program calls an app, a separate thread of execution is created, and the main -program continues without pausing. Thus in the example shown in the figure -below. There is initially a single task: the main program (black). The first -call to ``double`` creates a second task (red) and the second call to ``double`` -creates a third task (orange). The second and third task terminate as the -function that they execute returns. (The dashed lines represent the start and -finish of the tasks). The calling program will only block (wait) when it is -explicitly told to do so (in this case by calling ``result()``) - -.. image:: ../images/overview/parsl-concurrency.png - - -.. note:: - Note: We talk here about concurrency rather than parallelism for a reason. - Two activities are concurrent if they can execute at the same time. Two - activities occur in parallel if they do run at the same time. If a Parsl - program creates more tasks that there are available processors, not all - concurrent activities may run in parallel. - - -Parsl and Execution -------------------- -We have now seen that Parsl tasks are executed concurrently alongside the main -Python program and other Parsl tasks. We now turn to the question of how and -where are those tasks executed. Given the range of computers on which parallel -programs may be executed, Parsl allows tasks to be executed using different -executors (:py:class:`parsl.executors`). Executors are responsible for taking a queue of tasks and executing -them on local or remote resources. - -We briefly describe two of Parsl's most commonly used executors. -Other executors are described in :ref:`label-execution`. - -The `parsl.executors.HighThroughputExecutor` (HTEX) implements a *pilot job model* that enables -fine-grain task execution using across one or more provisioned nodes. -HTEX can be used on a single node (e.g., a laptop) and will make use of -multiple processes for concurrent execution. -As shown in the following figure, HTEX uses Parsl's provider abstraction (:py:class:`parsl.providers`) to -communicate with a resource manager (e.g., batch scheduler or cloud API) to -provision a set of nodes (e.g., Parsl will use Slurm’s sbatch command to request -nodes on a Slurm cluster) for the duration of execution. -HTEX deploys a lightweight worker agent on the nodes which subsequently connects -back to the main Parsl process. Parsl tasks are then sent from the main program -to the connected workers for execution and the results are sent back via the -same mechanism. This approach has a number of advantages over other methods: -it avoids long job scheduler queue delays by acquiring one set of resources -for the entire program and it allows for scheduling of many tasks on individual -nodes. - -.. image:: ../images/overview/htex-model.png - -.. Note: - Note: when deploying HTEX, or any pilot job model such as the - WorkQueueExecutor, it is important that the worker nodes be able to connect - back to the main Parsl process. Thus, you should verify that there is network - connectivity between the workers and the Parsl process and ensure that the - correct network address is used by the workers. Parsl provides a helper - function to automatically detect network addresses - (`parsl.addresses.address_by_query`). - - -The `parsl.executors.ThreadPoolExecutor` allows tasks to be executed on a pool of locally -accessible threads. As execution occurs on the same computer, on a pool of -threads forked from the main program, the tasks share memory with one another -(this is discussed further in the following sections). - - -Parsl and Communication ------------------------ -Parsl tasks typically need to communicate in order to perform useful work. -Parsl provides for two forms of communication: by parameter passing -and by file passing. -As described in the next section, Parsl programs may also communicate by -interacting with shared filesystems and services its environment. - -Parameter Passing -^^^^^^^^^^^^^^^^^ - -The figure above illustrates communication via parameter passing. -The call ``double(3)`` to the app ``double`` in the main program creates a new task -and passes the parameter value, 3, to that new task. When the task completes -execution, its return value, 6, is returned to the main program. Similarly, the -second task is passed the value 5 and returns the value 10. In this case, the -parameters passed are simple primitive types (i.e., integers); however, complex -objects (e.g., Numpy Arrays, Pandas DataFrames, custom objects) can also be -passed to/from tasks. - -File Passing -^^^^^^^^^^^^ -Parsl supports communication via files in both Bash apps and Python apps. -Files may be used in place of parameter passing for many reasons, such as for -apps are designed to support files, when data to be exchanged are large, -or when data cannot be easily serialized into Python objects. -As Parsl tasks may be executed on remote nodes, without shared file systems, -Parsl offers a Parsl :py:class:`parsl.data_provider.files.File` construct for location-independent reference -to files. Parsl will translate file objects to worker-accessible paths -when executing dependent apps. -Parsl is also able to transfer files in, out, and between Parsl -apps using one of several methods (e.g., FTP, HTTP(S), Globus and rsync). -To accommodate the asynchronous nature of file transfer, Parsl treats -data movement like a Parsl app, adding a dependency to the execution graph -and waiting for transfers to complete before executing dependent apps. -More information is provided in :ref:`label-data`). - -Futures -^^^^^^^ -Communication via parameter and file passing also serves a second purpose, namely -synchronization. As we discuss in more detail in :ref:`label-futures`, a call to an -app returns a special object called a future that has a special unassigned -state until such time as the app returns, at which time it takes the return -value. (In the example program, two futures are thus created, d1 and d2.) The -AppFuture function result() blocks until the future to which it is applied takes -a value. Thus the print statement in the main program blocks until both child -tasks created by the calls to the double app return. The following figure -captures this behavior, with time going from left to right rather than top to -bottom as in the preceding figure. Task 1 is initially active as it starts -Tasks 2 and 3, then blocks as a result of calls to d1.result() and d2.result(), -and when those values are available, is active again. - -.. image:: ../images/overview/communication.png - -The Parsl Environment ---------------------- -Regular Python and Parsl-enhanced Python differ in terms of the environment in -which code executes. We use the term *environment* here to refer to the -variables and modules (the *memory environment*), the file system(s) -(the *file system environment*), and the services (the *service environment*) -that are accessible to a function. - -An important question when it comes to understanding the behavior of Parsl -programs is the environment in which this new task executes: does it have the -same or different memory, file system, or service environment as its parent -task or any other task? The answer, depends on the executor used, and (in the -case of the file system environment) where the task executes. -Below we describe behavior for the most commonly used `parsl.executors.HighThroughputExecutor` -which is representative of all Parsl executors except the `parsl.executors.ThreadPoolExecutor`. - -.. Warning: - The `parsl.executors.ThreadPoolExecutor` behaves differently than other Parsl executors as - it allows tasks to share memory. - -Memory environment -^^^^^^^^^^^^^^^^^^ - -In Python, the variables and modules that are accessible to a function are defined -by Python scoping rules, by which a function has access to both variables defined -within the function (*local* variables) and those defined outside the function -(*global* variables). Thus in the following code, the print statement in the -print_answer function accesses the global variable "answer", and we see as output -"the answer is 42." - -.. code-block:: python - - answer = 42 - - def print_answer(): - print('the answer is', answer) - - print_answer() - - -In Parsl (except when using the `parsl.executors.ThreadPoolExecutor`) a Parsl app is executed -in a distinct environment that only has access to local variables associated -with the app function. Thus, if the program above is executed with say the -`parsl.executors.HighThroughputExecutor`, will print "the answer is 0" rather than "the answer -is 42," because the print statement in provide_answer does not have access to -the global variable that has been assigned the value 42. The program will -run without errors when using the `parsl.executors.ThreadPoolExecutor`. - -Similarly, the same scoping rules apply to import statements, and thus -the following program will run without errors with the `parsl.executors.ThreadPoolExecutor`, -but raise errors when run with any other executor, because the return statement -in ``ambiguous_double`` refers to a variable (factor) and a module (random) that are -not known to the function. - -.. code-block:: python - - import random - factor = 5 - - @python_app - def ambiguous_double(x): - return x * random.random() * factor - - print(ambiguous_double(42)) - - -To allow this program to run correctly with all Parsl executors, the random -library must be imported within the app, and the factor variable must be -passed as an argument, as follows. - -.. code-block:: python - - import random - factor = 5 - - @python_app - def good_double(factor, x): - import random - return x * random.random() * factor - - print(good_double(factor, 42)) - - -File system environment -^^^^^^^^^^^^^^^^^^^^^^^ - -In a regular Python program the environment that is accessible to a Python -program also includes the file system(s) of the computer on which it is -executing. -Thus in the following code, a value written to a file "answer.txt" in the -current directory can be retrieved by reading the same file, and the print -statement outputs "the answer is 42." - -.. code-block:: python - - def print_answer_file(): - with open('answer.txt','r') as f: - print('the answer is', f.read()) - - with open('answer.txt','w') as f: - f.write('42') - f.close() - - print_answer_file() - - -The question of which file system environment is accessible to a Parsl app -depends on where the app executes. If two tasks run on nodes that share a -file system, then those tasks (e.g., tasks A and B in the figure below, -but not task C) share a file system environment. Thus the program above will -output "the answer is 42" if the parent task and the child task run on -nodes 1 and 2, but not if they run on nodes 2 and 3. - -.. image:: ../images/overview/filesystem.png - :scale: 70 - :align: center - -Service Environment -^^^^^^^^^^^^^^^^^^^ - -We use the term service environment to refer to network services that may be -accessible to a Parsl program, such as a Redis server or Globus data management -service. These services are accessible to any task. - -Environment Summary -^^^^^^^^^^^^^^^^^^^ - -As we summarize in the table, if tasks execute with the `parsl.executors.ThreadPoolExecutor`, -they share the memory and file system environment of the parent task. If they -execute with any other executor, they have a separate memory environment, and -may or may not share their file system environment with other tasks, depending -on where they are placed. All tasks typically have access to the same network -services. - -+--------------------+--------------------+--------------------+---------------------------+------------------+ -| | Share memory | Share file system | Share file system | Share service | -| | environment with | environment with | environment with other | environment | -| | parent/other tasks | parent | tasks | with other tasks | -+====================+====================+====================+===========================+==================+ -+--------------------+--------------------+--------------------+---------------------------+------------------+ -| Python | Yes | Yes | N/A | N/A | -| without | | | | | -| Parsl | | | | | -+--------------------+--------------------+--------------------+---------------------------+------------------+ -| Parsl | Yes | Yes | Yes | N/A | -| ThreadPoolExecutor | | | | | -| | | | | | -+--------------------+--------------------+--------------------+---------------------------+------------------+ -| Other Parsl | No | If executed on the | If tasks are executed on | N/A | -| executors | | same node with | the same node or with | | -| | | file system access | access to the same file | | -| | | | system | | -+--------------------+--------------------+--------------------+---------------------------+------------------+ diff --git a/parsl/docs/userguide/parsl_perf.rst b/parsl/docs/userguide/parsl_perf.rst deleted file mode 100644 index 88c4f1a2..00000000 --- a/parsl/docs/userguide/parsl_perf.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. meta:: - :content http-equiv="refresh": 0;url=advanced/parsl_perf.html - -Redirect --------- - -This page has been `moved `_ diff --git a/parsl/docs/userguide/plugins.rst b/parsl/docs/userguide/plugins.rst deleted file mode 100644 index 2f4cdbcb..00000000 --- a/parsl/docs/userguide/plugins.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. meta:: - :content http-equiv="refresh": 0;url=advanced/plugins.html - -Redirect --------- - -This page has been `moved `_ diff --git a/parsl/docs/userguide/usage_tracking.rst b/parsl/docs/userguide/usage_tracking.rst deleted file mode 100644 index 1b891ce0..00000000 --- a/parsl/docs/userguide/usage_tracking.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. meta:: - :content http-equiv="refresh": 0;url=advanced/usage_tracking.html - -Redirect --------- - -This page has been `moved `_ diff --git a/parsl/docs/userguide/workflow.rst b/parsl/docs/userguide/workflow.rst deleted file mode 100644 index 139dd791..00000000 --- a/parsl/docs/userguide/workflow.rst +++ /dev/null @@ -1,9 +0,0 @@ -:orphan: - -.. meta:: - :content http-equiv="refresh": 0;url=workflows/workflow.html - -Redirect --------- - -This page has been `moved `_ diff --git a/parsl/docs/userguide/workflows/checkpoints.rst b/parsl/docs/userguide/workflows/checkpoints.rst deleted file mode 100644 index f1a179a4..00000000 --- a/parsl/docs/userguide/workflows/checkpoints.rst +++ /dev/null @@ -1,301 +0,0 @@ -.. _label-memos: - -Memoization and checkpointing ------------------------------ - -When an app is invoked several times with the same parameters, Parsl can -reuse the result from the first invocation without executing the app again. - -This can save time and computational resources. - -The memoization and checkpointing system is pluggable, with basic behaviour -provided by the `BasicMemoizer`. The rest of this chapter refers to the -behaviour of the `BasicMemoizer`. - -Memoization and checkpointing is done in two ways: - -* Firstly, *app caching* will allow reuse of results and exceptions within - the same run. This is also referred to as *memoization*. - -* Building on top of that, *checkpointing* will store results (but not - exceptions) on the filesystem and reuse those results in later runs. - -.. _label-appcaching: - -App caching -=========== - - -There are many situations in which a program may be re-executed -over time. Often, large fragments of the program will not have changed -and therefore, re-execution of apps will waste valuable time and -computation resources. Parsl's app caching solves this problem by -storing results from apps that have successfully completed -so that they can be re-used. - -App caching is enabled by setting the ``cache`` -argument in the :func:`~parsl.app.app.python_app` or :func:`~parsl.app.app.bash_app` -decorator to ``True`` (by default it is ``False``). - -.. code-block:: python - - @bash_app(cache=True) - def hello (msg, stdout=None): - return 'echo {}'.format(msg) - -App caching can be globally disabled by supplying a new memoizer in -:class:`~parsl.config.Config` defined as ``BasicMemoizer(memoize=False)``. - -App caching can be particularly useful when developing interactive programs such as when -using a Jupyter notebook. In this case, cells containing apps are often re-executed -during development. Using app caching will ensure that only modified apps are re-executed. - - -App equivalence -^^^^^^^^^^^^^^^ - -Parsl determines app equivalence using the name of the app function: -if two apps have the same name, then they are equivalent under this -relation. - -Changes inside the app, or by functions called by an app will not invalidate -cached values. - -There are lots of other ways functions might be compared for equivalence, -and `parsl.dataflow.memoization.id_for_memo` provides a hook to plug in -alternate application-specific implementations. - - -Invocation equivalence -^^^^^^^^^^^^^^^^^^^^^^ - -Two app invocations are determined to be equivalent if their -input arguments are identical. - -In simple cases, this follows obvious rules: - -.. code-block:: python - - # these two app invocations are the same and the second invocation will - # reuse any cached input from the first invocation - x = 7 - f(x).result() - - y = 7 - f(y).result() - - -Internally, equivalence is determined by hashing the input arguments, and -comparing the hash to hashes from previous app executions. - -This approach can only be applied to data types for which a deterministic hash -can be computed. - -By default Parsl can compute sensible hashes for basic data types: -str, int, float, None, as well as more some complex types: -functions, and dictionaries and lists containing hashable types. - -Attempting to cache apps invoked with other, non-hashable, data types will -lead to an exception at invocation. - -In that case, mechanisms to hash new types can be registered by a program by -implementing the `parsl.dataflow.memoization.id_for_memo` function for -the new type. - -Ignoring arguments -^^^^^^^^^^^^^^^^^^ - -On occasion one may wish to ignore particular arguments when determining -app invocation equivalence - for example, when generating log file -names automatically based on time or run information. -Parsl allows developers to list the arguments to be ignored -in the ``ignore_for_cache`` app decorator parameter: - -.. code-block:: python - - @bash_app(cache=True, ignore_for_cache=['stdout']) - def hello (msg, stdout=None): - return 'echo {}'.format(msg) - - -Caveats -^^^^^^^ - -It is important to consider several important issues when using app caching: - -- Determinism: App caching is generally useful only when the apps are deterministic. - If the outputs may be different for identical inputs, app caching will obscure - this non-deterministic behavior. For instance, caching an app that returns - a random number will result in every invocation returning the same result. - -- Timing: If several identical calls to an app are made concurrently having - not yet cached a result, many instances of the app will be launched. - Once one invocation completes and the result is cached - all subsequent calls will return immediately with the cached result. - -- Performance: If app caching is enabled, there may be some performance - overhead especially if a large number of short duration tasks are launched rapidly. - This overhead has not been quantified. - -.. _label-checkpointing: - -Checkpointing -============= - -Large-scale Parsl programs are likely to encounter errors due to node failures, -application or environment errors, and myriad other issues. Parsl offers an -application-level checkpointing model to improve resilience, fault tolerance, and -efficiency. - -.. note:: - Checkpointing builds on top of app caching, and so app caching must be - enabled. If app caching is disabled in the config ``Config.app_cache``, checkpointing will - not work. - -Parsl follows an incremental checkpointing model, where each checkpoint file contains -all results that have been updated since the last checkpoint. - -When a Parsl program loads a checkpoint file and is executed, it will use -checkpointed results for any apps that have been previously executed. -Like app caching, checkpoints -use the hash of the app and the invocation input parameters to identify previously computed -results. If multiple checkpoints exist for an app (with the same hash) -the most recent entry will be used. - -Parsl provides four checkpointing modes, which can be specified using the ``checkpoint_mode`` -parameter to ``memoizer=BasicMemoizer(...)`` - -1. ``task_exit``: a checkpoint is created each time an app completes or fails - (after retries if enabled). This mode minimizes the risk of losing information - from completed tasks. - - .. code-block:: python - - BasicMemoizer(checkpoint_mode = 'task_exit') - -2. ``periodic``: a checkpoint is created periodically using a user-specified - checkpointing interval. Results will be saved to the checkpoint file for - all tasks that have completed during this period. - - .. code-block:: python - - BasicMemoizer(checkpoint_mode = 'periodic', - checkpoint_period = '01:00:00') - -3. ``dfk_exit``: checkpoints are created when Parsl is - about to exit. This reduces the risk of losing results due to - premature program termination from exceptions, terminate signals, etc. However - it is still possible that information might be lost if the program is - terminated abruptly (machine failure, SIGKILL, etc.) - - .. code-block:: python - - BasicMemoizer(checkpoint_mode = 'dfk_exit') - -4. ``manual``: in addition to these automated checkpointing modes, it is also possible - to manually initiate a checkpoint by calling ``checkpoint()`` on the - `BasicMemoizer` in the Parsl program code. - - .. code-block:: python - - m = BasicMemoizer(checkpoint_mode = 'manual') - ... - with parsl.load(Config(memoizer=m, ...)): - ... - m.checkpoint() - -In all cases the checkpoint file is written out to the ``runinfo/RUN_ID/checkpoint/`` directory. - -.. Note:: Checkpoint modes ``periodic``, ``dfk_exit``, and ``manual`` can interfere with garbage collection. - In these modes task information will be retained after completion, until checkpointing events are triggered. - - -Creating a checkpoint -^^^^^^^^^^^^^^^^^^^^^ - -Automated checkpointing must be explicitly enabled in the Parsl configuration. -There is no need to modify a Parsl program as checkpointing will occur transparently. -In the following example, checkpointing is enabled at task exit. The results of -each invocation of the ``slow_double`` app will be stored in the checkpoint file. - -.. code-block:: python - - import parsl - from parsl.app.app import python_app - from parsl.configs.local_threads import config - - config.checkpoint_mode = 'task_exit' - - parsl.load(config) - - @python_app(cache=True) - def slow_double(x): - import time - time.sleep(5) - return x * 2 - - d = [] - for i in range(5): - d.append(slow_double(i)) - - print([d[i].result() for i in range(5)]) - -Alternatively, manual checkpointing can be used to explictly specify when the checkpoint -file should be saved. The following example shows how manual checkpointing can be used. -Here, the ``checkpoint()`` method will save the results of the prior invocations -of the ``slow_double`` app. - -.. code-block:: python - - import parsl - from parsl import python_app - from parsl.configs.local_threads import config - - dfk = parsl.load(config) - - @python_app(cache=True) - def slow_double(x, sleep_dur=1): - import time - time.sleep(sleep_dur) - return x * 2 - - N = 5 # Number of calls to slow_double - d = [] # List to store the futures - for i in range(0, N): - d.append(slow_double(i)) - - # Wait for the results - [i.result() for i in d] - - dfk.memoizer.checkpoint() - - -Resuming from a checkpoint -^^^^^^^^^^^^^^^^^^^^^^^^^^ - -When resuming a program from a checkpoint Parsl allows the user to select -which checkpoint file(s) to use. -Checkpoint files are stored in the ``runinfo/RUNID/checkpoint`` directory. - -The example below shows how to resume using all available checkpoints. -Here, the program re-executes the same calls to the ``slow_double`` app -as above and instead of waiting for results to be computed, the values -from the checkpoint file are are immediately returned. - -.. code-block:: python - - import parsl - from parsl.tests.configs.local_threads import config - from parsl.utils import get_all_checkpoints - - config.memoizer = BasicMemoizer(checkpoint_files = get_all_checkpoints()) - - parsl.load(config) - - # Rerun the same workflow - d = [] - for i in range(5): - d.append(slow_double(i)) - - # wait for results - print([d[i].result() for i in range(5)]) diff --git a/parsl/docs/userguide/workflows/concurrent.rst b/parsl/docs/userguide/workflows/concurrent.rst deleted file mode 100644 index ff5616f8..00000000 --- a/parsl/docs/userguide/workflows/concurrent.rst +++ /dev/null @@ -1,129 +0,0 @@ -.. _label-parslpool: - -ParslPoolExecutor Interface -=========================== - -The :class:`parsl.concurrent.ParslPoolExecutor` implements the "Executor" interface -from the :mod:`concurrent.futures` module in standard Python. -This reduced interface permits employing Parsl into applications already capable -of single-node parallelism using :class:`~concurrent.futures.ProcessPoolExecutor`. - -Creating a ParslPoolExecutor ----------------------------- - -.. note:: - - See the :ref:`Configuration section ` for how to define computational resources. - -Create a :class:`~parsl.concurrent.ParslPoolExecutor` using one of two methods: - -1. Supplying a Parsl :class:`~parsl.Config` that defines how to create new workers. - The executor will start a new Parsl Data Flow Kernel (DFK) when it is entered as a context manager. - - .. code-block:: python - - from parsl.concurrent import ParslPoolExecutor - from parsl.config.htex_local import config # Mimicks the PythonPool - - with ParslPoolExecutor(config=config) as pool: - ... - - All resources will be closed when the block exits. - - -2. Supplying an already-started Parsl :class:`~parsl.DataFlowKernel` (DFK). - The Parsl DFK must be started and stopped separate from the executor. - - .. code-block:: python - - from parsl.concurrent import ParslPoolExecutor - from parsl.config.htex_local import config - import parsl - - with parsl.load(dfk) as dfk - with ParslPoolExecutor(dfk=dfk) as pool: - ... - ... - - Parsl will only shut when the outer block exits. - -Use multiple types of resources within the same program -by creating multiple :class:`~parsl.concurrent.ParslPoolExecutors`, -each mapped to different types of Parsl workers (also called "executors"). -Start by loading a Parsl Config that includes :ref:`multiple executors `. -Then create ``ParslPoolExecutor`` with different lists of "executors" that -their tasks will use. - -.. code-block:: python - - with parsl.load(hybrid_config) as dfk, \ - ParslPoolExecutor(dfk=dfk, executors=['gpu']) as pool_gpu, \ - ParslPoolExecutor(dfk=dfk, executors=['cpu']) as pool_cpu: - ... - -Using a ParslPoolExecutor -------------------------- - -The :class:`~parsl.concurrent.ParslPoolExecutor` supports all functions from -the :class:`~concurrent.futures.Executor` interface *except task cancellation*. - -The ``submit`` and ``map`` functions behave just as in :class:`~concurrent.futures.ProcessPoolExecutor`, -and also include the task chaining supported in App-based Parsl workflows. - -.. code-block:: python - - from parsl.concurrent import ParslPoolExecutor - from parsl.config.htex_local import config # Mimicks the PythonPool - - def f(x): - return x + 1 - - with ParslPoolExecutor(config=config) as pool: - # Submit a task then a task which depends on the result - future_1 = pool.submit(f, 1) - future_2 = pool.submit(f, future_1) - - assert future_1.result() == 2 - assert future_2.result() == 3 - -Tasks from the Executor and App-based interface may also be used together. - - .. code-block:: python - - def f(x): - return x + 1 - - @python_app - def parity(x): - return 'odd' if x % 2 == 1 else 'even' - - with ParslPoolExecutor(config=my_parsl_config) as executor: - future_1 = executor.submit(f, 1) - assert parity(future_1).result() == 'even' # Function chaining, as expected - - -Differences ------------ - -The differences between the Parsl-based :class:`~parsl.concurrent.ParslPoolExecutor` -and the Python :class:`~concurrent.futures.ProcessPoolExecutor` are: - -1. *Task Cancellation*: Parsl does not support canceling tasks once submitted. - -2. *Defining Functions*: Functions defined in modules work the same in both - Parsl and the ``ProcessPoolExecutor``. However, those defined during execution - (e.g., in the "main" file) behave differently. - - Parsl will serialize functions defined at runtime but they will not be able - to access global variables (as is the case when using the - `spawn start method `_ - in the ``ProcessPoolExecutor``), which means modules must be imported - inside the function. - Follow the rules :ref:`in the App guide `. - -3. *No Multiprocessing Objects*: Tools such as the :class:`multiprocessing.Queue` and - `synchronization primitives `_ - are not compatible with ``ParslPoolExecutor``. - -4. *Worker Initialization*: The worker initialization functions from :class:`~concurrent.futures.ProcessPoolExecutor` - are not supported. Configure workers using the ``Config`` object instead. diff --git a/parsl/docs/userguide/workflows/exceptions.rst b/parsl/docs/userguide/workflows/exceptions.rst deleted file mode 100644 index d47cece4..00000000 --- a/parsl/docs/userguide/workflows/exceptions.rst +++ /dev/null @@ -1,172 +0,0 @@ -.. _label-exceptions: - -Error handling -============== - -Parsl provides various mechanisms to add resiliency and robustness to programs. - -Exceptions ----------- - -Parsl is designed to capture, track, and handle various errors occurring -during execution, including those related to the program, apps, execution -environment, and Parsl itself. -It also provides functionality to appropriately respond to failures during -execution. - -Failures might occur for various reasons: - -1. A task failed during execution. -2. A task failed to launch, for example, because an input dependency was not met. -3. There was a formatting error while formatting the command-line string in Bash apps. -4. A task completed execution but failed to produce one or more of its specified - outputs. -5. Task exceeded the specified walltime. - -Since Parsl tasks are executed asynchronously and remotely, it can be difficult to determine -when errors have occurred and to appropriately handle them in a Parsl program. - -For errors occurring in Python code, Parsl captures Python exceptions and returns -them to the main Parsl program. For non-Python errors, for example when a node -or worker fails, Parsl imposes a timeout, and considers a task to have failed -if it has not heard from the task by that timeout. Parsl also considers a task to have failed -if it does not meet the contract stated by the user during invocation, such as failing -to produce the stated output files. - -Parsl communicates these errors by associating Python exceptions with task futures. -These exceptions are raised only when a result is called on the future -of a failed task. For example: - -.. code-block:: python - - @python_app - def bad_divide(x): - return 6 / x - - # Call bad divide with 0, to cause a divide by zero exception - doubled_x = bad_divide(0) - - # Catch and handle the exception. - try: - doubled_x.result() - except ZeroDivisionError as e: - print('Oops! You tried to divide by 0.') - except Exception as e: - print('Oops! Something really bad happened.') - -.. _label-retry: - -Retries -------- - -Often errors in distributed/parallel environments are transient. -In these cases, retrying failed tasks can be a simple way -of overcoming transient (e.g., machine failure, -network failure) and intermittent failures. -When ``retries`` are enabled (and set to an integer > 0), Parsl will automatically -re-launch tasks that have failed until the retry limit is reached. -By default, retries are disabled and exceptions will be communicated -to the Parsl program. - -The following example shows how the number of retries can be set to 2: - -.. code-block:: python - - import parsl - from parsl.configs.htex_local import config - - config.retries = 2 - - parsl.load(config) - -More specific retry handling can be specified using retry handlers, documented -below. - - -Lazy fail ---------- - -Parsl implements a lazy failure model through which a workload will continue -to execute in the case that some tasks fail. That is, the program will not -halt as soon as it encounters a failure, rather it will continue to execute -unaffected apps. - -The following example shows how lazy failures affect execution. In this -case, task C fails and therefore tasks E and F that depend on results from -C cannot be executed; however, Parsl will continue to execute tasks B and D -as they are unaffected by task C's failure. - -.. code-block:: - - Here's a workflow graph, where - (X) is runnable, - [X] is completed, - (X*) is failed. - (!X) is dependency failed - - (A) [A] (A) - / \ / \ / \ - (B) (C) [B] (C*) [B] (C*) - | | => | | => | | - (D) (E) (D) (E) [D] (!E) - \ / \ / \ / - (F) (F) (!F) - - time -----> - - -Retry handlers --------------- - -The basic parsl retry mechanism keeps a count of the number of times a task -has been (re)tried, and will continue retrying that task until the configured -retry limit is reached. - -Retry handlers generalize this to allow more expressive retry handling: -parsl keeps a retry cost for a task, and the task will be retried until the -configured retry limit is reached. Instead of the cost being 1 for each -failure, user-supplied code can examine the failure and compute a custom -cost. - -This allows user knowledge about failures to influence the retry mechanism: -an exception which is almost definitely a non-recoverable failure (for example, -due to bad parameters) can be given a high retry cost (so that it will not -be retried many times, or at all), and exceptions which are likely to be -transient (for example, where a worker node has died) can be given a low -retry cost so they will be retried many times. - -A retry handler can be specified in the parsl configuration like this: - - -.. code-block:: python - - Config( - retries=2, - retry_handler=example_retry_handler - ) - - -``example_retry_handler`` should be a function defined by the user that will -compute the retry cost for a particular failure, given some information about -the failure. - -For example, the following handler will give a cost of 1 to all exceptions, -except when a bash app exits with unix exitcode 9, in which case the cost will -be 100. This will have the effect that retries will happen as normal for most -errors, but the bash app can indicate that there is little point in retrying -by exiting with exitcode 9. - -.. code-block:: python - - def example_retry_handler(exception, task_record): - if isinstance(exception, BashExitFailure) and exception.exitcode == 9: - return 100 - else - return 1 - -The retry handler is given two parameters: the exception from execution, and -the parsl internal task_record. The task record contains details such as the -app name, parameters and executor. - -If a retry handler raises an exception itself, then the task will be aborted -and no further tries will be attempted. diff --git a/parsl/docs/userguide/workflows/futures.rst b/parsl/docs/userguide/workflows/futures.rst deleted file mode 100644 index 13d22a21..00000000 --- a/parsl/docs/userguide/workflows/futures.rst +++ /dev/null @@ -1,165 +0,0 @@ -.. _label-futures: - -Futures -======= - -When an ordinary Python function is invoked in a Python program, the Python interpreter waits for the function to complete execution -before proceeding to the next statement. -But if a function is expected to execute for a long period of time, it may be preferable not to wait for -its completion but instead to proceed immediately with executing subsequent statements. -The function can then execute concurrently with that other computation. - -Concurrency can be used to enhance performance when independent activities -can execute on different cores or nodes in parallel. The following -code fragment demonstrates this idea, showing that overall execution time -may be reduced if the two function calls are executed concurrently. - -.. code-block:: python - - v1 = expensive_function(1) - v2 = expensive_function(2) - result = v1 + v2 - -However, concurrency also introduces a need for **synchronization**. -In the example, it is not possible to compute the sum of ``v1`` and ``v2`` -until both function calls have completed. -Synchronization provides a way of blocking execution of one activity -(here, the statement ``result = v1 + v2``) until other activities -(here, the two calls to ``expensive_function()``) have completed. - -Parsl supports concurrency and synchronization as follows. -Whenever a Parsl program calls a Parsl app (a function annotated with a Parsl -app decorator, see :ref:`apps`), -Parsl will create a new ``task`` and immediately return a -`future `_ in lieu of that function's result(s). -The program will then continue immediately to the next statement in the program. -At some point, for example when the task's dependencies are met and there -is available computing capacity, Parsl will execute the task. Upon -completion, Parsl will set the value of the future to contain the task's -output. - -A future can be used to track the status of an asynchronous task. -For example, after creation, the future may be interrogated to determine -the task's status (e.g., running, failed, completed), access results, -and capture exceptions. Further, futures may be used for synchronization, -enabling the calling Python program to block until the future -has completed execution. - -Parsl provides two types of futures: `AppFuture` and `DataFuture`. -While related, they enable subtly different parallel patterns. - -AppFutures ----------- - -AppFutures are the basic building block upon which Parsl programs are built. Every invocation of a Parsl app returns an AppFuture that may be used to monitor and manage the task's execution. -AppFutures are inherited from Python's `concurrent library `_. -They provide three key capabilities: - -1. An AppFuture's ``result()`` function can be used to wait for an app to complete, and then access any result(s). -This function is blocking: it returns only when the app completes or fails. -The following code fragment implements an example similar to the ``expensive_function()`` example above. -Here, the ``sleep_double`` app simply doubles the input value. The program invokes -the ``sleep_double`` app twice, and returns futures in place of results. The example -shows how the future's ``result()`` function can be used to wait for the results from the -two ``sleep_double`` app invocations to be computed. - -.. code-block:: python - - @python_app - def sleep_double(x): - import time - time.sleep(2) # Sleep for 2 seconds - return x*2 - - # Start two concurrent sleep_double apps. doubled_x1 and doubled_x2 are AppFutures - doubled_x1 = sleep_double(10) - doubled_x2 = sleep_double(5) - - # The result() function will block until each of the corresponding app calls have completed - print(doubled_x1.result() + doubled_x2.result()) - -2. An AppFuture's ``done()`` function can be used to check the status of an app, *without blocking*. -The following example shows that calling the future's ``done()`` function will not stop execution of the main Python program. - -.. code-block:: python - - @python_app - def double(x): - return x*2 - - # doubled_x is an AppFuture - doubled_x = double(10) - - # Check status of doubled_x, this will print True if the result is available, else False - print(doubled_x.done()) - -3. An AppFuture provides a safe way to handle exceptions and errors while asynchronously executing -apps. The example shows how exceptions can be captured in the same way as a standard Python program -when calling the future's ``result()`` function. - -.. code-block:: python - - @python_app - def bad_divide(x): - return 6/x - - # Call bad divide with 0, to cause a divide by zero exception - doubled_x = bad_divide(0) - - # Catch and handle the exception. - try: - doubled_x.result() - except ZeroDivisionError as ze: - print('Oops! You tried to divide by 0') - except Exception as e: - print('Oops! Something really bad happened') - - -In addition to being able to capture exceptions raised by a specific app, Parsl also raises ``DependencyErrors`` when apps are unable to execute due to failures in prior dependent apps. -That is, an app that is dependent upon the successful completion of another app will fail with a dependency error if any of the apps on which it depends fail. - - -DataFutures ------------ - -While an AppFuture represents the execution of an asynchronous app, -a DataFuture represents a file to be produced by that app. -Parsl's dataflow model requires such a construct so that it can determine -when dependent apps, apps that that are to consume a file produced by another app, -can start execution. - -When calling an app that produces files as outputs, Parsl requires that a list of output files be specified (as a list of `File` objects passed in via the ``outputs`` keyword argument). Parsl will return a DataFuture for each output file as part AppFuture when the app is executed. -These DataFutures are accessible in the AppFuture's ``outputs`` attribute. - -Each DataFuture will complete when the App has finished executing, -and the corresponding file has been created (and if specified, staged out). - -When a DataFuture is passed as an argument to a subsequent app invocation, -that subsequent app will not begin execution until the DataFuture is -completed. The input argument will then be replaced with an appropriate -File object. - -The following code snippet shows how DataFutures are used. In this -example, the call to the echo Bash app specifies that the results -should be written to an output file ("hello1.txt"). The main -program inspects the status of the output file (via the future's -``outputs`` attribute) and then blocks waiting for the file to -be created (``hello.outputs[0].result()``). - -.. code-block:: python - - # This app echoes the input string to the first file specified in the - # outputs list - @bash_app - def echo(message, outputs=()): - return 'echo {} &> {}'.format(message, outputs[0]) - - # Call echo specifying the output file - hello = echo('Hello World!', outputs=[File('hello1.txt')]) - - # The AppFuture's outputs attribute is a list of DataFutures - print(hello.outputs) - - # Print the contents of the output DataFuture when complete - with open(hello.outputs[0].result().filepath, 'r') as f: - print(f.read()) diff --git a/parsl/docs/userguide/workflows/index.rst b/parsl/docs/userguide/workflows/index.rst deleted file mode 100644 index 9b4a17d6..00000000 --- a/parsl/docs/userguide/workflows/index.rst +++ /dev/null @@ -1,38 +0,0 @@ -Running Workflows -================= - -Parsl workflows are a Python "main" program that defines Apps, -how the Apps are invoked, -and how results are passed between different Apps. - -The core concept of workflows is that Parsl Apps produce **Futures** -with all features from those in Python's :mod:`concurrent.futures` module and more. - -The Parsl programs typically contain a ``parsl.load`` statement to start -the services which manage scheduling computations. - -.. code-block:: python - - from parsl.configs.htex_local import config # See the "Configuration" documentation - from parsl import python_app # See the "Apps" documentation - import parsl - - @python_app - def f(x): - return x + 1 - - with parsl.load(config): - future = f(1) - print(f'1 + 1 = {future.result()}') - - -.. toctree:: - :maxdepth: 2 - - futures - workflow - exceptions - lifted_ops - logging - checkpoints - concurrent diff --git a/parsl/docs/userguide/workflows/lifted_ops.rst b/parsl/docs/userguide/workflows/lifted_ops.rst deleted file mode 100644 index 6e258b9b..00000000 --- a/parsl/docs/userguide/workflows/lifted_ops.rst +++ /dev/null @@ -1,56 +0,0 @@ -.. _label-liftedops: - -Lifted operators -================ - -Parsl allows some operators (``[]`` and ``.``) to be used on an AppFuture in -a way that makes sense with those operators on the eventually returned -result. - -Lifted [] operator ------------------- - -When an app returns a complex structure such as a ``dict`` or a ``list``, -it is sometimes useful to pass an element of that structure to a subsequent -task, without waiting for that subsequent task to complete. - -To help with this, Parsl allows the ``[]`` operator to be used on an -`AppFuture`. This operator will return another `AppFuture` that will -complete after the initial future, with the result of ``[]`` on the value -of the initial future. - -The end result is that this assertion will hold: - -.. code-block:: python - - fut = my_app() - assert fut['x'].result() == fut.result()[x] - -but more concurrency will be available, as execution of the main workflow -code will not stop to wait for ``result()`` to complete on the initial -future. - -`AppFuture` does not implement other methods commonly associated with -dicts and lists, such as ``len``, because those methods should return a -specific type of result immediately, and that is not possible when the -results are not available until the future. - -If a key does not exist in the returned result, then the exception will -appear in the Future returned by ``[]``, rather than at the point that -the ``[]`` operator is applied. This is because the valid values that can -be used are not known until the underlying result is available. - -Lifted . operator ------------------ - -The ``.`` operator works similarly to ``[]`` described above: - -.. code-block:: python - - fut = my_app - assert fut.x.result() == fut.result().x - -Attributes beginning with ``_`` are not lifted as this usually indicates an -attribute that is used for internal purposes, and to try to avoid mixing -protocols (such as iteration in for loops) defined on AppFutures vs protocols -defined on the underlying result object. diff --git a/parsl/docs/userguide/workflows/logging.rst b/parsl/docs/userguide/workflows/logging.rst deleted file mode 100644 index 7129b873..00000000 --- a/parsl/docs/userguide/workflows/logging.rst +++ /dev/null @@ -1,263 +0,0 @@ -.. - The logging examples are from the "examples" in the advanced user guide. - -Logging -======= - -Parsl produces a rich collection of output logs by default. -Different parts of Parsl write to separate files -and all are collected in a directory named "runinfo" by default. - -Log Directory Structure ------------------------ - -.. code-block:: text - - runinfo/ - └── 000 - ├── htex_local - │ ├── block-0 - │ │ └── ad5bf9a8d238 - │ │ ├── manager.log - │ │ ├── worker_0.log - │ │ ├── worker_1.log - │ │ ├── worker_2.log - │ │ └── worker_3.log - │ └── interchange.log - ├── parsl.log - └── submit_scripts - ├── parsl.htex_local.block-0.1735499186.6675148.sh - ├── parsl.htex_local.block-0.1735499186.6675148.sh.ec - ├── parsl.htex_local.block-0.1735499186.6675148.sh.err - └── parsl.htex_local.block-0.1735499186.6675148.sh.out - - -The ``runinfo`` directory holds a unique subdirectory for each time Parsl is executed. - -Components of Parsl run in separate processes -and each write to different files to avoid any need for synchronization. -The root directory for a run contains: - -1. ``parsl.log``: The core log file from main Parsl processes -2. ``submit_scripts``: A collection of the scripts which were run to launch workers. - These scripts may include batch submission files and the text printed to screen - during the batch job. -3. Subdirectories for logs from each of the Executors used by Parsl (here: ``htex_local``). - -Main Parsl Log -~~~~~~~~~~~~~~ - -The ``parsl.log`` file in a Parsl logging directory captures all Parsl-related activities -that occur on the Python process running the Parsl application. - -The log should begin with a message indicating Parsl DataFlowKernel (DFK) is starting -and a readout of the configuration settings. -The logs will end with a status message that the DFK has cleaned up successfully. - -.. code-block:: text - - 1735499181.631377 2024-12-29 14:06:21 MainProcess- MainThread- parsl.dataflow.dflow:95 __init__ INFO: Starting DataFlowKernel with config - [ ... ] - 1735499187.851164 2024-12-29 14:06:27 MainProcess- MainThread- parsl.dataflow.dflow:1265 cleanup INFO: DFK cleanup complete - 1735499187.851178 2024-12-29 14:06:27 MainProcess- MainThread- parsl.process_loggers:27 wrapped DEBUG: Normal ending for cleanup on thread MainThread - -As :ref:`exampled below `, the main Parsl log contains references for -status changes of blocks of workers and each task. - -Submission Scripts -~~~~~~~~~~~~~~~~~~ - -The ``submit_script`` directory contains the input and outputs from the :ref:`execution providers ` -used by a Parsl application. -The actual contents of the file vary between Parsl configurations, -but most providers will produce a shell script -used when requesting compute nodes and the outputs of the shell scripts. - -Executor Logs -~~~~~~~~~~~~~ - -Different Executors in the Parsl application write log files to their own directories. -The types of logs vary depending on the choice of Executor. -This section describes the output structure of a common configuration: -:ref:`HighThroughputExecutor ` using a batch scheduler. - -The log file at the root of the directory, ``interchange.log``, is from a process -which distributes task across the workers used by Parsl. -The interchange reports the connection between the main Parsl process -and managers which register to it. - -.. code-block:: text - - 2024-12-29 14:06:21.965 interchange:115 MainProcess(52425) MainThread __init__ [INFO] Attempting connection to client at 127.0.0.1 on ports: 55059,55181,55929 - 2024-12-29 14:06:21.966 interchange:127 MainProcess(52425) MainThread __init__ [INFO] Connected to client - 2024-12-29 14:06:21.966 interchange:160 MainProcess(52425) MainThread __init__ [INFO] Bound to ports 54160,54943 for incoming worker connections - [ ... ] - 2024-12-29 14:06:21.966 interchange:209 MainProcess(52425) Interchange-Task-Puller task_puller [INFO] Starting - 2024-12-29 14:06:27.316 interchange:411 MainProcess(52425) MainThread process_task_outgoing_incoming [INFO] Adding manager: b'ad5bf9a8d238' to ready queue - -Each manager writes logs to a unique subdirectory named by the block it belongs -to and the name assigned to it. -The structure shown above includes one manager directory: ``htex_local/block‑0/ad5bf9a8d238``. -The directory contains two types of files: - -1. A single manager log reporting connections to the other Parsl components, -2. Many worker logs reporting which CPUs or accelerators they use, and which tasks they have processed. - -.. _section-logging-tasks: - -Common Debugging Tasks ----------------------- - -.. note:: - - Consider the :ref:`monitoring module ` for developing - components that display the current status of a workflow. - -This section describes how to use the logs to check correctness -and locate errors in common operations. - -Launching Blocks -~~~~~~~~~~~~~~~~ - -Parsl groups compute nodes into :ref:`blocks ` that are acquired -and released as compute requirements change. -The process for launching a block is recorded in a sequence: - -1. A request for "scaling out" in ``parsl.log``. - - Log messages starting with ``[Scaling executor ]`` record the process for - deciding whether to request another block and status messages while acquiring - a new block of workers. - - Typical log lines for requesting a block start with a request - and end with a job ID from the :ref:`provider `: - - .. code-block:: text - - [...] parsl.jobs.strategy:195 _general_strategy DEBUG: [Scaling executor htex_local] Strategizing for executor - [...] parsl.jobs.strategy:198 _general_strategy DEBUG: [Scaling executor htex_local] Scaling out 1 initial blocks - [...] parsl.executors.status_handling:204 scale_out_facade INFO: [Scaling executor htex_local] Scaling out by 1 blocks - [...] parsl.executors.status_handling:207 scale_out_facade INFO: [Scaling executor htex_local] Allocated block ID 0 - [...] - [...] parsl.executors.status_handling:268 _launch_block DEBUG: [Scaling executor htex_local] Launched block 0 with job ID 20076 - - - The last message, "Launched block with job ID <>," indicates - a request was successfully made to the execution provider. - - The job ID is often the same used by the scheduling system underlying the Provider. - Use standard tools (e.g., ``squeue`` for Slurm) to monitor whether the requested nodes are available. - -2. The results from requesting nodes are found in the ``submit_scripts`` directory. - - Use the output files and the job ID from the previous step to determine whether - the block was launched properly. - The file names contain the name of the executor and ID associated with the block of workers. - - Common errors that can be found in the output files include: - - - Parsl worker services not found because the :ref:`Python environment was not activated ` - - The launcher (e.g., ``srun``) fails to work because of incorrect options. - - Workers fail to connect to the host process :ref:`due to network configuration problems ` - -Tracing Compute Nodes -~~~~~~~~~~~~~~~~~~~~~ - -.. note:: - - This sections describes the procedure for Parsl using - :ref:`the high-throughput executor ` - and may not be applicable to other executors. - -Use logs specific to an Executor to trace whether the workers on the compute nodes -are starting, detecting hardware properly, and connecting to the host process. - -Each node in a compute block writes to a unique subdirectory within -the directory of its associated executor and block. -For example, a compute node in the first block of the "htex_local" executor -is found in ``htex_local/block-0``. - -Use the logs written by processes on the compute node to ensure... - -- *Connectivity* to the host process. The manager log should declare that it connected - and identify which network it is using by printing a log line similar to - - .. code-block:: text - - 2025-12-29 09:22:12.683 parsl:322 20081 Interchange-Communicator [INFO] Successfully connected to interchange via URL: tcp://192.168.1.71:54495 - - The ``interchange.log`` in the root directory for the executor will include a matching line - after acknowledging the connection and registering the workers as ready for tasks. - - .. code-block:: text - - 2025-12-29 09:22:12.779 interchange:438 MainProcess(20069) MainThread process_manager_socket_message [INFO] Registered manager b'c4b55da1a90f' (py3.11, 1.3.0-dev) and added to ready queue - -- *Resource Pinning* for the workers. - Each worker log begins with a message that it has started then, - if affinity is enabled, - a list of hardware (CPU and/or GPU) that it will use for tasks. - - .. code-block:: text - - 2025-12-29 09:22:13.110 worker_log:658 20100 MainThread [INFO] Worker 7 started - 2025-12-29 09:22:13.112 worker_log:715 20100 MainThread [INFO] Set worker CPU affinity to [7] - - -Tracking Task Status -~~~~~~~~~~~~~~~~~~~~ - -The ``parsl.log`` provides information about a task across its lifespan. - -1. The task is submitted. Parsl logs which executor it is assigned to - and the name of the App it is running, and any dependencies. - - .. code-block:: text - - 1763128191.056197 2025-11-14 05:49:51 MainProcess- ThreadPoolExecutor-2_0- parsl.dataflow.dflow:973 submit DEBUG: Task 316 will be sent to executor _parsl_internal - 1763128191.058182 2025-11-14 05:49:51 MainProcess- ThreadPoolExecutor-2_0- parsl.dataflow.dflow:1052 submit INFO: Task 316 submitted for App launch_relaxations, waiting on task 315 - 1763128191.058387 2025-11-14 05:49:51 MainProcess- ThreadPoolExecutor-2_0- parsl.dataflow.dflow:1058 submit DEBUG: Task 316 set to pending state with AppFuture: - 1763128191.058833 2025-11-14 05:49:51 MainProcess- Task-Launch_0- parsl.dataflow.dflow:629 _launch_if_ready_async DEBUG: Task 316 has outstanding dependencies, so launch_if_ready skipping - - The above task is a :ref:`join app ` named ``launch_relaxations``, - and will run on the internal executor for Parsl (``_parsl_internal``). - It is dependent on task 315, so will not start immediately. - -2. The task is launched after all dependencies are done. - - .. code-block:: text - - 1763130591.542695 2025-11-14 06:29:51 MainProcess- Task-Launch_0- parsl.dataflow.memoization:269 check_memo DEBUG: Task 316 will not be memoized - 1763130591.543230 2025-11-14 06:29:51 MainProcess- Task-Launch_0- parsl.dataflow.dflow:739 launch_task INFO: Parsl task 316 try 0 launched on executor _parsl_internal - - A single task may be :ref:`tried multiple times ` and the attempt number is recorded in the logs as well. - -3. The result is received. If successful, Parsl will print a single completion message. - - .. code-block:: text - - 1763147214.980597 2025-11-14 11:06:54 MainProcess- HTEX-Result-Queue-Thread- parsl.dataflow.dflow:539 _complete_task_result INFO: Task 316 completed (joining -> exec_done) - - If unsuccessful, the task could be restarted and relaunched - - .. code-block:: text - - 1763129113.044855 2025-11-14 06:05:13 MainProcess- HTEX-Result-Queue-Thread- parsl.dataflow.dflow:331 handle_exec_update INFO: Task 204 try 0 failed with exception of type ValueError - 1763129113.045055 2025-11-14 06:05:13 MainProcess- HTEX-Result-Queue-Thread- parsl.dataflow.dflow:371 handle_exec_update INFO: Task 204 marked for retry - [...] - 1763129113.050172 2025-11-14 06:05:13 MainProcess- Task-Launch_0-140414082344704 parsl.dataflow.dflow:734 launch_task INFO: Parsl task 204 try 1 launched on executor xtb with executor id 445 - - or failed and the exception both recorded in the logs and - :ref:`exposed to the user as an exception in the app Future `. - - .. code-block:: text - - 1763137697.445044 2025-11-14 08:28:17 MainProcess- HTEX-Result-Queue-Thread- parsl.app.errors:110 reraise DEBUG: Reraising exception of type - 1763137697.445302 2025-11-14 08:28:17 MainProcess- HTEX-Result-Queue-Thread- parsl.dataflow.dflow:331 handle_exec_update INFO: Task 204 try 1 failed with exception of type ValueError - 1763137697.445468 2025-11-14 08:28:17 MainProcess- HTEX-Result-Queue-Thread- parsl.dataflow.dflow:374 handle_exec_update ERROR: Task 204 failed after 1 retry attempts - Traceback (most recent call last): - File "/global/cfs/env/lib/python3.12/site-packages/parsl/dataflow/dflow.py", line 328, in handle_exec_update - [...] - return umr_minimum(a, axis, None, out, keepdims, initial, where) - ^^^^^^^^^^^^^^^^^ - ValueError: zero-size array to reduction operation minimum which has no identity diff --git a/parsl/docs/userguide/workflows/workflow.rst b/parsl/docs/userguide/workflows/workflow.rst deleted file mode 100644 index f8b34fa6..00000000 --- a/parsl/docs/userguide/workflows/workflow.rst +++ /dev/null @@ -1,243 +0,0 @@ -.. _label-workflow: - -Example parallel patterns -========================= - -Parsl can be used to implement a wide range of parallel programming patterns, from bag of tasks -through to nested workflows. Parsl implicitly assembles a dataflow -dependency graph based on the data shared between apps. -The flexibility of this model allows for the implementation of a wide range -of parallel programming and workflow patterns. - -Parsl is also designed to address broad execution requirements, from programs -that run many short tasks to those that run a few long tasks. - -Below we illustrate a range of parallel programming and workflow patterns. It is important -to note that this set of examples is by no means comprehensive. - - -Bag of Tasks ------------- -Parsl can be used to execute a large bag of tasks. In this case, Parsl -assembles the set of tasks (represented as Parsl apps) and manages their concurrent -execution on available resources. - -.. code-block:: python - - from parsl import python_app - - parsl.load() - - # Map function that returns double the input integer - @python_app - def app_random(): - import random - return random.random() - - results = [] - for i in range(0, 10): - x = app_random() - results.append(x) - - for r in results: - print(r.result()) - - -Sequential workflows --------------------- - -Sequential workflows can be created by passing an AppFuture from one task to another. For example, in the following program the ``generate`` app (a Python app) generates a random number that is consumed by the ``save`` app (a Bash app), which writes it to a file. Because ``save`` cannot execute until it receives the ``message`` produced by ``generate``, the two apps execute in sequence. - -.. code-block:: python - - from parsl import python_app - - parsl.load() - - # Generate a random number - @python_app - def generate(limit): - from random import randint - """Generate a random integer and return it""" - return randint(1, limit) - - # Write a message to a file - @bash_app - def save(message, outputs=()): - return 'echo {} &> {}'.format(message, outputs[0]) - - message = generate(10) - - saved = save(message, outputs=['output.txt']) - - with open(saved.outputs[0].result(), 'r') as f: - print(f.read()) - - -Parallel workflows ------------------- - -Parallel execution occurs automatically in Parsl, respecting dependencies among app executions. In the following example, three instances of the ``wait_sleep_double`` app are created. The first two execute concurrently, as they have no dependencies; the third must wait until the first two complete and thus the ``doubled_x`` and ``doubled_y`` futures have values. Note that this sequencing occurs even though ``wait_sleep_double`` does not in fact use its second and third arguments. - -.. code-block:: python - - from parsl import python_app - - parsl.load() - - @python_app - def wait_sleep_double(x, foo_1, foo_2): - import time - time.sleep(2) # Sleep for 2 seconds - return x*2 - - # Launch two apps, which will execute in parallel, since they do not have to - # wait on any futures - doubled_x = wait_sleep_double(10, None, None) - doubled_y = wait_sleep_double(10, None, None) - - # The third app depends on the first two: - # doubled_x doubled_y (2 s) - # \ / - # doublex_z (2 s) - doubled_z = wait_sleep_double(10, doubled_x, doubled_y) - - # doubled_z will be done in ~4s - print(doubled_z.result()) - - -Parallel workflows with loops ------------------------------ - -A common approach to executing Parsl apps in parallel is via loops. The following example uses a loop to create many random numbers in parallel. - -.. code-block:: python - - from parsl import python_app - - parsl.load() - - @python_app - def generate(limit): - """Generate a random integer and return it""" - from random import randint - return randint(1, limit) - - rand_nums = [] - for i in range(1,5): - rand_nums.append(generate(i)) - - # Wait for all apps to finish and collect the results - outputs = [r.result() for r in rand_nums] - -The :class:`~parsl.concurrent.ParslPoolExecutor` simplifies this pattern using the same interface as -`Python's native Executors `_. - -.. code-block:: python - - from parsl.concurrent import ParslPoolExecutor - from parsl.configs.htex_local import config - - # NOTE: Functions used by the ParslPoolExecutor do _not_ use decorators - def generate(limit): - """Generate a random integer and return it""" - from random import randint - return randint(1, limit) - - - with ParslPoolExecutor(config) as pool: - outputs = pool.map(generate, range(1, 5)) - - -In the preceding example, the execution of different tasks is coordinated by passing Python objects from producers to consumers. -In other cases, it can be convenient to pass data in files, as in the following reformulation. Here, a set of files, each with a random number, is created by the ``generate`` app. These files are then concatenated into a single file, which is subsequently used to compute the sum of all numbers. - -.. code-block:: python - - from parsl import python_app, bash_app - - parsl.load() - - @bash_app - def generate(outputs=()): - return 'echo $(( RANDOM % (10 - 5 + 1 ) + 5 )) &> {}'.format(outputs[0]) - - @bash_app - def concat(inputs=(), outputs=(), stdout='stdout.txt', stderr='stderr.txt'): - return 'cat {0} >> {1}'.format(' '.join(inputs), outputs[0]) - - @python_app - def total(inputs=()): - total = 0 - with open(inputs[0].filepath, 'r') as f: - for l in f: - total += int(l) - return total - - # Create 5 files with random numbers - output_files = [] - for i in range (5): - output_files.append(generate(outputs=['random-%s.txt' % i])) - - # Concatenate the files into a single file - cc = concat(inputs=[i.outputs[0] for i in output_files], outputs=['all.txt']) - - # Calculate the average of the random numbers - totals = total(inputs=[cc.outputs[0]]) - - print(totals.result()) - - -MapReduce ---------- -MapReduce is a common pattern used in data analytics. It is composed of a map phase -that filters values and a reduce phase that aggregates values. -The following example demonstrates how Parsl can be used to specify a MapReduce computation -in which the map phase doubles a set of input integers and the reduce phase computes -the sum of those results. - -.. code-block:: python - - from parsl import python_app - - parsl.load() - - # Map function that returns double the input integer - @python_app - def app_double(x): - return x*2 - - # Reduce function that returns the sum of a list - @python_app - def app_sum(inputs=()): - return sum(inputs) - - # Create a list of integers - items = range(0,4) - - # Map phase: apply the double *app* function to each item in list - mapped_results = [] - for i in items: - x = app_double(i) - mapped_results.append(x) - - # Reduce phase: apply the sum *app* function to the set of results - total = app_sum(inputs=mapped_results) - - print(total.result()) - -The program first defines two Parsl apps, ``app_double`` and ``app_sum``. -It then makes calls to the ``app_double`` app with a set of input -values. It then passes the results from ``app_double`` to the ``app_sum`` app -to aggregate values into a single result. -These tasks execute concurrently, synchronized by the ``mapped_results`` variable. -The following figure shows the resulting task graph. - -.. image:: ../../images/MapReduce.png - -Caching expensive initialisation between tasks ----------------------------------------------- - -Many tasks in workflows require a expensive "initialization" steps that, once performed, can be used across successive invocations for that task. For example, you may want to reuse a machine learning model for multiple interface tasks and avoid loading it onto GPUs more than once. - -`This ExaWorks tutorial `_ gives examples of how to do this. diff --git a/parsl/mypy-stubs/typeguard.pyi b/parsl/mypy-stubs/typeguard.pyi deleted file mode 100644 index e182edfd..00000000 --- a/parsl/mypy-stubs/typeguard.pyi +++ /dev/null @@ -1,10 +0,0 @@ - -# this type for runtime_validation is based on the -# "Decorators that do not change the signature of the function" section of -# https://github.com/python/mypy/issues/3157 - -from typing import TypeVar - -Func = TypeVar('Func') - -def typechecked(f: Func) -> Func: ... diff --git a/parsl/mypy.ini b/parsl/mypy.ini deleted file mode 100644 index c268667a..00000000 --- a/parsl/mypy.ini +++ /dev/null @@ -1,212 +0,0 @@ -[mypy] -plugins = sqlalchemy.ext.mypy.plugin - -enable_error_code = ignore-without-code -no_implicit_reexport = True -warn_redundant_casts = True - -no_implicit_optional = True - -strict_equality = True -warn_unused_ignores = True -warn_unreachable = True - -[mypy-non_existent.*] -ignore_missing_imports = True - -# some subpackages in parsl are fully typed and should be checked with much -# stricter type checking options - a longer term goal might be for all of -# parsl to be checked that way, so these options could be default instead of -# listed per package; but unless/until that happens, the more-strictly-checked -# code should be listed here: - -[mypy-parsl.addresses.*] -disallow_untyped_defs = True -disallow_any_decorated = True - -[mypy-parsl.app.app.*] -disallow_untyped_defs = True - -[mypy-parsl.benchmark.*] -disallow_untyped_defs = True - -[mypy-parsl.dataflow.errors.*] -disallow_untyped_defs = True -disallow_any_expr = True -disallow_any_decorated = True - -[mypy-parsl.dataflow.futures.*] -disallow_untyped_defs = True -disallow_any_decorated = True - -[mypy-parsl.dataflow.memoization.*] -disallow_untyped_defs = True - -[mypy-parsl.dataflow.rundirs.*] -disallow_untyped_defs = True -disallow_any_expr = True -disallow_any_decorated = True - -[mypy-parsl.dataflow.states.*] -disallow_untyped_defs = True -disallow_any_expr = True -disallow_any_decorated = True - -[mypy-parsl.dataflow.taskrecord.*] -disallow_untyped_defs = True -disallow_any_expr = True -disallow_any_decorated = True - -[mypy-parsl.jobs.*] -disallow_untyped_defs = True - -[mypy-parsl.logconfigs.*] -disallow_untyped_defs = True -disallow_any_decorated = True - -[mypy-parsl.providers.base.*] -disallow_untyped_decorators = True -check_untyped_defs = True -disallow_subclassing_any = True -disallow_untyped_defs = True - -[mypy-parsl.serialize.*] -disallow_untyped_decorators = True -check_untyped_defs = True -disallow_subclassing_any = True -disallow_untyped_defs = True - -[mypy-parsl.serialize.proxystore.*] -# parsl/serialize/proxystore.py:9: error: Class cannot subclass "Pickler" (has type "Any") -disallow_subclassing_any = False - -[mypy-parsl.executors.base.*] -disallow_untyped_defs = True -disallow_any_expr = True - -[mypy-parsl.executors.high_throughput.interchange.*] -disallow_untyped_defs = True - -[mypy-parsl.monitoring.*] -disallow_untyped_decorators = True -check_untyped_defs = True -disallow_subclassing_any = True -disallow_untyped_defs = True - -# visualization typechecks much less well than the rest of monitoring, -# so reduce strictness for the visualization subtree. -[mypy-parsl.monitoring.visualization.*] -ignore_errors = True - -[mypy-parsl.tests.configs.local_user_opts] -ignore_missing_imports = True - -[mypy-parsl.usage_tracking] -check_untyped_defs = True -disallow_subclassing_any = True -disallow_untyped_decorators = True -disallow_untyped_defs = True -warn_unreachable = True - -[mypy-parsl.utils] -disallow_untyped_defs = True - -[mypy-flask_sqlalchemy.*] -ignore_missing_imports = True - -[mypy-networkx.*] -ignore_missing_imports = True - -[mypy-_pytest.*] -ignore_missing_imports = True - -[mypy-pytest.*] -ignore_missing_imports = True - -[mypy-dill.*] -ignore_missing_imports = True - -[mypy-copyreg.*] -ignore_missing_imports = True - -[mypy-tblib.*] -ignore_missing_imports = True - -[mypy-globus_sdk.*] -ignore_missing_imports = True - -[mypy-psutil.*] -ignore_missing_imports = True - -[mypy-cPickle.*] -ignore_missing_imports = True - -[mypy-copy_reg.*] -ignore_missing_imports = True - -[mypy-cmreslogging.handlers.*] -ignore_missing_imports = True - -[mypy-kubernetes.*] -ignore_missing_imports = True - -[mypy-azure.*] -ignore_missing_imports = True - -[mypy-msrestazure.*] -ignore_missing_imports = True - -[mypy-googleapiclient.*] -ignore_missing_imports = True - -[mypy-boto3.*] -ignore_missing_imports = True - -[mypy-botocore.*] -ignore_missing_imports = True - -[mypy-zmq.*] -ignore_missing_imports = True - -[mypy-mpi4py.*] -ignore_missing_imports = True - -[mypy-flask.*] -ignore_missing_imports = True - -# this is an internal undocumentated package -# of multiprocessing - trying to get Event -# to typecheck in monitoring, but it's not -# a top level class as far as mypy is concerned. -# but... when commented out seems ok? -# so lets see when happens when I try to merge -# in clean CI -#[mypy-multiprocessing.synchronization.*] -#ignore_missing_imports = True - -[mypy-globus_compute_sdk.*] -ignore_missing_imports = True - -[mypy-pandas.*] -ignore_missing_imports = True - -[mypy-ndcctools.*] -ignore_missing_imports = True - -[mypy-work_queue.*] -ignore_missing_imports = True - -[mypy-radical.*] -ignore_missing_imports = True - -[mypy-oauth_ssh.*] -ignore_missing_imports = True - -[mypy-flux.*] -ignore_missing_imports = True - -[mypy-setproctitle.*] -ignore_missing_imports = True - -[mypy-proxystore.*] -ignore_missing_imports = True diff --git a/parsl/parsl/__init__.py b/parsl/parsl/__init__.py deleted file mode 100644 index 8fa723eb..00000000 --- a/parsl/parsl/__init__.py +++ /dev/null @@ -1,75 +0,0 @@ -"""Parsl is a Parallel Scripting Library, designed to enable efficient workflow execution. - -Importing ---------- - -To get all the required functionality, we suggest importing the library as follows: - ->>> import parsl ->>> from parsl import * - -Constants ---------- -AUTO_LOGNAME - Special value that indicates Parsl should construct a filename for logging. - -""" -import logging -import os -import platform - -from parsl.app.app import bash_app, join_app, python_app -from parsl.config import Config -from parsl.data_provider.files import File -from parsl.dataflow.dflow import DataFlowKernel, DataFlowKernelLoader -from parsl.executors import ( - HighThroughputExecutor, - ThreadPoolExecutor, - WorkQueueExecutor, -) -from parsl.log_utils import set_file_logger, set_stream_logger -from parsl.monitoring import MonitoringHub -from parsl.version import VERSION - -__author__ = 'The Parsl Team' -__version__ = VERSION - -AUTO_LOGNAME = -1 - -__all__ = [ - - # decorators - 'bash_app', - 'python_app', - 'join_app', - - # core - 'Config', - 'DataFlowKernel', - 'File', - - # logging - 'set_stream_logger', - 'set_file_logger', - 'AUTO_LOGNAME', - - # executors - 'ThreadPoolExecutor', - 'HighThroughputExecutor', - 'WorkQueueExecutor', - 'RadicalPilotExecutor', - - # monitoring - 'MonitoringHub', -] - -clear = DataFlowKernelLoader.clear -load = DataFlowKernelLoader.load -dfk = DataFlowKernelLoader.dfk -wait_for_current_tasks = DataFlowKernelLoader.wait_for_current_tasks - - -logging.getLogger('parsl').addHandler(logging.NullHandler()) - -if platform.system() == 'Darwin': - os.environ['OBJC_DISABLE_INITIALIZE_FORK_SAFETY'] = 'YES' diff --git a/parsl/parsl/addresses.py b/parsl/parsl/addresses.py deleted file mode 100644 index 8f7ccb3b..00000000 --- a/parsl/parsl/addresses.py +++ /dev/null @@ -1,175 +0,0 @@ -"""This module contains several helper functions which can be used to -find an address of the submitting system, for example to use as the -address parameter for HighThroughputExecutor. - -The helper to use depends on the network environment around the submitter, -so some experimentation will probably be needed to choose the correct one. -""" - -import ipaddress -import logging -import platform -import socket - -import requests - -try: - import fcntl -except ImportError: - fcntl = None # type: ignore[assignment] -import struct -from typing import Callable, List, Set, Union - -import psutil -import typeguard - -logger = logging.getLogger(__name__) - - -def address_by_route() -> str: - """Finds an address for the local host by querying the local routing table - for the route to Google DNS. - - This will return an unusable value when the internet-facing address is - not reachable from workers. - """ - logger.debug("Finding address by querying local routing table") - - # original author unknown - import socket - s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) - s.connect(("8.8.8.8", 80)) - addr = s.getsockname()[0] - s.close() - logger.debug("Address found: {}".format(addr)) - return addr - - -@typeguard.typechecked -def address_by_query(timeout: float = 30) -> str: - """Finds an address for the local host by querying ipify. This may - return an unusable value when the host is behind NAT, or when the - internet-facing address is not reachable from workers. - Parameters: - ----------- - - timeout : float - Timeout for the request in seconds. Default: 30s - """ - logger.debug("Finding address by querying remote service") - response = requests.get('https://api.ipify.org', timeout=timeout) - - if response.status_code == 200: - addr = response.text - logger.debug("Address found: {}".format(addr)) - return addr - else: - raise RuntimeError("Remote service returned unexpected HTTP status code {}".format(response.status_code)) - - -def address_by_hostname() -> str: - """Returns the hostname of the local host. - - This will return an unusable value when the hostname cannot be - resolved from workers. - """ - logger.debug("Finding address by using local hostname") - addr = platform.node() - ip_addr = socket.gethostbyname(addr) - logger.debug("Address found: {}".format(ip_addr)) - return ip_addr - - -@typeguard.typechecked -def address_by_interface(ifname: str) -> str: - """Returns the IP address of the given interface name, e.g. 'eth0' - - This is taken from a Stack Overflow answer: - https://stackoverflow.com/questions/24196932/how-can-i-get-the-ip-address-of-eth0-in-python#24196955 - - - Parameters - ---------- - ifname : str - Name of the interface whose address is to be returned. Required. - - """ - assert fcntl is not None, "This function is not supported on your OS." - s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) - return socket.inet_ntoa(fcntl.ioctl( - s.fileno(), - 0x8915, # SIOCGIFADDR - struct.pack('256s', bytes(ifname[:15], 'utf-8')) - )[20:24]) - - -def get_all_addresses() -> Set[str]: - """ Uses a combination of methods to determine possible addresses. - - Returns: - list of addresses as strings - """ - net_interfaces = psutil.net_if_addrs() - - s_addresses = set() - for interface in net_interfaces: - try: - s_addresses.add(address_by_interface(interface)) - except Exception: - logger.debug("Ignoring failure to fetch address from interface {}".format(interface)) - - resolution_functions: List[Callable[[], str]] - resolution_functions = [address_by_hostname, address_by_route, address_by_query] - for f in resolution_functions: - try: - s_addresses.add(f()) - except Exception: - logger.debug("Ignoring an address finder exception") - - return s_addresses - - -def get_any_address() -> str: - """ Uses a combination of methods to find any address of the local machine. - - Returns: - one address in string - """ - net_interfaces = psutil.net_if_addrs() - - addr = '' - for interface in net_interfaces: - try: - addr = address_by_interface(interface) - return addr - except Exception: - logger.info("Ignoring failure to fetch address from interface {}".format(interface)) - - resolution_functions: List[Callable[[], str]] - resolution_functions = [address_by_hostname, address_by_route, address_by_query] - for f in resolution_functions: - try: - addr = f() - return addr - except Exception: - logger.info("Ignoring an address finder exception") - - if addr == '': - raise Exception('Cannot find address of the local machine.') - return addr - - -def tcp_url(address: str, port: Union[str, int, None] = None) -> str: - """Construct a tcp url safe for IPv4 and IPv6""" - port_suffix = f":{port}" if port else "" - if address == "*": - return "tcp://*" + port_suffix - - ip_addr = ipaddress.ip_address(address) - - if ip_addr.version == 6 and port_suffix: - url = f"tcp://[{address}]{port_suffix}" - else: - url = f"tcp://{address}{port_suffix}" - - return url diff --git a/parsl/parsl/app/__init__.py b/parsl/parsl/app/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/app/app.py b/parsl/parsl/app/app.py deleted file mode 100644 index 8d0d829b..00000000 --- a/parsl/parsl/app/app.py +++ /dev/null @@ -1,197 +0,0 @@ -"""Definitions for the @App decorator and the App classes. - -The App class encapsulates a generic leaf task that can be executed asynchronously. -""" -import logging -from abc import ABCMeta, abstractmethod -from inspect import signature -from typing import Any, Callable, Dict, List, Optional, Sequence, Union - -import typeguard -from typing_extensions import Literal - -from parsl.dataflow.dflow import DataFlowKernel -from parsl.dataflow.futures import AppFuture - -logger = logging.getLogger(__name__) - - -class AppBase(metaclass=ABCMeta): - """This is the base class that defines the two external facing functions that an App must define. - - The __init__ () which is called when the interpreter sees the definition of the decorated - function, and the __call__ () which is invoked when a decorated function is called by the user. - - """ - - @typeguard.typechecked - def __init__(self, func: Callable, - data_flow_kernel: Optional[DataFlowKernel] = None, - executors: Union[List[str], Literal['all']] = 'all', - cache: bool = False, - ignore_for_cache: Optional[Sequence[str]] = None) -> None: - """Construct the App object. - - Args: - - func (function): Takes the function to be made into an App - - Kwargs: - - data_flow_kernel (DataFlowKernel): The :class:`~parsl.dataflow.dflow.DataFlowKernel` responsible for - managing this app. This can be omitted only - after calling :meth:`parsl.dataflow.dflow.DataFlowKernelLoader.load`. - - executors (str|list) : Labels of the executors that this app can execute over. Default is 'all'. - - cache (Bool) : Enable caching of this app ? - - ignore_for_cache (sequence|None): Names of arguments which will be ignored by the caching mechanism. - - Returns: - - App object. - - """ - self.__name__ = func.__name__ - self.func = func - self.data_flow_kernel = data_flow_kernel - self.executors = executors - self.cache = cache - self.ignore_for_cache = ignore_for_cache - - params = signature(func).parameters - - self.kwargs: Dict[str, Any] - self.kwargs = {} - if 'stdout' in params: - self.kwargs['stdout'] = params['stdout'].default - if 'stderr' in params: - self.kwargs['stderr'] = params['stderr'].default - if 'walltime' in params: - self.kwargs['walltime'] = params['walltime'].default - if 'parsl_resource_specification' in params: - self.kwargs['parsl_resource_specification'] = params['parsl_resource_specification'].default - if 'outputs' in params: - self.kwargs['outputs'] = params['outputs'].default - if 'inputs' in params: - self.kwargs['inputs'] = params['inputs'].default - - @abstractmethod - def __call__(self, *args: Any, **kwargs: Any) -> AppFuture: - pass - - -@typeguard.typechecked -def python_app(function: Optional[Callable] = None, - data_flow_kernel: Optional[DataFlowKernel] = None, - cache: bool = False, - executors: Union[List[str], Literal['all']] = 'all', - ignore_for_cache: Optional[Sequence[str]] = None) -> Callable: - """Decorator function for making python apps. - - Parameters - ---------- - function : function - Do not pass this keyword argument directly. This is needed in order to allow for omitted parenthesis, - for example, ``@python_app`` if using all defaults or ``@python_app(walltime=120)``. If the - decorator is used alone, function will be the actual function being decorated, whereas if it - is called with arguments, function will be None. Default is None. - data_flow_kernel : DataFlowKernel - The :class:`~parsl.dataflow.dflow.DataFlowKernel` responsible for managing this app. This can - be omitted only after calling :meth:`parsl.dataflow.dflow.DataFlowKernelLoader.load`. Default is None. - executors : string or list - Labels of the executors that this app can execute over. Default is 'all'. - cache : bool - Enable caching of the app call. Default is False. - ignore_for_cache : (sequence|None) - Names of arguments which will be ignored by the caching mechanism. - """ - from parsl.app.python import PythonApp - - def decorator(func: Callable) -> Callable: - def wrapper(f: Callable) -> PythonApp: - return PythonApp(f, - data_flow_kernel=data_flow_kernel, - cache=cache, - executors=executors, - ignore_for_cache=ignore_for_cache, - join=False) - return wrapper(func) - if function is not None: - return decorator(function) - return decorator - - -@typeguard.typechecked -def join_app(function: Optional[Callable] = None, - data_flow_kernel: Optional[DataFlowKernel] = None, - cache: bool = False, - ignore_for_cache: Optional[Sequence[str]] = None) -> Callable: - """Decorator function for making join apps - - Parameters - ---------- - function : function - Do not pass this keyword argument directly. This is needed in order to allow for omitted parenthesis, - for example, ``@python_app`` if using all defaults or ``@python_app(walltime=120)``. If the - decorator is used alone, function will be the actual function being decorated, whereas if it - is called with arguments, function will be None. Default is None. - data_flow_kernel : DataFlowKernel - The :class:`~parsl.dataflow.dflow.DataFlowKernel` responsible for managing this app. This can - be omitted only after calling :meth:`parsl.dataflow.dflow.DataFlowKernelLoader.load`. Default is None. - cache : bool - Enable caching of the app call. Default is False. - ignore_for_cache : (sequence|None) - Names of arguments which will be ignored by the caching mechanism. - """ - from parsl.app.python import PythonApp - - def decorator(func: Callable) -> Callable: - def wrapper(f: Callable) -> PythonApp: - return PythonApp(f, - data_flow_kernel=data_flow_kernel, - cache=cache, - executors=["_parsl_internal"], - ignore_for_cache=ignore_for_cache, - join=True) - return wrapper(func) - if function is not None: - return decorator(function) - return decorator - - -@typeguard.typechecked -def bash_app(function: Optional[Callable] = None, - data_flow_kernel: Optional[DataFlowKernel] = None, - cache: bool = False, - executors: Union[List[str], Literal['all']] = 'all', - ignore_for_cache: Optional[Sequence[str]] = None) -> Callable: - """Decorator function for making bash apps. - - Parameters - ---------- - function : function - Do not pass this keyword argument directly. This is needed in order to allow for omitted parenthesis, - for example, ``@bash_app`` if using all defaults or ``@bash_app(walltime=120)``. If the - decorator is used alone, function will be the actual function being decorated, whereas if it - is called with arguments, function will be None. Default is None. - data_flow_kernel : DataFlowKernel - The :class:`~parsl.dataflow.dflow.DataFlowKernel` responsible for managing this app. This can - be omitted only after calling :meth:`parsl.dataflow.dflow.DataFlowKernelLoader.load`. Default is None. - walltime : int - Walltime for app in seconds. Default is 60. - executors : string or list - Labels of the executors that this app can execute over. Default is 'all'. - cache : bool - Enable caching of the app call. Default is False. - ignore_for_cache : (list|None) - Names of arguments which will be ignored by the caching mechanism. - """ - from parsl.app.bash import BashApp - - def decorator(func: Callable) -> Callable: - def wrapper(f: Callable) -> BashApp: - return BashApp(f, - data_flow_kernel=data_flow_kernel, - cache=cache, - executors=executors, - ignore_for_cache=ignore_for_cache) - return wrapper(func) - if function is not None: - return decorator(function) - return decorator diff --git a/parsl/parsl/app/bash.py b/parsl/parsl/app/bash.py deleted file mode 100644 index db632e1b..00000000 --- a/parsl/parsl/app/bash.py +++ /dev/null @@ -1,162 +0,0 @@ -import logging -from functools import partial -from inspect import Parameter, signature - -from parsl.app.app import AppBase -from parsl.app.errors import wrap_error -from parsl.data_provider.files import File -from parsl.dataflow.dflow import DataFlowKernelLoader - -logger = logging.getLogger(__name__) - - -def remote_side_bash_executor(func, *args, **kwargs): - """Executes the supplied function with *args and **kwargs to get a - command-line to run, and then run that command-line using bash. - """ - import os - import subprocess - - import parsl.app.errors as pe - from parsl.utils import get_std_fname_mode - - if hasattr(func, '__name__'): - func_name = func.__name__ - else: - logger.warning('No name for the function. Potentially a result of parsl#2233') - func_name = 'bash_app' - - executable = None - - # Try to run the func to compose the commandline - try: - # Execute the func to get the commandline - executable = func(*args, **kwargs) - - if not isinstance(executable, str): - raise ValueError(f"Expected a str for bash_app commandline, got {type(executable)}") - - except AttributeError as e: - if executable is not None: - raise pe.AppBadFormatting("App formatting failed for app '{}' with AttributeError: {}".format(func_name, e)) - else: - raise pe.BashAppNoReturn("Bash app '{}' did not return a value, or returned None - with this exception: {}".format(func_name, e)) - - except IndexError as e: - raise pe.AppBadFormatting("App formatting failed for app '{}' with IndexError: {}".format(func_name, e)) - except Exception as e: - raise e - - # Updating stdout, stderr if values passed at call time. - - def open_std_fd(fdname): - # fdname is 'stdout' or 'stderr' - stdfspec = kwargs.get(fdname) # spec is str name or tuple (name, mode) - if stdfspec is None: - return None - - if isinstance(stdfspec, File): - # a File is an os.PathLike and so we can use it directly for - # the subsequent file operations - fname = stdfspec - mode = "w" - else: - fname, mode = get_std_fname_mode(fdname, stdfspec) - - try: - if os.path.dirname(fname): - os.makedirs(os.path.dirname(fname), exist_ok=True) - fd = open(fname, mode) - except Exception as e: - raise pe.BadStdStreamFile(str(fname)) from e - return fd - - std_out = open_std_fd('stdout') - std_err = open_std_fd('stderr') - timeout = kwargs.get('walltime') - - if std_err is not None: - print('--> executable follows <--\n{}\n--> end executable <--'.format(executable), file=std_err, flush=True) - - returncode = None - try: - proc = subprocess.Popen(executable, stdout=std_out, stderr=std_err, shell=True, executable='/bin/bash', close_fds=False) - proc.wait(timeout=timeout) - returncode = proc.returncode - - except subprocess.TimeoutExpired: - raise pe.AppTimeout(f"App {func_name} exceeded walltime: {timeout} seconds") - - except Exception as e: - raise pe.AppException(f"App {func_name} caught exception", e) - - if returncode != 0: - raise pe.BashExitFailure(func_name, proc.returncode) - - # TODO : Add support for globs here - - missing = [] - for outputfile in kwargs.get('outputs', []): - fpath = outputfile.filepath - - if not os.path.exists(fpath): - missing.extend([outputfile]) - - if missing: - raise pe.MissingOutputs(f"Missing outputs from app {func_name}", missing) - - return returncode - - -class BashApp(AppBase): - - def __init__(self, func, data_flow_kernel=None, cache=False, executors='all', ignore_for_cache=None): - super().__init__(func, data_flow_kernel=data_flow_kernel, executors=executors, cache=cache, ignore_for_cache=ignore_for_cache) - self.kwargs = {} - - # We duplicate the extraction of parameter defaults - # to self.kwargs to ensure availability at point of - # command string format. Refer: #349 - sig = signature(func) - - for s in sig.parameters: - if sig.parameters[s].default is not Parameter.empty: - self.kwargs[s] = sig.parameters[s].default - - # partial is used to attach the first arg the "func" to the remote_side_bash_executor - # this is done to avoid passing a function type in the args which parsl.serializer - # doesn't support - remote_fn = partial(remote_side_bash_executor, self.func) - remote_fn.__name__ = self.func.__name__ - self.wrapped_remote_function = wrap_error(remote_fn) - - def __call__(self, *args, **kwargs): - """Handle the call to a Bash app. - - Args: - - Arbitrary - - Kwargs: - - Arbitrary - - Returns: - App_fut - - """ - invocation_kwargs = {} - invocation_kwargs.update(self.kwargs) - invocation_kwargs.update(kwargs) - - if self.data_flow_kernel is None: - dfk = DataFlowKernelLoader.dfk() - else: - dfk = self.data_flow_kernel - - app_fut = dfk.submit(self.wrapped_remote_function, - app_args=args, - executors=self.executors, - cache=self.cache, - ignore_for_cache=self.ignore_for_cache, - app_kwargs=invocation_kwargs) - - return app_fut diff --git a/parsl/parsl/app/errors.py b/parsl/parsl/app/errors.py deleted file mode 100644 index 8239c808..00000000 --- a/parsl/parsl/app/errors.py +++ /dev/null @@ -1,141 +0,0 @@ -"""Exceptions raised by Apps.""" -import logging -from functools import wraps -from types import TracebackType -from typing import Callable, List, Optional, TypeVar, Union - -from tblib import Traceback -from typing_extensions import ParamSpec - -from parsl.data_provider.files import File -from parsl.errors import ParslError - -logger = logging.getLogger(__name__) - - -class AppException(ParslError): - """An error raised during execution of an app. - - What this exception contains depends entirely on context - """ - - -class AppBadFormatting(ParslError): - """An error raised during formatting of a bash function. - """ - - -class BashExitFailure(AppException): - """A non-zero exit code returned from a @bash_app - - Contains: - app name (str) - exitcode (int) - """ - - def __init__(self, app_name: str, exitcode: int) -> None: - self.app_name = app_name - self.exitcode = exitcode - - def __str__(self) -> str: - return f"bash_app {self.app_name} failed with unix exit code {self.exitcode}" - - -class AppTimeout(AppException): - """An error raised during execution of an app when it exceeds its allotted walltime. - """ - - -class BashAppNoReturn(AppException): - """Bash app returned no string. - - Contains: - reason(string) - """ - - def __init__(self, reason: str) -> None: - super().__init__(reason) - self.reason = reason - - -class MissingOutputs(ParslError): - """Error raised at the end of app execution due to missing output files. - - Contains: - reason(string) - outputs(List of files) - """ - def __init__(self, reason: str, outputs: List[File]) -> None: - super().__init__(reason, outputs) - self.reason = reason - self.outputs = outputs - - def __str__(self) -> str: - return "Missing Outputs: {0}, Reason: {1}".format(self.outputs, self.reason) - - -class BadStdStreamFile(ParslError): - """Error raised due to bad filepaths specified for STDOUT/ STDERR. - - Contains: - reason(string) - """ - - def __init__(self, reason: str) -> None: - super().__init__(reason) - self._reason = reason - - def __str__(self) -> str: - return "Bad Stream File: {}".format(self._reason) - - -class RemoteExceptionWrapper: - def __init__(self, e_type: type, e_value: BaseException, traceback: Optional[TracebackType]) -> None: - - self.e_type = e_type - self.e_value = e_value - self.e_traceback = None if traceback is None else Traceback(traceback) - if e_value.__cause__ is None: - self.cause = None - else: - cause = e_value.__cause__ - self.cause = self.__class__(type(cause), cause, cause.__traceback__) - - def reraise(self) -> None: - - # the type is logged here before deserialising v and tb - # because occasionally there are problems deserialising the - # value (see #785, #548) and the fix is related to the - # specific exception type. - logger.debug("Reraising exception of type {}".format(self.e_type)) - - v = self.get_exception() - - raise v - - def get_exception(self) -> BaseException: - v = self.e_value - if self.cause is not None: - v.__cause__ = self.cause.get_exception() - if self.e_traceback is not None: - tb = self.e_traceback.as_traceback() - return v.with_traceback(tb) - else: - return v - - -P = ParamSpec('P') -R = TypeVar('R') - - -def wrap_error(func: Callable[P, R]) -> Callable[P, Union[R, RemoteExceptionWrapper]]: - @wraps(func) - def wrapper(*args: P.args, **kwargs: P.kwargs) -> Union[R, RemoteExceptionWrapper]: - import sys - - from parsl.app.errors import RemoteExceptionWrapper - try: - return func(*args, **kwargs) - except Exception: - return RemoteExceptionWrapper(*sys.exc_info()) - return wrapper diff --git a/parsl/parsl/app/futures.py b/parsl/parsl/app/futures.py deleted file mode 100644 index d85c4f25..00000000 --- a/parsl/parsl/app/futures.py +++ /dev/null @@ -1,99 +0,0 @@ -"""This module implements DataFutures. -""" -import logging -from concurrent.futures import Future - -import typeguard - -from parsl.data_provider.files import File - -logger = logging.getLogger(__name__) - - -class DataFuture(Future): - """A datafuture points at an AppFuture. - - We are simply wrapping a AppFuture, and adding the specific case where, if - the future is resolved i.e file exists, then the DataFuture is assumed to be - resolved. - """ - - def parent_callback(self, parent_fu): - """Callback from executor future to update the parent. - - Updates the future with the result (the File object) or the parent future's - exception. - - Args: - - parent_fu (Future): Future returned by the executor along with callback - - Returns: - - None - """ - - e = parent_fu._exception - if e: - self.set_exception(e) - else: - self.set_result(self.file_obj) - - @typeguard.typechecked - def __init__(self, fut: Future, file_obj: File, tid: int) -> None: - """Construct the DataFuture object. - - If the file_obj is a string convert to a File. - - Args: - - fut (Future) : Future that this DataFuture will track. - Completion of ``fut`` indicates that the data is - ready. - - file_obj (File) : File that this DataFuture represents the availability of - - Kwargs: - - tid (task_id) : Task id that this DataFuture tracks - """ - super().__init__() - self._tid = tid - self.file_obj = file_obj - self.parent = fut - - self.parent.add_done_callback(self.parent_callback) - - logger.debug("Creating DataFuture with parent: %s and file: %s", self.parent, repr(self.file_obj)) - - @property - def tid(self): - """Returns the task_id of the task that will resolve this DataFuture.""" - return self._tid - - @property - def filepath(self): - """Filepath of the File object this datafuture represents.""" - return self.file_obj.filepath - - @property - def filename(self): - """Filepath of the File object this datafuture represents.""" - return self.filepath - - def cancel(self): - raise NotImplementedError("Cancel not implemented") - - def cancelled(self): - return False - - def running(self): - if self.parent: - return self.parent.running() - else: - return False - - def __repr__(self) -> str: - type_ = type(self) - module = type_.__module__ - qualname = type_.__qualname__ - if self.done(): - done = "done" - else: - done = "not done" - return f"<{module}.{qualname} object at {hex(id(self))} representing {repr(self.file_obj)} {done}>" diff --git a/parsl/parsl/app/python.py b/parsl/parsl/app/python.py deleted file mode 100644 index 58839cff..00000000 --- a/parsl/parsl/app/python.py +++ /dev/null @@ -1,84 +0,0 @@ -import logging -import threading - -import tblib.pickling_support - -tblib.pickling_support.install() - -from functools import wraps - -from parsl.app.app import AppBase -from parsl.app.errors import wrap_error -from parsl.dataflow.dflow import DataFlowKernelLoader -from parsl.utils import AutoCancelTimer - -logger = logging.getLogger(__name__) - - -def timeout(f, seconds: float): - @wraps(f) - def wrapper(*args, **kwargs): - import ctypes - - import parsl.app.errors - - def inject_exception(thread): - ctypes.pythonapi.PyThreadState_SetAsyncExc( - ctypes.c_long(thread), - ctypes.py_object(parsl.app.errors.AppTimeout) - ) - - thread = threading.current_thread().ident - with AutoCancelTimer(seconds, inject_exception, args=[thread]): - return f(*args, **kwargs) - return wrapper - - -class PythonApp(AppBase): - """Extends AppBase to cover the Python App.""" - - def __init__(self, func, data_flow_kernel=None, cache=False, executors='all', ignore_for_cache=None, join=False): - super().__init__( - wrap_error(func), - data_flow_kernel=data_flow_kernel, - executors=executors, - cache=cache, - ignore_for_cache=ignore_for_cache - ) - self.join = join - - def __call__(self, *args, **kwargs): - """This is where the call to a python app is handled. - - Args: - - Arbitrary - Kwargs: - - Arbitrary - - Returns: - App_fut - - """ - invocation_kwargs = {} - invocation_kwargs.update(self.kwargs) - invocation_kwargs.update(kwargs) - - if self.data_flow_kernel is None: - dfk = DataFlowKernelLoader.dfk() - else: - dfk = self.data_flow_kernel - - walltime = invocation_kwargs.get('walltime') - if walltime is not None: - func = timeout(self.func, walltime) - else: - func = self.func - - app_fut = dfk.submit(func, app_args=args, - executors=self.executors, - cache=self.cache, - ignore_for_cache=self.ignore_for_cache, - app_kwargs=invocation_kwargs, - join=self.join) - - return app_fut diff --git a/parsl/parsl/benchmark/__init__.py b/parsl/parsl/benchmark/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/benchmark/perf.py b/parsl/parsl/benchmark/perf.py deleted file mode 100644 index 80347638..00000000 --- a/parsl/parsl/benchmark/perf.py +++ /dev/null @@ -1,157 +0,0 @@ -import argparse -import concurrent.futures -import importlib -import time -from typing import Any, Dict, Literal - -import parsl -from parsl.dataflow.dflow import DataFlowKernel -from parsl.errors import InternalConsistencyError - -VALID_NAMED_ITERATION_MODES = ("estimate", "exponential") - -min_iterations = 2 - - -# TODO: factor with conftest.py where this is copy/pasted from? -def load_dfk_from_config(filename: str) -> DataFlowKernel: - spec = importlib.util.spec_from_file_location('', filename) - - if spec is None: - raise RuntimeError("Could not import configuration") - - module = importlib.util.module_from_spec(spec) - - if spec.loader is None: - raise RuntimeError("Could not load configuration") - - spec.loader.exec_module(module) - - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - - if hasattr(module, 'config'): - return parsl.load(module.config) - elif hasattr(module, 'fresh_config'): - return parsl.load(module.fresh_config()) - else: - raise RuntimeError("Config module does not define config or fresh_config") - - -@parsl.python_app -def app(extra_payload: Any, parsl_resource_specification: Dict = {}) -> int: - return 7 - - -def performance(*, resources: dict, target_t: float, args_extra_size: int, iterate_mode: str | list[int]) -> None: - - delta_t: float - - iteration = 1 - - args_extra_payload = "x" * args_extra_size - - if isinstance(iterate_mode, list): - n = iterate_mode[0] - else: - n = 10 - - iterate = True - - while iterate: - print(f"==== Iteration {iteration} ====") - print(f"Will run {n} tasks") - start_t = time.time() - - fs = [] - print("Submitting tasks / invoking apps") - for _ in range(n): - fs.append(app(args_extra_payload, parsl_resource_specification=resources)) - - submitted_t = time.time() - print(f"All {n} tasks submitted ... waiting for completion") - print(f"Submission took {submitted_t - start_t:.3f} seconds = {n / (submitted_t - start_t):.3f} tasks/second") - - for f in concurrent.futures.as_completed(fs): - assert f.result() == 7 - - end_t = time.time() - - delta_t = end_t - start_t - - rate = n / delta_t - - print(f"Runtime: actual {delta_t:.3f}s vs target {target_t}s") - print(f"Tasks per second: {rate:.3f}") - - iteration += 1 - - # decide upon next iteration - - match iterate_mode: - case "estimate": - n = max(1, int(target_t * rate)) - iterate = delta_t < (0.75 * target_t) or iteration <= min_iterations - case "exponential": - n = int(n * 2) - iterate = delta_t < target_t or iteration <= min_iterations - case seq if isinstance(seq, list) and iteration <= len(seq): - n = seq[iteration - 1] - iterate = True - case seq if isinstance(seq, list): - iterate = False - case _: - raise InternalConsistencyError(f"Bad iterate mode {iterate_mode} - should have been validated at arg parse time") - - -def validate_int_list(v: str) -> list[int] | Literal[False]: - try: - return list(map(int, v.split(","))) - except ValueError: - return False - - -def iteration_mode(v: str) -> str | list[int]: - match v: - case s if s in VALID_NAMED_ITERATION_MODES: - return s - case _ if seq := validate_int_list(v): - return seq - case _: - raise argparse.ArgumentTypeError(f"Invalid iteration mode: {v}") - - -def cli_run() -> None: - parser = argparse.ArgumentParser( - prog="parsl-perf", - description="Measure performance of Parsl configurations", - epilog=""" -Example usage: python -m parsl.benchmark.perf --config parsl/tests/configs/workqueue_blocks.py --resources '{"cores":1, "memory":0, "disk":0}' - """) - - parser.add_argument("--config", required=True, help="path to Python file that defines a configuration") - parser.add_argument("--resources", metavar="EXPR", help="parsl_resource_specification dictionary") - parser.add_argument("--time", metavar="SECONDS", help="target number of seconds for an iteration", default=120, type=float) - parser.add_argument("--argsize", metavar="BYTES", help="extra bytes to add into app invocation arguments", default=0, type=int) - parser.add_argument("--version", action="version", version=f"parsl-perf from Parsl {parsl.__version__}") - parser.add_argument("--iterate", - metavar="MODE", - help="Iteration mode: " + ", ".join(VALID_NAMED_ITERATION_MODES) + ", or sequence of explicit sizes", - type=iteration_mode, - default="estimate") - - args = parser.parse_args() - - if args.resources: - resources = eval(args.resources) - else: - resources = {} - - with load_dfk_from_config(args.config): - performance(resources=resources, target_t=args.time, args_extra_size=args.argsize, iterate_mode=args.iterate) - print("Tests complete - leaving DFK block") - print("The end") - - -if __name__ == "__main__": - cli_run() diff --git a/parsl/parsl/concurrent/__init__.py b/parsl/parsl/concurrent/__init__.py deleted file mode 100644 index 02a9f818..00000000 --- a/parsl/parsl/concurrent/__init__.py +++ /dev/null @@ -1,146 +0,0 @@ -"""Interfaces modeled after Python's `concurrent library `_""" -import time -from concurrent.futures import Executor -from contextlib import AbstractContextManager -from typing import Callable, Dict, Iterable, Iterator, Literal, Optional, TypeVar -from warnings import warn - -from parsl import Config, DataFlowKernel, load -from parsl.app.python import PythonApp - -T = TypeVar('T') - - -class ParslPoolExecutor(Executor, AbstractContextManager): - """An executor that uses a pool of workers managed by Parsl - - Works just like a :class:`~concurrent.futures.ProcessPoolExecutor` except that tasks - are distributed across workers that can be on different machines. - - The futures returned by :meth:`submit` and :meth:`map` are Parsl futures and will work - with the same function chaining mechanisms as when using App-based Parsl. - - Parsl does not support canceling tasks. The :meth:`map` method does not cancel work - when one member of the run fails or a timeout is reached - and :meth:`shutdown` does not cancel work on completion. - """ - - def __init__(self, config: Config | None = None, dfk: DataFlowKernel | None = None, executors: Literal['all'] | list[str] = 'all'): - """Create the executor - - Args: - config: Configuration for the Parsl Data Flow Kernel (DFK) - dfk: DataFlowKernel of an already-started parsl - executors: List of executors to use for supplied functions - """ - if (config is not None) and (dfk is not None): - raise ValueError('Specify only one of config or dfk') - if (config is None) and (dfk is None): - raise ValueError('Must specify one of config or dfk') - self._config = config - self._app_cache: Dict[Callable, PythonApp] = {} # Cache specific to this instance: https://stackoverflow.com/questions/33672412 - self._dfk = dfk - self.executors = executors - - # Start workers immediately - if self._config is not None: - self._dfk = load(self._config) - - def __exit__(self, exc_type, exc_val, exc_tb): - if self._dfk is None: # Nothing has been started, do nothing - return - elif self._config is not None: # The executors are being managed by this class, shut them down - self.shutdown(wait=True) - return - else: # The DFK is managed elsewhere, do nothing - return - - @property - def app_count(self): - """Number of functions currently registered with the executor""" - return len(self._app_cache) - - def get_app(self, fn: Callable) -> PythonApp: - """Create a PythonApp for a function - - Args: - fn: Function to be turned into a Parsl app - Returns: - PythonApp version of that function - """ - if fn in self._app_cache: - return self._app_cache[fn] - app = PythonApp(fn, data_flow_kernel=self._dfk, executors=self.executors) - self._app_cache[fn] = app - return app - - def submit(self, fn, *args, **kwargs): - """Submits a callable to be executed with the given arguments. - - Schedules the callable to be executed as ``fn(*args, **kwargs)`` and returns - a Future instance representing the execution of the callable. - - Returns: - A Future representing the given call. - """ - - if self._dfk is None: - raise RuntimeError('Executor has been shut down.') - app = self.get_app(fn) - return app(*args, **kwargs) - - # TODO (wardlt): This override can go away when Parsl supports cancel - def map(self, fn: Callable[..., T], *iterables: Iterable, timeout: Optional[float] = None, - chunksize: int = 1, buffersize: Optional[int] = None) -> Iterator[T]: - """Returns an iterator equivalent to map(fn, iter). - - Args: - fn: A callable that will take as many arguments as there are - passed iterables. - timeout: The maximum number of seconds to wait. If None, then there - is no limit on the wait time. - chunksize: This parameter is ignored. Caution should be exercised - if expecting behaviour as documented in the base `concurrent.futures.Executor` class. - buffersize: This parameter is ignored. Caution should be exercised - if expecting behaviour as documented in the base `concurrent.futures.Executor` class. - - Returns: - An iterator equivalent to: map(func, ``*iterables``) but the calls may - be evaluated out-of-order. - - Raises: - TimeoutError: If the entire result iterator could not be generated - before the given timeout. - Exception: If ``fn(*args)`` raises for any values. - """ - # This is a version of the CPython 3.9 `.map` implementation modified to not use `cancel` - if timeout is not None: - end_time = timeout + time.monotonic() - - # Submit the applications - app = self.get_app(fn) - fs = [app(*args) for args in zip(*iterables)] - - # Yield the futures as completed - def result_iterator(): - # reverse to keep finishing order - fs.reverse() - while fs: - # Careful not to keep a reference to the popped future - if timeout is None: - yield fs.pop().result() - else: - yield fs.pop().result(end_time - time.monotonic()) - - return result_iterator() - - def shutdown(self, wait: bool = True, *, cancel_futures: bool = False) -> None: - if self._dfk is None: - return # Do nothing. Nothing is active - if cancel_futures: - warn(message="Canceling on-going tasks is not supported in Parsl") - if wait: - self._dfk.wait_for_current_tasks() - if self._config is not None: # The executors are being managed - self._dfk.cleanup() # Shutdown the DFK - self._dfk = None diff --git a/parsl/parsl/config.py b/parsl/parsl/config.py deleted file mode 100644 index 76bbb6eb..00000000 --- a/parsl/parsl/config.py +++ /dev/null @@ -1,159 +0,0 @@ -import logging -from typing import Callable, Iterable, Optional, Sequence, Union - -import typeguard -from typing_extensions import Literal - -from parsl.dataflow.dependency_resolvers import DependencyResolver -from parsl.dataflow.memoization import Memoizer -from parsl.dataflow.taskrecord import TaskRecord -from parsl.errors import ConfigurationError -from parsl.executors.base import ParslExecutor -from parsl.executors.threads import ThreadPoolExecutor -from parsl.logconfigs.base import LogConfig -from parsl.monitoring import MonitoringHub -from parsl.usage_tracking.api import UsageInformation -from parsl.usage_tracking.levels import DISABLED as USAGE_TRACKING_DISABLED -from parsl.usage_tracking.levels import LEVEL_3 as USAGE_TRACKING_LEVEL_3 -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - - -class Config(RepresentationMixin, UsageInformation): - """ - Specification of Parsl configuration options. - - Parameters - ---------- - executors : sequence of ParslExecutor, optional - List (or other iterable) of `ParslExecutor` instances to use for executing tasks. - Default is (:class:`~parsl.executors.threads.ThreadPoolExecutor()`,). - dependency_resolver: plugin point for custom dependency resolvers. Default: only resolve Futures, - using the `SHALLOW_DEPENDENCY_RESOLVER`. - exit_mode: str, optional - When Parsl is used as a context manager (using ``with parsl.load`` syntax) then this parameter - controls what will happen to running tasks and exceptions at exit. The options are: - - * ``cleanup``: cleanup the DFK on exit without waiting for any tasks - * ``skip``: skip all shutdown behaviour when exiting the context manager - * ``wait``: wait for all tasks to complete when exiting normally, but exit immediately when exiting due to an exception. - - Default is ``cleanup``. - garbage_collect : bool. optional. - Delete task records from DFK when tasks have completed. Default: True - internal_tasks_max_threads : int, optional - Maximum number of threads to allocate for submit side internal tasks such as some data transfers - or @joinapps - Default is 10. - monitoring : MonitoringHub, optional - The config to use for database monitoring. Default is None which does not log to a database. - retries : int, optional - Set the number of retries (or available retry budget when using retry_handler) in case of failure. Default is 0. - retry_handler : function, optional - A user pluggable handler to decide if/how a task retry should happen. - If no handler is specified, then each task failure incurs a retry cost - of 1. - run_dir : str, optional - Path to run directory. Default is 'runinfo'. - std_autopath : function, optional - Sets the function used to generate stdout/stderr specifications when parsl.AUTO_LOGPATH is used. If no function - is specified, generates paths that look like: ``rundir/NNN/task_logs/X/task_{id}_{name}{label}.{out/err}`` - strategy : str, optional - Strategy to use for scaling blocks according to workflow needs. Can be 'simple', 'htex_auto_scale', 'none' - or `None`. - If 'none' or `None`, dynamic scaling will be disabled. Default is 'simple'. The literal value `None` is - deprecated. - strategy_period : float or int, optional - How often the scaling strategy should be executed. Default is 5 seconds. - max_idletime : float, optional - The maximum idle time allowed for an executor before strategy could shut down unused blocks. Default is 120.0 seconds. - usage_tracking : int, optional - Set this field to 1, 2, or 3 to opt-in to Parsl's usage tracking system. - The value represents the level of usage tracking detail to be collected. - Setting this field to 0 will disable usage tracking. Default (this field is not set): usage tracking is not enabled. - Parsl only collects minimal, non personally-identifiable, - information used for reporting to our funding agencies. - project_name: str, optional - Option to deanonymize usage tracking data. - If set, this value will be used as the project name in the usage tracking data and placed on the leaderboard. - initialize_logging : bool or log config, optional - Make DFK optionally not initialize any logging. Log messages - will still be passed into the python logging system under the - ``parsl`` logger name, but the logging system will not by default - perform any further log system configuration. Most noticeably, - it will not create a parsl.log logfile. The use case for this - is when parsl is used as a library in a bigger system which - wants to configure logging in a way that makes sense for that - bigger system as a whole. - """ - - @typeguard.typechecked - def __init__(self, - executors: Optional[Iterable[ParslExecutor]] = None, - memoizer: Optional[Memoizer] = None, - dependency_resolver: Optional[DependencyResolver] = None, - exit_mode: Literal['cleanup', 'skip', 'wait'] = 'cleanup', - garbage_collect: bool = True, - internal_tasks_max_threads: int = 10, - retries: int = 0, - retry_handler: Optional[Callable[[Exception, TaskRecord], float]] = None, - run_dir: str = 'runinfo', - std_autopath: Optional[Callable] = None, - strategy: Optional[str] = 'simple', - strategy_period: Union[float, int] = 5, - max_idletime: float = 120.0, - monitoring: Optional[MonitoringHub] = None, - usage_tracking: int = 0, - project_name: Optional[str] = None, - initialize_logging: Union[bool, LogConfig] = True) -> None: - - executors = tuple(executors or []) - if not executors: - executors = (ThreadPoolExecutor(),) - - self._executors: Sequence[ParslExecutor] = executors - self._validate_executors() - - self.memoizer = memoizer - self.dependency_resolver = dependency_resolver - self.exit_mode = exit_mode - self.garbage_collect = garbage_collect - self.internal_tasks_max_threads = internal_tasks_max_threads - self.retries = retries - self.retry_handler = retry_handler - self.run_dir = run_dir - self.strategy = strategy - self.strategy_period = strategy_period - self.max_idletime = max_idletime - self.validate_usage_tracking(usage_tracking) - self.usage_tracking = usage_tracking - self.project_name = project_name - self.initialize_logging = initialize_logging - self.monitoring = monitoring - self.std_autopath: Optional[Callable] = std_autopath - - @property - def executors(self) -> Sequence[ParslExecutor]: - return self._executors - - def _validate_executors(self) -> None: - - if len(self.executors) == 0: - raise ConfigurationError('At least one executor must be specified') - - labels = [e.label for e in self.executors] - duplicates = [e for n, e in enumerate(labels) if e in labels[:n]] - if len(duplicates) > 0: - raise ConfigurationError('Executors must have unique labels ({})'.format( - ', '.join(['label={}'.format(repr(d)) for d in duplicates]))) - - def validate_usage_tracking(self, level: int) -> None: - if not USAGE_TRACKING_DISABLED <= level <= USAGE_TRACKING_LEVEL_3: - raise ConfigurationError( - f"Usage Tracking values must be 0, 1, 2, or 3 and not {level}" - ) - - def get_usage_information(self): - return {"executors_len": len(self.executors), - "dependency_resolver": self.dependency_resolver is not None} diff --git a/parsl/parsl/configs/ASPIRE1.py b/parsl/parsl/configs/ASPIRE1.py deleted file mode 100644 index 84d131f2..00000000 --- a/parsl/parsl/configs/ASPIRE1.py +++ /dev/null @@ -1,44 +0,0 @@ -from parsl.addresses import address_by_interface -from parsl.config import Config -from parsl.dataflow.memoization import BasicMemoizer -from parsl.executors import HighThroughputExecutor -from parsl.launchers import MpiRunLauncher -from parsl.monitoring.monitoring import MonitoringHub -from parsl.providers import PBSProProvider -from parsl.usage_tracking.levels import LEVEL_1 - -config = Config( - executors=[ - HighThroughputExecutor( - label="htex", - heartbeat_period=15, - heartbeat_threshold=120, - worker_debug=True, - max_workers_per_node=4, - address=address_by_interface('ib0'), - provider=PBSProProvider( - launcher=MpiRunLauncher(), - # PBS directives (header lines): for array jobs pass '-J' option - scheduler_options='#PBS -J 1-10', - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init='', - # number of compute nodes allocated for each block - nodes_per_block=3, - min_blocks=3, - max_blocks=5, - cpus_per_node=24, - # medium queue has a max walltime of 24 hrs - walltime='24:00:00' - ), - ), - ], - monitoring=MonitoringHub( - hub_address=address_by_interface('ib0'), - resource_monitoring_interval=10, - ), - strategy='simple', - retries=3, - memoizer=BasicMemoizer(checkpoint_mode='task_exit'), - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/Azure.py b/parsl/parsl/configs/Azure.py deleted file mode 100644 index 2a27db3f..00000000 --- a/parsl/parsl/configs/Azure.py +++ /dev/null @@ -1,39 +0,0 @@ -"""Config for Azure""" -import getpass - -from parsl.addresses import address_by_query -from parsl.config import Config -from parsl.data_provider.ftp import FTPInTaskStaging -from parsl.data_provider.http import HTTPInTaskStaging -from parsl.data_provider.rsync import RSyncStaging -from parsl.executors import HighThroughputExecutor -from parsl.providers import AzureProvider -from parsl.usage_tracking.levels import LEVEL_1 - -vm_reference = { - # All fields below are required - "admin_username": 'YOUR_ADMIN_USERNAME_FOR_INSTANCE', - "password": 'YOUR_PASSWORD_FOR_INSTANCE', - "vm_size": 'YOUR_VM_SIZE', - "disk_size_gb": 'YOUR_VM_DISK_SIZE', - "publisher": 'YOUR_IMAGE_PUBLISHER', - "offer": 'YOUR_VM_OS_OFFER', - "sku": 'YOUR_VM_OS_SKU', - "version": 'YOUR_VM_OS_VERSION', -} - -config = Config( - executors=[ - HighThroughputExecutor( - label='azure_single_node', - provider=AzureProvider( - vm_reference=vm_reference, - key_file='azure_key_file.json', - ), - storage_access=[HTTPInTaskStaging(), - FTPInTaskStaging(), - RSyncStaging(getpass.getuser() + "@" + address_by_query())], - ) - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/__init__.py b/parsl/parsl/configs/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/configs/anvil.py b/parsl/parsl/configs/anvil.py deleted file mode 100644 index f187b832..00000000 --- a/parsl/parsl/configs/anvil.py +++ /dev/null @@ -1,34 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SrunLauncher -from parsl.providers import SlurmProvider -from parsl.usage_tracking.levels import LEVEL_1 - -config = Config( - executors=[ - HighThroughputExecutor( - label='Anvil_GPU_Executor', - working_dir='/anvil/scratch/path/to/your/directory', - max_workers_per_node=1, - provider=SlurmProvider( - partition='gpu', - account='YOUR_ALLOCATION_ON_ANVIL', - nodes_per_block=1, - cores_per_node=4, - mem_per_node=50, - init_blocks=1, - max_blocks=1, - walltime='00:30:00', - # string to prepend to #SBATCH blocks in the submit - # script to the scheduler - scheduler_options='#SBATCH --gres gpu:1 --gpus-per-node=1', - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init='module load modtree/gpu', - exclusive=False, - launcher=SrunLauncher(), - ), - ) - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/bridges.py b/parsl/parsl/configs/bridges.py deleted file mode 100644 index 4cb0fba5..00000000 --- a/parsl/parsl/configs/bridges.py +++ /dev/null @@ -1,40 +0,0 @@ -from parsl.addresses import address_by_interface -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SrunLauncher -from parsl.providers import SlurmProvider -from parsl.usage_tracking.levels import LEVEL_1 - -""" This config assumes that it is used to launch parsl tasks from the login nodes -of Bridges at PSC. Each job submitted to the scheduler will request 2 nodes for 10 minutes. -""" - -config = Config( - executors=[ - HighThroughputExecutor( - label='Bridges_HTEX_multinode', - address=address_by_interface('ens3f0'), - max_workers_per_node=1, - provider=SlurmProvider( - 'YOUR_PARTITION_NAME', # Specify Partition / QOS, for eg. RM-small - nodes_per_block=2, - init_blocks=1, - # string to prepend to #SBATCH blocks in the submit - # script to the scheduler eg: '#SBATCH --gres=gpu:type:n' - scheduler_options='', - - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init='', - - # We request all hyperthreads on a node. - launcher=SrunLauncher(), - walltime='00:10:00', - # Slurm scheduler on Cori can be slow at times, - # increase the command timeouts - cmd_timeout=120, - ), - ) - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/cc_in2p3.py b/parsl/parsl/configs/cc_in2p3.py deleted file mode 100644 index f0140da5..00000000 --- a/parsl/parsl/configs/cc_in2p3.py +++ /dev/null @@ -1,22 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.providers import GridEngineProvider -from parsl.usage_tracking.levels import LEVEL_1 - -config = Config( - executors=[ - HighThroughputExecutor( - label='cc_in2p3_htex', - max_workers_per_node=2, - provider=GridEngineProvider( - nodes_per_block=1, - init_blocks=2, - max_blocks=2, - walltime="00:20:00", - scheduler_options='', # Input your scheduler_options if needed - worker_init='', # Input your worker_init if needed - ), - ) - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/delta.py b/parsl/parsl/configs/delta.py deleted file mode 100644 index a7f421e5..00000000 --- a/parsl/parsl/configs/delta.py +++ /dev/null @@ -1,35 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SrunLauncher -from parsl.providers import SlurmProvider -from parsl.usage_tracking.levels import LEVEL_1 - -config = Config( - executors=[ - HighThroughputExecutor( - label='Delta_GPU_Executor', - working_dir='/scratch/path/to/your/directory', - max_workers_per_node=1, - provider=SlurmProvider( - partition='gpuA100x4', - account='YOUR_ALLOCATION_ON_DELTA', - constraint="scratch", - nodes_per_block=1, - cores_per_node=32, - mem_per_node=220, - init_blocks=1, - max_blocks=1, - walltime='00:30:00', - # string to prepend to #SBATCH blocks in the submit - # script to the scheduler - scheduler_options='#SBATCH --gpus-per-node=1 --gpu-bind=closest', - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init='', - exclusive=False, - launcher=SrunLauncher(), - ), - ) - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/ec2.py b/parsl/parsl/configs/ec2.py deleted file mode 100644 index 8e85252a..00000000 --- a/parsl/parsl/configs/ec2.py +++ /dev/null @@ -1,30 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.providers import AWSProvider -from parsl.usage_tracking.levels import LEVEL_1 - -config = Config( - executors=[ - HighThroughputExecutor( - label='ec2_single_node', - provider=AWSProvider( - # Specify your EC2 AMI id - 'YOUR_AMI_ID', - # Specify the AWS region to provision from - # eg. us-east-1 - region='YOUR_AWS_REGION', - - # Specify the name of the key to allow ssh access to nodes - key_name='YOUR_KEY_NAME', - profile="default", - state_file='awsproviderstate.json', - nodes_per_block=1, - init_blocks=1, - max_blocks=1, - min_blocks=0, - walltime='01:00:00', - ), - ) - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/expanse.py b/parsl/parsl/configs/expanse.py deleted file mode 100644 index 35ef5e0f..00000000 --- a/parsl/parsl/configs/expanse.py +++ /dev/null @@ -1,30 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SrunLauncher -from parsl.providers import SlurmProvider -from parsl.usage_tracking.levels import LEVEL_1 - -config = Config( - executors=[ - HighThroughputExecutor( - label='Expanse_CPU_Multinode', - max_workers_per_node=32, - provider=SlurmProvider( - 'compute', - account='YOUR_ALLOCATION_ON_EXPANSE', - launcher=SrunLauncher(), - # string to prepend to #SBATCH blocks in the submit - # script to the scheduler - scheduler_options='', - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init='', - walltime='01:00:00', - init_blocks=1, - max_blocks=1, - nodes_per_block=2, - ), - ) - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/frontera.py b/parsl/parsl/configs/frontera.py deleted file mode 100644 index 25682bbe..00000000 --- a/parsl/parsl/configs/frontera.py +++ /dev/null @@ -1,35 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SrunLauncher -from parsl.providers import SlurmProvider -from parsl.usage_tracking.levels import LEVEL_1 - -""" This config assumes that it is used to launch parsl tasks from the login nodes -of Frontera at TACC. Each job submitted to the scheduler will request 2 nodes for 10 minutes. -""" -config = Config( - executors=[ - HighThroughputExecutor( - label="frontera_htex", - max_workers_per_node=1, # Set number of workers per node - provider=SlurmProvider( - cmd_timeout=60, # Add extra time for slow scheduler responses - nodes_per_block=2, - init_blocks=1, - min_blocks=1, - max_blocks=1, - partition='normal', # Replace with partition name - scheduler_options='#SBATCH -A ', # Enter scheduler_options if needed - - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init='', - - # Ideally we set the walltime to the longest supported walltime. - walltime='00:10:00', - launcher=SrunLauncher(), - ), - ) - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/gc_multisite.py b/parsl/parsl/configs/gc_multisite.py deleted file mode 100644 index 67862f9d..00000000 --- a/parsl/parsl/configs/gc_multisite.py +++ /dev/null @@ -1,27 +0,0 @@ -from globus_compute_sdk import Executor - -from parsl.config import Config -from parsl.executors import GlobusComputeExecutor -from parsl.usage_tracking.levels import LEVEL_1 - -# Please start your own endpoint on perlmutter following instructions below to use this config: -# https://globus-compute.readthedocs.io/en/stable/endpoints/endpoint_examples.html#perlmutter-nersc -perlmutter_endpoint = 'YOUR_PERLMUTTER_ENDPOINT_UUID' - -# Please start your own endpoint on expanse following instructions below to use this config: -# https://globus-compute.readthedocs.io/en/stable/endpoints/endpoint_examples.html#expanse-sdsc -expanse_endpoint = 'YOUR_EXPANSE_ENDPOINT_UUID' - -config = Config( - executors=[ - GlobusComputeExecutor( - executor=Executor(endpoint_id=perlmutter_endpoint), - label="Perlmutter", - ), - GlobusComputeExecutor( - executor=Executor(endpoint_id=expanse_endpoint), - label="Expanse", - ), - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/gc_tutorial.py b/parsl/parsl/configs/gc_tutorial.py deleted file mode 100644 index f3372a0c..00000000 --- a/parsl/parsl/configs/gc_tutorial.py +++ /dev/null @@ -1,18 +0,0 @@ -from globus_compute_sdk import Executor - -from parsl.config import Config -from parsl.executors import GlobusComputeExecutor -from parsl.usage_tracking.levels import LEVEL_1 - -# Public tutorial endpoint -tutorial_endpoint = '4b116d3c-1703-4f8f-9f6f-39921e5864df' - -config = Config( - executors=[ - GlobusComputeExecutor( - executor=Executor(endpoint_id=tutorial_endpoint), - label="Tutorial_Endpoint_py3.11", - ) - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/htex_local.py b/parsl/parsl/configs/htex_local.py deleted file mode 100644 index 57549a47..00000000 --- a/parsl/parsl/configs/htex_local.py +++ /dev/null @@ -1,18 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.providers import LocalProvider -from parsl.usage_tracking.levels import LEVEL_1 - -config = Config( - executors=[ - HighThroughputExecutor( - label="htex_local", - cores_per_worker=1, - provider=LocalProvider( - init_blocks=1, - max_blocks=1, - ), - ) - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/illinoiscluster.py b/parsl/parsl/configs/illinoiscluster.py deleted file mode 100644 index 216c910b..00000000 --- a/parsl/parsl/configs/illinoiscluster.py +++ /dev/null @@ -1,30 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SrunLauncher -from parsl.providers import SlurmProvider -from parsl.usage_tracking.levels import LEVEL_1 - -""" This config assumes that it is used to launch parsl tasks from the login nodes -of the Campus Cluster at UIUC. Each job submitted to the scheduler will request 2 nodes for 10 minutes. -""" -config = Config( - executors=[ - HighThroughputExecutor( - label="CC_htex", - worker_debug=False, - cores_per_worker=16.0, # each worker uses a full node - provider=SlurmProvider( - partition='secondary-fdr', # partition - nodes_per_block=2, # number of nodes - init_blocks=1, - max_blocks=1, - scheduler_options='', - cmd_timeout=60, - walltime='00:10:00', - launcher=SrunLauncher(), - worker_init='conda activate envParsl', # requires conda environment with parsl - ), - ) - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/improv.py b/parsl/parsl/configs/improv.py deleted file mode 100644 index 8a402828..00000000 --- a/parsl/parsl/configs/improv.py +++ /dev/null @@ -1,34 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import MpiRunLauncher -from parsl.providers import PBSProProvider - -config = Config( - executors=[ - HighThroughputExecutor( - label="Improv_multinode", - max_workers_per_node=32, - provider=PBSProProvider( - account="YOUR_ALLOCATION_ON_IMPROV", - # PBS directives (header lines), for example: - # scheduler_options='#PBS -l mem=4gb', - scheduler_options='', - - queue="compute", - - # Command to be run before starting a worker: - # **WARNING** Improv requires an openmpi module to be - # loaded for the MpiRunLauncher. Add additional env - # load commands to this multiline string. - worker_init=''' -module load gcc/13.2.0; -module load openmpi/5.0.3-gcc-13.2.0; ''', - launcher=MpiRunLauncher(), - - # number of compute nodes allocated for each block - nodes_per_block=2, - walltime='00:10:00' - ), - ), - ], -) diff --git a/parsl/parsl/configs/kubernetes.py b/parsl/parsl/configs/kubernetes.py deleted file mode 100644 index 5a460186..00000000 --- a/parsl/parsl/configs/kubernetes.py +++ /dev/null @@ -1,42 +0,0 @@ -from parsl.addresses import address_by_route -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.providers import KubernetesProvider -from parsl.usage_tracking.levels import LEVEL_1 - -config = Config( - executors=[ - HighThroughputExecutor( - label='kube-htex', - cores_per_worker=1, - max_workers_per_node=1, - worker_logdir_root='YOUR_WORK_DIR', - - # Address for the pod worker to connect back - address=address_by_route(), - provider=KubernetesProvider( - namespace="default", - - # Docker image url to use for pods - image='YOUR_DOCKER_URL', - - # Command to be run upon pod start, such as: - # 'module load Anaconda; source activate parsl_env'. - # or 'pip install parsl' - worker_init='', - - # The secret key to download the image - secret="YOUR_KUBE_SECRET", - - # Should follow the Kubernetes naming rules - pod_name='YOUR-POD-Name', - - nodes_per_block=1, - init_blocks=1, - # Maximum number of pods to scale up - max_blocks=10, - ), - ), - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/local_threads.py b/parsl/parsl/configs/local_threads.py deleted file mode 100644 index 6b6561ea..00000000 --- a/parsl/parsl/configs/local_threads.py +++ /dev/null @@ -1,8 +0,0 @@ -from parsl.config import Config -from parsl.executors.threads import ThreadPoolExecutor -from parsl.usage_tracking.levels import LEVEL_1 - -config = Config( - executors=[ThreadPoolExecutor()], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/midway.py b/parsl/parsl/configs/midway.py deleted file mode 100644 index 960c406c..00000000 --- a/parsl/parsl/configs/midway.py +++ /dev/null @@ -1,33 +0,0 @@ -from parsl.addresses import address_by_interface -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SrunLauncher -from parsl.providers import SlurmProvider -from parsl.usage_tracking.levels import LEVEL_1 - -config = Config( - executors=[ - HighThroughputExecutor( - label='Midway_HTEX_multinode', - address=address_by_interface('bond0'), - worker_debug=False, - max_workers_per_node=2, - provider=SlurmProvider( - 'YOUR_PARTITION', # Partition name, e.g 'broadwl' - launcher=SrunLauncher(), - nodes_per_block=2, - init_blocks=1, - min_blocks=1, - max_blocks=1, - # string to prepend to #SBATCH blocks in the submit - # script to the scheduler eg: '#SBATCH --constraint=knl,quad,cache' - scheduler_options='', - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init='', - walltime='00:30:00' - ), - ) - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/osg.py b/parsl/parsl/configs/osg.py deleted file mode 100644 index da0a1dcb..00000000 --- a/parsl/parsl/configs/osg.py +++ /dev/null @@ -1,32 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.providers import CondorProvider -from parsl.usage_tracking.levels import LEVEL_1 - -config = Config( - executors=[ - HighThroughputExecutor( - label='OSG_HTEX', - max_workers_per_node=1, - provider=CondorProvider( - nodes_per_block=1, - init_blocks=4, - max_blocks=4, - # This scheduler option string ensures that the compute nodes provisioned - # will have modules - scheduler_options=""" - +ProjectName = "MyProject" - Requirements = HAS_MODULES=?=TRUE - """, - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init='''unset HOME; unset PYTHONPATH; module load python/3.7.0; -python3 -m venv parsl_env; source parsl_env/bin/activate; python3 -m pip install parsl''', - walltime="00:20:00", - ), - worker_logdir_root='$OSG_WN_TMP', - worker_port=31000, - ) - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/polaris.py b/parsl/parsl/configs/polaris.py deleted file mode 100644 index 3d59991d..00000000 --- a/parsl/parsl/configs/polaris.py +++ /dev/null @@ -1,40 +0,0 @@ -from parsl.addresses import address_by_interface -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import MpiExecLauncher -from parsl.providers import PBSProProvider -from parsl.usage_tracking.levels import LEVEL_1 - -# There are three user parameters to change for the PBSProProvider: -# YOUR_ACCOUNT: Account to charge usage -# YOUR_ENV: Name or path of your Parsl environment -# YOUR_WALLTIME: How long each job should run for - -# You may also need to change the filesystem if your data is not on Eagle - -config = Config( - executors=[ - HighThroughputExecutor( - available_accelerators=4, # Ensures one worker per accelerator - address=address_by_interface('bond0'), - cpu_affinity="alternating", # Prevents thread contention - prefetch_capacity=0, # Increase if you have many more tasks than workers - provider=PBSProProvider( - account="YOUR_ACCOUNT", - worker_init="module load conda; conda activate YOUR_ENV", - walltime="YOUR_WALLTIME", - queue="prod", # Small runs should use "debug" or "debug-scaling" - scheduler_options="#PBS -l filesystems=home:eagle", # Change if data on other filesystem - launcher=MpiExecLauncher( - bind_cmd="--cpu-bind", overrides="--depth=64 --ppn 1" - ), # Ensures 1 manger per node and allows it to divide work to all 64 cores - select_options="ngpus=4", - nodes_per_block=128, - min_blocks=0, - max_blocks=1, - cpus_per_node=64, - ), - ), - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/stampede2.py b/parsl/parsl/configs/stampede2.py deleted file mode 100644 index b8e2aca9..00000000 --- a/parsl/parsl/configs/stampede2.py +++ /dev/null @@ -1,39 +0,0 @@ -from parsl.addresses import address_by_interface -from parsl.config import Config -from parsl.data_provider.globus import GlobusStaging -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SrunLauncher -from parsl.providers import SlurmProvider -from parsl.usage_tracking.levels import LEVEL_1 - -config = Config( - executors=[ - HighThroughputExecutor( - label='Stampede2_HTEX', - address=address_by_interface('em3'), - max_workers_per_node=2, - provider=SlurmProvider( - nodes_per_block=2, - init_blocks=1, - min_blocks=1, - max_blocks=1, - partition='YOUR_PARTITION', - # string to prepend to #SBATCH blocks in the submit - # script to the scheduler eg: '#SBATCH --constraint=knl,quad,cache' - scheduler_options='', - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init='', - launcher=SrunLauncher(), - walltime='00:30:00' - ), - storage_access=[GlobusStaging( - endpoint_uuid='ceea5ca0-89a9-11e7-a97f-22000a92523b', - endpoint_path='/', - local_path='/' - )] - ) - - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/summit.py b/parsl/parsl/configs/summit.py deleted file mode 100644 index 11e68ca2..00000000 --- a/parsl/parsl/configs/summit.py +++ /dev/null @@ -1,31 +0,0 @@ -from parsl.addresses import address_by_interface -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import JsrunLauncher -from parsl.providers import LSFProvider -from parsl.usage_tracking.levels import LEVEL_1 - -config = Config( - executors=[ - HighThroughputExecutor( - label='Summit_HTEX', - # On Summit ensure that the working dir is writeable from the compute nodes, - # for eg. paths below /gpfs/alpine/world-shared/ - working_dir='YOUR_WORKING_DIR_ON_SHARED_FS', - address=address_by_interface('ib0'), # This assumes Parsl is running on login node - worker_port_range=(50000, 55000), - provider=LSFProvider( - launcher=JsrunLauncher(), - walltime="00:10:00", - nodes_per_block=2, - init_blocks=1, - max_blocks=1, - worker_init='', # Input your worker environment initialization commands - project='YOUR_PROJECT_ALLOCATION', - cmd_timeout=60 - ), - ) - - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/toss3_llnl.py b/parsl/parsl/configs/toss3_llnl.py deleted file mode 100644 index 5c6b1c71..00000000 --- a/parsl/parsl/configs/toss3_llnl.py +++ /dev/null @@ -1,30 +0,0 @@ -from parsl.config import Config -from parsl.executors import FluxExecutor -from parsl.launchers import SrunLauncher -from parsl.providers import SlurmProvider -from parsl.usage_tracking.levels import LEVEL_1 - -config = Config( - executors=[ - FluxExecutor( - provider=SlurmProvider( - partition="YOUR_PARTITION", # e.g. "pbatch", "pdebug" - account="YOUR_ACCOUNT", - launcher=SrunLauncher(overrides="--mpibind=off"), - nodes_per_block=1, - init_blocks=1, - min_blocks=1, - max_blocks=1, - walltime="00:30:00", - # string to prepend to #SBATCH blocks in the submit - # script to the scheduler, e.g.: '#SBATCH -t 50' - scheduler_options='', - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init='', - cmd_timeout=120, - ), - ) - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/vineex_local.py b/parsl/parsl/configs/vineex_local.py deleted file mode 100644 index 755f1d1c..00000000 --- a/parsl/parsl/configs/vineex_local.py +++ /dev/null @@ -1,21 +0,0 @@ -import uuid - -from parsl.config import Config -from parsl.executors.taskvine import TaskVineExecutor, TaskVineManagerConfig -from parsl.usage_tracking.levels import LEVEL_1 - -config = Config( - executors=[ - TaskVineExecutor( - label="parsl-vine-example", - - # If a project_name is given, then TaskVine will periodically - # report its status and performance back to the global TaskVine catalog, - # which can be viewed here: http://ccl.cse.nd.edu/software/taskvine/status - - # To disable status reporting, comment out the project_name. - manager_config=TaskVineManagerConfig(project_name="parsl-vine-" + str(uuid.uuid4())), - ) - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/configs/wqex_local.py b/parsl/parsl/configs/wqex_local.py deleted file mode 100644 index fa583f38..00000000 --- a/parsl/parsl/configs/wqex_local.py +++ /dev/null @@ -1,27 +0,0 @@ -import uuid - -from parsl.config import Config -from parsl.executors import WorkQueueExecutor -from parsl.usage_tracking.levels import LEVEL_1 - -config = Config( - executors=[ - WorkQueueExecutor( - label="parsl-wq-example", - - # If a project_name is given, then Work Queue will periodically - # report its status and performance back to the global WQ catalog, - # which can be viewed here: http://ccl.cse.nd.edu/software/workqueue/status - - # To disable status reporting, comment out the project_name. - project_name="parsl-wq-" + str(uuid.uuid4()), - - # The port number that Work Queue will listen on for connecting workers. - port=9123, - - # A shared filesystem is not needed when using Work Queue. - shared_fs=False - ) - ], - usage_tracking=LEVEL_1, -) diff --git a/parsl/parsl/curvezmq.py b/parsl/parsl/curvezmq.py deleted file mode 100644 index 695602cb..00000000 --- a/parsl/parsl/curvezmq.py +++ /dev/null @@ -1,196 +0,0 @@ -import os -from abc import ABCMeta, abstractmethod -from typing import Optional, Tuple, Union - -import zmq -import zmq.auth -from zmq.auth.thread import ThreadAuthenticator - - -def create_certificates(base_dir: Union[str, os.PathLike]): - """Create server and client certificates in a private directory. - - This will overwrite existing certificate files. - - Parameters - ---------- - base_dir : str | os.PathLike - Parent directory of the private certificates directory. - """ - cert_dir = os.path.join(base_dir, "certificates") - os.makedirs(cert_dir, mode=0o700, exist_ok=True) - - zmq.auth.create_certificates(cert_dir, name="server") - zmq.auth.create_certificates(cert_dir, name="client") - - return cert_dir - - -def _load_certificate( - cert_dir: Union[str, os.PathLike], name: str -) -> Tuple[bytes, bytes]: - if os.stat(cert_dir).st_mode & 0o777 != 0o700: - raise OSError(f"The certificates directory must be private: {cert_dir}") - - # pyzmq creates secret key files with the '.key_secret' extension - # Ref: https://github.com/zeromq/pyzmq/blob/ae615d4097ccfbc6b5c17de60355cbe6e00a6065/zmq/auth/certs.py#L73 - secret_key_file = os.path.join(cert_dir, f"{name}.key_secret") - public_key, secret_key = zmq.auth.load_certificate(secret_key_file) - if secret_key is None: - raise ValueError(f"No secret key found in {secret_key_file}") - - return public_key, secret_key - - -class BaseContext(metaclass=ABCMeta): - """Base CurveZMQ context""" - - def __init__(self, cert_dir: Optional[Union[str, os.PathLike]]) -> None: - self.cert_dir = cert_dir - self._ctx = zmq.Context() - - @property - def encrypted(self): - """Indicates whether encryption is enabled. - - False (disabled) when self.cert_dir is set to None. - """ - return self.cert_dir is not None - - @property - def closed(self): - return self._ctx.closed - - @abstractmethod - def socket(self, socket_type: int, *args, **kwargs) -> zmq.Socket: - """Create a socket associated with this context. - - This method will apply all necessary certificates and socket options. - - Parameters - ---------- - socket_type : int - The socket type, which can be any of the 0MQ socket types: REQ, REP, - PUB, SUB, PAIR, DEALER, ROUTER, PULL, PUSH, etc. - - args: - passed to the zmq.Context.socket method. - - kwargs: - passed to the zmq.Context.socket method. - """ - ... - - def term(self): - """Terminate the context.""" - self._ctx.term() - - def destroy(self, linger: Optional[int] = None): - """Close all sockets associated with this context and then terminate - the context. - - .. warning:: - - destroy involves calling ``zmq_close()``, which is **NOT** threadsafe. - If there are active sockets in other threads, this must not be called. - - Parameters - ---------- - linger : int, optional - If specified, set LINGER on sockets prior to closing them. - """ - self._ctx.destroy(linger) - - -class ServerContext(BaseContext): - """CurveZMQ server context - - We create server sockets via the `ctx.socket` method, which automatically - applies the necessary certificates and socket options. - - We handle client certificate authentication in a separate dedicated thread. - - Parameters - ---------- - cert_dir : str | os.PathLike | None - Path to the certificate directory. Setting this to None will disable encryption. - """ - - def __init__(self, cert_dir: Optional[Union[str, os.PathLike]]) -> None: - super().__init__(cert_dir) - self.auth_thread = None - if self.encrypted: - self.auth_thread = self._start_auth_thread() - - def __del__(self): - # Avoid issues in which the auth_thread attr was - # previously deleted - if getattr(self, "auth_thread", None): - self.auth_thread.stop() - self.auth_thread = None - - def _start_auth_thread(self) -> ThreadAuthenticator: - auth_thread = ThreadAuthenticator(self._ctx) - auth_thread.start() - # Only allow certs that are in the cert dir - assert self.cert_dir # For mypy - auth_thread.configure_curve(domain="*", location=self.cert_dir) - return auth_thread - - def socket(self, socket_type: int, *args, **kwargs) -> zmq.Socket: - sock = self._ctx.socket(socket_type, *args, **kwargs) - if self.encrypted: - assert self.cert_dir # For mypy - _, secret_key = _load_certificate(self.cert_dir, name="server") - try: - # Only the clients need the server's public key to encrypt - # messages and verify the server's identity. - # Ref: http://curvezmq.org/page:read-the-docs - sock.setsockopt(zmq.CURVE_SECRETKEY, secret_key) - except zmq.ZMQError as e: - raise ValueError("Invalid CurveZMQ key format") from e - sock.setsockopt(zmq.CURVE_SERVER, True) # Must come before bind - - # This flag enables IPV6 in addition to IPV4 - sock.setsockopt(zmq.IPV6, True) - return sock - - def term(self): - if self.auth_thread: - self.auth_thread.stop() - self.auth_thread = None - super().term() - - def destroy(self, linger: Optional[int] = None): - if self.auth_thread: - self.auth_thread.stop() - self.auth_thread = None - super().destroy(linger) - - -class ClientContext(BaseContext): - """CurveZMQ client context - - We create client sockets via the `ctx.socket` method, which automatically - applies the necessary certificates and socket options. - - Parameters - ---------- - cert_dir : str | os.PathLike | None - Path to the certificate directory. Setting this to None will disable encryption. - """ - - def socket(self, socket_type: int, *args, **kwargs) -> zmq.Socket: - sock = self._ctx.socket(socket_type, *args, **kwargs) - if self.encrypted: - assert self.cert_dir # For mypy - public_key, secret_key = _load_certificate(self.cert_dir, name="client") - server_public_key, _ = _load_certificate(self.cert_dir, name="server") - try: - sock.setsockopt(zmq.CURVE_PUBLICKEY, public_key) - sock.setsockopt(zmq.CURVE_SECRETKEY, secret_key) - sock.setsockopt(zmq.CURVE_SERVERKEY, server_public_key) - except zmq.ZMQError as e: - raise ValueError("Invalid CurveZMQ key format") from e - sock.setsockopt(zmq.IPV6, True) - return sock diff --git a/parsl/parsl/data_provider/__init__.py b/parsl/parsl/data_provider/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/data_provider/data_manager.py b/parsl/parsl/data_provider/data_manager.py deleted file mode 100644 index b09ce2b0..00000000 --- a/parsl/parsl/data_provider/data_manager.py +++ /dev/null @@ -1,168 +0,0 @@ -import logging -from concurrent.futures import Future -from typing import TYPE_CHECKING, Any, Callable, List, Optional - -from parsl.app.futures import DataFuture -from parsl.data_provider.file_noop import NoOpFileStaging -from parsl.data_provider.files import File -from parsl.data_provider.ftp import FTPSeparateTaskStaging -from parsl.data_provider.http import HTTPSeparateTaskStaging -from parsl.data_provider.staging import Staging -from parsl.data_provider.zip import ZipFileStaging - -if TYPE_CHECKING: - from parsl.dataflow.dflow import DataFlowKernel - -logger = logging.getLogger(__name__) - -# these will be shared between all executors that do not explicitly -# override, so should not contain executor-specific state -default_staging: List[Staging] -default_staging = [NoOpFileStaging(), FTPSeparateTaskStaging(), HTTPSeparateTaskStaging(), ZipFileStaging()] - - -class DataManager: - """The DataManager is responsible for transferring input and output data. - - """ - - def __init__(self, dfk: "DataFlowKernel") -> None: - """Initialize the DataManager. - - Args: - - dfk (DataFlowKernel): The DataFlowKernel that this DataManager is managing data for. - - """ - - self.dfk = dfk - - def replace_task_stage_out(self, file: File, func: Callable, executor: str) -> Callable: - """This will give staging providers the chance to wrap (or replace entirely!) the task function.""" - executor_obj = self.dfk.executors[executor] - if hasattr(executor_obj, "storage_access") and executor_obj.storage_access is not None: - storage_access = executor_obj.storage_access # type: List[Staging] - else: - storage_access = default_staging - - for provider in storage_access: - logger.debug("stage_out checking Staging provider %r", provider) - if provider.can_stage_out(file): - newfunc = provider.replace_task_stage_out(self, executor, file, func) - if newfunc: - return newfunc - else: - return func - - logger.debug("reached end of staging provider list") - # if we reach here, we haven't found a suitable staging mechanism - raise ValueError("Executor {} cannot stage file {}".format(executor, repr(file))) - - def optionally_stage_in(self, input, func, executor): - if isinstance(input, DataFuture): - file = input.file_obj.cleancopy() - # replace the input DataFuture with a new DataFuture which will complete at - # the same time as the original one, but will contain the newly - # copied file - input = DataFuture(input, file, tid=input.tid) - elif isinstance(input, File): - file = input.cleancopy() - input = file - else: - return (input, func) - - replacement_input = self.stage_in(file, input, executor) - - func = self.replace_task(file, func, executor) - - return (replacement_input, func) - - def replace_task(self, file: File, func: Callable, executor: str) -> Callable: - """This will give staging providers the chance to wrap (or replace entirely!) the task function.""" - - executor_obj = self.dfk.executors[executor] - if hasattr(executor_obj, "storage_access") and executor_obj.storage_access is not None: - storage_access = executor_obj.storage_access - else: - storage_access = default_staging - - for provider in storage_access: - logger.debug("stage_in checking Staging provider %r", provider) - if provider.can_stage_in(file): - newfunc = provider.replace_task(self, executor, file, func) - if newfunc: - return newfunc - else: - return func - - logger.debug("reached end of staging provider list") - # if we reach here, we haven't found a suitable staging mechanism - raise ValueError("Executor {} cannot stage file {}".format(executor, repr(file))) - - def stage_in(self, file: File, input: Any, executor: str) -> Any: - """Transport the input from the input source to the executor, if it is file-like, - returning a DataFuture that wraps the stage-in operation. - - If no staging in is required - because the ``file`` parameter is not file-like, - then return that parameter unaltered. - - Args: - - self - - input (Any) : input to stage in. If this is a File or a - DataFuture, stage in tasks will be launched with appropriate - dependencies. Otherwise, no stage-in will be performed. - - executor (str) : an executor the file is going to be staged in to. - """ - - if isinstance(input, DataFuture): - parent_fut = input # type: Optional[Future] - elif isinstance(input, File): - parent_fut = None - else: - raise ValueError("Internal consistency error - should have checked DataFuture/File earlier") - - executor_obj = self.dfk.executors[executor] - if hasattr(executor_obj, "storage_access") and executor_obj.storage_access is not None: - storage_access = executor_obj.storage_access - else: - storage_access = default_staging - - for provider in storage_access: - logger.debug("stage_in checking Staging provider %r", provider) - if provider.can_stage_in(file): - staging_fut = provider.stage_in(self, executor, file, parent_fut=parent_fut) - if staging_fut: - return staging_fut - else: - return input - - logger.debug("reached end of staging provider list") - # if we reach here, we haven't found a suitable staging mechanism - raise ValueError("Executor {} cannot stage file {}".format(executor, repr(file))) - - def stage_out(self, file: File, executor: str, app_fu: Future) -> Optional[Future]: - """Transport the file from the local filesystem to the remote Globus endpoint. - - This function returns either a Future which should complete when the stageout - is complete, or None, if no staging needs to be waited for. - - Args: - - self - - file (File) - file to stage out - - executor (str) - Which executor the file is going to be staged out from. - - app_fu (Future) - a future representing the main body of the task that should - complete before stageout begins. - """ - executor_obj = self.dfk.executors[executor] - if hasattr(executor_obj, "storage_access") and executor_obj.storage_access is not None: - storage_access = executor_obj.storage_access - else: - storage_access = default_staging - - for provider in storage_access: - logger.debug("stage_out checking Staging provider %r", provider) - if provider.can_stage_out(file): - return provider.stage_out(self, executor, file, app_fu) - - logger.debug("reached end of staging provider list") - # if we reach here, we haven't found a suitable staging mechanism - raise ValueError("Executor {} cannot stage out file {}".format(executor, repr(file))) diff --git a/parsl/parsl/data_provider/file_noop.py b/parsl/parsl/data_provider/file_noop.py deleted file mode 100644 index 69c38669..00000000 --- a/parsl/parsl/data_provider/file_noop.py +++ /dev/null @@ -1,17 +0,0 @@ -import logging - -from parsl.data_provider.staging import Staging -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - - -class NoOpFileStaging(Staging, RepresentationMixin): - - def can_stage_in(self, file): - logger.debug("NoOpFileStaging checking file {}".format(repr(file))) - return file.scheme == 'file' - - def can_stage_out(self, file): - logger.debug("NoOpFileStaging checking file {}".format(repr(file))) - return file.scheme == 'file' diff --git a/parsl/parsl/data_provider/files.py b/parsl/parsl/data_provider/files.py deleted file mode 100644 index 4263753d..00000000 --- a/parsl/parsl/data_provider/files.py +++ /dev/null @@ -1,98 +0,0 @@ -"""Define the File Type. - -The primary purpose of the File object is to track the protocol to be used -to transfer the file as well as to give the appropriate filepath depending -on where (client-side, remote-side, intermediary-side) the File.filepath is -being called from. -""" -import logging -import os -from typing import Optional, Union -from urllib.parse import urlparse - -import typeguard - -logger = logging.getLogger(__name__) - - -class File: - """The Parsl File Class. - - This represents the global, and sometimes local, URI/path to a file. - - Staging-in mechanisms may annotate a file with a local path recording - the path at the far end of a staging action. It is up to the user of - the File object to track which local scope that local path actually - refers to. - - """ - - @typeguard.typechecked - def __init__(self, url: Union[os.PathLike, str]): - """Construct a File object from a url string. - - Args: - - url (string or PathLike) : url of the file e.g. - - 'input.txt' - - pathlib.Path('input.txt') - - 'file:///scratch/proj101/input.txt' - - 'globus://go#ep1/~/data/input.txt' - - 'globus://ddb59aef-6d04-11e5-ba46-22000b92c6ec/home/johndoe/data/input.txt' - """ - self.url = str(url) - parsed_url = urlparse(self.url) - self.scheme = parsed_url.scheme if parsed_url.scheme else 'file' - self.netloc = parsed_url.netloc - self.path = parsed_url.path - self.filename = os.path.basename(self.path) - self.local_path: Optional[str] = None - - def cleancopy(self) -> "File": - """Returns a copy of the file containing only the global immutable state, - without any mutable site-local local_path information. The returned File - object will be as the original object was when it was constructed. - """ - logger.debug("Making clean copy of File object {}".format(repr(self))) - return File(self.url) - - def __str__(self) -> str: - return self.filepath - - def __repr__(self) -> str: - content = [ - f"{type(self).__name__}", - f"at 0x{id(self):x}", - f"url={self.url}", - f"scheme={self.scheme}", - f"netloc={self.netloc}", - f"path={self.path}", - f"filename={self.filename}", - ] - if self.local_path is not None: - content.append(f"local_path={self.local_path}") - - return f"<{' '.join(content)}>" - - def __fspath__(self) -> str: - return self.filepath - - @property - def filepath(self) -> str: - """Return the resolved filepath on the side where it is called from. - - The appropriate filepath will be returned when called from within - an app running remotely as well as regular python on the submit side. - - Only file: scheme URLs make sense to have a submit-side path, as other - URLs are not accessible through POSIX file access. - - Returns: - - filepath - """ - if self.local_path is not None: - return self.local_path - - if self.scheme in ['file']: - return self.path - else: - raise ValueError("No local_path set for {}".format(repr(self))) diff --git a/parsl/parsl/data_provider/ftp.py b/parsl/parsl/data_provider/ftp.py deleted file mode 100644 index 88cefe61..00000000 --- a/parsl/parsl/data_provider/ftp.py +++ /dev/null @@ -1,82 +0,0 @@ -import ftplib -import logging -import os - -import parsl -from parsl.data_provider.staging import Staging -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - - -class FTPSeparateTaskStaging(Staging, RepresentationMixin): - """Performs FTP staging as a separate parsl level task.""" - - def can_stage_in(self, file): - logger.debug("FTPSeparateTaskStaging checking file {}".format(repr(file))) - return file.scheme == 'ftp' - - def stage_in(self, dm, executor, file, parent_fut): - working_dir = dm.dfk.executors[executor].working_dir - if working_dir: - file.local_path = os.path.join(working_dir, file.filename) - else: - file.local_path = file.filename - stage_in_app = _ftp_stage_in_app(dm, executor=executor) - app_fut = stage_in_app(working_dir, outputs=[file], _parsl_staging_inhibit=True, parent_fut=parent_fut) - return app_fut._outputs[0] - - -class FTPInTaskStaging(Staging, RepresentationMixin): - """Performs FTP staging as a wrapper around the application task.""" - - def can_stage_in(self, file): - logger.debug("FTPInTaskStaging checking file {}".format(file.__repr__())) - return file.scheme == 'ftp' - - def stage_in(self, dm, executor, file, parent_fut): - working_dir = dm.dfk.executors[executor].working_dir - if working_dir: - file.local_path = os.path.join(working_dir, file.filename) - else: - file.local_path = file.filename - - return None - - def replace_task(self, dm, executor, file, f): - working_dir = dm.dfk.executors[executor].working_dir - return in_task_transfer_wrapper(f, file, working_dir) - - -def in_task_transfer_wrapper(func, file, working_dir): - def wrapper(*args, **kwargs): - import ftplib - if working_dir: - os.makedirs(working_dir, exist_ok=True) - - with open(file.local_path, 'wb') as f: - ftp = ftplib.FTP(file.netloc) - ftp.login() - ftp.cwd(os.path.dirname(file.path)) - ftp.retrbinary('RETR {}'.format(file.filename), f.write) - ftp.quit() - - result = func(*args, **kwargs) - return result - return wrapper - - -def _ftp_stage_in(working_dir, parent_fut=None, outputs=[], _parsl_staging_inhibit=True): - file = outputs[0] - if working_dir: - os.makedirs(working_dir, exist_ok=True) - with open(file.local_path, 'wb') as f: - ftp = ftplib.FTP(file.netloc) - ftp.login() - ftp.cwd(os.path.dirname(file.path)) - ftp.retrbinary('RETR {}'.format(file.filename), f.write) - ftp.quit() - - -def _ftp_stage_in_app(dm, executor): - return parsl.python_app(executors=[executor], data_flow_kernel=dm.dfk)(_ftp_stage_in) diff --git a/parsl/parsl/data_provider/globus.py b/parsl/parsl/data_provider/globus.py deleted file mode 100644 index 9ad3d5fe..00000000 --- a/parsl/parsl/data_provider/globus.py +++ /dev/null @@ -1,311 +0,0 @@ -import json -import logging -import os -from functools import partial -from typing import Optional - -import typeguard - -import parsl -from parsl.app.app import python_app -from parsl.data_provider.staging import Staging -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - -# globus staging must be run explicitly in the same process/interpreter -# as the DFK. it relies on persisting globus state between actions in that -# process. - -""" -'Parsl Application' OAuth2 client registered with Globus Auth -by lukasz@globusid.org -""" -CLIENT_ID = '8b8060fd-610e-4a74-885e-1051c71ad473' -REDIRECT_URI = 'https://auth.globus.org/v2/web/auth-code' -SCOPES = ('openid ' - 'urn:globus:auth:scope:transfer.api.globus.org:all') - - -get_input = getattr(__builtins__, 'raw_input', input) - - -def _get_globus_provider(dfk, executor_label): - if executor_label is None: - raise ValueError("executor_label is mandatory") - executor = dfk.executors[executor_label] - if not hasattr(executor, "storage_access"): - raise ValueError("specified executor does not have storage_access attribute") - for provider in executor.storage_access: - if isinstance(provider, GlobusStaging): - return provider - - raise Exception('No suitable Globus endpoint defined for executor {}'.format(executor_label)) - - -def get_globus(): - Globus.init() - return Globus() - - -class Globus: - """ - All communication with the Globus Auth and Globus Transfer services is enclosed - in the Globus class. In particular, the Globus class is responsible for: - - managing an OAuth2 authorizer - getting access and refresh tokens, - refreshing an access token, storing to and retrieving tokens from - .globus.json file, - - submitting file transfers, - - monitoring transfers. - """ - - authorizer = None - - @classmethod - def init(cls): - token_path = os.path.join(os.path.expanduser('~'), '.parsl') - if not os.path.isdir(token_path): - os.mkdir(token_path) - cls.TOKEN_FILE = os.path.join(token_path, '.globus.json') - - if cls.authorizer: - return - cls.authorizer = cls._get_native_app_authorizer(CLIENT_ID) - - @classmethod - def get_authorizer(cls): - return cls.authorizer - - @classmethod - def transfer_file(cls, src_ep, dst_ep, src_path, dst_path): - import globus_sdk - tc = globus_sdk.TransferClient(authorizer=cls.authorizer) - td = globus_sdk.TransferData(tc, src_ep, dst_ep) - td.add_item(src_path, dst_path) - try: - task = tc.submit_transfer(td) - except Exception as e: - raise Exception('Globus transfer from {}{} to {}{} failed due to error: {}'.format( - src_ep, src_path, dst_ep, dst_path, e)) - - last_event_time = None - """ - A Globus transfer job (task) can be in one of the three states: ACTIVE, SUCCEEDED, FAILED. - Parsl every 15 seconds polls a status of the transfer job (task) from the Globus Transfer service, - with 60 second timeout limit. If the task is ACTIVE after time runs out 'task_wait' returns False, - and True otherwise. - """ - while not tc.task_wait(task['task_id'], timeout=60): - task = tc.get_task(task['task_id']) - # Get the last error Globus event - task_id = task['task_id'] - for event in tc.task_event_list(task_id): - if event['time'] != last_event_time: - last_event_time = event['time'] - logger.warning( - 'Non-critical Globus Transfer error event for globus://{}{}: "{}" at {}. Retrying...'.format( - src_ep, src_path, event['description'], event['time'])) - logger.debug('Globus Transfer error details: {}'.format(event['details'])) - """ - The Globus transfer job (task) has been terminated (is not ACTIVE). Check if the transfer - SUCCEEDED or FAILED. - """ - task = tc.get_task(task['task_id']) - if task['status'] == 'SUCCEEDED': - logger.debug('Globus transfer {}, from {}{} to {}{} succeeded'.format( - task['task_id'], src_ep, src_path, dst_ep, dst_path)) - else: - logger.debug('Globus Transfer task: {}'.format(task)) - events = tc.task_event_list(task['task_id']) - event = events.data[0] - raise Exception('Globus transfer {}, from {}{} to {}{} failed due to error: "{}"'.format( - task['task_id'], src_ep, src_path, dst_ep, dst_path, event['details'])) - - @classmethod - def _load_tokens_from_file(cls, filepath): - with open(filepath, 'r') as f: - tokens = json.load(f) - return tokens - - @classmethod - def _save_tokens_to_file(cls, filepath, tokens): - with open(filepath, 'w') as f: - json.dump(tokens, f) - - @classmethod - def _update_tokens_file_on_refresh(cls, token_response): - cls._save_tokens_to_file(cls.TOKEN_FILE, token_response.by_resource_server) - - @classmethod - def _do_native_app_authentication(cls, client_id, redirect_uri, - requested_scopes=None): - - import globus_sdk - client = globus_sdk.NativeAppAuthClient(client_id=client_id) - client.oauth2_start_flow( - requested_scopes=requested_scopes, - redirect_uri=redirect_uri, - refresh_tokens=True) - - url = client.oauth2_get_authorize_url() - print('Please visit the following URL to provide authorization: \n{}'.format(url)) - auth_code = get_input('Enter the auth code: ').strip() - token_response = client.oauth2_exchange_code_for_tokens(auth_code) - return token_response.by_resource_server - - @classmethod - def _get_native_app_authorizer(cls, client_id): - import globus_sdk - tokens = None - try: - tokens = cls._load_tokens_from_file(cls.TOKEN_FILE) - except Exception: - pass - - if not tokens: - tokens = cls._do_native_app_authentication( - client_id=client_id, - redirect_uri=REDIRECT_URI, - requested_scopes=SCOPES) - try: - cls._save_tokens_to_file(cls.TOKEN_FILE, tokens) - except Exception: - pass - - transfer_tokens = tokens['transfer.api.globus.org'] - - auth_client = globus_sdk.NativeAppAuthClient(client_id=client_id) - - return globus_sdk.RefreshTokenAuthorizer( - transfer_tokens['refresh_token'], - auth_client, - access_token=transfer_tokens['access_token'], - expires_at=transfer_tokens['expires_at_seconds'], - on_refresh=cls._update_tokens_file_on_refresh) - - -class GlobusStaging(Staging, RepresentationMixin): - """Specification for accessing data on a remote executor via Globus. - - Parameters - ---------- - endpoint_uuid : str - Universally unique identifier of the Globus endpoint at which the data can be accessed. - This can be found in the `Manage Endpoints `_ page. - endpoint_path : str, optional - FIXME - local_path : str, optional - FIXME - """ - - def can_stage_in(self, file): - logger.debug("Globus checking file {}".format(repr(file))) - return file.scheme == 'globus' - - def can_stage_out(self, file): - logger.debug("Globus checking file {}".format(repr(file))) - return file.scheme == 'globus' - - def stage_in(self, dm, executor, file, parent_fut): - globus_provider = _get_globus_provider(dm.dfk, executor) - globus_provider._update_local_path(file, executor, dm.dfk) - stage_in_app = globus_provider._globus_stage_in_app(executor=executor, dfk=dm.dfk) - app_fut = stage_in_app(outputs=[file], _parsl_staging_inhibit=True, parent_fut=parent_fut) - return app_fut._outputs[0] - - def stage_out(self, dm, executor, file, app_fu): - globus_provider = _get_globus_provider(dm.dfk, executor) - globus_provider._update_local_path(file, executor, dm.dfk) - stage_out_app = globus_provider._globus_stage_out_app(executor=executor, dfk=dm.dfk) - return stage_out_app(app_fu, _parsl_staging_inhibit=True, inputs=[file]) - - @typeguard.typechecked - def __init__(self, endpoint_uuid: str, endpoint_path: Optional[str] = None, local_path: Optional[str] = None): - self.endpoint_uuid = endpoint_uuid - self.endpoint_path = endpoint_path - self.local_path = local_path - self.globus = None - - def _globus_stage_in_app(self, executor, dfk): - executor_obj = dfk.executors[executor] - f = partial(_globus_stage_in, self, executor_obj) - return python_app(executors=['_parsl_internal'], data_flow_kernel=dfk)(f) - - def _globus_stage_out_app(self, executor, dfk): - executor_obj = dfk.executors[executor] - f = partial(_globus_stage_out, self, executor_obj) - return python_app(executors=['_parsl_internal'], data_flow_kernel=dfk)(f) - - # could this happen at __init__ time? - def initialize_globus(self): - if self.globus is None: - self.globus = get_globus() - - def _get_globus_endpoint(self, executor): - if executor.working_dir: - working_dir = os.path.normpath(executor.working_dir) - else: - raise ValueError("executor working_dir must be specified for GlobusStaging") - if self.endpoint_path and self.local_path: - endpoint_path = os.path.normpath(self.endpoint_path) - local_path = os.path.normpath(self.local_path) - common_path = os.path.commonpath((local_path, working_dir)) - if local_path != common_path: - raise Exception('"local_path" must be equal or an absolute subpath of "working_dir"') - relative_path = os.path.relpath(working_dir, common_path) - endpoint_path = os.path.join(endpoint_path, relative_path) - else: - endpoint_path = working_dir - return {'endpoint_uuid': self.endpoint_uuid, - 'endpoint_path': endpoint_path, - 'working_dir': working_dir} - - def _update_local_path(self, file, executor, dfk): - executor_obj = dfk.executors[executor] - globus_ep = self._get_globus_endpoint(executor_obj) - file.local_path = os.path.join(globus_ep['working_dir'], file.filename) - - -# this cannot be a class method, but must be a function, because I want -# to be able to use partial() on it - and partial() does not work on -# class methods -def _globus_stage_in(provider, executor, parent_fut=None, outputs=[], _parsl_staging_inhibit=True): - globus_ep = provider._get_globus_endpoint(executor) - file = outputs[0] - dst_path = os.path.join( - globus_ep['endpoint_path'], file.filename) - - provider.initialize_globus() - - provider.globus.transfer_file( - file.netloc, globus_ep['endpoint_uuid'], - file.path, dst_path) - - -def _globus_stage_out(provider, executor, app_fu, inputs=[], _parsl_staging_inhibit=True): - """ - Although app_fu isn't directly used in the stage out code, - it is needed as an input dependency to ensure this code - doesn't run until the app_fu is complete. The actual change - that is represented by the app_fu completing is that the - executor filesystem will now contain the file to stage out. - """ - globus_ep = provider._get_globus_endpoint(executor) - file = inputs[0] - src_path = os.path.join(globus_ep['endpoint_path'], file.filename) - - provider.initialize_globus() - - provider.globus.transfer_file( - globus_ep['endpoint_uuid'], file.netloc, - src_path, file.path - ) - - -def cli_run(): - parsl.set_stream_logger() - print("Parsl Globus command-line authorizer") - print("If authorization to Globus is necessary, the library will prompt you now.") - print("Otherwise it will do nothing") - get_globus() - print("Authorization complete") diff --git a/parsl/parsl/data_provider/http.py b/parsl/parsl/data_provider/http.py deleted file mode 100644 index 4553839b..00000000 --- a/parsl/parsl/data_provider/http.py +++ /dev/null @@ -1,88 +0,0 @@ -import logging -import os - -import requests - -import parsl -from parsl.data_provider.staging import Staging -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - - -class HTTPSeparateTaskStaging(Staging, RepresentationMixin): - """A staging provider that Performs HTTP and HTTPS staging - as a separate parsl-level task. This requires a shared file - system on the executor.""" - - def can_stage_in(self, file): - logger.debug("HTTPSeparateTaskStaging checking file {}".format(repr(file))) - return file.scheme == 'http' or file.scheme == 'https' - - def stage_in(self, dm, executor, file, parent_fut): - working_dir = dm.dfk.executors[executor].working_dir - - if working_dir: - file.local_path = os.path.join(working_dir, file.filename) - else: - file.local_path = file.filename - - stage_in_app = _http_stage_in_app(dm, executor=executor) - app_fut = stage_in_app(working_dir, outputs=[file], _parsl_staging_inhibit=True, parent_fut=parent_fut) - return app_fut._outputs[0] - - -class HTTPInTaskStaging(Staging, RepresentationMixin): - """A staging provider that performs HTTP and HTTPS staging - as in a wrapper around each task. In contrast to - HTTPSeparateTaskStaging, this provider does not require a - shared file system.""" - - def can_stage_in(self, file): - logger.debug("HTTPInTaskStaging checking file {}".format(repr(file))) - return file.scheme == 'http' or file.scheme == 'https' - - def stage_in(self, dm, executor, file, parent_fut): - working_dir = dm.dfk.executors[executor].working_dir - - if working_dir: - file.local_path = os.path.join(working_dir, file.filename) - else: - file.local_path = file.filename - - return None - - def replace_task(self, dm, executor, file, f): - working_dir = dm.dfk.executors[executor].working_dir - return in_task_transfer_wrapper(f, file, working_dir) - - -def in_task_transfer_wrapper(func, file, working_dir): - def wrapper(*args, **kwargs): - import requests - if working_dir: - os.makedirs(working_dir, exist_ok=True) - resp = requests.get(file.url, stream=True) - with open(file.local_path, 'wb') as f: - for chunk in resp.iter_content(chunk_size=1024): - if chunk: - f.write(chunk) - - result = func(*args, **kwargs) - return result - return wrapper - - -def _http_stage_in(working_dir, parent_fut=None, outputs=[], _parsl_staging_inhibit=True): - file = outputs[0] - if working_dir: - os.makedirs(working_dir, exist_ok=True) - resp = requests.get(file.url, stream=True) - with open(file.local_path, 'wb') as f: - for chunk in resp.iter_content(chunk_size=1024): - if chunk: - f.write(chunk) - - -def _http_stage_in_app(dm, executor): - return parsl.python_app(executors=[executor], data_flow_kernel=dm.dfk)(_http_stage_in) diff --git a/parsl/parsl/data_provider/rsync.py b/parsl/parsl/data_provider/rsync.py deleted file mode 100644 index d86b37bd..00000000 --- a/parsl/parsl/data_provider/rsync.py +++ /dev/null @@ -1,108 +0,0 @@ -import logging -import os - -from parsl.data_provider.staging import Staging -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - - -class RSyncStaging(Staging, RepresentationMixin): - """ - This staging provider will execute rsync on worker nodes - to stage in files from a remote location. - - Worker nodes must be able to authenticate to the rsync server - without interactive authentication - for example, worker - initialization could include an appropriate SSH key configuration. - - The submit side will need to run an rsync-compatible server (for example, - an ssh server with the rsync binary installed) - - """ - - def __init__(self, hostname): - self.hostname = hostname - - def can_stage_in(self, file): - return file.scheme == "file" - - def can_stage_out(self, file): - return file.scheme == "file" - - def stage_in(self, dm, executor, file, parent_fut): - # we need to make path an absolute path, because - # rsync remote name needs to include absolute path - file.path = os.path.abspath(file.path) - - working_dir = dm.dfk.executors[executor].working_dir - - if working_dir: - file.local_path = os.path.join(working_dir, file.filename) - else: - file.local_path = file.filename - - return None - - def stage_out(self, dm, executor, file, parent_fut): - - file.path = os.path.abspath(file.path) - - working_dir = dm.dfk.executors[executor].working_dir - - if working_dir: - file.local_path = os.path.join(working_dir, file.filename) - else: - file.local_path = file.filename - - return None - - def replace_task(self, dm, executor, file, f): - logger.debug("Replacing task for rsync stagein") - working_dir = dm.dfk.executors[executor].working_dir - return in_task_stage_in_wrapper(f, file, working_dir, self.hostname) - - def replace_task_stage_out(self, dm, executor, file, f): - logger.debug("Replacing task for rsync stageout") - working_dir = dm.dfk.executors[executor].working_dir - return in_task_stage_out_wrapper(f, file, working_dir, self.hostname) - - -def in_task_stage_in_wrapper(func, file, working_dir, hostname): - def wrapper(*args, **kwargs): - import logging - logger = logging.getLogger(__name__) - logger.debug("rsync in_task_stage_in_wrapper start") - if working_dir: - os.makedirs(working_dir, exist_ok=True) - - logger.debug("rsync in_task_stage_in_wrapper calling rsync") - r = os.system("rsync {hostname}:{permanent_filepath} {worker_filepath}".format(hostname=hostname, - permanent_filepath=file.path, - worker_filepath=file.local_path)) - if r != 0: - raise RuntimeError("rsync returned {}, a {}".format(r, type(r))) - logger.debug("rsync in_task_stage_in_wrapper calling wrapped function") - result = func(*args, **kwargs) - logger.debug("rsync in_task_stage_in_wrapper returned from wrapped function") - return result - return wrapper - - -def in_task_stage_out_wrapper(func, file, working_dir, hostname): - def wrapper(*args, **kwargs): - import logging - logger = logging.getLogger(__name__) - logger.debug("rsync in_task_stage_out_wrapper start") - - logger.debug("rsync in_task_stage_out_wrapper calling wrapped function") - result = func(*args, **kwargs) - logger.debug("rsync in_task_stage_out_wrapper returned from wrapped function, calling rsync") - r = os.system("rsync {worker_filepath} {hostname}:{permanent_filepath}".format(hostname=hostname, - permanent_filepath=file.path, - worker_filepath=file.local_path)) - if r != 0: - raise RuntimeError("rsync returned {}, a {}".format(r, type(r))) - logger.debug("rsync in_task_stage_out_wrapper returned from rsync") - return result - return wrapper diff --git a/parsl/parsl/data_provider/staging.py b/parsl/parsl/data_provider/staging.py deleted file mode 100644 index 37e5c7df..00000000 --- a/parsl/parsl/data_provider/staging.py +++ /dev/null @@ -1,106 +0,0 @@ -from concurrent.futures import Future -from typing import TYPE_CHECKING, Callable, Optional - -from parsl.app.futures import DataFuture -from parsl.data_provider.files import File - -if TYPE_CHECKING: - from parsl.data_provider.data_manager import DataManager - - -class Staging: - """ - This class defines the interface for file staging providers. - - For each file to be staged in, the data manager will present the file - to each configured Staging provider in turn: first, it will ask if the - provider can stage this file by calling ``can_stage_in``, and if so, it - will call both ``stage_in`` and ``replace_task`` to give the provider the - opportunity to perform staging. - - For each file to be staged out, the data manager will follow the same - pattern using the corresponding stage out methods of this class. - - The default implementation of this class rejects all files, and - performs no staging actions. - - To implement a concrete provider, one or both of the ``can_stage_*`` - methods should be overridden to match the appropriate files, and then - the corresponding ``stage_*`` and/or ``replace_task*`` methods should be - implemented. - """ - - def can_stage_in(self, file: File) -> bool: - """ - Given a File object, decide if this staging provider can - stage the file. Usually this is be based on the URL - scheme, but does not have to be. If this returns True, - then other methods of this Staging object will be called - to perform the staging. - """ - return False - - def can_stage_out(self, file: File) -> bool: - """ - Like can_stage_in, but for staging out. - """ - return False - - def stage_in(self, dm: "DataManager", executor: str, file: File, parent_fut: Optional[Future]) -> Optional[DataFuture]: - """ - This call gives the staging provider an opportunity to prepare for - stage-in and to launch arbitrary tasks which must complete as part - of stage-in. - - This call will be made with a fresh copy of the File that may be - modified for the purposes of this particular staging operation, - rather than the original application-provided File. This allows - staging specific information (primarily localpath) to be set on the - File without interfering with other stagings of the same File. - - The call can return a: - - DataFuture: the corresponding task input parameter will be - replaced by the DataFuture, and the main task will not - run until that DataFuture is complete. The DataFuture result - should be the file object as passed in. - - None: the corresponding task input parameter will be replaced - by a suitable automatically generated replacement that container - the File fresh copy, or is the fresh copy. - """ - return None - - def stage_out(self, dm: "DataManager", executor: str, file: File, app_fu: Future) -> Optional[Future]: - """ - This call gives the staging provider an opportunity to prepare for - stage-out and to launch arbitrary tasks which must complete as - part of stage-out. - - Even though it should set up stageout, it will be invoked before - the task executes. Any work which needs to happen after the main task - should depend on app_fu. - - For a given file, either return a Future which completes when stageout - is complete, or return None to indicate that no stageout action need - be waited for. When that Future completes, parsl will mark the relevant - output DataFuture complete. - - Note the asymmetry here between stage_in and stage_out: this can return - any Future, while stage_in must return a DataFuture. - """ - return None - - def replace_task(self, dm: "DataManager", executor: str, file: File, func: Callable) -> Optional[Callable]: - """ - For a file to be staged in, optionally return a replacement app - function, which usually should be the original app function wrapped - in staging code. - """ - return None - - def replace_task_stage_out(self, dm: "DataManager", executor: str, file: File, func: Callable) -> Optional[Callable]: - """ - For a file to be staged out, optionally return a replacement app - function, which usually should be the original app function wrapped - in staging code. - """ - return None diff --git a/parsl/parsl/data_provider/zip.py b/parsl/parsl/data_provider/zip.py deleted file mode 100644 index 167ddbbd..00000000 --- a/parsl/parsl/data_provider/zip.py +++ /dev/null @@ -1,135 +0,0 @@ -import logging -import os -import zipfile -from typing import Tuple - -import filelock - -import parsl -from parsl.data_provider.files import File -from parsl.data_provider.staging import Staging -from parsl.errors import ParslError - -logger = logging.getLogger(__name__) - - -class ZipAuthorityError(ParslError): - def __init__(self, file): - self.file = file - - def __str__(self): - return f"ZipFileStaging cannot stage Files with an authority (netloc) section ({self.file.netloc}), for {self.file.url}" - - -class ZipFileStaging(Staging): - """A stage-out provider for zip files. - - This provider will stage out files by writing them into the specified zip - file. - - The filename of both the zip file and the file contained in that zip are - specified using a zip: URL, like this: - - zip:/tmp/foo/this.zip/inside/here.txt - - This URL names a zip file ``/tmp/foo/this.zip`` containing a file - ``inside/here.txt``. - - The provider will use the Python filelock package to lock the zip file so - that it does not conflict with other instances of itself. This lock will - not protect against other modifications to the zip file. - """ - - def can_stage_out(self, file: File) -> bool: - return self.is_zip_url(file) - - def can_stage_in(self, file: File) -> bool: - return self.is_zip_url(file) - - def is_zip_url(self, file: File) -> bool: - logger.debug("archive provider checking File {}".format(repr(file))) - - # First check if this is the scheme we care about - if file.scheme != "zip": - return False - - # This is some basic validation to check that the user isn't specifying - # an authority section and expecting it to mean something. - if file.netloc != "": - raise ZipAuthorityError(file) - - # If we got this far, we can stage this file - return True - - def stage_out(self, dm, executor, file, parent_fut): - assert file.scheme == 'zip' - - zip_path, inside_path = zip_path_split(file.path) - - working_dir = dm.dfk.executors[executor].working_dir - - if working_dir: - file.local_path = os.path.join(working_dir, inside_path) - - # TODO: I think its the right behaviour that a staging out provider should create the directory structure - # for the file to be placed in? - os.makedirs(os.path.dirname(file.local_path), exist_ok=True) - else: - raise RuntimeError("zip file staging requires a working_dir to be specified") - - stage_out_app = _zip_stage_out_app(dm) - app_fut = stage_out_app(zip_path, inside_path, working_dir, inputs=[file], _parsl_staging_inhibit=True, parent_fut=parent_fut) - return app_fut - - def stage_in(self, dm, executor, file, parent_fut): - assert file.scheme == 'zip' - - zip_path, inside_path = zip_path_split(file.path) - - working_dir = dm.dfk.executors[executor].working_dir - - if working_dir: - file.local_path = os.path.join(working_dir, inside_path) - - stage_in_app = _zip_stage_in_app(dm) - app_fut = stage_in_app(zip_path, inside_path, working_dir, outputs=[file], _parsl_staging_inhibit=True, parent_fut=parent_fut) - return app_fut._outputs[0] - - -def _zip_stage_out(zip_file, inside_path, working_dir, parent_fut=None, inputs=[], _parsl_staging_inhibit=True): - file = inputs[0] - - os.makedirs(os.path.dirname(zip_file), exist_ok=True) - - with filelock.FileLock(zip_file + ".lock"): - with zipfile.ZipFile(zip_file, mode='a', compression=zipfile.ZIP_DEFLATED) as z: - z.write(file, arcname=inside_path) - - os.remove(file) - - -def _zip_stage_out_app(dm): - return parsl.python_app(executors=['_parsl_internal'], data_flow_kernel=dm.dfk)(_zip_stage_out) - - -def _zip_stage_in(zip_file, inside_path, working_dir, *, parent_fut, outputs, _parsl_staging_inhibit=True): - with filelock.FileLock(zip_file + ".lock"): - with zipfile.ZipFile(zip_file, mode='r') as z: - content = z.read(inside_path) - with open(outputs[0], "wb") as of: - of.write(content) - - -def _zip_stage_in_app(dm): - return parsl.python_app(executors=['_parsl_internal'], data_flow_kernel=dm.dfk)(_zip_stage_in) - - -def zip_path_split(path: str) -> Tuple[str, str]: - """Split zip: path into a zipfile name and a contained-file name. - """ - index = path.find(".zip/") - - zip_path = path[:index + 4] - inside_path = path[index + 5:] - - return (zip_path, inside_path) diff --git a/parsl/parsl/dataflow/__init__.py b/parsl/parsl/dataflow/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/dataflow/dependency_resolvers.py b/parsl/parsl/dataflow/dependency_resolvers.py deleted file mode 100644 index a2987cd7..00000000 --- a/parsl/parsl/dataflow/dependency_resolvers.py +++ /dev/null @@ -1,115 +0,0 @@ -from concurrent.futures import Future -from dataclasses import dataclass -from functools import singledispatch -from typing import Callable, Sequence - - -@dataclass -class DependencyResolver: - """A DependencyResolver describes how app dependencies can be resolved. - It is specified as two functions: `traverse_to_gather` which turns an - app parameter into a sequence of futures which must be waited for before - the task can be executed (for example, in the case of - `DEEP_DEPENDENCY_RESOLVER` this traverses structures such as lists to - find every contained ``Future``), and `traverse_to_unwrap` which turns an - app parameter into its value to be passed to the app on execution - (for example in the case of `DEEP_DEPENDENCY_RESOLVER` this replaces a - list containing futures with a new list containing the values of those - resolved futures). - - By default, Parsl will use `SHALLOW_DEPENDENCY_RESOLVER` which only - resolves Futures passed directly as arguments. - """ - traverse_to_gather: Callable[[object], Sequence[Future]] - traverse_to_unwrap: Callable[[object], object] - - -@singledispatch -def shallow_traverse_to_gather(o): - # objects in general do not expose futures that we can see - return [] - - -@singledispatch -def shallow_traverse_to_unwrap(o): - # objects in general unwrap to themselves - return o - - -@shallow_traverse_to_gather.register -def _(fut: Future): - return [fut] - - -@shallow_traverse_to_unwrap.register -@singledispatch -def _(fut: Future): - assert fut.done() - return fut.result() - - -@singledispatch -def deep_traverse_to_gather(o): - # objects in general do not expose futures that we can see - return [] - - -@singledispatch -def deep_traverse_to_unwrap(o): - # objects in general unwrap to themselves - return o - - -@deep_traverse_to_gather.register -def _(fut: Future): - return [fut] - - -@deep_traverse_to_unwrap.register -@singledispatch -def _(fut: Future): - assert fut.done() - return fut.result() - - -@deep_traverse_to_gather.register(tuple) -@deep_traverse_to_gather.register(list) -@deep_traverse_to_gather.register(set) -def _(iterable): - return [e for v in iterable for e in deep_traverse_to_gather(v)] - - -@deep_traverse_to_unwrap.register(tuple) -@deep_traverse_to_unwrap.register(list) -@deep_traverse_to_unwrap.register(set) -@singledispatch -def _(iterable): - - type_ = type(iterable) - return type_(map(deep_traverse_to_unwrap, iterable)) - - -@deep_traverse_to_gather.register(dict) -def _(dictionary): - futures = [] - for key, value in dictionary.items(): - futures.extend(deep_traverse_to_gather(key)) - futures.extend(deep_traverse_to_gather(value)) - return futures - - -@deep_traverse_to_unwrap.register(dict) -def _(dictionary): - unwrapped_dict = {} - for key, value in dictionary.items(): - key = deep_traverse_to_unwrap(key) - value = deep_traverse_to_unwrap(value) - unwrapped_dict[key] = value - return unwrapped_dict - - -DEEP_DEPENDENCY_RESOLVER = DependencyResolver(traverse_to_gather=deep_traverse_to_gather, - traverse_to_unwrap=deep_traverse_to_unwrap) - -SHALLOW_DEPENDENCY_RESOLVER = DependencyResolver(traverse_to_gather=shallow_traverse_to_gather, - traverse_to_unwrap=shallow_traverse_to_unwrap) diff --git a/parsl/parsl/dataflow/dflow.py b/parsl/parsl/dataflow/dflow.py deleted file mode 100644 index f1a120d7..00000000 --- a/parsl/parsl/dataflow/dflow.py +++ /dev/null @@ -1,1331 +0,0 @@ -from __future__ import annotations - -import atexit -import concurrent.futures as cf -import datetime -import inspect -import logging -import os -import pathlib -import random -import sys -import threading -import time -from concurrent.futures import Future -from functools import partial -from getpass import getuser -from socket import gethostname -from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union -from uuid import uuid4 - -import typeguard -from typeguard import typechecked - -import parsl -from parsl.app.errors import RemoteExceptionWrapper -from parsl.app.futures import DataFuture -from parsl.config import Config -from parsl.data_provider.data_manager import DataManager -from parsl.data_provider.files import File -from parsl.dataflow.dependency_resolvers import SHALLOW_DEPENDENCY_RESOLVER -from parsl.dataflow.errors import DependencyError, JoinError -from parsl.dataflow.futures import AppFuture -from parsl.dataflow.memoization import BasicMemoizer, Memoizer -from parsl.dataflow.rundirs import make_rundir -from parsl.dataflow.states import FINAL_FAILURE_STATES, FINAL_STATES, States -from parsl.dataflow.taskrecord import TaskRecord -from parsl.errors import ( - ConfigurationError, - InternalConsistencyError, - NoDataFlowKernelError, -) -from parsl.executors.base import ParslExecutor -from parsl.executors.status_handling import BlockProviderExecutor -from parsl.executors.threads import ThreadPoolExecutor -from parsl.jobs.job_status_poller import JobStatusPoller -from parsl.logconfigs.base import ( - LogConfig, - oneshot_initialize_logging, - oneshot_uninitialize_logging, -) -from parsl.logconfigs.file import FileLogging -from parsl.monitoring import MonitoringHub -from parsl.monitoring.errors import RadioRequiredError -from parsl.monitoring.message_type import MessageType -from parsl.monitoring.radios.multiprocessing import MultiprocessingQueueRadioSender -from parsl.monitoring.remote import monitor_wrapper -from parsl.process_loggers import wrap_with_logs -from parsl.usage_tracking.usage import UsageTracker -from parsl.utils import get_std_fname_mode, get_version - -logger = logging.getLogger(__name__) - - -class DataFlowKernel: - """The DataFlowKernel adds dependency awareness to an existing executor. - - It is responsible for managing futures, such that when dependencies are resolved, - pending tasks move to the runnable state. - - Here is a simplified diagram of what happens internally:: - - User | DFK | Executor - ---------------------------------------------------------- - | | - Task-------+> +Submit | - App_Fu<------+--| | - | Dependencies met | - | task-------+--> +Submit - | Ex_Fu<------+----| - - """ - - @typechecked - def __init__(self, config: Config) -> None: - """Initialize the DataFlowKernel. - - Parameters - ---------- - config : Config - A specification of all configuration options. For more details see the - :class:~`parsl.config.Config` documentation. - """ - - # this will be used to check cleanup only happens once - self.cleanup_called = False - - self._config = config - self.run_dir = make_rundir(config.run_dir) - - self._logging_unregister_callback: Optional[Callable[[], None]] - - self.log_config: Optional[LogConfig] - - if config.initialize_logging is True: - # This legacy behaviour deliberately does not pass log configuration - # to other components, so that they can preserve their own legacy - # logging behaviour. - self.log_config = None - dfk_log_config = FileLogging(level=logging.DEBUG) - self._logging_unregister_callback = dfk_log_config.initialize_logging(log_dir=pathlib.Path(self.run_dir), log_name="parsl") - elif config.initialize_logging is False: - self.log_config = None - self._logging_unregister_callback = None - else: - c = config.initialize_logging - assert c is not None, "type of initialize_logging was not properly narrowed to LogConfig" - self.log_config = config.initialize_logging - oneshot_initialize_logging(log_config=c, - log_dir=pathlib.Path(self.run_dir), - log_name="parsl") - self._logging_unregister_callback = lambda: oneshot_uninitialize_logging(log_config=c) - - logger.info("Starting DataFlowKernel with config\n%r", config) - - logger.info("Parsl version: %s", get_version()) - self.usage_tracker = UsageTracker(self) - self.usage_tracker.send_start_message() - - self.task_state_counts_lock = threading.Lock() - self.task_state_counts = {state: 0 for state in States} - - # Monitoring - self.run_id = str(uuid4()) - - self.monitoring: Optional[MonitoringHub] - self.monitoring = config.monitoring - - self.monitoring_radio = None - - if self.monitoring: - self.monitoring.start(self.run_dir, self.config.run_dir, self.log_config) - self.monitoring_radio = MultiprocessingQueueRadioSender(self.monitoring.resource_msgs) - - time_began = datetime.datetime.now() - - logger.info("Run id is: " + self.run_id) - - self.workflow_name = None - if self.monitoring is not None and self.monitoring.workflow_name is not None: - self.workflow_name = self.monitoring.workflow_name - else: - for frame in inspect.stack(): - logger.debug("Considering candidate for workflow name: %s", frame.filename) - fname = os.path.basename(str(frame.filename)) - parsl_file_names = ['dflow.py', 'typeguard.py', '__init__.py'] - # Find first file name not considered a parsl file - if fname not in parsl_file_names: - self.workflow_name = fname - logger.debug("Using %s as workflow name", fname) - break - else: - logger.debug("Could not choose a name automatically") - self.workflow_name = "unnamed" - - self.workflow_version = str(time_began.replace(microsecond=0)) - if self.monitoring is not None and self.monitoring.workflow_version is not None: - self.workflow_version = self.monitoring.workflow_version - - workflow_info = { - 'python_version': "{}.{}.{}".format(sys.version_info.major, - sys.version_info.minor, - sys.version_info.micro), - 'parsl_version': get_version(), - "time_began": time_began, - 'run_id': self.run_id, - 'workflow_name': self.workflow_name, - 'workflow_version': self.workflow_version, - 'rundir': self.run_dir, - 'tasks_completed_count': self.task_state_counts[States.exec_done], - 'tasks_failed_count': self.task_state_counts[States.failed], - 'user': getuser(), - 'host': gethostname(), - } - - loggable_workflow_info = {"parsl.workflow_info." + k: v for k, v in workflow_info.items()} - logger.info("Workflow startup information: %r", workflow_info, extra={"parsl.dfk": self.run_id} | loggable_workflow_info) - if self.monitoring_radio: - self.monitoring_radio.send((MessageType.WORKFLOW_INFO, - workflow_info)) - - self.memoizer: Memoizer = config.memoizer if config.memoizer is not None else BasicMemoizer() - self.memoizer.start(run_dir=self.run_dir, config_run_dir=self.config.run_dir) - - # this must be set before executors are added since add_executors calls - # job_status_poller.add_executors. - self.job_status_poller = JobStatusPoller(strategy=self.config.strategy, - strategy_period=self.config.strategy_period, - max_idletime=self.config.max_idletime) - - self.executors: Dict[str, ParslExecutor] = {} - - self.data_manager = DataManager(self) - parsl_internal_executor = ThreadPoolExecutor(max_threads=config.internal_tasks_max_threads, - label='_parsl_internal') - self.add_executors(config.executors) - self.add_executors([parsl_internal_executor]) - - self.task_count = 0 - self.tasks: Dict[int, TaskRecord] = {} - self.submitter_lock = threading.Lock() - - self._task_launch_pool = cf.ThreadPoolExecutor(max_workers=1, thread_name_prefix="Task-Launch") - - self.dependency_resolver = self.config.dependency_resolver if self.config.dependency_resolver is not None \ - else SHALLOW_DEPENDENCY_RESOLVER - - atexit.register(self.atexit_cleanup) - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_value, traceback) -> None: - mode = self.config.exit_mode - logger.debug("Exiting context manager, with exit mode '%s'", mode) - if mode == "cleanup": - logger.info("Calling cleanup for DFK") - self.cleanup() - elif mode == "skip": - logger.info("Skipping all cleanup handling") - elif mode == "wait": - if exc_type is None: - logger.info("Waiting for all tasks to complete") - self.wait_for_current_tasks() - self.cleanup() - else: - logger.info("There was an exception - cleaning up without waiting for task completion") - self.cleanup() - else: - raise InternalConsistencyError(f"Exit case for {mode} should be unreachable, validated by typeguard on Config()") - - def _send_task_info(self, task_record: TaskRecord) -> None: - if self.monitoring_radio: - task_log_info = self._create_task_log_info(task_record) - self.monitoring_radio.send((MessageType.TASK_INFO, task_log_info)) - - def _create_task_log_info(self, task_record: TaskRecord) -> Dict[str, Any]: - """ - Create the dictionary that will be included in the log. - """ - info_to_monitor = ['func_name', 'memoize', 'hashsum', 'fail_count', 'fail_cost', 'status', - 'id', 'time_invoked', 'try_time_launched', 'time_returned', 'try_time_returned', 'executor'] - - # mypy cannot verify that these task_record[k] references are valid: - # They are valid if all entries in info_to_monitor are declared in the definition of TaskRecord - # This type: ignore[literal-required] asserts that fact. - task_log_info = {"task_" + k: task_record[k] for k in info_to_monitor} # type: ignore[literal-required] - - task_log_info['run_id'] = self.run_id - task_log_info['try_id'] = task_record['try_id'] - task_log_info['timestamp'] = datetime.datetime.now() - task_log_info['task_status_name'] = task_record['status'].name - task_log_info['tasks_failed_count'] = self.task_state_counts[States.failed] - task_log_info['tasks_completed_count'] = self.task_state_counts[States.exec_done] - task_log_info['tasks_memo_completed_count'] = self.task_state_counts[States.memo_done] - task_log_info['from_memo'] = task_record['from_memo'] - task_log_info['task_inputs'] = str(task_record['kwargs'].get('inputs', None)) - task_log_info['task_outputs'] = str(task_record['kwargs'].get('outputs', None)) - task_log_info['task_stdin'] = task_record['kwargs'].get('stdin', None) - - def std_spec_to_name(name, spec): - if spec is None: - name = "" - elif isinstance(spec, File): - name = spec.url - else: - # fallthrough case is various str, os.PathLike, tuple modes that - # can be interpreted by get_std_fname_mode. - try: - name, _ = get_std_fname_mode(name, spec) - except Exception: - logger.exception(f"Could not parse {name} specification {spec} for task {task_record['id']}") - name = "" - return name - - stdout_spec = task_record['kwargs'].get('stdout') - task_log_info['task_stdout'] = std_spec_to_name('stdout', stdout_spec) - - stderr_spec = task_record['kwargs'].get('stderr') - task_log_info['task_stderr'] = std_spec_to_name('stderr', stderr_spec) - - task_log_info['task_fail_history'] = ",".join(task_record['fail_history']) - task_log_info['task_depends'] = None - if task_record['depends'] is not None: - task_log_info['task_depends'] = ",".join([str(t.tid) for t in task_record['depends'] - if isinstance(t, AppFuture) or isinstance(t, DataFuture)]) - task_log_info['task_joins'] = None - - if isinstance(task_record['joins'], list): - task_log_info['task_joins'] = ",".join([str(t.tid) for t in task_record['joins'] - if isinstance(t, AppFuture) or isinstance(t, DataFuture)]) - elif isinstance(task_record['joins'], Future): - task_log_info['task_joins'] = ",".join([str(t.tid) for t in [task_record['joins']] - if isinstance(t, AppFuture) or isinstance(t, DataFuture)]) - - return task_log_info - - def _count_deps(self, depends: Sequence[Future]) -> int: - """Count the number of unresolved futures in the list depends. - """ - count = 0 - for dep in depends: - if not dep.done(): - count += 1 - - return count - - @property - def config(self) -> Config: - """Returns the fully initialized config that the DFK is actively using. - - Returns: - - Config object - """ - return self._config - - def handle_exec_update(self, task_record: TaskRecord, future: Future) -> None: - """This function is called only as a callback from an execution - attempt reaching a final state (either successfully or failing). - - It will launch retries if necessary, and update the task - structure. - - Args: - task_record (dict) : Task record - future (Future) : The future object corresponding to the task which - makes this callback - """ - task_id = task_record['id'] - - task_record['try_time_returned'] = datetime.datetime.now() - - if not future.done(): - raise InternalConsistencyError("done callback called, despite future not reporting itself as done") - - try: - res = self._unwrap_remote_exception_wrapper(future) - - except Exception as e: - logger.info(f"Task {task_id} try {task_record['try_id']} failed with exception of type {type(e).__name__}") - # We keep the history separately, since the future itself could be - # tossed. - task_record['fail_history'].append(repr(e)) - task_record['fail_count'] += 1 - if self._config.retry_handler: - try: - cost = self._config.retry_handler(e, task_record) - except Exception as retry_handler_exception: - logger.exception("retry_handler raised an exception - will not retry") - - # this can be any amount > self._config.retries, to stop any more - # retries from happening - task_record['fail_cost'] = self._config.retries + 1 - - # make the reported exception be the retry handler's exception, - # rather than the execution level exception - e = retry_handler_exception - else: - task_record['fail_cost'] += cost - else: - task_record['fail_cost'] += 1 - - if isinstance(e, DependencyError): - logger.info("Task %s failed due to dependency failure so skipping retries", task_id) - self._complete_task_exception(task_record, States.dep_fail, e) - - elif task_record['fail_cost'] <= self._config.retries: - - # record the final state for this try before we mutate for retries - self._update_task_state(task_record, States.fail_retryable) - - task_record['try_id'] += 1 - task_record['try_time_launched'] = None - task_record['try_time_returned'] = None - task_record['fail_history'] = [] - self._update_task_state(task_record, States.pending) - - else: - logger.exception("Task %s failed after %s retry attempts", task_id, task_record['try_id']) - self._complete_task_exception(task_record, States.failed, e) - - else: - if task_record['from_memo']: - self._complete_task_result(task_record, States.memo_done, res) - elif not task_record['join']: - self._complete_task_result(task_record, States.exec_done, res) - else: - # This is a join task, and the original task's function code has - # completed. That means that the future returned by that code - # will be available inside the executor future, so we can now - # record the inner app ID in monitoring, and add a completion - # listener to that inner future. - - joinable = future.result() - - # Fail with a TypeError if the joinapp python body returned - # something we can't join on. - if isinstance(joinable, Future): - task_record['joins'] = joinable - task_record['join_lock'] = threading.Lock() - self._update_task_state(task_record, States.joining) - joinable.add_done_callback(partial(self.handle_join_update, task_record)) - elif joinable == []: # got a list, but it had no entries, and specifically, no Futures. - task_record['joins'] = joinable - task_record['join_lock'] = threading.Lock() - self._update_task_state(task_record, States.joining) - self.handle_join_update(task_record, None) - elif isinstance(joinable, list) and [j for j in joinable if not isinstance(j, Future)] == []: - task_record['joins'] = joinable - task_record['join_lock'] = threading.Lock() - self._update_task_state(task_record, States.joining) - for inner_future in joinable: - inner_future.add_done_callback(partial(self.handle_join_update, task_record)) - else: - self._complete_task_exception( - task_record, - States.failed, - TypeError(f"join_app body must return a Future or list of Futures, got {joinable} of type {type(joinable)}")) - - self._log_std_streams(task_record) - - # it might be that in the course of the update, we've gone back to being - # pending - in which case, we should consider ourself for relaunch - if task_record['status'] == States.pending: - self.launch_if_ready(task_record) - - def handle_join_update(self, task_record: TaskRecord, inner_app_future: Optional[AppFuture]) -> None: - with task_record['join_lock']: - # inner_app_future has completed, which is one (potentially of many) - # futures the outer task is joining on. - - # If the outer task is joining on a single future, then - # use the result of the inner_app_future as the final result of - # the outer app future. - - # If the outer task is joining on a list of futures, then - # check if the list is all done, and if so, return a list - # of the results. Otherwise, this callback can do nothing and - # processing will happen in another callback (on the final Future - # to complete) - - # There is no retry handling here: inner apps are responsible for - # their own retrying, and joining state is responsible for passing - # on whatever the result of that retrying was (if any). - - outer_task_id = task_record['id'] - logger.debug(f"Join callback for task {outer_task_id}, inner_app_future {inner_app_future}") - - if task_record['status'] != States.joining: - logger.debug(f"Join callback for task {outer_task_id} skipping because task is not in joining state") - return - - joinable = task_record['joins'] - - if isinstance(joinable, list): - for future in joinable: - if not future.done(): - logger.debug(f"A joinable future {future} is not done for task {outer_task_id} - skipping callback") - return # abandon this callback processing if joinables are not all done - - # now we know each joinable Future is done - # so now look for any exceptions - exceptions_tids: List[Tuple[BaseException, str]] - exceptions_tids = [] - if isinstance(joinable, Future): - je = joinable.exception() - if je is not None: - tid = self.render_future_description(joinable) - exceptions_tids = [(je, tid)] - elif isinstance(joinable, list): - for future in joinable: - je = future.exception() - if je is not None: - tid = self.render_future_description(future) - exceptions_tids.append((je, tid)) - else: - raise TypeError(f"Unknown joinable type {type(joinable)}") - - if exceptions_tids: - logger.debug("Task %s failed due to failure of an inner join future", outer_task_id) - e = JoinError(exceptions_tids, outer_task_id) - # We keep the history separately, since the future itself could be - # tossed. - task_record['fail_history'].append(repr(e)) - task_record['fail_count'] += 1 - # no need to update the fail cost because join apps are never - # retried - - self._complete_task_exception(task_record, States.failed, e) - - else: - # all the joinables succeeded, so construct a result: - if isinstance(joinable, Future): - assert inner_app_future is joinable - res = joinable.result() - elif isinstance(joinable, list): - res = [] - for future in joinable: - res.append(future.result()) - else: - raise TypeError(f"Unknown joinable type {type(joinable)}") - self._complete_task_result(task_record, States.exec_done, res) - - self._log_std_streams(task_record) - - def _complete_task_result(self, task_record: TaskRecord, new_state: States, result: Any) -> None: - """Set a task into a completed state - """ - assert new_state in FINAL_STATES - assert new_state not in FINAL_FAILURE_STATES - - task_record['time_returned'] = datetime.datetime.now() - - self.memoizer.update_memo_result(task_record, result) - - self._update_task_state(task_record, new_state) - - self.wipe_task(task_record['id']) - - with task_record['app_fu']._update_lock: - task_record['app_fu'].set_result(result) - - def _complete_task_exception(self, task_record: TaskRecord, new_state: States, exception: BaseException) -> None: - """Set a task into a failure state - """ - assert new_state in FINAL_STATES - assert new_state in FINAL_FAILURE_STATES - - task_record['time_returned'] = datetime.datetime.now() - - self.memoizer.update_memo_exception(task_record, exception) - - self._update_task_state(task_record, new_state) - - self.wipe_task(task_record['id']) - - with task_record['app_fu']._update_lock: - task_record['app_fu'].set_exception(exception) - - def _update_task_state(self, task_record: TaskRecord, new_state: States) -> None: - """Updates a task record state including accompanying consistency-keeping: - - * record change in task state counters - * record change in monitoring - """ - - with self.task_state_counts_lock: - extra = {"parsl.dfk": self.run_id, - "parsl.task": task_record['id'], - "parsl.task_state": new_state.name - } - if 'status' in task_record: - self.task_state_counts[task_record['status']] -= 1 - logger.info(f"Task {task_record['id']} changing state from {task_record['status'].name} to {new_state.name}", extra=extra) - else: - logger.info(f"Task {task_record['id']} initializing state to {new_state.name}", extra=extra) - - self.task_state_counts[new_state] += 1 - task_record['status'] = new_state - - self._send_task_info(task_record) - - @staticmethod - def _unwrap_remote_exception_wrapper(future: Future) -> Any: - result = future.result() - if isinstance(result, RemoteExceptionWrapper): - result.reraise() - return result - - def wipe_task(self, task_id: int) -> None: - """Remove task with task_id from the internal tasks table - """ - if self.config.garbage_collect: - del self.tasks[task_id] - - @staticmethod - def check_staging_inhibited(kwargs: Dict[str, Any]) -> bool: - return kwargs.get('_parsl_staging_inhibit', False) - - def launch_if_ready(self, task_record: TaskRecord) -> None: - """Schedules a task record for re-inspection to see if it is ready - for launch and for launch if it is ready. The call will return - immediately. - - This should be called by any piece of the DataFlowKernel that - thinks a task may have become ready to run. - - It is not an error to call launch_if_ready on a task that is not - ready to run - launch_if_ready will not incorrectly launch that - task. - - launch_if_ready is thread safe, so may be called from any thread - or callback. - """ - return self._task_launch_pool.submit(self._launch_if_ready_async, task_record) - - @wrap_with_logs - def _launch_if_ready_async(self, task_record: TaskRecord) -> None: - """ - _launch_if_ready will launch the specified task, if it is ready - to run (for example, without dependencies, and in pending state). - """ - exec_fu: Future - - task_id = task_record['id'] - with task_record['task_launch_lock']: - - if task_record['status'] != States.pending: - logger.debug(f"Task {task_id} is not pending, so launch_if_ready skipping") - return - - if self._count_deps(task_record['depends']) != 0: - logger.debug(f"Task {task_id} has outstanding dependencies, so launch_if_ready skipping") - return - - # We can now launch the task or handle any dependency failures - - new_args, kwargs, exceptions_tids = self._unwrap_futures(task_record['args'], - task_record['kwargs']) - task_record['args'] = new_args - task_record['kwargs'] = kwargs - - if not exceptions_tids: - # There are no dependency errors - try: - exec_fu = self.launch_task(task_record) - assert isinstance(exec_fu, Future) - except Exception as e: - # task launched failed somehow. the execution might - # have been launched and an exception raised after - # that, though. that's hard to detect from here. - # we don't attempt retries here. This is an error with submission - # even though it might come from user code such as a plugged-in - # executor or memoization hash function. - - logger.debug("Got an exception launching task", exc_info=True) - exec_fu = Future() - exec_fu.set_exception(e) - else: - logger.info("Task %s failed due to dependency failure", task_id) - - exec_fu = Future() - exec_fu.set_exception(DependencyError(exceptions_tids, - task_id)) - - assert isinstance(exec_fu, Future), "Every code path leading here needs to define exec_fu" - - try: - exec_fu.add_done_callback(partial(self.handle_exec_update, task_record)) - except Exception: - # this exception is ignored here because it is assumed that exception - # comes from directly executing handle_exec_update (because exec_fu is - # done already). If the callback executes later, then any exception - # coming out of the callback will be ignored and not propate anywhere, - # so this block attempts to keep the same behaviour here. - logger.error("add_done_callback got an exception which will be ignored", exc_info=True) - - task_record['exec_fu'] = exec_fu - - def launch_task(self, task_record: TaskRecord) -> Future: - """Handle the actual submission of the task to the executor layer. - - Args: - task_record : The task record - - Returns: - Future that tracks the execution of the submitted function - """ - task_id = task_record['id'] - function = task_record['func'] - args = task_record['args'] - kwargs = task_record['kwargs'] - - task_record['try_time_launched'] = datetime.datetime.now() - - memo_fu = self.memoizer.check_memo(task_record) - if memo_fu: - logger.info("Reusing cached result for task %s", task_id) - task_record['from_memo'] = True - assert isinstance(memo_fu, Future) - return memo_fu - - task_record['from_memo'] = False - executor_label = task_record["executor"] - try: - executor = self.executors[executor_label] - except Exception: - logger.exception("Task %s requested invalid executor %s", task_id, executor_label) - raise ValueError("Task {} requested invalid executor {}".format(task_id, executor_label)) - - try_id = task_record['fail_count'] - - if self.monitoring is not None and self.monitoring.resource_monitoring_enabled: - if executor.remote_monitoring_radio is None: - raise RadioRequiredError() - - wrapper_logging_level = logging.DEBUG if self.monitoring.monitoring_debug else logging.INFO - (function, args, kwargs) = monitor_wrapper(f=function, - args=args, - kwargs=kwargs, - x_try_id=try_id, - x_task_id=task_id, - radio_config=executor.remote_monitoring_radio, - run_id=self.run_id, - logging_level=wrapper_logging_level, - sleep_dur=self.monitoring.resource_monitoring_interval, - monitor_resources=executor.monitor_resources(), - run_dir=self.run_dir) - - with self.submitter_lock: - exec_fu = executor.submit(function, task_record['resource_specification'], *args, **kwargs) - - self._update_task_state(task_record, States.launched) - - extra = {"parsl.dfk": self.run_id, - "parsl.task": task_id, - "parsl.try": try_id, - "parsl.executor": executor.label - } - - if hasattr(exec_fu, "parsl_executor_task_id"): - extra['parsl.executor_task'] = exec_fu.parsl_executor_task_id - logger.info( - f"Parsl task {task_id} try {try_id} launched on executor {executor.label} " - f"with executor id {exec_fu.parsl_executor_task_id}", extra=extra) - - else: - logger.info(f"Parsl task {task_id} try {try_id} launched on executor {executor.label}", extra=extra) - - self._log_std_streams(task_record) - - return exec_fu - - def _add_input_deps(self, executor: str, args: Sequence[Any], kwargs: Dict[str, Any], func: Callable) -> Tuple[Sequence[Any], Dict[str, Any], - Callable]: - """Look for inputs of the app that are files. Give the data manager - the opportunity to replace a file with a data future for that file, - for example wrapping the result of a staging action. - - Args: - - executor (str) : executor where the app is going to be launched - - args (List) : Positional args to app function - - kwargs (Dict) : Kwargs to app function - """ - - # Return if the task is a data management task, rather than doing - # data management on it. - if self.check_staging_inhibited(kwargs): - logger.debug("Not performing input staging") - return args, kwargs, func - - inputs = kwargs.get('inputs', []) - for idx, f in enumerate(inputs): - (inputs[idx], func) = self.data_manager.optionally_stage_in(f, func, executor) - - for kwarg, f in kwargs.items(): - # stdout and stderr files should not be staging in (they will be staged *out* - # in _add_output_deps) - if kwarg in ['stdout', 'stderr']: - continue - (kwargs[kwarg], func) = self.data_manager.optionally_stage_in(f, func, executor) - - newargs = list(args) - for idx, f in enumerate(newargs): - (newargs[idx], func) = self.data_manager.optionally_stage_in(f, func, executor) - - return tuple(newargs), kwargs, func - - def _add_output_deps(self, executor: str, args: Sequence[Any], kwargs: Dict[str, Any], app_fut: AppFuture, func: Callable) -> Callable: - logger.debug("Adding output dependencies") - outputs = kwargs.get('outputs', []) - app_fut._outputs = [] - - # Pass over all possible outputs: the outputs kwarg, stdout and stderr - # and for each of those, perform possible stage-out. This can result in: - # a DataFuture to be exposed in app_fut to represent the completion of - # that stageout (sometimes backed by a new sub-workflow for separate-task - # stageout), a replacement for the function to be executed (intended to - # be the original function wrapped with an in-task stageout wrapper), a - # rewritten File object to be passed to task to be executed - - def stageout_one_file(file: File, rewritable_func: Callable): - if not self.check_staging_inhibited(kwargs): - # replace a File with a DataFuture - either completing when the stageout - # future completes, or if no stage out future is returned, then when the - # app itself completes. - - # The staging code will get a clean copy which it is allowed to mutate, - # while the DataFuture-contained original will not be modified by any staging. - f_copy = file.cleancopy() - - logger.debug("Submitting stage out for output file %r", file) - stageout_fut = self.data_manager.stage_out(f_copy, executor, app_fut) - if stageout_fut: - logger.debug("Adding a dependency on stageout future for %r", file) - df = DataFuture(stageout_fut, file, tid=app_fut.tid) - else: - logger.debug("No stageout dependency for %r", file) - df = DataFuture(app_fut, file, tid=app_fut.tid) - - # this is a hook for post-task stageout - # note that nothing depends on the output - which is maybe a bug - # in the not-very-tested stageout system? - rewritable_func = self.data_manager.replace_task_stage_out(f_copy, rewritable_func, executor) - return rewritable_func, f_copy, df - else: - logger.debug("Not performing output staging for: %r", file) - return rewritable_func, file, DataFuture(app_fut, file, tid=app_fut.tid) - - for idx, file in enumerate(outputs): - func, outputs[idx], o = stageout_one_file(file, func) - app_fut._outputs.append(o) - - file = kwargs.get('stdout') - if isinstance(file, File): - func, kwargs['stdout'], app_fut._stdout_future = stageout_one_file(file, func) - - file = kwargs.get('stderr') - if isinstance(file, File): - func, kwargs['stderr'], app_fut._stderr_future = stageout_one_file(file, func) - - return func - - def _gather_all_deps(self, args: Sequence[Any], kwargs: Dict[str, Any]) -> List[Future]: - """Assemble a list of all Futures passed as arguments, kwargs or in the inputs kwarg. - - Args: - - args: The list of args pass to the app - - kwargs: The dict of all kwargs passed to the app - - Returns: - - list of dependencies - - """ - depends: List[Future] = [] - - def check_dep(d: Any) -> None: - try: - depends.extend(self.dependency_resolver.traverse_to_gather(d)) - except Exception: - logger.exception("Exception in dependency_resolver.traverse_to_gather") - raise - - # Check the positional args - for dep in args: - check_dep(dep) - - # Check for explicit kwargs ex, fu_1= - for key in kwargs: - dep = kwargs[key] - check_dep(dep) - - # Check for futures in inputs=[...] - for dep in kwargs.get('inputs', []): - check_dep(dep) - - return depends - - def _unwrap_futures(self, args: Sequence[Any], kwargs: Dict[str, Any]) \ - -> Tuple[Sequence[Any], Dict[str, Any], Sequence[Tuple[Exception, str]]]: - """This function should be called when all dependencies have completed. - - It will rewrite the arguments for that task, replacing each Future - with the result of that future. - - If the user hid futures a level below, we will not catch - it, and will (most likely) result in a type error. - - Args: - args (List) : Positional args to app function - kwargs (Dict) : Kwargs to app function - - Return: - a rewritten args list - a rewritten kwargs dict - pairs of exceptions, task ids from any Futures which stored - exceptions rather than results. - """ - dep_failures = [] - - def append_failure(e: Exception, dep: Future) -> None: - tid = self.render_future_description(dep) - dep_failures.extend([(e, tid)]) - - # Replace item in args - new_args = [] - for dep in args: - try: - new_args.extend([self.dependency_resolver.traverse_to_unwrap(dep)]) - except Exception as e: - append_failure(e, dep) - - # Check for explicit kwargs ex, fu_1= - for key in kwargs: - dep = kwargs[key] - try: - kwargs[key] = self.dependency_resolver.traverse_to_unwrap(dep) - except Exception as e: - append_failure(e, dep) - - # Check for futures in inputs=[...] - if 'inputs' in kwargs: - new_inputs = [] - for dep in kwargs['inputs']: - try: - new_inputs.extend([self.dependency_resolver.traverse_to_unwrap(dep)]) - except Exception as e: - append_failure(e, dep) - kwargs['inputs'] = new_inputs - - return new_args, kwargs, dep_failures - - def submit(self, - func: Callable, - app_args: Sequence[Any], - executors: Union[str, Sequence[str]], - cache: bool, - ignore_for_cache: Optional[Sequence[str]], - app_kwargs: Dict[str, Any], - join: bool = False) -> AppFuture: - """Add task to the dataflow system. - - If the app task has the executors attributes not set (default=='all') - the task will be launched on a randomly selected executor from the - list of executors. If the app task specifies a particular set of - executors, it will be targeted at the specified executors. - - Args: - - func : A function object - - KWargs : - - app_args : Args to the function - - executors (list or string) : List of executors this call could go to. - Default='all' - - cache (Bool) : To enable memoization or not - - ignore_for_cache (sequence) : List of kwargs to be ignored for memoization/checkpointing - - app_kwargs (dict) : Rest of the kwargs to the fn passed as dict. - - Returns: - AppFuture - - """ - - if ignore_for_cache is None: - ignore_for_cache = [] - else: - # duplicate so that it can be modified safely later - ignore_for_cache = list(ignore_for_cache) - - if self.cleanup_called: - raise NoDataFlowKernelError("Cannot submit to a DFK that has been cleaned up") - - task_id = self.task_count - self.task_count += 1 - if isinstance(executors, str) and executors.lower() == 'all': - choices = list(e for e in self.executors if e != '_parsl_internal') - elif isinstance(executors, list): - choices = executors - else: - raise ValueError("Task {} supplied invalid type for executors: {}".format(task_id, type(executors))) - executor = random.choice(choices) - logger.debug("Task %s will be sent to executor %s", task_id, executor) - - resource_specification = app_kwargs.get('parsl_resource_specification', {}) - - task_record: TaskRecord - task_record = {'args': app_args, - 'depends': [], - 'dfk': self, - 'executor': executor, - 'func': func, - 'func_name': func.__name__, - 'kwargs': app_kwargs, - 'memoize': cache, - 'hashsum': None, - 'exec_fu': None, - 'fail_count': 0, - 'fail_cost': 0, - 'fail_history': [], - 'from_memo': None, - 'ignore_for_cache': ignore_for_cache, - 'join': join, - 'joins': None, - 'try_id': 0, - 'id': task_id, - 'task_launch_lock': threading.Lock(), - 'time_invoked': datetime.datetime.now(), - 'time_returned': None, - 'try_time_launched': None, - 'try_time_returned': None, - 'resource_specification': resource_specification} - - for kw in ['stdout', 'stderr']: - if kw in app_kwargs: - if app_kwargs[kw] == parsl.AUTO_LOGNAME: - if kw not in ignore_for_cache: - ignore_for_cache += [kw] - if self.config.std_autopath is None: - app_kwargs[kw] = self.default_std_autopath(task_record, kw) - else: - app_kwargs[kw] = self.config.std_autopath(task_record, kw) - - app_fu = AppFuture(task_record) - task_record['app_fu'] = app_fu - - # Transform remote input files to data futures - app_args, app_kwargs, func = self._add_input_deps(executor, app_args, app_kwargs, func) - - func = self._add_output_deps(executor, app_args, app_kwargs, app_fu, func) - - logger.debug("Added output dependencies") - - # Replace the function invocation in the TaskRecord with whatever file-staging - # substitutions have been made. - task_record.update({ - 'args': app_args, - 'func': func, - 'kwargs': app_kwargs}) - - logger.debug("Gathering dependencies") - # Get the list of dependencies for the task - depends = self._gather_all_deps(app_args, app_kwargs) - logger.debug("Gathered dependencies") - task_record['depends'] = depends - - depend_descs = [] - for d in depends: - if isinstance(d, AppFuture) and d.task_record['dfk'] == self: - logger.info("Task %s has dependency task %s", task_id, d.task_record['id'], - extra={"parsl.task": task_id, "parsl.dependency_task": d.task_record['id']}) - else: - logger.info("Task %s has dependency future %r", task_id, d, - extra={"parsl.task": task_id, "parsl.dependency_object": repr(d)}) - - depend_descs.append(self.render_future_description(d)) - - if depend_descs != []: - waiting_message = "waiting on {}".format(", ".join(depend_descs)) - else: - waiting_message = "not waiting on any dependency" - - logger.info("Task %s submitted for App %s, %s", task_id, task_record['func_name'], waiting_message, - extra={"parsl.task": task_id, "parsl.app_name": task_record['func_name']}) - - logger.debug("Task %s has AppFuture: %r", task_id, task_record['app_fu']) - self._update_task_state(task_record, States.pending) - - assert task_id not in self.tasks - self.tasks[task_id] = task_record - - # at this point add callbacks to all dependencies to do a launch_if_ready - # call whenever a dependency completes. - - # we need to be careful about the order of setting the state to pending, - # adding the callbacks, and caling launch_if_ready explicitly once always below. - - # I think as long as we call launch_if_ready once after setting pending, then - # we can add the callback dependencies at any point: if the callbacks all fire - # before then, they won't cause a launch, but the one below will. if they fire - # after we set it pending, then the last one will cause a launch, and the - # explicit one won't. - - for d in depends: - - def callback_adapter(dep_fut: Future) -> None: - self.launch_if_ready(task_record) - - try: - d.add_done_callback(callback_adapter) - except Exception as e: - logger.error("add_done_callback got an exception %s which will be ignored", e) - - self.launch_if_ready(task_record) - - return app_fu - - # it might also be interesting to assert that all DFK - # tasks are in a "final" state (3,4,5) when the DFK - # is closed down, and report some kind of warning. - # although really I'd like this to drain properly... - # and a drain function might look like this. - # If tasks have their states changed, this won't work properly - # but we can validate that... - def log_task_states(self) -> None: - logger.info("Summary of tasks in DFK:") - - with self.task_state_counts_lock: - for state in States: - logger.info("Tasks in state %s: %s", state, self.task_state_counts[state]) - - logger.info("End of summary") - - def add_executors(self, executors: Sequence[ParslExecutor]) -> None: - for executor in executors: - logger.info("Adding executor %s of type %s.%s", executor.label, type(executor).__module__, type(executor).__name__, - extra={"parsl.executor.label": executor.label, - "parsl.executor.type": type(executor).__module__ + "." + type(executor).__name__ - }) - executor.run_id = self.run_id - executor.run_dir = self.run_dir - executor.log_config = self.log_config - if self.monitoring and executor.remote_monitoring_radio is not None: - executor.monitoring_messages = self.monitoring.resource_msgs - logger.debug("Starting monitoring receiver for executor %s " - "with remote monitoring radio config %s", - executor.label, executor.remote_monitoring_radio) - - executor.monitoring_receiver = executor.remote_monitoring_radio.create_receiver(resource_msgs=executor.monitoring_messages, - run_dir=executor.run_dir) - if hasattr(executor, 'provider'): - if hasattr(executor.provider, 'script_dir'): - executor.provider.script_dir = os.path.join(self.run_dir, 'submit_scripts') - os.makedirs(executor.provider.script_dir, exist_ok=True) - - self.executors[executor.label] = executor - executor.start() - block_executors = [e for e in executors if isinstance(e, BlockProviderExecutor)] - self.job_status_poller.add_executors(block_executors) - - def atexit_cleanup(self) -> None: - logger.warning("Python is exiting with a DFK still running. " - "You should call parsl.dfk().cleanup() before " - "exiting to release any resources") - - def wait_for_current_tasks(self) -> None: - """Waits for all tasks in the task list to be completed, by waiting for their - AppFuture to be completed. This method will not necessarily wait for any tasks - added after cleanup has started (such as data stageout?) - """ - logger.info("Waiting for all remaining tasks to complete") - - # .values is made into a list immediately to reduce (although not - # eliminate) a race condition where self.tasks can be modified - # elsewhere by a completing task being removed from the dictionary. - task_records = list(self.tasks.values()) - for task_record in task_records: - # .exception() is a less exception throwing way of - # waiting for completion than .result() - fut = task_record['app_fu'] - if not fut.done(): - ex = fut.exception() - # now app future is done, poll until DFK state is final: a - # DFK state being final and the app future being done do not imply each other. - while task_record['status'] not in FINAL_STATES: - time.sleep(0.1) - - logger.info("All remaining tasks completed") - - @wrap_with_logs - def cleanup(self) -> None: - """Clean-up by closing all of the components used by the DFK - """ - - logger.info("DFK cleanup initiated") - - # this check won't detect two DFK cleanups happening from - # different threads extremely close in time because of - # non-atomic read/modify of self.cleanup_called - if self.cleanup_called: - raise Exception("attempt to clean up DFK when it has already been cleaned-up") - self.cleanup_called = True - - self.log_task_states() - - self.memoizer.close() - - # Send final stats - self.usage_tracker.send_end_message() - self.usage_tracker.close() - - logger.info("Closing job status poller") - self.job_status_poller.close() - logger.info("Terminated job status poller") - - logger.info("Shutting down executors") - - for executor in self.executors.values(): - logger.info(f"Shutting down executor {executor.label}") - executor.shutdown() - logger.info(f"Shut down executor {executor.label}") - - logger.info("Terminated executors") - time_completed = datetime.datetime.now() - - workflow_info = {'tasks_failed_count': self.task_state_counts[States.failed], - 'tasks_completed_count': self.task_state_counts[States.exec_done], - 'time_completed': time_completed, - 'run_id': self.run_id, 'rundir': self.run_dir} - - loggable_workflow_info = {"parsl.workflow_info." + k: v for k, v in workflow_info.items()} - logger.info("Workflow shutdown information: %r", workflow_info, extra={"parsl.dfk": self.run_id} | loggable_workflow_info) - if self.monitoring_radio: - logger.info("Sending final monitoring message") - self.monitoring_radio.send((MessageType.WORKFLOW_INFO, workflow_info)) - - if self.monitoring: - logger.info("Terminating monitoring") - self.monitoring.close() - logger.info("Terminated monitoring") - - logger.info("Terminating task launch pool") - self._task_launch_pool.shutdown() - logger.info("Terminated task launch pool") - - logger.info("Unregistering atexit hook") - atexit.unregister(self.atexit_cleanup) - logger.info("Unregistered atexit hook") - - if DataFlowKernelLoader._dfk is self: - logger.info("Unregistering default DFK") - parsl.clear() - logger.info("Unregistered default DFK") - else: - logger.debug("Cleaning up non-default DFK - not unregistering") - - if self._logging_unregister_callback: - logger.info("Unregistering log handler") - self._logging_unregister_callback() - logger.info("Unregistered log handler") - - # This message won't go to the default parsl.log, but other handlers - # should still see it. - logger.info("DFK cleanup complete") - - @staticmethod - def _log_std_streams(task_record: TaskRecord) -> None: - tid = task_record['id'] - - def log_std_stream(name: str, target) -> None: - if target is None: - logger.info(f"{name} for task {tid} will not be redirected.") - elif isinstance(target, str): - logger.info(f"{name} for task {tid} will be redirected to {target}") - elif isinstance(target, os.PathLike): - logger.info(f"{name} for task {tid} will be redirected to {os.fspath(target)}") - elif isinstance(target, tuple) and len(target) == 2 and isinstance(target[0], str): - logger.info(f"{name} for task {tid} will be redirected to {target[0]} with mode {target[1]}") - elif isinstance(target, tuple) and len(target) == 2 and isinstance(target[0], os.PathLike): - logger.info(f"{name} for task {tid} will be redirected to {os.fspath(target[0])} with mode {target[1]}") - elif isinstance(target, DataFuture): - logger.info(f"{name} for task {tid} will staged to {target.file_obj.url}") - else: - logger.error(f"{name} for task {tid} has unknown specification: {target!r}") - - log_std_stream("Standard out", task_record['app_fu'].stdout) - log_std_stream("Standard error", task_record['app_fu'].stderr) - - def default_std_autopath(self, taskrecord, kw): - label = taskrecord['kwargs'].get('label') - task_id = taskrecord['id'] - return os.path.join( - self.run_dir, - 'task_logs', - str(int(task_id / 10000)).zfill(4), # limit logs to 10k entries per directory - 'task_{}_{}{}.{}'.format( - str(task_id).zfill(4), - taskrecord['func_name'], - '' if label is None else '_{}'.format(label), - kw)) - - def render_future_description(self, dep: Future) -> str: - """Renders a description of the future in the context of the - current DFK. - """ - if isinstance(dep, AppFuture) and dep.task_record['dfk'] == self: - tid = "task " + repr(dep.task_record['id']) - elif isinstance(dep, DataFuture): - tid = "DataFuture from task " + repr(dep.tid) - else: - tid = repr(dep) - return tid - - -class DataFlowKernelLoader: - """Manage which DataFlowKernel is active. - - This is a singleton class containing only class methods. You should not - need to instantiate this class. - """ - - _dfk: Optional[DataFlowKernel] = None - - @classmethod - def clear(cls) -> None: - """Clear the active DataFlowKernel so that a new one can be loaded.""" - cls._dfk = None - - @classmethod - @typeguard.typechecked - def load(cls, config: Optional[Config] = None) -> DataFlowKernel: - """Load a DataFlowKernel. - - Args: - - config (Config) : Configuration to load. This config will be passed to a - new DataFlowKernel instantiation which will be set as the active DataFlowKernel. - Returns: - - DataFlowKernel : The loaded DataFlowKernel object. - """ - if cls._dfk is not None: - raise ConfigurationError('Config has already been loaded') - - if config is None: - cls._dfk = DataFlowKernel(Config()) - else: - cls._dfk = DataFlowKernel(config) - - return cls._dfk - - @classmethod - def wait_for_current_tasks(cls) -> None: - """Waits for all tasks in the task list to be completed, by waiting for their - AppFuture to be completed. This method will not necessarily wait for any tasks - added after cleanup has started such as data stageout. - """ - cls.dfk().wait_for_current_tasks() - - @classmethod - def dfk(cls) -> DataFlowKernel: - """Return the currently-loaded DataFlowKernel.""" - if cls._dfk is None: - raise NoDataFlowKernelError('Must first load config') - return cls._dfk diff --git a/parsl/parsl/dataflow/errors.py b/parsl/parsl/dataflow/errors.py deleted file mode 100644 index 83fff06d..00000000 --- a/parsl/parsl/dataflow/errors.py +++ /dev/null @@ -1,105 +0,0 @@ -from typing import List, Sequence, Tuple - -from parsl.errors import ParslError - - -class DataFlowException(ParslError): - """Base class for all exceptions. - - Only to be invoked when only a more specific error is not available. - - """ - - -class BadCheckpoint(DataFlowException): - """Error raised at the end of app execution due to missing output files. - - Args: - - reason - - Contains: - reason (string) - dependent_exceptions - """ - - def __init__(self, reason: str) -> None: - self.reason = reason - - def __str__(self) -> str: - return self.reason - - -class PropagatedException(DataFlowException): - """Error raised if an app fails because there was an error - in a related task. This is intended to be subclassed for - dependency and join_app errors. - - Args: - - dependent_exceptions_tids: List of exceptions and brief descriptions - for dependencies which failed. The description might be a task ID or - the repr of a non-AppFuture. - - task_id: Task ID of the task that failed because of the dependency error - """ - - def __init__(self, - dependent_exceptions_tids: Sequence[Tuple[BaseException, str]], - task_id: int, - *, - failure_description: str) -> None: - self.dependent_exceptions_tids = dependent_exceptions_tids - self.task_id = task_id - self._failure_description = failure_description - - (cause, cause_sequence) = self._find_any_root_cause() - self.__cause__ = cause - self._cause_sequence = cause_sequence - - def __str__(self) -> str: - sequence_text = " <- ".join(self._cause_sequence) - return f"{self._failure_description} for task {self.task_id}. " \ - f"The representative cause is via {sequence_text}" - - def _find_any_root_cause(self) -> Tuple[BaseException, List[str]]: - """Looks recursively through self.dependent_exceptions_tids to find - an exception that caused this propagated error, that is not itself - a propagated error. - """ - e: BaseException = self - dep_ids = [] - while isinstance(e, PropagatedException) and len(e.dependent_exceptions_tids) >= 1: - id_txt = e.dependent_exceptions_tids[0][1] - assert isinstance(id_txt, str) - # if there are several causes for this exception, label that - # there are more so that we know that the representative fail - # sequence is not the full story. - if len(e.dependent_exceptions_tids) > 1: - id_txt += " (+ others)" - dep_ids.append(id_txt) - e = e.dependent_exceptions_tids[0][0] - return e, dep_ids - - -class DependencyError(PropagatedException): - """Error raised if an app cannot run because there was an error - in a dependency. There can be several exceptions (one from each - dependency) and DependencyError collects them all together. - - Args: - - dependent_exceptions_tids: List of exceptions and brief descriptions - for dependencies which failed. The description might be a task ID or - the repr of a non-AppFuture. - - task_id: Task ID of the task that failed because of the dependency error - """ - def __init__(self, dependent_exceptions_tids: Sequence[Tuple[BaseException, str]], task_id: int) -> None: - super().__init__(dependent_exceptions_tids, task_id, - failure_description="Dependency failure") - - -class JoinError(PropagatedException): - """Error raised if apps joining into a join_app raise exceptions. - There can be several exceptions (one from each joining app), - and JoinError collects them all together. - """ - def __init__(self, dependent_exceptions_tids: Sequence[Tuple[BaseException, str]], task_id: int) -> None: - super().__init__(dependent_exceptions_tids, task_id, - failure_description="Join failure") diff --git a/parsl/parsl/dataflow/futures.py b/parsl/parsl/dataflow/futures.py deleted file mode 100644 index 616a3d3b..00000000 --- a/parsl/parsl/dataflow/futures.py +++ /dev/null @@ -1,163 +0,0 @@ -from __future__ import annotations - -import logging -import threading -from concurrent.futures import Future -from typing import Any, Optional, Sequence, Union - -import parsl.app.app as app -from parsl.app.futures import DataFuture -from parsl.dataflow.taskrecord import TaskRecord - -logger = logging.getLogger(__name__) - -# Possible future states (for internal use by the futures package). -PENDING = 'PENDING' -RUNNING = 'RUNNING' -# The future was cancelled by the user... -CANCELLED = 'CANCELLED' -# ...and _Waiter.add_cancelled() was called by a worker. -CANCELLED_AND_NOTIFIED = 'CANCELLED_AND_NOTIFIED' -FINISHED = 'FINISHED' - -_STATE_TO_DESCRIPTION_MAP = { - PENDING: "pending", - RUNNING: "running", - CANCELLED: "cancelled", - CANCELLED_AND_NOTIFIED: "cancelled", - FINISHED: "finished" -} - - -class AppFuture(Future): - """An AppFuture wraps a sequence of Futures which may fail and be retried. - - The AppFuture will wait for the DFK to provide a result from an appropriate - parent future, through ``parent_callback``. It will set its result to the - result of that parent future, if that parent future completes without an - exception. This result setting should cause .result(), .exception() and done - callbacks to fire as expected. - - The AppFuture will not set its result to the result of the parent future, if - that parent future completes with an exception, and if that parent future - has retries left. In that case, no result(), exception() or done callbacks should - report a result. - - The AppFuture will set its result to the result of the parent future, if that - parent future completes with an exception and if that parent future has no - retries left, or if it has no retry field. .result(), .exception() and done callbacks - should give a result as expected when a Future has a result set - - The parent future may return a RemoteExceptionWrapper as a result - and AppFuture will treat this an an exception for the above - retry and result handling behaviour. - - """ - - def __init__(self, task_record: TaskRecord) -> None: - """Initialize the AppFuture. - - Args: - - KWargs: - - task_record : The TaskRecord for the task represented by - this future. - """ - super().__init__() - self._update_lock = threading.Lock() - self._outputs: Sequence[DataFuture] - self._outputs = [] - self.task_record = task_record - - self._stdout_future: Optional[DataFuture] = None - self._stderr_future: Optional[DataFuture] = None - - @property - def stdout(self) -> Union[None, str, DataFuture]: - """Return app stdout. If stdout was specified as a string, then this - property will return that string. If stdout was specified as a File, - then this property will return a DataFuture representing that file - stageout. - TODO: this can be a tuple too I think?""" - if self._stdout_future: - return self._stdout_future - else: - # this covers the str and None cases - return self.task_record['kwargs'].get('stdout') - - @property - def stderr(self) -> Union[None, str, DataFuture]: - """Return app stderr. If stdout was specified as a string, then this - property will return that string. If stdout was specified as a File, - then this property will return a DataFuture representing that file - stageout. - TODO: this can be a tuple too I think?""" - if self._stderr_future: - return self._stderr_future - else: - # this covers the str and None cases - return self.task_record['kwargs'].get('stderr') - - @property - def tid(self) -> int: - return self.task_record['id'] - - def cancel(self) -> bool: - raise NotImplementedError("Cancel not implemented") - - def cancelled(self) -> bool: - return False - - def task_status(self) -> str: - """Returns the status of the task that will provide the value - for this future. This may not be in-sync with the result state - of this future - for example, task_status might return 'done' but - self.done() might not be true (which in turn means self.result() - and self.exception() might block). - - The actual status description strings returned by this method are - likely to change over subsequent versions of parsl, as use-cases - and infrastructure are worked out. - - It is expected that the status values will be from a limited set - of strings (so that it makes sense, for example, to group and - count statuses from many futures). - - It is expected that might be a non-trivial cost in acquiring the - status in future (for example, by communicating with a remote - worker). - - Returns: str - """ - return self.task_record['status'].name - - @property - def outputs(self) -> Sequence[DataFuture]: - return self._outputs - - def __getitem__(self, key: Any) -> AppFuture: - # This is decorated on each invocation because the getitem task - # should be bound to the same DFK as the task associated with this - # Future. - deferred_getitem_app = app.python_app(deferred_getitem, executors=['_parsl_internal'], data_flow_kernel=self.task_record['dfk']) - - return deferred_getitem_app(self, key) - - def __getattr__(self, name: str) -> AppFuture: - # this will avoid lifting behaviour on private methods and attributes, - # including __double_underscore__ methods which implement other - # Python syntax (such as iterators in for loops) - if name.startswith("_"): - raise AttributeError() - - deferred_getattr_app = app.python_app(deferred_getattr, executors=['_parsl_internal'], data_flow_kernel=self.task_record['dfk']) - - return deferred_getattr_app(self, name) - - -def deferred_getitem(o: Any, k: Any) -> Any: - return o[k] - - -def deferred_getattr(o: Any, name: str) -> Any: - return getattr(o, name) diff --git a/parsl/parsl/dataflow/memoization.py b/parsl/parsl/dataflow/memoization.py deleted file mode 100644 index 15059243..00000000 --- a/parsl/parsl/dataflow/memoization.py +++ /dev/null @@ -1,584 +0,0 @@ -from __future__ import annotations - -import hashlib -import logging -import os -import pickle -import threading -import types -from abc import ABCMeta, abstractmethod -from concurrent.futures import Future -from functools import lru_cache, singledispatch -from typing import Any, Dict, List, Literal, Optional, Sequence - -import typeguard - -from parsl.dataflow.errors import BadCheckpoint -from parsl.dataflow.taskrecord import TaskRecord -from parsl.errors import ConfigurationError -from parsl.utils import Timer, get_all_checkpoints - -logger = logging.getLogger(__name__) - - -class CheckpointCommand: - def __init__(self, task_record: TaskRecord, *, result: Optional[object] = None, exception: Optional[BaseException] = None): - """Create a checkpoint command. This specifies a checkpoint entry of - either a result or an exception. If exception is set, then this object - specifies an exception checkpoint. Otherwise, it specifies a result. - This is almost: one of exception or result must be non-None, but not - quite because this data model also needs to accomodate a valid result - of None.""" - - assert result is None or exception is None, "CheckpointCommand cannot specify both a result and exception" - - self._task_record = task_record - self._result = result - self._exception = exception - - @property - def task_record(self) -> TaskRecord: - return self._task_record - - @property - def result(self) -> Optional[object]: - return self._result - - @property - def exception(self) -> Optional[BaseException]: - return self._exception - - -@singledispatch -def id_for_memo(obj: object, output_ref: bool = False) -> bytes: - """This should return a byte sequence which identifies the supplied - value for memoization purposes: for any two calls of id_for_memo, - the byte sequence should be the same when the "same" value is supplied, - and different otherwise. - - "same" is in quotes about because sameness is not as straightforward as - serialising out the content. - - For example, for two dicts x, y: - - x = {"a":3, "b":4} - y = {"b":4, "a":3} - - then: x == y, but their serialization is not equal, and some other - functions on x and y are not equal: list(x.keys()) != list(y.keys()) - - - id_for_memo is invoked with output_ref=True when the parameter is an - output reference (a value in the outputs=[] parameter of an app - invocation). - - Memo hashing might be different for such parameters: for example, a - user might choose to hash input File content so that changing the - content of an input file invalidates memoization. This does not make - sense to do for output files: there is no meaningful content stored - where an output filename points at memoization time. - """ - logger.error("id_for_memo attempted on unknown type {}".format(type(obj))) - raise ValueError("unknown type for memoization: {}".format(type(obj))) - - -@id_for_memo.register(str) -@id_for_memo.register(int) -@id_for_memo.register(float) -@id_for_memo.register(type(None)) -def id_for_memo_pickle(obj: object, output_ref: bool = False) -> bytes: - return pickle.dumps(obj) - - -@id_for_memo.register(list) -def id_for_memo_list(denormalized_list: list, output_ref: bool = False) -> bytes: - if type(denormalized_list) is not list: - raise ValueError("id_for_memo_list cannot work on subclasses of list") - - normalized_list = [] - - for e in denormalized_list: - normalized_list.append(id_for_memo(e, output_ref=output_ref)) - - return pickle.dumps(normalized_list) - - -@id_for_memo.register(tuple) -def id_for_memo_tuple(denormalized_tuple: tuple, output_ref: bool = False) -> bytes: - if type(denormalized_tuple) is not tuple: - raise ValueError("id_for_memo_tuple cannot work on subclasses of tuple") - - normalized_list = [] - - for e in denormalized_tuple: - normalized_list.append(id_for_memo(e, output_ref=output_ref)) - - return pickle.dumps(normalized_list) - - -@id_for_memo.register(dict) -def id_for_memo_dict(denormalized_dict: dict, output_ref: bool = False) -> bytes: - """This normalises the keys and values of the supplied dictionary. - - When output_ref=True, the values are normalised as output refs, but - the keys are not. - """ - if type(denormalized_dict) is not dict: - raise ValueError("id_for_memo_dict cannot work on subclasses of dict") - - keys = sorted(denormalized_dict) - - normalized_list = [] - for k in keys: - normalized_list.append(id_for_memo(k)) - normalized_list.append(id_for_memo(denormalized_dict[k], output_ref=output_ref)) - return pickle.dumps(normalized_list) - - -# the LRU cache decorator must be applied closer to the id_for_memo_function call -# that the .register() call, so that the cache-decorated version is registered. -@id_for_memo.register(types.FunctionType) -@lru_cache() -def id_for_memo_function(f: types.FunctionType, output_ref: bool = False) -> bytes: - """This will checkpoint a function based only on its name and module name. - This means that changing source code (other than the function name) will - not cause a checkpoint invalidation. - """ - return pickle.dumps(["types.FunctionType", f.__name__, f.__module__]) - - -def make_hash(task: TaskRecord) -> str: - """Create a hash of the task inputs. - - Args: - - task (dict) : Task dictionary from dfk.tasks - - Returns: - - hash (str) : A unique hash string - """ - - t: List[bytes] = [] - - # if kwargs contains an outputs parameter, that parameter is removed - # and normalised differently - with output_ref set to True. - # kwargs listed in ignore_for_cache will also be removed - - filtered_kw = task['kwargs'].copy() - - ignore_list = task['ignore_for_cache'] - - logger.debug("Ignoring these kwargs for checkpointing: %s", ignore_list) - for k in ignore_list: - logger.debug("Ignoring kwarg %s", k) - del filtered_kw[k] - - if 'outputs' in task['kwargs']: - outputs = task['kwargs']['outputs'] - del filtered_kw['outputs'] - t.append(id_for_memo(outputs, output_ref=True)) - - t.extend(map(id_for_memo, (filtered_kw, task['func'], task['args']))) - - x = b''.join(t) - return hashlib.md5(x).hexdigest() - - -class Memoizer(metaclass=ABCMeta): - """Defines the interface for the DFK to talk to the memoization/checkpoint system. - - The DFK will invoke these methods on an instance of a Memoizer at suitable points - in the lifecycle of a task. These methods are not intended for users to invoke - directly. - """ - - @abstractmethod - def update_memo_exception(self, task: TaskRecord, e: BaseException) -> None: - """Called by the DFK when a task completes with an exception. - - On every task completion, either this method or `update_memo_result` - will be called, but not both. - - This is an opportunity for the memoization/checkpoint system to record - the outcome of a task for later discovery by a call to check_memo. - """ - raise NotImplementedError - - @abstractmethod - def update_memo_result(self, task: TaskRecord, r: Any) -> None: - """Called by the DFK when a task completes with a successful result. - - On every task completion, either this method or `update_memo_exception` - will be called, but not both. - - This is an opportunity for the memoization/checkpoint system to record - the outcome of a task for later discovery by a call to check_memo. - """ - raise NotImplementedError - - @abstractmethod - def start(self, *, run_dir: str, config_run_dir: str) -> None: - """Called by the DFK when it starts up. - - This is an opportunity for the memoization/checkpoint system to - initialize itself. - - The path to the per-run run directory and the base run directory - are passed as parameters. - """ - raise NotImplementedError - - @abstractmethod - def check_memo(self, task: TaskRecord) -> Optional[Future[Any]]: - """Asks the checkpoint system for a result recorded for the described - task. ``check_memo`` should return a `Future` that will be used as - an executor future, in place of sending the task to an executor for - execution. That future should be populated with a result or exception. - """ - raise NotImplementedError - - @abstractmethod - def close(self) -> None: - """Called at DFK shutdown. This gives the checkpoint system an - opportunity for graceful shutdown. - """ - raise NotImplementedError - - -class BasicMemoizer(Memoizer): - """Memoizer is responsible for ensuring that identical work is not repeated. - - When a task is repeated, i.e., the same function is called with the same exact arguments, the - result from a previous execution is reused. `wiki `_ - - The memoizer implementation here does not collapse duplicate calls - at call time, but works **only** when the result of a previous - call is available at the time the duplicate call is made. - - For instance:: - - No advantage from Memoization helps - memoization here: here: - - TaskA TaskB - | TaskA | - | | TaskA done (TaskB) - | | | (TaskB) - done | | - done | - done - - The memoizer creates a lookup table by hashing the function name - and its inputs, and storing the results of the function. - - When a task is ready for launch, i.e., all of its arguments - have resolved, we add its hash to the task datastructure. - - """ - - run_dir: str - - def __init__(self, *, - checkpoint_files: Sequence[str] | None = None, - checkpoint_period: Optional[str] = None, - checkpoint_mode: Literal['task_exit', 'periodic', 'dfk_exit', 'manual'] | None = None, - memoize: bool = True): - """Initialize the memoizer. - - KWargs: - - - checkpoint_files : sequence of str, optional - List of paths to checkpoint files to load. By default, all checkpoints from the - run directory will be restored. This is usually the right behaviour, but this - parameter allows that behaviour to be overridden. - See :func:`parsl.utils.get_all_checkpoints` and :func:`parsl.utils.get_last_checkpoint` - for helpers. - - checkpoint_period : str, optional - Time interval (in "HH:MM:SS") at which to checkpoint completed tasks. Only has an effect if - ``checkpoint_mode='periodic'``. - - checkpoint_mode : str, optional - Checkpoint mode to use, can be ``'dfk_exit'``, ``'task_exit'``, ``'periodic'`` or ``'manual'``. - If set to `None`, checkpointing will be disabled. Default is None. - - memoize : str, enable memoization or not. - - """ - self.checkpointed_tasks = 0 - - # this lock must be held when: - # * writing to any checkpoint files - # * interacting with self.checkpointable_tasks - self._checkpoint_lock = threading.Lock() - - self.checkpoint_files = checkpoint_files - self.checkpoint_mode = checkpoint_mode - self.checkpoint_period = checkpoint_period - - self.checkpointable_tasks: List[CheckpointCommand] = [] - - self._checkpoint_timer: Timer | None = None - self.memoize = memoize - - def start(self, *, run_dir: str, config_run_dir: str) -> None: - - self.run_dir = run_dir - self.config_run_dir = config_run_dir - - if self.checkpoint_files is not None: - checkpoint_files = self.checkpoint_files - elif self.checkpoint_files is None and self.checkpoint_mode is not None: - checkpoint_files = get_all_checkpoints(self.config_run_dir) - else: - checkpoint_files = [] - - checkpoint = self.load_checkpoints(checkpoint_files) - - if self.memoize: - logger.info("App caching initialized") - self.memo_lookup_table = checkpoint - else: - logger.info("App caching disabled for all apps") - self.memo_lookup_table = {} - - if self.checkpoint_mode == "periodic": - if self.checkpoint_period is None: - raise ConfigurationError("Checkpoint period must be specified with periodic checkpoint mode") - else: - try: - h, m, s = map(int, self.checkpoint_period.split(':')) - except Exception: - raise ConfigurationError("invalid checkpoint_period provided: {0} expected HH:MM:SS".format(self.checkpoint_period)) - checkpoint_period = (h * 3600) + (m * 60) + s - self._checkpoint_timer = Timer(self.checkpoint_queue, interval=checkpoint_period, name="Checkpoint") - - def close(self) -> None: - if self.checkpoint_mode is not None: - logger.info("Making final checkpoint") - self.checkpoint_queue() - - if self._checkpoint_timer: - logger.info("Stopping checkpoint timer") - self._checkpoint_timer.close() - - def check_memo(self, task: TaskRecord) -> Optional[Future[Any]]: - """Create a hash of the task and its inputs and check the lookup table for this hash. - - If present, the results are returned. - - Args: - - task(task) : task from the dfk.tasks table - - Returns: - - Result of the function if present in table, wrapped in a Future - - This call will also set task['hashsum'] to the unique hashsum for the func+inputs. - """ - - task_id = task['id'] - - if not self.memoize or not task['memoize']: - task['hashsum'] = None - logger.debug("Task {} will not be memoized".format(task_id)) - return None - - hashsum = make_hash(task) - logger.debug("Task {} has memoization hash {}".format(task_id, hashsum)) - result = None - if hashsum in self.memo_lookup_table: - result = self.memo_lookup_table[hashsum] - logger.info("Task %s using result from cache", task_id) - else: - logger.info("Task %s had no result in cache", task_id) - - task['hashsum'] = hashsum - - assert isinstance(result, Future) or result is None - return result - - def update_memo_result(self, task: TaskRecord, r: Any) -> None: - self._update_memo(task) - if self.checkpoint_mode is not None: - self._update_checkpoint(CheckpointCommand(task, result=r)) - - def update_memo_exception(self, task: TaskRecord, e: BaseException) -> None: - self._update_memo(task) - if self.checkpoint_mode is not None: - self._update_checkpoint(CheckpointCommand(task, exception=e)) - - def _update_memo(self, task: TaskRecord) -> None: - """Updates the memoization lookup table with the result from a task. - This doesn't move any values around but associates the memoization - hashsum with the completed (by success or failure) AppFuture. - - Args: - - task (TaskRecord) : A task record from dfk.tasks - """ - task_id = task['id'] - - if not self.memoize or not task['memoize'] or 'hashsum' not in task: - return - - if not isinstance(task['hashsum'], str): - logger.error("Attempting to update app cache entry but hashsum is not a string key") - return - - if task['hashsum'] in self.memo_lookup_table: - logger.info(f"Replacing app cache entry {task['hashsum']} with result from task {task_id}") - else: - logger.debug(f"Storing app cache entry {task['hashsum']} with result from task {task_id}") - self.memo_lookup_table[task['hashsum']] = task['app_fu'] - - def _load_checkpoints(self, checkpointDirs: Sequence[str]) -> Dict[str, Future[Any]]: - """Load a checkpoint file into a lookup table. - - The data being loaded from the pickle file mostly contains input - attributes of the task: func, args, kwargs, env... - To simplify the check of whether the exact task has been completed - in the checkpoint, we hash these input params and use it as the key - for the memoized lookup table. - - Args: - - checkpointDirs (list) : List of filepaths to checkpoints - Eg. ['runinfo/001', 'runinfo/002'] - - Returns: - - memoized_lookup_table (dict) - """ - memo_lookup_table = {} - - for checkpoint_dir in checkpointDirs: - logger.info("Loading checkpoints from {}".format(checkpoint_dir)) - checkpoint_file = os.path.join(checkpoint_dir, 'tasks.pkl') - try: - with open(checkpoint_file, 'rb') as f: - while True: - try: - data = pickle.load(f) - # Copy and hash only the input attributes - memo_fu: Future = Future() - assert data['exception'] is None - memo_fu.set_result(data['result']) - memo_lookup_table[data['hash']] = memo_fu - - except EOFError: - # Done with the checkpoint file - break - except FileNotFoundError: - reason = "Checkpoint file was not found: {}".format( - checkpoint_file) - logger.error(reason) - raise BadCheckpoint(reason) - except Exception: - reason = "Failed to load checkpoint: {}".format( - checkpoint_file) - logger.error(reason) - raise BadCheckpoint(reason) - - logger.info("Completed loading checkpoint: {0} with {1} tasks".format(checkpoint_file, - len(memo_lookup_table.keys()))) - return memo_lookup_table - - @typeguard.typechecked - def load_checkpoints(self, checkpointDirs: Optional[Sequence[str]]) -> Dict[str, Future]: - """Load checkpoints from the checkpoint files into a dictionary. - - The results are used to pre-populate the memoizer's lookup_table - - Kwargs: - - checkpointDirs (list) : List of run folder to use as checkpoints - Eg. ['runinfo/001', 'runinfo/002'] - - Returns: - - dict containing, hashed -> future mappings - """ - if checkpointDirs: - return self._load_checkpoints(checkpointDirs) - else: - return {} - - def _update_checkpoint(self, command: CheckpointCommand) -> None: - if self.checkpoint_mode == 'task_exit': - self.checkpoint_one(command) - elif self.checkpoint_mode in ('manual', 'periodic', 'dfk_exit'): - with self._checkpoint_lock: - self.checkpointable_tasks.append(command) - elif self.checkpoint_mode is None: - pass - else: - assert False, "Invalid checkpoint mode {self.checkpoint_mode} - should have been validated at initialization" - - def checkpoint_one(self, cc: CheckpointCommand) -> None: - """Checkpoint a single task to a checkpoint file. - - By default the checkpoints are written to the RUNDIR of the current - run under RUNDIR/checkpoints/tasks.pkl - - Kwargs: - - task : A task to checkpoint. - - .. note:: - Checkpointing only works if memoization is enabled - - """ - with self._checkpoint_lock: - self._checkpoint_these_tasks([cc]) - - def checkpoint_queue(self) -> None: - """Checkpoint all tasks registered in self.checkpointable_tasks. - - By default the checkpoints are written to the RUNDIR of the current - run under RUNDIR/checkpoints/tasks.pkl - - .. note:: - Checkpointing only works if memoization is enabled - - """ - with self._checkpoint_lock: - self._checkpoint_these_tasks(self.checkpointable_tasks) - self.checkpointable_tasks = [] - - def checkpoint(self) -> None: - """This is the user-facing interface to manual checkpointing. - """ - self.checkpoint_queue() - - def _checkpoint_these_tasks(self, checkpoint_queue: List[CheckpointCommand]) -> None: - """Play a sequence of CheckpointCommands into a checkpoint file. - - The checkpoint lock must be held when invoking this method. - """ - - # This checks that the lock is held, at least, but does not check that - # it is held by the current thread - threading.Lock does not have a - # concept of locking thread for threading.Lock. - assert self._checkpoint_lock.locked(), "checkpoint system should be locked" - - checkpoint_dir = '{0}/checkpoint'.format(self.run_dir) - checkpoint_tasks = checkpoint_dir + '/tasks.pkl' - - if not os.path.exists(checkpoint_dir): - os.makedirs(checkpoint_dir, exist_ok=True) - - count = 0 - - with open(checkpoint_tasks, 'ab') as f: - for cc in checkpoint_queue: - if cc.exception is None: - hashsum = cc.task_record['hashsum'] - if not hashsum: - continue - t = {'hash': hashsum, 'exception': None, 'result': cc.result} - - # We are using pickle here since pickle dumps to a file in 'ab' - # mode behave like a incremental log. - pickle.dump(t, f) - count += 1 - logger.debug("Task %s checkpointed", cc.task_record['id']) - - self.checkpointed_tasks += count - - if count == 0: - if self.checkpointed_tasks == 0: - logger.warning("No tasks checkpointed so far in this run. Please ensure caching is enabled") - else: - logger.debug("No tasks checkpointed in this pass.") - else: - logger.info("Done checkpointing {} tasks".format(count)) diff --git a/parsl/parsl/dataflow/rundirs.py b/parsl/parsl/dataflow/rundirs.py deleted file mode 100644 index 14177308..00000000 --- a/parsl/parsl/dataflow/rundirs.py +++ /dev/null @@ -1,39 +0,0 @@ -import logging -import os -from glob import glob - -logger = logging.getLogger(__name__) - - -def make_rundir(path: str) -> str: - """Create a numbered run directory under the specified path. - - ./runinfo <- specified path - |----000 - |----001 <- Directories for each run - | .... - |----NNN - - Args: - - path (str): String path to root of all rundirs - """ - try: - if not os.path.exists(path): - os.makedirs(path) - - prev_rundirs = glob(os.path.join(path, "[0-9]*[0-9]")) - - current_rundir = os.path.join(path, '000') - - if prev_rundirs: - # Since we globbed on files named as 0-9 - x = sorted([int(os.path.basename(x)) for x in prev_rundirs])[-1] - current_rundir = os.path.join(path, '{0:03}'.format(x + 1)) - - os.makedirs(current_rundir) - logger.debug("Parsl run initializing in rundir: {0}".format(current_rundir)) - return os.path.abspath(current_rundir) - - except Exception: - logger.exception("Failed to create run directory") - raise diff --git a/parsl/parsl/dataflow/states.py b/parsl/parsl/dataflow/states.py deleted file mode 100644 index d7222111..00000000 --- a/parsl/parsl/dataflow/states.py +++ /dev/null @@ -1,75 +0,0 @@ -from enum import IntEnum - - -class States(IntEnum): - """Enumerates the states a parsl task may be in. - - These states occur inside the task record for a task inside - a `DataFlowKernel` and in the monitoring database. - - In a single successful task execution, tasks will progress in this - sequence: - - pending -> launched -> running -> running_ended -> exec_done - - Other states represent deviations from this path, either due to - failures, or to deliberate changes to how tasks are executed (for - example due to join_app, or memoization). - - - All tasks should end up in one of the states listed in `FINAL_STATES`. - """ - - pending = 0 - """Task is known to parsl but cannot run yet. Usually, a task cannot - run because it is waiting for dependency tasks to complete. - """ - - running = 2 - """Task is running on a resource. This state is special - a DFK task - record never goes to States.running state; but the monitoring database - may represent a task in this state based on non-DFK information received - from monitor_wrapper.""" - - exec_done = 3 - """Task has been executed successfully.""" - - failed = 4 - """Task has failed and no more attempts will be made to run it.""" - - dep_fail = 5 - """Dependencies of this task failed, so it is marked as failed without - even an attempt to launch it.""" - - launched = 7 - """Task has been passed to a `ParslExecutor` for execution.""" - - fail_retryable = 8 - """Task has failed, but can be retried""" - - memo_done = 9 - """Task was found in the memoization table, so it is marked as done - without even an attempt to launch it.""" - - joining = 10 - """Task is a join_app, joining on internal tasks. The task has run its - own Python code, and is now waiting on other tasks before it can make - further progress (to a done/failed state).""" - - running_ended = 11 - """Like States.running, this state is also not observed by the DFK, - but instead only by monitoring. This state does not record - anything about task success or failure, merely that the wrapper - ran long enough to record it as finished.""" - - def __str__(self) -> str: - return self.__class__.__name__ + "." + self.name - - -FINAL_FAILURE_STATES = {States.failed, States.dep_fail} -"""States which are final and which indicate a failure. This must -be a subset of FINAL_STATES""" - -FINAL_STATES = {States.exec_done, States.memo_done, *FINAL_FAILURE_STATES} -"""States from which we will never move to another state, because the job has -either definitively completed or failed.""" diff --git a/parsl/parsl/dataflow/taskrecord.py b/parsl/parsl/dataflow/taskrecord.py deleted file mode 100644 index 0621ab8f..00000000 --- a/parsl/parsl/dataflow/taskrecord.py +++ /dev/null @@ -1,103 +0,0 @@ -from __future__ import annotations - -import datetime -import threading -from concurrent.futures import Future - -# only for type checking: -from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Sequence, Union - -from typing_extensions import TypedDict - -if TYPE_CHECKING: - from parsl.dataflow.futures import AppFuture - -import parsl.dataflow.dflow as dflow -from parsl.dataflow.states import States - - -class TaskRecord(TypedDict, total=False): - """This stores most information about a Parsl task""" - - dfk: dflow.DataFlowKernel - """The DataFlowKernel which is managing this task. - """ - - func_name: str - - status: States - - depends: List[Future] - - app_fu: AppFuture - """The Future which was returned to the user when an app was invoked. - """ - - exec_fu: Optional[Future] - """When a task has been launched on an executor, stores the Future - returned by that executor. - """ - - executor: str - """The name of the executor which this task will be/is being/was - executed on. - """ - - fail_count: int - fail_cost: float - fail_history: List[str] - - checkpoint: bool - """Should this task be checkpointed? - """ - - hashsum: Optional[str] # hash for checkpointing/memoization. - """The hash used for checkpointing and memoisation. This is not known - until at least all relevant dependencies have completed, and will be - None before that. - """ - - task_launch_lock: threading.Lock - """This lock is used to ensure that task launch only happens once. - A task can be launched by dependencies completing from arbitrary - threads, and a race condition would exist when dependencies complete - in multiple threads very close together in time, which this lock - prevents. - """ - - # these three could be more strongly typed perhaps but I'm not thinking about that now - func: Callable - args: Sequence[Any] - # in some places we uses a Tuple[Any, ...] and in some places a List[Any]. - # This is an attempt to correctly type both of those. - kwargs: Dict[str, Any] - - time_invoked: Optional[datetime.datetime] - time_returned: Optional[datetime.datetime] - try_time_launched: Optional[datetime.datetime] - try_time_returned: Optional[datetime.datetime] - - memoize: bool - """Should this task be memoized?""" - ignore_for_cache: Sequence[str] - from_memo: Optional[bool] - - id: int - try_id: int - - resource_specification: Dict[str, Any] - """Dictionary containing relevant info for a task execution. - Includes resources to allocate and execution mode as a given - executor permits.""" - - join: bool - """Is this a join_app?""" - - joins: Union[None, Future, List[Future]] - """If this is a join app and the python body has executed, then this - contains the Future or list of Futures that the join app will join.""" - - join_lock: threading.Lock - """Restricts access to end-of-join behavior to ensure that joins - only complete once, even if several joining Futures complete close - together in time.""" diff --git a/parsl/parsl/errors.py b/parsl/parsl/errors.py deleted file mode 100644 index 5de6010d..00000000 --- a/parsl/parsl/errors.py +++ /dev/null @@ -1,37 +0,0 @@ -from typing import List - - -class ParslError(Exception): - """Base class for all exceptions. - - Only to be invoked when a more specific error is not available. - """ - - -class ConfigurationError(ParslError): - """Raised when a component receives an invalid configuration. - """ - - -class OptionalModuleMissing(ParslError): - ''' Error raised when a required module is missing for a optional/extra component - ''' - - def __init__(self, module_names: List[str], reason: str): - self.module_names = module_names - self.reason = reason - - def __str__(self) -> str: - return "The functionality requested requires optional modules {0} which could not be imported, because: {1}".format( - self.module_names, self.reason - ) - - -class InternalConsistencyError(ParslError): - """Raised when a component enounters an internal inconsistency. - """ - - -class NoDataFlowKernelError(ParslError): - """Raised when no DataFlowKernel is available for an operation that needs one. - """ diff --git a/parsl/parsl/executors/__init__.py b/parsl/parsl/executors/__init__.py deleted file mode 100644 index 81955aab..00000000 --- a/parsl/parsl/executors/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -from parsl.executors.flux.executor import FluxExecutor -from parsl.executors.globus_compute import GlobusComputeExecutor -from parsl.executors.high_throughput.executor import HighThroughputExecutor -from parsl.executors.high_throughput.mpi_executor import MPIExecutor -from parsl.executors.threads import ThreadPoolExecutor -from parsl.executors.workqueue.executor import WorkQueueExecutor - -__all__ = ['ThreadPoolExecutor', - 'HighThroughputExecutor', - 'MPIExecutor', - 'WorkQueueExecutor', - 'FluxExecutor', - 'GlobusComputeExecutor'] diff --git a/parsl/parsl/executors/base.py b/parsl/parsl/executors/base.py deleted file mode 100644 index 8c53b8f4..00000000 --- a/parsl/parsl/executors/base.py +++ /dev/null @@ -1,146 +0,0 @@ -from __future__ import annotations - -import logging -import os -from abc import ABCMeta, abstractmethod -from concurrent.futures import Future -from multiprocessing.queues import Queue -from typing import Any, Callable, Dict, Optional - -from typing_extensions import Literal, Self - -from parsl.logconfigs.base import LogConfig -from parsl.monitoring.radios.base import MonitoringRadioReceiver, RadioConfig -from parsl.monitoring.types import TaggedMonitoringMessage - -logger = logging.getLogger(__name__) - - -class ParslExecutor(metaclass=ABCMeta): - """Executors are abstractions that represent available compute resources - to which you could submit arbitrary App tasks. - - This is an abstract base class that only enforces concrete implementations - of functionality by the child classes. - - Can be used as a context manager. On exit, calls ``self.shutdown()`` with - no arguments and re-raises any thrown exception. - - In addition to the listed methods, a ParslExecutor instance must always - have a member field: - - label: str - a human readable label for the executor, unique - with respect to other executors. - - remote_monitoring_radio: RadioConfig describing how tasks on this executor - should report task resource status - - An executor may optionally expose: - - storage_access: List[parsl.data_provider.staging.Staging] - a list of staging - providers that will be used for file staging. In the absence of this - attribute, or if this attribute is `None`, then a default value of - ``parsl.data_provider.staging.default_staging`` will be used by the - staging code. - - Typechecker note: Ideally storage_access would be declared on executor - __init__ methods as List[Staging] - however, lists are by default - invariant, not co-variant, and it looks like @typeguard cannot be - persuaded otherwise. So if you're implementing an executor and want to - @typeguard the constructor, you'll have to use List[Any] here. - - The DataFlowKernel will set this attribute before calling .start(), - if monitoring is enabled: - - monitoring_messages: Optional[Queue[TaggedMonitoringMessage]] - an executor - can send messages to the monitoring hub by putting them into - this queue. - """ - - label: str = "undefined" - - def __init__( - self, - *, - monitoring_messages: Optional[Queue[TaggedMonitoringMessage]] = None, - run_dir: str = ".", - run_id: Optional[str] = None, - ): - self.monitoring_messages = monitoring_messages - - self.remote_monitoring_radio: Optional[RadioConfig] = None - self.monitoring_receiver: Optional[MonitoringRadioReceiver] = None - - self.run_dir = os.path.abspath(run_dir) - self.run_id = run_id - - self.log_config: Optional[LogConfig] = None - - def __enter__(self) -> Self: - return self - - def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> Literal[False]: - self.shutdown() - return False - - def start(self) -> None: - """Start the executor. - - By default, this does nothing, but this method should be overridden to - perform any spin-up operations (for example: starting thread pools). - """ - pass - - @abstractmethod - def submit(self, func: Callable, resource_specification: Dict[str, Any], *args: Any, **kwargs: Any) -> Future: - """Submit. - - The executor can optionally set a parsl_executor_task_id attribute on - the Future that it returns, and in that case, parsl will log a - relationship between the executor's task ID and parsl level try/task - IDs. - """ - pass - - @abstractmethod - def shutdown(self) -> None: - """Shutdown the executor. - - Executors should call super().shutdown() as part of their overridden - implementation. - """ - if self.monitoring_receiver is not None: - logger.info("Starting monitoring receiver shutdown") - self.monitoring_receiver.shutdown() - logger.info("Done with monitoring receiver shutdown") - - def monitor_resources(self) -> bool: - """Should resource monitoring happen for tasks on running on this executor? - - Parsl resource monitoring conflicts with execution styles which do - not directly use a process tree - for example, the ThreadPoolExecutor - and the MPIExecutor. - - This function allows resource monitoring to be disabled per executor implementation. - """ - return True - - @property - def run_dir(self) -> str: - """Path to the run directory. - """ - return self._run_dir - - @run_dir.setter - def run_dir(self, value: str) -> None: - self._run_dir = value - - @property - def run_id(self) -> Optional[str]: - """UUID for the enclosing DFK. - """ - return self._run_id - - @run_id.setter - def run_id(self, value: Optional[str]) -> None: - self._run_id = value diff --git a/parsl/parsl/executors/errors.py b/parsl/parsl/executors/errors.py deleted file mode 100644 index fed43228..00000000 --- a/parsl/parsl/executors/errors.py +++ /dev/null @@ -1,75 +0,0 @@ -"""Exceptions raise by Executors.""" -from typing import Set - -from parsl.errors import ParslError -from parsl.executors.base import ParslExecutor - - -class ExecutorError(ParslError): - """Base class for executor related exceptions. - - Only to be invoked when only a more specific error is not available. - """ - - def __init__(self, executor: ParslExecutor, reason: str): - self.executor = executor - self.reason = reason - - def __str__(self): - return "Executor {0} failed due to: {1}".format(self.executor.label, self.reason) - - -class BadStateException(ExecutorError): - """Error returned by task Futures when an executor is in a bad state. - """ - - def __init__(self, executor, exception): - super().__init__(executor, str(exception)) - - -class UnsupportedFeatureError(ExecutorError): - """Error raised when attemping to use unsupported feature in an Executor""" - - def __init__(self, feature, current_executor, target_executor): - self.feature = feature - self.current_executor = current_executor - self.target_executor = target_executor - - def __str__(self): - if self.target_executor: - return "The {} feature is unsupported in {}. \ - Please checkout {} for this feature".format(self.feature, - self.current_executor, - self.target_executor) - else: - return "The {} feature is unsupported in {}.".format(self.feature, - self.current_executor) - - -class InvalidResourceSpecification(ExecutorError): - """Error raised when Invalid input is supplied via resource Specification""" - - def __init__(self, invalid_keys: Set[str], message: str = ''): - self.invalid_keys = invalid_keys - self.message = message - - def __str__(self): - return f"Invalid Resource Specification Supplied: {self.invalid_keys}. {self.message}" - - -class ScalingFailed(ExecutorError): - """Scaling failed due to error in Execution provider.""" - - def __str__(self): - return f"Executor {self.executor.label} failed to scale due to: {self.reason}" - - -class BadMessage(ParslError): - """ Mangled/Poorly formatted/Unsupported message received - """ - - def __init__(self, reason): - self.reason = reason - - def __str__(self): - return "Received an unsupported message. Reason:{}".format(self.reason) diff --git a/parsl/parsl/executors/execute_task.py b/parsl/parsl/executors/execute_task.py deleted file mode 100644 index 3eb8fd2d..00000000 --- a/parsl/parsl/executors/execute_task.py +++ /dev/null @@ -1,31 +0,0 @@ -from parsl.serialize import unpack_apply_message - - -def execute_task(bufs: bytes): - """Deserialize the buffer and execute the task. - Returns the result or throws exception. - """ - f, args, kwargs = unpack_apply_message(bufs) - - # We might need to look into callability of the function from itself - # since we change it's name in the new namespace - prefix = "parsl_" - fname = prefix + "f" - argname = prefix + "args" - kwargname = prefix + "kwargs" - resultname = prefix + "result" - - code = "{0} = {1}(*{2}, **{3})".format(resultname, fname, - argname, kwargname) - - user_ns = locals() - user_ns.update({ - '__builtins__': __builtins__, - fname: f, - argname: args, - kwargname: kwargs, - resultname: resultname - }) - - exec(code, user_ns, user_ns) - return user_ns.get(resultname) diff --git a/parsl/parsl/executors/flux/__init__.py b/parsl/parsl/executors/flux/__init__.py deleted file mode 100644 index d9324a34..00000000 --- a/parsl/parsl/executors/flux/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -"""Package defining FluxExecutor.""" - -import collections - -TaskResult = collections.namedtuple("TaskResult", ("returnval", "exception")) diff --git a/parsl/parsl/executors/flux/execute_parsl_task.py b/parsl/parsl/executors/flux/execute_parsl_task.py deleted file mode 100644 index 4372578e..00000000 --- a/parsl/parsl/executors/flux/execute_parsl_task.py +++ /dev/null @@ -1,45 +0,0 @@ -"""Script for executing tasks inside of Flux jobs.""" - -import argparse -import logging -import os - -from parsl.executors.execute_task import execute_task -from parsl.executors.flux import TaskResult -from parsl.serialize import serialize - - -def main(): - """Execute one rank of an MPI application.""" - logging.basicConfig( - level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s" - ) - parser = argparse.ArgumentParser() - parser.add_argument("-i", "--input", required=True, help="Input pickle file") - parser.add_argument("-o", "--output", required=True, help="Output pickle file") - args = parser.parse_args() - logging.info("Input : %s", args.input) - logging.info("Output : %s", args.output) - returnval = None - exception = None - # open and deserialize the task's pickled input package - with open(args.input, "rb") as file_handle: - fn_buf = file_handle.read() - logging.info("Read input pickle file") - try: - returnval = execute_task(fn_buf) - except Exception as exc: - logging.exception("Parsl task execution failed:") - exception = exc - else: - logging.info("Finished execution") - # only rank 0 should write/return a result; other ranks exit - if int(os.environ["FLUX_TASK_RANK"]) == 0: - # write the result to the output file - result_buf = serialize(TaskResult(returnval, exception)) - with open(args.output, "wb") as file_handle: - file_handle.write(result_buf) - - -if __name__ == "__main__": - main() diff --git a/parsl/parsl/executors/flux/executor.py b/parsl/parsl/executors/flux/executor.py deleted file mode 100644 index 0f563384..00000000 --- a/parsl/parsl/executors/flux/executor.py +++ /dev/null @@ -1,440 +0,0 @@ -"""Defines the FluxExecutor class.""" - -import collections -import concurrent.futures as cf -import functools -import itertools -import os -import queue -import shutil -import sys -import threading -import uuid -import weakref -from collections.abc import Callable, Mapping -from socket import gethostname -from typing import Any, Dict, Optional - -import zmq - -from parsl.app.errors import AppException -from parsl.executors.base import ParslExecutor -from parsl.executors.errors import ScalingFailed -from parsl.executors.flux.execute_parsl_task import __file__ as _WORKER_PATH -from parsl.executors.flux.flux_instance_manager import __file__ as _MANAGER_PATH -from parsl.providers import LocalProvider -from parsl.providers.base import ExecutionProvider -from parsl.serialize import deserialize, pack_apply_message -from parsl.serialize.errors import SerializationError -from parsl.utils import RepresentationMixin - -_WORKER_PATH = os.path.realpath(_WORKER_PATH) -_MANAGER_PATH = os.path.realpath(_MANAGER_PATH) - - -_FluxJobInfo = collections.namedtuple( - "_FluxJobInfo", ("future", "task_id", "infile", "outfile", "resource_spec") -) - - -class FluxFutureWrapper(cf.Future): - """Wrapper class around a ``flux.job.FluxExecutorFuture.`` - - Forwards methods onto the underlying FluxExecutorFuture. - """ - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self._flux_future = None - self._cancellation_lock = threading.Lock() - - def cancel(self): - # potential race condition: executor sets _flux_future after check - # that _flux_future is None. Protect the check and cancel() call with a lock. - with self._cancellation_lock: - if self._flux_future is None: - return super().cancel() - if self._flux_future.cancelled(): - # due to a bug, flux futures can only have ``cancel`` called once - return True - if self._flux_future.cancel(): # cancel underlying future and then self - if not super().cancel(): - raise RuntimeError("Unexpected state") - self.set_running_or_notify_cancel() # also should be called only once - return True - return False - - cancel.__doc__ = cf.Future.cancel.__doc__ - - def running(self): - if self._flux_future is None: - return False # no race condition since ``running()`` just advisory - return self._flux_future.running() - - running.__doc__ = cf.Future.running.__doc__ - - -def _complete_future( - expected_file: str, future_wrapper: FluxFutureWrapper, flux_future: Any -): - """Callback triggered when a FluxExecutorFuture completes. - - When the FluxExecutorFuture completes, check for the Parsl task's - output file, and assign the result to the FluxWrapperFuture future. - - Parameters - ---------- - expected_file : str - The path to the Parsl task's output file, storing the result of the task. - future_wrapper : FluxFutureWrapper - The user-facing future. - flux_future : FluxExecutorFuture - The future wrapped by ``future_wrapper``. Also accessible via - ``future_wrapper``, but the flux_future must be accepted as an argument - due to how ``concurrent.futures.add_done_callback`` works. - """ - if flux_future.cancelled(): # if underlying future was cancelled, return - return # no need to set a result on the wrapper future - try: - returncode = flux_future.result() - except Exception as unknown_err: - future_wrapper.set_exception(unknown_err) - return - if returncode == 0: - try: # look for the output file - with open(expected_file, "rb") as file_handle: - task_result = deserialize(file_handle.read()) - except FileNotFoundError: - future_wrapper.set_exception( - FileNotFoundError( - f"No result found for Parsl task, expected {expected_file}" - ) - ) - except Exception as unknown_err: - future_wrapper.set_exception(unknown_err) - else: # task package deserialized successfully - if task_result.exception is not None: - future_wrapper.set_exception(task_result.exception) - else: - future_wrapper.set_result(task_result.returnval) - else: # the job exited abnormally - future_wrapper.set_exception( - AppException(f"Parsl task exited abnormally: returned {returncode}") - ) - - -class FluxExecutor(ParslExecutor, RepresentationMixin): - """Executor that uses Flux to schedule and run jobs. - - Every callable submitted to the executor is wrapped into a Flux job. - - This executor requires that there be a Flux installation available - locally, and that it can be located either in PATH or through the - ``flux_path`` argument. - - Flux jobs are fairly heavyweight. As of Flux v0.25, a single Flux - instance is (on many systems) capped at 50 jobs per second. As such, - this executor is not a good fit for use-cases consisting of large numbers - of small, fast jobs. - - However, Flux is great at handling jobs with large resource requirements, - and collections of jobs with varying resource requirements. - - Note that due to vendor-specific extensions, on certain Cray machines like - ALCF's Theta or LANL's Trinitite/Trinity, Flux cannot run applications - that use the default MPI library. Generally the only workaround is to - recompile with another MPI library like OpenMPI. - - This executor acts as a sort of wrapper around a ``flux.job.FluxExecutor``, - which can be confusing since both wrapped and wrapper classes share the same name. - Whenever possible, the underlying executor is referred by its fully qualified name, - ``flux.job.FluxExecutor``. - - Parameters - ---------- - working_dir: str - Directory in which the executor should place its files, possibly overwriting - existing files. If ``None``, generate a unique directory. - label: str - Label for this executor instance. - flux_handle_args: collections.abc.Sequence - Positional arguments to ``flux.Flux()`` instance, if any. - The first positional argument, ``url``, is provided by this executor. - flux_executor_kwargs: collections.abc.Mapping - Keyword arguments to pass to the underlying ``flux.job.FluxExecutor()`` - instance, if any. Note that the ``handle_args`` keyword - argument is provided by this executor, - in order to supply the URL of a remote Flux instance. - flux_path: str - Path to flux installation to use, or None to search PATH for flux. - launch_cmd: str - The command to use when launching the executor's backend. The default - command is available as the ``DEFAULT_LAUNCH_COMMAND`` attribute. The - default command starts a new Flux instance, which may not be desirable - if a Flux instance will already be provisioned (this is not likely). - """ - - DEFAULT_LAUNCH_CMD = "{flux} start {python} {manager} {protocol} {hostname} {port}" - - def __init__( - self, - provider: Optional[ExecutionProvider] = None, - working_dir: Optional[str] = None, - label: str = "FluxExecutor", - flux_executor_kwargs: Mapping = {}, - flux_path: Optional[str] = None, - launch_cmd: Optional[str] = None, - ): - super().__init__() - if provider is None: - provider = LocalProvider() - self.provider = provider - self.label = label - if working_dir is None: - working_dir = self.label + "_" + str(uuid.uuid4()) - self.working_dir = os.path.abspath(working_dir) - # check that flux_path is an executable, or look for flux in PATH - if flux_path is None: - flux_path = shutil.which("flux") - if flux_path is None: - raise EnvironmentError("Cannot find Flux installation in PATH") - self.flux_path = os.path.abspath(flux_path) - self._task_id_counter = itertools.count() - # Assumes a launch command cannot be None or empty - self.launch_cmd = launch_cmd or self.DEFAULT_LAUNCH_CMD - self._submission_queue: queue.Queue = queue.Queue() - self._stop_event = threading.Event() - # lock to protect self._task_id_counter and also submission/shutdown race - self._submission_lock = threading.Lock() - self.flux_executor_kwargs = flux_executor_kwargs - self._submission_thread = threading.Thread( - target=_submit_wrapper, - args=( - self._submission_queue, - self._stop_event, - self.working_dir, - self.flux_executor_kwargs, - self.provider, - self, - self.flux_path, - self.launch_cmd, - ), - daemon=True, - ) - # add a ``weakref.finalize()`` function for joining the executor thread - weakref.finalize( - self, - lambda x, y: x.set() or y.join(), # type: ignore[func-returns-value] - self._stop_event, - self._submission_thread, - ) - - def start(self): - """Called when DFK starts the executor when the config is loaded.""" - super().start() - os.makedirs(self.working_dir, exist_ok=True) - self._submission_thread.start() - - def shutdown(self, wait=True): - """Shut down the executor, causing further calls to ``submit`` to fail. - - Parameters - ---------- - wait - If ``True``, do not return until all submitted Futures are done. - """ - with self._submission_lock: - self._stop_event.set() - if wait: - self._submission_thread.join() - - super().shutdown() - - def submit( - self, - func: Callable, - resource_specification: Dict[str, Any], - *args: Any, - **kwargs: Any, - ): - """Wrap a callable in a Flux job and submit it to Flux. - - :param func: The callable to submit as a job to Flux - - :param resource_specification: A mapping defining the resources to allocate to the Flux job. - - Only the following keys are checked for: - - - num_tasks: the number of tasks to launch (MPI ranks for an MPI job), default 1 - - cores_per_task: cores per task, default 1 - - gpus_per_task: gpus per task, default 1 - - num_nodes: if > 0, evenly distribute the allocated cores/gpus - across the given number of nodes. Does *not* give the job exclusive - access to those nodes; this option only affects distribution. - - :param args: positional arguments for the callable - - :param kwargs: keyword arguments for the callable - """ - # protect self._task_id_counter and shutdown/submit race - with self._submission_lock: - if self._stop_event.is_set(): - raise RuntimeError("`shutdown()` already called") - task_id = str(next(self._task_id_counter)) - infile = os.path.join(self.working_dir, f"{task_id}_in{os.extsep}pkl") - outfile = os.path.join(self.working_dir, f"{task_id}_out{os.extsep}pkl") - try: - fn_buf = pack_apply_message( - func, args, kwargs, buffer_threshold=1 << 20, - ) - except TypeError: - raise SerializationError(func.__name__) - with open(infile, "wb") as infile_handle: - infile_handle.write(fn_buf) - future = FluxFutureWrapper() - self._submission_queue.put( - _FluxJobInfo(future, task_id, infile, outfile, resource_specification) - ) - return future - - -def _submit_wrapper( - submission_queue: queue.Queue, stop_event: threading.Event, *args, **kwargs -): - """Wrap the ``_submit_flux_jobs`` function in a try/except. - - If an exception is thrown, error out all submitted tasks. - """ - with zmq.Context() as ctx: - with ctx.socket(zmq.REP) as socket: - try: - _submit_flux_jobs(submission_queue, stop_event, socket, *args, **kwargs) - except Exception as exc: - _error_out_jobs(submission_queue, stop_event, exc) - raise - - -def _error_out_jobs( - submission_queue: queue.Queue, stop_event: threading.Event, exc: Exception -): - """Clear out ``submission_queue``, setting errors on all futures.""" - while not stop_event.is_set() or not submission_queue.empty(): - try: - jobinfo = submission_queue.get(timeout=0.05) - except queue.Empty: - pass - else: - jobinfo.future.set_exception(exc) - - -def _submit_flux_jobs( - submission_queue: queue.Queue, - stop_event: threading.Event, - socket: zmq.Socket, - working_dir: str, - flux_executor_kwargs: Mapping, - provider: ExecutionProvider, - executor: FluxExecutor, - flux_path: str, - launch_cmd: str, -): - """Function to be run in a separate thread by executor. - - Pull ``_FluxJobInfo`` job packages from a queue and submit them to Flux. - """ - provider.script_dir = working_dir - job_id = provider.submit( - launch_cmd.format( - port=socket.bind_to_random_port("tcp://*"), - protocol="tcp", - hostname=gethostname(), - python=sys.executable, - flux=flux_path, - manager=_MANAGER_PATH, - ), - 1, - ) - if not job_id: - raise ScalingFailed( - executor, "Attempt to provision nodes via provider has failed", - ) - # wait for the flux package path to be sent - _check_provider_job(socket, provider, job_id) - # receive path to the ``flux`` package from the ZMQ socket - flux_pkg_path = socket.recv().decode() - # load the package. Unfortunately the only good way to do this is to - # modify sys.path - if flux_pkg_path not in sys.path: - sys.path.append(flux_pkg_path) - import flux.job - - socket.send(b"ack") # dummy message - # receive the URI of the Flux instance launched by provider - _check_provider_job(socket, provider, job_id) - flux_instance_uri = socket.recv() - # create a ``flux.job.FluxExecutor`` connected to remote Flux instance - with flux.job.FluxExecutor( - handle_args=(flux_instance_uri,), **flux_executor_kwargs - ) as flux_executor: - # need to ensure that no jobs submitted after stop_event set - # exit loop when event is set and queue is drained - while not stop_event.is_set() or not submission_queue.empty(): - try: - jobinfo = submission_queue.get(timeout=0.05) - except queue.Empty: - pass - else: - _submit_single_job(flux_executor, working_dir, jobinfo) - socket.send(b"shutdown") - - -def _check_provider_job(socket: zmq.Socket, provider: ExecutionProvider, job_id: Any): - """Poll for messages, checking that the provider's allocation is alive.""" - while not socket.poll(1000, zmq.POLLIN): - if provider.status([job_id])[0].terminal: - raise RuntimeError("Provider job has terminated") - - -def _submit_single_job(flux_executor: Any, working_dir: str, jobinfo: _FluxJobInfo): - """Submit a single job to Flux. Link the Flux future with a user-facing future.""" - import flux.job - - jobspec = flux.job.JobspecV1.from_command( - command=[ - sys.executable, - _WORKER_PATH, - "-i", - jobinfo.infile, - "-o", - jobinfo.outfile, - ], - num_tasks=jobinfo.resource_spec.get("num_tasks", 1), - num_nodes=jobinfo.resource_spec.get("num_nodes"), - cores_per_task=jobinfo.resource_spec.get("cores_per_task", 1), - gpus_per_task=jobinfo.resource_spec.get("gpus_per_task"), - ) - jobspec.cwd = os.getcwd() - jobspec.environment = dict(os.environ) - jobspec.stdout = os.path.abspath( - os.path.join(working_dir, f"{jobinfo.task_id}_stdout{os.extsep}txt") - ) - jobspec.stderr = os.path.abspath( - os.path.join(working_dir, f"{jobinfo.task_id}_stderr{os.extsep}txt") - ) - # need to shield user future from cancellation while setting its underlying future - with jobinfo.future._cancellation_lock: - if jobinfo.future.cancelled(): - return - try: - # flux_executor.submit() raises if the executor is broken for any reason - # most importantly, it raises if the remote flux instance dies - flux_future = flux_executor.submit(jobspec) - except Exception as exc: - jobinfo.future.set_exception(exc) - return - jobinfo.future._flux_future = flux_future - # Trigger the user-facing wrapper future to complete when the - # wrapped ``flux.job.FluxExecutor`` future completes. - flux_future.add_done_callback( - functools.partial(_complete_future, jobinfo.outfile, jobinfo.future) - ) diff --git a/parsl/parsl/executors/flux/flux_instance_manager.py b/parsl/parsl/executors/flux/flux_instance_manager.py deleted file mode 100644 index e6111796..00000000 --- a/parsl/parsl/executors/flux/flux_instance_manager.py +++ /dev/null @@ -1,56 +0,0 @@ -"""Script meant to be the initial program of a Flux instance.""" - -import argparse -import logging -import os -from os.path import dirname -from socket import gethostbyname, gethostname - -import zmq - - -def main(): - """Run a Flux instance to completion. - - Send the path to the Flux Python package and the URI of the - encapsulating Flux instance. - """ - # flux imports only available when launched under Flux instance - import flux - import flux.job - - logging.basicConfig( - level=logging.DEBUG, format="%(asctime)s [%(levelname)s] %(message)s" - ) - parser = argparse.ArgumentParser() - parser.add_argument("protocol", help="Protocol of the parent executor's socket") - parser.add_argument("hostname", help="hostname of the parent executor's socket") - parser.add_argument("port", help="Port of the parent executor's socket") - args = parser.parse_args() - with zmq.Context() as context, context.socket(zmq.REQ) as socket: - socket.connect( - args.protocol + "://" + gethostbyname(args.hostname) + ":" + args.port - ) - # send the path to the ``flux.job`` package - socket.send(dirname(dirname(os.path.realpath(flux.__file__))).encode()) - logging.debug("Flux package path sent.") - # collect the encapsulating Flux instance's URI - local_uri = flux.Flux().attr_get("local-uri") - hostname = gethostname() - if args.hostname == hostname: - flux_uri = local_uri - else: - flux_uri = "ssh://" + gethostname() + local_uri.replace("local://", "") - logging.debug("Flux URI is %s", flux_uri) - response = socket.recv() # get acknowledgment - logging.debug("Received acknowledgment %s", response) - socket.send(flux_uri.encode()) # send URI - logging.debug("URI sent. Blocking for response...") - response = socket.recv() # wait for shutdown message - logging.debug("Response %s received, draining flux jobs...", response) - flux.Flux().rpc("job-manager.drain").get() - logging.debug("Flux jobs drained, exiting.") - - -if __name__ == "__main__": - main() diff --git a/parsl/parsl/executors/globus_compute.py b/parsl/parsl/executors/globus_compute.py deleted file mode 100644 index 2e215df7..00000000 --- a/parsl/parsl/executors/globus_compute.py +++ /dev/null @@ -1,134 +0,0 @@ -from __future__ import annotations - -import copy -from concurrent.futures import Future -from typing import Any, Callable, Dict, List, Optional - -import typeguard - -from parsl.data_provider.staging import Staging -from parsl.errors import OptionalModuleMissing -from parsl.executors.base import ParslExecutor -from parsl.utils import RepresentationMixin - -try: - from globus_compute_sdk import Executor - _globus_compute_enabled = True -except ImportError: - _globus_compute_enabled = False - - -class GlobusComputeExecutor(ParslExecutor, RepresentationMixin): - """ GlobusComputeExecutor enables remote execution on Globus Compute endpoints - - GlobusComputeExecutor is a thin wrapper over globus_compute_sdk.Executor - Refer to `globus-compute user documentation `_ - and `reference documentation `_ - for more details. - - .. note:: - As a remote execution system, Globus Compute relies on serialization to ship - tasks and results between the Parsl client side and the remote Globus Compute - Endpoint side. Serialization is unreliable across python versions, and - wrappers used by Parsl assume identical Parsl versions across on both sides. - We recommend using matching Python, Parsl and Globus Compute version on both - the client side and the endpoint side for stable behavior. - - """ - - @typeguard.typechecked - def __init__( - self, - executor: Executor, - label: str = 'GlobusComputeExecutor', - storage_access: Optional[List[Staging]] = None, - working_dir: Optional[str] = None, - ): - """ - Parameters - ---------- - - executor: globus_compute_sdk.Executor - Pass a globus_compute_sdk Executor that will be used to execute - tasks on a globus_compute endpoint. Refer to `globus-compute docs - `_ - - label: - a label to name the executor - - storage_access: - a list of staging providers that will be used for file staging - - working_dir: - The working dir to be used for file staging - """ - if not _globus_compute_enabled: - raise OptionalModuleMissing( - ['globus-compute-sdk'], - "GlobusComputeExecutor requires globus-compute-sdk installed" - ) - - super().__init__() - self.executor: Executor = executor - self.resource_specification = self.executor.resource_specification - self.user_endpoint_config = self.executor.user_endpoint_config - self.label = label - self.storage_access = storage_access - self.working_dir = working_dir - - def submit(self, func: Callable, resource_specification: Dict[str, Any], *args: Any, **kwargs: Any) -> Future: - """ Submit func to globus-compute - - - Parameters - ---------- - - func: Callable - Python function to execute remotely - - resource_specification: Dict[str, Any] - Resource specification can be used specify MPI resources required by MPI applications on - Endpoints configured to use globus compute's MPIEngine. GCE also accepts *user_endpoint_config* - to configure endpoints when the endpoint is a `Multi-User Endpoint - `_ - - args: - Args to pass to the function - - kwargs: - kwargs to pass to the function - - Returns - ------- - - Future - """ - res_spec = copy.deepcopy(resource_specification or self.resource_specification) - # Pop user_endpoint_config since it is illegal in resource_spec for globus_compute - if res_spec: - user_endpoint_config = res_spec.pop('user_endpoint_config', self.user_endpoint_config) - else: - user_endpoint_config = self.user_endpoint_config - - try: - self.executor.resource_specification = res_spec - self.executor.user_endpoint_config = user_endpoint_config - return self.executor.submit(func, *args, **kwargs) - finally: - # Reset executor state to defaults set at configuration time - self.executor.resource_specification = self.resource_specification - self.executor.user_endpoint_config = self.user_endpoint_config - - def shutdown(self): - """Clean-up the resources associated with the Executor. - - GCE.shutdown will cancel all futures that have not yet registered with - Globus Compute and will not wait for the launched futures to complete. - This method explicitly shutsdown the result_watcher thread to avoid - it waiting for outstanding futures at thread exit. - """ - self.executor.shutdown(wait=False, cancel_futures=True) - result_watcher = self.executor._get_result_watcher() - result_watcher.shutdown(wait=False, cancel_futures=True) - - super().shutdown() diff --git a/parsl/parsl/executors/high_throughput/__init__.py b/parsl/parsl/executors/high_throughput/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/executors/high_throughput/errors.py b/parsl/parsl/executors/high_throughput/errors.py deleted file mode 100644 index cb2ba5d2..00000000 --- a/parsl/parsl/executors/high_throughput/errors.py +++ /dev/null @@ -1,52 +0,0 @@ -import time - - -class ManagerLost(Exception): - """ - Task lost due to manager loss. Manager is considered lost when multiple heartbeats - have been missed. - """ - def __init__(self, manager_id: bytes, hostname: str) -> None: - self.manager_id = manager_id - self.tstamp = time.time() - self.hostname = hostname - - def __str__(self) -> str: - return ( - f"Task failure due to loss of manager {self.manager_id.decode()} on" - f" host {self.hostname}" - ) - - -class VersionMismatch(Exception): - """Manager and Interchange versions do not match""" - def __init__(self, interchange_version: str, manager_version: str): - self.interchange_version = interchange_version - self.manager_version = manager_version - - def __str__(self) -> str: - return ( - f"Manager version info {self.manager_version} does not match interchange" - f" version info {self.interchange_version}" - ) - - -class WorkerLost(Exception): - """Exception raised when a worker is lost - """ - def __init__(self, worker_id, hostname): - self.worker_id = worker_id - self.hostname = hostname - - def __str__(self): - return "Task failure due to loss of worker {} on host {}".format(self.worker_id, self.hostname) - - -class CommandClientTimeoutError(Exception): - """Raised when the command client times out waiting for a response. - """ - - -class CommandClientBadError(Exception): - """Raised when the command client is bad from an earlier timeout. - """ diff --git a/parsl/parsl/executors/high_throughput/executor.py b/parsl/parsl/executors/high_throughput/executor.py deleted file mode 100644 index aff0f735..00000000 --- a/parsl/parsl/executors/high_throughput/executor.py +++ /dev/null @@ -1,976 +0,0 @@ -import logging -import math -import os -import pickle -import platform -import subprocess -import sys -import threading -import typing -import warnings -from collections import defaultdict -from concurrent.futures import Future -from dataclasses import dataclass -from pathlib import Path -from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union - -import typeguard - -from parsl import curvezmq -from parsl.addresses import get_all_addresses -from parsl.app.errors import RemoteExceptionWrapper -from parsl.data_provider.staging import Staging -from parsl.executors.errors import ( - BadMessage, - InvalidResourceSpecification, - ScalingFailed, -) -from parsl.executors.high_throughput import zmq_pipes -from parsl.executors.high_throughput.errors import CommandClientTimeoutError -from parsl.executors.high_throughput.manager_selector import ( - ManagerSelector, - RandomManagerSelector, -) -from parsl.executors.status_handling import BlockProviderExecutor -from parsl.jobs.states import TERMINAL_STATES, JobState, JobStatus -from parsl.monitoring.radios.base import RadioConfig -from parsl.monitoring.radios.htex import HTEXRadio -from parsl.monitoring.radios.zmq_router import ZMQRadioReceiver, start_zmq_receiver -from parsl.process_loggers import wrap_with_logs -from parsl.providers import LocalProvider -from parsl.providers.base import ExecutionProvider -from parsl.serialize import deserialize, pack_apply_message -from parsl.serialize.errors import DeserializationError, SerializationError -from parsl.usage_tracking.api import UsageInformation -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - -if platform.system() == 'Windows': - process_worker_pool_script = Path( - sys.executable).parent / 'Scripts' / 'process_worker_pool.py' -else: - process_worker_pool_script = 'process_worker_pool.py' -DEFAULT_LAUNCH_CMD = (f"{process_worker_pool_script} " - "{debug} {max_workers_per_node} " - "-a {addresses} " - "-p {prefetch_capacity} " - "-c {cores_per_worker} " - "-m {mem_per_worker} " - "--poll {poll_period} " - "--port={worker_port} " - "--cert_dir {cert_dir} " - "--logdir={logdir} " - "--logconf={logconf} " - "--block_id={{block_id}} " - "--hb_period={heartbeat_period} " - "{address_probe_timeout_string} " - "--hb_threshold={heartbeat_threshold} " - "--drain_period={drain_period} " - "--cpu-affinity {cpu_affinity} " - "{enable_mpi_mode} " - "--mpi-launcher={mpi_launcher} " - "--available-accelerators {accelerators}") - -DEFAULT_INTERCHANGE_LAUNCH_CMD = ["interchange.py"] - -GENERAL_HTEX_PARAM_DOCS = """provider : :class:`~parsl.providers.base.ExecutionProvider` - Provider to access computation resources. Can be one of :class:`~parsl.providers.aws.aws.EC2Provider`, - :class:`~parsl.providers.condor.condor.Condor`, - :class:`~parsl.providers.googlecloud.googlecloud.GoogleCloud`, - :class:`~parsl.providers.gridEngine.gridEngine.GridEngine`, - :class:`~parsl.providers.local.local.Local`, - :class:`~parsl.providers.sge.sge.GridEngine`, - :class:`~parsl.providers.slurm.slurm.Slurm`, or - :class:`~parsl.providers.torque.torque.Torque`. - - label : str - Label for this executor instance. - - launch_cmd : str - Command line string to launch the process_worker_pool from the provider. The command line string - will be formatted with appropriate values for the following values (debug, task_url, result_url, - cores_per_worker, nodes_per_block, heartbeat_period, heartbeat_threshold, logdir). For example: - launch_cmd="process_worker_pool.py {debug} -c {cores_per_worker} --task_url={task_url} --result_url={result_url}" - - interchange_launch_cmd : Sequence[str] - Custom sequence of command line tokens to launch the interchange process from the executor. If - undefined, the executor will use the default "interchange.py" command. - - address : string - An address to connect to the main Parsl process which is reachable from the network in which - workers will be running. This field expects an IPv4 or IPv6 address. - Most login nodes on clusters have several network interfaces available, only some of which - can be reached from the compute nodes. This field can be used to limit the executor to listen - only on a specific interface, and limiting connections to the internal network. - By default, the executor will attempt to enumerate and connect through all possible addresses. - Setting an address here overrides the default behavior. - default=None - - loopback_address: string - Specify address used for internal communication between executor and interchange. - Supports IPv4 and IPv6 addresses - default=127.0.0.1 - - worker_port : int - Specify the port to be used by workers to connect to Parsl. If this option is specified, - worker_port_range will not be honored. - - worker_port_range : (int, int) - Worker ports will be chosen between the two integers provided. - - interchange_port_range : (int, int) - Port range used by Parsl to communicate with the Interchange. - - working_dir : str - Working dir to be used by the executor. - - worker_debug : Bool - Enables worker debug logging. - - prefetch_capacity : int - Number of tasks that could be prefetched over available worker capacity. - When there are a few tasks (<100) or when tasks are long running, this option should - be set to 0 for better load balancing. Default is 0. - - address_probe_timeout : int | None - Managers attempt connecting over many different addresses to determine a viable address. - This option sets a time limit in seconds on the connection attempt. - Default of None implies 30s timeout set on worker. - - heartbeat_threshold : int - Seconds since the last message from the counterpart in the communication pair: - (interchange, manager) after which the counterpart is assumed to be un-available. Default: 120s - - heartbeat_period : int - Number of seconds after which a heartbeat message indicating liveness is sent to the - counterpart (interchange, manager). Default: 30s - - poll_period : int - Timeout period to be used by the executor components in milliseconds. Increasing poll_periods - trades performance for cpu efficiency. Default: 10ms - - drain_period : int - The number of seconds after start when workers will begin to drain - and then exit. Set this to a time that is slightly less than the - maximum walltime of batch jobs to avoid killing tasks while they - execute. For example, you could set this to the walltime minus a grace - period for the batch job to start the workers, minus the expected - maximum length of an individual task. - - worker_logdir_root : string - In case of a remote file system, specify the path to where logs will be kept. - - encrypted : bool - Flag to enable/disable encryption (CurveZMQ). Default is True. - - manager_selector: ManagerSelector - Determines what strategy the interchange uses to select managers during task distribution. - See API reference under "Manager Selectors" regarding the various manager selectors. - Default: 'RandomManagerSelector' -""" # Documentation for params used by both HTEx and MPIEx - - -class HTEXFuture(Future): - def __init__(self, task_id) -> None: - super().__init__() - self.parsl_executor_task_id = task_id - - -class HighThroughputExecutor(BlockProviderExecutor, RepresentationMixin, UsageInformation): - __doc__ = f"""Executor designed for cluster-scale - - The HighThroughputExecutor system has the following components: - 1. The HighThroughputExecutor instance which is run as part of the Parsl script. - 2. The Interchange which acts as a load-balancing proxy between workers and Parsl - 3. The multiprocessing based worker pool which coordinates task execution over several - cores on a node. - 4. ZeroMQ pipes connect the HighThroughputExecutor, Interchange and the process_worker_pool - - Here is a diagram - - .. code:: python - - - | Data | Executor | Interchange | External Process(es) - | Flow | | | - Task | Kernel | | | - +----->|-------->|------------>|->outgoing_q---|-> process_worker_pool - | | | | batching | | | - Parsl<---Fut-| | | load-balancing| result exception - ^ | | | watchdogs | | | - | | | Result | | | | - | | | Queue | | V V - | | | Thread<--|-incoming_q<---|--- +---------+ - | | | | | | - | | | | | | - +----update_fut-----+ - - - Each of the workers in each process_worker_pool has access to its local rank through - an environmental variable, ``PARSL_WORKER_RANK``. The local rank is unique for each process - and is an integer in the range from 0 to the number of workers per in the pool minus 1. - The workers also have access to the ID of the worker pool as ``PARSL_WORKER_POOL_ID`` - and the size of the worker pool as ``PARSL_WORKER_COUNT``. - - - Parameters - ---------- - - {GENERAL_HTEX_PARAM_DOCS} - - cores_per_worker : float - cores to be assigned to each worker. Oversubscription is possible - by setting cores_per_worker < 1.0. Default=1 - - mem_per_worker : float - GB of memory required per worker. If this option is specified, the node manager - will check the available memory at startup and limit the number of workers such that - the there's sufficient memory for each worker. Default: None - - max_workers_per_node : int - Caps the number of workers launched per node. Default: None - - cpu_affinity: string - Whether or how each worker process sets thread affinity. Options include "none" to forgo - any CPU affinity configuration, "block" to assign adjacent cores to workers - (ex: assign 0-1 to worker 0, 2-3 to worker 1), and - "alternating" to assign cores to workers in round-robin - (ex: assign 0,2 to worker 0, 1,3 to worker 1). - The "block-reverse" option assigns adjacent cores to workers, but assigns - the CPUs with large indices to low index workers (ex: assign 2-3 to worker 1, 0,1 to worker 2) - - available_accelerators: int | list - Accelerators available for workers to use. Each worker will be pinned to exactly one of the provided - accelerators, and no more workers will be launched than the number of accelerators. - - Either provide the list of accelerator names or the number available. If a number is provided, - Parsl will create names as integers starting with 0. - - default: empty list - - """ - - @typeguard.typechecked - def __init__(self, - label: str = 'HighThroughputExecutor', - provider: Optional[ExecutionProvider] = None, - launch_cmd: Optional[str] = None, - interchange_launch_cmd: Optional[Sequence[str]] = None, - address: Optional[str] = None, - loopback_address: str = "127.0.0.1", - worker_port: Optional[int] = None, - worker_port_range: Optional[Tuple[int, int]] = (54000, 55000), - interchange_port_range: Optional[Tuple[int, int]] = (55000, 56000), - storage_access: Optional[List[Staging]] = None, - working_dir: Optional[str] = None, - worker_debug: bool = False, - cores_per_worker: float = 1.0, - mem_per_worker: Optional[float] = None, - max_workers_per_node: Optional[int] = None, - cpu_affinity: str = 'none', - available_accelerators: Union[int, Sequence[str]] = (), - prefetch_capacity: int = 0, - heartbeat_threshold: int = 120, - heartbeat_period: int = 30, - drain_period: Optional[int] = None, - poll_period: int = 10, - address_probe_timeout: Optional[int] = None, - worker_logdir_root: Optional[str] = None, - manager_selector: ManagerSelector = RandomManagerSelector(), - block_error_handler: Union[bool, Callable[[BlockProviderExecutor, Dict[str, JobStatus]], None]] = True, - encrypted: bool = True, - remote_monitoring_radio: Optional[RadioConfig] = None): - - logger.debug("Initializing HighThroughputExecutor") - - BlockProviderExecutor.__init__(self, - provider=provider if provider else LocalProvider(), - block_error_handler=block_error_handler) - self.label = label - self.worker_debug = worker_debug - self.storage_access = storage_access - self.working_dir = working_dir - self.cores_per_worker = cores_per_worker - self.mem_per_worker = mem_per_worker - self.prefetch_capacity = prefetch_capacity - self.address = address - self.address_probe_timeout = address_probe_timeout - self.manager_selector = manager_selector - self.loopback_address = loopback_address - - if self.address: - self.all_addresses = {self.address} - else: - self.all_addresses = get_all_addresses() - - self.max_workers_per_node = max_workers_per_node or float("inf") - - mem_slots = self.max_workers_per_node - cpu_slots = self.max_workers_per_node - assert self.provider is not None, "htex always has a provider" - if hasattr(self.provider, 'mem_per_node') and \ - self.provider.mem_per_node is not None and \ - mem_per_worker is not None and \ - mem_per_worker > 0: - mem_slots = math.floor(self.provider.mem_per_node / mem_per_worker) - if hasattr(self.provider, 'cores_per_node') and \ - self.provider.cores_per_node is not None: - cpu_slots = math.floor(self.provider.cores_per_node / cores_per_worker) - - # Set the list of available accelerators - if isinstance(available_accelerators, int): - # If the user provide an integer, create some names for them - available_accelerators = list(map(str, range(available_accelerators))) - self.available_accelerators = list(available_accelerators) - - # Determine the number of workers per node - self._workers_per_node = min(self.max_workers_per_node, mem_slots, cpu_slots) - if len(self.available_accelerators) > 0: - self._workers_per_node = min(self._workers_per_node, len(available_accelerators)) - if self._workers_per_node == float('inf'): - self._workers_per_node = 1 # our best guess-- we do not have any provider hints - - self._task_counter = 0 - - if remote_monitoring_radio is not None: - self.remote_monitoring_radio = remote_monitoring_radio - else: - self.remote_monitoring_radio = HTEXRadio() - - self.worker_port = worker_port - self.worker_port_range = worker_port_range - self.interchange_proc: Optional[subprocess.Popen] = None - self.interchange_port_range = interchange_port_range - self.heartbeat_threshold = heartbeat_threshold - self.heartbeat_period = heartbeat_period - self.drain_period = drain_period - self.poll_period = poll_period - self.run_dir = '.' - self.worker_logdir_root = worker_logdir_root - self.cpu_affinity = cpu_affinity - self.encrypted = encrypted - self.cert_dir = None - - # This flag will enable/disable internal Python mismatch checks - # between the interchange and worker managers. This serves as a - # temporary workaround for Globus Compute to support different - # Python versions at the endpoint and worker layers. We can drop - # the flag once we implement modular internal message protocols. - self._check_python_mismatch: bool = True - - if not launch_cmd: - launch_cmd = DEFAULT_LAUNCH_CMD - self.launch_cmd = launch_cmd - - if not interchange_launch_cmd: - interchange_launch_cmd = DEFAULT_INTERCHANGE_LAUNCH_CMD - self.interchange_launch_cmd = interchange_launch_cmd - - self._result_queue_thread_exit = threading.Event() - self._result_queue_thread: Optional[threading.Thread] = None - - self.zmq_monitoring: Optional[ZMQRadioReceiver] - self.zmq_monitoring = None - self.hub_zmq_port = None - - enable_mpi_mode: bool = False - mpi_launcher: str = "mpiexec" - - def _warn_deprecated(self, old: str, new: str): - warnings.warn( - f"{old} is deprecated and will be removed in a future release. " - f"Please use {new} instead.", - DeprecationWarning, - stacklevel=2 - ) - - @property - def logdir(self): - return "{}/{}".format(self.run_dir, self.label) - - @property - def worker_logdir(self): - if self.worker_logdir_root is not None: - return "{}/{}".format(self.worker_logdir_root, self.label) - return self.logdir - - def validate_resource_spec(self, resource_specification: dict): - if resource_specification: - """HTEX supports the following *Optional* resource specifications: - priority: lower value is higher priority""" - acceptable_fields = {'priority'} # add new resource spec field names here to make htex accept them - keys = set(resource_specification.keys()) - invalid_keys = keys - acceptable_fields - if invalid_keys: - message = "Task resource specification only accepts these types of resources: {}".format( - ', '.join(acceptable_fields)) - logger.error(message) - raise InvalidResourceSpecification(set(invalid_keys), message) - return - - def initialize_scaling(self): - """Compose the launch command and scale out the initial blocks. - """ - debug_opts = "--debug" if self.worker_debug else "" - max_workers_per_node = "" if self.max_workers_per_node == float('inf') else "--max_workers_per_node={}".format(self.max_workers_per_node) - enable_mpi_opts = "--enable_mpi_mode " if self.enable_mpi_mode else "" - - address_probe_timeout_string = "" - if self.address_probe_timeout: - address_probe_timeout_string = "--address_probe_timeout={}".format(self.address_probe_timeout) - - l_cmd = self.launch_cmd.format(debug=debug_opts, - prefetch_capacity=self.prefetch_capacity, - address_probe_timeout_string=address_probe_timeout_string, - addresses=",".join(self.all_addresses), - worker_port=self.worker_port, - cores_per_worker=self.cores_per_worker, - mem_per_worker=self.mem_per_worker, - max_workers_per_node=max_workers_per_node, - nodes_per_block=self.provider.nodes_per_block, - heartbeat_period=self.heartbeat_period, - heartbeat_threshold=self.heartbeat_threshold, - drain_period=self.drain_period, - poll_period=self.poll_period, - cert_dir=self.cert_dir, - logdir=self.worker_logdir, - logconf=self.logconf_path, - cpu_affinity=self.cpu_affinity, - enable_mpi_mode=enable_mpi_opts, - mpi_launcher=self.mpi_launcher, - accelerators=" ".join(self.available_accelerators)) - self.launch_cmd = l_cmd - logger.debug("Launch command: {}".format(self.launch_cmd)) - - logger.debug("Starting HighThroughputExecutor with provider:\n%s", self.provider) - - def start(self): - """Create the Interchange process and connect to it. - """ - super().start() - if self.encrypted and self.cert_dir is None: - logger.debug("Creating CurveZMQ certificates") - self.cert_dir = curvezmq.create_certificates(self.logdir) - elif not self.encrypted and self.cert_dir: - raise AttributeError( - "The certificates directory path attribute (cert_dir) is defined, but the " - "encrypted attribute is set to False. You must either change cert_dir to " - "None or encrypted to True." - ) - - if self.log_config: - log_config_dir = os.path.join(self.logdir, "log_config") - os.makedirs(log_config_dir, mode=0o700, exist_ok=True) - - self.logconf_path = os.path.join(log_config_dir, "log_config.pkl") - with open(self.logconf_path, "wb") as f: - pickle.dump(self.log_config, f) - else: - self.logconf_path = None - - self.outgoing_q = zmq_pipes.TasksOutgoing( - self.loopback_address, self.interchange_port_range, self.cert_dir - ) - self.incoming_q = zmq_pipes.ResultsIncoming( - self.loopback_address, self.interchange_port_range, self.cert_dir - ) - self.command_client = zmq_pipes.CommandClient( - self.loopback_address, self.interchange_port_range, self.cert_dir - ) - - if self.monitoring_messages is not None: - self.zmq_monitoring = start_zmq_receiver(monitoring_messages=self.monitoring_messages, - loopback_address=self.loopback_address, - port_range=self.interchange_port_range, - logdir=self.logdir, - worker_debug=self.worker_debug, - cert_dir=self.cert_dir, - ) - self.hub_zmq_port = self.zmq_monitoring.port - - self._result_queue_thread = None - self._start_result_queue_thread() - self._start_local_interchange_process() - - self.initialize_scaling() - - @wrap_with_logs - def _result_queue_worker(self): - """Listen to the queue for task result messages and handle them. - - Depending on the message, tasks will be updated with results or exceptions. - - .. code:: python - - { - "task_id" : - "result" : serialized result object, if task succeeded - ... more tags could be added later - } - - { - "task_id" : - "exception" : serialized exception object, on failure - } - """ - logger.debug("Result queue worker starting") - - while not self.bad_state_is_set and not self._result_queue_thread_exit.is_set(): - try: - serialized_msg = self.incoming_q.get(timeout_ms=self.poll_period) - if serialized_msg is None: # timeout - continue - - except IOError as e: - logger.exception("Caught broken queue with exception code {}: {}".format(e.errno, e)) - return - - except Exception as e: - logger.exception("Caught unknown exception: {}".format(e)) - return - - else: - - msg = pickle.loads(serialized_msg) - - if msg['type'] == 'result': - try: - tid = msg['task_id'] - except Exception: - raise BadMessage("Message received does not contain 'task_id' field") - - if tid == -1 and 'exception' in msg: - logger.warning("Executor shutting down due to exception from interchange") - exception = deserialize(msg['exception']) - self.set_bad_state_and_fail_all(exception) - break - - task_fut = self.tasks.pop(tid) - - if 'result' in msg: - result = deserialize(msg['result']) - task_fut.set_result(result) - - elif 'exception' in msg: - try: - s = deserialize(msg['exception']) - # s should be a RemoteExceptionWrapper... so we can reraise it - if isinstance(s, RemoteExceptionWrapper): - try: - s.reraise() - except Exception as e: - task_fut.set_exception(e) - elif isinstance(s, Exception): - task_fut.set_exception(s) - else: - raise ValueError("Unknown exception-like type received: {}".format(type(s))) - except Exception as e: - # TODO could be a proper wrapped exception? - task_fut.set_exception( - DeserializationError("Received exception, but handling also threw an exception: {}".format(e))) - else: - raise BadMessage("Message received is neither result or exception") - else: - raise BadMessage("Message received with unknown type {}".format(msg['type'])) - - logger.info("Closing result ZMQ pipe") - self.incoming_q.close() - logger.info("Result queue worker finished") - - def _start_local_interchange_process(self) -> None: - """ Starts the interchange process locally - - Starts the interchange process locally and uses the command queue to - get the worker task and result ports that the interchange has bound to. - """ - - assert self.interchange_proc is None, f"Already exists! {self.interchange_proc!r}" - - interchange_config = {"client_address": self.loopback_address, - "client_ports": (self.outgoing_q.port, - self.incoming_q.port, - self.command_client.port), - "interchange_address": self.address, - "worker_port": self.worker_port, - "worker_port_range": self.worker_port_range, - "hub_address": self.loopback_address, - "hub_zmq_port": self.hub_zmq_port, - "logdir": self.logdir, - "heartbeat_threshold": self.heartbeat_threshold, - "poll_period": self.poll_period, - "logging_level": logging.DEBUG if self.worker_debug else logging.INFO, - "cert_dir": self.cert_dir, - "manager_selector": self.manager_selector, - "run_id": self.run_id, - "log_config": self.log_config, - "_check_python_mismatch": self._check_python_mismatch, - } - - config_pickle = pickle.dumps(interchange_config) - - self.interchange_proc = subprocess.Popen(self.interchange_launch_cmd, stdin=subprocess.PIPE) - stdin = self.interchange_proc.stdin - assert stdin is not None, "Popen should have created an IO object (vs default None) because of PIPE mode" - - logger.debug("Popened interchange process. Writing config object") - stdin.write(config_pickle) - stdin.flush() - stdin.close() - logger.debug("Sent config object. Requesting worker ports") - try: - self.worker_port = self.command_client.run("WORKER_BINDS", timeout_s=120) - except CommandClientTimeoutError: - logger.error("Interchange has not completed initialization. Aborting") - raise Exception("Interchange failed to start") - logger.debug( - "Interchange process started (%r). Worker port: %d", - self.interchange_proc, - self.worker_port, - ) - - def _start_result_queue_thread(self): - """Method to start the result queue thread as a daemon. - - Checks if a thread already exists, then starts it. - Could be used later as a restart if the result queue thread dies. - """ - assert self._result_queue_thread is None, f"Already exists! {self._result_queue_thread!r}" - - logger.debug("Starting result queue thread") - self._result_queue_thread = threading.Thread(target=self._result_queue_worker, name="HTEX-Result-Queue-Thread") - self._result_queue_thread.daemon = True - self._result_queue_thread.start() - logger.debug("Started result queue thread: %r", self._result_queue_thread) - - def _hold_manager(self, manager_id: str) -> None: - """Puts a manager on hold, preventing scheduling of additional tasks to it. - - This is called "hold" mostly because this only stops scheduling of tasks, - and does not actually kill the manager or workers. - - Parameters - ---------- - - manager_id : str - Manager id to be put on hold - """ - self.command_client.run(["HOLD_MANAGER", manager_id]) - logger.debug("Sent hold request to manager: {}".format(manager_id)) - - def outstanding(self) -> int: - """Returns the count of tasks outstanding across the interchange - and managers""" - return len(self.tasks) - - def connected_managers(self) -> List[Dict[str, typing.Any]]: - """Returns a list of dicts one for each connected managers. - The dict contains info on manager(str:manager_id), block_id, - worker_count, tasks(int), idle_durations(float), active(bool) - """ - return self.command_client.run("MANAGERS") - - def connected_managers_packages(self) -> Dict[str, Dict[str, str]]: - """Returns a dict mapping each manager ID to a dict of installed - packages and their versions - """ - return self.command_client.run("MANAGERS_PACKAGES") - - def connected_blocks(self) -> List[str]: - """List of connected block ids""" - return self.command_client.run("CONNECTED_BLOCKS") - - def _hold_block(self, block_id): - """ Sends hold command to all managers which are in a specific block - - Parameters - ---------- - block_id : str - Block identifier of the block to be put on hold - """ - - managers = self.connected_managers() - - for manager in managers: - if manager['block_id'] == block_id: - logger.debug("Sending hold for manager: %s", manager['manager']) - self._hold_manager(manager['manager']) - - def submit(self, func: Callable, resource_specification: dict, *args, **kwargs) -> HTEXFuture: - """Submits work to the outgoing_q. - - The outgoing_q is an external process listens on this - queue for new work. This method behaves like a submit call as described here `Python docs: `_ - - Args: - - func (callable) : Callable function - - resource_specification (dict): Dictionary containing relevant info about task that is needed by underlying executors. - - args (list) : List of arbitrary positional arguments. - - Kwargs: - - kwargs (dict) : A dictionary of arbitrary keyword args for func. - - Returns: - Future - """ - - self.validate_resource_spec(resource_specification) - - # handle people sending blobs gracefully - if logger.getEffectiveLevel() <= logging.DEBUG: - args_to_print = tuple([ar if len(ar := repr(arg)) < 100 else (ar[:100] + '...') for arg in args]) - logger.debug("Pushing function {} to queue with args {}".format(func, args_to_print)) - - try: - fn_buf = pack_apply_message(func, args, kwargs, buffer_threshold=1 << 20) - except TypeError: - raise SerializationError(func.__name__) - - context = {} - if resource_specification: - context["resource_spec"] = resource_specification - - fu = self.submit_payload(context, fn_buf) - return fu - - def submit_payload(self, context: dict, buffer: bytes) -> HTEXFuture: - """ - Submit specially crafted payloads. - - For use-cases where the ``HighThroughputExecutor`` consumer needs the payload - handled by the worker in a special way. For example, if the function is - serialized differently than Parsl's default approach, or if the task must - be setup more precisely than Parsl's default ``execute_task`` allows. - - An example interaction: - - .. code-block: python - - >>> htex: HighThroughputExecutor # setup prior to this example - >>> ctxt = { - ... "task_executor": { - ... "f": "full.import.path.of.custom_execute_task", - ... "a": ("additional", "arguments"), - ... "k": {"some": "keyword", "args": "here"} - ... } - ... } - >>> fn_buf = custom_serialize(task_func, *task_args, **task_kwargs) - >>> fut = htex.submit_payload(ctxt, fn_buf) - - The custom ``custom_execute_task`` would be dynamically imported, and - invoked as: - - .. code-block: python - - args = ("additional", "arguments") - kwargs = {"some": "keyword", "args": "here"} - result = custom_execute_task(fn_buf, *args, **kwargs) - - Parameters - ---------- - context: - A task-specific context associated with the function buffer. Parsl - currently implements the keys ``task_executor`` and ``resource_spec`` - - buffer: - A serialized function, that will be deserialized and executed by - ``execute_task`` (or custom function, if ``task_executor`` is specified) - - Returns - ------- - An HTEXFuture (a normal Future, with the attribute ``.parsl_executor_task_id`` - set). The future will be set to done when the associated function buffer has - been invoked and completed. - """ - if self.bad_state_is_set: - raise self.executor_exception - - self._task_counter += 1 - task_id = self._task_counter - - fut = HTEXFuture(task_id) - self.tasks[task_id] = fut - - msg = {"task_id": task_id, "context": context, "buffer": buffer} - self.outgoing_q.put(msg) - - return fut - - @property - def workers_per_node(self) -> Union[int, float]: - return self._workers_per_node - - def scale_in(self, blocks: int, max_idletime: Optional[float] = None) -> List[str]: - """Scale in the number of active blocks by specified amount. - - The scale in method here is very rude. It doesn't give the workers - the opportunity to finish current tasks or cleanup. This is tracked - in issue #530 - - Parameters - ---------- - - blocks : int - Number of blocks to terminate and scale_in by - - max_idletime: float - A time to indicate how long a block should be idle to be a - candidate for scaling in. - - If None then blocks will be force scaled in even if they are busy. - - If a float, then only idle blocks will be terminated, which may be less than - the requested number. - - Returns - ------- - List of block IDs scaled in - """ - logger.debug(f"Scale in called, blocks={blocks}") - - @dataclass - class BlockInfo: - tasks: int # sum of tasks in this block - idle: float # shortest idle time of any manager in this block - - # block_info will be populated from two sources: - # the Job Status Poller mutable block list, and the list of blocks - # which have connected to the interchange. - - def new_block_info(): - return BlockInfo(tasks=0, idle=float('inf')) - - block_info: Dict[str, BlockInfo] = defaultdict(new_block_info) - - for block_id, job_status in self._status.items(): - if job_status.state not in TERMINAL_STATES: - block_info[block_id] = new_block_info() - - managers = self.connected_managers() - for manager in managers: - if not manager['active']: - continue - b_id = manager['block_id'] - block_info[b_id].tasks += manager['tasks'] - block_info[b_id].idle = min(block_info[b_id].idle, manager['idle_duration']) - - # The scaling policy is that longest idle blocks should be scaled down - # in preference to least idle (most recently used) blocks. - # Other policies could be implemented here. - - sorted_blocks = sorted(block_info.items(), key=lambda item: (-item[1].idle, item[1].tasks)) - - logger.debug(f"Scale in selecting from {len(sorted_blocks)} blocks") - if max_idletime is None: - block_ids_to_kill = [x[0] for x in sorted_blocks[:blocks]] - else: - block_ids_to_kill = [] - for x in sorted_blocks: - if x[1].idle > max_idletime and x[1].tasks == 0: - block_ids_to_kill.append(x[0]) - if len(block_ids_to_kill) == blocks: - break - - logger.debug("Selected idle block ids to kill: {}".format( - block_ids_to_kill)) - if len(block_ids_to_kill) < blocks: - logger.warning(f"Could not find enough blocks to kill: wanted {blocks} but only selected {len(block_ids_to_kill)}") - - # Hold the block - for block_id in block_ids_to_kill: - self._hold_block(block_id) - - # Now kill via provider - # Potential issue with multiple threads trying to remove the same blocks - to_kill = [self.blocks_to_job_id[bid] for bid in block_ids_to_kill if bid in self.blocks_to_job_id] - - assert self.provider is not None, "htex always has a provider" - r = self.provider.cancel(to_kill) - job_ids = self._filter_scale_in_ids(to_kill, r) - - # to_kill block_ids are fetched from self.blocks_to_job_id - # If a block_id is in self.blocks_to_job_id, it must exist in self.job_ids_to_block - block_ids_killed = [self.job_ids_to_block[jid] for jid in job_ids] - - return block_ids_killed - - def _get_launch_command(self, block_id: str) -> str: - if self.launch_cmd is None: - raise ScalingFailed(self, "No launch command") - launch_cmd = self.launch_cmd.format(block_id=block_id) - return launch_cmd - - def status(self) -> Dict[str, JobStatus]: - job_status = super().status() - connected_blocks = self.connected_blocks() - for job_id in job_status: - job_info = job_status[job_id] - if job_info.terminal and job_id not in connected_blocks and job_info.state != JobState.SCALED_IN: - logger.debug("Rewriting job %s from status %s to MISSING", job_id, job_info) - job_status[job_id].state = JobState.MISSING - if job_status[job_id].message is None: - job_status[job_id].message = ( - "Job is marked as MISSING since the workers failed to register " - "to the executor. Check the stdout/stderr logs in the submit_scripts " - "directory for more debug information" - ) - return job_status - - def shutdown(self, timeout: float = 10.0): - """Shutdown the executor, including the interchange. This does not - shut down any workers directly - workers should be terminated by the - scaling mechanism or by heartbeat timeout. - - Parameters - ---------- - - timeout : float - Amount of time to wait for the Interchange process to terminate before - we forcefully kill it. - """ - if self.interchange_proc is None: - logger.info("HighThroughputExecutor has not started; skipping shutdown") - return - - logger.info("Attempting HighThroughputExecutor shutdown") - - logger.info("Terminating interchange and result queue thread") - self._result_queue_thread_exit.set() - self.interchange_proc.terminate() - try: - self.interchange_proc.wait(timeout=timeout) - except subprocess.TimeoutExpired: - logger.warning("Unable to terminate Interchange process; sending SIGKILL") - self.interchange_proc.kill() - - logger.info("Closing ZMQ pipes") - - # These pipes are used in a thread unsafe manner. If you have traced a - # problem to this block of code, you might consider what is happening - # with other threads that access these. - - # incoming_q is not closed here because it is used by the results queue - # worker which is not shut down at this point. - - if hasattr(self, 'outgoing_q'): - logger.info("Closing outgoing_q") - self.outgoing_q.close() - - if hasattr(self, 'command_client'): - logger.info("Closing command client") - self.command_client.close() - - logger.info("Waiting for result queue thread exit") - if self._result_queue_thread: - self._result_queue_thread.join() - - if self.zmq_monitoring: - self.zmq_monitoring.close() - - super().shutdown() - - logger.info("Finished HighThroughputExecutor shutdown attempt") - - def get_usage_information(self): - return {"mpi": self.enable_mpi_mode} diff --git a/parsl/parsl/executors/high_throughput/interchange.py b/parsl/parsl/executors/high_throughput/interchange.py deleted file mode 100644 index 80b9955a..00000000 --- a/parsl/parsl/executors/high_throughput/interchange.py +++ /dev/null @@ -1,607 +0,0 @@ -#!/usr/bin/env python -import datetime -import logging -import os -import pathlib -import pickle -import platform -import sys -import threading -import time -from typing import Any, Dict, List, Optional, Sequence, Set, Tuple, cast - -import zmq -from sortedcontainers import SortedList - -from parsl import curvezmq -from parsl.addresses import tcp_url -from parsl.app.errors import RemoteExceptionWrapper -from parsl.executors.high_throughput.errors import ManagerLost, VersionMismatch -from parsl.executors.high_throughput.manager_record import ManagerRecord -from parsl.executors.high_throughput.manager_selector import ManagerSelector -from parsl.log_utils import set_file_logger -from parsl.logconfigs.base import LogConfig, oneshot_initialize_logging -from parsl.monitoring.message_type import MessageType -from parsl.monitoring.radios.base import MonitoringRadioSender -from parsl.monitoring.radios.zmq import ZMQRadioSender -from parsl.process_loggers import wrap_with_logs -from parsl.serialize import serialize as serialize_object -from parsl.version import VERSION as PARSL_VERSION - -PKL_HEARTBEAT_CODE = pickle.dumps((2 ** 32) - 1) -PKL_SHUTDOWN_CODE = pickle.dumps((2 ** 32) - 2) - -logger = logging.getLogger("parsl.executors.high_throughput.interchange") - - -class Interchange: - """ Interchange is a task orchestrator for distributed systems. - - 1. Asynchronously queue large volume of tasks (>100K) - 2. Allow for workers to join and leave the union - 3. Detect workers that have failed using heartbeats - """ - def __init__(self, - *, - client_address: str, - interchange_address: Optional[str], - client_ports: Tuple[int, int, int], - worker_port: Optional[int], - worker_port_range: Tuple[int, int], - hub_address: Optional[str], - hub_zmq_port: Optional[int], - heartbeat_threshold: int, - logdir: str, - logging_level: int, - poll_period: int, - cert_dir: Optional[str], - manager_selector: ManagerSelector, - run_id: str, - log_config: Optional[LogConfig], - _check_python_mismatch: bool, - ) -> None: - """ - Parameters - ---------- - client_address : str - The ip address at which the parsl client can be reached. Default: "127.0.0.1" - - interchange_address : Optional str - If specified the interchange will only listen on this address for connections from workers - else, it binds to all addresses. - - client_ports : tuple(int, int, int) - The ports at which the client can be reached - - worker_port : int - The specific port to which workers will connect to the Interchange. - - worker_port_range : tuple(int, int) - The interchange picks ports at random from the range which will be used by workers. - This is overridden when the worker_port option is set. - - hub_address : str - The IP address at which the interchange can send info about managers to when monitoring is enabled. - When None, monitoring is disabled. - - hub_zmq_port : str - The port at which the interchange can send info about managers to when monitoring is enabled. - When None, monitoring is disabled. - - heartbeat_threshold : int - Number of seconds since the last heartbeat after which worker is considered lost. - - logdir : str - Parsl log directory paths. Logs and temp files go here. - - logging_level : int - Logging level as defined in the logging module. - - poll_period : int - The main thread polling period, in milliseconds. - - cert_dir : str | None - Path to the certificate directory. - - _check_python_mismatch : bool - If True, the interchange and worker managers must run the same version of - Python and Parsl. Running different versions can cause inter-process - communication errors, so proceed with caution. - Default: True - """ - self.cert_dir = cert_dir - self.logdir = logdir - os.makedirs(self.logdir, exist_ok=True) - - if log_config: - oneshot_initialize_logging(log_config=log_config, log_dir=pathlib.Path(self.logdir), log_name="interchange") - # discard the returned callback because the interchange doesn't usually do - # a normal shutdown - else: - set_file_logger("{}/interchange.log".format(self.logdir), level=logging_level) - - logger.debug("Initializing Interchange process") - - self.client_address = client_address - self.interchange_address: str = interchange_address or "*" - self.poll_period = poll_period - - logger.info("Attempting connection to client at {} on ports: {},{},{}".format( - client_address, client_ports[0], client_ports[1], client_ports[2])) - self.zmq_context = curvezmq.ServerContext(self.cert_dir) - self.task_incoming = self.zmq_context.socket(zmq.DEALER) - self.task_incoming.set_hwm(0) - self.task_incoming.connect(tcp_url(client_address, client_ports[0])) - self.results_outgoing = self.zmq_context.socket(zmq.DEALER) - self.results_outgoing.set_hwm(0) - self.results_outgoing.connect(tcp_url(client_address, client_ports[1])) - - self.command_channel = self.zmq_context.socket(zmq.REP) - self.command_channel.connect(tcp_url(client_address, client_ports[2])) - logger.info("Connected to client") - - self.run_id = run_id - self._check_python_mismatch = _check_python_mismatch - - self.hub_address = hub_address - self.hub_zmq_port = hub_zmq_port - - self.pending_task_queue: SortedList[Any] = SortedList(key=lambda tup: (tup[0], tup[1])) - - # count of tasks that have been sent out to worker pools - self.count = 0 - - self.manager_sock = self.zmq_context.socket(zmq.ROUTER) - self.manager_sock.set_hwm(0) - - if worker_port: - task_addy = tcp_url(self.interchange_address, worker_port) - self.manager_sock.bind(task_addy) - - else: - worker_port = self.manager_sock.bind_to_random_port( - tcp_url(self.interchange_address), - min_port=worker_port_range[0], - max_port=worker_port_range[1], - max_tries=100, - ) - self.worker_port = worker_port - - logger.info(f"Bound to port {worker_port} for incoming worker connections") - - self._ready_managers: Dict[bytes, ManagerRecord] = {} - self._logged_manager_count_token: object = None - self.connected_block_history: List[str] = [] - - self.heartbeat_threshold = heartbeat_threshold - - self.manager_selector = manager_selector - - self.current_platform = {'parsl_v': PARSL_VERSION, - 'python_v': "{}.{}.{}".format(sys.version_info.major, - sys.version_info.minor, - sys.version_info.micro), - 'os': platform.system(), - 'hostname': platform.node(), - 'dir': os.getcwd()} - - logger.info("Platform info: {}".format(self.current_platform)) - - def get_tasks(self, count: int) -> Sequence[dict]: - """ Obtains a batch of tasks from the internal pending_task_queue - - Parameters - ---------- - count: int - Count of tasks to get from the queue - - Returns - ------- - List of upto count tasks. May return fewer than count down to an empty list - eg. [{'task_id':, 'buffer':} ... ] - """ - tasks = [] - try: - for _ in range(count): - *_, task = self.pending_task_queue.pop() - tasks.append(task) - except IndexError: - pass - - return tasks - - def _send_monitoring_info(self, monitoring_radio: Optional[MonitoringRadioSender], manager: ManagerRecord) -> None: - if monitoring_radio: - logger.info("Sending message {} to MonitoringHub".format(manager)) - - d: Dict = cast(Dict, manager.copy()) - d['timestamp'] = datetime.datetime.now() - d['last_heartbeat'] = datetime.datetime.fromtimestamp(d['last_heartbeat']) - d['run_id'] = self.run_id - - monitoring_radio.send((MessageType.NODE_INFO, d)) - - def process_command(self, monitoring_radio: Optional[MonitoringRadioSender]) -> None: - """ Command server to run async command to the interchange - """ - - reply: Any # the type of reply depends on the command_req received (aka this needs dependent types...) - - if self.socks.get(self.command_channel) == zmq.POLLIN: - logger.debug("entering command_server section") - - command_req = self.command_channel.recv_pyobj() - logger.debug("Received command request: {}".format(command_req)) - if command_req == "CONNECTED_BLOCKS": - reply = self.connected_block_history - - elif command_req == "MANAGERS": - reply = [] - now = time.time() - for manager_id, m in self._ready_managers.items(): - idle_duration = now - (m['idle_since'] or now) - resp = { - 'manager': manager_id.decode('utf-8'), - 'block_id': m['block_id'], - 'worker_count': m['worker_count'], - 'tasks': len(m['tasks']), - 'idle_duration': idle_duration, - 'active': m['active'], - 'parsl_version': m['parsl_version'], - 'python_version': m['python_version'], - 'draining': m['draining'] - } - reply.append(resp) - - elif command_req == "MANAGERS_PACKAGES": - reply = {} - for manager_id, m in self._ready_managers.items(): - manager_id_str = manager_id.decode('utf-8') - reply[manager_id_str] = m["packages"] - - elif isinstance(command_req, list) and len(command_req) == 2 and command_req[0] == "HOLD_MANAGER": - manager_id = command_req[1].encode('utf-8') - logger.info("Received HOLD_MANAGER for {!r}".format(manager_id)) - if manager_id in self._ready_managers: - m = self._ready_managers[manager_id] - m['active'] = False - self._send_monitoring_info(monitoring_radio, m) - else: - logger.warning("Worker to hold was not in ready managers list") - - reply = None - - elif command_req == "WORKER_BINDS": - reply = self.worker_port - - else: - logger.error(f"Received unknown command: {command_req}") - reply = None - - logger.debug("Reply: {}".format(reply)) - self.command_channel.send_pyobj(reply) - - @wrap_with_logs - def start(self) -> None: - """ Start the interchange - """ - - logger.info("Starting main interchange method") - - if self.hub_address is not None and self.hub_zmq_port is not None: - logger.debug("Creating monitoring radio to %s:%s", self.hub_address, self.hub_zmq_port) - monitoring_radio = ZMQRadioSender(self.hub_address, self.hub_zmq_port, self.cert_dir) - logger.debug("Created monitoring radio") - else: - monitoring_radio = None - - poll_period = self.poll_period - - start = time.time() - - kill_event = threading.Event() - - poller = zmq.Poller() - poller.register(self.manager_sock, zmq.POLLIN) - poller.register(self.task_incoming, zmq.POLLIN) - poller.register(self.command_channel, zmq.POLLIN) - - # These are managers which we should examine in an iteration - # for scheduling a job (or maybe any other attention?). - # Anything altering the state of the manager should add it - # onto this list. - interesting_managers: Set[bytes] = set() - - while not kill_event.is_set(): - self.socks = dict(poller.poll(timeout=poll_period)) - - self.process_command(monitoring_radio) - self.process_task_incoming() - self.process_manager_socket_message(interesting_managers, monitoring_radio, kill_event) - self.expire_bad_managers(interesting_managers, monitoring_radio) - self.expire_drained_managers(interesting_managers, monitoring_radio) - self.log_manager_counts(interesting_managers) - self.process_tasks_to_send(interesting_managers, monitoring_radio) - - self.zmq_context.destroy() - delta = time.time() - start - logger.info(f"Processed {self.count} tasks in {delta} seconds") - logger.warning("Exiting") - - def process_task_incoming(self) -> None: - """Process incoming task message(s). - """ - - if self.socks.get(self.task_incoming) == zmq.POLLIN: - logger.debug("start task_incoming section") - msg = self.task_incoming.recv_pyobj() - - # Process priority, higher number = lower priority - task_id = msg['task_id'] - resource_spec = msg['context'].get('resource_spec', {}) - priority = resource_spec.get('priority', float('inf')) - queue_entry = (-priority, -task_id, msg) - - logger.debug("Putting task %s onto pending_task_queue", task_id) - - self.pending_task_queue.add(queue_entry) - logger.debug("Put task %s onto pending_task_queue", task_id) - - def process_manager_socket_message( - self, - interesting_managers: Set[bytes], - monitoring_radio: Optional[MonitoringRadioSender], - kill_event: threading.Event, - ) -> None: - """Process one message from manager on the manager_sock channel.""" - if not self.socks.get(self.manager_sock) == zmq.POLLIN: - return - - logger.debug('starting worker message section') - msg_parts = self.manager_sock.recv_multipart() - try: - manager_id, msg_b = msg_parts - msg = pickle.loads(msg_b) - mtype = msg['type'] - except Exception as e: - logger.warning( - 'Failed to read manager message; ignoring message' - f' (Exception: [{type(e).__name__}] {e})' - ) - logger.debug('Raw message bytes:\n %r\n', msg_parts, exc_info=e) - return - - logger.debug( - 'Processing message type %r from manager %r', mtype, manager_id - ) - - if mtype == 'connection_probe': - self.manager_sock.send_multipart([manager_id, b'']) - return - - elif mtype == 'registration': - ix_minor_py = self.current_platform['python_v'].rsplit('.', 1)[0] - ix_parsl_v = self.current_platform['parsl_v'] - mgr_minor_py = msg['python_v'].rsplit('.', 1)[0] - mgr_parsl_v = msg['parsl_v'] - - # legacy draining handling. remove in ~6 months (ca Apr 2027) - drain_time = msg.get('drain_time', float('inf')) - - new_rec = ManagerRecord( - block_id=None, - start_time=msg['start_time'], - tasks=[], - worker_count=0, - max_capacity=0, - active=True, - draining=False, - drain_time=drain_time, - last_heartbeat=time.time(), - idle_since=time.time(), - parsl_version=mgr_parsl_v, - python_version=msg['python_v'], - ) - - # m is a ManagerRecord, but meta is a dict[Any,Any] and so can - # contain arbitrary fields beyond those in ManagerRecord (and - # indeed does - for example, python_v) which are then ignored - # later. - new_rec.update(msg) - - logger.info(f'Registration info for manager {manager_id!r}: {msg}') - self._send_monitoring_info(monitoring_radio, new_rec) - - python_mismatch: bool = ix_minor_py != mgr_minor_py - parsl_mismatch: bool = ix_parsl_v != mgr_parsl_v - if self._check_python_mismatch and (parsl_mismatch or python_mismatch): - kill_event.set() - vm_exc = VersionMismatch( - f"py.v={ix_minor_py} parsl.v={ix_parsl_v}", - f"py.v={mgr_minor_py} parsl.v={mgr_parsl_v}", - ) - result_package = { - 'type': 'result', - 'task_id': -1, - 'exception': serialize_object(vm_exc), - } - pkl_package = pickle.dumps(result_package) - self.results_outgoing.send(pkl_package) - logger.error( - 'Manager has incompatible version info with the interchange;' - ' sending failure reports and shutting down:' - f'\n Interchange: {vm_exc.interchange_version}' - f'\n Manager: {vm_exc.manager_version}' - ) - - else: - # We really should update the associated data structure; but not - # at this time. *kicks can down the road* - assert new_rec['block_id'] is not None, 'Verified externally' - - # set up entry only if we accept the registration - self._ready_managers[manager_id] = new_rec - self.connected_block_history.append(new_rec['block_id']) - - interesting_managers.add(manager_id) - - logger.info( - f"Registered manager {manager_id!r} (py{mgr_minor_py}," - f" {mgr_parsl_v}) and added to ready queue" - ) - logger.debug("Manager %r -> %s", manager_id, new_rec) - - return - - if not (m := self._ready_managers.get(manager_id)): - logger.warning(f"Ignoring message from unknown manager: {manager_id!r}") - return - - if mtype == 'result': - # process this for task ID and forward to executor - tid = msg['task_id'] - logger.debug("Removing task %s from manager", tid) - try: - m['tasks'].remove(tid) - except Exception: - logger.exception( - 'Ignoring exception removing task_id %s from manager' - ' task list %s', - tid, - m['tasks'] - ) - return - - logger.debug('Sending result on results_outgoing') - self.results_outgoing.send(msg_b) - logger.debug('Sent result on results_outgoing') - - # Result received, so manager now has idle capacity - interesting_managers.add(manager_id) - - if len(m['tasks']) == 0 and m['idle_since'] is None: - m['idle_since'] = time.time() - - self._send_monitoring_info(monitoring_radio, m) - - elif mtype == 'monitoring': - # the monitoring code makes the assumption that no - # monitoring messages will be received if monitoring - # is not configured, and that monitoring_radio will only - # be None when monitoring is not configurated. - assert monitoring_radio is not None - - monitoring_radio.send(msg['payload']) - - elif mtype == 'heartbeat': - m['last_heartbeat'] = time.time() - self.manager_sock.send_multipart([manager_id, PKL_HEARTBEAT_CODE]) - - # legacy draining handling. remove in ~6 months (ca Apr 2027) - elif mtype == 'drain': - m['draining'] = True - - else: - logger.error(f"Unexpected message type received from manager: {mtype}") - - logger.debug("leaving worker message section") - - def expire_drained_managers(self, interesting_managers: Set[bytes], monitoring_radio: Optional[MonitoringRadioSender]) -> None: - now = time.time() - for manager_id, m in list(self._ready_managers.items()): - if not m['draining'] and now >= m['drain_time']: - logger.info(f"Manager {manager_id!r} has reached its drain time - draining") - m['draining'] = True - - if m['draining'] and len(m['tasks']) == 0: - logger.info(f"Manager {manager_id!r} is drained - telling manager to shutdown") - self.manager_sock.send_multipart([manager_id, PKL_SHUTDOWN_CODE]) - interesting_managers.discard(manager_id) - self._ready_managers.pop(manager_id) - - m['active'] = False - self._send_monitoring_info(monitoring_radio, m) - - def log_manager_counts(self, interesting_managers: Set[bytes]) -> None: - count_interesting = len(interesting_managers) - count_ready = len(self._ready_managers) - - new_logged_manager_count_token = (count_interesting, count_ready) - - if self._logged_manager_count_token != new_logged_manager_count_token: - - logger.debug( - "Managers count (interesting/total): %d/%d", - count_interesting, - count_ready - ) - self._logged_manager_count_token = new_logged_manager_count_token - - def process_tasks_to_send(self, interesting_managers: Set[bytes], monitoring_radio: Optional[MonitoringRadioSender]) -> None: - # Check if there are tasks that could be sent to managers - - if interesting_managers and self.pending_task_queue: - shuffled_managers = self.manager_selector.sort_managers(self._ready_managers, interesting_managers) - - while shuffled_managers and self.pending_task_queue: # cf. the if statement above... - manager_id = shuffled_managers.pop() - m = self._ready_managers[manager_id] - tasks_inflight = len(m['tasks']) - real_capacity = m['max_capacity'] - tasks_inflight - - if real_capacity and m["active"] and not m["draining"]: - tasks = self.get_tasks(real_capacity) - if tasks: - self.manager_sock.send_multipart([manager_id, pickle.dumps(tasks)]) - task_count = len(tasks) - self.count += task_count - tids = [t['task_id'] for t in tasks] - m['tasks'].extend(tids) - m['idle_since'] = None - logger.debug("Sent tasks: %s to manager %r", tids, manager_id) - # recompute real_capacity after sending tasks - real_capacity -= task_count - if real_capacity > 0: - logger.debug("Manager %r has free capacity %s", manager_id, real_capacity) - # ... so keep it in the interesting_managers list - else: - logger.debug("Manager %r is now saturated", manager_id) - interesting_managers.remove(manager_id) - self._send_monitoring_info(monitoring_radio, m) - else: - interesting_managers.remove(manager_id) - logger.debug("leaving _ready_managers section, with %s managers still interesting", len(interesting_managers)) - - def expire_bad_managers(self, interesting_managers: Set[bytes], monitoring_radio: Optional[MonitoringRadioSender]) -> None: - bad_managers = [(manager_id, m) for (manager_id, m) in self._ready_managers.items() if - time.time() - m['last_heartbeat'] > self.heartbeat_threshold] - for (manager_id, m) in bad_managers: - logger.debug("Last: {} Current: {}".format(m['last_heartbeat'], time.time())) - logger.warning(f"Too many heartbeats missed for manager {manager_id!r} - removing manager") - if m['active']: - m['active'] = False - self._send_monitoring_info(monitoring_radio, m) - - logger.warning(f"Cancelling htex tasks {m['tasks']} on removed manager") - for tid in m['tasks']: - try: - raise ManagerLost(manager_id, m['hostname']) - except Exception: - result_package = {'type': 'result', 'task_id': tid, 'exception': serialize_object(RemoteExceptionWrapper(*sys.exc_info()))} - pkl_package = pickle.dumps(result_package) - self.results_outgoing.send(pkl_package) - logger.warning("Sent failure reports, unregistering manager") - self._ready_managers.pop(manager_id, 'None') - if manager_id in interesting_managers: - interesting_managers.remove(manager_id) - - -if __name__ == "__main__": - from parsl.utils import setproctitle - - setproctitle("parsl: HTEX interchange") - - config = pickle.load(sys.stdin.buffer) - - ic = Interchange(**config) - ic.start() diff --git a/parsl/parsl/executors/high_throughput/manager_record.py b/parsl/parsl/executors/high_throughput/manager_record.py deleted file mode 100644 index 8f16716a..00000000 --- a/parsl/parsl/executors/high_throughput/manager_record.py +++ /dev/null @@ -1,22 +0,0 @@ -from datetime import datetime -from typing import Any, Dict, List, Optional - -from typing_extensions import TypedDict - - -class ManagerRecord(TypedDict, total=False): - block_id: Optional[str] - start_time: float - tasks: List[Any] - worker_count: int - max_capacity: int - active: bool - draining: bool - drain_time: float - hostname: str - last_heartbeat: float - idle_since: Optional[float] - timestamp: datetime - parsl_version: str - python_version: str - packages: Dict[str, str] diff --git a/parsl/parsl/executors/high_throughput/manager_selector.py b/parsl/parsl/executors/high_throughput/manager_selector.py deleted file mode 100644 index 25a9c49e..00000000 --- a/parsl/parsl/executors/high_throughput/manager_selector.py +++ /dev/null @@ -1,55 +0,0 @@ -import random -from abc import ABCMeta, abstractmethod -from typing import Dict, List, Set - -from parsl.executors.high_throughput.manager_record import ManagerRecord - - -class ManagerSelector(metaclass=ABCMeta): - - @abstractmethod - def sort_managers(self, ready_managers: Dict[bytes, ManagerRecord], manager_list: Set[bytes]) -> List[bytes]: - """ Sort a given list of managers. - - Any operations pertaining to the sorting and rearrangement of the - interesting_managers Set should be performed here. - """ - pass - - -class RandomManagerSelector(ManagerSelector): - - """Returns a shuffled list of interesting_managers - - By default this strategy is used by the interchange. Works well - in distributing workloads equally across all availble compute - resources. The random workload strategy is not effective in - conjunction with elastic scaling behavior as the even task - distribution does not allow the scaling down of blocks, leading - to wasted resource consumption. - """ - - def sort_managers(self, ready_managers: Dict[bytes, ManagerRecord], manager_list: Set[bytes]) -> List[bytes]: - c_manager_list = list(manager_list) - random.shuffle(c_manager_list) - return c_manager_list - - -class BlockIdManagerSelector(ManagerSelector): - - """Returns an interesting_managers list sorted by block ID - - Observations: - 1. BlockID manager selector helps with workloads that see a varying - amount of tasks over time. New blocks are prioritized with the - blockID manager selector, when used with 'htex_auto_scaling', results - in compute cost savings. - - 2. Doesn't really work with bag-of-tasks workloads. When all the tasks - are put into the queue upfront, all blocks operate at near full - utilization for the majority of the workload, which task goes where - doesn't really matter. - """ - - def sort_managers(self, ready_managers: Dict[bytes, ManagerRecord], manager_list: Set[bytes]) -> List[bytes]: - return sorted(manager_list, key=lambda x: (ready_managers[x]['block_id'] is not None, ready_managers[x]['block_id'])) diff --git a/parsl/parsl/executors/high_throughput/monitoring_info.py b/parsl/parsl/executors/high_throughput/monitoring_info.py deleted file mode 100644 index f39f4693..00000000 --- a/parsl/parsl/executors/high_throughput/monitoring_info.py +++ /dev/null @@ -1,9 +0,0 @@ -# this is a global that will be worker-specific -# and can be set to the result queue which can -# then be acquired by any other code running in -# a worker context - specifically the monitoring -# wrapper code. -from queue import Queue -from typing import Optional - -result_queue: Optional[Queue] = None diff --git a/parsl/parsl/executors/high_throughput/mpi_executor.py b/parsl/parsl/executors/high_throughput/mpi_executor.py deleted file mode 100644 index de1aeb8e..00000000 --- a/parsl/parsl/executors/high_throughput/mpi_executor.py +++ /dev/null @@ -1,125 +0,0 @@ -"""A simplified interface for HTEx when running in MPI mode""" -from typing import Callable, Dict, List, Optional, Tuple, Union - -import typeguard - -from parsl.data_provider.staging import Staging -from parsl.executors.high_throughput.executor import ( - GENERAL_HTEX_PARAM_DOCS, - HighThroughputExecutor, -) -from parsl.executors.high_throughput.mpi_prefix_composer import ( - VALID_LAUNCHERS, - validate_resource_spec, -) -from parsl.executors.status_handling import BlockProviderExecutor -from parsl.jobs.states import JobStatus -from parsl.launchers import SimpleLauncher -from parsl.monitoring.radios.base import RadioConfig -from parsl.providers.base import ExecutionProvider - - -class MPIExecutor(HighThroughputExecutor): - __doc__ = f"""A version of :class:`~parsl.HighThroughputExecutor` tuned for executing multi-node (e.g., MPI) tasks. - - The Provider _must_ use the :class:`~parsl.launchers.SimpleLauncher`, - which places a single pool of workers on the first node of a block. - Each worker can then make system calls which use an MPI launcher (e.g., ``mpirun``, ``srun``) - to spawn multi-node tasks. - - Specify the maximum number of multi-node tasks to run at once using ``max_workers_per_block``. - The value should be less than or equal to the ``nodes_per_block`` in the Provider. - - Parameters - ---------- - max_workers_per_block: int - Maximum number of MPI applications to run at once per block - - mpi_launcher: str - Select one from the list of supported MPI launchers: - ("srun", "aprun", "mpiexec"). - default: "mpiexec" - - {GENERAL_HTEX_PARAM_DOCS} - """ - - @typeguard.typechecked - def __init__(self, - label: str = 'MPIExecutor', - provider: Optional[ExecutionProvider] = None, - launch_cmd: Optional[str] = None, - interchange_launch_cmd: Optional[str] = None, - address: Optional[str] = None, - loopback_address: str = "127.0.0.1", - worker_port: Optional[int] = None, - worker_port_range: Optional[Tuple[int, int]] = (54000, 55000), - interchange_port_range: Optional[Tuple[int, int]] = (55000, 56000), - storage_access: Optional[List[Staging]] = None, - working_dir: Optional[str] = None, - worker_debug: bool = False, - max_workers_per_block: int = 1, - prefetch_capacity: int = 0, - heartbeat_threshold: int = 120, - heartbeat_period: int = 30, - drain_period: Optional[int] = None, - poll_period: int = 10, - address_probe_timeout: Optional[int] = None, - worker_logdir_root: Optional[str] = None, - mpi_launcher: str = "mpiexec", - block_error_handler: Union[bool, Callable[[BlockProviderExecutor, Dict[str, JobStatus]], None]] = True, - encrypted: bool = False, - remote_monitoring_radio: Optional[RadioConfig] = None): - super().__init__( - # Hard-coded settings - cores_per_worker=1e-9, # Ensures there will be at least an absurd number of workers - max_workers_per_node=max_workers_per_block, - - # Everything else - label=label, - provider=provider, - launch_cmd=launch_cmd, - interchange_launch_cmd=interchange_launch_cmd, - address=address, - loopback_address=loopback_address, - worker_port=worker_port, - worker_port_range=worker_port_range, - interchange_port_range=interchange_port_range, - storage_access=storage_access, - working_dir=working_dir, - worker_debug=worker_debug, - prefetch_capacity=prefetch_capacity, - heartbeat_threshold=heartbeat_threshold, - heartbeat_period=heartbeat_period, - drain_period=drain_period, - poll_period=poll_period, - address_probe_timeout=address_probe_timeout, - worker_logdir_root=worker_logdir_root, - block_error_handler=block_error_handler, - encrypted=encrypted, - remote_monitoring_radio=remote_monitoring_radio - ) - self.enable_mpi_mode = True - self.mpi_launcher = mpi_launcher - - self.max_workers_per_block = max_workers_per_block - - assert isinstance(self.provider, ExecutionProvider), \ - "HTEX initialization should always make self.provider a provider" - - if not hasattr(self.provider, 'launcher') or not isinstance(self.provider.launcher, SimpleLauncher): - raise TypeError("mpi_mode requires the provider to be configured to use a SimpleLauncher") - - if mpi_launcher not in VALID_LAUNCHERS: - raise ValueError(f"mpi_launcher set to:{mpi_launcher} must be set to one of {VALID_LAUNCHERS}") - - self.mpi_launcher = mpi_launcher - - def validate_resource_spec(self, resource_specification: dict): - return validate_resource_spec(resource_specification) - - def monitor_resources(self): - """Resource monitoring does not make sense when using the - MPIExecutor, as the process tree launched for each task is spread - across multiple OS images/worker nodes. - """ - return False diff --git a/parsl/parsl/executors/high_throughput/mpi_prefix_composer.py b/parsl/parsl/executors/high_throughput/mpi_prefix_composer.py deleted file mode 100644 index 689fbb77..00000000 --- a/parsl/parsl/executors/high_throughput/mpi_prefix_composer.py +++ /dev/null @@ -1,136 +0,0 @@ -import logging -from typing import Dict, List, Tuple - -from parsl.executors.errors import InvalidResourceSpecification - -logger = logging.getLogger(__name__) - -VALID_LAUNCHERS = ('srun', - 'aprun', - 'mpiexec') - - -def validate_resource_spec(resource_spec: Dict[str, str]): - """Basic validation of keys in the resource_spec - - Raises: InvalidResourceSpecification if the resource_spec - is invalid (e.g, contains invalid keys) - """ - user_keys = set(resource_spec.keys()) - - # empty resource_spec when mpi_mode is set causes parsl to hang - # ref issue #3427 - if len(user_keys) == 0: - raise InvalidResourceSpecification(user_keys, - 'MPI mode requires optional parsl_resource_specification keyword argument to be configured') - - legal_keys = set(("ranks_per_node", - "num_nodes", - "num_ranks", - "launcher_options", - )) - invalid_keys = user_keys - legal_keys - if invalid_keys: - raise InvalidResourceSpecification(invalid_keys) - if "num_nodes" in resource_spec: - if not resource_spec.get("num_ranks") and resource_spec.get("ranks_per_node"): - resource_spec["num_ranks"] = str(int(resource_spec["num_nodes"]) * int(resource_spec["ranks_per_node"])) - if not resource_spec.get("ranks_per_node") and resource_spec.get("num_ranks"): - resource_spec["ranks_per_node"] = str(int(resource_spec["num_ranks"]) / int(resource_spec["num_nodes"])) - return - - -def compose_mpiexec_launch_cmd( - resource_spec: Dict, node_hostnames: List[str] -) -> Tuple[str, str]: - """Compose mpiexec launch command prefix""" - - node_str = ",".join(node_hostnames) - args = [ - "mpiexec", - "-n", - resource_spec.get("num_ranks"), - "-ppn", - resource_spec.get("ranks_per_node"), - "-hosts", - node_str, - resource_spec.get("launcher_options", ""), - ] - prefix = " ".join(str(arg) for arg in args) - return "PARSL_MPIEXEC_PREFIX", prefix - - -def compose_srun_launch_cmd( - resource_spec: Dict, node_hostnames: List[str] -) -> Tuple[str, str]: - """Compose srun launch command prefix""" - - num_nodes = str(len(node_hostnames)) - args = [ - "srun", - "--ntasks", - resource_spec.get("num_ranks"), - "--ntasks-per-node", - resource_spec.get("ranks_per_node"), - "--nodelist", - ",".join(node_hostnames), - "--nodes", - num_nodes, - resource_spec.get("launcher_options", ""), - ] - - prefix = " ".join(str(arg) for arg in args) - return "PARSL_SRUN_PREFIX", prefix - - -def compose_aprun_launch_cmd( - resource_spec: Dict, node_hostnames: List[str] -) -> Tuple[str, str]: - """Compose aprun launch command prefix""" - - node_str = ",".join(node_hostnames) - args = [ - "aprun", - "-n", - resource_spec.get("num_ranks"), - "-N", - resource_spec.get("ranks_per_node"), - "-node-list", - node_str, - resource_spec.get("launcher_options", ""), - ] - prefix = " ".join(str(arg) for arg in args) - return "PARSL_APRUN_PREFIX", prefix - - -def compose_all( - mpi_launcher: str, resource_spec: Dict, node_hostnames: List[str] -) -> Dict[str, str]: - """Compose all launch command prefixes and set the default""" - - all_prefixes = {} - composers = [ - compose_aprun_launch_cmd, - compose_srun_launch_cmd, - compose_mpiexec_launch_cmd, - ] - for composer in composers: - try: - key, prefix = composer(resource_spec, node_hostnames) - all_prefixes[key] = prefix - except Exception: - logging.exception( - f"Failed to compose launch prefix with {composer} from {resource_spec}" - ) - pass - - if mpi_launcher == "srun": - all_prefixes["PARSL_MPI_PREFIX"] = all_prefixes["PARSL_SRUN_PREFIX"] - elif mpi_launcher == "aprun": - all_prefixes["PARSL_MPI_PREFIX"] = all_prefixes["PARSL_APRUN_PREFIX"] - elif mpi_launcher == "mpiexec": - all_prefixes["PARSL_MPI_PREFIX"] = all_prefixes["PARSL_MPIEXEC_PREFIX"] - else: - raise RuntimeError(f"Unknown mpi_launcher:{mpi_launcher}") - - return all_prefixes diff --git a/parsl/parsl/executors/high_throughput/mpi_resource_management.py b/parsl/parsl/executors/high_throughput/mpi_resource_management.py deleted file mode 100644 index 0d14b2d1..00000000 --- a/parsl/parsl/executors/high_throughput/mpi_resource_management.py +++ /dev/null @@ -1,211 +0,0 @@ -import logging -import multiprocessing -import os -import pickle -import queue -import subprocess -from dataclasses import dataclass, field -from enum import Enum -from typing import Dict, List, Optional - -from parsl.multiprocessing import SpawnContext - -logger = logging.getLogger(__name__) - - -class Scheduler(Enum): - Unknown = 0 - Slurm = 1 - PBS = 2 - - -def get_slurm_hosts_list() -> List[str]: - """Get list of slurm hosts from scontrol""" - cmd = "scontrol show hostname $SLURM_NODELIST" - b_output = subprocess.check_output( - cmd, stderr=subprocess.STDOUT, shell=True - ) # bytes - output = b_output.decode().strip().split() - return output - - -def get_pbs_hosts_list() -> List[str]: - """Get list of PBS hosts from envvar: PBS_NODEFILE""" - nodefile_name = os.environ["PBS_NODEFILE"] - with open(nodefile_name) as f: - return [line.strip() for line in f.readlines()] - - -def get_nodes_in_batchjob(scheduler: Scheduler) -> List[str]: - """Get nodelist from all supported schedulers""" - nodelist = [] - if scheduler == Scheduler.Slurm: - nodelist = get_slurm_hosts_list() - elif scheduler == Scheduler.PBS: - nodelist = get_pbs_hosts_list() - else: - raise RuntimeError(f"mpi_mode does not support scheduler:{scheduler}") - return nodelist - - -def identify_scheduler() -> Scheduler: - """Use envvars to determine batch scheduler""" - if os.environ.get("SLURM_NODELIST"): - return Scheduler.Slurm - elif os.environ.get("PBS_NODEFILE"): - return Scheduler.PBS - else: - return Scheduler.Unknown - - -class MPINodesUnavailable(Exception): - """Raised if there are no free nodes available for an MPI request""" - - def __init__(self, requested: int, available: int): - self.requested = requested - self.available = available - - def __str__(self): - return f"MPINodesUnavailable(requested={self.requested} available={self.available})" - - -@dataclass(order=True) -class PrioritizedTask: - # Comparing dict will fail since they are unhashable - # This dataclass limits comparison to the priority field - priority: int - task: Dict = field(compare=False) - - -class TaskScheduler: - """Default TaskScheduler that does no taskscheduling - - This class simply acts as an abstraction over the task_q and result_q - that can be extended to implement more complex task scheduling logic - """ - def __init__( - self, - pending_task_q: multiprocessing.Queue, - pending_result_q: multiprocessing.Queue, - ): - self.pending_task_q = pending_task_q - self.pending_result_q = pending_result_q - - def put_task(self, task) -> None: - return self.pending_task_q.put(task) - - def get_result(self, block: bool = True, timeout: Optional[float] = None): - return self.pending_result_q.get(block, timeout) - - -class MPITaskScheduler(TaskScheduler): - """Extends TaskScheduler to schedule MPI functions over provisioned nodes - The MPITaskScheduler runs on a Manager on the lead node of a batch job, as - such it is expected to control task placement over this single batch job. - - The MPITaskScheduler adds the following functionality: - 1) Determine list of nodes attached to current batch job - 2) put_task for execution onto workers: - a) if resources are available attach resource list - b) if unavailable place tasks into backlog - 3) get_result will fetch a result and relinquish nodes, - and attempt to schedule tasks in backlog if any. - """ - def __init__( - self, - pending_task_q: multiprocessing.Queue, - pending_result_q: multiprocessing.Queue, - ): - super().__init__(pending_task_q, pending_result_q) - self.scheduler = identify_scheduler() - # PriorityQueue is threadsafe - self._backlog_queue: queue.PriorityQueue[PrioritizedTask] = queue.PriorityQueue() - self._map_tasks_to_nodes: Dict[str, List[str]] = {} - self.available_nodes = get_nodes_in_batchjob(self.scheduler) - self._free_node_counter = SpawnContext.Value("i", len(self.available_nodes)) - # mp.Value has issues with mypy - # issue https://github.com/python/typeshed/issues/8799 - # from mypy 0.981 onwards - self.nodes_q: queue.Queue = queue.Queue() - for node in self.available_nodes: - self.nodes_q.put(node) - - logger.info( - f"Starting MPITaskScheduler with {len(self.available_nodes)}" - ) - - def _get_nodes(self, num_nodes: int) -> List[str]: - """Thread safe method to acquire num_nodes from free resources - - Raises: MPINodesUnavailable if there aren't enough resources - Returns: List of nodenames:str - """ - logger.debug( - f"Requesting : {num_nodes=} we have {self._free_node_counter}" - ) - acquired_nodes = [] - with self._free_node_counter.get_lock(): - if num_nodes <= self._free_node_counter.value: - self._free_node_counter.value -= num_nodes - else: - raise MPINodesUnavailable( - requested=num_nodes, available=self._free_node_counter.value - ) - - for i in range(num_nodes): - node = self.nodes_q.get() - acquired_nodes.append(node) - return acquired_nodes - - def _return_nodes(self, nodes: List[str]) -> None: - """Threadsafe method to return a list of nodes""" - for node in nodes: - self.nodes_q.put(node) - with self._free_node_counter.get_lock(): - self._free_node_counter.value += len(nodes) - - def put_task(self, task_package: dict): - """Schedule task if resources are available otherwise backlog the task""" - resource_spec = task_package.get("context", {}).get("resource_spec", {}) - - if nodes_needed := resource_spec.get("num_nodes"): - tid = task_package["task_id"] - try: - allocated_nodes = self._get_nodes(nodes_needed) - except MPINodesUnavailable: - logger.info(f"Not enough resources, placing task {tid} into backlog") - self._backlog_queue.put(PrioritizedTask(nodes_needed, task_package)) - return - else: - resource_spec["MPI_NODELIST"] = ",".join(allocated_nodes) - self._map_tasks_to_nodes[tid] = allocated_nodes - - self.pending_task_q.put(task_package) - - def _schedule_backlog_tasks(self): - """Attempt to schedule backlogged tasks""" - try: - prioritized_task = self._backlog_queue.get(block=False) - self.put_task(prioritized_task.task) - except queue.Empty: - return - else: - # Keep attempting to schedule tasks till we are out of resources - self._schedule_backlog_tasks() - - def get_result(self, block: bool = True, timeout: Optional[float] = None): - """Return result and relinquish provisioned nodes""" - result_pkl = self.pending_result_q.get(block, timeout) - if result_pkl is None: - return None - result_dict = pickle.loads(result_pkl) - # TODO (wardlt): If the task did not request nodes, it won't be in `self._map_tasks_to_nodes`. - # Causes Parsl to hang. See Issue #3427 - if result_dict["type"] == "result": - task_id = result_dict["task_id"] - assert task_id in self._map_tasks_to_nodes, "You are about to experience issue #3427" - nodes_to_reallocate = self._map_tasks_to_nodes[task_id] - self._return_nodes(nodes_to_reallocate) - self._schedule_backlog_tasks() - - return result_pkl diff --git a/parsl/parsl/executors/high_throughput/probe.py b/parsl/parsl/executors/high_throughput/probe.py deleted file mode 100644 index 52582b7d..00000000 --- a/parsl/parsl/executors/high_throughput/probe.py +++ /dev/null @@ -1,65 +0,0 @@ -import logging -import pickle -from contextlib import ExitStack -from typing import Optional - -import zmq - -from parsl import curvezmq - -logger = logging.getLogger(__name__) - - -def probe_addresses( - zmq_context: curvezmq.ClientContext, - addresses: set[str], - timeout_ms: int = 120_000, - identity: Optional[bytes] = None, -): - """ - Given a set of addresses, return the first proven valid address. - - This function will connect to each address in ``addresses`` and attempt to send a - CONNECTION_PROBE packet. Returns the first address that receives a response. - - If no address receives a response within the ``timeout_ms`` (specified in - milliseconds), then raise ``ConnectionError``. - - :param zmq_context: A ZMQ Context; the call-site may provide an encrypted ZMQ - context for assurance that the returned address is the expected and correct - endpoint - :param addresses: a set of addresses to attempt. Example: - ``{"tcp://127.0.0.1:1234", "tcp://[3812::03aa]:5678"}`` - :param timeout_ms: how long to wait for a response from the probes. The probes - are initiated and await concurrently, so this timeout will be the total wall - time in the worst case of "no addresses are valid." - :param identity: a ZMQ connection identity; used for logging connection probes - at the interchange - :raises: ``ConnectionError`` if no addresses are determined valid - :returns: a single address, the first one that received a response - """ - if not addresses: - raise ValueError("No address to probe!") - - sock_map = {} - with ExitStack() as stk: - poller = zmq.Poller() - for url in addresses: - logger.debug("Testing ZMQ connection to url: %s", url) - s: zmq.Socket = stk.enter_context(zmq_context.socket(zmq.DEALER)) - s.setsockopt(zmq.LINGER, 0) - s.setsockopt(zmq.IPV6, True) - if identity: - s.setsockopt(zmq.IDENTITY, identity) - stk.enter_context(s.connect(url)) - poller.register(s, zmq.POLLIN) - sock_map[s] = url - - s.send(pickle.dumps({'type': 'connection_probe'})) - - for sock, evt in poller.poll(timeout=timeout_ms): - sock.recv() # clear the buffer for good netizenry - return sock_map.get(sock) - - addys = ", ".join(addresses) # just slightly more human friendly - raise ConnectionError(f"No viable ZMQ url from: {addys}") diff --git a/parsl/parsl/executors/high_throughput/process_worker_pool.py b/parsl/parsl/executors/high_throughput/process_worker_pool.py deleted file mode 100755 index b747db96..00000000 --- a/parsl/parsl/executors/high_throughput/process_worker_pool.py +++ /dev/null @@ -1,1016 +0,0 @@ -#!/usr/bin/env python3 - -import argparse -import importlib -import logging -import math -import multiprocessing -import os -import pathlib -import pickle -import platform -import queue -import subprocess -import sys -import threading -import time -import uuid -from importlib.metadata import distributions -from multiprocessing.context import SpawnProcess -from multiprocessing.managers import DictProxy -from multiprocessing.sharedctypes import Synchronized -from typing import Callable, Dict, List, Optional, Sequence, Union - -import psutil -import zmq - -from parsl import curvezmq -from parsl.addresses import tcp_url -from parsl.app.errors import RemoteExceptionWrapper -from parsl.executors.execute_task import execute_task -from parsl.executors.high_throughput.errors import WorkerLost -from parsl.executors.high_throughput.mpi_prefix_composer import ( - VALID_LAUNCHERS, - compose_all, -) -from parsl.executors.high_throughput.mpi_resource_management import ( - MPITaskScheduler, - TaskScheduler, -) -from parsl.executors.high_throughput.probe import probe_addresses -from parsl.log_utils import set_file_logger -from parsl.logconfigs.base import LogConfig, oneshot_initialize_logging -from parsl.multiprocessing import SpawnContext -from parsl.process_loggers import wrap_with_logs -from parsl.serialize import serialize -from parsl.version import VERSION as PARSL_VERSION - -HEARTBEAT_CODE = (2 ** 32) - 1 -SHUTDOWN_CODE = (2 ** 32) - 2 - -# this is specified explicitly because when this file is run as a script (the -# expected use), __name__ which is the normal idiom here is set to __main__, -# rather than a parsl submodule name. -logger = logging.getLogger("parsl.executors.high_throughput.process_worker_pool") - - -class Manager: - """ Manager manages task execution by the workers - - | zmq | Manager | Worker Processes - | | | - | <-----Register with -----+ | Request task<--+ - | N task capacity | | | | - Interchange | -------------------------+->Receive task batch| | | - | | Distribute tasks--+----> Get(block) & | - | | | Execute task | - | | | | | - | <------------------------+--Return results----+---- Post result | - | | | | | - | | | +----------+ - | | IPC-Qeueues - - """ - def __init__(self, *, - addresses: str, - address_probe_timeout: int, - port: int, - cores_per_worker, - mem_per_worker: Optional[float], - max_workers_per_node, - prefetch_capacity: int, - uid: str, - block_id: str, - heartbeat_threshold: int, - heartbeat_period: int, - poll_period: int, - cpu_affinity: str, - enable_mpi_mode: bool = False, - mpi_launcher: str = "mpiexec", - available_accelerators: Sequence[str], - cert_dir: Optional[str], - drain_period: Optional[int], - log_config: Optional[LogConfig]) -> None: - """ - Parameters - ---------- - addresses : str - comma separated list of addresses for the interchange - - address_probe_timeout : int - Timeout in seconds for the address probe to detect viable addresses - to the interchange. - - uid : str - string unique identifier - - block_id : str - Block identifier that maps managers to the provider blocks they belong to. - - cores_per_worker : float - cores to be assigned to each worker. Oversubscription is possible - by setting cores_per_worker < 1.0. - - mem_per_worker : optional float - GB of memory required per worker. If this option is specified, the node manager - will check the available memory at startup and limit the number of workers such that - the there's sufficient memory for each worker. If set to None, memory on node is not - considered in the determination of workers to be launched on node by the manager. - - max_workers_per_node : int | float - Caps the maximum number of workers that can be launched. - - prefetch_capacity : int - Number of tasks that could be prefetched over available worker capacity. - When there are a few tasks (<100) or when tasks are long running, this option should - be set to 0 for better load balancing. - - heartbeat_threshold : int - Seconds since the last message from the interchange after which the - interchange is assumed to be un-available, and the manager initiates shutdown. - - Number of seconds since the last message from the interchange after which the worker - assumes that the interchange is lost and the manager shuts down. - - heartbeat_period : int - Number of seconds after which a heartbeat message is sent to the interchange, and workers - are checked for liveness. - - poll_period : int - Timeout period used by the manager in milliseconds. - - cpu_affinity : str - Whether or how each worker should force its affinity to different CPUs - - available_accelerators: list of str - List of accelerators available to the workers. - - enable_mpi_mode: bool - When set to true, the manager assumes ownership of the batch job and each worker - claims a subset of nodes from a shared pool to execute multi-node mpi tasks. Node - info is made available to workers via env vars. - - mpi_launcher: str - Set to one of the supported MPI launchers: ("srun", "aprun", "mpiexec") - - cert_dir : str | None - Path to the certificate directory. - - drain_period: int | None - Number of seconds to drain after TODO: could be a nicer timespec involving m,s,h qualifiers for user friendliness? - """ - - logger.info("Manager initializing") - - self._start_time = time.time() - - self.log_config = log_config - - self.cert_dir = cert_dir - self.zmq_context = curvezmq.ClientContext(self.cert_dir) - - address_set = {tcp_url(a, port) for a in addresses.split(',')} - try: - self._ix_url = probe_addresses( - self.zmq_context, - address_set, - timeout_ms=1_000 * address_probe_timeout, - identity=uid.encode('utf-8'), - ) - except ConnectionError: - addys = ", ".join(addresses.split(",")) - logger.error(f"Unable to connect to interchange; attempted addresses: {addys}") - raise - - logger.info(f"Probe discovered interchange url: {self._ix_url}") - - self.uid = uid - self.block_id = block_id - self.start_time = time.time() - - self.enable_mpi_mode = enable_mpi_mode - self.mpi_launcher = mpi_launcher - - if os.environ.get('PARSL_CORES'): - cores_on_node = int(os.environ['PARSL_CORES']) - else: - cores_on_node = SpawnContext.cpu_count() - - if os.environ.get('PARSL_MEMORY_GB'): - available_mem_on_node = float(os.environ['PARSL_MEMORY_GB']) - else: - available_mem_on_node = round(psutil.virtual_memory().available / (2**30), 1) - - self.max_workers_per_node = max_workers_per_node - self.prefetch_capacity = prefetch_capacity - - mem_slots = max_workers_per_node - # Avoid a divide by 0 error. - if mem_per_worker and mem_per_worker > 0: - mem_slots = math.floor(available_mem_on_node / mem_per_worker) - - self.worker_count: int = min(max_workers_per_node, - mem_slots, - math.floor(cores_on_node / cores_per_worker)) - - self._mp_manager = SpawnContext.Manager() # Starts a server process - self._tasks_in_progress = self._mp_manager.dict() - self._stop_event = threading.Event() # when set, will begin shutdown process - - self.monitoring_queue = self._mp_manager.Queue() - self.pending_task_queue = SpawnContext.Queue() - self.pending_result_queue = SpawnContext.Queue() - self.task_scheduler: TaskScheduler - if self.enable_mpi_mode: - self.task_scheduler = MPITaskScheduler( - self.pending_task_queue, - self.pending_result_queue, - ) - else: - self.task_scheduler = TaskScheduler( - self.pending_task_queue, - self.pending_result_queue - ) - self.ready_worker_count = SpawnContext.Value("i", 0) - - self.heartbeat_period = heartbeat_period - self.heartbeat_threshold = heartbeat_threshold - self.poll_period = poll_period - - self.drain_time: float - if drain_period: - self.drain_time = self._start_time + drain_period - logger.info(f"Will request drain at {self.drain_time}") - else: - self.drain_time = float('inf') - - self.cpu_affinity = cpu_affinity - - # Define accelerator available, adjust worker count accordingly - self.available_accelerators = available_accelerators - self.accelerators_available = len(available_accelerators) > 0 - if self.accelerators_available: - self.worker_count = min(len(self.available_accelerators), self.worker_count) - logger.info("Manager will spawn {} workers".format(self.worker_count)) - - def create_reg_message(self) -> dict: - """ Creates a registration message to identify the worker to the interchange - """ - return { - 'type': 'registration', - 'parsl_v': PARSL_VERSION, - 'python_v': "{}.{}.{}".format( - sys.version_info.major, - sys.version_info.minor, - sys.version_info.micro - ), - 'packages': {d.metadata['Name']: d.version for d in distributions()}, - 'worker_count': self.worker_count, - 'uid': self.uid, - 'block_id': self.block_id, - 'start_time': self.start_time, - 'drain_time': self.drain_time, - 'prefetch_capacity': self.prefetch_capacity, - 'max_capacity': self.worker_count + self.prefetch_capacity, - 'os': platform.system(), - 'hostname': platform.node(), - 'dir': os.getcwd(), - 'cpu_count': psutil.cpu_count(logical=False), - 'total_memory': psutil.virtual_memory().total, - } - - @staticmethod - def heartbeat_to_incoming(task_incoming: zmq.Socket) -> None: - """ Send heartbeat to the incoming task queue - """ - # don't need to dumps and encode this every time - could do as a global on import? - b_msg = pickle.dumps({'type': 'heartbeat'}) - task_incoming.send(b_msg) - logger.debug("Sent heartbeat") - - @wrap_with_logs - def interchange_communicator(self, pair_setup: threading.Event) -> None: - """ Pull tasks from the incoming tasks zmq pipe onto the internal - pending task queue - """ - logger.info("starting") - - results_sock = self.zmq_context.socket(zmq.PAIR) - results_sock.setsockopt(zmq.LINGER, 0) - results_sock.bind("inproc://results") - pair_setup.set() - - # Linger is set to 0, so that the manager can exit even when there might be - # messages in the pipe - ix_sock = self.zmq_context.socket(zmq.DEALER) - ix_sock.setsockopt(zmq.IDENTITY, self.uid.encode('utf-8')) - ix_sock.setsockopt(zmq.LINGER, 0) - ix_sock.connect(self._ix_url) - logger.info("Manager task pipe connected to interchange") - - poller = zmq.Poller() - poller.register(results_sock, zmq.POLLIN) - poller.register(ix_sock, zmq.POLLIN) - - ix_sock.send(pickle.dumps({"type": "connection_probe"})) - evts = dict(poller.poll(timeout=self.heartbeat_period)) - if evts.get(ix_sock) is None: - logger.error(f"Failed to connect to interchange ({self._ix_url}") - - ix_sock.recv() - logger.info(f"Successfully connected to interchange via URL: {self._ix_url}") - - # Send a registration message - msg = self.create_reg_message() - logger.debug("Sending registration message: %s", msg) - ix_sock.send(pickle.dumps(msg)) - last_beat = time.time() - last_interchange_contact = time.time() - task_recv_counter = 0 - - while not self._stop_event.is_set(): - - # This loop will sit inside poller.poll until either a message - # arrives or one of these event times is reached. This code - # assumes that the event times won't change except on iteration - # of this loop - so will break if a different thread does - # anything to bring one of the event times earlier - and that the - # time here are correctly copy-pasted from the relevant if - # statements. - next_interesting_event_time = min(last_beat + self.heartbeat_period, - self.drain_time, # legacy, remove ca Apr 2027 - last_interchange_contact + self.heartbeat_threshold) - try: - pending_task_count = self.pending_task_queue.qsize() - except NotImplementedError: - # Ref: https://github.com/python/cpython/blob/6d5e0dc0e330f4009e8dc3d1642e46b129788877/Lib/multiprocessing/queues.py#L125 - pending_task_count = f"pending task count is not available on {platform.system()}" # type: ignore[assignment] - - logger.debug( - f'ready workers: {self.ready_worker_count.value}, pending tasks: {pending_task_count}' - ) - - if time.time() >= last_beat + self.heartbeat_period: - self.heartbeat_to_incoming(ix_sock) - last_beat = time.time() - - # legacy draining handling. remove in ~6 months (ca Apr 2027) - if time.time() > self.drain_time: - # ask to start draining (old interchanges don't track that for us). this - # stops ours from sending more tasks; it will tell us to shut down when - # we finish all active work. - logger.info("Requesting drain") - ix_sock.send(pickle.dumps({'type': 'drain'})) - logger.debug("Sent drain") - - # now set drain time to the far future so we don't send a drain - # message every iteration. - self.drain_time = float('inf') - - poll_duration_s = max(0, next_interesting_event_time - time.time()) - socks = dict(poller.poll(timeout=math.floor(poll_duration_s * 1000))) - - if socks.get(ix_sock) == zmq.POLLIN: - pkl_msg = ix_sock.recv() - tasks = pickle.loads(pkl_msg) - del pkl_msg - - last_interchange_contact = time.time() - - if tasks == HEARTBEAT_CODE: - logger.debug("Got heartbeat response from interchange") - elif tasks == SHUTDOWN_CODE: - logger.info("Got shutdown message from interchange - setting kill flag") - self._stop_event.set() - else: - task_recv_counter += len(tasks) - logger.debug("Got executor tasks: {}, cumulative count of tasks: {}".format( - [t['task_id'] for t in tasks], task_recv_counter - )) - - for task in tasks: - self.task_scheduler.put_task(task) - - elif socks.get(results_sock) == zmq.POLLIN: - ix_sock.send(results_sock.recv()) - logger.debug("Result sent to interchange") - - else: - logger.debug("No incoming tasks") - - # Only check if no messages were received. - if time.time() >= last_interchange_contact + self.heartbeat_threshold: - logger.critical("Missing contact with interchange beyond heartbeat_threshold") - self._stop_event.set() - logger.critical("Exiting") - break - - ix_sock.close() - logger.info("Exiting") - - @wrap_with_logs - def ferry_result(self, may_connect: threading.Event) -> None: - """ Listens on the pending_result_queue and ferries results to the interchange - connected thread - """ - logger.debug("Begin") - - # Linger is set to 0, so that the manager can exit even when there might be - # messages in the pipe - notify_sock = self.zmq_context.socket(zmq.PAIR) - notify_sock.setsockopt(zmq.LINGER, 0) - may_connect.wait() - notify_sock.connect("inproc://results") - - while not self._stop_event.is_set(): - try: - r = self.task_scheduler.get_result() - if r is None: - continue - logger.debug("Result received from worker") - notify_sock.send(r) - except Exception: - logger.exception("Failed to send result to interchange") - - notify_sock.close() - logger.debug("Exiting") - - def worker_watchdog(self, procs: dict[int, SpawnProcess]) -> None: - """Keeps workers alive.""" - logger.debug("Starting worker watchdog") - - while not self._stop_event.wait(self.heartbeat_period): - for worker_id, p in procs.items(): - if not p.is_alive(): - logger.error("Worker {} has died".format(worker_id)) - try: - task = self._tasks_in_progress.pop(worker_id) - logger.info("Worker {} was busy when it died".format(worker_id)) - try: - raise WorkerLost(worker_id, platform.node()) - except Exception: - logger.info("Putting exception for executor task {} in the pending result queue".format(task['task_id'])) - result_package = {'type': 'result', - 'task_id': task['task_id'], - 'exception': serialize(RemoteExceptionWrapper(*sys.exc_info()))} - pkl_package = pickle.dumps(result_package) - self.pending_result_queue.put(pkl_package) - del pkl_package - except KeyError: - logger.info("Worker {} was not busy when it died".format(worker_id)) - - procs[worker_id] = self._start_worker(worker_id) - logger.info("Worker {} has been restarted".format(worker_id)) - - logger.debug("Exiting") - - @wrap_with_logs - def handle_monitoring_messages(self) -> None: - """Transfer messages from the managed monitoring queue to the result queue. - - We separate the queues so that the result queue does not rely on a manager - process, which adds overhead that causes slower queue operations but enables - use across processes started in fork and spawn contexts. - - We transfer the messages to the result queue to reuse the ZMQ connection between - the manager and the interchange. - """ - logger.debug("Starting monitoring handler thread") - - while not self._stop_event.is_set(): - try: - logger.debug("Starting monitor_queue.get()") - msg = self.monitoring_queue.get(block=True) - if msg is None: - continue - logger.debug("Got a monitoring message") - self.pending_result_queue.put(msg) - logger.debug("Put monitoring message on pending_result_queue") - except Exception: - logger.exception("Failed to forward monitoring message") - - logger.debug("Exiting") - - def start(self) -> None: - """ Start the worker processes. - """ - procs: dict[int, SpawnProcess] = {} - for worker_id in range(self.worker_count): - procs[worker_id] = self._start_worker(worker_id) - - logger.debug("Workers started") - - pair_setup = threading.Event() - - thr_task_puller = threading.Thread( - target=self.interchange_communicator, - args=(pair_setup,), - name="Interchange-Communicator", - ) - thr_result_ferry = threading.Thread( - target=self.ferry_result, args=(pair_setup,), name="Result-Shovel") - thr_worker_watchdog = threading.Thread( - target=self.worker_watchdog, args=(procs,), name="worker-watchdog" - ) - thr_monitoring_handler = threading.Thread( - target=self.handle_monitoring_messages, name="Monitoring-Handler" - ) - - thr_task_puller.start() - thr_result_ferry.start() - thr_worker_watchdog.start() - thr_monitoring_handler.start() - - logger.info("Manager threads started") - - # This might need a multiprocessing event to signal back. - self._stop_event.wait() - logger.info("Stop event set; terminating worker processes") - - # Invite blocking threads to quit - self.monitoring_queue.put(None) - self.pending_result_queue.put(None) - - thr_monitoring_handler.join() - thr_worker_watchdog.join() - thr_result_ferry.join() - thr_task_puller.join() - - for worker_id in procs: - p = procs[worker_id] - proc_info = f"(PID: {p.pid}, Worker ID: {worker_id})" - logger.debug(f"Signaling worker {p.name} (TERM). {proc_info}") - p.terminate() - - self.zmq_context.term() - - # give processes 1 second to gracefully shut themselves down, based on the - # SIGTERM (.terminate()) just sent; after then, we pull the plug. - force_child_shutdown_at = time.monotonic() + 1 - while procs: - worker_id, p = procs.popitem() - timeout = max(force_child_shutdown_at - time.monotonic(), 0.000001) - p.join(timeout=timeout) - proc_info = f"(PID: {p.pid}, Worker ID: {worker_id})" - if p.exitcode is not None: - logger.debug( - "Worker joined successfully. %s (exitcode: %s)", proc_info, p.exitcode - ) - - else: - logger.warning( - f"Worker {p.name} ({worker_id}) failed to terminate in a timely" - f" manner; sending KILL signal to process. {proc_info}" - ) - p.kill() - p.join() - p.close() - - delta = time.time() - self._start_time - logger.info("process_worker_pool ran for {} seconds".format(delta)) - - def _start_worker(self, worker_id: int) -> SpawnProcess: - p = SpawnContext.Process( - target=worker, - args=( - worker_id, - self.uid, - self.worker_count, - self.pending_task_queue, - self.pending_result_queue, - self.monitoring_queue, - self.ready_worker_count, - self._tasks_in_progress, - self.cpu_affinity, - self.available_accelerators[worker_id] if self.accelerators_available else None, - self.block_id, - self.heartbeat_period, - os.getpid(), - args.logdir, - self.log_config, - args.debug, - self.mpi_launcher, - ), - name="HTEX-Worker-{}".format(worker_id), - ) - p.start() - return p - - -def update_resource_spec_env_vars(mpi_launcher: str, resource_spec: Dict, node_info: List[str]) -> None: - prefix_table = compose_all(mpi_launcher, resource_spec=resource_spec, node_hostnames=node_info) - for key in prefix_table: - os.environ[key] = prefix_table[key] - - -def _init_mpi_env(mpi_launcher: str, resource_spec: Dict) -> None: - for varname in resource_spec: - envname = "PARSL_" + str(varname).upper() - os.environ[envname] = str(resource_spec[varname]) - - node_list = resource_spec.get("MPI_NODELIST") - if node_list is None: - return - nodes_for_task = node_list.split(',') - logger.info(f"Launching task on provisioned nodes: {nodes_for_task}") - update_resource_spec_env_vars(mpi_launcher=mpi_launcher, resource_spec=resource_spec, node_info=nodes_for_task) - - -@wrap_with_logs -def worker( - worker_id: int, - pool_id: str, - pool_size: int, - task_queue: multiprocessing.Queue, - result_queue: multiprocessing.Queue, - monitoring_queue: queue.Queue, - ready_worker_count: Synchronized, - tasks_in_progress: DictProxy, - cpu_affinity: str, - accelerator: Optional[str], - block_id: str, - task_queue_timeout: int, - manager_pid: int, - logdir: str, - log_config: LogConfig, - debug: bool, - mpi_launcher: str, -) -> None: - - if log_config: - path = pathlib.Path(logdir) / f"block-{block_id}" / pool_id - oneshot_initialize_logging(log_config=log_config, log_dir=path, log_name=f"worker_{worker_id}") - else: - set_file_logger('{}/block-{}/{}/worker_{}.log'.format(logdir, block_id, pool_id, worker_id), - level=logging.DEBUG if debug else logging.INFO) - - # Store worker ID as an environment variable - os.environ['PARSL_WORKER_RANK'] = str(worker_id) - os.environ['PARSL_WORKER_COUNT'] = str(pool_size) - os.environ['PARSL_WORKER_POOL_ID'] = str(pool_id) - os.environ['PARSL_WORKER_BLOCK_ID'] = str(block_id) - - import parsl.executors.high_throughput.monitoring_info as mi - mi.result_queue = monitoring_queue - - logger.info('Worker {} started'.format(worker_id)) - if debug: - logger.debug("Debug logging enabled") - - # If desired, set process affinity - if cpu_affinity != "none": - # Count the number of cores per worker - # OSX does not implement os.sched_getaffinity - avail_cores = sorted(os.sched_getaffinity(0)) # type: ignore[attr-defined, unused-ignore] - cores_per_worker = len(avail_cores) // pool_size - assert cores_per_worker > 0, "Affinity does not work if there are more workers than cores" - - # Determine this worker's cores - if cpu_affinity == "block": - my_cores = avail_cores[cores_per_worker * worker_id:cores_per_worker * (worker_id + 1)] - elif cpu_affinity == "block-reverse": - cpu_worker_id = pool_size - worker_id - 1 # To assign in reverse order - my_cores = avail_cores[cores_per_worker * cpu_worker_id:cores_per_worker * (cpu_worker_id + 1)] - elif cpu_affinity == "alternating": - my_cores = avail_cores[worker_id::pool_size] - elif cpu_affinity[0:4] == "list": - thread_ranks = cpu_affinity.split(":")[1:] - if len(thread_ranks) != pool_size: - raise ValueError("Affinity list {} has wrong number of thread ranks".format(cpu_affinity)) - threads = thread_ranks[worker_id] - thread_list = threads.split(",") - my_cores = [] - for tl in thread_list: - thread_range = tl.split("-") - if len(thread_range) == 1: - my_cores.append(int(thread_range[0])) - elif len(thread_range) == 2: - start_thread = int(thread_range[0]) - end_thread = int(thread_range[1]) + 1 - my_cores += list(range(start_thread, end_thread)) - else: - raise ValueError("Affinity list formatting is not expected {}".format(cpu_affinity)) - else: - raise ValueError("Affinity strategy {} is not supported".format(cpu_affinity)) - - # Set the affinity for OpenMP - # See: https://hpc-tutorials.llnl.gov/openmp/ProcessThreadAffinity.pdf - proc_list = ",".join(map(str, my_cores)) - os.environ["OMP_NUM_THREADS"] = str(len(my_cores)) - os.environ["GOMP_CPU_AFFINITY"] = proc_list # Compatible with GCC OpenMP - os.environ["KMP_AFFINITY"] = f"explicit,proclist=[{proc_list}]" # For Intel OpenMP - - # Set the affinity for this worker - # OSX does not implement os.sched_setaffinity so type checking - # is ignored here in two ways: - # On a platform without sched_setaffinity, that attribute will not - # be defined, so ignore[attr-defined] will tell mypy to ignore this - # incorrect-for-OS X attribute access. - # On a platform with sched_setaffinity, that type: ignore message - # will be redundant, and ignore[unused-ignore] tells mypy to ignore - # that this ignore is unneeded. - os.sched_setaffinity(0, my_cores) # type: ignore[attr-defined, unused-ignore] - logger.info("Set worker CPU affinity to {}".format(my_cores)) - - # If desired, pin to accelerator - if accelerator is not None: - - # If CUDA devices, find total number of devices to allow for MPS - # See: https://developer.nvidia.com/system-management-interface - nvidia_smi_cmd = "nvidia-smi -L > /dev/null && nvidia-smi -L | wc -l" - nvidia_smi_ret = subprocess.run(nvidia_smi_cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) - if nvidia_smi_ret.returncode == 0: - num_cuda_devices = int(nvidia_smi_ret.stdout.split()[0]) - else: - num_cuda_devices = None - - try: - if num_cuda_devices is not None: - procs_per_cuda_device = pool_size // num_cuda_devices - partitioned_accelerator = str(int(accelerator) // procs_per_cuda_device) # multiple workers will share a GPU - os.environ["CUDA_VISIBLE_DEVICES"] = partitioned_accelerator - logger.info(f'Pinned worker to partitioned cuda device: {partitioned_accelerator}') - else: - os.environ["CUDA_VISIBLE_DEVICES"] = accelerator - except (TypeError, ValueError, ZeroDivisionError): - os.environ["CUDA_VISIBLE_DEVICES"] = accelerator - os.environ["ROCR_VISIBLE_DEVICES"] = accelerator - os.environ["ZE_AFFINITY_MASK"] = accelerator - os.environ["ZE_ENABLE_PCI_ID_DEVICE_ORDER"] = '1' - - logger.info(f'Pinned worker to accelerator: {accelerator}') - - def manager_is_alive() -> bool: - try: - # This does not kill the process, but instead raises - # an exception if the process doesn't exist - os.kill(manager_pid, 0) - except OSError: - logger.critical(f"Manager ({manager_pid}) died; worker {worker_id} shutting down") - return False - else: - return True - - worker_enqueued = False - while manager_is_alive(): - if not worker_enqueued: - with ready_worker_count.get_lock(): - ready_worker_count.value += 1 - worker_enqueued = True - - try: - req = task_queue.get(timeout=task_queue_timeout) - # req is {'task_id':, 'buffer':, 'resource_spec':} - except queue.Empty: - continue - - tasks_in_progress[worker_id] = req - tid = req['task_id'] - logger.info("Received executor task {}".format(tid)) - - with ready_worker_count.get_lock(): - ready_worker_count.value -= 1 - worker_enqueued = False - - ctxt = req["context"] - res_spec = ctxt.get("resource_spec", {}) - - _init_mpi_env(mpi_launcher=mpi_launcher, resource_spec=res_spec) - - exec_func: Callable = execute_task - exec_args = () - exec_kwargs = {} - - try: - if task_executor := ctxt.get("task_executor", None): - mod_name, _, fn_name = task_executor["f"].rpartition(".") - exec_mod = importlib.import_module(mod_name) - exec_func = getattr(exec_mod, fn_name) - - exec_args = task_executor.get("a", ()) - exec_kwargs = task_executor.get("k", {}) - - result = exec_func(req['buffer'], *exec_args, **exec_kwargs) - serialized_result = serialize(result, buffer_threshold=1000000) - except Exception as e: - logger.info('Caught an exception: {}'.format(e)) - result_package = {'type': 'result', 'task_id': tid, 'exception': serialize(RemoteExceptionWrapper(*sys.exc_info()))} - else: - result_package = {'type': 'result', 'task_id': tid, 'result': serialized_result} - del serialized_result - del req - - logger.info("Completed executor task {}".format(tid)) - try: - pkl_package = pickle.dumps(result_package) - except Exception: - logger.exception("Caught exception while trying to pickle the result package") - pkl_package = pickle.dumps({'type': 'result', 'task_id': tid, - 'exception': serialize(RemoteExceptionWrapper(*sys.exc_info())) - }) - - result_queue.put(pkl_package) - del pkl_package, result_package - tasks_in_progress.pop(worker_id) - logger.info("All processing finished for executor task {}".format(tid)) - - -def get_arg_parser() -> argparse.ArgumentParser: - - def strategyorlist(s: str) -> Union[str, list]: - s = s.lower() - allowed_strategies = ("none", "block", "alternating", "block-reverse") - if s in allowed_strategies: - return s - elif s[0:4] == "list": - return s - err_msg = f"cpu-affinity must be one of {allowed_strategies} or a list format" - raise argparse.ArgumentTypeError(err_msg) - - parser = argparse.ArgumentParser() - parser.add_argument( - "-d", "--debug", action='store_true', help="Enable logging at DEBUG level", - ) - parser.add_argument( - "-a", - "--addresses", - required=True, - help="Comma separated list of addresses at which the interchange could be reached", - ) - parser.add_argument( - "--cert_dir", required=True, help="Path to certificate directory." - ) - parser.add_argument( - "-l", - "--logdir", - default="process_worker_pool_logs", - help="Process worker pool log directory", - ) - parser.add_argument( - "-L", - "--logconf", - default=None, - help="Path to pickled log configuration object, default=None", - ) - parser.add_argument( - "-u", - "--uid", - default=str(uuid.uuid4()).split('-')[-1], - help="Unique identifier string for Manager", - ) - parser.add_argument( - "-b", "--block_id", default=None, help="Block identifier for Manager" - ) - parser.add_argument( - "-c", - "--cores_per_worker", - default="1.0", - help="Number of cores assigned to each worker process. Default=1.0", - ) - parser.add_argument( - "-m", - "--mem_per_worker", - help="GB of memory assigned to each worker process. 'None' means no assignment.", - ) - parser.add_argument( - "-P", - "--port", - required=True, - help="Port for communication with the interchange", - ) - parser.add_argument( - "--max_workers_per_node", - default=float('inf'), - help="Caps the maximum workers that can be launched, default:infinity", - ) - parser.add_argument( - "-p", - "--prefetch_capacity", - default=0, - help="Number of tasks that can be prefetched to the manager. Default is 0.", - ) - parser.add_argument( - "--hb_period", - default=30, - help="Heartbeat period in seconds. Uses manager default unless set", - ) - parser.add_argument( - "--hb_threshold", - default=120, - help="Heartbeat threshold in seconds. Uses manager default unless set", - ) - parser.add_argument( - "--drain_period", - default=None, - help="Drain this pool after specified number of seconds. By default, does not drain.", - ) - parser.add_argument( - "--address_probe_timeout", - default=30, - help="Timeout to probe for viable address to interchange. Default: 30s", - ) - parser.add_argument( - "--poll", default=10, help="Poll period used in milliseconds" - ) - parser.add_argument( - "--cpu-affinity", - type=strategyorlist, - required=True, - help="Whether/how workers should control CPU affinity.", - ) - parser.add_argument( - "--available-accelerators", - type=str, - nargs="*", - default=[], - help="Names of available accelerators, if not given assumed to be zero accelerators available", - ) - parser.add_argument( - "--enable_mpi_mode", action='store_true', help="Enable MPI mode" - ) - parser.add_argument( - "--mpi-launcher", - type=str, - choices=VALID_LAUNCHERS, - help="MPI launcher to use iff enable_mpi_mode=true", - ) - - return parser - - -if __name__ == "__main__": - parser = get_arg_parser() - args = parser.parse_args() - - if args.logconf is None or args.logconf == "None": - os.makedirs(os.path.join(args.logdir, "block-{}".format(args.block_id), args.uid), exist_ok=True) - - set_file_logger( - f'{args.logdir}/block-{args.block_id}/{args.uid}/manager.log', - level=logging.DEBUG if args.debug is True else logging.INFO - ) - logconf = None - else: - with open(args.logconf, "rb") as f: - # This deliberately does not make the path directory. Creating directories - # can be extremely expensive on shared filesystems, and avoiding this for - # logging is a specific use case for this log configurability. - path = os.path.join(args.logdir, "block-{}".format(args.block_id), args.uid) - logconf = pickle.load(f) - oneshot_initialize_logging(log_config=logconf, log_dir=pathlib.Path(path), log_name="manager") - - logger.info( - f"\n Python version: {sys.version}" - f"\n Debug logging: {args.debug}" - f"\n Certificates dir: {args.cert_dir}" - f"\n Log dir: {args.logdir}" - f"\n Log config path: {args.logconf}" - f"\n Manager ID: {args.uid}" - f"\n Block ID: {args.block_id}" - f"\n cores_per_worker: {args.cores_per_worker}" - f"\n mem_per_worker: {args.mem_per_worker}" - f"\n Interchange port: {args.port}" - f"\n addresses: {args.addresses}" - f"\n max_workers_per_node: {args.max_workers_per_node}" - f"\n poll_period: {args.poll}" - f"\n address_probe_timeout: {args.address_probe_timeout}" - f"\n Prefetch capacity: {args.prefetch_capacity}" - f"\n Heartbeat threshold: {args.hb_threshold}" - f"\n Heartbeat period: {args.hb_period}" - f"\n Drain period: {args.drain_period}" - f"\n CPU affinity: {args.cpu_affinity}" - f"\n Accelerators: {' '.join(args.available_accelerators)}" - f"\n enable_mpi_mode: {args.enable_mpi_mode}" - f"\n mpi_launcher: {args.mpi_launcher}" - ) - try: - manager = Manager(port=args.port, - addresses=args.addresses, - address_probe_timeout=int(args.address_probe_timeout), - uid=args.uid, - block_id=args.block_id, - cores_per_worker=float(args.cores_per_worker), - mem_per_worker=None if args.mem_per_worker == 'None' else float(args.mem_per_worker), - max_workers_per_node=( - args.max_workers_per_node if args.max_workers_per_node == float('inf') - else int(args.max_workers_per_node) - ), - prefetch_capacity=int(args.prefetch_capacity), - heartbeat_threshold=int(args.hb_threshold), - heartbeat_period=int(args.hb_period), - drain_period=None if args.drain_period == "None" else int(args.drain_period), - poll_period=int(args.poll), - cpu_affinity=args.cpu_affinity, - enable_mpi_mode=args.enable_mpi_mode, - mpi_launcher=args.mpi_launcher, - available_accelerators=args.available_accelerators, - cert_dir=None if args.cert_dir == "None" else args.cert_dir, - log_config=logconf) - manager.start() - - except Exception: - logger.critical("Process worker pool exiting with an exception", exc_info=True) - raise - else: - logger.info("Process worker pool exiting normally") - print("Process worker pool exiting normally") diff --git a/parsl/parsl/executors/high_throughput/zmq_pipes.py b/parsl/parsl/executors/high_throughput/zmq_pipes.py deleted file mode 100644 index b06cc05c..00000000 --- a/parsl/parsl/executors/high_throughput/zmq_pipes.py +++ /dev/null @@ -1,195 +0,0 @@ -#!/usr/bin/env python3 - -import logging -import threading -import time -from typing import Optional - -import zmq - -from parsl import curvezmq -from parsl.addresses import tcp_url -from parsl.errors import InternalConsistencyError -from parsl.executors.high_throughput.errors import ( - CommandClientBadError, - CommandClientTimeoutError, -) - -logger = logging.getLogger(__name__) - - -class CommandClient: - """ CommandClient - """ - def __init__(self, ip_address, port_range, cert_dir: Optional[str] = None): - """ - Parameters - ---------- - - ip_address: str - IP address of the client (where Parsl runs) - - port_range: tuple(int, int) - Port range for the comms between client and interchange - - cert_dir: str | None - Path to the certificate directory. Setting this to None will disable encryption. - default: None - - """ - self.zmq_context = curvezmq.ClientContext(cert_dir) - self.ip_address = ip_address - self.port_range = port_range - self.port = None - self.create_socket_and_bind() - self._lock = threading.Lock() - self.ok = True - - def create_socket_and_bind(self): - """ Creates socket and binds to a port. - - Upon recreating the socket, we bind to the same port. - """ - self.zmq_socket = self.zmq_context.socket(zmq.REQ) - self.zmq_socket.setsockopt(zmq.LINGER, 0) - if self.port is None: - self.port = self.zmq_socket.bind_to_random_port(tcp_url(self.ip_address), - min_port=self.port_range[0], - max_port=self.port_range[1]) - else: - self.zmq_socket.bind(tcp_url(self.ip_address, self.port)) - - def run(self, message, max_retries=3, timeout_s=None): - """ This function needs to be fast at the same time aware of the possibility of - ZMQ pipes overflowing. - - We could set copy=False and get slightly better latency but this results - in ZMQ sockets reaching a broken state once there are ~10k tasks in flight. - This issue can be magnified if each the serialized buffer itself is larger. - """ - if not self.ok: - raise CommandClientBadError() - - start_time_s = time.monotonic() - - reply = '__PARSL_ZMQ_PIPES_MAGIC__' - with self._lock: - logger.debug("Sending command client command") - - if timeout_s is not None: - remaining_time_s = start_time_s + timeout_s - time.monotonic() - poll_result = self.zmq_socket.poll(timeout=remaining_time_s * 1000, flags=zmq.POLLOUT) - if poll_result == zmq.POLLOUT: - pass # this is OK, so continue - elif poll_result == 0: - raise CommandClientTimeoutError("Waiting for command channel to be ready for a command") - else: - raise InternalConsistencyError(f"ZMQ poll returned unexpected value: {poll_result}") - - self.zmq_socket.send_pyobj(message, copy=True) - - if timeout_s is not None: - logger.debug("Polling for command client response or timeout") - remaining_time_s = start_time_s + timeout_s - time.monotonic() - poll_result = self.zmq_socket.poll(timeout=remaining_time_s * 1000, flags=zmq.POLLIN) - if poll_result == zmq.POLLIN: - pass # this is OK, so continue - elif poll_result == 0: - logger.error("Command timed-out - command client is now bad forever") - self.ok = False - raise CommandClientTimeoutError("Waiting for a reply from command channel") - else: - raise InternalConsistencyError(f"ZMQ poll returned unexpected value: {poll_result}") - - logger.debug("Receiving command client response") - reply = self.zmq_socket.recv_pyobj() - logger.debug("Received command client response") - return reply - - def close(self): - self.zmq_socket.close() - self.zmq_context.term() - - -class TasksOutgoing: - """ Outgoing task queue from the executor to the Interchange - """ - def __init__(self, ip_address, port_range, cert_dir: Optional[str] = None): - """ - Parameters - ---------- - - ip_address: str - IP address of the client (where Parsl runs) - - port_range: tuple(int, int) - Port range for the comms between client and interchange - - cert_dir: str | None - Path to the certificate directory. Setting this to None will disable encryption. - default: None - - """ - self.zmq_context = curvezmq.ClientContext(cert_dir) - self.zmq_socket = self.zmq_context.socket(zmq.DEALER) - self.zmq_socket.set_hwm(0) - self.port = self.zmq_socket.bind_to_random_port(tcp_url(ip_address), - min_port=port_range[0], - max_port=port_range[1]) - - def put(self, message): - """ This function needs to be fast at the same time aware of the possibility of - ZMQ pipes overflowing. - - We could set copy=False and get slightly better latency but this results - in ZMQ sockets reaching a broken state once there are ~10k tasks in flight. - This issue can be magnified if each the serialized buffer itself is larger. - """ - logger.debug("Sending TasksOutgoing message") - self.zmq_socket.send_pyobj(message) - logger.debug("Sent TasksOutgoing message") - - def close(self): - self.zmq_socket.close() - self.zmq_context.term() - - -class ResultsIncoming: - """ Incoming results queue from the Interchange to the executor - """ - - def __init__(self, ip_address, port_range, cert_dir: Optional[str] = None): - """ - Parameters - ---------- - - ip_address: str - IP address of the client (where Parsl runs) - - port_range: tuple(int, int) - Port range for the comms between client and interchange - - cert_dir: str | None - Path to the certificate directory. Setting this to None will disable encryption. - default: None - - """ - self.zmq_context = curvezmq.ClientContext(cert_dir) - self.results_receiver = self.zmq_context.socket(zmq.DEALER) - self.results_receiver.set_hwm(0) - self.port = self.results_receiver.bind_to_random_port(tcp_url(ip_address), - min_port=port_range[0], - max_port=port_range[1]) - - def get(self, timeout_ms=None): - """Get a message from the queue, returning None if timeout expires - without a message. timeout is measured in milliseconds. - """ - if zmq.POLLIN == self.results_receiver.poll(timeout_ms, zmq.POLLIN): - logger.debug("Receiving ResultsIncoming message") - return self.results_receiver.recv() - return None - - def close(self): - self.results_receiver.close() - self.zmq_context.term() diff --git a/parsl/parsl/executors/radical/__init__.py b/parsl/parsl/executors/radical/__init__.py deleted file mode 100644 index 43b61e81..00000000 --- a/parsl/parsl/executors/radical/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from parsl.executors.radical.executor import RadicalPilotExecutor -from parsl.executors.radical.rpex_resources import ResourceConfig - -__all__ = ['RadicalPilotExecutor', 'ResourceConfig'] diff --git a/parsl/parsl/executors/radical/executor.py b/parsl/parsl/executors/radical/executor.py deleted file mode 100644 index 48597f92..00000000 --- a/parsl/parsl/executors/radical/executor.py +++ /dev/null @@ -1,605 +0,0 @@ -"""RadicalPilotExecutor builds on the RADICAL-Pilot/Parsl -""" -import inspect -import logging -import os -import queue -import sys -import threading as mt -import time -from concurrent.futures import Future -from functools import partial -from pathlib import PosixPath -from typing import Dict, Optional - -import requests -import typeguard - -import parsl -from parsl.app.errors import BashExitFailure, RemoteExceptionWrapper -from parsl.app.python import timeout -from parsl.data_provider.files import File -from parsl.executors.base import ParslExecutor -from parsl.serialize import deserialize, pack_apply_message -from parsl.serialize.errors import DeserializationError, SerializationError -from parsl.utils import RepresentationMixin - -from .rpex_resources import CLIENT, MPI, ResourceConfig - -try: - import radical.pilot as rp - import radical.utils as ru -except ImportError: - _rp_enabled = False -else: - _rp_enabled = True - - -RPEX = 'RPEX' -BASH = 'bash' -PYTHON = 'python' - -CWD = os.getcwd() -PWD = os.path.abspath(os.path.dirname(__file__)) - -PARSL_RP_RESOURCE_MAP = {'cores': 'ranks', - 'disk': 'lfs_per_rank', - 'memory': 'mem_per_rank'} - -logger = logging.getLogger(__name__) - - -class RadicalPilotExecutor(ParslExecutor, RepresentationMixin): - """Executor is designed for executing heterogeneous tasks - in terms of type/resource. - - The RadicalPilotExecutor system has the following main components: - - 1. "start": Create and start the RADICAL-Pilot runtime components ``rp.Session``, - ``rp.PilotManager`` and ``rp.TaskManager``. - 2. "translate": Unwrap, identify, and parse Parsl ``apps`` into ``rp.TaskDescription``. - 3. "submit": Submit Parsl apps to ``rp.TaskManager``. - 4. "shutdown": Shut down the RADICAL-Pilot runtime and all associated components. - - Here is a diagram - - .. code:: python - - ---------------------------------------------------------------------------- - Parsl Data Flow Kernel | Task Translator | rp.TaskManager - ---------------------------------------|-------------------|---------------- - | | - -> Dep. check ------> Parsl_tasks{} <--+--> Parsl Task | - Data management +dfk.submit | | | - | v | - | RP Task(s) -> | submit(task) - ---------------------------------------------------------------------------- - - The RadicalPilotExecutor creates a ``rp.Session``, ``rp.TaskManager``, - and ``rp.PilotManager``. The executor receives the Parsl apps from the - DFK and translates these apps (in-memory) into ``rp.TaskDescription`` - object to be passed to the ``rp.TaskManager``. This executor has two - submission mechanisms: - - 1. Default_mode: where the executor submits the tasks directly to - RADICAL-Pilot. - - 2. Bulk_mode: where the executor accumulates N tasks (functions and - executables) and submit them. - - Parameters - ---------- - rpex_cfg : :class: `~parsl.executors.rpex_resources.ResourceConfig` - a dataclass specifying resource configuration. - Default is ResourceConfig instance. - - label : str - Label for this executor instance. - Default is "RPEX". - - bulk_mode : bool - Enable bulk mode submission and execution. Default is False (stream). - - resource : Optional[str] - The resource name of the targeted HPC machine or cluster. - Default is local.localhost (user local machine). - - runtime : int - The maximum runtime for the entire job in minutes. - Default is 30. - - working_dir : str - The working dir to be used by the executor. - - rpex_pilot_kwargs: Dict of kwargs that are passed directly to the rp.PilotDescription object. - - For more information: https://radicalpilot.readthedocs.io/en/stable/ - """ - - @typeguard.typechecked - def __init__(self, - resource: str, - label: str = RPEX, - bulk_mode: bool = False, - working_dir: Optional[str] = None, - rpex_cfg: Optional[ResourceConfig] = None, **rpex_pilot_kwargs): - - super().__init__() - self.pmgr = None - self.tmgr = None - self.run_dir = '.' - self.label = label - self.session = None - self.resource = resource - self._uid = RPEX.lower() - self.bulk_mode = bulk_mode - self._terminate = mt.Event() - self.working_dir = working_dir - self.pilot_kwargs = rpex_pilot_kwargs - self.future_tasks: Dict[str, Future] = {} - - if rpex_cfg: - self.rpex_cfg = rpex_cfg.get_config() - elif not rpex_cfg and 'local' in resource: - self.rpex_cfg = ResourceConfig().get_config() - else: - raise ValueError('Resource config must be ' - 'specified for a non-local resources') - - def task_state_cb(self, task, state): - """ - Update the state of Parsl Future apps - Based on RP task state callbacks. - """ - # check the Master/Worker state - if task.mode in [rp.RAPTOR_MASTER, rp.RAPTOR_WORKER]: - if state == rp.FAILED: - exception = RuntimeError(f'{task.uid} failed with internal error: {task.stderr}') - self._fail_all_tasks(exception) - - # check all other tasks state - else: - parsl_task = self.future_tasks[task.uid] - - if state == rp.DONE: - if task.description['mode'] in [rp.TASK_EXEC, - rp.TASK_PROC, - rp.TASK_EXECUTABLE]: - parsl_task.set_result(int(task.exit_code)) - else: - # we do not support MPI function output - # serialization. TODO: To be fixed soon. - if not task.description.get('use_mpi'): - result = deserialize(eval(task.return_value)) - parsl_task.set_result(result) - else: - parsl_task.set_result(task.return_value) - - elif state == rp.CANCELED: - parsl_task.cancel() - - elif state == rp.FAILED: - if task.description['mode'] in [rp.TASK_EXEC, - rp.TASK_EXECUTABLE]: - parsl_task.set_exception(BashExitFailure(task.name, - task.exit_code)) - else: - if task.exception: - # unpack a serialized exception - if not task.description.get('use_mpi') or task.description['mode'] == rp.TASK_PROC: - self._unpack_and_set_parsl_exception(parsl_task, task.exception) - # we do not serialize mpi function exception - else: - parsl_task.set_exception(eval(task.exception)) - else: - parsl_task.set_exception('Task failed for an unknown reason') - - def _fail_all_tasks(self, exception): - """ - Fail all outstanding tasks with the given exception. - - This method iterates through all outstanding tasks in the - `_future_tasks` dictionary, which have not yet completed, - and sets the provided exception as their result, indicating - a failure. - - Parameters: - - exception: The exception to be set as the result for all - outstanding tasks. - """ - for fut_task in self.future_tasks.values(): - if not fut_task.done(): - fut_task.set_exception(exception) - - def start(self): - """Create the Pilot component and pass it. - """ - logger.info("starting RadicalPilotExecutor") - super().start() - logger.info('Parsl: {0}'.format(parsl.__version__)) - logger.info('RADICAL pilot: {0}'.format(rp.version)) - self.session = rp.Session(cfg={'base': self.run_dir}, - uid=ru.generate_id('rpex.session', - mode=ru.ID_PRIVATE)) - logger.info("RPEX session is created: {0}".format(self.session.path)) - - pd_init = {**self.pilot_kwargs, - 'exit_on_error': True, - 'resource': self.resource} - - if not self.resource or 'local' in self.resource: - os.environ["RADICAL_LOG_LVL"] = "DEBUG" - logger.info("RPEX will be running in local mode") - - pd = rp.PilotDescription(pd_init) - pd.verify() - - # start RP's main components TMGR, PMGR and Pilot - self.tmgr = rp.TaskManager(session=self.session) - self.pmgr = rp.PilotManager(session=self.session) - self.pilot = self.pmgr.submit_pilots(pd) - - if not self.pilot.description.get('cores') or not self.pilot.description.get('nodes'): - logger.warning('no "cores/nodes" per pilot were set, using default resources') - - self.tmgr.add_pilots(self.pilot) - self.tmgr.register_callback(self.task_state_cb) - - worker_path = '{0}/rpex_worker.py'.format(PWD) - - self.masters = [] - - logger.info(f'Starting {self.rpex_cfg.n_masters} masters and {self.rpex_cfg.n_workers} workers for each master') - - # create N masters - for _ in range(self.rpex_cfg.n_masters): - md = rp.TaskDescription(self.rpex_cfg.master_descr) - md.uid = ru.generate_id('rpex.master.%(item_counter)06d', ru.ID_CUSTOM, - ns=self.session.uid) - - # submit the master to the TMGR - master = self.tmgr.submit_raptors(md)[0] - self.masters.append(master) - - workers = [] - # create N workers for each master and submit them to the TMGR - for _ in range(self.rpex_cfg.n_workers): - wd = rp.TaskDescription(self.rpex_cfg.worker_descr) - wd.uid = ru.generate_id('rpex.worker.%(item_counter)06d', ru.ID_CUSTOM, - ns=self.session.uid) - wd.raptor_id = master.uid - wd.input_staging = self._stage_files([File(worker_path)], mode='in') - workers.append(wd) - - self.tmgr.submit_workers(workers) - - self.select_master = self._cyclic_master_selector() - - # prepare or use the current env for the agent/pilot side environment - if self.rpex_cfg.pilot_env_mode != CLIENT: - logger.info("creating {0} environment for the executor".format(self.rpex_cfg.pilot_env.name)) - self.pilot.prepare_env(env_name=self.rpex_cfg.pilot_env.name, - env_spec=self.rpex_cfg.pilot_env.as_dict()) - else: - client_env = sys.prefix - logger.info("reusing ({0}) environment for the executor".format(client_env)) - - # create a bulking thread to run the actual task submission - # to RP in bulks - if self.bulk_mode: - self._max_bulk_size = 1024 - self._max_bulk_time = 3 # seconds - self._min_bulk_time = 0.1 # seconds - - self._bulk_queue = queue.Queue() - self._bulk_thread = mt.Thread(target=self._bulk_collector) - - self._bulk_thread.daemon = True - self._bulk_thread.start() - - logger.info('bulk mode is on, submitting tasks in bulks') - - return True - - def _cyclic_master_selector(self): - """ - Balance tasks submission across N masters and N workers - """ - current_master = 0 - masters_uids = [m.uid for m in self.masters] - - while True: - yield masters_uids[current_master] - current_master = (current_master + 1) % len(self.masters) - - def unwrap(self, func, args): - """ - Unwrap a Parsl app and its args for further processing. - - Parameters - ---------- - func : callable - The function to be unwrapped. - - args : tuple - The arguments associated with the function. - - Returns - ------- - tuple - A tuple containing the unwrapped function, adjusted arguments, - and task type information. - """ - - task_type = '' - - while hasattr(func, '__wrapped__'): - func = func.__wrapped__ - - try: - if isinstance(func, partial): - try: - task_type = inspect.getsource(func.args[0]).split('\n')[0] - if BASH in task_type: - task_type = BASH - func = func.args[0] - else: - task_type = PYTHON - - except Exception: - logger.exception('unwrap failed') - - return func, args, task_type - - else: - task_type = inspect.getsource(func).split('\n')[0] - if PYTHON in task_type: - task_type = PYTHON - else: - task_type = '' - except Exception as e: - raise Exception('failed to obtain task type: {0}'.format(e)) - - return func, args, task_type - - def task_translate(self, tid, func, parsl_resource_specification, args, kwargs): - """ - Convert Parsl function to RADICAL-Pilot rp.TaskDescription - """ - - task = rp.TaskDescription() - task.name = func.__name__ - - if parsl_resource_specification and isinstance(parsl_resource_specification, dict): - logger.debug('mapping Parsl resource specifications >> RP resource specifications') - for key, val in parsl_resource_specification.items(): - if key not in task.as_dict(): - key = PARSL_RP_RESOURCE_MAP.get(key, None) - if not key: - logger.warning('ignoring "{0}" key from task resource specification as it is not supported by RP'.format(key)) - continue - setattr(task, key, val) - - func, args, task_type = self.unwrap(func, args) - - if BASH in task_type: - if callable(func): - # if the user specifies the executable mode then we expect the - # a code in a file that need to be executed in an isolated env. - if parsl_resource_specification.get('mode') == rp.TASK_EXECUTABLE: - # These lines of code are from parsl/app/bash.py - try: - # Execute the func to get the command - bash_app = func(*args, **kwargs) - if not isinstance(bash_app, str): - raise ValueError("Expected a str for bash_app cmd," - "got: {0}".format(type(bash_app))) - except AttributeError as e: - raise Exception("failed to obtain bash app cmd") from e - - task.executable = bash_app - task.mode = rp.TASK_EXECUTABLE - - # This is the default mode where the bash_app will be executed as - # as a single core process by RP. For cores > 1 the user must use - # task.mode=rp.TASK_EXECUTABLE (above) or use MPI functions if their - # code is Python. - else: - task.mode = rp.TASK_PROC - task.raptor_id = next(self.select_master) - task.executable = self._pack_and_apply_message(func, args, kwargs) - - elif PYTHON in task_type or not task_type: - task.mode = rp.TASK_FUNCTION - task.raptor_id = next(self.select_master) - if kwargs.get('walltime'): - func = timeout(func, kwargs['walltime']) - - # Check how to serialize the function object - if MPI in self.rpex_cfg.worker_type.lower(): - task.use_mpi = True - task.function = rp.PythonTask(func, *args, **kwargs) - else: - task.use_mpi = False - task.function = self._pack_and_apply_message(func, args, kwargs) - - task.input_staging = self._stage_files(kwargs.get("inputs", []), - mode='in') - task.output_staging = self._stage_files(kwargs.get("outputs", []), - mode='out') - - task.input_staging.extend(self._stage_files(list(args), mode='in')) - - self._set_stdout_stderr(task, kwargs) - - try: - task.verify() - except ru.typeddict.TDKeyError as e: - raise Exception(f'{e}. Please check: https://radicalpilot.readthedocs.io/en/stable/ documentation') - - return task - - def _pack_and_apply_message(self, func, args, kwargs): - try: - buffer = pack_apply_message(func, args, kwargs, buffer_threshold=1 << 20) - task_func = rp.utils.serialize_bson(buffer) - except TypeError: - raise SerializationError(func.__name__) - - return task_func - - def _unpack_and_set_parsl_exception(self, parsl_task, exception): - try: - try: - s = rp.utils.deserialize_bson(exception) - except Exception: - s = exception - - if isinstance(s, RemoteExceptionWrapper): - try: - s.reraise() - except Exception as e: - parsl_task.set_exception(e) - elif isinstance(s, Exception): - parsl_task.set_exception(s) - elif isinstance(s, str): - parsl_task.set_exception(eval(s)) - else: - raise ValueError("Unknown exception-like type received: {}".format(type(s))) - except Exception as e: - parsl_task.set_exception( - DeserializationError("Received exception, but handling also threw an exception: {}".format(e))) - - def _set_stdout_stderr(self, task, kwargs): - """ - set the stdout and stderr of a task - """ - for k in ['stdout', 'stderr']: - k_val = kwargs.get(k, '') - if k_val: - # check the type of the stderr/out - if isinstance(k_val, File): - k_val = k_val.filepath - elif isinstance(k_val, PosixPath): - k_val = k_val.__str__() - - # set the stderr/out to the desired - # name by the user - setattr(task, k, k_val) - task.sandbox = CWD - - def _stage_files(self, files, mode): - """ - a function to stage list of input/output - files between two locations. - """ - to_stage = [] - files = [f for f in files if isinstance(f, File)] - for file in files: - if mode == 'in': - # a workaround RP not supporting - # staging https file - if file.scheme == 'https': - r = requests.get(file.url) - p = CWD + '/' + file.filename - with open(p, 'wb') as ff: - ff.write(r.content) - file = File(p) - - f = {'source': file.url, - 'action': rp.TRANSFER} - to_stage.append(f) - - elif mode == 'out': - # this indicates that the user - # did not provided a specific - # output file and RP will stage out - # the task.stdout from pilot://task_folder - # to the CWD or file.url - if '/' not in file.url: - f = {'source': file.filename, - 'target': file.url, - 'action': rp.TRANSFER} - to_stage.append(f) - else: - raise ValueError('unknown staging mode') - - return to_stage - - def _bulk_collector(self): - - bulk = list() - - while not self._terminate.is_set(): - - now = time.time() # time of last submission - - # collect tasks for min bulk time - # NOTE: total collect time could actually be max_time + min_time - while time.time() - now < self._max_bulk_time: - - try: - task = self._bulk_queue.get(block=True, - timeout=self._min_bulk_time) - except queue.Empty: - task = None - - if task: - bulk.append(task) - - if len(bulk) >= self._max_bulk_size: - break - - if self._terminate.is_set(): - break - - if bulk: - logger.debug('submit bulk: %d', len(bulk)) - self.tmgr.submit_tasks(bulk) - bulk = list() - - def submit(self, func, resource_specification, *args, **kwargs): - """ - Submits tasks in stream mode or bulks (bulk mode) - to RADICAL-Pilot rp.TaskManager. - """ - rp_tid = ru.generate_id('task.%(item_counter)06d', ru.ID_CUSTOM, - ns=self.session.uid) - parsl_tid = int(rp_tid.split('task.')[1]) - - logger.debug("got Task {0} from Parsl-dfk".format(parsl_tid)) - task = self.task_translate(parsl_tid, func, resource_specification, args, kwargs) - - # assign task id for rp task - task.uid = rp_tid - - # set the future with corresponding id - self.future_tasks[rp_tid] = Future() - - if self.bulk_mode: - # push task to rp submit thread - self._bulk_queue.put(task) - else: - # submit the task to rp - logger.debug("put {0} to rp-TMGR".format(rp_tid)) - self.tmgr.submit_tasks(task) - - return self.future_tasks[rp_tid] - - def shutdown(self, hub=True, targets='all', block=False): - """Shutdown the executor, including all RADICAL-Pilot components.""" - logger.info("RadicalPilotExecutor is terminating...") - - self._terminate.set() - - # ensure we are in the bulk submssion mode - if self.bulk_mode: - self._bulk_thread.join() - - self.session.close(download=True) - - super().shutdown() - - logger.info("RadicalPilotExecutor is terminated.") - - return True diff --git a/parsl/parsl/executors/radical/rpex_resources.py b/parsl/parsl/executors/radical/rpex_resources.py deleted file mode 100644 index c337ee33..00000000 --- a/parsl/parsl/executors/radical/rpex_resources.py +++ /dev/null @@ -1,167 +0,0 @@ -import json -import sys -from typing import List - -_setup_paths: List[str] = [] -try: - import radical.pilot as rp - import radical.utils as ru -except ImportError: - pass - - -MPI = "mpi" -RP_ENV = "rp" -CLIENT = "client" -RPEX_ENV = "ve_rpex" -MPI_WORKER = "MPIWorker" -DEFAULT_WORKER = "DefaultWorker" - - -class ResourceConfig: - """ - This ResourceConfig class is an abstraction of the resource - configuration of the RAPTOR layer in the RADICAL-Pilot runtime system. - - This class sets up the default configuration values for the executor and - allows the user to specify different resource requirements flexibly. - - For more information: - https://radicalpilot.readthedocs.io/en/stable/tutorials/raptor.html - - Parameters - ---------- - masters : int - The number of masters to be deployed by RAPTOR. - Default is 1. - - workers : int - The number of workers to be deployed by RAPTOR. - Default is 1. - - worker_gpus_per_node : int - The number of GPUs a worker will operate on per node. - Default is 0. - - worker_cores_per_node : int - The number of CPU cores a worker will operate on per node. - Default is 4. - - cores_per_master : int - The number of cores a master will operate on per node. - Default is 1. - - nodes_per_worker : int - The number of nodes to be occupied by every worker. - Default is 1. - - pilot_env_path : str - The path to an exisitng pilot environment. - Default is an empty string (RADICAL-Pilot will create one). - - pilot_env_name : str - The name of the pilot environment. - Default is "ve_rpex". - - pilot_env_pre_exec : list - List of commands to be executed before starting the pilot environment. - Default is an empty list. - - pilot_env_type : str - The type of the pilot environment (e.g., 'venv', 'conda'). - Default is "venv". - - pilot_env_setup : list - List of setup commands/packages for the pilot environment. - Default is an empty list. - - python_v : str - The Python version to be used in the pilot environment. - Default is determined by the system's Python version. - - worker_type : str - The type of worker(s) to be deployed by RAPTOR on the compute - resources. - Default is "DefaultWorker". - """ - - masters: int = 1 - workers: int = 1 - - worker_gpus_per_node: int = 0 - worker_cores_per_node: int = 4 - - cores_per_master: int = 1 - nodes_per_worker: int = 1 - - pilot_env_mode: str = CLIENT - pilot_env_path: str = "" - pilot_env_type: str = "venv" - pilot_env_name: str = RP_ENV - pilot_env_pre_exec: List[str] = [] - pilot_env_setup: List[str] = _setup_paths - - python_v: str = f'{sys.version_info[0]}.{sys.version_info[1]}' - worker_type: str = DEFAULT_WORKER - - def get_config(cls, path=None): - - # Default ENV mode for RP is to reuse - # the client side. If this is not the case, - # then RP will create a new env named ve_rpex - # The user need to make sure that under: - # $HOME/.radical/pilot/configs/*_resource.json - # that virtenv_mode = local - if cls.pilot_env_mode != CLIENT: - cls.pilot_env_name = RPEX_ENV - - if MPI in cls.worker_type.lower() and \ - "mpi4py" not in cls.pilot_env_setup: - cls.pilot_env_setup.append("mpi4py") - - cfg = { - 'n_masters': cls.masters, - 'n_workers': cls.workers, - 'worker_type': cls.worker_type, - 'gpus_per_node': cls.worker_gpus_per_node, - 'cores_per_node': cls.worker_cores_per_node, - 'cores_per_master': cls.cores_per_master, - 'nodes_per_worker': cls.nodes_per_worker, - - 'pilot_env': { - "version": cls.python_v, - "name": cls.pilot_env_name, - "path": cls.pilot_env_path, - "type": cls.pilot_env_type, - "setup": cls.pilot_env_setup, - "pre_exec": cls.pilot_env_pre_exec - }, - - 'pilot_env_mode': cls.pilot_env_mode, - - 'master_descr': { - "ranks": 1, - "cores_per_rank": 1, - "mode": rp.RAPTOR_MASTER, - "named_env": cls.pilot_env_name, - }, - - 'worker_descr': { - "mode": rp.RAPTOR_WORKER, - "named_env": cls.pilot_env_name, - "raptor_file": "./rpex_worker.py", - "raptor_class": cls.worker_type if - cls.worker_type.lower() != MPI else MPI_WORKER, - "ranks": cls.nodes_per_worker * cls.worker_cores_per_node, - "gpus_per_rank": cls.nodes_per_worker * cls.worker_gpus_per_node, - }} - - # Convert the class instance to a Json file or a Config dict. - if path: - config_path = 'rpex.cfg' - config_path = path + '/' + config_path - with open(config_path, 'w') as f: - json.dump(cfg, f, indent=4) - else: - config_obj = ru.Config(from_dict=cfg) - return config_obj diff --git a/parsl/parsl/executors/radical/rpex_worker.py b/parsl/parsl/executors/radical/rpex_worker.py deleted file mode 100644 index 9e4f17aa..00000000 --- a/parsl/parsl/executors/radical/rpex_worker.py +++ /dev/null @@ -1,62 +0,0 @@ -import sys - -import radical.pilot as rp - -import parsl.app.errors as pe -from parsl.app.bash import remote_side_bash_executor -from parsl.executors.execute_task import execute_task -from parsl.serialize import serialize, unpack_apply_message - - -class ParslWorker: - - def _dispatch_func(self, task): - - try: - buffer = rp.utils.deserialize_bson(task['description']['function']) - result = execute_task(buffer) - val = str(serialize(result, buffer_threshold=1000000)) - exc = (None, None) - ret = 0 - out = None - err = None - except Exception: - val = None - exc = (rp.utils.serialize_bson(pe.RemoteExceptionWrapper(*sys.exc_info())), None) - ret = 1 - out = None - err = None - - return out, err, ret, val, exc - - def _dispatch_proc(self, task): - - try: - buffer = rp.utils.deserialize_bson(task['description']['executable']) - func, args, kwargs = unpack_apply_message(buffer) - ret = remote_side_bash_executor(func, *args, **kwargs) - exc = (None, None) - val = None - out = None - err = None - except Exception: - val = None - exc = (rp.utils.serialize_bson(pe.RemoteExceptionWrapper(*sys.exc_info())), None) - ret = 1 - out = None - err = None - - return out, err, ret, val, exc - - -class MPIWorker(rp.raptor.MPIWorker): - def _dispatch_func(self, task): - return super()._dispatch_func(task) - - -class DefaultWorker(rp.raptor.DefaultWorker): - def _dispatch_func(self, task): - return ParslWorker()._dispatch_func(task) - - def _dispatch_proc(self, task): - return ParslWorker()._dispatch_proc(task) diff --git a/parsl/parsl/executors/status_handling.py b/parsl/parsl/executors/status_handling.py deleted file mode 100644 index 5d6063ff..00000000 --- a/parsl/parsl/executors/status_handling.py +++ /dev/null @@ -1,373 +0,0 @@ -from __future__ import annotations - -import datetime -import logging -import threading -import time -from abc import abstractmethod, abstractproperty -from concurrent.futures import Future -from functools import cached_property -from itertools import compress -from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union - -from parsl.executors.base import ParslExecutor -from parsl.executors.errors import BadStateException, ScalingFailed -from parsl.jobs.error_handlers import noop_error_handler, simple_error_handler -from parsl.jobs.states import TERMINAL_STATES, JobState, JobStatus -from parsl.monitoring.message_type import MessageType -from parsl.monitoring.radios.multiprocessing import MultiprocessingQueueRadioSender -from parsl.providers.base import ExecutionProvider -from parsl.utils import AtomicIDCounter - -logger = logging.getLogger(__name__) - - -class BlockProviderExecutor(ParslExecutor): - """A base class for executors which scale using blocks. - - This base class is intended to help with executors which: - - - use blocks of workers to execute tasks - - blocks of workers are launched on a batch system through - an `ExecutionProvider` - - An implementing class should implement the abstract methods required by - `ParslExecutor` to submit tasks, as well as BlockProviderExecutor - abstract methods to provide the executor-specific command to start a block - of workers (the ``_get_launch_command`` method), and some basic scaling - information (``outstanding`` and ``workers_per_node`` properties). - - This base class provides a ``scale_out`` method which will launch new - blocks. It does not provide a ``scale_in`` method, because scale-in - behaviour is not well defined in the Parsl scaling model and so behaviour - is left to individual executors. - - Parsl scaling will provide scaling between min_blocks and max_blocks by - invoking scale_out, but it will not initialize the blocks requested by - any init_blocks parameter. Subclasses must implement that behaviour - themselves. - """ - - def __init__(self, *, - provider: Optional[ExecutionProvider], - block_error_handler: Union[bool, Callable[[BlockProviderExecutor, Dict[str, JobStatus]], None]]): - super().__init__() - self._provider = provider - self.block_error_handler: Callable[[BlockProviderExecutor, Dict[str, JobStatus]], None] - if isinstance(block_error_handler, bool): - if block_error_handler: - self.block_error_handler = simple_error_handler - else: - self.block_error_handler = noop_error_handler - else: - self.block_error_handler = block_error_handler - - self._executor_bad_state = threading.Event() - self._executor_exception: Optional[Exception] = None - - self._block_id_counter = AtomicIDCounter() - - self._tasks = {} # type: Dict[object, Future] - - self._last_poll_time = 0.0 - - # these four structures track, in loosely coordinated fashion, the - # existence of blocks and jobs and how to map between their - # identifiers. - self.blocks_to_job_id = {} # type: Dict[str, str] - self.job_ids_to_block = {} # type: Dict[str, str] - - # errors can happen during the submit call to the provider; this is used - # to keep track of such errors so that they can be handled in one place - # together with errors reported by status() - self._simulated_status: Dict[str, JobStatus] = {} - - # this stores an approximation (sometimes delayed) of the latest status - # of pending, active and recently terminated blocks - self._status = {} # type: Dict[str, JobStatus] - - self.submit_monitoring_radio: Optional[MultiprocessingQueueRadioSender] = None - - @cached_property - def _scale_prefix(self) -> str: - """Prefix used for logging messages about scaling""" - return f'[Scaling executor {self.label}]' - - def start(self): - super().start() - if self.monitoring_messages: - self.submit_monitoring_radio = MultiprocessingQueueRadioSender(self.monitoring_messages) - - def _make_status_dict(self, block_ids: Sequence[str], status_list: Sequence[JobStatus]) -> Dict[str, JobStatus]: - """Given a sequence of block ids and a sequence of corresponding status strings, - returns a dictionary mapping each block id to the corresponding status - - :param block_ids: the sequence of block ids - :param status_list: the sequence of job status strings - :return: the resulting dictionary - """ - if len(block_ids) != len(status_list): - raise IndexError("block id list and status string list differ in size") - d = {} - for i in range(len(block_ids)): - d[block_ids[i]] = status_list[i] - - return d - - @property - def status_polling_interval(self): - """Returns the interval, in seconds, at which the status method should be called. The - assumption here is that, once initialized, an executor's polling interval is fixed. - In practice, at least given the current situation, the executor uses a single task provider - and this method is a delegate to the corresponding method in the provider. - - :return: the number of seconds to wait between calls to status() or zero if no polling - should be done - """ - if self._provider is None: - return 0 - else: - return self._provider.status_polling_interval - - @abstractmethod - def outstanding(self) -> int: - """This should return the number of tasks that the executor has been given to run (waiting to run, and running now)""" - - raise NotImplementedError("Classes inheriting from BlockProviderExecutor must implement " - "outstanding()") - - def set_bad_state_and_fail_all(self, exception: Exception): - """Allows external error handlers to mark this executor as irrecoverably bad and cause - all tasks submitted to it now and in the future to fail. The executor is responsible - for checking :method:bad_state_is_set() in the :method:submit() method and raising the - appropriate exception, which is available through :method:executor_exception(). - """ - logger.exception("Setting bad state due to exception", exc_info=exception) - self._executor_exception = exception - # Set bad state to prevent new tasks from being submitted - self._executor_bad_state.set() - # We set all current tasks to this exception to make sure that - # this is raised in the main context. - for task in self._tasks: - self._tasks[task].set_exception(BadStateException(self, self._executor_exception)) - - @property - def bad_state_is_set(self): - """Returns true if this executor is in an irrecoverable error state. If this method - returns true, :property:executor_exception should contain an exception indicating the - cause. - """ - return self._executor_bad_state.is_set() - - @property - def executor_exception(self): - """Returns an exception that indicates why this executor is in an irrecoverable state.""" - return self._executor_exception - - def handle_errors(self, status: Dict[str, JobStatus]) -> None: - """This method is called by the error management infrastructure after a status poll. The - executor implementing this method is then responsible for detecting abnormal conditions - based on the status of submitted jobs. If the executor does not implement any special - error handling, this method should return False, in which case a generic error handling - scheme will be used. - :param status: status of all jobs launched by this executor - """ - self.block_error_handler(self, status) - - @property - def tasks(self) -> Dict[object, Future]: - return self._tasks - - @property - def provider(self) -> Optional[ExecutionProvider]: - return self._provider - - def _filter_scale_in_ids(self, to_kill: Sequence[Any], killed: Sequence[bool]) -> Sequence[Any]: - """ Filter out job id's that were not killed - """ - assert len(to_kill) == len(killed) - - if False in killed: - killed_job_ids = [jid for jid, k in zip(to_kill, killed) if k] - not_killed_job_ids = [jid for jid, k in zip(to_kill, killed) if not k] - logger.warning("Some jobs were not killed successfully: " - f"killed jobs: {killed_job_ids}, " - f"not-killed jobs: {not_killed_job_ids}") - - # Filters first iterable by bool values in second - return list(compress(to_kill, killed)) - - def scale_out_facade(self, n: int) -> List[str]: - """Scales out the number of blocks by "blocks" - """ - assert self.provider is not None, \ - "scale_out_facade should only be called on executors with a valid provider" - block_ids = [] - monitoring_status_changes = {} - logger.info("%s Scaling out by %d blocks", self._scale_prefix, n) - for _ in range(n): - block_id = str(self._block_id_counter.get_id()) - logger.info("%s Allocated block ID %s", self._scale_prefix, block_id) - try: - job_id = self._launch_block(block_id) - - pending_status = JobStatus(JobState.PENDING) - - self.blocks_to_job_id[block_id] = job_id - self.job_ids_to_block[job_id] = block_id - self._status[block_id] = pending_status - - monitoring_status_changes[block_id] = pending_status - block_ids.append(block_id) - - except Exception as ex: - failed_status = JobStatus(JobState.FAILED, "Failed to start block {}: {}".format(block_id, ex)) - self._simulated_status[block_id] = failed_status - self._status[block_id] = failed_status - - self.send_monitoring_info(monitoring_status_changes) - return block_ids - - def scale_in(self, blocks: int) -> List[str]: - """Scale in method. - - Cause the executor to reduce the number of blocks by count. - - The default implementation will kill blocks without regard to their - status or whether they are executing tasks. Executors with more - nuanced scaling strategies might overload this method to work with - that strategy - see the HighThroughputExecutor for an example of that. - - :return: A list of block ids corresponding to the blocks that were removed. - """ - - active_blocks = [block_id for block_id, status in self._status.items() - if status.state not in TERMINAL_STATES] - - block_ids_to_kill = active_blocks[:blocks] - - job_ids_to_kill = [self.blocks_to_job_id[block] for block in block_ids_to_kill] - - # Cancel the blocks provisioned - if self.provider: - logger.info("%s Scaling in jobs: %s", self._scale_prefix, str(job_ids_to_kill)) - r = self.provider.cancel(job_ids_to_kill) - job_ids = self._filter_scale_in_ids(job_ids_to_kill, r) - block_ids_killed = [self.job_ids_to_block[job_id] for job_id in job_ids] - return block_ids_killed - else: - logger.error("%s No execution provider available to scale in", self._scale_prefix) - return [] - - def _launch_block(self, block_id: str) -> Any: - assert self.provider is not None, \ - "_launch_block should only be called on executors with a valid provider" - - launch_cmd = self._get_launch_command(block_id) - job_name = f"parsl.{self.label}.block-{block_id}" - logger.debug("%s Submitting to provider with job_name %s", self._scale_prefix, job_name) - job_id = self.provider.submit(launch_cmd, 1, job_name) - if job_id: - logger.debug("%s Launched block %s with job ID %s", self._scale_prefix, block_id, str(job_id)) - else: - raise ScalingFailed(self, "Attempt to provision nodes did not return a job ID") - return job_id - - @abstractmethod - def _get_launch_command(self, block_id: str) -> str: - pass - - def _get_block_and_job_ids(self) -> Tuple[List[str], List[Any]]: - # Not using self.blocks.keys() and self.blocks.values() simultaneously - # The dictionary may be changed during invoking this function - # As scale_in and scale_out are invoked in multiple threads - block_ids = list(self.blocks_to_job_id.keys()) - job_ids = [] # types: List[Any] - for bid in block_ids: - job_ids.append(self.blocks_to_job_id[bid]) - return block_ids, job_ids - - @abstractproperty - def workers_per_node(self) -> Union[int, float]: - pass - - def send_monitoring_info(self, status: Dict) -> None: - # Send monitoring info for HTEX when monitoring enabled - if self.submit_monitoring_radio: - msg = self.create_monitoring_info(status) - logger.debug("Sending block monitoring message: %r", msg) - self.submit_monitoring_radio.send((MessageType.BLOCK_INFO, msg)) - - def create_monitoring_info(self, status: Dict[str, JobStatus]) -> Sequence[Dict[str, Any]]: - """Create a monitoring message for each block based on the poll status. - """ - return [ - { - "run_id": self.run_id, - "status": s.status_name, - "timestamp": datetime.datetime.now(), - "executor_label": self.label, - "job_id": self.blocks_to_job_id.get(bid, None), - "block_id": bid - } - for bid, s in status.items() - ] - - def poll_facade(self) -> None: - now = time.time() - if now >= self._last_poll_time + self.status_polling_interval: - previous_status = self._status - self._status = self.status() - self._last_poll_time = now - delta_status = {} - for block_id in self._status: - if block_id not in previous_status \ - or previous_status[block_id].state != self._status[block_id].state: - delta_status[block_id] = self._status[block_id] - - if delta_status: - self.send_monitoring_info(delta_status) - - def status(self) -> Dict[str, JobStatus]: - """Return the status of all jobs/blocks currently known to this executor. - - :return: a dictionary mapping block ids (in string) to job status - """ - if self._provider: - block_ids, job_ids = self._get_block_and_job_ids() - status = self._make_status_dict(block_ids, self._provider.status(job_ids)) - else: - status = {} - status.update(self._simulated_status) - - return status - - @property - def status_facade(self) -> Dict[str, JobStatus]: - """Return the status of all jobs/blocks of the executor of this poller. - - :return: a dictionary mapping block ids (in string) to job status - """ - return self._status - - def scale_in_facade(self, n: int, max_idletime: Optional[float] = None) -> List[str]: - - if max_idletime is None: - block_ids = self.scale_in(n) - else: - # This is a HighThroughputExecutor-specific interface violation. - # This code hopes, through pan-codebase reasoning, that this - # scale_in method really does come from HighThroughputExecutor, - # and so does have an extra max_idletime parameter not present - # in the executor interface. - block_ids = self.scale_in(n, max_idletime=max_idletime) # type: ignore[call-arg] - if block_ids is not None: - new_status = {} - for block_id in block_ids: - logger.debug("Marking block %s as SCALED_IN", block_id) - s = JobStatus(JobState.SCALED_IN) - new_status[block_id] = s - self._status[block_id] = s - self._simulated_status[block_id] = s - self.send_monitoring_info(new_status) - return block_ids diff --git a/parsl/parsl/executors/taskvine/__init__.py b/parsl/parsl/executors/taskvine/__init__.py deleted file mode 100644 index a9f0c723..00000000 --- a/parsl/parsl/executors/taskvine/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from parsl.executors.taskvine.executor import TaskVineExecutor -from parsl.executors.taskvine.factory_config import TaskVineFactoryConfig -from parsl.executors.taskvine.manager_config import TaskVineManagerConfig - -__all__ = ['TaskVineExecutor', 'TaskVineManagerConfig', 'TaskVineFactoryConfig'] diff --git a/parsl/parsl/executors/taskvine/errors.py b/parsl/parsl/executors/taskvine/errors.py deleted file mode 100644 index 3d7aac94..00000000 --- a/parsl/parsl/executors/taskvine/errors.py +++ /dev/null @@ -1,29 +0,0 @@ -from parsl.app.errors import AppException -from parsl.errors import ParslError - - -class TaskVineTaskFailure(AppException): - """A failure executing a task in TaskVine - - Contains: - reason(string) - status(int) - """ - - def __init__(self, reason: str, status: int): - self.reason = reason - self.status = status - - -class TaskVineManagerFailure(ParslError): - """A failure in the taskvine executor that prevented the task to be - executed. - """ - pass - - -class TaskVineFactoryFailure(ParslError): - """A failure in the TaskVine factory that prevents the factory from - supplying workers to the manager. - """ - pass diff --git a/parsl/parsl/executors/taskvine/exec_parsl_function.py b/parsl/parsl/executors/taskvine/exec_parsl_function.py deleted file mode 100644 index cf02ced0..00000000 --- a/parsl/parsl/executors/taskvine/exec_parsl_function.py +++ /dev/null @@ -1,185 +0,0 @@ -import sys -import traceback - -from parsl.app.errors import RemoteExceptionWrapper -from parsl.data_provider.files import File -from parsl.serialize import deserialize, serialize -from parsl.utils import get_std_fname_mode - -# This scripts executes a parsl function which is pickled in 4 files: -# -# exec_parsl_function.py map_file function_file argument_file result_file -# -# map_file: Contains a pickled dictionary that indicates which local_paths the -# parsl Files should take. -# -# function_file: Contains a pickle parsl function. Function might be serialized in advance. -# See @parsl.serialize.concretes.py -# -# argument_file: Contains the serialized arguments to the function call. -# -# result_file: A file path, whose content will contain the result of the function, including any -# exception generated. Exceptions will be wrapped with RemoteExceptionWrapper. -# -# Exit codes: -# 0: The function was evaluated to completion. The result or any exception -# wrapped with RemoteExceptionWrapper were written to result_file. -# anything else: There was an error that prevented writing to the result file altogether. -# The exit code corresponds to whatever the python interpreter gives. -# - - -def dump_result_to_file(result_file: str, result): - """ Dump a result to the given result file.""" - with open(result_file, "wb") as f_out: - f_out.write(serialize(result)) - - -def remap_location(mapping, parsl_file): - """ Remap files from local name (on manager) to remote name (on worker).""" - if not isinstance(parsl_file, File): - return - # Below we rewrite .local_path when scheme != file only when the local_name - # was given by the main parsl process. This is the case when scheme != - # 'file' but .local_path (via filepath) is in mapping. - if parsl_file.scheme == 'file' or parsl_file.local_path: - master_location = parsl_file.filepath - if master_location in mapping: - parsl_file.local_path = mapping[master_location] - - -def remap_list_of_files(mapping, maybe_files): - """ Remap a list of potential files.""" - for maybe_file in maybe_files: - remap_location(mapping, maybe_file) - - -def remap_all_files(mapping, fn_args, fn_kwargs): - # remap any positional argument given to the function that looks like a - # File - remap_list_of_files(mapping, fn_args) - - # remap any keyword argument in the same way, but we need to treat - # "inputs" and "outputs" specially because they are lists, and - # "stdout" and "stderr", because they are not File's. - for kwarg, maybe_file in fn_kwargs.items(): - if kwarg in ["inputs", "outputs"]: - remap_list_of_files(mapping, maybe_file) - if kwarg in ["stdout", "stderr"]: - if maybe_file: - (fname, mode) = get_std_fname_mode(kwarg, maybe_file) - if fname in mapping: - fn_kwargs[kwarg] = (mapping[fname], mode) - else: - # Treat anything else as a possible File to be remapped. - remap_location(mapping, maybe_file) - - -def unpack_object_from_file(path): - with open(path, 'rb') as f: - obj = deserialize(f.read()) - return obj - - -def encode_function(user_namespace, fn, fn_name, fn_args, fn_kwargs): - """ Register the given function to the given namespace.""" - # Returns a tuple (code, result_name) - # code can be exec in the user_namespace to produce result_name. - prefix = "parsl_" - args_name = prefix + "args" - kwargs_name = prefix + "kwargs" - result_name = prefix + "result" - - # Add variables to the namespace to make function call - user_namespace.update({args_name: fn_args, - kwargs_name: fn_kwargs, - result_name: result_name}) - - if callable(fn): - code = encode_byte_code_function(user_namespace, fn, fn_name, args_name, kwargs_name, result_name) - else: - raise ValueError("Function object does not look like a function.") - - return (code, result_name) - - -def encode_byte_code_function(user_namespace, fn, fn_name, args_name, kwargs_name, result_name): - user_namespace.update({fn_name: fn}) - code = "{0} = {1}(*{2}, **{3})".format(result_name, fn_name, args_name, kwargs_name) - return code - - -def load_function(map_file, function_file, argument_file): - # Decodes the function and its file arguments to be executed into - # function_code, and updates a user namespace with the function name and - # the variable named `result_name`. When the function is executed, its result - # will be stored in this variable in the user namespace. - # Returns (namespace, function_code, result_name) - - fn = unpack_object_from_file(function_file) - args_dict = unpack_object_from_file(argument_file) - fn_args = args_dict['args'] - fn_kwargs = args_dict['kwargs'] - fn_name = 'parsl_tmp_func_name' - - mapping = unpack_object_from_file(map_file) - remap_all_files(mapping, fn_args, fn_kwargs) - - # Create the namespace to isolate the function execution. - user_ns = locals() - user_ns.update({'__builtins__': __builtins__}) - - (code, result_name) = encode_function(user_ns, fn, fn_name, fn_args, fn_kwargs) - - return (user_ns, code, result_name) - - -def execute_function(namespace, function_code, result_name): - # On executing the function inside the namespace, its result will be in a - # variable named result_name. - exec(function_code, namespace, namespace) - result = namespace.get(result_name) - - return result - - -def run(map_file, function_file, argument_file, result_file): - try: - try: - (namespace, function_code, result_name) = load_function(map_file, function_file, argument_file) - except Exception: - print("There was an error setting up the function for execution.") - raise - - try: - result = execute_function(namespace, function_code, result_name) - except Exception: - print("There was an error executing the function.") - raise - except Exception: - traceback.print_exc() - result = RemoteExceptionWrapper(*sys.exc_info()) - - # Write out function result to the result file - try: - dump_result_to_file(result_file, result) - except Exception: - print("Could not write to result file.") - traceback.print_exc() - sys.exit(1) - - -if __name__ == "__main__": - # parse the four required command line arguments: - # map_file: contains a pickled dictionary to map original names to - # names at the execution site. - # function_file: contains the pickled parsl function to execute. - # argument_file: contains the pickled arguments to the function call. - # result_file: any output (including exceptions) will be written to - # this file. - try: - (map_file, function_file, argument_file, result_file) = sys.argv[1:] - except ValueError: - print("Usage:\n\t{} function argument result mapping\n".format(sys.argv[0])) - raise - run(map_file, function_file, argument_file, result_file) diff --git a/parsl/parsl/executors/taskvine/executor.py b/parsl/parsl/executors/taskvine/executor.py deleted file mode 100644 index 1da10415..00000000 --- a/parsl/parsl/executors/taskvine/executor.py +++ /dev/null @@ -1,673 +0,0 @@ -""" TaskVineExecutor utilizes the TaskVine distributed framework developed by the -Cooperative Computing Lab (CCL) at Notre Dame to provide a fault-tolerant, -high-throughput system for delegating Parsl tasks to thousands of remote machines -""" - -import getpass -import hashlib -import inspect -import itertools -import logging -import multiprocessing -import os -import queue -import shutil -import subprocess -import tempfile - -# Import Python built-in libraries -import threading -import uuid -from concurrent.futures import Future -from datetime import datetime -from typing import List, Literal, Optional, Union - -# Import other libraries -import typeguard - -# Import Parsl constructs -import parsl.utils as putils -from parsl.addresses import get_any_address -from parsl.data_provider.files import File -from parsl.data_provider.staging import Staging -from parsl.errors import OptionalModuleMissing -from parsl.executors.errors import ExecutorError -from parsl.executors.status_handling import BlockProviderExecutor -from parsl.executors.taskvine import exec_parsl_function -from parsl.executors.taskvine.errors import TaskVineManagerFailure, TaskVineTaskFailure -from parsl.executors.taskvine.factory import _taskvine_factory -from parsl.executors.taskvine.factory_config import TaskVineFactoryConfig -from parsl.executors.taskvine.manager import _taskvine_submit_wait -from parsl.executors.taskvine.manager_config import TaskVineManagerConfig -from parsl.executors.taskvine.utils import ParslFileToVine, ParslTaskToVine -from parsl.monitoring.radios.base import RadioConfig -from parsl.monitoring.radios.filesystem import FilesystemRadio -from parsl.multiprocessing import SpawnContext -from parsl.process_loggers import wrap_with_logs -from parsl.providers import CondorProvider, LocalProvider -from parsl.providers.base import ExecutionProvider -from parsl.serialize import deserialize, serialize - -logger = logging.getLogger(__name__) - - -class TaskVineExecutor(BlockProviderExecutor, putils.RepresentationMixin): - """Executor to use TaskVine dynamic workflow system - - The TaskVineExecutor system utilizes the TaskVine framework to - efficiently delegate Parsl apps to remote machines in clusters and - grids using a fault-tolerant system. Users can run the - vine_worker program on remote machines to connect to the - TaskVineExecutor, and Parsl apps will then be sent out to these - machines for execution and retrieval. - - This Executor sets up configurations for the TaskVine manager, TaskVine - factory, and run both in separate processes. Sending tasks and receiving - results are done through multiprocessing module native to Python. - - Parameters - ---------- - - label: str - A human readable label for the executor, unique - with respect to other executors. - Default is "TaskVineExecutor". - - worker_launch_method: Union[Literal['provider'], Literal['factory'], Literal['manual']] - Choose to use Parsl provider, TaskVine factory, or - manual user-provided workers to scale workers. - Options are among {'provider', 'factory', 'manual'}. - Default is 'factory'. - - function_exec_mode: Union[Literal['regular'], Literal['serverless']] - Choose to execute functions with a regular fresh python process or a - pre-warmed forked python process. - Default is 'regular'. - - manager_config: TaskVineManagerConfig - Configuration for the TaskVine manager. Default - - factory_config: TaskVineFactoryConfig - Configuration for the TaskVine factory. - Use of factory is disabled by default. - - provider: ExecutionProvider - The Parsl provider that will spawn worker processes. - Default to spawning one local vine worker process. - - storage_access: List[Staging] - Define Parsl file staging providers for this executor. - Default is None. - """ - - @typeguard.typechecked - def __init__(self, - label: str = "TaskVineExecutor", - worker_launch_method: Union[Literal['provider'], Literal['factory'], Literal['manual']] = 'factory', - function_exec_mode: Union[Literal['regular'], Literal['serverless']] = 'regular', - manager_config: TaskVineManagerConfig = TaskVineManagerConfig(), - factory_config: TaskVineFactoryConfig = TaskVineFactoryConfig(), - provider: Optional[ExecutionProvider] = None, - storage_access: Optional[List[Staging]] = None, - remote_monitoring_radio: Optional[RadioConfig] = None): - - # Set worker launch option for this executor - if worker_launch_method == 'factory' or worker_launch_method == 'manual': - provider = None - elif worker_launch_method == 'provider' and provider is None: - # provider method chosen, but no explicit provider supplied to __init__ - # so default to LocalProvider - provider = LocalProvider(init_blocks=1) - - # Initialize the parent class with the execution provider and block error handling enabled. - # If provider is None, then no worker is launched via the provider method. - BlockProviderExecutor.__init__(self, provider=provider, - block_error_handler=True) - - # Raise an exception if there's a problem importing TaskVine - try: - import ndcctools.taskvine - logger.debug(f'TaskVine default port: {ndcctools.taskvine.cvine.VINE_DEFAULT_PORT}') - except ImportError: - raise OptionalModuleMissing(['taskvine'], "TaskVineExecutor requires the taskvine module.") - - # Executor configurations - self.label = label - self.worker_launch_method = worker_launch_method - self.function_exec_mode = function_exec_mode - self.manager_config = manager_config - self.factory_config = factory_config - self.storage_access = storage_access - - if remote_monitoring_radio is not None: - self.remote_monitoring_radio = remote_monitoring_radio - else: - self.remote_monitoring_radio = FilesystemRadio() - - # Queue to send ready tasks from TaskVine executor process to TaskVine manager process - self._ready_task_queue: multiprocessing.Queue = SpawnContext.Queue() - - # Queue to send finished tasks from TaskVine manager process to TaskVine executor process - self._finished_task_queue: multiprocessing.Queue = SpawnContext.Queue() - - # Event to signal whether the manager and factory processes should stop running - self._should_stop = SpawnContext.Event() - - # TaskVine manager process - self._submit_process = None - - # TaskVine factory process - self._factory_process = None - - # Executor thread to collect results from TaskVine manager and set - # tasks' futures to done status. - self._collector_thread = None - - # track task id of submitted parsl tasks - # task ids are incremental and start from 0 - self._executor_task_counter = 0 - - # track number of tasks that are waiting/running - self._outstanding_tasks = 0 - - # Lock for threads to access self._outstanding_tasks attribute - self._outstanding_tasks_lock = threading.Lock() - - # Threading lock to manage self.tasks dictionary object, which maps a task id - # to its future object. - self._tasks_lock = threading.Lock() - - # Worker command to be given to an execution provider (e.g., local or Condor) - self._worker_command = "" - - # Path to directory that holds all tasks' data and results. - self._function_data_dir = "" - - # Helper scripts to prepare package tarballs for Parsl apps - self._package_analyze_script = shutil.which("poncho_package_analyze") - self._package_create_script = shutil.which("poncho_package_create") - if self._package_analyze_script is None or self._package_create_script is None: - self._poncho_available = False - else: - self._poncho_available = True - - def _get_launch_command(self, block_id): - # Implements BlockProviderExecutor's abstract method. - # This executor uses different terminology for worker/launch - # commands than in htex. - return f"PARSL_WORKER_BLOCK_ID={block_id} {self._worker_command}" - - def __synchronize_manager_factory_comm_settings(self): - # Synchronize the communication settings between the manager and the factory - # so the factory can direct workers to contact the manager. - - # If the manager can choose any available port (port number = 0), - # then it must have a project name - # so the factory can look it up. Otherwise the factory process will not know the - # port number as it's only chosen when the TaskVine manager process is run. - if self.manager_config.port == 0 and self.manager_config.project_name is None: - self.manager_config.project_name = "parsl-vine-" + str(uuid.uuid4()) - - # guess the host name if the project name is not given and none has been supplied - # explicitly in the manager config. - if not self.manager_config.project_name and self.manager_config.address is None: - self.manager_config.address = get_any_address() - - # Factory communication settings are overridden by manager communication settings. - self.factory_config._project_port = self.manager_config.port - self.factory_config._project_address = self.manager_config.address - self.factory_config._project_name = self.manager_config.project_name - self.factory_config._project_password_file = self.manager_config.project_password_file - logger.debug('Communication settings between TaskVine manager and factory synchronized.') - - def __create_data_and_logging_dirs(self): - # Create neccessary data and logging directories - - # Use the current run directory from Parsl - run_dir = self.run_dir - - # Create directories for data and results - log_dir = os.path.join(run_dir, self.label) - os.makedirs(log_dir) - tmp_prefix = f'{self.label}-{getpass.getuser()}-{datetime.now().strftime("%Y%m%d%H%M%S%f")}-' - self._function_data_dir = tempfile.TemporaryDirectory(prefix=tmp_prefix) - - # put TaskVine logs outside of a Parsl run as TaskVine caches between runs while - # Parsl does not. - vine_log_dir = os.path.join(os.path.dirname(run_dir), self.label) - if self.manager_config.vine_log_dir is None: - self.manager_config.vine_log_dir = vine_log_dir - - # factory logs go with manager logs regardless - self.factory_config.scratch_dir = self.manager_config.vine_log_dir - logger.debug(f"Function data directory: {self._function_data_dir.name}, log directory: {log_dir}") - logger.debug( - f"TaskVine manager log directory: {self.manager_config.vine_log_dir}, " - f"factory log directory: {self.factory_config.scratch_dir}") - - def start(self): - """Create submit process and collector thread to create, send, and - retrieve Parsl tasks within the TaskVine system. - """ - - super().start() - # Synchronize connection and communication settings between the manager and factory - self.__synchronize_manager_factory_comm_settings() - - # Create data and logging dirs - self.__create_data_and_logging_dirs() - - logger.debug("Starting TaskVineExecutor") - - # Create a process to run the TaskVine manager. - submit_process_kwargs = {"ready_task_queue": self._ready_task_queue, - "finished_task_queue": self._finished_task_queue, - "should_stop": self._should_stop, - "manager_config": self.manager_config} - self._submit_process = SpawnContext.Process(target=_taskvine_submit_wait, - name="TaskVine-Submit-Process", - kwargs=submit_process_kwargs) - - # Create a process to run the TaskVine factory if enabled. - if self.worker_launch_method == 'factory': - factory_process_kwargs = {"should_stop": self._should_stop, - "factory_config": self.factory_config} - self._factory_process = SpawnContext.Process(target=_taskvine_factory, - name="TaskVine-Factory-Process", - kwargs=factory_process_kwargs) - - # Run thread to collect results and set tasks' futures. - self._collector_thread = threading.Thread(target=self._collect_taskvine_results, - name="TaskVine-Collector-Thread") - # Interpreter can exit without waiting for this thread. - self._collector_thread.daemon = True - - # Begin work - self._submit_process.start() - - # Run worker scaler either with Parsl provider or TaskVine factory. - # Skip if workers are launched manually. - if self.worker_launch_method == 'factory': - self._factory_process.start() - elif self.worker_launch_method == 'provider': - self.initialize_scaling() - - self._collector_thread.start() - - logger.debug("All components in TaskVineExecutor started") - - def _path_in_task(self, executor_task_id, *path_components): - """ - Returns a filename fixed and specific to a task. - It is used for the following filename's: - (not given): The subdirectory per task that contains function, result, etc. - 'function': Pickled file that contains the function to be executed. - 'argument': Pickled file that contains the arguments of the function call. - 'result': Pickled file that (will) contain the result of the function. - 'map': Pickled file with a dict between local parsl names, and remote taskvine names. - """ - task_dir = "{:04d}".format(executor_task_id) - return os.path.join(self._function_data_dir.name, task_dir, *path_components) - - def submit(self, func, resource_specification, *args, **kwargs): - """Processes the Parsl app by its arguments and submits the function - information to the task queue, to be executed using the TaskVine - system. The args and kwargs are processed for input and output files to - the Parsl app, so that the files are appropriately specified for the TaskVine task. - - Parameters - ---------- - - func : function - Parsl app to be submitted to the TaskVine system - resource_specification: dict - Dictionary containing relevant info about task. - Include information about resources of task, execution mode - of task (out of {regular, serverless}). - args : list - Arguments to the Parsl app - kwargs : dict - Keyword arguments to the Parsl app - """ - - logger.debug(f'Got resource specification: {resource_specification}') - - # Default execution mode of apps is regular - exec_mode = resource_specification.get('exec_mode', self.function_exec_mode) - - # Detect resources and features of a submitted Parsl app - cores = None - memory = None - disk = None - gpus = None - priority = None - category = None - running_time_min = None - if resource_specification and isinstance(resource_specification, dict): - for k in resource_specification: - if k == 'cores': - cores = resource_specification[k] - elif k == 'memory': - memory = resource_specification[k] - elif k == 'disk': - disk = resource_specification[k] - elif k == 'gpus': - gpus = resource_specification[k] - elif k == 'priority': - priority = resource_specification[k] - elif k == 'category': - category = resource_specification[k] - elif k == 'running_time_min': - running_time_min = resource_specification[k] - - # Assign executor task id to app - executor_task_id = self._executor_task_counter - self._executor_task_counter += 1 - - # Create a per task directory for the function, argument, map, and result files - os.mkdir(self._path_in_task(executor_task_id)) - - input_files = [] - output_files = [] - - # Determine whether to stage input files that will exist at the workers - # Input and output files are always cached - input_files += [self._register_file(f) for f in kwargs.get("inputs", []) if isinstance(f, File)] - output_files += [self._register_file(f) for f in kwargs.get("outputs", []) if isinstance(f, File)] - - # Also consider any *arg that looks like a file as an input: - input_files += [self._register_file(f) for f in args if isinstance(f, File)] - - for kwarg, maybe_file in kwargs.items(): - # Add appropriate input and output files from "stdout" and "stderr" keyword arguments - if kwarg == "stdout" or kwarg == "stderr": - if maybe_file: - output_files.append(self._std_output_to_vine(kwarg, maybe_file)) - # For any other keyword that looks like a file, assume it is an input file - elif isinstance(maybe_file, File): - input_files.append(self._register_file(maybe_file)) - - # Create a Future object and have it be mapped from the task ID in the tasks dictionary - fu = Future() - fu.parsl_executor_task_id = executor_task_id - with self._tasks_lock: - self.tasks[str(executor_task_id)] = fu - - # Setup files to be used on a worker to execute the function - function_file = None - argument_file = None - result_file = None - map_file = None - - # Get path to files that will contain the pickled function, - # arguments, result, and map of input and output files - function_file = self._path_in_task(executor_task_id, "function") - argument_file = self._path_in_task(executor_task_id, "argument") - result_file = self._path_in_task(executor_task_id, "result") - map_file = self._path_in_task(executor_task_id, "map") - - logger.debug("Creating executor task {} with function at: {}, argument at: {}, \ - and result to be found at: {}".format(executor_task_id, function_file, argument_file, result_file)) - - # Serialize function object and arguments, separately - self._serialize_object_to_file(function_file, func) - args_dict = {'args': args, 'kwargs': kwargs} - self._serialize_object_to_file(argument_file, args_dict) - - # Construct the map file of local filenames at worker - self._construct_map_file(map_file, input_files, output_files) - - # Register a tarball containing all package dependencies for this app if instructed - if self.manager_config.app_pack: - env_pkg = self._prepare_package(func, self.extra_pkgs) - else: - env_pkg = None - - # Create message to put into the message queue - logger.debug("Placing task {} on message queue".format(executor_task_id)) - - # Put apps into their categories based on function name if enabled - if category is None: - category = func.__name__ if self.manager_config.autocategory else 'parsl-default' - - task_info = ParslTaskToVine(executor_id=executor_task_id, - exec_mode=exec_mode, - category=category, - input_files=input_files, - output_files=output_files, - map_file=map_file, - function_file=function_file, - argument_file=argument_file, - result_file=result_file, - cores=cores, - memory=memory, - disk=disk, - gpus=gpus, - priority=priority, - running_time_min=running_time_min, - env_pkg=env_pkg) - - # Send ready task to manager process - if not self._submit_process.is_alive(): - raise ExecutorError(self, "taskvine Submit Process is not alive") - - self._ready_task_queue.put_nowait(task_info) - - # Increment outstanding task counter - with self._outstanding_tasks_lock: - self._outstanding_tasks += 1 - - # Return the future for this function, will be set by collector thread when result - # comes back from the TaskVine manager. - return fu - - def _construct_worker_command(self): - worker_command = self.factory_config.worker_executable - if self.factory_config._project_password_file: - worker_command += ' --password {}'.format(self.factory_config._project_password_file) - if self.factory_config.worker_options: - worker_command += ' {}'.format(self.factory_config.worker_options) - if self.factory_config._project_name: - worker_command += ' -M {}'.format(self.factory_config._project_name) - else: - worker_command += ' {} {}'.format(self.factory_config._project_address, self.factory_config._project_port) - - logger.debug("Using worker command: {}".format(worker_command)) - return worker_command - - def _patch_providers(self): - # Add the worker and password file to files that the provider needs to stage. - # (Currently only for the CondorProvider) - if isinstance(self.provider, CondorProvider): - path_to_worker = shutil.which('vine_worker') - self._worker_command = './' + self._worker_command - self.provider.transfer_input_files.append(path_to_worker) - if self.project_password_file: - self.provider.transfer_input_files.append(self.project_password_file) - - def _serialize_object_to_file(self, path, obj): - """Takes any object and serializes it to the file path.""" - serialized_obj = serialize(obj, buffer_threshold=1024 * 1024) - with open(path, 'wb') as f_out: - written = 0 - while written < len(serialized_obj): - written += f_out.write(serialized_obj[written:]) - - def _construct_map_file(self, map_file, input_files, output_files): - """ Map local filepath of parsl files to the filenames at the execution worker. - If using a shared filesystem, the filepath is mapped to its absolute filename. - Otherwise, to its original relative filename. In this later case, taskvine - recreates any directory hierarchy needed.""" - file_translation_map = {} - for spec in itertools.chain(input_files, output_files): - local_name = spec.parsl_name - if self.manager_config.shared_fs: - remote_name = os.path.abspath(local_name) - else: - remote_name = local_name - file_translation_map[local_name] = remote_name - self._serialize_object_to_file(map_file, file_translation_map) - - def _register_file(self, parsl_file): - """Generates a tuple (parsl_file.filepath, stage, cache) to give to - taskvine. cache is always True. - stage is True if the file has a relative path. (i.e., not - a URL or an absolute path)""" - to_cache = True - to_stage = False - if parsl_file.scheme == 'file' or \ - (parsl_file.local_path and os.path.exists(parsl_file.local_path)): - to_stage = not os.path.isabs(parsl_file.filepath) - - return ParslFileToVine(parsl_file.filepath, to_stage, to_cache) - - def _std_output_to_vine(self, fdname, stdfspec): - """Find the name of the file that will contain stdout or stderr and - return a ParslFileToVine with it. These files are never cached""" - fname, mode = putils.get_std_fname_mode(fdname, stdfspec) - to_stage = not os.path.isabs(fname) - return ParslFileToVine(fname, stage=to_stage, cache=False) - - def _prepare_package(self, fn, extra_pkgs): - """ Look at source code of apps to figure out their package depedencies - and output a tarball containing those to send along with tasks for execution.""" - - if not self._poncho_available: - raise ExecutorError(self, 'poncho package is not available to individually pack apps.') - - fn_id = id(fn) - fn_name = fn.__name__ - if fn_id in self.cached_envs: - logger.debug("Skipping analysis of %s, previously got %s", fn_name, self.cached_envs[fn_id]) - return self.cached_envs[fn_id] - source_code = inspect.getsource(fn).encode() - pkg_dir = os.path.join(tempfile.gettempdir(), "python_package-{}".format(os.geteuid())) - os.makedirs(pkg_dir, exist_ok=True) - with tempfile.NamedTemporaryFile(suffix='.yaml') as spec: - logger.info("Analyzing dependencies of %s", fn_name) - analyze_cmdline = [self._package_analyze_script, exec_parsl_function.__file__, '-', spec.name] - for p in extra_pkgs: - analyze_cmdline += ["--extra-pkg", p] - subprocess.run(analyze_cmdline, input=source_code, check=True) - with open(spec.name, mode='rb') as f: - spec_hash = hashlib.sha256(f.read()).hexdigest() - logger.debug("Spec hash for %s is %s", fn_name, spec_hash) - pkg = os.path.join(pkg_dir, "pack-{}.tar.gz".format(spec_hash)) - if os.access(pkg, os.R_OK): - self.cached_envs[fn_id] = pkg - logger.debug("Cached package for %s found at %s", fn_name, pkg) - return pkg - (fd, tarball) = tempfile.mkstemp(dir=pkg_dir, prefix='.tmp', suffix='.tar.gz') - os.close(fd) - logger.info("Creating dependency package for %s", fn_name) - logger.debug("Writing deps for %s to %s", fn_name, tarball) - subprocess.run([self._package_create_script, spec.name, tarball], stdout=subprocess.DEVNULL, check=True) - logger.debug("Done with conda-pack; moving %s to %s", tarball, pkg) - os.rename(tarball, pkg) - self.cached_envs[fn_id] = pkg - return pkg - - def initialize_scaling(self): - """ Compose the launch command and call scale out - - Scales the workers to the appropriate nodes with provider - """ - # Start scaling in/out - logger.debug("Starting TaskVineExecutor with provider: %s", self.provider) - self._worker_command = self._construct_worker_command() - self._patch_providers() - - def outstanding(self) -> int: - """Count the number of outstanding tasks.""" - logger.debug(f"Counted {self._outstanding_tasks} outstanding tasks") - return self._outstanding_tasks - - @property - def workers_per_node(self) -> Union[int, float]: - return 1 - - def shutdown(self, *args, **kwargs): - """Shutdown the executor. Sets flag to cancel the submit process and - collector thread, which shuts down the TaskVine system submission. - """ - logger.debug("TaskVine shutdown started") - self._should_stop.set() - - # Join all processes before exiting - logger.debug("Joining on submit process") - self._submit_process.join() - self._submit_process.close() - logger.debug("Joining on collector thread") - self._collector_thread.join() - if self.worker_launch_method == 'factory': - logger.debug("Joining on factory process") - self._factory_process.join() - self._factory_process.close() - - # Shutdown multiprocessing queues - self._ready_task_queue.close() - self._ready_task_queue.join_thread() - self._finished_task_queue.close() - self._finished_task_queue.join_thread() - - super().shutdown() - - logger.debug("TaskVine shutdown completed") - - @wrap_with_logs - def _collect_taskvine_results(self): - """Sets the values of tasks' futures completed by taskvine. - """ - logger.debug("Starting Collector Thread") - try: - while not self._should_stop.is_set(): - if not self._submit_process.is_alive(): - raise ExecutorError(self, "taskvine Submit Process is not alive") - - # Get the result message from the _finished_task_queue - try: - task_report = self._finished_task_queue.get(timeout=1) - except queue.Empty: - continue - - # Obtain the future from the tasks dictionary - with self._tasks_lock: - future = self.tasks.pop(task_report.executor_id) - - logger.debug(f'Updating Future for Parsl Task: {task_report.executor_id}. ' - f'Task {task_report.executor_id} has result_received set to {task_report.result_received}') - if task_report.result_received: - try: - with open(task_report.result_file, 'rb') as f_in: - result = deserialize(f_in.read()) - except Exception as e: - logger.error(f'Cannot load result from result file {task_report.result_file}. Exception: {e}') - ex = TaskVineTaskFailure('Cannot load result from result file', None) - ex.__cause__ = e - future.set_exception(ex) - else: - if isinstance(result, Exception): - ex = TaskVineTaskFailure('Task execution raises an exception', result) - ex.__cause__ = result - future.set_exception(ex) - else: - future.set_result(result) - else: - # If there are no results, then the task failed according to one of - # taskvine modes, such as resource exhaustion. - ex = TaskVineTaskFailure(task_report.reason, None) - future.set_exception(ex) - - # decrement outstanding task counter - with self._outstanding_tasks_lock: - self._outstanding_tasks -= 1 - finally: - logger.debug(f"Marking all {self.outstanding()} outstanding tasks as failed") - logger.debug("Acquiring tasks_lock") - with self._tasks_lock: - logger.debug("Acquired tasks_lock") - # set exception for tasks waiting for results that taskvine did not execute - for fu in self.tasks.values(): - if not fu.done(): - fu.set_exception(TaskVineManagerFailure("taskvine executor failed to execute the task.")) - logger.debug("Exiting Collector Thread") diff --git a/parsl/parsl/executors/taskvine/factory.py b/parsl/parsl/executors/taskvine/factory.py deleted file mode 100644 index 0790af51..00000000 --- a/parsl/parsl/executors/taskvine/factory.py +++ /dev/null @@ -1,73 +0,0 @@ -import logging -import os - -from parsl.executors.taskvine.errors import TaskVineFactoryFailure -from parsl.process_loggers import wrap_with_logs - -# This try except clause prevents import errors -# when TaskVine is not used in Parsl. -try: - from ndcctools.taskvine import Factory - taskvine_available = True -except ImportError: - taskvine_available = False - -logger = logging.getLogger(__name__) - - -@wrap_with_logs -def _taskvine_factory(should_stop, factory_config): - if not taskvine_available: - logger.debug("TaskVine package cannot be found. Please install the ndcctools package.") - return - logger.debug("Starting TaskVine factory process") - - try: - # create the factory according to the project name if given - if factory_config._project_name: - factory = Factory(batch_type=factory_config.batch_type, - manager_name=factory_config._project_name, - ) - else: - factory = Factory(batch_type=factory_config.batch_type, - manager_host_port=f"{factory_config._project_address}:{factory_config._project_port}", - ) - except Exception as e: - raise TaskVineFactoryFailure(f'Cannot create factory with exception {e}') - - # Set attributes of this factory - if factory_config._project_password_file: - factory.password = factory_config._project_password_file - factory.factory_timeout = factory_config.factory_timeout - factory.scratch_dir = factory_config.scratch_dir - factory.min_workers = factory_config.min_workers - factory.max_workers = factory_config.max_workers - factory.workers_per_cycle = factory_config.workers_per_cycle - - # create scratch dir if factory process gets ahead of the manager. - os.makedirs(factory.scratch_dir, exist_ok=True) - - if factory_config.worker_options: - factory.extra_options = factory_config.worker_options - factory.timeout = factory_config.worker_timeout - if factory_config.cores: - factory.cores = factory_config.cores - if factory_config.gpus: - factory.gpus = factory_config.gpus - if factory_config.memory: - factory.memory = factory_config.memory - if factory_config.disk: - factory.disk = factory_config.disk - if factory_config.python_env: - factory.python_env = factory_config.python_env - - if factory_config.condor_requirements: - factory.condor_requirements = factory_config.condor_requirements - if factory_config.batch_options: - factory.batch_options = factory_config.batch_options - - # run factory through Python context and wait for signal to stop. - with factory: - should_stop.wait() - - logger.debug("Exiting TaskVine factory process") diff --git a/parsl/parsl/executors/taskvine/factory_config.py b/parsl/parsl/executors/taskvine/factory_config.py deleted file mode 100644 index f198e874..00000000 --- a/parsl/parsl/executors/taskvine/factory_config.py +++ /dev/null @@ -1,121 +0,0 @@ -from dataclasses import dataclass -from typing import Optional - - -@dataclass -class TaskVineFactoryConfig: - """ - Configuration of a TaskVine factory. - Default is to spawn one local worker. - - Parameters - ---------- - factory_timeout: int - Exit if no manager seen in $factory_timeout seconds. - Kill all workers on upon exit. - - scratch_dir: Optional[str] - Directory for factory to operate. - Default is None, which TaskVine factory will use a directory - residing inside Parsl logging directory. - - min_workers: int - Number of minimum workers connecting to manager at all times. - Default is 1. - - max_workers: int - Number of maximum workers connecting to manager at all times. - Default is 1. - - workers_per_cycle: int - Maximum number of new workers per 30s. - - worker_options: Optional[str] - Additional options to pass to workers. Run - ``vine_worker --help`` for more details. - Default is None. - - worker_executable: str - Executable to run workers. - Default is 'vine_worker', which finds the executable - on $PATH. - - worker_timeout: int - Number of seconds for workers to wait for manager's contact - before exiting. - Default is 300. - - cores: Optional[int] - Number of cores a worker should have. - Default is None, which allows a worker to use all available - cores of the machine it lands on. - - gpus: Optional[int] - Number of gpus a worker should have. - Default is None, which allows a worker to use all available - gpus of the machine it lands on. - - memory: Optional[int] - Amount of memory in MBs a worker should have. - Default is None, which allows a worker to use all available - memory of the machine it lands on. - - disk: Optional[int] - Amount of disk in MBs a worker should have. - Default is None, which allows a worker to use all available - disk space of the machine it lands on. - - python_env: Optional[str] - Run workers inside this python environment tarball. - Default is None. - - batch_type: str - Batch systems the factory should submit workers as jobs. - 'local': spawn local workers. - 'condor': Submit workers through HTCondor batch system. - 'sge': Submit workers through SGE batch system. - 'slurm': Submit workers through SLURM batch system. - Default is 'local'. - - condor_requirements: Optional[str] - String describing the condor requirements for slots - that workers should land on. - Default is None. - - batch_options: Optional[str] - String describing generic batch options. Varies with the - type of batch system. - Default is None. - """ - - # Connection and communication settings - factory_timeout: int = 300 - - # Logging settings - scratch_dir: Optional[str] = None - - # Scale settings - min_workers: int = 1 - max_workers: int = 1 - workers_per_cycle: int = 1 - - # Worker settings - worker_options: Optional[str] = None - worker_executable: str = 'vine_worker' - worker_timeout: int = 300 - cores: Optional[int] = None - gpus: Optional[int] = None - memory: Optional[int] = None - disk: Optional[int] = None - python_env: Optional[str] = None - - # Batch settings - batch_type: str = "local" - condor_requirements: Optional[str] = None - batch_options: Optional[str] = None - - # Private attributes, users should not touch - _project_port: int = 0 - _project_address: Optional[str] = None - _project_name: Optional[str] = None - _project_password_file: Optional[str] = None diff --git a/parsl/parsl/executors/taskvine/manager.py b/parsl/parsl/executors/taskvine/manager.py deleted file mode 100644 index d11e81be..00000000 --- a/parsl/parsl/executors/taskvine/manager.py +++ /dev/null @@ -1,490 +0,0 @@ -import hashlib -import logging -import os -import queue -import shutil -import subprocess -import uuid - -import parsl -from parsl.executors.taskvine import exec_parsl_function -from parsl.executors.taskvine.utils import VineTaskToParsl, run_parsl_function -from parsl.process_loggers import wrap_with_logs -from parsl.utils import setproctitle - -try: - from ndcctools.taskvine import FunctionCall, Manager, Task, cvine - from ndcctools.taskvine.cvine import ( - VINE_ALLOCATION_MODE_EXHAUSTIVE_BUCKETING, - VINE_ALLOCATION_MODE_MAX, - VINE_ALLOCATION_MODE_MAX_THROUGHPUT, - ) -except ImportError: - _taskvine_enabled = False -else: - _taskvine_enabled = True - -logger = logging.getLogger(__name__) - - -def _set_manager_attributes(m, config): - """ Set various manager global attributes.""" - if config.project_password_file: - m.set_password_file(config.project_password_file) - - # Autolabeling resources require monitoring to be enabled - if config.autolabel: - m.enable_monitoring() - if config.autolabel_window is not None: - m.tune('category-steady-n-tasks', config.autolabel_window) - - # Specify number of workers to wait for before sending the first task - if config.wait_for_workers: - m.tune("wait-for-workers", config.wait_for_workers) - - # Enable peer transfer feature between workers if specified - if config.enable_peer_transfers: - m.enable_peer_transfers() - else: - m.disable_peer_transfers() - - # Set catalog report to parsl if project name exists - if m.name: - m.set_property("framework", "parsl") - - if config.tune_parameters is not None: - for k, v in config.tune_parameters.items(): - m.tune(k, v) - - -def _prepare_environment_serverless(manager_config, env_cache_dir, poncho_create_script): - # Return path to a packaged poncho environment - poncho_env_path = '' - if not manager_config.shared_fs: - if manager_config.env_pack is None: - raise Exception('TaskVine manager needs env_pack to be specified when running tasks in serverless mode and with no shared_fs') - - poncho_env_path = manager_config.env_pack - - # If a conda environment name or path is given, then use the hash of the headers of - # all contained packages as the name of the to-be-packaged poncho tarball, - # and package it if it's not cached. - if not poncho_env_path.endswith('tar.gz'): - if os.path.isabs(poncho_env_path): - conda_env_signature = hashlib.md5(subprocess.check_output(['conda', 'list', '-p', poncho_env_path, '--json'])).hexdigest() - logger.debug(f'Signature of conda environment at {poncho_env_path}: {conda_env_signature}') - else: - conda_env_signature = hashlib.md5(subprocess.check_output(['conda', 'list', '-n', poncho_env_path, '--json'])).hexdigest() - logger.debug(f'Signature of conda environment named {poncho_env_path}: {conda_env_signature}') - - # If env is cached then use it, - # else create a new env tarball - poncho_env_path = os.path.join(env_cache_dir, '.'.join([conda_env_signature, 'tar.gz'])) - if not os.path.isfile(poncho_env_path): - logger.debug(f'No cached poncho environment. Creating poncho environment for library task at {poncho_env_path}') - try: - subprocess.run([poncho_create_script, manager_config.env_pack, poncho_env_path], stdout=subprocess.DEVNULL, check=True) - except Exception: - logger.error('Cannot create a poncho environment. Removing it.') - if os.path.isfile(poncho_env_path): - os.remove(poncho_env_path) - raise - else: - logger.debug(f'Found cached poncho environment at {poncho_env_path}. Reusing it.') - else: - logger.debug(f'Use the given poncho environment at {manager_config.env_pack} to setup library task.') - return poncho_env_path - - -def _prepare_environment_regular(m, manager_config, t, task, poncho_env_to_file, poncho_create_script): - # Add environment file to the task if possible - # Prioritize local poncho environment over global poncho environment - # (local: use app_pack, global: use env_pack) - poncho_env_file = None - - # check if env_pack is specified - if manager_config.env_pack is not None: - - # check if the environment file is not already created - if manager_config.env_pack not in poncho_env_to_file: - - # if the environment is already packaged as a tarball, then add the file - # otherwise it is an environment name or path, so create a poncho tarball then add it - if not manager_config.env_pack.endswith('.tar.gz'): - env_tarball = str(uuid.uuid4()) + '.tar.gz' - logger.debug(f'Creating a poncho environment at {env_tarball} from conda environment {manager_config.env_pack}') - subprocess.run([poncho_create_script, manager_config.env_pack, env_tarball], stdout=subprocess.DEVNULL, check=True) - else: - env_tarball = manager_config.env_pack - poncho_env_file = m.declare_poncho(env_tarball, cache=True, peer_transfer=True) - poncho_env_to_file[manager_config.env_pack] = poncho_env_file - else: - poncho_env_file = poncho_env_to_file[manager_config.env_pack] - logger.debug(f'Found cached poncho environment for {manager_config.env_pack}. Reusing it.') - - # check if app_pack is used, override if possible - if task.env_pkg is not None: - if task.env_pkg not in poncho_env_to_file: - poncho_env_file = m.declare_poncho(task.env_pkg, cache=True, peer_transfer=True) - poncho_env_to_file[task.env_pkg] = poncho_env_file - else: - poncho_env_file = poncho_env_to_file[task.env_pkg] - - # Add environment to the task - if poncho_env_file is not None: - t.add_environment(poncho_env_file) - - -@wrap_with_logs -def _taskvine_submit_wait(ready_task_queue=None, - finished_task_queue=None, - should_stop=None, - manager_config=None - ): - """Process to handle Parsl app submissions to the TaskVine objects. - Takes in Parsl functions submitted using submit(), and creates a - TaskVine task with the appropriate specifications, which is then - submitted to TaskVine. After tasks are completed, processes the - exit status and exit code of the task, and sends results to the - TaskVine collector thread. - To avoid python's global interpreter lock with taskvine's wait, this - function should be launched as a process, not as a lightweight thread. This - means that any communication should be done using the multiprocessing - module capabilities, rather than shared memory. - """ - logger.debug("Starting TaskVine Submit/Wait Process") - setproctitle("parsl: TaskVine submit/wait") - - # Enable debugging flags and create logging file - if manager_config.vine_log_dir is not None: - logger.debug("Setting debugging flags and creating logging file at {}".format(manager_config.vine_log_dir)) - - # Create TaskVine queue object - logger.debug("Creating TaskVine Object") - try: - logger.debug("Listening on port {}".format(manager_config.port)) - m = Manager(port=manager_config.port, - name=manager_config.project_name, - run_info_path=manager_config.vine_log_dir) - except Exception as e: - logger.error("Unable to create TaskVine object: {}".format(e)) - raise e - - # Specify TaskVine manager attributes - _set_manager_attributes(m, manager_config) - - # Get parent pid, useful to shutdown this process when its parent, the taskvine - # executor process, exits. - orig_ppid = os.getppid() - - result_file_of_task_id = {} # Mapping executor task id -> result file. - - poncho_env_to_file = {} # Mapping poncho_env id to File object in TaskVine - - # Mapping of parsl local file name to TaskVine File object - # dict[str] -> vine File object - parsl_file_name_to_vine_file = {} - - # Mapping of tasks from vine id to parsl id - # Dict[str] -> str - vine_id_to_executor_task_id = {} - - # Find poncho scripts to create and activate an environment tarball - poncho_create_script = shutil.which("poncho_package_create") - - # Declare helper script as cache-able and peer-transferable - exec_parsl_function_file = m.declare_file(exec_parsl_function.__file__, cache=True, peer_transfer=True) - - # Flag to make sure library for serverless tasks is declared and installed only once. - lib_installed = False - - # Create cache dir for environment files - env_cache_dir = os.path.join(manager_config.vine_log_dir, 'vine-cache', 'vine-poncho-env-cache') - os.makedirs(env_cache_dir, exist_ok=True) - - logger.debug("Entering main loop of TaskVine manager") - - while not should_stop.is_set(): - # Check if executor process is still running - ppid = os.getppid() - if ppid != orig_ppid: - logger.debug("Executor process is detected to have exited. Exiting..") - break - - # Submit tasks - while ready_task_queue.qsize() > 0 or m.empty() and not should_stop.is_set(): - # Obtain task from ready_task_queue - try: - task = ready_task_queue.get(timeout=1) - logger.debug("Removing executor task from queue") - except queue.Empty: - logger.debug("Queue is empty") - continue - if task.exec_mode == 'regular': - # Create command string - launch_cmd = "python3 exec_parsl_function.py {mapping} {function} {argument} {result}" - if manager_config.init_command != '': - launch_cmd = "{init_cmd} " + launch_cmd - command_str = launch_cmd.format(init_cmd=manager_config.init_command, - mapping=os.path.basename(task.map_file), - function=os.path.basename(task.function_file), - argument=os.path.basename(task.argument_file), - result=os.path.basename(task.result_file)) - logger.debug("Sending executor task {} (mode: regular) with command: {}".format(task.executor_id, command_str)) - try: - t = Task(command_str) - except Exception as e: - logger.error("Unable to create executor task (mode:regular): {}".format(e)) - finished_task_queue.put_nowait(VineTaskToParsl(executor_id=task.executor_id, - result_received=False, - result_file=None, - reason="task could not be created by taskvine", - status=-1)) - continue - elif task.exec_mode == 'serverless': - if not lib_installed: - # Declare and install common library for serverless tasks. - # Library requires an environment setup properly, which is - # different from setup of regular tasks. - # If shared_fs is True, then no environment preparation is done. - # Only the core serverless code is created. - poncho_env_path = _prepare_environment_serverless(manager_config, env_cache_dir, poncho_create_script) - - # Don't automatically add environment so manager can declare and cache the vine file associated with the environment file - add_env = False - serverless_lib = m.create_library_from_functions('common-parsl-taskvine-lib', - run_parsl_function, - poncho_env=poncho_env_path, - init_command=manager_config.init_command, - add_env=add_env, - hoisting_modules=[parsl.serialize, run_parsl_function]) - - # Configure the library if provided - if manager_config.library_config: - lib_cores = manager_config.library_config.get('cores', None) - lib_memory = manager_config.library_config.get('memory', None) - lib_disk = manager_config.library_config.get('disk', None) - lib_slots = manager_config.library_config.get('num_slots', None) - if lib_cores: - serverless_lib.set_cores(lib_cores) - if lib_memory: - serverless_lib.set_memory(lib_memory) - if lib_disk: - serverless_lib.set_disk(lib_disk) - if lib_slots: - serverless_lib.set_function_slots(lib_slots) - - if poncho_env_path: - serverless_lib_env_file = m.declare_poncho(poncho_env_path, cache=True, peer_transfer=True) - serverless_lib.add_environment(serverless_lib_env_file) - poncho_env_to_file[manager_config.env_pack] = serverless_lib_env_file - logger.debug(f'Created library task using poncho environment at {poncho_env_path}.') - else: - logger.debug('Created minimal library task with no environment.') - - m.install_library(serverless_lib) - lib_installed = True - try: - # run_parsl_function only needs remote names of map_file, function_file, argument_file, - # and result_file, which are simply named map, function, argument, result. - # These names are given when these files are declared below. - t = FunctionCall('common-parsl-taskvine-lib', run_parsl_function.__name__, 'map', 'function', 'argument', 'result') - except Exception as e: - logger.error("Unable to create executor task (mode:serverless): {}".format(e)) - finished_task_queue.put_nowait(VineTaskToParsl(executor_id=task.executor_id, - result_received=False, - result_file=None, - reason="task could not be created by taskvine", - status=-1)) - else: - raise Exception(f'Unrecognized task mode {task.exec_mode}. Exiting...') - - # prepare environment for regular tasks if not using shared_fs - if task.exec_mode == 'regular' and not manager_config.shared_fs: - _prepare_environment_regular(m, manager_config, t, task, poncho_env_to_file, poncho_create_script) - - t.set_category(task.category) - - # Set autolabel mode - if manager_config.autolabel: - if manager_config.autolabel_algorithm == 'max-xput': - m.set_category_mode(task.category, VINE_ALLOCATION_MODE_MAX_THROUGHPUT) - elif manager_config.autolabel_algorithm == 'bucketing': - m.set_category_mode(task.category, VINE_ALLOCATION_MODE_EXHAUSTIVE_BUCKETING) - elif manager_config.autolabel_algorithm == 'max': - m.set_category_mode(task.category, VINE_ALLOCATION_MODE_MAX) - else: - logger.warning(f'Unrecognized autolabeling algorithm named {manager_config.autolabel_algorithm} for taskvine manager.') - raise Exception(f'Unrecognized autolabeling algorithm named {manager_config.autolabel_algorithm} for taskvine manager.') - - if task.cores is not None: - t.set_cores(task.cores) - if task.memory is not None: - t.set_memory(task.memory) - if task.disk is not None: - t.set_disk(task.disk) - if task.gpus is not None: - t.set_gpus(task.gpus) - if task.priority is not None: - t.set_priority(task.priority) - if task.running_time_min is not None: - t.set_time_min(task.running_time_min) - - if manager_config.max_retries is not None: - logger.debug(f"Specifying max_retries {manager_config.max_retries}") - t.set_retries(manager_config.max_retries) - - # Specify environment variables for the task - if manager_config.env_vars is not None: - for var in manager_config.env_vars: - t.set_env_var(str(var), str(manager_config.env_vars[var])) - - if task.exec_mode == 'regular': - # Add helper files that execute parsl functions on remote nodes - # only needed to add as file for tasks with 'regular' mode - t.add_input(exec_parsl_function_file, "exec_parsl_function.py") - - # Declare and add task-specific function, data, and result files to task - task_function_file = m.declare_file(task.function_file, cache=False, peer_transfer=False) - t.add_input(task_function_file, "function") - - task_argument_file = m.declare_file(task.argument_file, cache=False, peer_transfer=False) - t.add_input(task_argument_file, "argument") - - task_map_file = m.declare_file(task.map_file, cache=False, peer_transfer=False) - t.add_input(task_map_file, "map") - - task_result_file = m.declare_file(task.result_file, cache=False, peer_transfer=False) - t.add_output(task_result_file, "result") - - result_file_of_task_id[str(task.executor_id)] = task.result_file - - logger.debug("Executor task id: {}".format(task.executor_id)) - - # Specify input/output files that need to be staged. - # Absolute paths are assumed to be in shared filesystem, and thus - # not staged by taskvine. - # Files that share the same local path are assumed to be the same - # and thus use the same Vine File object if detected. - if not manager_config.shared_fs: - for spec in task.input_files: - if spec.stage: - if spec.parsl_name in parsl_file_name_to_vine_file: - task_in_file = parsl_file_name_to_vine_file[spec.parsl_name] - else: - task_in_file = m.declare_file(spec.parsl_name, cache=spec.cache, peer_transfer=True) - parsl_file_name_to_vine_file[spec.parsl_name] = task_in_file - t.add_input(task_in_file, spec.parsl_name) - - for spec in task.output_files: - if spec.stage: - if spec.parsl_name in parsl_file_name_to_vine_file: - task_out_file = parsl_file_name_to_vine_file[spec.parsl_name] - else: - task_out_file = m.declare_file(spec.parsl_name, cache=spec.cache, peer_transfer=True) - parsl_file_name_to_vine_file[spec.parsl_name] = task_out_file - t.add_output(task_out_file, spec.parsl_name) - - # Submit the task to the TaskVine object - logger.debug("Submitting executor task {}, {} to TaskVine".format(task.executor_id, t)) - try: - vine_id = m.submit(t) - logger.debug("Submitted executor task {} to TaskVine".format(task.executor_id)) - vine_id_to_executor_task_id[str(vine_id)] = str(task.executor_id), task.exec_mode - except Exception as e: - logger.error("Unable to submit task to taskvine: {}".format(e)) - finished_task_queue.put_nowait(VineTaskToParsl(executor_id=task.executor_id, - result_received=False, - result_file=None, - reason="task could not be submited to taskvine", - status=-1)) - continue - - logger.debug("Executor task {} submitted as TaskVine task with id {}".format(task.executor_id, vine_id)) - - # If the queue is not empty wait on the TaskVine queue for a task - task_found = True - while not m.empty() and task_found and not should_stop.is_set(): - # Obtain the task from the queue - t = m.wait(1) - if t is None: - task_found = False - continue - logger.debug('Found a task') - executor_task_id = vine_id_to_executor_task_id[str(t.id)][0] - vine_id_to_executor_task_id.pop(str(t.id)) - - # When a task is found - result_file = result_file_of_task_id.pop(executor_task_id) - - logger.debug(f"completed executor task info: {executor_task_id}, {t.category}, {t.command}, {t.std_output}") - - # A tasks completes 'succesfully' if it has result file. - # A check whether the Python object represented using this file can be - # deserialized happens later in the collector thread of the executor - # process. - logger.debug("Looking for result in {}".format(result_file)) - if os.path.exists(result_file): - logger.debug("Found result in {}".format(result_file)) - finished_task_queue.put_nowait(VineTaskToParsl(executor_id=executor_task_id, - result_received=True, - result_file=result_file, - reason=None, - status=t.exit_code)) - # If a result file could not be generated, explain the - # failure according to taskvine error codes. - else: - reason = _explain_taskvine_result(t) - logger.debug("Did not find result in {}".format(result_file)) - logger.debug("Wrapper Script status: {}\nTaskVine Status: {}".format(t.exit_code, t.result)) - logger.debug("Task with executor id {} / vine id {} failed because:\n{}".format(executor_task_id, t.id, reason)) - finished_task_queue.put_nowait(VineTaskToParsl(executor_id=executor_task_id, - result_received=False, - result_file=None, - reason=reason, - status=t.exit_code)) - - logger.debug("Exiting TaskVine Monitoring Process") - return 0 - - -def _explain_taskvine_result(vine_task): - """Returns a string with the reason why a task failed according to taskvine.""" - - vine_result = vine_task.result - reason = "taskvine result: " - if vine_result == cvine.VINE_RESULT_SUCCESS: - reason += "succesful execution with exit code {}".format(vine_task.return_status) - elif vine_result == cvine.VINE_RESULT_OUTPUT_MISSING: - reason += "The result file was not transfered from the worker.\n" - reason += "This usually means that there is a problem with the python setup,\n" - reason += "or the wrapper that executes the function." - reason += "\nTrace:\n" + str(vine_task.output) - elif vine_result == cvine.VINE_RESULT_INPUT_MISSING: - reason += "missing input file" - elif vine_result == cvine.VINE_RESULT_STDOUT_MISSING: - reason += "stdout has been truncated" - elif vine_result == cvine.VINE_RESULT_SIGNAL: - reason += "task terminated with a signal" - elif vine_result == cvine.VINE_RESULT_RESOURCE_EXHAUSTION: - reason += "task used more resources than requested" - elif vine_result == cvine.VINE_RESULT_MAX_END_TIME: - reason += "task ran past the specified end time" - elif vine_result == cvine.VINE_RESULT_UNKNOWN: - reason += "result could not be classified" - elif vine_result == cvine.VINE_RESULT_FORSAKEN: - reason += "task failed, but not a task error" - elif vine_result == cvine.VINE_RESULT_MAX_RETRIES: - reason += "unable to complete after specified number of retries" - elif vine_result == cvine.VINE_RESULT_MAX_WALL_TIME: - reason += "task ran for more than the specified time" - elif vine_result == cvine.VINE_RESULT_RMONITOR_ERROR: - reason += "task failed because the monitor did not produce an output" - elif vine_result == cvine.VINE_RESULT_OUTPUT_TRANSFER_ERROR: - reason += "task failed because output transfer fails" - elif vine_result == cvine.VINE_RESULT_FIXED_LOCATION_MISSING: - reason += "task failed because no worker could satisfy the fixed \n" - reason += "location input file requirements" - else: - reason += "unable to process TaskVine system failure" - return reason diff --git a/parsl/parsl/executors/taskvine/manager_config.py b/parsl/parsl/executors/taskvine/manager_config.py deleted file mode 100644 index 26b1e460..00000000 --- a/parsl/parsl/executors/taskvine/manager_config.py +++ /dev/null @@ -1,191 +0,0 @@ -from dataclasses import dataclass -from typing import Optional - -# This try except clause prevents import errors -# when TaskVine is not used in Parsl. -try: - from ndcctools.taskvine.cvine import VINE_DEFAULT_PORT -except ImportError: - VINE_DEFAULT_PORT = 0 # use any available port. - - -@dataclass -class TaskVineManagerConfig: - """ - Configuration of a TaskVine manager - - Parameters - ---------- - - port: int - Network port for the manager to communicate with workers. - A value of 0 means TaskVine chooses any available port. - Default is VINE_DEFAULT_PORT. - - address: Optional[str] - Address of the local machine. - If None, :py:func:`parsl.addresses.get_any_address` will be used to determine the address. - - project_name: Optional[str] - If given, TaskVine will periodically report its status and performance - back to the global TaskVine catalog at - http://ccl.cse.nd.edu/software/taskvine/status. - Recommended mode of communication between manager and workers. - Default is None. Overrides address. - - project_password_file: Optional[str] - If given, manager and workers will authenticate each other - with the given password file. - Default is None. - - env_vars: Optional[dict] - Dictionary of environment variables to set in a shell - before executing a task. - Default is None - - init_command: str - Command line to run before executing a task in a worker. - Default is ''. - - env_pack: Optional[str] - Used to encapsulate package dependencies of tasks to - execute them remotely without needing a shared filesystem. - Recommended way to manage tasks' dependency requirements. - All tasks will be executed in the encapsulated environment. - If an absolute path to a conda environment or a conda - environment name is given, TaskVine will package the conda - environment in a tarball and send it along with tasks to be - executed in a replicated conda environment. - If a tarball of packages (``*.tar.gz``) is given, TaskVine - skips the packaging step and sends the tarball along with - tasks to be executed in a replicated conda environment. - - app_pack: bool - Use conda-pack to prepare a self-contained Python environment - for each app. Enabling this increases first task latency but - does not require a common environment or a shared filesystem - on workers. - If env_pack is specified, app_pack if set to True will override - env_pack. - Default is False. - - extra_pkgs: Optional[list] - Used when app_pack is True. - List of extra pip/conda package names to include when packing - the environment. Useful when app executes other (possibly - non-Python) programs provided via pip or conda. - Default is None. - - max_retries: int - Set the number of retries that TaskVine will make when a task - fails. This is distinct from Parsl level retries configured in - parsl.config.Config. Set to None to allow TaskVine to retry tasks - forever. - Default is 1. - - library_config: Optional[dict] - Only and must specify when functions are executed in the serverless mode. - Configure the number of function slots and amount of resources - a library task can run. A library task is a stateful object that executes - functions in the serverless way. Accept the following keywords: - 'num_slots', 'cores', 'memory (MBs)', 'disk (MBs)'. - Default is {'num_slots': 1, 'cores': None, 'memory': None, 'disk': None}, - which will take all resources of a worker node and run at most 1 function - invocation at any given time. - E.g., {'num_slots': 4, 'cores': 16, 'memory': 16000, 'disk': 16000} will - reserve those resources to the library task to run at most 4 function - invocations. - - shared_fs: bool - Whether workers will use a shared filesystem or not. If so, TaskVine - will not track and transfer files for execution, in exchange for - I/O pressure on the shared filesystem. - Default is False. - - autolabel: bool - Use the Resource Monitor to automatically determine resource - labels based on observed task behavior. - Default is False. - - autolabel_window: int - Set the number of tasks considered for autolabeling. TaskVine - will wait for a series of N tasks with steady resource - requirements before making a decision on labels. Increasing - this parameter will reduce the number of failed tasks due to - resource exhaustion when autolabeling, at the cost of increased - resources spent collecting stats. - - autolabel_algorithm: str - Choose an autolabeling algorithm to label resources to tasks. - 'max-xput': smartly guess a resource value, then fall back if task - fails due to overconsumption. - 'bucketing': group tasks into buckets based on resource consumption - similarity, and randomly choose a bucket to assign resources to the - next task. - 'max': always allocate max resources seen so far. Allocate a whole - machine if task fails. - Default is max-xput. - - autolabel_window: Optional[int] - Only apply to 'max-xput' and 'max'. - Set the number of tasks considered for autolabeling. TaskVine - will wait for a series of N tasks with steady resource - requirements before making a decision on labels. Increasing - this parameter will reduce the number of failed tasks due to - resource exhaustion when autolabeling, at the cost of increased - resources spent collecting stats. - - autocategory: bool - Place each app in its own category by default. If all - invocations of an app have similar performance characteristics, - this will provide a reasonable set of categories automatically. - Default is True. - - enable_peer_transfers: bool - Whether to enable workers' capability to transfer files among - themselves or not. Helpful if TaskVine manages all files - tasks in the workflow use. Require that workers can communicate - with each other via TCP ports. - Default is True. - - wait_for_workers: Optional[int] - Number of workers to wait for before the manager starts sending - tasks for execution. - - vine_log_dir: Optional[str] - Directory to store TaskVine logging facilities. - Default is None, in which all TaskVine logs will be contained - in the Parsl logging directory. - - tune_parameters: Optional[dict] - Extended vine_tune parameters, expressed in a dictionary - by { 'tune-parameter' : value }. - """ - - # Connection and communication settings - port: int = VINE_DEFAULT_PORT - address: Optional[str] = None - project_name: Optional[str] = None - project_password_file: Optional[str] = None - - # Global task settings - env_vars: Optional[dict] = None - init_command: str = "" - env_pack: Optional[str] = None - app_pack: bool = False - extra_pkgs: Optional[list] = None - max_retries: int = 1 - library_config: Optional[dict] = None - - # Performance-specific settings - shared_fs: bool = False - autolabel: bool = False - autolabel_algorithm: str = 'max-xput' - autolabel_window: Optional[int] = None - autocategory: bool = True - enable_peer_transfers: bool = True - wait_for_workers: Optional[int] = None - tune_parameters: Optional[dict] = None - - # Logging settings - vine_log_dir: Optional[str] = None diff --git a/parsl/parsl/executors/taskvine/utils.py b/parsl/parsl/executors/taskvine/utils.py deleted file mode 100644 index 86cf446b..00000000 --- a/parsl/parsl/executors/taskvine/utils.py +++ /dev/null @@ -1,85 +0,0 @@ -from typing import Optional - - -class ParslTaskToVine: - """ Helper class to transform a Parsl function into a TaskVine task.""" - - def __init__(self, - executor_id: int, # executor id of Parsl function - exec_mode: str, # execution mode of function, out of {regular, python, serverless} - category: str, # category of Parsl function - input_files: list, # list of input files to this function - output_files: list, # list of output files to this function - map_file: Optional[str], # pickled file containing mapping of local to remote names of files - function_file: Optional[str], # pickled file containing the function information - argument_file: Optional[str], # pickled file containing the arguments to the function call - result_file: Optional[str], # path to the pickled result object of the function execution - cores: Optional[float], # number of cores to allocate - memory: Optional[int], # amount of memory in MBs to allocate - disk: Optional[int], # amount of disk in MBs to allocate - gpus: Optional[float], # number of gpus to allocate - priority: Optional[float], # priority of function, the higher the more priority - running_time_min: Optional[int], # minimum amount of time for the function to run on a worker - env_pkg: Optional[str], # path to a poncho environment tarball - ): - self.executor_id = executor_id - self.exec_mode = exec_mode - self.category = category - self.map_file = map_file - self.function_file = function_file - self.argument_file = argument_file - self.result_file = result_file - self.input_files = input_files - self.output_files = output_files - self.cores = cores - self.memory = memory - self.disk = disk - self.gpus = gpus - self.priority = priority - self.running_time_min = running_time_min - self.env_pkg = env_pkg - - -class VineTaskToParsl: - """ - Support structure to communicate final status of TaskVine tasks to Parsl. - result_file is only valid if result_received is True. - Reason and status are only valid if result_received is False. - """ - def __init__(self, - executor_id: int, # executor id of task - result_received: bool, # whether result is received or not - result_file: Optional[str], # path to file that contains the serialized result object - reason: Optional[str], # string describing why execution fails - status: Optional[int] # exit code of execution of task - ): - self.executor_id = executor_id - self.result_received = result_received - self.result_file = result_file - self.reason = reason - self.status = status - - -class ParslFileToVine: - """ - Support structure to report Parsl filenames to TaskVine. - parsl_name is the local_name or filepath attribute of a Parsl file object. - stage tells whether the file should be copied by TaskVine to the workers. - cache tells whether the file should be cached at workers. Only valid if stage == True - """ - def __init__(self, - parsl_name: str, # name of file - stage: bool, # whether TaskVine should know about this file - cache: bool # whether TaskVine should cache this file - ): - self.parsl_name = parsl_name - self.stage = stage - self.cache = cache - - -def run_parsl_function(map_file, function_file, argument_file, result_file): - """ - Wrapper function to deploy with FunctionCall as serverless tasks. - """ - from parsl.executors.taskvine.exec_parsl_function import run - run(map_file, function_file, argument_file, result_file) diff --git a/parsl/parsl/executors/threads.py b/parsl/parsl/executors/threads.py deleted file mode 100644 index 4ec3c4cb..00000000 --- a/parsl/parsl/executors/threads.py +++ /dev/null @@ -1,102 +0,0 @@ -import concurrent.futures as cf -import logging -from typing import List, Optional - -import typeguard - -from parsl.data_provider.staging import Staging -from parsl.executors.base import ParslExecutor -from parsl.executors.errors import InvalidResourceSpecification -from parsl.monitoring.radios.base import RadioConfig -from parsl.monitoring.radios.multiprocessing import MultiprocessingQueueRadio -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - - -class ThreadPoolExecutor(ParslExecutor, RepresentationMixin): - """A thread-based executor. - - Parameters - ---------- - max_threads : Optional[int] - Number of threads. Default is 2. - thread_name_prefix : string - Thread name prefix - storage_access : list of :class:`~parsl.data_provider.staging.Staging` - Specifications for accessing data this executor remotely. - """ - - @typeguard.typechecked - def __init__(self, label: str = 'threads', max_threads: Optional[int] = 2, - thread_name_prefix: str | None = None, storage_access: Optional[List[Staging]] = None, - working_dir: Optional[str] = None, remote_monitoring_radio: Optional[RadioConfig] = None): - ParslExecutor.__init__(self) - self.label = label - self.max_threads = max_threads - if thread_name_prefix is None: - self.thread_name_prefix = "ThreadPoolExecutor-" + label - else: - self.thread_name_prefix = thread_name_prefix - - # we allow storage_access to be None now, which means something else to [] now - # None now means that a default storage access list will be used, while - # [] is a list with no storage access in it at all - self.storage_access = storage_access - self.working_dir = working_dir - - if remote_monitoring_radio is not None: - self.remote_monitoring_radio = remote_monitoring_radio - else: - self.remote_monitoring_radio = MultiprocessingQueueRadio() - - def start(self): - self.executor = cf.ThreadPoolExecutor(max_workers=self.max_threads, - thread_name_prefix=self.thread_name_prefix) - - def submit(self, func, resource_specification, *args, **kwargs): - """Submits work to the thread pool. - - This method is simply pass through and behaves like a submit call as described - here `Python docs: `_ - - """ - if resource_specification: - logger.error("Ignoring the resource specification. " - "Parsl resource specification is not supported in ThreadPool Executor.") - raise InvalidResourceSpecification(set(resource_specification.keys()), - "Parsl resource specification is not supported in ThreadPool Executor.") - - fu = self.executor.submit(func, *args, **kwargs) - print('future exception:', fu.exception()) - return fu - - def shutdown(self, block=True): - """Shutdown the ThreadPool. The underlying concurrent.futures thread pool - implementation will not terminate tasks that are being executed, because it - does not provide a mechanism to do that. With block set to false, this will - return immediately and it will appear as if the DFK is shut down, but - the python process will not be able to exit until the thread pool has - emptied out by task completions. In either case, this can be a very long wait. - - Kwargs: - - block (Bool): To block for confirmations or not - - """ - logger.debug("Shutting down executor, which involves waiting for running tasks to complete") - self.executor.shutdown(wait=block) - - super().shutdown() - - logger.debug("Done with executor shutdown") - - def monitor_resources(self): - """Resource monitoring does not make sense when using the - ThreadPoolExecutor, as there is no per-task process tree: all tasks - run inside the same single submitting process. - - In addition, the use of fork-based multiprocessing in the remote - wrapper in parsl/monitoring/remote.py was especially prone to deadlock - with this executor. - """ - return False diff --git a/parsl/parsl/executors/workqueue/__init__.py b/parsl/parsl/executors/workqueue/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/executors/workqueue/errors.py b/parsl/parsl/executors/workqueue/errors.py deleted file mode 100644 index 3b12d1ad..00000000 --- a/parsl/parsl/executors/workqueue/errors.py +++ /dev/null @@ -1,24 +0,0 @@ -from typing import Optional - -from parsl.app.errors import AppException -from parsl.errors import ParslError - - -class WorkQueueTaskFailure(AppException): - """A failure executing a task in workqueue - - Contains: - reason(string) - status(optional exception) - """ - - def __init__(self, reason: str, status: Optional[Exception]): - self.reason = reason - self.status = status - - -class WorkQueueFailure(ParslError): - """A failure in the work queue executor that prevented the task to be - executed."" - """ - pass diff --git a/parsl/parsl/executors/workqueue/exec_parsl_function.py b/parsl/parsl/executors/workqueue/exec_parsl_function.py deleted file mode 100644 index d19d92ef..00000000 --- a/parsl/parsl/executors/workqueue/exec_parsl_function.py +++ /dev/null @@ -1,209 +0,0 @@ -import pickle -import sys -import traceback - -from parsl.app.errors import RemoteExceptionWrapper -from parsl.data_provider.files import File -from parsl.serialize import serialize -from parsl.utils import get_std_fname_mode - -# This scripts executes a parsl function which is pickled in a file: -# -# exec_parsl_function.py map_file function_file result_file -# -# map_file: Contains a pickled dictionary that indicates which local_paths the -# parsl Files should take. -# -# function_file: Contains a pickle parsl function. -# -# result_file: It will contain the result of the function, including any -# exception generated. Exceptions will be wrapped with RemoteExceptionWrapper. -# -# Exit codes: -# 0: The function was evaluated to completion. The result or any exception -# wrapped with RemoteExceptionWrapper were written to result_file. -# anything else: There was an error that prevented writing to the result file altogether. -# The exit code corresponds to whatever the python interpreter gives. -# - - -def load_pickled_file(filename): - with open(filename, "rb") as f_in: - return pickle.load(f_in) - - -def dump_result_to_file(result_file, result_package): - with open(result_file, "wb") as f_out: - f_out.write(serialize(result_package)) - - -def remap_location(mapping, parsl_file): - if not isinstance(parsl_file, File): - return - # Below we rewrite .local_path when scheme != file only when the local_name - # was given by the main parsl process. This is the case when scheme != - # 'file' but .local_path (via filepath) is in mapping. - if parsl_file.scheme == 'file' or parsl_file.local_path: - master_location = parsl_file.filepath - if master_location in mapping: - parsl_file.local_path = mapping[master_location] - - -def remap_list_of_files(mapping, maybe_files): - for maybe_file in maybe_files: - remap_location(mapping, maybe_file) - - -def remap_all_files(mapping, fn_args, fn_kwargs): - # remap any positional argument given to the function that looks like a - # File - remap_list_of_files(mapping, fn_args) - - # remap any keyword argument in the same way, but we need to treat - # "inputs" and "outputs" specially because they are lists, and - # "stdout" and "stderr", because they are not File's. - for kwarg, maybe_file in fn_kwargs.items(): - if kwarg in ["inputs", "outputs"]: - remap_list_of_files(mapping, maybe_file) - if kwarg in ["stdout", "stderr"]: - if maybe_file: - (fname, mode) = get_std_fname_mode(kwarg, maybe_file) - if fname in mapping: - fn_kwargs[kwarg] = (mapping[fname], mode) - else: - # Treat anything else as a possible File to be remapped. - remap_location(mapping, maybe_file) - - -def unpack_function(function_info, user_namespace): - if "source code" in function_info: - return unpack_source_code_function(function_info, user_namespace) - elif "byte code" in function_info: - return unpack_byte_code_function(function_info, user_namespace) - else: - raise ValueError("Function file does not have a valid function representation.") - - -def unpack_source_code_function(function_info, user_namespace): - source_code = function_info["source code"] - name = function_info["name"] - args = function_info["args"] - kwargs = function_info["kwargs"] - return (source_code, name, args, kwargs) - - -def unpack_byte_code_function(function_info, user_namespace): - from parsl.serialize import unpack_apply_message - func, args, kwargs = unpack_apply_message(function_info["byte code"]) - return (func, 'parsl_function_name', args, kwargs) - - -def encode_function(user_namespace, fn, fn_name, fn_args, fn_kwargs): - # Returns a tuple (code, result_name) - # code can be exec in the user_namespace to produce result_name. - prefix = "parsl_" - args_name = prefix + "args" - kwargs_name = prefix + "kwargs" - result_name = prefix + "result" - - # Add variables to the namespace to make function call - user_namespace.update({args_name: fn_args, - kwargs_name: fn_kwargs, - result_name: result_name}) - - if isinstance(fn, str): - code = encode_source_code_function(user_namespace, fn, fn_name, args_name, kwargs_name, result_name) - elif callable(fn): - code = encode_byte_code_function(user_namespace, fn, fn_name, args_name, kwargs_name, result_name) - else: - raise ValueError("Function object does not look like a function.") - - return (code, result_name) - - -def encode_source_code_function(user_namespace, fn, fn_name, args_name, kwargs_name, result_name): - # We drop the first line as it names the parsl decorator used (i.e., @python_app) - source = fn.split('\n')[1:] - fn_app = "{0} = {1}(*{2}, **{3})".format(result_name, fn_name, args_name, kwargs_name) - - source.append(fn_app) - - code = "\n".join(source) - return code - - -def encode_byte_code_function(user_namespace, fn, fn_name, args_name, kwargs_name, result_name): - user_namespace.update({fn_name: fn}) - code = "{0} = {1}(*{2}, **{3})".format(result_name, fn_name, args_name, kwargs_name) - return code - - -def load_function(map_file, function_file): - # Decodes the function and its file arguments to be executed into - # function_code, and updates a user namespace with the function name and - # the variable named result_name. When the function is executed, its result - # will be stored in this variable in the user namespace. - # Returns (namespace, function_code, result_name) - - # Create the namespace to isolate the function execution. - user_ns = locals() - user_ns.update({'__builtins__': __builtins__}) - - function_info = load_pickled_file(function_file) - - (fn, fn_name, fn_args, fn_kwargs) = unpack_function(function_info, user_ns) - - mapping = load_pickled_file(map_file) - remap_all_files(mapping, fn_args, fn_kwargs) - - (code, result_name) = encode_function(user_ns, fn, fn_name, fn_args, fn_kwargs) - - return (user_ns, code, result_name) - - -def execute_function(namespace, function_code, result_name): - # On executing the function inside the namespace, its result will be in a - # variable named result_name. - - exec(function_code, namespace, namespace) - result = namespace.get(result_name) - - return result - - -if __name__ == "__main__": - try: - # parse the three required command line arguments: - # map_file: contains a pickled dictionary to map original names to - # names at the execution site. - # function_file: contains the pickled parsl function to execute. - # result_file: any output (including exceptions) will be written to - # this file. - try: - (map_file, function_file, result_file) = sys.argv[1:] - except ValueError: - print("Usage:\n\t{} function result mapping\n".format(sys.argv[0])) - raise - - try: - (namespace, function_code, result_name) = load_function(map_file, function_file) - except Exception: - print("There was an error setting up the function for execution.") - raise - - try: - result = execute_function(namespace, function_code, result_name) - except Exception: - print("There was an error executing the function.") - raise - except Exception: - traceback.print_exc() - result = RemoteExceptionWrapper(*sys.exc_info()) - - # Write out function result to the result file - try: - dump_result_to_file(result_file, result) - except Exception: - print("Could not write to result file.") - traceback.print_exc() - sys.exit(1) diff --git a/parsl/parsl/executors/workqueue/executor.py b/parsl/parsl/executors/workqueue/executor.py deleted file mode 100644 index 5370ad6d..00000000 --- a/parsl/parsl/executors/workqueue/executor.py +++ /dev/null @@ -1,1054 +0,0 @@ -""" WorkQueueExecutor utilizes the Work Queue distributed framework developed by the -Cooperative Computing Lab (CCL) at Notre Dame to provide a fault-tolerant, -high-throughput system for delegating Parsl tasks to thousands of remote machines -""" - -import hashlib -import inspect -import itertools -import logging -import multiprocessing -import os -import pickle -import queue -import shutil -import socket -import subprocess -import tempfile -import threading -import time -from collections import namedtuple -from concurrent.futures import Future -from ctypes import c_bool -from typing import Dict, List, Optional, Set, Union - -import typeguard - -import parsl.utils as putils -from parsl.data_provider.files import File -from parsl.data_provider.staging import Staging -from parsl.errors import OptionalModuleMissing -from parsl.executors.errors import ExecutorError, InvalidResourceSpecification -from parsl.executors.status_handling import BlockProviderExecutor -from parsl.executors.workqueue import exec_parsl_function -from parsl.monitoring.radios.base import RadioConfig -from parsl.monitoring.radios.filesystem import FilesystemRadio -from parsl.multiprocessing import SpawnContext, SpawnProcess -from parsl.process_loggers import wrap_with_logs -from parsl.providers import CondorProvider, LocalProvider -from parsl.providers.base import ExecutionProvider -from parsl.serialize import deserialize, pack_apply_message -from parsl.utils import setproctitle - -from .errors import WorkQueueFailure, WorkQueueTaskFailure - -IMPORT_EXCEPTION = None -try: - from ndcctools import work_queue as wq - from ndcctools.work_queue import ( - WORK_QUEUE_ALLOCATION_MODE_MAX_THROUGHPUT, - WORK_QUEUE_DEFAULT_PORT, - WorkQueue, - ) -except ImportError as e: - _work_queue_enabled = False - WORK_QUEUE_DEFAULT_PORT = 0 - IMPORT_EXCEPTION = e -else: - _work_queue_enabled = True - -package_analyze_script = shutil.which("python_package_analyze") -package_create_script = shutil.which("python_package_create") -package_run_script = shutil.which("python_package_run") - -logger = logging.getLogger(__name__) - - -# Support structure to communicate parsl tasks to the work queue submit thread. -ParslTaskToWq = namedtuple( - 'ParslTaskToWq', - 'id ' - 'category ' - 'cores memory disk gpus priority running_time_min env_pkg map_file function_file result_file input_files output_files') - -# Support structure to communicate final status of work queue tasks to parsl -# if result_received is True: -# result_file is the path to the file containing the result. -# if result_received is False: -# reason and status are only valid if result_received is False -# result_file is None -WqTaskToParsl = namedtuple('WqTaskToParsl', 'id result_received result_file reason status') - -# Support structure to report parsl filenames to work queue. -# parsl_name is the local_name or filepath attribute of a parsl file object. -# stage tells whether the file should be copied by work queue to the workers. -# cache tells whether the file should be cached at workers. Only valid if stage == True -ParslFileToWq = namedtuple('ParslFileToWq', 'parsl_name stage cache') - - -class WorkQueueExecutor(BlockProviderExecutor, putils.RepresentationMixin): - """Executor to use Work Queue batch system - - The WorkQueueExecutor system utilizes the Work Queue framework to - efficiently delegate Parsl apps to remote machines in clusters and - grids using a fault-tolerant system. Users can run the - work_queue_worker program on remote machines to connect to the - WorkQueueExecutor, and Parsl apps will then be sent out to these - machines for execution and retrieval. - - - Parameters - ---------- - - label: str - A human readable label for the executor, unique - with respect to other Work Queue master programs. - Default is "WorkQueueExecutor". - - working_dir: str - Location for Parsl to perform app delegation to the Work - Queue system. Defaults to current directory. - - project_name: str - If a project_name is given, then Work Queue will periodically - report its status and performance back to the global WQ catalog, - which can be viewed here: http://ccl.cse.nd.edu/software/workqueue/status - Default is None. Overrides address. - - project_password_file: str - Optional password file for the work queue project. Default is None. - - address: str - The ip to contact this work queue master process. - If not given, uses the address of the current machine as returned - by socket.gethostname(). - Ignored if project_name is specified. - - port: int - TCP port on Parsl submission machine for Work Queue workers - to connect to. Workers will connect to Parsl using this port. - - If 0, Work Queue will allocate a port number automatically. - In this case, environment variables can be used to influence the - choice of port, documented here: - https://ccl.cse.nd.edu/software/manuals/api/html/work__queue_8h.html#a21714a10bcdfcf5c3bd44a96f5dcbda6 - Default: WORK_QUEUE_DEFAULT_PORT. - - env: dict{str} - Dictionary that contains the environmental variables that - need to be set on the Work Queue worker machine. - - shared_fs: bool - Define if working in a shared file system or not. If Parsl - and the Work Queue workers are on a shared file system, Work - Queue does not need to transfer and rename files for execution. - Default is False. - - use_cache: bool - Whether workers should cache files that are common to tasks. - Warning: Two files are considered the same if they have the same - filepath name. Use with care when reusing the executor instance - across multiple parsl workflows. Default is False. - - source: bool - Choose whether to transfer parsl app information as - source code. (Note: this increases throughput for - @python_apps, but the implementation does not include - functionality for @bash_apps, and thus source=False - must be used for programs utilizing @bash_apps.) - Default is False. Set to True if pack is True - - pack: bool - Use conda-pack to prepare a self-contained Python evironment for - each task. This greatly increases task latency, but does not - require a common environment or shared FS on execution nodes. - Implies source=True. - - extra_pkgs: list - List of extra pip/conda package names to include when packing - the environment. This may be useful if the app executes other - (possibly non-Python) programs provided via pip or conda. - Scanning the app source for imports would not detect these - dependencies, so they need to be manually specified. - - autolabel: bool - Use the Resource Monitor to automatically determine resource - labels based on observed task behavior. - - autolabel_window: int - Set the number of tasks considered for autolabeling. Work Queue - will wait for a series of N tasks with steady resource - requirements before making a decision on labels. Increasing - this parameter will reduce the number of failed tasks due to - resource exhaustion when autolabeling, at the cost of increased - resources spent collecting stats. - - autocategory: bool - Place each app in its own category by default. If all - invocations of an app have similar performance characteristics, - this will provide a reasonable set of categories automatically. - - max_retries: int - Set the number of retries that Work Queue will make when a task - fails. This is distinct from Parsl level retries configured in - parsl.config.Config. Set to None to allow Work Queue to retry - tasks forever. By default, this is set to 1, so that all retries - will be managed by Parsl. - - init_command: str - Command line to run before executing a task in a worker. - Default is ''. - - worker_options: str - Extra options passed to work_queue_worker. Default is ''. - - worker_executable: str - The command used to invoke work_queue_worker. This can be used - when the worker needs to be wrapped inside some other command - (for example, to run the worker inside a container). Default is - 'work_queue_worker'. - - function_dir: str - The directory where serialized function invocations are placed - to be sent to workers. If undefined, this defaults to a directory - under runinfo/. If shared_filesystem=True, then this directory - must be visible from both the submitting side and workers. - - coprocess: bool - Use Work Queue's coprocess facility to avoid launching a new Python - process for each task. Experimental. - This requires a version of Work Queue / cctools after commit - 874df524516441da531b694afc9d591e8b134b73 (release 7.5.0 is too early). - Default is False. - - scaling_cores_per_worker: int - When using Parsl scaling, this specifies the number of cores that a - worker is expected to have available for computation. Default 1. This - parameter can be ignored when using a fixed number of blocks, or when - using one task per worker (by omitting a ``cores`` resource - specifiation for each task). - """ - - @typeguard.typechecked - def __init__(self, - label: str = "WorkQueueExecutor", - provider: ExecutionProvider = LocalProvider(), - working_dir: str = ".", - project_name: Optional[str] = None, - project_password_file: Optional[str] = None, - address: Optional[str] = None, - port: int = WORK_QUEUE_DEFAULT_PORT, - env: Optional[Dict] = None, - shared_fs: bool = False, - storage_access: Optional[List[Staging]] = None, - use_cache: bool = False, - source: bool = False, - pack: bool = False, - extra_pkgs: Optional[List[str]] = None, - autolabel: bool = False, - autolabel_window: int = 1, - autocategory: bool = True, - max_retries: int = 1, - init_command: str = "", - worker_options: str = "", - full_debug: bool = True, - worker_executable: str = 'work_queue_worker', - function_dir: Optional[str] = None, - coprocess: bool = False, - scaling_cores_per_worker: int = 1, - remote_monitoring_radio: Optional[RadioConfig] = None): - BlockProviderExecutor.__init__(self, provider=provider, - block_error_handler=True) - if not _work_queue_enabled: - raise OptionalModuleMissing(['work_queue'], f"WorkQueueExecutor requires the work_queue module. More info: {IMPORT_EXCEPTION}") - - self.scaling_cores_per_worker = scaling_cores_per_worker - self.label = label - self.task_queue: multiprocessing.Queue = SpawnContext.Queue() - self.collector_queue: multiprocessing.Queue = SpawnContext.Queue() - self.address = address - self.port = port - self.executor_task_counter = -1 - self.project_name = project_name - self.project_password_file = project_password_file - self.env = env - self.init_command = init_command - self.shared_fs = shared_fs - self.storage_access = storage_access - self.use_cache = use_cache - self.working_dir = working_dir - self.registered_files: Set[str] = set() - self.full_debug = full_debug - self.source = True if pack else source - self.pack = pack - self.extra_pkgs = extra_pkgs or [] - self.autolabel = autolabel - self.autolabel_window = autolabel_window - self.autocategory = autocategory - self.max_retries = max_retries - self.should_stop = SpawnContext.Value(c_bool, False) - self.cached_envs = {} # type: Dict[int, str] - self.worker_options = worker_options - self.worker_executable = worker_executable - self.function_dir = function_dir - self.coprocess = coprocess - - if not self.address: - self.address = socket.gethostname() - - if self.project_password_file is not None and not os.path.exists(self.project_password_file): - raise WorkQueueFailure('Could not find password file: {}'.format(self.project_password_file)) - - if self.project_password_file is not None: - if os.path.exists(self.project_password_file) is False: - logger.debug("Password File does not exist, no file used") - self.project_password_file = None - - # Build foundations of the launch command - self.launch_cmd = ("{package_prefix}python3 exec_parsl_function.py {mapping} {function} {result}") - if self.init_command != "": - self.launch_cmd = self.init_command + "; " + self.launch_cmd - - if remote_monitoring_radio is not None: - self.remote_monitoring_radio = remote_monitoring_radio - else: - self.remote_monitoring_radio = FilesystemRadio() - - def _get_launch_command(self, block_id): - # this executor uses different terminology for worker/launch - # commands than in htex - return f"PARSL_WORKER_BLOCK_ID={block_id} {self.worker_command}" - - def start(self): - """Create submit process and collector thread to create, send, and - retrieve Parsl tasks within the Work Queue system. - """ - super().start() - self.tasks_lock = threading.Lock() - - # Create directories for data and results - if not self.function_dir: - self.function_data_dir = os.path.join(self.run_dir, self.label, "function_data") - else: - tp = str(time.time()) - tx = os.path.join(self.function_dir, tp) - os.makedirs(tx) - self.function_data_dir = os.path.join(self.function_dir, tp, self.label, "function_data") - self.package_dir = os.path.join(self.run_dir, self.label, "package_data") - self.wq_log_dir = os.path.join(self.run_dir, self.label) - logger.debug("function data directory: {}\nlog directory: {}".format(self.function_data_dir, self.wq_log_dir)) - os.makedirs(self.wq_log_dir) - os.makedirs(self.function_data_dir) - os.makedirs(self.package_dir) - - logger.debug("Starting WorkQueueExecutor") - - port_mailbox = SpawnContext.Queue() - - # Create a Process to perform WorkQueue submissions - submit_process_kwargs = {"task_queue": self.task_queue, - "launch_cmd": self.launch_cmd, - "data_dir": self.function_data_dir, - "collector_queue": self.collector_queue, - "full": self.full_debug, - "shared_fs": self.shared_fs, - "autolabel": self.autolabel, - "autolabel_window": self.autolabel_window, - "autocategory": self.autocategory, - "max_retries": self.max_retries, - "should_stop": self.should_stop, - "port": self.port, - "wq_log_dir": self.wq_log_dir, - "project_password_file": self.project_password_file, - "project_name": self.project_name, - "port_mailbox": port_mailbox, - "coprocess": self.coprocess - } - self.submit_process = SpawnProcess(target=_work_queue_submit_wait, - name="WorkQueue-Submit-Process", - kwargs=submit_process_kwargs) - - self.collector_thread = threading.Thread(target=self._collect_work_queue_results, - name="WorkQueue-collector-thread") - self.collector_thread.daemon = True - - # Begin both processes - self.submit_process.start() - self.collector_thread.start() - - self._chosen_port = port_mailbox.get(timeout=60) - - logger.debug(f"Chosen listening port is {self._chosen_port}") - - # Initialize scaling for the provider - self.initialize_scaling() - - def _path_in_task(self, executor_task_id, *path_components): - """Returns a filename specific to a task. - It is used for the following filename's: - (not given): The subdirectory per task that contains function, result, etc. - 'function': Pickled file that contains the function to be executed. - 'result': Pickled file that (will) contain the result of the function. - 'map': Pickled file with a dict between local parsl names, and remote work queue names. - """ - task_dir = "{:04d}".format(executor_task_id) - return os.path.join(self.function_data_dir, task_dir, *path_components) - - def submit(self, func, resource_specification, *args, **kwargs): - """Processes the Parsl app by its arguments and submits the function - information to the task queue, to be executed using the Work Queue - system. The args and kwargs are processed for input and output files to - the Parsl app, so that the files are appropriately specified for the Work - Queue task. - - Parameters - ---------- - - func : function - Parsl app to be submitted to the Work Queue system - args : list - Arguments to the Parsl app - kwargs : dict - Keyword arguments to the Parsl app - """ - cores = None - memory = None - disk = None - gpus = None - priority = None - category = None - running_time_min = None - if resource_specification and isinstance(resource_specification, dict): - logger.debug("Got resource_specification: {}".format(resource_specification)) - - required_resource_types = set(['cores', 'memory', 'disk']) - acceptable_fields = set(['cores', 'memory', 'disk', 'gpus', 'priority', 'running_time_min']) - keys = set(resource_specification.keys()) - - if not keys.issubset(acceptable_fields): - message = "Task resource specification only accepts these types of resources: {}".format( - ', '.join(acceptable_fields)) - logger.error(message) - raise InvalidResourceSpecification(keys, message) - - # this checks that either all of the required resource types are specified, or - # that none of them are: the `required_resource_types` are not actually required, - # but if one is specified, then they all must be. - key_check = required_resource_types.intersection(keys) - required_keys_ok = len(key_check) == 0 or key_check == required_resource_types - if not self.autolabel and not required_keys_ok: - logger.error("Running with `autolabel=False`. In this mode, " - "task resource specification requires " - "three resources to be specified simultaneously: cores, memory, and disk") - raise InvalidResourceSpecification(keys, - "Task resource specification requires " - "three resources to be specified simultaneously: cores, memory, and disk. " - "Try setting autolabel=True if you are unsure of the resource usage") - - for k in keys: - if k == 'cores': - cores = resource_specification[k] - elif k == 'memory': - memory = resource_specification[k] - elif k == 'disk': - disk = resource_specification[k] - elif k == 'gpus': - gpus = resource_specification[k] - elif k == 'priority': - priority = resource_specification[k] - elif k == 'category': - category = resource_specification[k] - elif k == 'running_time_min': - running_time_min = resource_specification[k] - - self.executor_task_counter += 1 - executor_task_id = self.executor_task_counter - - # Create a per task directory for the function, result, map, and result files - os.mkdir(self._path_in_task(executor_task_id)) - - input_files = [] - output_files = [] - - # Determine the input and output files that will exist at the workers: - input_files += [self._register_file(f) for f in kwargs.get("inputs", []) if isinstance(f, File)] - output_files += [self._register_file(f) for f in kwargs.get("outputs", []) if isinstance(f, File)] - - # Also consider any *arg that looks like a file as an input: - input_files += [self._register_file(f) for f in args if isinstance(f, File)] - - for kwarg, maybe_file in kwargs.items(): - # Add appropriate input and output files from "stdout" and "stderr" keyword arguments - if kwarg == "stdout" or kwarg == "stderr": - if maybe_file: - output_files.append(self._std_output_to_wq(kwarg, maybe_file)) - # For any other keyword that looks like a file, assume it is an input file - elif isinstance(maybe_file, File): - input_files.append(self._register_file(maybe_file)) - - # Create a Future object and have it be mapped from the task ID in the tasks dictionary - fu = Future() - fu.parsl_executor_task_id = executor_task_id - assert isinstance(resource_specification, dict) - fu.resource_specification = resource_specification - logger.debug("Getting tasks_lock to set WQ-level task entry") - with self.tasks_lock: - logger.debug("Got tasks_lock to set WQ-level task entry") - self.tasks[str(executor_task_id)] = fu - - logger.debug("Creating executor task {} for function {} with args {}".format(executor_task_id, func, args)) - - function_file = self._path_in_task(executor_task_id, "function") - result_file = self._path_in_task(executor_task_id, "result") - map_file = self._path_in_task(executor_task_id, "map") - - logger.debug("Creating executor task {} with function at: {}".format(executor_task_id, function_file)) - logger.debug("Creating executor task {} with result to be found at: {}".format(executor_task_id, result_file)) - - self._serialize_function(function_file, func, args, kwargs) - - if self.pack: - env_pkg = self._prepare_package(func, self.extra_pkgs) - else: - env_pkg = None - - logger.debug("Constructing map for local filenames at worker for executor task {}".format(executor_task_id)) - self._construct_map_file(map_file, input_files, output_files) - - if not self.submit_process.is_alive(): - raise ExecutorError(self, "Workqueue Submit Process is not alive") - - # Create message to put into the message queue - logger.debug("Placing executor task {} on message queue".format(executor_task_id)) - if category is None: - category = func.__name__ if self.autocategory else 'parsl-default' - self.task_queue.put_nowait(ParslTaskToWq(executor_task_id, - category, - cores, - memory, - disk, - gpus, - priority, - running_time_min, - env_pkg, - map_file, - function_file, - result_file, - input_files, - output_files)) - - return fu - - def _construct_worker_command(self): - worker_command = self.worker_executable - if self.coprocess: - worker_command += " --coprocess parsl_coprocess.py" - if self.project_password_file: - worker_command += ' --password {}'.format(self.project_password_file) - if self.worker_options: - worker_command += ' {}'.format(self.worker_options) - if self.project_name: - worker_command += ' -M {}'.format(self.project_name) - else: - worker_command += ' {} {}'.format(self.address, self._chosen_port) - - logger.debug("Using worker command: {}".format(worker_command)) - return worker_command - - def _patch_providers(self): - # Add the worker and password file to files that the provider needs to stage. - # (Currently only for the CondorProvider) - if isinstance(self.provider, CondorProvider): - path_to_worker = shutil.which('work_queue_worker') - self.worker_command = './' + self.worker_command - self.provider.transfer_input_files.append(path_to_worker) - if self.project_password_file: - self.provider.transfer_input_files.append(self.project_password_file) - - def _serialize_function(self, fn_path, parsl_fn, parsl_fn_args, parsl_fn_kwargs): - """Takes the function application parsl_fn(*parsl_fn_args, **parsl_fn_kwargs) - and serializes it to the file fn_path.""" - - # Either build a dictionary with the source of the function, or pickle - # the function directly: - if self.source: - function_info = {"source code": inspect.getsource(parsl_fn), - "name": parsl_fn.__name__, - "args": parsl_fn_args, - "kwargs": parsl_fn_kwargs} - else: - function_info = {"byte code": pack_apply_message(parsl_fn, parsl_fn_args, parsl_fn_kwargs, - buffer_threshold=1024 * 1024)} - - with open(fn_path, "wb") as f_out: - pickle.dump(function_info, f_out) - - def _construct_map_file(self, map_file, input_files, output_files): - """ Map local filepath of parsl files to the filenames at the execution worker. - If using a shared filesystem, the filepath is mapped to its absolute filename. - Otherwise, to its original relative filename. In this later case, work queue - recreates any directory hierarchy needed.""" - file_translation_map = {} - for spec in itertools.chain(input_files, output_files): - local_name = spec[0] - if self.shared_fs: - remote_name = os.path.abspath(local_name) - else: - remote_name = local_name - file_translation_map[local_name] = remote_name - with open(map_file, "wb") as f_out: - pickle.dump(file_translation_map, f_out) - - def _register_file(self, parsl_file): - """Generates a tuple (parsl_file.filepath, stage, cache) to give to - work queue. cache is always False if self.use_cache is False. - Otherwise, it is set to True if parsl_file is used more than once. - stage is True if the file needs to be copied by work queue. (i.e., not - a URL or an absolute path) - - It has the side-effect of adding parsl_file to a list of registered - files. - - Note: The first time a file is used cache is set to False. Since - tasks are generated dynamically, without other information this is - the best we can do.""" - to_cache = False - if self.use_cache: - to_cache = parsl_file in self.registered_files - - to_stage = False - if parsl_file.scheme == 'file' or (parsl_file.local_path and os.path.exists(parsl_file.local_path)): - to_stage = not os.path.isabs(parsl_file.filepath) - - self.registered_files.add(parsl_file) - - return ParslFileToWq(parsl_file.filepath, to_stage, to_cache) - - def _std_output_to_wq(self, fdname, stdfspec): - """Find the name of the file that will contain stdout or stderr and - return a ParslFileToWq with it. These files are never cached""" - fname, mode = putils.get_std_fname_mode(fdname, stdfspec) - to_stage = not os.path.isabs(fname) - return ParslFileToWq(fname, stage=to_stage, cache=False) - - def _prepare_package(self, fn, extra_pkgs): - fn_id = id(fn) - fn_name = fn.__name__ - if fn_id in self.cached_envs: - logger.debug("Skipping analysis of %s, previously got %s", fn_name, self.cached_envs[fn_id]) - return self.cached_envs[fn_id] - source_code = inspect.getsource(fn).encode() - pkg_dir = os.path.join(tempfile.gettempdir(), "python_package-{}".format(os.geteuid())) - os.makedirs(pkg_dir, exist_ok=True) - with tempfile.NamedTemporaryFile(suffix='.yaml') as spec: - logger.info("Analyzing dependencies of %s", fn_name) - analyze_cmdline = [package_analyze_script, exec_parsl_function.__file__, '-', spec.name] - for p in extra_pkgs: - analyze_cmdline += ["--extra-pkg", p] - subprocess.run(analyze_cmdline, input=source_code, check=True) - with open(spec.name, mode='rb') as f: - spec_hash = hashlib.sha256(f.read()).hexdigest() - logger.debug("Spec hash for %s is %s", fn_name, spec_hash) - pkg = os.path.join(pkg_dir, "pack-{}.tar.gz".format(spec_hash)) - if os.access(pkg, os.R_OK): - self.cached_envs[fn_id] = pkg - logger.debug("Cached package for %s found at %s", fn_name, pkg) - return pkg - (fd, tarball) = tempfile.mkstemp(dir=pkg_dir, prefix='.tmp', suffix='.tar.gz') - os.close(fd) - logger.info("Creating dependency package for %s", fn_name) - logger.debug("Writing deps for %s to %s", fn_name, tarball) - subprocess.run([package_create_script, spec.name, tarball], stdout=subprocess.DEVNULL, check=True) - logger.debug("Done with conda-pack; moving %s to %s", tarball, pkg) - os.rename(tarball, pkg) - self.cached_envs[fn_id] = pkg - return pkg - - def initialize_scaling(self): - """ Compose the launch command and call scale out - - Scales the workers to the appropriate nodes with provider - """ - # Start scaling in/out - logger.debug("Starting WorkQueueExecutor with provider: %s", self.provider) - self.worker_command = self._construct_worker_command() - self._patch_providers() - - def outstanding(self) -> int: - """Count the number of outstanding slots required. This is inefficiently - implemented and probably could be replaced with a counter. - """ - logger.debug("Calculating outstanding task slot load") - outstanding = 0 - tasks = 0 # only for log message... - with self.tasks_lock: - for fut in self.tasks.values(): - if not fut.done(): - # if a task does not specify a core count, Work Queue will allocate an entire - # worker node to that task. That's approximated here by saying that it uses - # scaling_cores_per_worker. - resource_spec = getattr(fut, 'resource_specification', {}) - cores = resource_spec.get('cores', self.scaling_cores_per_worker) - - outstanding += cores - tasks += 1 - logger.debug(f"Counted {tasks} outstanding tasks with {outstanding} outstanding slots") - return outstanding - - @property - def workers_per_node(self) -> Union[int, float]: - return self.scaling_cores_per_worker - - def shutdown(self, *args, **kwargs): - """Shutdown the executor. Sets flag to cancel the submit process and - collector thread, which shuts down the Work Queue system submission. - """ - logger.debug("Work Queue shutdown started") - self.should_stop.value = True - - logger.debug("Joining on submit process") - self.submit_process.join() - self.submit_process.close() - - logger.debug("Joining on collector thread") - self.collector_thread.join() - - logger.debug("Closing multiprocessing queues") - self.task_queue.close() - self.task_queue.join_thread() - self.collector_queue.close() - self.collector_queue.join_thread() - - super().shutdown() - - logger.debug("Work Queue shutdown completed") - - @wrap_with_logs - def _collect_work_queue_results(self): - """Sets the values of tasks' futures of tasks completed by work queue. - """ - logger.debug("Starting Collector Thread") - try: - while not self.should_stop.value: - if not self.submit_process.is_alive(): - raise ExecutorError(self, "Workqueue Submit Process is not alive") - - # Get the result message from the collector_queue - try: - task_report = self.collector_queue.get(timeout=1) - except queue.Empty: - continue - - # Obtain the future from the tasks dictionary - with self.tasks_lock: - future = self.tasks.pop(task_report.id) - logger.debug("Updating Future for executor task {}".format(task_report.id)) - # If result_received, then there's a result file. The object inside the file - # may be a valid result or an exception caused within the function invocation. - # Otherwise there's no result file, implying errors from WorkQueue. - if task_report.result_received: - try: - with open(task_report.result_file, 'rb') as f_in: - result = deserialize(f_in.read()) - except Exception as e: - logger.error(f'Cannot load result from result file {task_report.result_file}. Exception: {e}') - ex = WorkQueueTaskFailure('Cannot load result from result file', None) - ex.__cause__ = e - future.set_exception(ex) - else: - if isinstance(result, Exception): - ex = WorkQueueTaskFailure('Task execution raises an exception', result) - ex.__cause__ = result - future.set_exception(ex) - else: - future.set_result(result) - else: - # If there are no results, then the task failed according to one of - # work queue modes, such as resource exhaustion. - ex = WorkQueueTaskFailure(task_report.reason, None) - future.set_exception(ex) - finally: - logger.debug("Marking all outstanding tasks as failed") - logger.debug("Acquiring tasks_lock") - with self.tasks_lock: - logger.debug("Acquired tasks_lock") - # set exception for tasks waiting for results that work queue did not execute - for fu in self.tasks.values(): - if not fu.done(): - fu.set_exception(WorkQueueFailure("work queue executor failed to execute the task.")) - logger.debug("Exiting Collector Thread") - - -@wrap_with_logs -def _work_queue_submit_wait(*, - port_mailbox: multiprocessing.Queue, - task_queue: multiprocessing.Queue, - launch_cmd: str, - collector_queue: multiprocessing.Queue, - data_dir: str, - full: bool, - shared_fs: bool, - autolabel: bool, - autolabel_window: int, - autocategory: bool, - max_retries: Optional[int], - should_stop, # multiprocessing.Value is an awkward type alias from inside multiprocessing - port: int, - wq_log_dir: str, - project_password_file: Optional[str], - project_name: Optional[str], - coprocess: bool) -> int: - """Thread to handle Parsl app submissions to the Work Queue objects. - Takes in Parsl functions submitted using submit(), and creates a - Work Queue task with the appropriate specifications, which is then - submitted to Work Queue. After tasks are completed, processes the - exit status and exit code of the task, and sends results to the - Work Queue collector thread. - To avoid python's global interpreter lock with work queue's wait, this - function should be launched as a process, not as a lightweight thread. This - means that any communication should be done using the multiprocessing - module capabilities, rather than shared memory. - """ - logger.debug("Starting WorkQueue Submit/Wait Process") - setproctitle("parsl: Work Queue submit/wait") - - # Enable debugging flags and create logging file - wq_debug_log = None - if wq_log_dir is not None: - logger.debug("Setting debugging flags and creating logging file") - wq_debug_log = os.path.join(wq_log_dir, "debug_log") - - # Create WorkQueue queue object - logger.debug("Creating WorkQueue Object") - try: - logger.debug("Requested port {}".format(port)) - q = WorkQueue(port, debug_log=wq_debug_log) - port_mailbox.put(q.port) - logger.debug("Listening on port {}".format(q.port)) - except Exception as e: - logger.error("Unable to create WorkQueue object: {}".format(e)) - port_mailbox.put(None) - raise e - - # Specify WorkQueue queue attributes - if project_name: - q.specify_name(project_name) - - if project_password_file: - q.specify_password_file(project_password_file) - - if autolabel: - q.enable_monitoring() - if autolabel_window is not None: - q.tune('category-steady-n-tasks', autolabel_window) - - # Only write logs when the wq_log_dir is specified, which it most likely will be - if wq_log_dir is not None: - wq_master_log = os.path.join(wq_log_dir, "master_log") - wq_trans_log = os.path.join(wq_log_dir, "transaction_log") - if full and autolabel: - wq_resource_log = os.path.join(wq_log_dir, "resource_logs") - q.enable_monitoring_full(dirname=wq_resource_log) - q.specify_log(wq_master_log) - q.specify_transactions_log(wq_trans_log) - - orig_ppid = os.getppid() - - result_file_of_task_id = {} # Mapping executor task id -> result file for active tasks. - - while not should_stop.value: - # Monitor the task queue - ppid = os.getppid() - if ppid != orig_ppid: - logger.debug("new Process") - break - - # Submit tasks - while task_queue.qsize() > 0 or q.empty() and not should_stop.value: - # Obtain task from task_queue - try: - task = task_queue.get(timeout=1) - logger.debug("Removing task from queue") - except queue.Empty: - continue - - try: - pkg_pfx = "" - if task.env_pkg is not None: - if package_run_script is None: - raise ValueError("package_run_script must be specified") - pkg_pfx = "./{} -e {} ".format(os.path.basename(package_run_script), - os.path.basename(task.env_pkg)) - - if not coprocess: - # Create command string - logger.debug(launch_cmd) - command_str = launch_cmd.format(package_prefix=pkg_pfx, - mapping=os.path.basename(task.map_file), - function=os.path.basename(task.function_file), - result=os.path.basename(task.result_file)) - logger.debug(command_str) - - # Create WorkQueue task for the command - logger.debug("Sending executor task {} with command: {}".format(task.id, command_str)) - t = wq.Task(command_str) - else: - t = wq.RemoteTask("run_parsl_task", - "parsl_coprocess", - os.path.basename(task.map_file), - os.path.basename(task.function_file), - os.path.basename(task.result_file)) - t.specify_exec_method("direct") - logger.debug("Sending executor task {} to coprocess".format(task.id)) - - except Exception as e: - logger.error("Unable to create task: {}".format(e)) - collector_queue.put_nowait(WqTaskToParsl(id=task.id, - result_received=False, - result_file=None, - reason="task could not be created by work queue", - status=-1)) - continue - - t.specify_category(task.category) - if autolabel: - q.specify_category_mode(task.category, WORK_QUEUE_ALLOCATION_MODE_MAX_THROUGHPUT) - - if task.cores is not None: - t.specify_cores(task.cores) - if task.memory is not None: - t.specify_memory(task.memory) - if task.disk is not None: - t.specify_disk(task.disk) - if task.gpus is not None: - t.specify_gpus(task.gpus) - if task.priority is not None: - t.specify_priority(task.priority) - if task.running_time_min is not None: - t.specify_running_time_min(task.running_time_min) - - if max_retries is not None: - logger.debug(f"Specifying max_retries {max_retries}") - t.specify_max_retries(max_retries) - else: - logger.debug("Not specifying max_retries") - - if task.env_pkg is not None: - t.specify_input_file(package_run_script, cache=True) - t.specify_input_file(task.env_pkg, cache=True) - - # Specify script, and data/result files for task - t.specify_input_file(exec_parsl_function.__file__, cache=True) - t.specify_input_file(task.function_file, cache=False) - t.specify_input_file(task.map_file, cache=False) - t.specify_output_file(task.result_file, cache=False) - t.specify_tag(str(task.id)) - result_file_of_task_id[str(task.id)] = task.result_file - - logger.debug("Executor task id: {}".format(task.id)) - - # Specify input/output files that need to be staged. - # Absolute paths are assumed to be in shared filesystem, and thus - # not staged by work queue. - if not shared_fs: - for spec in task.input_files: - if spec.stage: - t.specify_input_file(spec.parsl_name, spec.parsl_name, cache=spec.cache) - for spec in task.output_files: - if spec.stage: - t.specify_output_file(spec.parsl_name, spec.parsl_name, cache=spec.cache) - - # Submit the task to the WorkQueue object - logger.debug("Submitting executor task {} to WorkQueue".format(task.id)) - try: - wq_id = q.submit(t) - except Exception as e: - logger.error("Unable to submit task to work queue: {}".format(e)) - collector_queue.put_nowait(WqTaskToParsl(id=task.id, - result_received=False, - result_file=None, - reason="task could not be submited to work queue", - status=-1)) - continue - logger.info("Executor task {} submitted as Work Queue task {}".format(task.id, wq_id)) - - # If the queue is not empty wait on the WorkQueue queue for a task - task_found = True - while not q.empty() and task_found and not should_stop.value: - # Obtain the task from the queue - t = q.wait(1) - if t is None: - task_found = False - continue - # When a task is found: - executor_task_id = t.tag - logger.info("Completed Work Queue task {}, executor task {}".format(t.id, t.tag)) - result_file = result_file_of_task_id.pop(t.tag) - - # A tasks completes 'succesfully' if it has result file. - # The check whether this file can load a serialized Python object - # happens later in the collector thread of the executor process. - logger.debug("Looking for result in {}".format(result_file)) - if os.path.exists(result_file): - logger.debug("Found result in {}".format(result_file)) - collector_queue.put_nowait(WqTaskToParsl(id=executor_task_id, - result_received=True, - result_file=result_file, - reason=None, - status=t.return_status)) - # If a result file could not be generated, explain the - # failure according to work queue error codes. - else: - reason = _explain_work_queue_result(t) - logger.debug("Did not find result in {}".format(result_file)) - logger.debug("Wrapper Script status: {}\nWorkQueue Status: {}" - .format(t.return_status, t.result)) - logger.debug("Task with executor id {} / Work Queue id {} failed because:\n{}" - .format(executor_task_id, t.id, reason)) - collector_queue.put_nowait(WqTaskToParsl(id=executor_task_id, - result_received=False, - result_file=None, - reason=reason, - status=t.return_status)) - logger.debug("Exiting WorkQueue Monitoring Process") - return 0 - - -def _explain_work_queue_result(wq_task): - """Returns a string with the reason why a task failed according to work queue.""" - - wq_result = wq_task.result - - reason = "work queue result: " - if wq_result == wq.WORK_QUEUE_RESULT_SUCCESS: - reason += "succesful execution with exit code {}".format(wq_task.return_status) - elif wq_result == wq.WORK_QUEUE_RESULT_OUTPUT_MISSING: - reason += "The result file was not transfered from the worker.\n" - reason += "This usually means that there is a problem with the python setup,\n" - reason += "or the wrapper that executes the function." - reason += "\nTrace:\n" + str(wq_task.output) - elif wq_result == wq.WORK_QUEUE_RESULT_INPUT_MISSING: - reason += "missing input file" - elif wq_result == wq.WORK_QUEUE_RESULT_STDOUT_MISSING: - reason += "stdout has been truncated" - elif wq_result == wq.WORK_QUEUE_RESULT_SIGNAL: - reason += "task terminated with a signal" - elif wq_result == wq.WORK_QUEUE_RESULT_RESOURCE_EXHAUSTION: - reason += "task used more resources than requested" - elif wq_result == wq.WORK_QUEUE_RESULT_TASK_TIMEOUT: - reason += "task ran past the specified end time" - elif wq_result == wq.WORK_QUEUE_RESULT_UNKNOWN: - reason += "result could not be classified" - elif wq_result == wq.WORK_QUEUE_RESULT_FORSAKEN: - reason += "task failed, but not a task error" - elif wq_result == wq.WORK_QUEUE_RESULT_MAX_RETRIES: - reason += "unable to complete after specified number of retries" - elif wq_result == wq.WORK_QUEUE_RESULT_TASK_MAX_RUN_TIME: - reason += "task ran for more than the specified time" - elif wq_result == wq.WORK_QUEUE_RESULT_DISK_ALLOC_FULL: - reason += "task needed more space to complete task" - elif wq_result == wq.WORK_QUEUE_RESULT_RMONITOR_ERROR: - reason += "task failed because the monitor did not produce an output" - else: - reason += "unable to process Work Queue system failure" - return reason diff --git a/parsl/parsl/executors/workqueue/parsl_coprocess.py b/parsl/parsl/executors/workqueue/parsl_coprocess.py deleted file mode 100755 index 0bbcde89..00000000 --- a/parsl/parsl/executors/workqueue/parsl_coprocess.py +++ /dev/null @@ -1,166 +0,0 @@ -#! /usr/bin/env python3 - -import json -import os -import socket -import sys - -# If enabled, coprocess will print to stdout -debug_mode = False - -# Send a message on a binary I/O stream by sending the message length and then the (string) message. -def send_message(stream, data): - size = len(data) - size_msg = "{}\n".format(size) - stream.write(size_msg) - stream.write(data) - -# Receive a standard message from a binary I/O stream by reading length and then returning the (string) message -def recv_message(stream): - line = stream.readline() - length = int(line) - return stream.read(length) - -# Decorator for remotely execution functions to package things as json. -def remote_execute(func): - def remote_wrapper(event): - kwargs = event["fn_kwargs"] - args = event["fn_args"] - try: - response = { - "Result": func(*args, **kwargs), - "StatusCode": 200 - } - except Exception as e: - response = { - "Result": str(e), - "StatusCode": 500 - } - return response - return remote_wrapper - -# Main loop of coprocess for executing network functions. -def main(): - # Listen on an arbitrary port to be reported to the worker. - s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - try: - s.bind(('localhost', 0)) - except Exception as e: - s.close() - print(e, file=sys.stderr) - sys.exit(1) - - # Inform the worker of name and port for later connection. - config = { - "name": name(), # noqa: F821 - "port": s.getsockname()[1], - } - send_message(sys.stdout, json.dumps(config)) - sys.stdout.flush() - - # Remember original working directory b/c we change for each invocation. - abs_working_dir = os.getcwd() - - # Create pipe for communication with child process - rpipe, wpipe = os.pipe() - rpipestream = os.fdopen(rpipe, "r") - - while True: - s.listen() - conn, addr = s.accept() - connstream = conn.makefile("rw", encoding="utf-8") - - if debug_mode: - print('Network function: connection from {}'.format(addr), file=sys.stderr) - - while True: - # Read the invocation header from the worker - line = connstream.readline() - - # If end of file, then break out and accept again - if not line: - break - - # Parse the invocation header. - input_spec = line.split() - function_name = input_spec[0] - task_id = int(input_spec[1]) - event_size = int(input_spec[2]) - - # then read the contents of the event itself - event_str = connstream.read(event_size) - event = json.loads(event_str) - exec_method = event.get("remote_task_exec_method", None) - - try: - # First move to target directory (is undone in finally block) - os.chdir(os.path.join(abs_working_dir, f't.{task_id}')) - - # Then invoke function by desired method, resulting in - # response containing the text representation of the result. - - if exec_method == "direct": - response = json.dumps(globals()[function_name](event)) - else: - p = os.fork() - if p == 0: - response = globals()[function_name](event) - wpipestream = os.fdopen(wpipe, "w") - send_message(wpipestream, json.dumps(response)) - wpipestream.flush() - os._exit(0) - elif p < 0: - if debug_mode: - print(f'Network function: unable to fork to execute {function_name}', file=sys.stderr) - response = { - "Result": "unable to fork", - "StatusCode": 500 - } - response = json.dumps(response) - else: - # Get response string from child process. - response = recv_message(rpipestream) - # Wait for child process to complete - os.waitpid(p, 0) - - # At this point, response is set to a value one way or the other - - except Exception as e: - if debug_mode: - print("Network function encountered exception ", str(e), file=sys.stderr) - response = { - 'Result': f'network function encountered exception {e}', - 'Status Code': 500 - } - response = json.dumps(response) - finally: - # Restore the working directory, no matter how the function ended. - os.chdir(abs_working_dir) - - # Send response string back to parent worker process. - send_message(connstream, response) - connstream.flush() - - return 0 -def name(): - return 'parsl_coprocess' -@remote_execute -def run_parsl_task(a, b, c): - import parsl.executors.workqueue.exec_parsl_function as epf - try: - (map_file, function_file, result_file) = (a, b, c) - try: - (namespace, function_code, result_name) = epf.load_function(map_file, function_file) - except Exception: - raise - try: - result = epf.execute_function(namespace, function_code, result_name) - except Exception: - raise - except Exception: - result = RemoteExceptionWrapper(*sys.exc_info()) - epf.dump_result_to_file(result_file, result) - return None -if __name__ == "__main__": - main() - diff --git a/parsl/parsl/executors/workqueue/parsl_coprocess_stub.py b/parsl/parsl/executors/workqueue/parsl_coprocess_stub.py deleted file mode 100755 index e914f6fa..00000000 --- a/parsl/parsl/executors/workqueue/parsl_coprocess_stub.py +++ /dev/null @@ -1,31 +0,0 @@ -#! /usr/bin/env python3 - -import sys - -from parsl.app.errors import RemoteExceptionWrapper - - -def name(): - return 'parsl_coprocess' - - -def run_parsl_task(a, b, c): - import parsl.executors.workqueue.exec_parsl_function as epf - try: - (map_file, function_file, result_file) = (a, b, c) - - try: - (namespace, function_code, result_name) = epf.load_function(map_file, function_file) - except Exception: - raise - - try: - result = epf.execute_function(namespace, function_code, result_name) - except Exception: - raise - except Exception: - result = RemoteExceptionWrapper(*sys.exc_info()) - - epf.dump_result_to_file(result_file, result) - - return None diff --git a/parsl/parsl/jobs/__init__.py b/parsl/parsl/jobs/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/jobs/error_handlers.py b/parsl/parsl/jobs/error_handlers.py deleted file mode 100644 index 246f8c30..00000000 --- a/parsl/parsl/jobs/error_handlers.py +++ /dev/null @@ -1,68 +0,0 @@ -from __future__ import annotations - -from typing import Dict, Tuple - -import parsl.executors.status_handling as status_handling -from parsl.jobs.errors import TooManyJobFailuresError -from parsl.jobs.states import JobState, JobStatus - - -def noop_error_handler(executor: status_handling.BlockProviderExecutor, status: Dict[str, JobStatus], threshold: int = 3) -> None: - pass - - -def simple_error_handler(executor: status_handling.BlockProviderExecutor, status: Dict[str, JobStatus], threshold: int = 3) -> None: - (total_jobs, failed_jobs) = _count_jobs(status) - assert executor.provider is not None, "error handlers should only be called on executors which have a provider" - if hasattr(executor.provider, "init_blocks"): - threshold = max(1, executor.provider.init_blocks) - - if total_jobs >= threshold and failed_jobs == total_jobs: - executor.set_bad_state_and_fail_all(_get_error(status)) - - -def windowed_error_handler(executor: status_handling.BlockProviderExecutor, status: Dict[str, JobStatus], threshold: int = 3) -> None: - sorted_status = [(key, status[key]) for key in sorted(status, key=lambda x: int(x))] - current_window = dict(sorted_status[-threshold:]) - total, failed = _count_jobs(current_window) - if failed == threshold: - executor.set_bad_state_and_fail_all(_get_error(status)) - - -def _count_jobs(status: Dict[str, JobStatus]) -> Tuple[int, int]: - total = 0 - failed = 0 - for js in status.values(): - total += 1 - if js.state == JobState.FAILED or js.state == JobState.MISSING: - failed += 1 - return total, failed - - -def _get_error(status: Dict[str, JobStatus]) -> Exception: - """Concatenate all errors.""" - err = "" - count = 1 - for js in status.values(): - err = err + f"Error {count}:\n" - count += 1 - - if js.message is not None: - err = err + f"\t{js.message}\n" - - if js.exit_code is not None: - err = err + f"\tEXIT CODE: {js.exit_code}\n" - - stdout = js.stdout_summary - if stdout: - err = err + "\tSTDOUT: {}\n".format(stdout) - - stderr = js.stderr_summary - if stderr: - err = err + "\tSTDERR: {}\n".format(stderr) - - if len(err) == 0: - err = "No error messages received" - # wrapping things in an exception here doesn't really help in providing more information - # than the string itself - return TooManyJobFailuresError(err) diff --git a/parsl/parsl/jobs/errors.py b/parsl/parsl/jobs/errors.py deleted file mode 100644 index 6d42f429..00000000 --- a/parsl/parsl/jobs/errors.py +++ /dev/null @@ -1,7 +0,0 @@ -from parsl.errors import ParslError - - -class TooManyJobFailuresError(ParslError): - """Indicates that executor is shut down because of too many block failures. - """ - pass diff --git a/parsl/parsl/jobs/job_status_poller.py b/parsl/parsl/jobs/job_status_poller.py deleted file mode 100644 index c5d86b63..00000000 --- a/parsl/parsl/jobs/job_status_poller.py +++ /dev/null @@ -1,66 +0,0 @@ -import logging -from typing import List, Optional, Sequence, Union - -from parsl.executors.status_handling import BlockProviderExecutor -from parsl.jobs.strategy import Strategy -from parsl.utils import Timer - -logger = logging.getLogger(__name__) - - -class JobStatusPoller(Timer): - - def __init__(self, *, strategy: Optional[str], max_idletime: float, - strategy_period: Union[float, int]) -> None: - """Initalize the poller. - - strategy: str - names the scaling strategy to use - - max_idletime: float, seconds - used by some scaling strategies to decide how long a block is idle - - strategy_period: float|int, seconds - how often the scaling strategy should be executed - """ - # only executors which should be polled and which have a valid - # provider should be added to this list: so it is safe to assume - # that e.provider is not None for e in self._executors - self._executors: List[BlockProviderExecutor] = [] - - self._strategy = Strategy(strategy=strategy, - max_idletime=max_idletime) - super().__init__(self.poll, interval=strategy_period, name="JobStatusPoller") - - def poll(self) -> None: - for executor in self._executors: - # Update status - executor.poll_facade() - executor.handle_errors(executor.status_facade) - - self._strategy.strategize(self._executors) - - def add_executors(self, executors: Sequence[BlockProviderExecutor]) -> None: - for executor in executors: - if executor.status_polling_interval > 0: - assert executor.provider is not None, \ - "BlockProviderExecutor.status_polling_interval implementation guarantee: None => status_polling_interval == 0" - logger.debug("Adding executor {}".format(executor.label)) - self._executors.append(executor) - self._strategy.add_executors(executors) - - def close(self, timeout: Optional[float] = None) -> None: - super().close(timeout) - for executor in self._executors: - if not executor.bad_state_is_set: - logger.info(f"Scaling in executor {executor.label}") - - # this code needs to be at least as many blocks as need - # cancelling, but it is safe to be more, as the scaling - # code will cope with being asked to cancel more blocks - # than exist. - block_count = len(executor.status_facade) - executor.scale_in_facade(block_count) - - else: # and bad_state_is_set - logger.warning(f"Not scaling in executor {executor.label} because it is in bad state") diff --git a/parsl/parsl/jobs/states.py b/parsl/parsl/jobs/states.py deleted file mode 100644 index b1cbdc82..00000000 --- a/parsl/parsl/jobs/states.py +++ /dev/null @@ -1,150 +0,0 @@ -import logging -import os -from enum import IntEnum -from typing import Optional - -logger = logging.getLogger(__name__) - - -class JobState(IntEnum): - """Defines a set of states that a job can be in""" - - UNKNOWN = 0 - """The batch provider is unable to determine a state for this job""" - - PENDING = 1 - """"This job is in the batch queue but has not started running""" - - RUNNING = 2 - """This job is running in the batch system""" - - CANCELLED = 3 - """This job has been cancelled""" - - COMPLETED = 4 - """This job has completed""" - - FAILED = 5 - """This job has failed""" - - TIMEOUT = 6 - """This job has ended due to walltime expiry. This is different from - other error states, because in the pilot job model, a timeout is usually - expected and not a failure. Timeouts should not be reported with FAILED - state: if they are reported as FAILED, Parsl's error handling code - in `simple_error_handler` will eventually regard the batch system as - broken and shut down. - """ - - HELD = 7 - """This job is held/suspended in the batch system""" - - MISSING = 8 - """This job has reached a terminal state without the resources (managers/workers) - launched in the job connecting back to the Executor. This state is set by HTEX - when it is able to infer that the block failed to start workers for eg due to - bad worker environment or network connectivity issues. - """ - - SCALED_IN = 9 - """This job has been deliberately scaled in. Scaling code should not be concerned - that the job never ran (for example for error handling purposes). - """ - - def __str__(self) -> str: - return f"{self.__class__.__name__}.{self.name}" - - -TERMINAL_STATES = [JobState.CANCELLED, JobState.COMPLETED, JobState.FAILED, - JobState.TIMEOUT, JobState.MISSING, JobState.SCALED_IN] - - -class JobStatus: - """Encapsulates a job state together with other details: - - Args: - state: The machine-readable state of the job this status refers to - message: Optional human readable message - exit_code: Optional exit code - stdout_path: Optional path to a file containing the job's stdout - stderr_path: Optional path to a file containing the job's stderr - """ - SUMMARY_TRUNCATION_THRESHOLD = 2048 - - def __init__(self, state: JobState, message: Optional[str] = None, exit_code: Optional[int] = None, - stdout_path: Optional[str] = None, stderr_path: Optional[str] = None): - self.state = state - self.message = message - self.exit_code = exit_code - self.stdout_path = stdout_path - self.stderr_path = stderr_path - - @property - def terminal(self) -> bool: - return self.state in TERMINAL_STATES - - @property - def status_name(self) -> str: - return self.state.name - - def __repr__(self) -> str: - if self.message is not None: - extra = f"state={self.state} message={self.message}" - else: - extra = f"state={self.state}" - return f"<{type(self).__module__}.{type(self).__qualname__} object at {hex(id(self))}, {extra}>" - - def __str__(self) -> str: - if self.message is not None: - return f"{self.state} ({self.message})" - else: - return f"{self.state}" - - @property - def stdout(self) -> Optional[str]: - return self._read_file(self.stdout_path) - - @property - def stderr(self) -> Optional[str]: - return self._read_file(self.stderr_path) - - def _read_file(self, path: Optional[str]) -> Optional[str]: - if path is None: - return None - try: - with open(path, 'r') as f: - return f.read() - except Exception: - logger.exception("Converting exception to None") - return None - - @property - def stdout_summary(self) -> Optional[str]: - return self._read_summary(self.stdout_path) - - @property - def stderr_summary(self) -> Optional[str]: - return self._read_summary(self.stderr_path) - - def _read_summary(self, path: Optional[str]) -> Optional[str]: - if not path: - # can happen for synthetic job failures - return None - try: - with open(path, 'r') as f: - f.seek(0, os.SEEK_END) - size = f.tell() - f.seek(0, os.SEEK_SET) - if size > JobStatus.SUMMARY_TRUNCATION_THRESHOLD: - half_threshold = int(JobStatus.SUMMARY_TRUNCATION_THRESHOLD / 2) - head = f.read(half_threshold) - f.seek(size - half_threshold, os.SEEK_SET) - tail = f.read(half_threshold) - return head + '\n...\n' + tail - else: - f.seek(0, os.SEEK_SET) - return f.read() - except FileNotFoundError: - # When output is redirected to a file, but the process does not produce any output - # bytes, no file is actually created. This handles that case. - return None diff --git a/parsl/parsl/jobs/strategy.py b/parsl/parsl/jobs/strategy.py deleted file mode 100644 index aa9d36f4..00000000 --- a/parsl/parsl/jobs/strategy.py +++ /dev/null @@ -1,316 +0,0 @@ -from __future__ import annotations - -import logging -import math -import time -import warnings -from typing import Dict, List, Optional, Sequence, TypedDict - -from parsl.executors import HighThroughputExecutor -from parsl.executors.base import ParslExecutor -from parsl.executors.status_handling import BlockProviderExecutor -from parsl.jobs.states import JobState -from parsl.process_loggers import wrap_with_logs - -logger = logging.getLogger(__name__) - - -class ExecutorState(TypedDict): - """Strategy relevant state for an executor - """ - - idle_since: Optional[float] - """The timestamp at which an executor became idle. - If the executor is not idle, then None. - """ - - first: bool - """True if this executor has not yet had a strategy poll. - """ - - -class Strategy: - """Scaling strategy. - - As a workflow dag is processed by Parsl, new tasks are added and completed - asynchronously. Parsl interfaces executors with execution providers to construct - scalable executors to handle the variable work-load generated by the - workflow. This component is responsible for periodically checking outstanding - tasks and available compute capacity and trigger scaling events to match - workflow needs. - - Here's a diagram of an executor. An executor consists of blocks, which are usually - created by single requests to a Local Resource Manager (LRM) such as slurm, - condor, torque, or even AWS API. The blocks could contain several task blocks - which are separate instances on workers. - - - .. code:: python - - |<--min_blocks |<-init_blocks max_blocks-->| - +----------------------------------------------------------+ - | +--------block----------+ +--------block--------+ | - executor = | | task task | ... | task task | | - | +-----------------------+ +---------------------+ | - +----------------------------------------------------------+ - - The relevant specification options are: - 1. min_blocks: Minimum number of blocks to maintain - 2. init_blocks: number of blocks to provision at initialization of workflow - 3. max_blocks: Maximum number of blocks that can be active due to one workflow - - - .. code:: python - - active_tasks = pending_tasks + running_tasks - - Parallelism = slots / tasks - = [0, 1] (i.e, 0 <= p <= 1) - - For example: - - When p = 0, - => compute with the least resources possible. - infinite tasks are stacked per slot. - - .. code:: python - - blocks = min_blocks { if active_tasks = 0 - max(min_blocks, 1) { else - - When p = 1, - => compute with the most resources. - one task is stacked per slot. - - .. code:: python - - blocks = min ( max_blocks, - ceil( active_tasks / slots ) ) - - - When p = 1/2, - => We stack upto 2 tasks per slot before we overflow - and request a new block - - - let's say min:init:max = 0:0:4 and task_blocks=2 - Consider the following example: - min_blocks = 0 - init_blocks = 0 - max_blocks = 4 - tasks_per_node = 2 - nodes_per_block = 1 - - In the diagram, X <- task - - at 2 tasks: - - .. code:: python - - +---Block---| - | | - | X X | - |slot slot| - +-----------+ - - at 5 tasks, we overflow as the capacity of a single block is fully used. - - .. code:: python - - +---Block---| +---Block---| - | X X | ----> | | - | X X | | X | - |slot slot| |slot slot| - +-----------+ +-----------+ - - """ - - def __init__(self, *, strategy: Optional[str], max_idletime: float) -> None: - """Initialize strategy.""" - self.executors: Dict[str, ExecutorState] - self.executors = {} - self.max_idletime = max_idletime - - self.strategies = {None: self._strategy_init_only, - 'none': self._strategy_init_only, - 'simple': self._strategy_simple, - 'htex_auto_scale': self._strategy_htex_auto_scale} - - if strategy is None: - warnings.warn("literal None for strategy choice is deprecated. Use string 'none' instead.", - DeprecationWarning) - - self.strategize = self.strategies[strategy] - - logger.debug("Scaling strategy: {0}".format(strategy)) - - def add_executors(self, executors: Sequence[ParslExecutor]) -> None: - for executor in executors: - self.executors[executor.label] = {'idle_since': None, 'first': True} - - def _strategy_init_only(self, executors: List[BlockProviderExecutor]) -> None: - """Scale up to init_blocks at the start, then nothing more. - """ - for executor in executors: - if self.executors[executor.label]['first']: - assert executor.provider is not None, \ - "strategy must only be called with executors that have a provider." - logger.debug(f"strategy_init_only: scaling out {executor.provider.init_blocks} initial blocks for {executor.label}") - executor.scale_out_facade(executor.provider.init_blocks) - self.executors[executor.label]['first'] = False - else: - logger.debug("strategy_init_only: doing nothing") - - def _strategy_simple(self, executors: List[BlockProviderExecutor]) -> None: - self._general_strategy(executors, strategy_type='simple') - - def _strategy_htex_auto_scale(self, executors: List[BlockProviderExecutor]) -> None: - """HTEX specific auto scaling strategy - - This strategy works only for HTEX. This strategy will scale out by - requesting additional compute resources via the provider when the - workload requirements exceed the provisioned capacity. The scale out - behavior is exactly like the 'simple' strategy. - - If there are idle blocks during execution, this strategy will terminate - those idle blocks specifically. When # of tasks >> # of blocks, HTEX places - tasks evenly across blocks, which makes it rather difficult to ensure that - some blocks will reach 0% utilization. Consequently, this strategy can be - expected to scale in effectively only when # of workers, or tasks executing - per block is close to 1. - """ - self._general_strategy(executors, strategy_type='htex') - - @wrap_with_logs - def _general_strategy(self, executors: List[BlockProviderExecutor], *, strategy_type: str) -> None: - logger.debug(f"general strategy starting with strategy_type {strategy_type} for {len(executors)} executors") - - for executor in executors: - assert executor.provider is not None, \ - "strategy must only be called with executors that have a provider." - label = executor.label - prefix = f"[Scaling executor {label}]" - - if executor.bad_state_is_set: - logger.info("%s Not strategizing for executor because bad state is set", prefix) - continue - - logger.debug("%s Strategizing for executor", prefix) - - if self.executors[label]['first']: - logger.debug("%s Scaling out %d initial blocks", prefix, executor.provider.init_blocks) - executor.scale_out_facade(executor.provider.init_blocks) - self.executors[label]['first'] = False - - # Tasks that are either pending completion - active_tasks = executor.outstanding() - - status = executor.status_facade - - # FIXME we need to handle case where provider does not define these - # FIXME probably more of this logic should be moved to the provider - min_blocks = executor.provider.min_blocks - max_blocks = executor.provider.max_blocks - tasks_per_node = executor.workers_per_node - - nodes_per_block = executor.provider.nodes_per_block - parallelism = executor.provider.parallelism - - running = sum([1 for x in status.values() if x.state == JobState.RUNNING]) - pending = sum([1 for x in status.values() if x.state == JobState.PENDING]) - active_blocks = running + pending - active_slots = active_blocks * tasks_per_node * nodes_per_block - - logger.debug('%s Executor %d active tasks, %d active slots, and %d/%d running/pending blocks', - prefix, active_tasks, active_slots, running, pending) - - # reset idle timer if executor has active tasks - if active_tasks > 0 and self.executors[executor.label]['idle_since']: - self.executors[executor.label]['idle_since'] = None - - # Case 1 - # No tasks. - if active_tasks == 0: - # Case 1a - # Fewer blocks that min_blocks - if active_blocks <= min_blocks: - logger.debug("%s Strategy case 1a: Executor has no active tasks and minimum blocks. Taking no action.", prefix) - # Case 1b - # More blocks than min_blocks. Scale in - else: - # We want to make sure that max_idletime is reached - # before killing off resources - logger.debug("%s Strategy case 1b: Executor has no active tasks, and more (%d)" - " than minimum blocks (%d)", prefix, active_blocks, min_blocks) - - if not self.executors[executor.label]['idle_since']: - logger.debug("%s Starting idle timer. If idle time exceeds %.1fs, blocks will be scaled in", prefix, self.max_idletime) - self.executors[executor.label]['idle_since'] = time.time() - idle_since = self.executors[executor.label]['idle_since'] - assert idle_since is not None, "The `if` statement above this assert should have forced idle time to be not-None" - - idle_duration = time.time() - idle_since - if idle_duration > self.max_idletime: - # We have resources idle for the max duration, - # we have to scale_in now. - logger.debug("%s Idle time has reached %.1fs for executor; scaling in", prefix, self.max_idletime) - executor.scale_in_facade(active_blocks - min_blocks) - - else: - logger.debug( - "%s Idle time %.1fs is less than max_idletime %.1fs; not scaling in", prefix, idle_duration, self.max_idletime, - ) - - # Case 2 - # More tasks than the available slots. - elif (float(active_slots) / active_tasks) < parallelism: - logger.debug("%s Strategy case 2: slots are overloaded - (slot_ratio = active_slots/active_tasks) < parallelism", prefix) - - # Case 2a - # We have the max blocks possible - if active_blocks >= max_blocks: - # Ignore since we already have the max nodes - logger.debug("%s Strategy case 2a: active_blocks %d >= max_blocks %d so not scaling out", prefix, active_blocks, max_blocks) - # Case 2b - else: - logger.debug("%s Strategy case 2b: active_blocks %d < max_blocks %d so scaling out", prefix, active_blocks, max_blocks) - excess_slots = math.ceil((active_tasks * parallelism) - active_slots) - excess_blocks = math.ceil(float(excess_slots) / (tasks_per_node * nodes_per_block)) - excess_blocks = min(excess_blocks, max_blocks - active_blocks) - logger.debug("%s Requesting %d more blocks", prefix, excess_blocks) - executor.scale_out_facade(excess_blocks) - - # Case 4a - # Tasks but no slots - elif active_slots == 0 and active_tasks > 0: - logger.debug("%s Strategy case 4a: No active slots but some active tasks - could scale out by a single block", prefix) - - if active_blocks < max_blocks: - logger.debug("%s Requesting single block", prefix) - - executor.scale_out_facade(1) - else: - logger.debug("%s Not requesting any blocks, because at maxblocks already", prefix) - - # Case 4b - # More slots than tasks - elif active_slots > 0 and active_slots > active_tasks: - logger.debug("%s Strategy case 4b: more slots than tasks", prefix) - if strategy_type == 'htex': - # Scale in for htex - if isinstance(executor, HighThroughputExecutor): - if active_blocks > min_blocks: - excess_slots = math.floor(active_slots - (active_tasks * parallelism)) - excess_blocks = math.floor(float(excess_slots) / (tasks_per_node * nodes_per_block)) - excess_blocks = min(excess_blocks, active_blocks - min_blocks) - logger.debug("%s Requesting scaling in by %d blocks with idle time %.1fs", prefix, excess_blocks, self.max_idletime) - executor.scale_in_facade(excess_blocks, max_idletime=self.max_idletime) - else: - logger.error("%s This strategy only supports scaling in for HighThroughputExecutor - taking no action", prefix) - else: - logger.debug("%s This strategy does not support scaling in", prefix) - - # Case 3 - # tasks ~ slots - else: - logger.debug("%s Strategy case 3: no changes necessary to current block load", prefix) diff --git a/parsl/parsl/launchers/__init__.py b/parsl/parsl/launchers/__init__.py deleted file mode 100644 index 155ed556..00000000 --- a/parsl/parsl/launchers/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -from parsl.launchers.launchers import ( - AprunLauncher, - GnuParallelLauncher, - JsrunLauncher, - MpiExecLauncher, - MpiRunLauncher, - SimpleLauncher, - SingleNodeLauncher, - SrunLauncher, - SrunMPILauncher, - WrappedLauncher, -) - -__all__ = ['SimpleLauncher', - 'WrappedLauncher', - 'SingleNodeLauncher', - 'SrunLauncher', - 'AprunLauncher', - 'SrunMPILauncher', - 'JsrunLauncher', - 'GnuParallelLauncher', - 'MpiExecLauncher', - 'MpiRunLauncher', - 'WrappedLauncher'] diff --git a/parsl/parsl/launchers/base.py b/parsl/parsl/launchers/base.py deleted file mode 100644 index a4ee7e52..00000000 --- a/parsl/parsl/launchers/base.py +++ /dev/null @@ -1,17 +0,0 @@ -from abc import ABCMeta, abstractmethod - -from parsl.utils import RepresentationMixin - - -class Launcher(RepresentationMixin, metaclass=ABCMeta): - """Launchers are basically wrappers for user submitted scripts as they - are submitted to a specific execution resource. - """ - def __init__(self, debug: bool = True): - self.debug = debug - - @abstractmethod - def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: - """ Wraps the command with the Launcher calls. - """ - pass diff --git a/parsl/parsl/launchers/errors.py b/parsl/parsl/launchers/errors.py deleted file mode 100644 index 2882a85a..00000000 --- a/parsl/parsl/launchers/errors.py +++ /dev/null @@ -1,13 +0,0 @@ -from parsl.launchers.base import Launcher -from parsl.providers.errors import ExecutionProviderException - - -class BadLauncher(ExecutionProviderException, TypeError): - """Error raised when an object of inappropriate type is supplied as a Launcher - """ - - def __init__(self, launcher: Launcher): - self.launcher = launcher - - def __str__(self) -> str: - return f"Bad Launcher provided: {self.launcher}, expecting a parsl.launcher.launcher.Launcher" diff --git a/parsl/parsl/launchers/launchers.py b/parsl/parsl/launchers/launchers.py deleted file mode 100644 index cbef89d9..00000000 --- a/parsl/parsl/launchers/launchers.py +++ /dev/null @@ -1,574 +0,0 @@ -import logging -import platform -import sys -import textwrap - -from parsl.launchers.base import Launcher - -logger = logging.getLogger(__name__) - - -class SimpleLauncher(Launcher): - """ Does no wrapping. Just returns the command as-is - """ - def __init__(self, debug: bool = True) -> None: - super().__init__(debug=debug) - - def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: - - if nodes_per_block > 1: - logger.warning('Simple Launcher only supports single node per block. ' - f'Requested nodes: {nodes_per_block}. ' - 'You may be getting fewer workers than expected') - - return command - - -class WrappedLauncher(Launcher): - """Wraps the command by prepending commands before a user's command - - As an example, the wrapped launcher can be used to launch a command - inside a docker container by prepending the proper docker invocation""" - - def __init__(self, prepend: str, debug: bool = True): - """ - Args: - prepend (str): Command to use before the launcher (e.g., ``time``) - """ - super().__init__(debug=debug) - self.prepend = prepend - - def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: - if tasks_per_node > 1: - logger.warning('WrappedLauncher ignores the number of tasks per node. ' - 'You may be getting fewer workers than expected') - if nodes_per_block > 1: - logger.warning('WrappedLauncher ignores the number of nodes per block. ' - 'You may be getting fewer workers than expected') - return "{0} {1}".format(self.prepend, command) - - -class SingleNodeLauncher(Launcher): - """ Worker launcher that wraps the user's command with the framework to - launch multiple command invocations in parallel. This wrapper sets the - bash env variable CORES to the number of cores on the machine. By setting - task_blocks to an integer or to a bash expression the number of invocations - of the command to be launched can be controlled. - """ - - def __init__(self, debug: bool = True, fail_on_any: bool = False): - super().__init__(debug=debug) - self.fail_on_any = fail_on_any - - def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: - """ - Args: - - command (str): The command string to be launched - - fail_on_any: If True, return a nonzero exit code if any worker failed, otherwise zero; - if False, return a nonzero exit code if all workers failed, otherwise zero. - - script_dir (str): Path to the submit script directory - - """ - task_blocks = tasks_per_node * nodes_per_block - fail_on_any_num = int(self.fail_on_any) - debug_num = int(self.debug) - - if platform.system() == 'Windows': - x = textwrap.dedent(f''' - $ErrorActionPreference = "Stop" - - $env:CORES = [Environment]::ProcessorCount - - if ("{debug_num}" -eq "1") {{ Write-Host "Found cores : $env:CORES" }} - - $WORKERCOUNT = {task_blocks} - $FAILONANY = {fail_on_any_num} - $processes = @() - - for ($COUNT = 1; $COUNT -le $WORKERCOUNT; $COUNT++) {{ - if ("{debug_num}" -eq "1") {{ Write-Host "Launching worker: $COUNT" }} - $proc = Start-Process -FilePath "{sys.executable}" -ArgumentList "{command} $COUNT" -NoNewWindow -PassThru - $processes += $proc - }} - - $ALLFAILED = 1 - $ANYFAILED = 0 - - foreach ($proc in $processes) {{ - $proc.WaitForExit() - if ($proc.ExitCode -ne 0) {{ - $ANYFAILED = 1 - }} else {{ - $ALLFAILED = 0 - }} - }} - - if ("{debug_num}" -eq "1") {{ Write-Host "All workers done" }} - - if ($FAILONANY -eq 1) {{ - exit $ANYFAILED - }} else {{ - exit $ALLFAILED - }} - ''') - else: - x = textwrap.dedent(f''' - set -e - export CORES=$(getconf _NPROCESSORS_ONLN) - [[ "{debug_num}" == "1" ]] && echo "Found cores : $CORES" - WORKERCOUNT={task_blocks} - FAILONANY={fail_on_any_num} - PIDS="" - - CMD() {{ - {command} - }} - for COUNT in $(seq 1 1 $WORKERCOUNT); do - [[ "{debug_num}" == "1" ]] && echo "Launching worker: $COUNT" - CMD $COUNT & - PIDS="$PIDS $!" - done - - ALLFAILED=1 - ANYFAILED=0 - for PID in $PIDS ; do - wait $PID - if [ "$?" != "0" ]; then - ANYFAILED=1 - else - ALLFAILED=0 - fi - done - - [[ "{debug_num}" == "1" ]] && echo "All workers done" - if [ "$FAILONANY" == "1" ]; then - exit $ANYFAILED - else - exit $ALLFAILED - fi - ''') - return x - - -class GnuParallelLauncher(Launcher): - """ Worker launcher that wraps the user's command with the framework to - launch multiple command invocations via GNU parallel sshlogin. - - This wrapper sets the bash env variable CORES to the number of cores on the - machine. - - This launcher makes the following assumptions: - - - GNU parallel is installed and can be located in $PATH - - Paswordless SSH login is configured between the controller node and the - target nodes. - - The provider makes available the $PBS_NODEFILE environment variable - """ - def __init__(self, debug: bool = True): - super().__init__(debug=debug) - - def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: - """ - Args: - - command (str): The command string to be launched - - script_dir (str): Path to the submit script directory - - """ - task_blocks = tasks_per_node * nodes_per_block - debug_num = int(self.debug) - - x = '''set -e -export CORES=$(getconf _NPROCESSORS_ONLN) -[[ "{debug}" == "1" ]] && echo "Found cores : $CORES" -WORKERCOUNT={task_blocks} -SCRIPT_DIR="{script_dir}" -[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR="" - -# Deduplicate the nodefile -SSHLOGINFILE="$JOBNAME.nodes" -if [ -z "$PBS_NODEFILE" ]; then - echo "localhost" > $SSHLOGINFILE -else - sort -u $PBS_NODEFILE > $SSHLOGINFILE -fi - -cat << PARALLEL_CMD_EOF > ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh -{command} -PARALLEL_CMD_EOF -chmod u+x ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh - -#file to contain the commands to parallel -PFILE=${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_${{JOBNAME}}.sh.parallel - -# Truncate the file -cp /dev/null $PFILE - -for COUNT in $(seq 1 1 $WORKERCOUNT) -do - echo "sh ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh" >> $PFILE -done - -parallel --env _ --joblog "$JOBNAME.sh.parallel.log" \ - --sshloginfile $SSHLOGINFILE --jobs {tasks_per_node} < $PFILE - -[[ "{debug}" == "1" ]] && echo "All workers done" -'''.format(command=command, - tasks_per_node=tasks_per_node, - task_blocks=task_blocks, - debug=debug_num, - script_dir=script_dir) - return x - - -class MpiExecLauncher(Launcher): - """ Worker launcher that wraps the user's command with the framework to - launch multiple command invocations via mpiexec. - - This wrapper sets the bash env variable CORES to the number of cores on the - machine. - - This launcher makes the following assumptions: - - mpiexec is installed and can be located in $PATH - - The provider makes available the $PBS_NODEFILE environment variable - """ - def __init__(self, debug: bool = True, bind_cmd: str = '--bind-to', overrides: str = ''): - """ - - Parameters - ---------- - bind_cmd: str - Name of the argument for binding ranks to CPUs - - overrides: str - Additional arguments to add to the invocation - - """ - super().__init__(debug=debug) - self.bind_cmd = bind_cmd - self.overrides = overrides - - def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: - """ - Args: - - command (str): The command string to be launched - - script_dir (str): Path to the submit script directory - - """ - task_blocks = tasks_per_node * nodes_per_block - debug_num = int(self.debug) - - x = '''set -e -export CORES=$(getconf _NPROCESSORS_ONLN) -[[ "{debug}" == "1" ]] && echo "Found cores : $CORES" -WORKERCOUNT={task_blocks} -SCRIPT_DIR="{script_dir}" -[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR="" - -# Deduplicate the nodefile -HOSTFILE="$JOBNAME.nodes" -if [ -z "$PBS_NODEFILE" ]; then - echo "localhost" > $HOSTFILE -else - sort -u $PBS_NODEFILE > $HOSTFILE -fi - -cat << MPIEXEC_EOF > ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh -{command} -MPIEXEC_EOF -chmod u+x ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh - -mpiexec {bind_cmd} none {overrides} -n $WORKERCOUNT --hostfile $HOSTFILE /usr/bin/sh ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh - -[[ "{debug}" == "1" ]] && echo "All workers done" -'''.format(command=command, - task_blocks=task_blocks, - debug=debug_num, - overrides=self.overrides, - bind_cmd=self.bind_cmd, - script_dir=script_dir) - return x - - -class MpiRunLauncher(Launcher): - """ Worker launcher that wraps the user's command with the framework to - launch multiple command invocations via mpirun. - - This wrapper sets the bash env variable CORES to the number of cores on the - machine. - - This launcher makes the following assumptions: - - mpirun is installed and can be located in $PATH - - The provider makes available the $PBS_NODEFILE environment variable - """ - def __init__(self, debug: bool = True, bash_location: str = '/bin/bash', overrides: str = ''): - super().__init__(debug=debug) - self.bash_location = bash_location - self.overrides = overrides - - def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: - """ - Args: - - command (str): The command string to be launched - - script_dir (str): Path to the submit script directory - - """ - task_blocks = tasks_per_node * nodes_per_block - debug_num = int(self.debug) - - x = '''set -e -export CORES=$(getconf _NPROCESSORS_ONLN) -[[ "{debug}" == "1" ]] && echo "Found cores : $CORES" -WORKERCOUNT={task_blocks} -SCRIPT_DIR="{script_dir}" -[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR="" - -cat << MPIRUN_EOF > ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh -{command} -MPIRUN_EOF -chmod u+x ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh - -mpirun -np $WORKERCOUNT {overrides} {bash_location} ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh - -[[ "{debug}" == "1" ]] && echo "All workers done" -'''.format(command=command, - task_blocks=task_blocks, - overrides=self.overrides, - bash_location=self.bash_location, - debug=debug_num, - script_dir=script_dir) - return x - - -class SrunLauncher(Launcher): - """ Worker launcher that wraps the user's command with the SRUN launch framework - to launch multiple cmd invocations in parallel on a single job allocation. - """ - - def __init__(self, debug: bool = True, overrides: str = ''): - """ - Parameters - ---------- - - overrides: str - This string will be passed to the srun launcher. Default: '' - """ - - super().__init__(debug=debug) - self.overrides = overrides - - def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: - """ - Args: - - command (str): The command string to be launched - - script_dir (str): Path to the submit script directory - - """ - task_blocks = tasks_per_node * nodes_per_block - debug_num = int(self.debug) - - x = '''set -e -export CORES=$SLURM_CPUS_ON_NODE -export NODES=$SLURM_JOB_NUM_NODES - -[[ "{debug}" == "1" ]] && echo "Found cores : $CORES" -[[ "{debug}" == "1" ]] && echo "Found nodes : $NODES" -WORKERCOUNT={task_blocks} -SCRIPT_DIR="{script_dir}" -[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR="" - -cat << SLURM_EOF > ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$SLURM_JOB_NAME.sh -{command} -SLURM_EOF -chmod a+x ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$SLURM_JOB_NAME.sh - -srun --ntasks {task_blocks} -l {overrides} bash ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$SLURM_JOB_NAME.sh - -[[ "{debug}" == "1" ]] && echo "Done" -'''.format(command=command, - task_blocks=task_blocks, - overrides=self.overrides, - debug=debug_num, - script_dir=script_dir) - return x - - -class SrunMPILauncher(Launcher): - """Launches as many workers as MPI tasks to be executed concurrently within a block. - - Use this launcher instead of SrunLauncher if each block will execute multiple MPI applications - at the same time. Workers should be launched with independent Srun calls so as to setup the - environment for MPI application launch. - """ - def __init__(self, debug: bool = True, overrides: str = ''): - """ - Parameters - ---------- - - overrides: str - This string will be passed to the launcher. Default: '' - """ - - super().__init__(debug=debug) - self.overrides = overrides - - def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: - """ - Args: - - command (str): The command string to be launched - - script_dir (str): Path to the submit script directory - - """ - task_blocks = tasks_per_node * nodes_per_block - debug_num = int(self.debug) - - x = '''set -e -export CORES=$SLURM_CPUS_ON_NODE -export NODES=$SLURM_JOB_NUM_NODES - -[[ "{debug}" == "1" ]] && echo "Found cores : $CORES" -[[ "{debug}" == "1" ]] && echo "Found nodes : $NODES" -WORKERCOUNT={task_blocks} -SCRIPT_DIR="{script_dir}" -[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR="" - -cat << SLURM_EOF > ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$SLURM_JOB_NAME.sh -{command} -SLURM_EOF -chmod a+x ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$SLURM_JOB_NAME.sh - -TASKBLOCKS={task_blocks} - -# If there are more taskblocks to be launched than nodes use -if (( "$TASKBLOCKS" > "$NODES" )) -then - [[ "{debug}" == "1" ]] && echo "TaskBlocks:$TASKBLOCKS > Nodes:$NODES" - CORES_PER_BLOCK=$(($NODES * $CORES / $TASKBLOCKS)) - for blk in $(seq 1 1 $TASKBLOCKS): - do - srun --ntasks $CORES_PER_BLOCK -l {overrides} bash ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$SLURM_JOB_NAME.sh & - done - wait -else - # A Task block could be integer multiples of Nodes - [[ "{debug}" == "1" ]] && echo "TaskBlocks:$TASKBLOCKS <= Nodes:$NODES" - NODES_PER_BLOCK=$(( $NODES / $TASKBLOCKS )) - for blk in $(seq 1 1 $TASKBLOCKS): - do - srun --exclusive --nodes $NODES_PER_BLOCK -l {overrides} bash ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$SLURM_JOB_NAME.sh & - done - wait - -fi - - -[[ "{debug}" == "1" ]] && echo "Done" -'''.format(command=command, - task_blocks=task_blocks, - overrides=self.overrides, - debug=debug_num, - script_dir=script_dir) - return x - - -class AprunLauncher(Launcher): - """ Worker launcher that wraps the user's command with the Aprun launch framework - to launch multiple cmd invocations in parallel on a single job allocation - - """ - def __init__(self, debug: bool = True, overrides: str = ''): - """ - Parameters - ---------- - - overrides: str - This string will be passed to the aprun launcher. Default: '' - """ - super().__init__(debug=debug) - self.overrides = overrides - - def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: - """ - Args: - - command (str): The command string to be launched - - tasks_per_node (int) : Workers to launch per node - - nodes_per_block (int) : Number of nodes in a block - - script_dir (str): Path to the submit script directory - - """ - - tasks_per_block = tasks_per_node * nodes_per_block - debug_num = int(self.debug) - - x = '''set -e -WORKERCOUNT={tasks_per_block} -SCRIPT_DIR="{script_dir}" -[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR="" - -cat << APRUN_EOF > ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh -{command} -APRUN_EOF -chmod a+x ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh - -aprun -n {tasks_per_block} -N {tasks_per_node} {overrides} /bin/bash ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh & -wait - -[[ "{debug}" == "1" ]] && echo "Done" -'''.format(command=command, - tasks_per_block=tasks_per_block, - tasks_per_node=tasks_per_node, - overrides=self.overrides, - debug=debug_num, - script_dir=script_dir) - return x - - -class JsrunLauncher(Launcher): - """ Worker launcher that wraps the user's command with the Jsrun launch framework - to launch multiple cmd invocations in parallel on a single job allocation - - """ - def __init__(self, debug: bool = True, overrides: str = ''): - """ - Parameters - ---------- - - overrides: str - This string will be passed to the JSrun launcher. Default: '' - """ - super().__init__(debug=debug) - self.overrides = overrides - - def __call__(self, command: str, tasks_per_node: int, nodes_per_block: int, script_dir: str = "") -> str: - """ - Args: - - command (str): The command string to be launched - - tasks_per_node (int) : Workers to launch per node - - nodes_per_block (int) : Number of nodes in a block - - script_dir (str): Path to the submit script directory - - """ - - tasks_per_block = tasks_per_node * nodes_per_block - debug_num = int(self.debug) - - x = '''set -e -WORKERCOUNT={tasks_per_block} -SCRIPT_DIR="{script_dir}" -[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR="" - -cat << JSRUN_EOF > ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh -{command} -JSRUN_EOF -chmod a+x ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh - -jsrun -n {tasks_per_block} -r {tasks_per_node} {overrides} /bin/bash ${{SCRIPT_DIR:+$SCRIPT_DIR/}}cmd_$JOBNAME.sh & -wait - -[[ "{debug}" == "1" ]] && echo "Done" -'''.format(command=command, - tasks_per_block=tasks_per_block, - tasks_per_node=tasks_per_node, - overrides=self.overrides, - debug=debug_num, - script_dir=script_dir) - return x diff --git a/parsl/parsl/log_utils.py b/parsl/parsl/log_utils.py deleted file mode 100644 index f9a93afb..00000000 --- a/parsl/parsl/log_utils.py +++ /dev/null @@ -1,99 +0,0 @@ -"""This module contains helpers for configuring logging. By default, -`set_file_logger` is invoked by the DataFlowKernel initializer to log -parsl messages to parsl.log. - -`set_stream_logger` which by default logs to stderr, can be useful -when working in a Jupyter notebook. -""" -import io -import logging -from typing import Callable, Optional - -import typeguard - -DEFAULT_FORMAT = ( - "%(created)f %(asctime)s %(processName)s-%(process)d " - "%(threadName)s-%(thread)d %(name)s:%(lineno)d %(funcName)s %(levelname)s: " - "%(message)s" -) - - -@typeguard.typechecked -def set_stream_logger(name: str = 'parsl', - level: int = logging.DEBUG, - format_string: Optional[str] = None, - stream: Optional[io.TextIOBase] = None) -> Callable[[], None]: - """Add a stream log handler. - - Args: - - name (string) : Set the logger name. - - level (logging.LEVEL) : Set to logging.DEBUG by default. - - format_string (string) : Set to None by default. - - stream (io.TextIOWrapper) : Specify sys.stdout or sys.stderr for stream. - If not specified, the default stream for logging.StreamHandler is used. - """ - if format_string is None: - # format_string = "%(asctime)s %(name)s [%(levelname)s] Thread:%(thread)d %(message)s" - format_string = "%(asctime)s %(name)s:%(lineno)d [%(levelname)s] %(message)s" - - logger = logging.getLogger(name) - logger.setLevel(logging.DEBUG) - handler = logging.StreamHandler(stream) - handler.setLevel(level) - formatter = logging.Formatter(format_string, datefmt='%Y-%m-%d %H:%M:%S') - handler.setFormatter(formatter) - logger.addHandler(handler) - - # Concurrent.futures errors are also of interest, as exceptions - # which propagate out of the top of a callback are logged this way - # and then discarded. (see #240) - futures_logger = logging.getLogger("concurrent.futures") - futures_logger.addHandler(handler) - - def unregister_callback(): - logger.removeHandler(handler) - futures_logger.removeHandler(handler) - - return unregister_callback - - -@typeguard.typechecked -def set_file_logger(filename: str, - name: str = 'parsl', - level: int = logging.DEBUG, - format_string: Optional[str] = None) -> Callable[[], None]: - """Add a file log handler. - - Args: - - filename (string): Name of the file to write logs to - - name (string): Logger name - - level (logging.LEVEL): Set the logging level. - - format_string (string): Set the format string - - Returns: - - a callable which, when invoked, will reverse the log handler - attachments made by this call. (compare to how object based pieces - of parsl model this as a close/shutdown/cleanup method on the - object)) - """ - if format_string is None: - format_string = DEFAULT_FORMAT - - logger = logging.getLogger(name) - logger.setLevel(logging.DEBUG) - handler = logging.FileHandler(filename) - handler.setLevel(level) - formatter = logging.Formatter(format_string, datefmt='%Y-%m-%d %H:%M:%S') - handler.setFormatter(formatter) - logger.addHandler(handler) - - # see note in set_stream_logger for notes about logging - # concurrent.futures - futures_logger = logging.getLogger("concurrent.futures") - futures_logger.addHandler(handler) - - def unregister_callback(): - logger.removeHandler(handler) - futures_logger.removeHandler(handler) - - return unregister_callback diff --git a/parsl/parsl/logconfigs/__init__.py b/parsl/parsl/logconfigs/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/logconfigs/base.py b/parsl/parsl/logconfigs/base.py deleted file mode 100644 index de8788a9..00000000 --- a/parsl/parsl/logconfigs/base.py +++ /dev/null @@ -1,118 +0,0 @@ -from __future__ import annotations - -import abc -import logging -import pathlib -import threading -import uuid -from collections.abc import Callable - -logger = logging.getLogger(__name__) - - -class LogConfig(abc.ABC): - """Implementations of this class can initialize Parsl logging. - - Parsl logging is built around Python's `logging` system, with - a Parsl-provided configuration system that allows multiple - configurations to be initialized and deinitialized within the - same process. - - Configurations which might be shared between processes should expect to - be pickled/unpickled multiple times as they move around a Parsl - distributed system. - """ - - def __init__(self) -> None: - self.uuid = uuid.uuid4() - - @abc.abstractmethod - def initialize_logging(self, *, log_dir: pathlib.Path, log_name: str) -> Callable[[], None]: - """Initialize logging in current process. - - This should be implemented by users wanting to define their own log - configuration policies. - - This should be called by Parsl components to initialize logging - according to a user supplied policy, rather than a hard-coded log - policy. - - This should return a callback to uninitialize the logging initialized - by this call. - """ - ... - - -class _ConfigReference: - def __init__(self, uninit_callback: Callable) -> None: - self.uninit_callback = uninit_callback - self.count: int = 1 - - -# This holds the collection of initialized configurations for the current -# Python interpreter. It is scoped at the interpreter level because that is -# the scope of Python `logging` module log configuration. -_initialized_log_contexts: dict[uuid.UUID, _ConfigReference] = {} -_initialized_log_context_lock: threading.Lock = threading.Lock() - - -def oneshot_initialize_logging(*, log_config: LogConfig, log_dir: pathlib.Path, log_name: str) -> None: - """Initialized the LogConfig if it is not currently initialized. - - This is scoped per-interpreter, the same as the global state for the - Python logging module. - - A first oneshot_initialize_logging for any given LogConfig will call - the initialize_logging method of that config. Subsequent calls will - be reference counted, but not cause a reinitialization. - - Calling the callback will decrease the reference count, and when that - count reaches zero, the LogConfig will be uninitialized via its own - callback. - - The API syntax mimics the syntax of the LogConfig abstract class, with - these reference counting semantics added on top. - - The intention is to allow a log configuration to be initialized by - multiple pieces of Parsl without duplicating logs: for example, when - using the thread pool executor, a task does not need the global log - configuration to be initialized (again) because the task runs in the - same process as the DFK; but in, for example, the Work Queue executor - (at least now), there is no per-worker log configuration, and the task - *does* need to initialize logging. This reference counting is the - mechanism by which that different behaviour can occur. - - There is an ambiguity on the use of log_name and log_dir across multiple - initializations: the name and directory of the first initialization - will be used and the name and directory of subsequent initializations - will be ignored. - """ - with _initialized_log_context_lock: - if log_config.uuid in _initialized_log_contexts: - _initialized_log_contexts[log_config.uuid].count += 1 - logger.debug('Configuration %r already initialized, now with %s references', - log_config, - _initialized_log_contexts[log_config.uuid].count) - else: - logger.debug('Initializing %r', log_config) - _initialized_log_contexts[log_config.uuid] = _ConfigReference( - log_config.initialize_logging(log_dir=log_dir, log_name=log_name) - ) - assert callable(_initialized_log_contexts[log_config.uuid].uninit_callback), ( - f'Log config {log_config} should have returned a callable on initialization' - ) - logger.debug('Initialized %r', log_config) - - assert log_config.uuid in _initialized_log_contexts - - -def oneshot_uninitialize_logging(*, log_config: LogConfig) -> None: - with _initialized_log_context_lock: - assert log_config.uuid in _initialized_log_contexts, f"log context for {log_config} not found, perhaps too many uninitializations" - assert _initialized_log_contexts[log_config.uuid].count >= 1, \ - f"log context for {log_config} has bad reference count {_initialized_log_contexts[log_config.uuid].count}" - _initialized_log_contexts[log_config.uuid].count -= 1 - if _initialized_log_contexts[log_config.uuid].count < 1: - logger.debug('All references removed for %r', log_config) - _initialized_log_contexts[log_config.uuid].uninit_callback() - del _initialized_log_contexts[log_config.uuid] diff --git a/parsl/parsl/logconfigs/file.py b/parsl/parsl/logconfigs/file.py deleted file mode 100644 index c3bf8bb5..00000000 --- a/parsl/parsl/logconfigs/file.py +++ /dev/null @@ -1,50 +0,0 @@ -import logging -import pathlib -from typing import Callable - -from parsl.log_utils import DEFAULT_FORMAT -from parsl.logconfigs.base import LogConfig - - -class FileLogging(LogConfig): - """A log configuration which configures traditional logging to files.""" - - def __init__(self, *, format_string: str = DEFAULT_FORMAT, level: int = logging.DEBUG): - super().__init__() - self.format_string = format_string - self.level = level - - def initialize_logging(self, *, log_dir: pathlib.Path, log_name: str) -> Callable[[], None]: - - log_dir.mkdir(exist_ok=True, parents=True) - filename = log_dir.joinpath(log_name + ".log") - - handler = logging.FileHandler(filename) - handler.setLevel(self.level) - formatter = logging.Formatter(self.format_string, datefmt='%Y-%m-%d %H:%M:%S') - handler.setFormatter(formatter) - - logger = logging.getLogger("parsl") - - if logger.level == logging.NOTSET: - logger.setLevel(self.level) - else: - logger.setLevel(min(logger.level, self.level)) - - logger.addHandler(handler) - - # Concurrent.futures errors are also of interest, as exceptions - # which propagate out of the top of a callback are logged this way - # and then discarded. (see #240) - futures_logger = logging.getLogger("concurrent.futures") - if futures_logger.level == logging.NOTSET: - futures_logger.setLevel(self.level) - else: - futures_logger.setLevel(min(futures_logger.level, self.level)) - futures_logger.addHandler(handler) - - def unregister_callback() -> None: - logger.removeHandler(handler) - futures_logger.removeHandler(handler) - - return unregister_callback diff --git a/parsl/parsl/logconfigs/json.py b/parsl/parsl/logconfigs/json.py deleted file mode 100644 index 860e2543..00000000 --- a/parsl/parsl/logconfigs/json.py +++ /dev/null @@ -1,74 +0,0 @@ -import json -import logging -import pathlib -from typing import Any, Callable - -from parsl.logconfigs.base import LogConfig - - -class JSONLogging(LogConfig): - """A log configuration which logs as JSON objects. - - This configuration will write each log event as a JSON object into a - log file, one per line. - """ - - def __init__(self, *, level: int = logging.DEBUG): - super().__init__() - self.level = level - - def initialize_logging(self, *, log_dir: pathlib.Path, log_name: str) -> Callable[[], None]: - log_dir.mkdir(exist_ok=True, parents=True) - - filename = log_dir.joinpath(log_name + ".jsonlog") - - logger = logging.getLogger("") - if logger.level == logging.NOTSET: - logger.setLevel(self.level) - else: - logger.setLevel(min(logger.level, self.level)) - - handler = JSONHandler(filename) - handler.setLevel(self.level) - formatter = logging.Formatter("%(message)s", datefmt='%Y-%m-%d %H:%M:%S') - handler.setFormatter(formatter) - logger.addHandler(handler) - - def unregister_callback() -> None: - logger.removeHandler(handler) - - return unregister_callback - - -class JSONHandler(logging.Handler): - def __init__(self, filename: pathlib.Path, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) - self.f = open(filename, "w") - - def emit(self, record: logging.LogRecord) -> None: - - d: dict[str, object] = { - "created": record.created, - "process": record.process, - "processName": record.processName, - "threadName": record.threadName, - "thread": record.thread, - "name": record.name, - "lineno": record.lineno, - "funcname": record.funcName, - "levelname": record.levelname, - "msg": record.msg, - "formatted": self.format(record) - } - - for (k, v) in record.__dict__.items(): - if k.startswith("parsl."): - try: - d[k] = str(v) - except Exception as e: - # present the exception rather than omitting - d[k] = f"UNREPRESENTABLE: {e!r}" - - json.dump(d, fp=self.f) - print("", file=self.f) - self.f.flush() diff --git a/parsl/parsl/logconfigs/noop.py b/parsl/parsl/logconfigs/noop.py deleted file mode 100644 index dc678e17..00000000 --- a/parsl/parsl/logconfigs/noop.py +++ /dev/null @@ -1,20 +0,0 @@ -import pathlib -from typing import Callable - -from parsl.logconfigs.base import LogConfig - - -class NoopLogging(LogConfig): - """A log configuration which doesn't configure any logging. - - This is intended to help with benchmarking, but can also be used to - configure an environment with no logging from any LogConfig aware - component. - """ - - def initialize_logging(self, *, log_dir: pathlib.Path, log_name: str) -> Callable[[], None]: - - def unregister_callback() -> None: - pass - - return unregister_callback diff --git a/parsl/parsl/monitoring/__init__.py b/parsl/parsl/monitoring/__init__.py deleted file mode 100644 index 4f72c82e..00000000 --- a/parsl/parsl/monitoring/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from parsl.monitoring.monitoring import MonitoringHub - -__all__ = ['MonitoringHub'] diff --git a/parsl/parsl/monitoring/db_manager.py b/parsl/parsl/monitoring/db_manager.py deleted file mode 100644 index 12921add..00000000 --- a/parsl/parsl/monitoring/db_manager.py +++ /dev/null @@ -1,724 +0,0 @@ -import datetime -import logging -import multiprocessing.queues as mpq -import multiprocessing.synchronize as mpe -import os -import pathlib -import queue -import threading -import time -from typing import Any, Dict, List, Optional, Set, Tuple, TypeVar, cast - -import typeguard - -from parsl.dataflow.states import States -from parsl.errors import OptionalModuleMissing -from parsl.log_utils import set_file_logger -from parsl.logconfigs.base import LogConfig, oneshot_initialize_logging -from parsl.monitoring.message_type import MessageType -from parsl.monitoring.types import MonitoringMessage, TaggedMonitoringMessage -from parsl.process_loggers import wrap_with_logs -from parsl.utils import setproctitle - -logger = logging.getLogger(__name__) - -X = TypeVar('X') - -try: - import sqlalchemy as sa - from sqlalchemy import ( - BigInteger, - Boolean, - Column, - DateTime, - Float, - Integer, - PrimaryKeyConstraint, - Table, - Text, - ) - from sqlalchemy.orm import Mapper, declarative_base, mapperlib, sessionmaker -except ImportError: - _sqlalchemy_enabled = False -else: - _sqlalchemy_enabled = True - - -WORKFLOW = 'workflow' # Workflow table includes workflow metadata -TASK = 'task' # Task table includes task metadata -TRY = 'try' # Try table includes information about each attempt to run a task -STATUS = 'status' # Status table includes task status -RESOURCE = 'resource' # Resource table includes task resource utilization -NODE = 'node' # Node table include node info -BLOCK = 'block' # Block table include the status for block polling - - -class Database: - - if not _sqlalchemy_enabled: - raise OptionalModuleMissing(['sqlalchemy'], - ("Monitoring requires the sqlalchemy library." - " Install monitoring dependencies with: pip install 'parsl[monitoring]'")) - Base = declarative_base() - - def __init__(self, - url: str = 'sqlite:///runinfomonitoring.db', - ): - - self.eng = sa.create_engine(url) - self.meta = self.Base.metadata - - # TODO: this code wants a read lock on the sqlite3 database, and fails if it cannot - # - for example, if someone else is querying the database at the point that the - # monitoring system is initialized. See PR #1917 for related locked-for-read fixes - # elsewhere in this file. - self.meta.create_all(self.eng) - - self.meta.reflect(bind=self.eng) - - Session = sessionmaker(bind=self.eng) - self.session = Session() - - def _get_mapper(self, table_obj: Table) -> Mapper: - all_mappers: Set[Mapper] = set() - for mapper_registry in mapperlib._all_registries(): - all_mappers.update(mapper_registry.mappers) - mapper_gen = ( - mapper for mapper in all_mappers - if table_obj in mapper.tables - ) - try: - mapper = next(mapper_gen) - second_mapper = next(mapper_gen, False) - except StopIteration: - raise ValueError(f"Could not get mapper for table {table_obj}") - - if second_mapper: - raise ValueError(f"Multiple mappers for table {table_obj}") - return mapper - - def update(self, *, table: str, columns: List[str], messages: List[MonitoringMessage]) -> None: - table_obj = self.meta.tables[table] - mappings = self._generate_mappings(table_obj, columns=columns, - messages=messages) - mapper = self._get_mapper(table_obj) - self.session.bulk_update_mappings(mapper, mappings) - self.session.commit() - - def insert(self, *, table: str, messages: List[MonitoringMessage]) -> None: - table_obj = self.meta.tables[table] - mappings = self._generate_mappings(table_obj, messages=messages) - mapper = self._get_mapper(table_obj) - self.session.bulk_insert_mappings(mapper, mappings) - self.session.commit() - - def rollback(self) -> None: - self.session.rollback() - - def _generate_mappings( - self, - table: Table, - columns: Optional[List[str]] = None, - messages: List[MonitoringMessage] = [], - ) -> List[Dict[str, Any]]: - - mappings = [] - for msg in messages: - m = {} - if columns is None: - columns = table.c.keys() - for column in columns: - m[column] = msg.get(column, None) - mappings.append(m) - return mappings - - class Workflow(Base): - __tablename__ = WORKFLOW - run_id = Column(Text, nullable=False, primary_key=True) - workflow_name = Column(Text, nullable=True) - workflow_version = Column(Text, nullable=True) - time_began = Column(DateTime, nullable=False) - time_completed = Column(DateTime, nullable=True) - host = Column(Text, nullable=False) - user = Column(Text, nullable=False) - rundir = Column(Text, nullable=False) - tasks_failed_count = Column(Integer, nullable=False) - tasks_completed_count = Column(Integer, nullable=False) - - class Status(Base): - __tablename__ = STATUS - task_id = Column(Integer, nullable=False) - task_status_name = Column(Text, nullable=False) - timestamp = Column(DateTime, nullable=False) - run_id = Column(Text, sa.ForeignKey('workflow.run_id'), nullable=False) - try_id = Column('try_id', Integer, nullable=False) - __table_args__ = ( - PrimaryKeyConstraint('task_id', 'run_id', - 'task_status_name', 'timestamp'), - ) - - class Task(Base): - __tablename__ = TASK - task_id = Column('task_id', Integer, nullable=False) - run_id = Column('run_id', Text, nullable=False) - task_depends = Column('task_depends', Text, nullable=True) - task_func_name = Column('task_func_name', Text, nullable=False) - task_memoize = Column('task_memoize', Text, nullable=False) - task_hashsum = Column('task_hashsum', Text, nullable=True, index=True) - task_inputs = Column('task_inputs', Text, nullable=True) - task_outputs = Column('task_outputs', Text, nullable=True) - task_stdin = Column('task_stdin', Text, nullable=True) - task_stdout = Column('task_stdout', Text, nullable=True) - task_stderr = Column('task_stderr', Text, nullable=True) - - task_time_invoked = Column( - 'task_time_invoked', DateTime, nullable=True) - - task_time_returned = Column( - 'task_time_returned', DateTime, nullable=True) - - task_fail_count = Column('task_fail_count', Integer, nullable=False) - task_fail_cost = Column('task_fail_cost', Float, nullable=False) - - __table_args__ = ( - PrimaryKeyConstraint('task_id', 'run_id'), - ) - - class Try(Base): - __tablename__ = TRY - try_id = Column('try_id', Integer, nullable=False) - task_id = Column('task_id', Integer, nullable=False) - run_id = Column('run_id', Text, nullable=False) - - block_id = Column('block_id', Text, nullable=True) - hostname = Column('hostname', Text, nullable=True) - - task_executor = Column('task_executor', Text, nullable=False) - - task_try_time_launched = Column( - 'task_try_time_launched', DateTime, nullable=True) - - task_try_time_running = Column( - 'task_try_time_running', DateTime, nullable=True) - - task_try_time_returned = Column( - 'task_try_time_returned', DateTime, nullable=True) - - task_fail_history = Column('task_fail_history', Text, nullable=True) - - task_joins = Column('task_joins', Text, nullable=True) - - __table_args__ = ( - PrimaryKeyConstraint('try_id', 'task_id', 'run_id'), - ) - - class Node(Base): - __tablename__ = NODE - id = Column('id', Integer, nullable=False, primary_key=True, autoincrement=True) - run_id = Column('run_id', Text, nullable=False) - hostname = Column('hostname', Text, nullable=False) - uid = Column('uid', Text, nullable=False) - block_id = Column('block_id', Text, nullable=False) - cpu_count = Column('cpu_count', Integer, nullable=False) - total_memory = Column('total_memory', BigInteger, nullable=False) - active = Column('active', Boolean, nullable=False) - worker_count = Column('worker_count', Integer, nullable=False) - python_v = Column('python_v', Text, nullable=False) - timestamp = Column('timestamp', DateTime, nullable=False) - last_heartbeat = Column('last_heartbeat', DateTime, nullable=False) - - class Block(Base): - __tablename__ = BLOCK - run_id = Column('run_id', Text, nullable=False) - executor_label = Column('executor_label', Text, nullable=False) - block_id = Column('block_id', Text, nullable=False) - job_id = Column('job_id', Text, nullable=True) - timestamp = Column('timestamp', DateTime, nullable=False) - status = Column("status", Text, nullable=False) - __table_args__ = ( - PrimaryKeyConstraint('run_id', 'block_id', 'executor_label', 'timestamp'), - ) - - class Resource(Base): - __tablename__ = RESOURCE - try_id = Column('try_id', Integer, nullable=False) - task_id = Column('task_id', Integer, nullable=False) - run_id = Column('run_id', Text, sa.ForeignKey( - 'workflow.run_id'), nullable=False) - timestamp = Column('timestamp', DateTime, nullable=False) - resource_monitoring_interval = Column( - 'resource_monitoring_interval', Float, nullable=True) - psutil_process_pid = Column( - 'psutil_process_pid', Integer, nullable=True) - psutil_process_memory_percent = Column( - 'psutil_process_memory_percent', Float, nullable=True) - psutil_process_children_count = Column( - 'psutil_process_children_count', Float, nullable=True) - psutil_process_time_user = Column( - 'psutil_process_time_user', Float, nullable=True) - psutil_process_time_system = Column( - 'psutil_process_time_system', Float, nullable=True) - psutil_process_memory_virtual = Column( - 'psutil_process_memory_virtual', Float, nullable=True) - psutil_process_memory_resident = Column( - 'psutil_process_memory_resident', Float, nullable=True) - psutil_process_disk_read = Column( - 'psutil_process_disk_read', Float, nullable=True) - psutil_process_disk_write = Column( - 'psutil_process_disk_write', Float, nullable=True) - psutil_process_status = Column( - 'psutil_process_status', Text, nullable=True) - psutil_cpu_num = Column( - 'psutil_cpu_num', Text, nullable=True) - psutil_process_num_ctx_switches_voluntary = Column( - 'psutil_process_num_ctx_switches_voluntary', Float, nullable=True) - psutil_process_num_ctx_switches_involuntary = Column( - 'psutil_process_num_ctx_switches_involuntary', Float, nullable=True) - __table_args__ = ( - PrimaryKeyConstraint('try_id', 'task_id', 'run_id', 'timestamp'), - ) - - -class DatabaseManager: - def __init__(self, - *, - db_url: str = 'sqlite:///runinfo/monitoring.db', - run_dir: str = '.', - logging_level: int = logging.INFO, - log_config: Optional[LogConfig] = None, - batching_interval: float = 1, - batching_threshold: float = 99999, - exit_event: mpe.Event - ): - - self.workflow_end = False - self.workflow_start_message: Optional[MonitoringMessage] = None - self.run_dir = run_dir - - if log_config: - oneshot_initialize_logging(log_config=log_config, log_dir=pathlib.Path(self.run_dir), log_name="database_manager") - else: - os.makedirs(self.run_dir, exist_ok=True) - - set_file_logger(f"{self.run_dir}/database_manager.log", level=logging_level, - format_string="%(asctime)s.%(msecs)03d %(name)s:%(lineno)d [%(levelname)s] [%(threadName)s %(thread)d] %(message)s") - - logger.info("Initializing Database Manager process") - - self.db = Database(db_url) - self.batching_interval = batching_interval - self.batching_threshold = batching_threshold - - self.pending_priority_queue: queue.Queue[TaggedMonitoringMessage] = queue.Queue() - self.pending_node_queue: queue.Queue[MonitoringMessage] = queue.Queue() - self.pending_block_queue: queue.Queue[MonitoringMessage] = queue.Queue() - self.pending_resource_queue: queue.Queue[MonitoringMessage] = queue.Queue() - self.pending_worker_task_queue: queue.Queue[MonitoringMessage] = queue.Queue() - - self.external_exit_event = exit_event - - def start(self, - resource_queue: mpq.Queue) -> None: - - self._kill_event = threading.Event() - - self._resource_queue_pull_thread = threading.Thread(target=self._migrate_logs_to_internal, - args=( - resource_queue, self._kill_event,), - name="Monitoring-migrate-resource", - daemon=True, - ) - self._resource_queue_pull_thread.start() - - # The following three variables work together to maintain sets to - # track the tasks and tries that are already INSERTed into database - # to address a race condition that the first resource message - # (indicating that the task has begun running on a worker) - # arrives before the first task message from the DFK. In such a case, - # the code cannot UPDATE the relevant try table row, because it - # does not exist yet. - - # When such a message arrives, it is added to deferred_resource_messages - # and processed later, after the relevant DFK-side message has been - # received and processed to create a row in the try table. - - # Exactly one message per task/try can be deferred, which may not be - # enough when a try happens several times (for example when an - # executor performs its own Parsl-try unaware retries. - inserted_tasks: Set[object] = set() - inserted_tries: Set[object] = set() - deferred_worker_task_messages: dict[str, MonitoringMessage] = {} - - exception_happened = False - - while (not self._kill_event.is_set() or - not self.pending_priority_queue.empty() or not self.pending_resource_queue.empty() or - not self.pending_worker_task_queue.empty() or - not self.pending_node_queue.empty() or not self.pending_block_queue.empty() or - not resource_queue.empty()): - - """ - WORKFLOW_INFO and TASK_INFO messages (i.e. priority messages) - - """ - try: - logger.debug("""Checking STOP conditions: {}, {}, {}, {}, {}, {}""".format( - self._kill_event.is_set(), - not self.pending_priority_queue.empty(), not self.pending_resource_queue.empty(), - not self.pending_node_queue.empty(), not self.pending_block_queue.empty(), - not resource_queue.empty())) - - # This is the list of resource messages which can be reprocessed as if they - # had just arrived because the corresponding first task message has been - # processed (corresponding by task id) - reprocessable_first_worker_task_messages = [] - - # end-of-task-run status messages - handled in similar way as - # for last resource messages to try to have symmetry... this - # needs a type annotation though reprocessable_first_worker_task_messages - # doesn't... not sure why. Too lazy right now to figure out what, - # if any, more specific type than Any the messages have. - reprocessable_last_worker_task_messages: List[Any] = [] - - # Get a batch of priority messages - priority_messages = self._get_messages_in_batch(self.pending_priority_queue) - if priority_messages: - logger.debug( - "Got {} messages from priority queue".format(len(priority_messages))) - task_info_update_messages, task_info_insert_messages, task_info_all_messages = [], [], [] - try_update_messages, try_insert_messages, try_all_messages = [], [], [] - for msg_type, msg in priority_messages: - if msg_type == MessageType.WORKFLOW_INFO: - if "python_version" in msg: # workflow start message - logger.debug( - "Inserting workflow start info to WORKFLOW table") - self._insert(table=WORKFLOW, messages=[msg]) - self.workflow_start_message = msg - else: # workflow end message - logger.debug( - "Updating workflow end info to WORKFLOW table") - self._update(table=WORKFLOW, - columns=['run_id', 'tasks_failed_count', - 'tasks_completed_count', 'time_completed'], - messages=[msg]) - self.workflow_end = True - - elif msg_type == MessageType.TASK_INFO: - task_try_id = str(msg['task_id']) + "." + str(msg['try_id']) - task_info_all_messages.append(msg) - if msg['task_id'] in inserted_tasks: - task_info_update_messages.append(msg) - else: - inserted_tasks.add(msg['task_id']) - task_info_insert_messages.append(msg) - - try_all_messages.append(msg) - if task_try_id in inserted_tries: - try_update_messages.append(msg) - else: - inserted_tries.add(task_try_id) - try_insert_messages.append(msg) - - # check if there is a deferred message for this task - if task_try_id in deferred_worker_task_messages: - reprocessable_first_worker_task_messages.append( - deferred_worker_task_messages.pop(task_try_id)) - else: - raise RuntimeError("Unexpected message type {} received on priority queue".format(msg_type)) - - logger.debug("Updating and inserting TASK_INFO to all tables") - logger.debug("Updating {} TASK_INFO into workflow table".format(len(task_info_update_messages))) - self._update(table=WORKFLOW, - columns=['run_id', 'tasks_failed_count', - 'tasks_completed_count'], - messages=task_info_all_messages) - - if task_info_insert_messages: - self._insert(table=TASK, messages=task_info_insert_messages) - logger.debug( - "There are {} inserted task records".format(len(inserted_tasks))) - - if task_info_update_messages: - logger.debug("Updating {} TASK_INFO into task table".format(len(task_info_update_messages))) - self._update(table=TASK, - columns=['task_time_invoked', - 'task_time_returned', - 'run_id', 'task_id', - 'task_fail_count', - 'task_fail_cost', - 'task_hashsum', - 'task_inputs'], - messages=task_info_update_messages) - logger.debug("Inserting {} task_info_all_messages into status table".format(len(task_info_all_messages))) - - self._insert(table=STATUS, messages=task_info_all_messages) - - if try_insert_messages: - logger.debug("Inserting {} TASK_INFO to try table".format(len(try_insert_messages))) - self._insert(table=TRY, messages=try_insert_messages) - logger.debug( - "There are {} inserted task records".format(len(inserted_tasks))) - - if try_update_messages: - logger.debug("Updating {} TASK_INFO into try table".format(len(try_update_messages))) - self._update(table=TRY, - columns=['run_id', 'task_id', 'try_id', - 'task_fail_history', - 'task_try_time_launched', - 'task_try_time_returned', - 'task_joins'], - messages=try_update_messages) - - """ - NODE_INFO messages - - """ - node_info_messages = self._get_messages_in_batch(self.pending_node_queue) - if node_info_messages: - logger.debug( - "Got {} messages from node queue".format(len(node_info_messages))) - self._insert(table=NODE, messages=node_info_messages) - - """ - BLOCK_INFO messages - - """ - block_info_messages = self._get_messages_in_batch(self.pending_block_queue) - if block_info_messages: - logger.debug( - "Got {} messages from block queue".format(len(block_info_messages))) - # block_info_messages is possibly a nested list of dict (at different polling times) - # Each dict refers to the info of a job/block at one polling time - block_messages_to_insert: List[Any] = [] - for block_msg in block_info_messages: - block_messages_to_insert.extend(block_msg) - self._insert(table=BLOCK, messages=block_messages_to_insert) - - """ - Worker task status messages - """ - worker_task_messages = self._get_messages_in_batch(self.pending_worker_task_queue) - - if worker_task_messages: - logger.debug( - "Got {} messages from worker task queue, " - "{} reprocessable as first messages, " - "{} reprocessable as last messages".format(len(worker_task_messages), - len(reprocessable_first_worker_task_messages), - len(reprocessable_last_worker_task_messages))) - - for msg in worker_task_messages: - task_try_id = str(msg['task_id']) + "." + str(msg['try_id']) - if msg['first_msg']: - # Update the running time to try table if first message - msg['task_status_name'] = States.running.name - msg['task_try_time_running'] = msg['timestamp'] - - if task_try_id in inserted_tries: - reprocessable_first_worker_task_messages.append(msg) - else: - if task_try_id in deferred_worker_task_messages: - # this might happen if a task/try is run multiple - # times by an executor (for example, the work queue - # executor can be configured to do this) - logger.error( - "Task {} already has a deferred resource message. " - "Discarding previous message.".format(msg['task_id']) - ) - deferred_worker_task_messages[task_try_id] = msg - elif msg['last_msg']: - # This assumes that the primary key has been added - # to the try table already, so doesn't use the same - # deferral logic as the first_msg case. - msg['task_status_name'] = States.running_ended.name - reprocessable_last_worker_task_messages.append(msg) - else: - raise RuntimeError("Bad worker task message that is neither first nor last") - - if reprocessable_first_worker_task_messages: - self._insert(table=STATUS, messages=reprocessable_first_worker_task_messages) - self._update(table=TRY, - columns=['task_try_time_running', - 'run_id', 'task_id', 'try_id', - 'block_id', 'hostname'], - messages=reprocessable_first_worker_task_messages) - - if reprocessable_last_worker_task_messages: - self._insert(table=STATUS, messages=reprocessable_last_worker_task_messages) - - """ - Resource info messages - - """ - resource_messages = self._get_messages_in_batch(self.pending_resource_queue) - if resource_messages: - logger.debug("Got {} messages from resource queue".format(len(resource_messages))) - self._insert(table=RESOURCE, messages=resource_messages) - - except Exception: - logger.exception( - "Exception in db loop: this might have been a malformed message, " - "or some other error. monitoring data may have been lost" - ) - exception_happened = True - - if self.external_exit_event.is_set(): - self.close() - - if exception_happened: - raise RuntimeError("An exception happened sometime during database processing and should have been logged in database_manager.log") - - @wrap_with_logs - def _migrate_logs_to_internal(self, logs_queue: mpq.Queue, kill_event: threading.Event) -> None: - logger.info("Starting _migrate_logs_to_internal") - - while not kill_event.is_set() or not logs_queue.empty(): - logger.debug("Checking STOP conditions: kill event: %s, queue has entries: %s", - kill_event.is_set(), not logs_queue.empty()) - - try: - x = logs_queue.get(timeout=0.1) - except queue.Empty: - continue - else: - self._dispatch_to_internal(x) - - def _dispatch_to_internal(self, x: Tuple) -> None: - assert isinstance(x, tuple) - assert len(x) == 2, "expected message tuple to have exactly two elements" - - if x[0] in [MessageType.WORKFLOW_INFO, MessageType.TASK_INFO]: - self.pending_priority_queue.put(cast(Any, x)) - elif x[0] == MessageType.RESOURCE_INFO: - self.pending_resource_queue.put(x[1]) - elif x[0] == MessageType.WORKER_TASK_INFO: - self.pending_worker_task_queue.put(x[1]) - elif x[0] == MessageType.NODE_INFO: - logger.debug("Will put {} to pending node queue".format(x[1])) - self.pending_node_queue.put(x[1]) - elif x[0] == MessageType.BLOCK_INFO: - logger.debug("Will put {} to pending block queue".format(x[1])) - self.pending_block_queue.put(x[1]) - else: - logger.error("Discarding message of unknown type {}".format(x[0])) - - def _update(self, table: str, columns: List[str], messages: List[MonitoringMessage]) -> None: - try: - done = False - while not done: - try: - self.db.update(table=table, columns=columns, messages=messages) - done = True - except sa.exc.OperationalError as e: - # This code assumes that an OperationalError is something that will go away eventually - # if retried - for example, the database being locked because someone else is readying - # the tables we are trying to write to. If that assumption is wrong, then this loop - # may go on forever. - logger.warning("Got a database OperationalError. " - "Ignoring and retrying on the assumption that it is recoverable: {}".format(e)) - self.db.rollback() - time.sleep(1) # hard coded 1s wait - this should be configurable or exponential backoff or something - - except KeyboardInterrupt: - logger.exception("KeyboardInterrupt when trying to update Table {}".format(table)) - try: - self.db.rollback() - except Exception: - logger.exception("Rollback failed") - raise - except Exception: - logger.exception("Got exception when trying to update table {}".format(table)) - try: - self.db.rollback() - except Exception: - logger.exception("Rollback failed") - - def _insert(self, table: str, messages: List[MonitoringMessage]) -> None: - try: - done = False - while not done: - try: - self.db.insert(table=table, messages=messages) - done = True - except sa.exc.OperationalError as e: - # hoping that this is a database locked error during _update, not some other problem - logger.warning("Got a database OperationalError. " - "Ignoring and retrying on the assumption that it is recoverable: {}".format(e)) - self.db.rollback() - time.sleep(1) # hard coded 1s wait - this should be configurable or exponential backoff or something - except KeyboardInterrupt: - logger.exception("KeyboardInterrupt when trying to update Table {}".format(table)) - try: - self.db.rollback() - except Exception: - logger.exception("Rollback failed") - raise - except Exception: - logger.exception("Got exception when trying to insert to table {}".format(table)) - try: - self.db.rollback() - except Exception: - logger.exception("Rollback failed") - - def _get_messages_in_batch(self, msg_queue: "queue.Queue[X]") -> List[X]: - messages: List[X] = [] - start = time.time() - while True: - if time.time() - start >= self.batching_interval or len(messages) >= self.batching_threshold: - break - try: - x = msg_queue.get(timeout=0.1) - # logger.debug("Database manager receives a message {}".format(x)) - except queue.Empty: - logger.debug("Database manager has not received any message.") - break - else: - messages.append(x) - return messages - - def close(self) -> None: - logger.info("Database Manager cleanup initiated.") - if not self.workflow_end and self.workflow_start_message: - logger.info("Logging workflow end info to database due to abnormal exit") - time_completed = datetime.datetime.now() - msg = {'time_completed': time_completed, - 'workflow_duration': (time_completed - self.workflow_start_message['time_began']).total_seconds()} - self.workflow_start_message.update(msg) - self._update(table=WORKFLOW, - columns=['run_id', 'time_completed', - 'workflow_duration'], - messages=[self.workflow_start_message]) - self.batching_interval = float('inf') - self.batching_threshold = float('inf') - self._kill_event.set() - - -@wrap_with_logs -@typeguard.typechecked -def dbm_starter(resource_msgs: mpq.Queue, - db_url: str, - run_dir: str, - logging_level: int, - exit_event: mpe.Event, - log_config: Optional[LogConfig]) -> None: - """Start the database manager process - """ - setproctitle("parsl: monitoring database") - - try: - dbm = DatabaseManager(db_url=db_url, - run_dir=run_dir, - logging_level=logging_level, - exit_event=exit_event, - log_config=log_config) - logger.info("Starting dbm in dbm starter") - dbm.start(resource_msgs) - except KeyboardInterrupt: - logger.exception("KeyboardInterrupt signal caught") - dbm.close() - raise - except Exception: - logger.exception("dbm.start exception") - dbm.close() - - logger.info("End of dbm_starter") diff --git a/parsl/parsl/monitoring/errors.py b/parsl/parsl/monitoring/errors.py deleted file mode 100644 index c5a85425..00000000 --- a/parsl/parsl/monitoring/errors.py +++ /dev/null @@ -1,11 +0,0 @@ -from parsl.errors import ParslError - - -class MonitoringRouterStartError(ParslError): - def __str__(self) -> str: - return "Monitoring router failed to start" - - -class RadioRequiredError(ParslError): - def __str__(self) -> str: - return "A radio must be configured for remote task monitoring" diff --git a/parsl/parsl/monitoring/message_type.py b/parsl/parsl/monitoring/message_type.py deleted file mode 100644 index 26c1633e..00000000 --- a/parsl/parsl/monitoring/message_type.py +++ /dev/null @@ -1,22 +0,0 @@ -from enum import Enum - - -class MessageType(Enum): - - # Reports any task related info such as launch, completion etc. - TASK_INFO = 0 - - # Reports of resource utilization on a per-task basis - RESOURCE_INFO = 1 - - # Top level workflow information - WORKFLOW_INFO = 2 - - # Reports of the resource capacity for each node - NODE_INFO = 3 - - # Reports of the block info - BLOCK_INFO = 4 - - # Reports of a task starting or ending execution on a worker - WORKER_TASK_INFO = 5 diff --git a/parsl/parsl/monitoring/monitoring.py b/parsl/parsl/monitoring/monitoring.py deleted file mode 100644 index f4171795..00000000 --- a/parsl/parsl/monitoring/monitoring.py +++ /dev/null @@ -1,162 +0,0 @@ -from __future__ import annotations - -import logging -import multiprocessing.synchronize as ms -import os -import warnings -from multiprocessing.queues import Queue -from typing import Any, Optional, Union - -import typeguard - -from parsl.logconfigs.base import LogConfig -from parsl.monitoring.types import TaggedMonitoringMessage -from parsl.multiprocessing import ( - SpawnEvent, - SpawnProcess, - SpawnQueue, - join_terminate_close_proc, -) -from parsl.utils import RepresentationMixin - -_db_manager_excepts: Optional[Exception] - - -try: - from parsl.monitoring.db_manager import dbm_starter -except Exception as e: - _db_manager_excepts = e -else: - _db_manager_excepts = None - -logger = logging.getLogger(__name__) - - -@typeguard.typechecked -class MonitoringHub(RepresentationMixin): - def __init__(self, - hub_address: Any = None, # unused, so no type enforcement - hub_port_range: Any = None, # unused, so no type enforcement - hub_port: Any = None, # unused, so no type enforcement - - workflow_name: Optional[str] = None, - workflow_version: Optional[str] = None, - logging_endpoint: Optional[str] = None, - monitoring_debug: bool = False, - resource_monitoring_enabled: bool = True, - resource_monitoring_interval: float = 30): # in seconds - """ - Parameters - ---------- - hub_address : unused - hub_port : unused - Unused, but probably retained until 2026-06-01 to give deprecation warning. - These two values previously configured UDP parameters when UDP was used - for monitoring messages from workers. These are now configured on the - relevant UDPRadio. - hub_port_range : unused - Unused, but probably retained until 2026-06-01 to give deprecation warning. - This value previously configured one ZMQ channel inside the - HighThroughputExecutor. That ZMQ channel is now configured by the - interchange_port_range parameter of HighThroughputExecutor. - workflow_name : str - The name for the workflow. Default to the name of the parsl script - workflow_version : str - The version of the workflow. Default to the beginning datetime of the parsl script - logging_endpoint : str - The database connection url for monitoring to log the information. - These URLs follow RFC-1738, and can include username, password, hostname, database name. - Default: sqlite, in the configured run_dir. - monitoring_debug : Bool - Enable monitoring debug logging. Default: False - resource_monitoring_enabled : boolean - Set this field to True to enable logging of information from the worker side. - This will include environment information such as start time, hostname and block id, - along with periodic resource usage of each task. Default: True - resource_monitoring_interval : float - The time interval, in seconds, at which the monitoring records the resource usage of each task. - If set to 0, only start and end information will be logged, and no periodic monitoring will - be made. - Default: 30 seconds - """ - - if _db_manager_excepts: - raise _db_manager_excepts - - # The following three parameters need to exist as attributes to be - # output by RepresentationMixin. - if hub_address is not None: - message = "Instead of MonitoringHub.hub_address, specify UDPRadio(address=...)" - warnings.warn(message, DeprecationWarning) - logger.warning(message) - - self.hub_address = hub_address - - if hub_port is not None: - message = "Instead of MonitoringHub.hub_port, specify UDPRadio(port=...)" - warnings.warn(message, DeprecationWarning) - logger.warning(message) - - self.hub_port = hub_port - - if hub_port_range is not None: - message = "Instead of MonitoringHub.hub_port_range, Use HighThroughputExecutor.interchange_port_range" - warnings.warn(message, DeprecationWarning) - logger.warning(message) - - self.hub_port_range = hub_port_range - - self.logging_endpoint = logging_endpoint - self.monitoring_debug = monitoring_debug - - self.workflow_name = workflow_name - self.workflow_version = workflow_version - - self.resource_monitoring_enabled = resource_monitoring_enabled - self.resource_monitoring_interval = resource_monitoring_interval - - def start(self, dfk_run_dir: str, config_run_dir: Union[str, os.PathLike], log_config: Optional[LogConfig]) -> None: - - logger.debug("Starting MonitoringHub") - - if self.logging_endpoint is None: - self.logging_endpoint = f"sqlite:///{os.fspath(config_run_dir)}/monitoring.db" - - os.makedirs(dfk_run_dir, exist_ok=True) - - self.monitoring_hub_active = True - - self.resource_msgs: Queue[TaggedMonitoringMessage] - self.resource_msgs = SpawnQueue() - - self.dbm_exit_event: ms.Event - self.dbm_exit_event = SpawnEvent() - - self.dbm_proc = SpawnProcess(target=dbm_starter, - args=(self.resource_msgs,), - kwargs={"run_dir": dfk_run_dir, - "logging_level": logging.DEBUG if self.monitoring_debug else logging.INFO, - "db_url": self.logging_endpoint, - "exit_event": self.dbm_exit_event, - "log_config": log_config, - }, - name="Monitoring-DBM-Process", - daemon=True, - ) - self.dbm_proc.start() - logger.info("Started DBM process %s", self.dbm_proc.pid) - logger.info("Monitoring Hub initialized") - - def close(self) -> None: - logger.info("Terminating Monitoring Hub") - if self.monitoring_hub_active: - self.monitoring_hub_active = False - logger.debug("Waiting for DB termination") - self.dbm_exit_event.set() - join_terminate_close_proc(self.dbm_proc) - logger.debug("Finished waiting for DBM termination") - - logger.info("Closing monitoring multiprocessing queues") - self.resource_msgs.close() - self.resource_msgs.join_thread() - logger.info("Closed monitoring multiprocessing queues") diff --git a/parsl/parsl/monitoring/queries/__init__.py b/parsl/parsl/monitoring/queries/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/monitoring/queries/pandas.py b/parsl/parsl/monitoring/queries/pandas.py deleted file mode 100644 index 9bda8422..00000000 --- a/parsl/parsl/monitoring/queries/pandas.py +++ /dev/null @@ -1,73 +0,0 @@ -from typing import Any - -import pandas as pd - -# pandas can take several different types of database connection, -# and itself exposes its connection parameters as "Any". -# This alias might help things be a bit clearer in our documentation. -DB = Any - - -def app_counts_for_workflow(workflow_id: Any, db: DB) -> pd.DataFrame: - return pd.read_sql_query(""" - SELECT task_func_name, count(*) as 'frequency' - FROM task - WHERE run_id='%s' - GROUP BY task_func_name; - """ % workflow_id, db) - - -def nodes_for_workflow(workflow_id: Any, db: Any) -> pd.DataFrame: - return pd.read_sql_query(""" - SELECT * - FROM node - WHERE run_id='%s'; - """ % (workflow_id), db) - - -def resources_for_workflow(workflow_id: Any, db: Any) -> pd.DataFrame: - return pd.read_sql_query(""" - SELECT * - FROM resource - WHERE run_id='%s'; - """ % (workflow_id), db) - - -def resources_for_task(workflow_id: Any, task_id: Any, db: Any) -> pd.DataFrame: - return pd.read_sql_query(""" - SELECT * - FROM resource - WHERE run_id='%s' AND task_id='%s'; - """ % (workflow_id, task_id), db) - - -def status_for_workflow(workflow_id: Any, db: Any) -> pd.DataFrame: - return pd.read_sql_query(""" - SELECT run_id, task_id, task_status_name, timestamp - FROM status - WHERE run_id='%s'; - """ % workflow_id, db) - - -def completion_times_for_workflow(workflow_id: Any, db: Any) -> pd.DataFrame: - return pd.read_sql_query(""" - SELECT task_id, task_func_name, task_time_returned - FROM task - WHERE run_id='%s' - """ % (workflow_id), db) - - -def tasks_for_workflow(workflow_id: Any, db: Any) -> pd.DataFrame: - return pd.read_sql_query(""" - SELECT * - FROM task - WHERE run_id='%s' - """ % (workflow_id), db) - - -def tries_for_workflow(workflow_id: Any, db: Any) -> pd.DataFrame: - return pd.read_sql_query(""" - SELECT task.task_id, task_func_name, task_time_returned, task_try_time_running, task_try_time_returned - FROM task, try - WHERE task.task_id = try.task_id AND task.run_id='%s' AND try.run_id='%s' - """ % (workflow_id, workflow_id), db) diff --git a/parsl/parsl/monitoring/radios/__init__.py b/parsl/parsl/monitoring/radios/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/monitoring/radios/base.py b/parsl/parsl/monitoring/radios/base.py deleted file mode 100644 index c870a07c..00000000 --- a/parsl/parsl/monitoring/radios/base.py +++ /dev/null @@ -1,71 +0,0 @@ -from abc import ABCMeta, abstractmethod -from multiprocessing.queues import Queue - - -class MonitoringRadioReceiver(metaclass=ABCMeta): - @abstractmethod - def shutdown(self) -> None: - pass - - -class MonitoringRadioSender(metaclass=ABCMeta): - @abstractmethod - def send(self, message: object) -> None: - pass - - -class RadioConfig(metaclass=ABCMeta): - """Base class for radio plugin configuration. - - This provides the configuration for a particular way of sending monitoring - messages from a source of monitoring messages into the submit side - monitoring database. - - This uses staged initialization like lots of Parsl configuration, but in - a slightly different form. - - A RadioConfig object must be pickleable, because it will be sent to remote - workers to configure senders. The MonitoringRadioSender and - MonitoringRadioReceiver objects do not need to be pickleable (and often - will not be - for example, when they hold references to other processes). - - The RadioConfig object will be used by Parsl in this sequence: - - * A user creates a RadioConfig object from the appropriate subclass for - radio mechanism they want to use, and specifies it as part of their - executor configuration. - - Methods on the RadioConfig will then be invoked by Parsl like this: - - * one create_receiver call, on the submit side - - this call can modify the state of radioconfig to contain information - about how a sender can connect back to the receiver. for example, - after binding to a particular port, can store that port so that the - sender knows which port to connect to. - - * Possibly many serializations to get the RadioConfig to remote workers - - * Many (0 or more) create_sender calls, possibly on remote workers, to - create the sending side of the radio (MonitoringRadioSender instances) - - * Those senders are used to send messages - - * At executor shutdown, the receiver is shut down. - - This object cannot be re-used across parsl configurations - like many other - pieces of parsl config it is single use in that respect. - """ - - @abstractmethod - def create_receiver(self, *, run_dir: str, resource_msgs: Queue) -> MonitoringRadioReceiver: - """Create a receiver for this RadioConfig, and update this RadioConfig - with enough context to create senders. - """ - pass - - @abstractmethod - def create_sender(self) -> MonitoringRadioSender: - """Create a sender to connect to the receiver created by an - earlier call to create_receiver. - """ - pass diff --git a/parsl/parsl/monitoring/radios/filesystem.py b/parsl/parsl/monitoring/radios/filesystem.py deleted file mode 100644 index 7967949e..00000000 --- a/parsl/parsl/monitoring/radios/filesystem.py +++ /dev/null @@ -1,68 +0,0 @@ -import logging -import os -import pickle -import uuid -from multiprocessing.queues import Queue - -from parsl.monitoring.radios.base import ( - MonitoringRadioReceiver, - MonitoringRadioSender, - RadioConfig, -) -from parsl.monitoring.radios.filesystem_router import FilesystemRadioReceiver -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - - -class FilesystemRadio(RadioConfig, RepresentationMixin): - """A MonitoringRadioSender that sends messages over a shared filesystem. - - The messsage directory structure is based on maildir, - https://en.wikipedia.org/wiki/Maildir - - The writer creates a message in tmp/ and then when it is fully - written, moves it atomically into new/ - - The reader ignores tmp/ and only reads and deletes messages from - new/ - - This avoids a race condition of reading partially written messages. - - This radio is likely to give higher shared filesystem load compared to - the UDP radio, but should be much more reliable. - """ - - def create_sender(self) -> MonitoringRadioSender: - return FilesystemRadioSender(run_dir=self.run_dir) - - def create_receiver(self, *, run_dir: str, resource_msgs: Queue) -> MonitoringRadioReceiver: - self.run_dir = run_dir - return FilesystemRadioReceiver(resource_msgs, run_dir) - - -class FilesystemRadioSender(MonitoringRadioSender): - def __init__(self, *, run_dir: str): - logger.info("filesystem based monitoring radio initializing") - self.base_path = f"{run_dir}/monitor-fs-radio/" - self.tmp_path = f"{self.base_path}/tmp" - self.new_path = f"{self.base_path}/new" - - os.makedirs(self.tmp_path, exist_ok=True) - os.makedirs(self.new_path, exist_ok=True) - - def send(self, message: object) -> None: - logger.info("Sending a monitoring message via filesystem") - - unique_id = str(uuid.uuid4()) - - tmp_filename = f"{self.tmp_path}/{unique_id}" - new_filename = f"{self.new_path}/{unique_id}" - buffer = message - - # this will write the message out then atomically - # move it into new/, so that a partially written - # file will never be observed in new/ - with open(tmp_filename, "wb") as f: - pickle.dump(buffer, f) - os.rename(tmp_filename, new_filename) diff --git a/parsl/parsl/monitoring/radios/filesystem_router.py b/parsl/parsl/monitoring/radios/filesystem_router.py deleted file mode 100644 index 2ae5c3fc..00000000 --- a/parsl/parsl/monitoring/radios/filesystem_router.py +++ /dev/null @@ -1,84 +0,0 @@ -from __future__ import annotations - -import logging -import os -import pickle -from multiprocessing.queues import Queue -from multiprocessing.synchronize import Event -from typing import cast - -from parsl.log_utils import set_file_logger -from parsl.monitoring.radios.base import MonitoringRadioReceiver -from parsl.monitoring.radios.multiprocessing import MultiprocessingQueueRadioSender -from parsl.monitoring.types import TaggedMonitoringMessage -from parsl.multiprocessing import SpawnEvent, SpawnProcess, join_terminate_close_proc -from parsl.process_loggers import wrap_with_logs -from parsl.utils import setproctitle - -logger = logging.getLogger(__name__) - -# how often the router will scan the new message directory -POLL_PERIOD_S = 10 - - -@wrap_with_logs -def filesystem_router_starter(*, q: Queue[TaggedMonitoringMessage], run_dir: str, exit_event: Event) -> None: - set_file_logger(f"{run_dir}/monitoring_filesystem_radio.log", - level=logging.INFO) - - logger.info("Starting filesystem radio receiver") - setproctitle("parsl: monitoring filesystem receiver") - base_path = f"{run_dir}/monitor-fs-radio/" - tmp_dir = f"{base_path}/tmp/" - new_dir = f"{base_path}/new/" - logger.debug("Creating new and tmp paths under %s", base_path) - - target_radio = MultiprocessingQueueRadioSender(q) - - os.makedirs(tmp_dir, exist_ok=True) - os.makedirs(new_dir, exist_ok=True) - - loop = True - - while loop: - logger.debug("Start filesystem radio receiver loop") - - # this happens before the final poll of the directory so that - # one complete pass over the new_dir will happen strictly after - # the exit_event is set. Without forcing that final pass, there - # can be a race between exiting on exit_event after the files - # are added. - if exit_event.wait(POLL_PERIOD_S): - loop = False - - # iterate over files in new_dir - for filename in os.listdir(new_dir): - try: - logger.info("Processing filesystem radio file %s", filename) - full_path_filename = f"{new_dir}/{filename}" - with open(full_path_filename, "rb") as f: - message = pickle.load(f) - logger.debug("Message received is: %s", message) - assert isinstance(message, tuple) - target_radio.send(cast(TaggedMonitoringMessage, message)) - os.remove(full_path_filename) - except Exception: - logger.exception("Exception processing %s - probably will be retried next iteration", filename) - - logger.info("Ending filesystem radio receiver") - - -class FilesystemRadioReceiver(MonitoringRadioReceiver): - def __init__(self, resource_msgs: Queue, run_dir: str) -> None: - self.exit_event = SpawnEvent() - self.process = SpawnProcess(target=filesystem_router_starter, - kwargs={"q": resource_msgs, "run_dir": run_dir, "exit_event": self.exit_event}, - name="Monitoring-Filesystem-Process", - daemon=True - ) - self.process.start() - logger.info("Started filesystem radio receiver process %s", self.process.pid) - - def shutdown(self) -> None: - self.exit_event.set() - join_terminate_close_proc(self.process) diff --git a/parsl/parsl/monitoring/radios/htex.py b/parsl/parsl/monitoring/radios/htex.py deleted file mode 100644 index 6d42a753..00000000 --- a/parsl/parsl/monitoring/radios/htex.py +++ /dev/null @@ -1,65 +0,0 @@ -import logging -import pickle -from multiprocessing.queues import Queue - -from parsl.monitoring.radios.base import ( - MonitoringRadioReceiver, - MonitoringRadioSender, - RadioConfig, -) -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - - -class HTEXRadio(RadioConfig, RepresentationMixin): - def create_sender(self) -> MonitoringRadioSender: - return HTEXRadioSender() - - def create_receiver(self, *, run_dir: str, resource_msgs: Queue) -> MonitoringRadioReceiver: - return HTEXRadioReceiver() - - -class HTEXRadioSender(MonitoringRadioSender): - - def __init__(self) -> None: - # there is nothing to initialize - pass - - def send(self, message: object) -> None: - """ Sends a message via HTEX result channel. - - Parameter - --------- - - message: object - Arbitrary pickle-able object that is to be sent - - Returns: - None - """ - - import parsl.executors.high_throughput.monitoring_info - - result_queue = parsl.executors.high_throughput.monitoring_info.result_queue - - # this message needs to go in the result queue tagged so that it is treated - # as a monitoring message by the interchange. - - # for the interchange, the outer wrapper, this needs to be a dict: - - interchange_msg = { - 'type': 'monitoring', - 'payload': message - } - - if result_queue: - result_queue.put(pickle.dumps(interchange_msg)) - else: - logger.error("result_queue is uninitialized - cannot put monitoring message") - - -class HTEXRadioReceiver(MonitoringRadioReceiver): - def shutdown(self) -> None: - # there is nothing to shut down - pass diff --git a/parsl/parsl/monitoring/radios/multiprocessing.py b/parsl/parsl/monitoring/radios/multiprocessing.py deleted file mode 100644 index 52773a73..00000000 --- a/parsl/parsl/monitoring/radios/multiprocessing.py +++ /dev/null @@ -1,38 +0,0 @@ -from multiprocessing import Queue - -from parsl.monitoring.radios.base import ( - MonitoringRadioReceiver, - MonitoringRadioSender, - RadioConfig, -) -from parsl.utils import RepresentationMixin - - -class MultiprocessingQueueRadioSender(MonitoringRadioSender, RepresentationMixin): - """A monitoring radio which connects over a multiprocessing Queue. - This radio is intended to be used on the submit side, where components - in the submit process, or processes launched by multiprocessing, will have - access to a Queue shared with the monitoring database code (bypassing the - monitoring router). - """ - def __init__(self, queue: Queue) -> None: - self.queue = queue - - def send(self, message: object) -> None: - self.queue.put(message) - - -class MultiprocessingQueueRadio(RadioConfig): - def create_sender(self) -> MonitoringRadioSender: - return MultiprocessingQueueRadioSender(self._queue) - - def create_receiver(self, *, run_dir: str, resource_msgs: Queue) -> MonitoringRadioReceiver: - # This object is only for use with an in-process thread-pool so it - # is fine to store a reference to the message queue directly. - self._queue = resource_msgs - return MultiprocessingQueueRadioReceiver() - - -class MultiprocessingQueueRadioReceiver(MonitoringRadioReceiver): - def shutdown(self) -> None: - pass diff --git a/parsl/parsl/monitoring/radios/udp.py b/parsl/parsl/monitoring/radios/udp.py deleted file mode 100644 index 54b32fa7..00000000 --- a/parsl/parsl/monitoring/radios/udp.py +++ /dev/null @@ -1,95 +0,0 @@ -import hashlib -import hmac -import logging -import pickle -import secrets -import socket -from multiprocessing.queues import Queue -from typing import Optional - -from parsl.monitoring.radios.base import ( - MonitoringRadioReceiver, - MonitoringRadioSender, - RadioConfig, -) -from parsl.monitoring.radios.udp_router import start_udp_receiver -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - - -class UDPRadio(RadioConfig, RepresentationMixin): - def __init__(self, *, port: Optional[int] = None, atexit_timeout: int = 3, address: str, debug: bool = False, hmac_digest: str = 'sha512'): - self.port = port - self.atexit_timeout = atexit_timeout - self.address = address - self.debug = debug - self.hmac_digest = hmac_digest - - def create_sender(self) -> MonitoringRadioSender: - assert self.port is not None, "self.port should have been initialized by create_receiver" - return UDPRadioSender(self.address, self.port, self.hmac_key, self.hmac_digest) - - def create_receiver(self, run_dir: str, resource_msgs: Queue) -> MonitoringRadioReceiver: - # RFC 2104 section 2 recommends that the key length be at - # least as long as the hash output (64 bytes in the case of SHA512). - # RFC 2014 section 3 talks about periodic key refreshing. This key is - # not refreshed inside a workflow run, but each separate workflow run - # uses a new key. - keysize = hashlib.new(self.hmac_digest).digest_size - self.hmac_key = secrets.token_bytes(keysize) - - udp_receiver = start_udp_receiver(logdir=run_dir, - monitoring_messages=resource_msgs, - port=self.port, - debug=self.debug, - atexit_timeout=self.atexit_timeout, - hmac_key=self.hmac_key, - hmac_digest=self.hmac_digest - ) - self.port = udp_receiver.port - return udp_receiver - - -class UDPRadioSender(MonitoringRadioSender): - - def __init__(self, address: str, port: int, hmac_key: bytes, hmac_digest: str, *, timeout: int = 10) -> None: - self.sock_timeout = timeout - self.address = address - self.port = port - self.hmac_key = hmac_key - self.hmac_digest = hmac_digest - - self.sock = socket.socket(socket.AF_INET, - socket.SOCK_DGRAM, - socket.IPPROTO_UDP) # UDP - self.sock.settimeout(self.sock_timeout) - - def send(self, message: object) -> None: - """ Sends a message to the UDP receiver - - Parameter - --------- - - message: object - Arbitrary pickle-able object that is to be sent - - Returns: - None - """ - logger.info("Starting UDP radio message send") - try: - data = pickle.dumps(message) - origin_hmac = hmac.digest(self.hmac_key, data, self.hmac_digest) - buffer = origin_hmac + data - except Exception: - logging.exception("Exception during pickling", exc_info=True) - return - - try: - self.sock.sendto(buffer, (self.address, self.port)) - except socket.timeout: - logging.error("Could not send message within timeout limit") - return - logger.info("Normal ending for UDP radio message send") - return diff --git a/parsl/parsl/monitoring/radios/udp_router.py b/parsl/parsl/monitoring/radios/udp_router.py deleted file mode 100644 index edfac5ee..00000000 --- a/parsl/parsl/monitoring/radios/udp_router.py +++ /dev/null @@ -1,233 +0,0 @@ -from __future__ import annotations - -import hmac -import logging -import multiprocessing.queues as mpq -import os -import pickle -import queue -import socket -import time -from multiprocessing.context import SpawnProcess as SpawnProcessType -from multiprocessing.queues import Queue -from multiprocessing.synchronize import Event -from multiprocessing.synchronize import Event as EventType -from typing import Optional, Union - -import typeguard - -from parsl.log_utils import set_file_logger -from parsl.monitoring.errors import MonitoringRouterStartError -from parsl.monitoring.radios.base import MonitoringRadioReceiver -from parsl.monitoring.radios.multiprocessing import MultiprocessingQueueRadioSender -from parsl.multiprocessing import ( - SpawnEvent, - SpawnProcess, - SpawnQueue, - join_terminate_close_proc, -) -from parsl.process_loggers import wrap_with_logs -from parsl.utils import setproctitle - -logger = logging.getLogger(__name__) - - -class MonitoringRouter: - - def __init__(self, - *, - udp_port: Optional[int] = None, - run_dir: str = ".", - logging_level: int = logging.INFO, - atexit_timeout: int, # in seconds - resource_msgs: mpq.Queue, - exit_event: Event, - hmac_key: bytes, - hmac_digest: str, - ): - """ Initializes a monitoring configuration class. - - Parameters - ---------- - udp_port : int - The specific port at which workers will be able to reach the Hub via UDP. Default: None - run_dir : str - Parsl log directory paths. Logs and temp files go here. Default: '.' - logging_level : int - Logging level as defined in the logging module. Default: logging.INFO - atexit_timeout : float, optional - The amount of time in seconds to terminate the hub without receiving any messages, after the last dfk workflow message is received. - resource_msgs : multiprocessing.Queue - A multiprocessing queue to receive messages to be routed onwards to the database process - exit_event : Event - An event that the main Parsl process will set to signal that the monitoring router should shut down. - """ - os.makedirs(run_dir, exist_ok=True) - set_file_logger(f"{run_dir}/monitoring_udp_router.log", - level=logging_level) - logger.debug("Monitoring router starting") - - self.atexit_timeout = atexit_timeout - - self.hmac_key = hmac_key - self.hmac_digest = hmac_digest - - self.loop_freq = 10.0 # milliseconds - - # Initialize the UDP socket - self.udp_sock = socket.socket(socket.AF_INET, - socket.SOCK_DGRAM, - socket.IPPROTO_UDP) - - # We are trying to bind to all interfaces with 0.0.0.0 - if not udp_port: - self.udp_sock.bind(('0.0.0.0', 0)) - self.udp_port = self.udp_sock.getsockname()[1] - else: - self.udp_port = udp_port - try: - self.udp_sock.bind(('0.0.0.0', self.udp_port)) - except Exception as e: - raise RuntimeError(f"Could not bind to udp_port {udp_port} because: {e}") - self.udp_sock.settimeout(self.loop_freq / 1000) - logger.info("Initialized the UDP socket on 0.0.0.0:{}".format(self.udp_port)) - - self.target_radio = MultiprocessingQueueRadioSender(resource_msgs) - self.exit_event = exit_event - - @wrap_with_logs - def start(self) -> None: - logger.info("Starting UDP listener") - try: - while not self.exit_event.is_set(): - try: - self.process_message() - except socket.timeout: - pass - - logger.info("UDP listener draining") - last_msg_received_time = time.time() - while time.time() - last_msg_received_time < self.atexit_timeout: - try: - self.process_message() - last_msg_received_time = time.time() - except socket.timeout: - pass - - logger.info("UDP listener finishing normally") - finally: - logger.info("UDP listener finished") - - def process_message(self) -> None: - hmdata, addr = self.udp_sock.recvfrom(2048) - h = hmac.HMAC(key=self.hmac_key, digestmod=self.hmac_digest) - origin_hmac = hmdata[0:h.digest_size] - h.update(hmdata[h.digest_size:]) - data = hmdata[h.digest_size:] - - # Check hmac before pickle load. - # If data is wrong, do not log it because it is suspect, - # but it should be safe to log the addr, at error level. - - recomputed_hmac = h.digest() - - if not hmac.compare_digest(origin_hmac, recomputed_hmac): - logger.error("HMAC does not match on received message") - # No exception, because this can be arbitrary network noise - # that shouldn't break the receiver. - else: - resource_msg = pickle.loads(data) - logger.debug("Got UDP Message from {}: {}".format(addr, resource_msg)) - self.target_radio.send(resource_msg) - - -@wrap_with_logs -@typeguard.typechecked -def udp_router_starter(*, - comm_q: mpq.Queue, - resource_msgs: mpq.Queue, - exit_event: Event, - - udp_port: Optional[int], - hmac_key: bytes, - run_dir: str, - logging_level: int, - atexit_timeout: int, - hmac_digest: str) -> None: - setproctitle("parsl: monitoring UDP router") - try: - router = MonitoringRouter(udp_port=udp_port, - run_dir=run_dir, - logging_level=logging_level, - resource_msgs=resource_msgs, - exit_event=exit_event, - atexit_timeout=atexit_timeout, - hmac_key=hmac_key, - hmac_digest=hmac_digest) - except Exception as e: - logger.error("MonitoringRouter construction failed.", exc_info=True) - comm_q.put(f"Monitoring router construction failed: {e}") - else: - comm_q.put(router.udp_port) - - logger.info("Starting MonitoringRouter in router_starter") - try: - router.start() - except Exception: - logger.exception("UDP router start exception") - - -class UDPRadioReceiver(MonitoringRadioReceiver): - def __init__(self, *, process: SpawnProcessType, exit_event: EventType, port: int) -> None: - self.process = process - self.exit_event = exit_event - self.port = port - - def shutdown(self) -> None: - self.exit_event.set() - join_terminate_close_proc(self.process) - - -def start_udp_receiver(*, - monitoring_messages: Queue, - port: Optional[int], - logdir: str, - debug: bool, - atexit_timeout: int, - hmac_key: bytes, - hmac_digest: str) -> UDPRadioReceiver: - - udp_comm_q: Queue[Union[int, str]] - udp_comm_q = SpawnQueue(maxsize=10) - - router_exit_event = SpawnEvent() - - router_proc = SpawnProcess(target=udp_router_starter, - kwargs={"comm_q": udp_comm_q, - "resource_msgs": monitoring_messages, - "exit_event": router_exit_event, - "udp_port": port, - "run_dir": logdir, - "logging_level": logging.DEBUG if debug else logging.INFO, - "atexit_timeout": atexit_timeout, - "hmac_key": hmac_key, - "hmac_digest": hmac_digest, - }, - name="Monitoring-UDP-Router-Process", - daemon=True, - ) - router_proc.start() - - try: - udp_comm_q_result = udp_comm_q.get(block=True, timeout=120) - udp_comm_q.close() - udp_comm_q.join_thread() - except queue.Empty: - logger.error("Monitoring UDP router has not reported port in 120s. Aborting") - raise MonitoringRouterStartError() - - if isinstance(udp_comm_q_result, str): - logger.error("MonitoringRouter sent an error message: %s", udp_comm_q_result) - raise RuntimeError(f"MonitoringRouter failed to start: {udp_comm_q_result}") - - return UDPRadioReceiver(process=router_proc, exit_event=router_exit_event, port=udp_comm_q_result) diff --git a/parsl/parsl/monitoring/radios/zmq.py b/parsl/parsl/monitoring/radios/zmq.py deleted file mode 100644 index cb3bf94e..00000000 --- a/parsl/parsl/monitoring/radios/zmq.py +++ /dev/null @@ -1,20 +0,0 @@ -from typing import Optional - -import zmq - -import parsl.curvezmq as curvezmq -from parsl.monitoring.radios.base import MonitoringRadioSender - - -class ZMQRadioSender(MonitoringRadioSender): - """A monitoring radio which connects over ZMQ. This radio is not - thread-safe, because its use of ZMQ is not thread-safe. - """ - - def __init__(self, hub_address: str, hub_zmq_port: int, cert_dir: Optional[str]) -> None: - self._hub_channel = curvezmq.ClientContext(cert_dir=cert_dir).socket(zmq.DEALER) - self._hub_channel.set_hwm(0) - self._hub_channel.connect(f"tcp://{hub_address}:{hub_zmq_port}") - - def send(self, message: object) -> None: - self._hub_channel.send_pyobj(message) diff --git a/parsl/parsl/monitoring/radios/zmq_router.py b/parsl/parsl/monitoring/radios/zmq_router.py deleted file mode 100644 index 0774f896..00000000 --- a/parsl/parsl/monitoring/radios/zmq_router.py +++ /dev/null @@ -1,198 +0,0 @@ -from __future__ import annotations - -import logging -import multiprocessing.queues as mpq -import os -import queue -import time -from multiprocessing.context import SpawnProcess as SpawnProcessType -from multiprocessing.queues import Queue as QueueType -from multiprocessing.synchronize import Event as EventType -from typing import Optional, Tuple - -import typeguard -import zmq - -import parsl.curvezmq as curvezmq -from parsl.addresses import tcp_url -from parsl.log_utils import set_file_logger -from parsl.monitoring.errors import MonitoringRouterStartError -from parsl.monitoring.radios.multiprocessing import MultiprocessingQueueRadioSender -from parsl.monitoring.types import TaggedMonitoringMessage -from parsl.multiprocessing import ( - SpawnEvent, - SpawnProcess, - SpawnQueue, - join_terminate_close_proc, -) -from parsl.process_loggers import wrap_with_logs -from parsl.utils import setproctitle - -logger = logging.getLogger(__name__) - - -class MonitoringRouter: - - def __init__(self, - *, - address: str, - port_range: Tuple[int, int] = (55050, 56000), - - run_dir: str = ".", - logging_level: int = logging.INFO, - resource_msgs: mpq.Queue, - exit_event: EventType, - cert_dir: Optional[str], - ): - """ Initializes a monitoring configuration class. - - Parameters - ---------- - address : str - The ip address at which the workers will be able to reach the Hub. - port_range : tuple(int, int) - The MonitoringHub picks ports at random from the range which will be used by Hub. - Default: (55050, 56000) - run_dir : str - Parsl log directory paths. Logs and temp files go here. Default: '.' - logging_level : int - Logging level as defined in the logging module. Default: logging.INFO - resource_msgs : multiprocessing.Queue - A multiprocessing queue to receive messages to be routed onwards to the database process - exit_event : Event - An event that the main Parsl process will set to signal that the monitoring router should shut down. - """ - os.makedirs(run_dir, exist_ok=True) - set_file_logger(f"{run_dir}/monitoring_zmq_router.log", - level=logging_level) - logger.debug("Monitoring router starting") - - self.address = address - - self.loop_freq = 10.0 # milliseconds - - self._context = curvezmq.ServerContext(cert_dir=cert_dir) - self.zmq_receiver_channel = self._context.socket(zmq.DEALER) - self.zmq_receiver_channel.setsockopt(zmq.LINGER, 0) - self.zmq_receiver_channel.set_hwm(0) - self.zmq_receiver_channel.RCVTIMEO = int(self.loop_freq) # in milliseconds - logger.debug("address: {}. port_range {}".format(address, port_range)) - self.zmq_receiver_port = self.zmq_receiver_channel.bind_to_random_port(tcp_url(address), - min_port=port_range[0], - max_port=port_range[1]) - - self.target_radio = MultiprocessingQueueRadioSender(resource_msgs) - self.exit_event = exit_event - - @wrap_with_logs - def start(self) -> None: - logger.info("Starting ZMQ listener") - try: - while not self.exit_event.is_set(): - try: - dfk_loop_start = time.time() - while time.time() - dfk_loop_start < 1.0: # TODO make configurable - # note that nothing checks that msg really is of the annotated type - msg: TaggedMonitoringMessage - msg = self.zmq_receiver_channel.recv_pyobj() - - assert isinstance(msg, tuple), "ZMQ Receiver expects only tuples, got {}".format(msg) - assert len(msg) >= 1, "ZMQ Receiver expects tuples of length at least 1, got {}".format(msg) - assert len(msg) == 2, "ZMQ Receiver expects message tuples of exactly length 2, got {}".format(msg) - - self.target_radio.send(msg) - except zmq.Again: - pass - except Exception: - # This will catch malformed messages. What happens if the - # channel is broken in such a way that it always raises - # an exception? Looping on this would maybe be the wrong - # thing to do. - logger.warning("Failure processing a ZMQ message", exc_info=True) - - logger.info("ZMQ listener finishing normally") - finally: - logger.info("ZMQ listener finished") - - -@wrap_with_logs -@typeguard.typechecked -def zmq_router_starter(*, - comm_q: mpq.Queue, - resource_msgs: mpq.Queue, - exit_event: EventType, - - address: str, - port_range: Tuple[int, int], - - run_dir: str, - logging_level: int, - cert_dir: Optional[str]) -> None: - setproctitle("parsl: monitoring zmq router") - try: - router = MonitoringRouter(address=address, - port_range=port_range, - run_dir=run_dir, - logging_level=logging_level, - resource_msgs=resource_msgs, - exit_event=exit_event, - cert_dir=cert_dir) - except Exception as e: - logger.error("MonitoringRouter construction failed.", exc_info=True) - comm_q.put(f"Monitoring router construction failed: {e}") - else: - comm_q.put(router.zmq_receiver_port) - router.start() - - -class ZMQRadioReceiver(): - def __init__(self, *, process: SpawnProcessType, exit_event: EventType, port: int) -> None: - self.process = process - self.exit_event = exit_event - self.port = port - - def close(self) -> None: - self.exit_event.set() - join_terminate_close_proc(self.process) - - -def start_zmq_receiver(*, - monitoring_messages: QueueType, - loopback_address: str, - port_range: Tuple[int, int], - logdir: str, - worker_debug: bool, - cert_dir: Optional[str]) -> ZMQRadioReceiver: - comm_q = SpawnQueue(maxsize=10) - - router_exit_event = SpawnEvent() - - router_proc = SpawnProcess(target=zmq_router_starter, - kwargs={"comm_q": comm_q, - "resource_msgs": monitoring_messages, - "exit_event": router_exit_event, - "address": loopback_address, - "port_range": port_range, - "run_dir": logdir, - "logging_level": logging.DEBUG if worker_debug else logging.INFO, - "cert_dir": cert_dir, - }, - name="Monitoring-ZMQ-Router-Process", - daemon=True, - ) - router_proc.start() - - try: - logger.debug("Waiting for router process to report port") - comm_q_result = comm_q.get(block=True, timeout=120) - comm_q.close() - comm_q.join_thread() - except queue.Empty: - logger.error("Monitoring ZMQ Router has not reported port in 120s") - raise MonitoringRouterStartError() - - if isinstance(comm_q_result, str): - logger.error("MonitoringRouter sent an error message: %s", comm_q_result) - raise RuntimeError(f"MonitoringRouter failed to start: {comm_q_result}") - - return ZMQRadioReceiver(process=router_proc, exit_event=router_exit_event, port=comm_q_result) diff --git a/parsl/parsl/monitoring/remote.py b/parsl/parsl/monitoring/remote.py deleted file mode 100644 index 121b60ea..00000000 --- a/parsl/parsl/monitoring/remote.py +++ /dev/null @@ -1,292 +0,0 @@ -import datetime -import logging -import os -import time -from functools import wraps -from multiprocessing import Event -from typing import Any, Callable, Dict, List, Sequence, Tuple - -from parsl.monitoring.message_type import MessageType -from parsl.monitoring.radios.base import RadioConfig -from parsl.multiprocessing import ForkProcess -from parsl.process_loggers import wrap_with_logs - -logger = logging.getLogger(__name__) - - -def monitor_wrapper(*, - f: Any, # per app - args: Sequence, # per invocation - kwargs: Dict, # per invocation - x_try_id: int, # per invocation - x_task_id: int, # per invocation - radio_config: RadioConfig, # per executor - run_id: str, # per workflow - logging_level: int, # per workflow - sleep_dur: float, # per workflow - monitor_resources: bool, # per workflow - run_dir: str) -> Tuple[Callable, Sequence, Dict]: - """Wrap the Parsl app with a function that will call the monitor function and point it at the correct pid when the task begins. - """ - - @wraps(f) - def wrapped(*args: List[Any], **kwargs: Dict[str, Any]) -> Any: - task_id = kwargs.pop('_parsl_monitoring_task_id') - try_id = kwargs.pop('_parsl_monitoring_try_id') - terminate_event = Event() - # Send first message to monitoring router - send_first_message(try_id, - task_id, - radio_config, - run_id, - run_dir) - - if monitor_resources and sleep_dur > 0: - # create the monitor process and start - pp = ForkProcess(target=monitor, - args=(os.getpid(), - try_id, - task_id, - radio_config, - run_id, - logging_level, - sleep_dur, - run_dir, - terminate_event), - daemon=True, - name="Monitor-Wrapper-{}".format(task_id)) - pp.start() - p = pp - # TODO: awkwardness because ForkProcess is not directly a constructor - # and type-checking is expecting p to be optional and cannot - # narrow down the type of p in this block. - - else: - p = None - - try: - return f(*args, **kwargs) - finally: - # There's a chance of zombification if the workers are killed by some signals (?) - if p: - terminate_event.set() - p.join(30) # 30 second delay for this -- this timeout will be hit in the case of an unusually long end-of-loop - if p.exitcode is None: - logger.warn("Event-based termination of monitoring helper took too long. Using process-based termination.") - p.terminate() - # DANGER: this can corrupt shared queues according to docs. - # So, better that the above termination event worked. - # This is why this log message is a warning - p.join() - - send_last_message(try_id, - task_id, - radio_config, - run_id, - run_dir) - - new_kwargs = kwargs.copy() - new_kwargs['_parsl_monitoring_task_id'] = x_task_id - new_kwargs['_parsl_monitoring_try_id'] = x_try_id - - return (wrapped, args, new_kwargs) - - -@wrap_with_logs -def send_first_message(try_id: int, - task_id: int, - radio_config: RadioConfig, - run_id: str, run_dir: str) -> None: - send_first_last_message(try_id, task_id, radio_config, run_id, - run_dir, False) - - -@wrap_with_logs -def send_last_message(try_id: int, - task_id: int, - radio_config: RadioConfig, - run_id: str, run_dir: str) -> None: - send_first_last_message(try_id, task_id, radio_config, run_id, - run_dir, True) - - -def send_first_last_message(try_id: int, - task_id: int, - radio_config: RadioConfig, - run_id: str, run_dir: str, - is_last: bool) -> None: - import os - import platform - - radio = radio_config.create_sender() - - msg = (MessageType.WORKER_TASK_INFO, - {'run_id': run_id, - 'try_id': try_id, - 'task_id': task_id, - 'hostname': platform.node(), - 'block_id': os.environ.get('PARSL_WORKER_BLOCK_ID'), - 'first_msg': not is_last, - 'last_msg': is_last, - 'timestamp': datetime.datetime.now() - }) - radio.send(msg) - return - - -@wrap_with_logs -def monitor(pid: int, - try_id: int, - task_id: int, - radio_config: RadioConfig, - run_id: str, - logging_level: int, - sleep_dur: float, - run_dir: str, - # removed all defaults because unused and there's no meaningful default for terminate_event. - # these probably should become named arguments, with a *, and named at invocation. - terminate_event: Any) -> None: # cannot be Event because of multiprocessing type weirdness. - """Monitors the Parsl task's resources by pointing psutil to the task's pid and watching it and its children. - - This process makes calls to logging, but deliberately does not attach - any log handlers. Previously, there was a handler which logged to a - file in /tmp, but this was usually not useful or even accessible. - In some circumstances, it might be useful to hack in a handler so the - logger calls remain in place. - """ - import logging - import platform - - import psutil - - from parsl.utils import setproctitle - - setproctitle("parsl: task resource monitor") - - radio = radio_config.create_sender() - - logging.debug("start of monitor") - - # these values are simple to log. Other information is available in special formats such as memory below. - simple = ["cpu_num", 'create_time', 'cwd', 'exe', 'memory_percent', 'nice', 'name', 'num_threads', 'pid', 'ppid', 'status', 'username'] - # values that can be summed up to see total resources used by task process and its children - summable_values = ['memory_percent', 'num_threads'] - - pm = psutil.Process(pid) - - children_user_time: Dict[int, float] = {} - children_system_time: Dict[int, float] = {} - children_num_ctx_switches_voluntary: Dict[int, float] = {} - children_num_ctx_switches_involuntary: Dict[int, float] = {} - - def accumulate_and_prepare() -> Dict[str, Any]: - d = {"psutil_process_" + str(k): v for k, v in pm.as_dict().items() if k in simple} - d["run_id"] = run_id - d["task_id"] = task_id - d["try_id"] = try_id - d['resource_monitoring_interval'] = sleep_dur - d['hostname'] = platform.node() - d['timestamp'] = datetime.datetime.now() - - logging.debug("getting children") - children = pm.children(recursive=True) - logging.debug("got children") - - d["psutil_cpu_count"] = psutil.cpu_count() - - # note that this will be the CPU number of the base process, not anything launched by it - d["psutil_cpu_num"] = pm.cpu_num() - - pctxsw = pm.num_ctx_switches() - - d["psutil_process_num_ctx_switches_voluntary"] = pctxsw.voluntary - d["psutil_process_num_ctx_switches_involuntary"] = pctxsw.involuntary - - d['psutil_process_memory_virtual'] = pm.memory_info().vms - d['psutil_process_memory_resident'] = pm.memory_info().rss - d['psutil_process_time_user'] = pm.cpu_times().user - d['psutil_process_time_system'] = pm.cpu_times().system - d['psutil_process_children_count'] = len(children) - try: - d['psutil_process_disk_write'] = pm.io_counters().write_chars - d['psutil_process_disk_read'] = pm.io_counters().read_chars - except Exception: - # occasionally pid temp files that hold this information are unvailable to be read so set to zero - logging.exception("Exception reading IO counters for main process. Recorded IO usage may be incomplete", exc_info=True) - d['psutil_process_disk_write'] = 0 - d['psutil_process_disk_read'] = 0 - for child in children: - for k, v in child.as_dict(attrs=summable_values).items(): - d['psutil_process_' + str(k)] += v - child_user_time = child.cpu_times().user - child_system_time = child.cpu_times().system - children_user_time[child.pid] = child_user_time - children_system_time[child.pid] = child_system_time - - pctxsw = child.num_ctx_switches() - children_num_ctx_switches_voluntary[child.pid] = pctxsw.voluntary - children_num_ctx_switches_involuntary[child.pid] = pctxsw.involuntary - - d['psutil_process_memory_virtual'] += child.memory_info().vms - d['psutil_process_memory_resident'] += child.memory_info().rss - try: - d['psutil_process_disk_write'] += child.io_counters().write_chars - d['psutil_process_disk_read'] += child.io_counters().read_chars - except Exception: - # occassionally pid temp files that hold this information are unvailable to be read so add zero - logging.exception("Exception reading IO counters for child {k}. Recorded IO usage may be incomplete".format(k=k), exc_info=True) - d['psutil_process_disk_write'] += 0 - d['psutil_process_disk_read'] += 0 - - total_children_user_time = 0.0 - for child_pid in children_user_time: - total_children_user_time += children_user_time[child_pid] - - total_children_system_time = 0.0 - for child_pid in children_system_time: - total_children_system_time += children_system_time[child_pid] - - total_children_num_ctx_switches_voluntary = 0.0 - for child_pid in children_num_ctx_switches_voluntary: - total_children_num_ctx_switches_voluntary += children_num_ctx_switches_voluntary[child_pid] - - total_children_num_ctx_switches_involuntary = 0.0 - for child_pid in children_num_ctx_switches_involuntary: - total_children_num_ctx_switches_involuntary += children_num_ctx_switches_involuntary[child_pid] - - d['psutil_process_time_user'] += total_children_user_time - d['psutil_process_time_system'] += total_children_system_time - d['psutil_process_num_ctx_switches_voluntary'] += total_children_num_ctx_switches_voluntary - d['psutil_process_num_ctx_switches_involuntary'] += total_children_num_ctx_switches_involuntary - logging.debug("sending message") - return d - - next_send = time.time() - accumulate_dur = 5.0 # TODO: make configurable? - - while not terminate_event.is_set() and pm.is_running(): - logging.debug("start of monitoring loop") - try: - d = accumulate_and_prepare() - if time.time() >= next_send: - logging.debug("Sending intermediate resource message") - radio.send((MessageType.RESOURCE_INFO, d)) - next_send += sleep_dur - except Exception: - logging.exception("Exception getting the resource usage. Not sending usage to Hub", exc_info=True) - logging.debug("sleeping") - - # wait either until approx next send time, or the accumulation period - # so the accumulation period will not be completely precise. - # but before this, the sleep period was also not completely precise. - # with a minimum floor of 0 to not upset wait - - terminate_event.wait(max(0, min(next_send - time.time(), accumulate_dur))) - - logging.debug("Sending final resource message") - try: - d = accumulate_and_prepare() - radio.send((MessageType.RESOURCE_INFO, d)) - except Exception: - logging.exception("Exception getting the resource usage. Not sending final usage to Hub", exc_info=True) - logging.debug("End of monitoring helper") diff --git a/parsl/parsl/monitoring/types.py b/parsl/parsl/monitoring/types.py deleted file mode 100644 index 662d1421..00000000 --- a/parsl/parsl/monitoring/types.py +++ /dev/null @@ -1,11 +0,0 @@ -from typing import Any, Dict, Tuple - -from typing_extensions import TypeAlias - -from parsl.monitoring.message_type import MessageType - -# A MonitoringMessage dictionary can be tagged, giving a -# TaggedMonitoringMessage. - -MonitoringMessage: TypeAlias = Dict[str, Any] -TaggedMonitoringMessage: TypeAlias = Tuple[MessageType, MonitoringMessage] diff --git a/parsl/parsl/monitoring/visualization/__init__.py b/parsl/parsl/monitoring/visualization/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/monitoring/visualization/app.py b/parsl/parsl/monitoring/visualization/app.py deleted file mode 100644 index e0c9510e..00000000 --- a/parsl/parsl/monitoring/visualization/app.py +++ /dev/null @@ -1,36 +0,0 @@ -import argparse -import os - -from flask import Flask - -from parsl.monitoring.visualization.models import db - - -def cli_run(): - """ Instantiates the Monitoring viz server - """ - parser = argparse.ArgumentParser(description='Parsl visualization tool') - parser.add_argument('db_path', type=str, default="sqlite:///{cwd}/runinfo/monitoring.db".format(cwd=os.getcwd()), - nargs="?", help='Database path in the format sqlite:///') - parser.add_argument('-p', '--port', type=int, default=8080, - help='Port at which the monitoring Viz Server is hosted. Default: 8080') - parser.add_argument("-d", "--debug", action='store_true', - help="Enable debug logging") - parser.add_argument("-l", "--listen", type=str, default="127.0.0.1", metavar="ADDRESS", - help="Choose address to listen for connections on. Default: 127.0.0.1. Choose 0.0.0.0 to listen on all addresses.") - args = parser.parse_args() - - app = Flask(__name__) - app.config['SQLALCHEMY_DATABASE_URI'] = args.db_path - app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False - db.init_app(app) - - with app.app_context(): - db.create_all() - from parsl.monitoring.visualization import views - views.dummy = False - app.run(host=args.listen, port=args.port, debug=args.debug) - - -if __name__ == "__main__": - cli_run() diff --git a/parsl/parsl/monitoring/visualization/models.py b/parsl/parsl/monitoring/visualization/models.py deleted file mode 100644 index dd39c1d9..00000000 --- a/parsl/parsl/monitoring/visualization/models.py +++ /dev/null @@ -1,112 +0,0 @@ -from flask_sqlalchemy import SQLAlchemy - -WORKFLOW = 'workflow' # Workflow table includes workflow metadata -TASK = 'task' # Task table includes task metadata -STATUS = 'status' # Status table includes task status -RESOURCE = 'resource' # Resource table includes task resource utilization -NODE = 'node' # Node table include node info - -db = SQLAlchemy() - - -class Workflow(db.Model): - __tablename__ = WORKFLOW - run_id = db.Column(db.Text, nullable=False, primary_key=True) - workflow_name = db.Column(db.Text, nullable=True) - workflow_version = db.Column(db.Text, nullable=True) - time_began = db.Column(db.DateTime, nullable=False) # Why not date? - time_completed = db.Column(db.DateTime) - host = db.Column(db.Text, nullable=False) - user = db.Column(db.Text, nullable=False) - rundir = db.Column(db.Text, nullable=False) - tasks_failed_count = db.Column(db.Integer, nullable=False) - tasks_completed_count = db.Column(db.Integer, nullable=False) - - -class Node(db.Model): - __tablename__ = NODE - id = db.Column('id', db.Integer, nullable=False, primary_key=True, autoincrement=True) - run_id = db.Column('run_id', db.Text, nullable=False) - hostname = db.Column('hostname', db.Text, nullable=False) - cpu_count = db.Column('cpu_count', db.Integer, nullable=False) - total_memory = db.Column('total_memory', db.Integer, nullable=False) - active = db.Column('active', db.Boolean, nullable=False) - worker_count = db.Column('worker_count', db.Integer, nullable=False) - python_v = db.Column('python_v', db.Text, nullable=False) - reg_time = db.Column('reg_time', db.DateTime, nullable=False) - - -# TODO: expand to full set of info -class Status(db.Model): - __tablename__ = STATUS - task_id = db.Column(db.Integer, db.ForeignKey( - 'task.task_id'), nullable=False) - task_status_name = db.Column(db.Text, nullable=False) - timestamp = db.Column(db.DateTime, nullable=False) - run_id = db.Column(db.Text, db.ForeignKey( - 'workflow.run_id'), nullable=False) - __table_args__ = ( - db.PrimaryKeyConstraint('task_id', 'run_id', - 'task_status_name', 'timestamp'), - ) - - -class Task(db.Model): - __tablename__ = TASK - task_id = db.Column('task_id', db.Integer, nullable=False) - run_id = db.Column('run_id', db.Text, nullable=False) - task_func_name = db.Column('task_func_name', db.Text, nullable=False) - task_depends = db.Column('task_depends', db.Text, nullable=True) - task_time_invoked = db.Column( - 'task_time_invoked', db.DateTime, nullable=True) - task_time_returned = db.Column( - 'task_time_returned', db.DateTime, nullable=True) - task_memoize = db.Column('task_memoize', db.Text, nullable=False) - task_inputs = db.Column('task_inputs', db.Text, nullable=True) - task_outputs = db.Column('task_outputs', db.Text, nullable=True) - task_stdin = db.Column('task_stdin', db.Text, nullable=True) - task_stdout = db.Column('task_stdout', db.Text, nullable=True) - task_stderr = db.Column('task_stderr', db.Text, nullable=True) - __table_args__ = ( - db.PrimaryKeyConstraint('task_id', 'run_id'), - ) - - -class Resource(db.Model): - __tablename__ = RESOURCE - task_id = db.Column('task_id', db.Integer, db.ForeignKey( - 'task.task_id'), nullable=False) - timestamp = db.Column('timestamp', db.DateTime, nullable=False) - run_id = db.Column('run_id', db.Text, db.ForeignKey( - 'workflow.run_id'), nullable=False) - resource_monitoring_interval = db.Column( - 'resource_monitoring_interval', db.Float, nullable=True) - psutil_process_pid = db.Column( - 'psutil_process_pid', db.Integer, nullable=True) - psutil_process_memory_percent = db.Column( - 'psutil_process_memory_percent', db.Float, nullable=True) - psutil_process_children_count = db.Column( - 'psutil_process_children_count', db.Float, nullable=True) - psutil_process_time_user = db.Column( - 'psutil_process_time_user', db.Float, nullable=True) - psutil_process_time_system = db.Column( - 'psutil_process_time_system', db.Float, nullable=True) - psutil_process_memory_virtual = db.Column( - 'psutil_process_memory_virtual', db.Float, nullable=True) - psutil_process_memory_resident = db.Column( - 'psutil_process_memory_resident', db.Float, nullable=True) - psutil_process_disk_read = db.Column( - 'psutil_process_disk_read', db.Float, nullable=True) - psutil_process_disk_write = db.Column( - 'psutil_process_disk_write', db.Float, nullable=True) - psutil_process_status = db.Column( - 'psutil_process_status', db.Text, nullable=True) - psutil_cpu_num = db.Column( - 'psutil_cpu_num', db.Text, nullable=True) - psutil_process_num_ctx_switches_voluntary = db.Column( - 'psutil_process_num_ctx_switches_voluntary', db.Float, nullable=True) - psutil_process_num_ctx_switches_involuntary = db.Column( - 'psutil_process_num_ctx_switches_involuntary', db.Float, nullable=True) - - __table_args__ = ( - db.PrimaryKeyConstraint('task_id', 'run_id', 'timestamp'),) diff --git a/parsl/parsl/monitoring/visualization/plots/__init__.py b/parsl/parsl/monitoring/visualization/plots/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/monitoring/visualization/plots/default/__init__.py b/parsl/parsl/monitoring/visualization/plots/default/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/monitoring/visualization/plots/default/task_plots.py b/parsl/parsl/monitoring/visualization/plots/default/task_plots.py deleted file mode 100644 index 9885859a..00000000 --- a/parsl/parsl/monitoring/visualization/plots/default/task_plots.py +++ /dev/null @@ -1,33 +0,0 @@ -import plotly.graph_objs as go -from plotly.offline import plot - - -def time_series_cpu_per_task_plot(df_resources, resource_type, label): - yaxis = dict(title='Accumulated CPU user time (seconds)') - fig = go.Figure(data=[go.Scatter(x=df_resources['timestamp'], - y=df_resources[resource_type])], - layout=go.Layout(xaxis=dict(tickformat='%m-%d\n%H:%M:%S', - autorange=True, - title='Time'), - yaxis=yaxis, - title=label)) - - return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) - - -def time_series_memory_per_task_plot(df_resources, resource_type, label): - if resource_type == "psutil_process_memory_percent": - yaxis = dict(title="Memory utilization") - data = [go.Scatter(x=df_resources['timestamp'], - y=df_resources[resource_type])] - else: - yaxis = dict(title='Memory usage (GB)') - data = [go.Scatter(x=df_resources['timestamp'], - y=[num / 1000000000 for num in df_resources[resource_type].astype(float)])] - fig = go.Figure(data=data, - layout=go.Layout(xaxis=dict(tickformat='%m-%d\n%H:%M:%S', - autorange=True, - title='Time'), - yaxis=yaxis, - title=label)) - return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) diff --git a/parsl/parsl/monitoring/visualization/plots/default/workflow_plots.py b/parsl/parsl/monitoring/visualization/plots/default/workflow_plots.py deleted file mode 100644 index 7c972374..00000000 --- a/parsl/parsl/monitoring/visualization/plots/default/workflow_plots.py +++ /dev/null @@ -1,294 +0,0 @@ -import math - -import networkx as nx -import numpy as np -import pandas as pd -import plotly.figure_factory as ff -import plotly.graph_objs as go -from plotly.offline import plot - -from parsl.dataflow.states import States -from parsl.monitoring.visualization.utils import ( - DB_DATE_FORMAT, - num_to_timestamp, - timestamp_to_int, -) - -gantt_colors = { - "pending": 'rgb(168, 168, 168)', - "launched": 'rgb(100, 255, 255)', - "running": 'rgb(0, 0, 255)', - "dep_fail": 'rgb(255, 128, 255)', - "failed": 'rgb(200, 0, 0)', - "exec_done": 'rgb(0, 200, 0)', - "memo_done": 'rgb(64, 200, 64)', - "fail_retryable": 'rgb(200, 128, 128)', - "joining": 'rgb(128, 128, 255)', - "running_ended": 'rgb(64, 64, 255)', -} -dag_state_colors = {} -for enum_i, (state, color) in enumerate(gantt_colors.items()): - dag_state_colors[state] = (enum_i, color) - -assert len(States) == len(gantt_colors), "must assign color for every state" -assert all(s.name in gantt_colors for s in States), "must assign color for every state" - - -def task_gantt_plot(df_task, df_status, time_completed=None): - - if df_task.empty: - return None - - # if the workflow is not recorded as completed, then assume - # that tasks should continue in their last state until now, - # rather than the workflow end time. - if not time_completed: - time_completed = df_status['timestamp'].max() - - df_task = df_task.sort_values(by=['task_id'], ascending=False) - - parsl_tasks = [] - for i, task in df_task.iterrows(): - task_id = task['task_id'] - - description = "Task ID: {}, app: {}".format(task['task_id'], task['task_func_name']) - - statuses = df_status.loc[df_status['task_id'] == task_id].sort_values(by=['timestamp']) - - last_status = None - for j, status in statuses.iterrows(): - if last_status is not None: - last_status_bar = {'Task': description, - 'Start': last_status['timestamp'], - 'Finish': status['timestamp'], - 'Resource': last_status['task_status_name'] - } - parsl_tasks.extend([last_status_bar]) - last_status = status - - # TODO: factor with above? - if last_status is not None: - last_status_bar = {'Task': description, - 'Start': last_status['timestamp'], - 'Finish': time_completed, - 'Resource': last_status['task_status_name'] - } - parsl_tasks.extend([last_status_bar]) - - fig = ff.create_gantt(parsl_tasks, - title="", - colors=gantt_colors, - group_tasks=True, - show_colorbar=True, - index_col='Resource', - ) - fig['layout']['yaxis']['title'] = 'Task' - fig['layout']['yaxis']['showticklabels'] = False - fig['layout']['xaxis']['title'] = 'Time' - return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) - - -def task_per_app_plot(task, status, time_completed): - - try: - task['epoch_time_running'] = (pd.to_datetime( - task['task_try_time_running']) - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') - task['epoch_time_returned'] = (pd.to_datetime( - task['task_time_returned']) - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') - start = int(task['epoch_time_running'].min()) - - end = int(task['epoch_time_returned'].max()) - - tasks_per_app = {} - all_tasks = [0] * (end - start + 1) - for i, row in task.iterrows(): - if math.isnan(row['epoch_time_running']): - # Skip rows with no running start time. - continue - if math.isnan(row['epoch_time_returned']): - time_returned = end - else: - time_returned = int(row['epoch_time_returned']) - - if row['task_func_name'] not in tasks_per_app: - tasks_per_app[row['task_func_name']] = [0] * (end - start + 1) - for j in range(int(row['epoch_time_running']) + 1, time_returned + 1): - tasks_per_app[row['task_func_name']][j - start] += 1 - all_tasks[j - start] += 1 - fig = go.Figure( - data=[go.Scatter(x=list(range(0, end - start + 1)), - y=all_tasks, - name='All', - )] + - [go.Scatter(x=list(range(0, end - start + 1)), - y=tasks_per_app[app], - name=app, - ) for app in tasks_per_app], - - layout=go.Layout(xaxis=dict(autorange=True, - title='Time (seconds)'), - yaxis=dict(title='Number of tasks'), - title="Execution tries per app")) - return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) - except Exception as e: - return "The tasks per app plot cannot be generated because of exception {}.".format(e) - - -def total_tasks_plot(df_task, df_status, columns=20): - - min_time = timestamp_to_int(min(df_status['timestamp'])) - max_time = timestamp_to_int(max(df_status['timestamp'])) - time_step = (max_time - min_time) / columns - - x_axis = [] - for i in np.arange(min_time, max_time + time_step, time_step): - x_axis.append(num_to_timestamp(i).strftime(DB_DATE_FORMAT)) - - # Fill up dict "apps" like: {app1: [#task1, #task2], app2: [#task4], app3: [#task3]} - apps_dict = dict() - for i in range(len(df_task)): - row = df_task.iloc[i] - if row['task_func_name'] in apps_dict: - apps_dict[row['task_func_name']].append(row['task_id']) - else: - apps_dict[row['task_func_name']] = [row['task_id']] - - def y_axis_setup(value): - items = [] - for app, tasks in apps_dict.items(): - tmp = [] - task = df_status[df_status['task_id'].isin(tasks)] - for i in range(len(x_axis) - 1): - x = task['timestamp'] >= x_axis[i] - y = task['timestamp'] < x_axis[i + 1] - tmp.append(sum(task.loc[x & y]['task_status_name'] == value)) - items = np.sum([items, tmp], axis=0) - - return items - - y_axis_done = y_axis_setup('done') - y_axis_failed = y_axis_setup('failed') - - fig = go.Figure(data=[go.Bar(x=x_axis[:-1], - y=y_axis_done, - name='done'), - go.Bar(x=x_axis[:-1], - y=y_axis_failed, - name='failed')], - layout=go.Layout(xaxis=dict(tickformat='%m-%d\n%H:%M:%S', - autorange=True, - title='Time'), - yaxis=dict(tickformat=',d', - title='Running tasks.' ' Bin width: ' + num_to_timestamp(time_step).strftime('%Mm%Ss')), - annotations=[ - dict( - x=0, - y=1.07, - showarrow=False, - text='Total Done: ' + - str(sum(y_axis_done)), - xref='paper', - yref='paper' - ), - dict( - x=0, - y=1.05, - showarrow=False, - text='Total Failed: ' + - str(sum(y_axis_failed)), - xref='paper', - yref='paper' - ), - ], - barmode='stack', - title="Total tasks")) - - return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) - - -def workflow_dag_plot(df_tasks, group_by_apps=True): - G = nx.DiGraph(directed=True) - nodes = df_tasks['task_id'].unique() - dic = df_tasks.set_index('task_id').to_dict() - G.add_nodes_from(nodes) - - # Add edges or links between the nodes: - edges = [] - for k, v in dic['task_depends'].items(): - if v: - adj = v.split(",") - for e in adj: - edges.append((int(e), k)) - G.add_edges_from(edges) - - node_positions = nx.nx_pydot.pydot_layout(G, prog='dot') - - if group_by_apps: - groups_list = {app: (i, None) for i, app in enumerate( - df_tasks['task_func_name'].unique())} - else: - groups_list = dag_state_colors - - node_traces = [...] * len(groups_list) - - for k, (index, color) in groups_list.items(): - node_trace = go.Scatter( - x=[], - y=[], - text=[], - mode='markers', - textposition='top center', - textfont=dict( - family='arial', - size=18, - color='rgb(0,0,0)' - ), - hoverinfo='text', - name=k, # legend app_name here - marker=dict( - showscale=False, - color=color, - size=11, - line=dict(width=1, color='rgb(0,0,0)'))) - node_traces[index] = node_trace - - for node in node_positions: - x, y = node_positions[node] - if group_by_apps: - name = dic['task_func_name'][node] - else: - name = dic['task_status_name'][node] - index, _ = groups_list[name] - node_traces[index]['x'] += tuple([x]) - node_traces[index]['y'] += tuple([y]) - node_traces[index]['text'] += tuple( - ["{}:{}".format(dic['task_func_name'][node], node)]) - - # The edges will be drawn as lines: - edge_trace = go.Scatter( - x=[], - y=[], - line=dict(width=1, color='rgb(160,160,160)'), - hoverinfo='none', - # showlegend=False, - name='Dependency', - mode='lines') - - for edge in G.edges: - x0, y0 = node_positions[edge[0]] - x1, y1 = node_positions[edge[1]] - edge_trace['x'] += tuple([x0, x1, None]) - edge_trace['y'] += tuple([y0, y1, None]) - - # Create figure: - title = go.layout.Title(text='Workflow DAG', font=dict(size=16)) - fig = go.Figure(data=[edge_trace] + node_traces, - layout=go.Layout( - title=title, - showlegend=True, - hovermode='closest', - margin=dict(b=20, l=5, r=5, t=40), # noqa: E741 - xaxis=dict(showgrid=False, zeroline=False, - showticklabels=False), - yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))) - return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) diff --git a/parsl/parsl/monitoring/visualization/plots/default/workflow_resource_plots.py b/parsl/parsl/monitoring/visualization/plots/default/workflow_resource_plots.py deleted file mode 100644 index c7cd776d..00000000 --- a/parsl/parsl/monitoring/visualization/plots/default/workflow_resource_plots.py +++ /dev/null @@ -1,242 +0,0 @@ -import math - -import numpy as np -import pandas as pd -import plotly.graph_objs as go -from plotly.offline import plot - - -def resource_distribution_plot(df_resources, df_task, type, label, option, columns=20,): - assert type == "psutil_process_time_user" or type == "psutil_process_memory_resident" - assert option == "avg" or option == "max" - - min_range = min(df_resources[type].astype('float')) - max_range = max(df_resources[type].astype('float')) - time_step = (max_range - min_range) / columns - - if min_range == max_range: - x_axis = [min_range] - else: - x_axis = [] - for i in np.arange(min_range, max_range + time_step, time_step): - x_axis.append(i) - - tasks_dict = dict() - for i in range(len(df_task)): - row = df_task.iloc[i] - tasks_dict[row['task_id']] = [] - - def y_axis_setup(): - items = [0] * len(x_axis) - - for task_id, tasks in tasks_dict.items(): - if option == 'avg': - task = df_resources[df_resources['task_id'] == - task_id][type].astype('float').mean() - elif option == 'max': - task = df_resources[df_resources['task_id'] == task_id][type].astype('float').max() - else: - raise ValueError(f"Cannot plot unknown aggregation option {option}") - - for i in range(len(x_axis) - 1): - a = task >= x_axis[i] - b = task < x_axis[i + 1] - if a and b: - items[i] += 1 - break - if task >= x_axis[-1]: - items[-1] += 1 - return items - - if type == "psutil_process_time_user": - xaxis = dict(autorange=True, - title='CPU user time (seconds)') - elif type == "psutil_process_memory_resident": - xaxis = dict(autorange=True, - title='Memory usage (bytes)') - else: - raise ValueError(f"Cannot plot unknown type {type}") - - fig = go.Figure( - data=[go.Bar(x=x_axis, - y=y_axis_setup(), - name='tasks')], - layout=go.Layout(xaxis=xaxis, - yaxis=dict(title='Tasks'), - title=label + '(' + option + ')')) - - return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) - - -def resource_time_series(tasks, type='psutil_process_time_user', label='CPU user time'): - tasks['epoch_time'] = (pd.to_datetime( - tasks['timestamp']) - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') - step = int(tasks['resource_monitoring_interval'][0]) - start = tasks['epoch_time'].min() - end = tasks['epoch_time'].max() - tasks['relative_time'] = tasks['epoch_time'] - start - if end != start: - bins = pd.cut(tasks['relative_time'], - range(0, end - start + 1, step), - include_lowest=True) - df = tasks.groupby(bins, as_index=False)[type].mean() - df['time'] = step * df.index - fig = go.Figure( - data=[go.Scatter(x=df['time'], - y=df[type], - )], - layout=go.Layout(xaxis=dict(autorange=True, - title='Time (seconds)'), - yaxis=dict(title=label), - title=label)) - else: - fig = go.Figure( - data=[go.Scatter(x=[0], - y=[tasks[type].mean()], - )], - layout=go.Layout(xaxis=dict(autorange=True, - title='Time (seconds)'), - yaxis=dict(title=label), - title=label)) - return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) - - -def worker_efficiency(task, node): - try: - node['epoch_time'] = (pd.to_datetime( - node['timestamp']) - pd.Timestamp("1970-01-01")) / pd.Timedelta('1s') - task['epoch_time_start'] = (pd.to_datetime( - task['task_try_time_launched']) - pd.Timestamp("1970-01-01")) / pd.Timedelta('1s') - task['epoch_time_running'] = (pd.to_datetime( - task['task_try_time_running']) - pd.Timestamp("1970-01-01")) / pd.Timedelta('1s') - task['epoch_time_returned'] = (pd.to_datetime( - task['task_try_time_returned']) - pd.Timestamp("1970-01-01")) / pd.Timedelta('1s') - start = int(min(task['epoch_time_start'].min(), node['epoch_time'].min())) - end = int(task['epoch_time_returned'].max()) - - # worker_plot will be used to make a histogram of worker usage, one bucket per - # second - worker_plot = [0] * (end - start + 1) - total_workers = node['worker_count'].sum() - - for i, row in task.iterrows(): - if math.isnan(row['epoch_time_running']): - # skip tasks with no running start time - continue - if math.isnan(row['epoch_time_returned']): - # there is no end time for this, so we should assume the "end" time - etr = end - else: - etr = row['epoch_time_returned'] - - # there's a two cases here: - # The task starts and ends in same one-second bucket: - # populate one bucket with the fraction of second between - # the start and end - # - # or - # - # The task starts in one bucket and ends in another: - # populate middle buckets with 1, and start/end buckets with fraction - - start_bucket = int(row['epoch_time_running']) - start - end_bucket = int(etr) - start - - if start_bucket == end_bucket: - fraction = etr - row['epoch_time_running'] - worker_plot[start_bucket] += fraction - - else: - fraction_before = row['epoch_time_running'] - int(row['epoch_time_running']) - worker_plot[start_bucket] += (1 - fraction_before) - - for j in range(start_bucket + 1, end_bucket): - worker_plot[j] += 1 - - fraction_after = etr - int(etr) - worker_plot[end_bucket] += fraction_after - - fig = go.Figure( - data=[go.Scatter(x=list(range(0, end - start + 1)), - y=worker_plot, - name='Total busy workers', - ), - go.Scatter(x=list(range(0, end - start + 1)), - y=[total_workers] * (end - start + 1), - name='Total of workers in whole run', - ) - ], - layout=go.Layout(xaxis=dict(autorange=True, - title='Time (seconds)'), - yaxis=dict(title='Number of workers'), - title="Worker efficiency")) - return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) - except Exception as e: - print(e) - return "The worker efficiency plot cannot be generated due to missing data." - - -def resource_efficiency(resource, node, label): - try: - resource['epoch_time'] = (pd.to_datetime( - resource['timestamp']) - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') - node['epoch_time'] = (pd.to_datetime( - node['timestamp']) - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') - resource = resource.sort_values(by='epoch_time') - start = min(resource['epoch_time'].min(), node['epoch_time'].min()) - end = resource['epoch_time'].max() - resource['relative_time'] = resource['epoch_time'] - start - node['relative_time'] = node['epoch_time'] - start - resource['key'] = resource['task_id'].astype(str) + "-" + resource['try_id'].astype(str) - - task_plot = [0] * (end - start + 1) - if label == 'CPU': - total = node['cpu_count'].sum() - elif label == 'mem': - total = node['total_memory'].sum() / 1024 / 1024 / 1024 - - resource['total_cpu_time'] = resource['psutil_process_time_user'] + resource['psutil_process_time_system'] - for key in resource['key'].unique(): - tmp = resource[resource['key'] == key] - tmp['last_timestamp'] = tmp['relative_time'].shift(1) - if label == 'CPU': - tmp['last_cputime'] = tmp['total_cpu_time'].shift(1) - for index, row in tmp.iterrows(): - if np.isnan(row['last_timestamp']): - continue - for i in range(int(row['last_timestamp']), int(row['relative_time'])): - if label == 'CPU': - diff = (row['total_cpu_time'] - row['last_cputime']) / (row['relative_time'] - row['last_timestamp']) - elif label == 'mem': - diff = row['psutil_process_memory_resident'] / 1024 / 1024 / 1024 - task_plot[i] += diff - - if label == 'CPU': - name1 = 'Used CPU cores' - name2 = 'Total CPU cores' - yaxis = 'Number of CPU cores' - title = 'CPU usage' - elif label == 'mem': - name1 = 'Used memory' - name2 = 'Total memory' - yaxis = 'Memory (GB)' - title = 'Memory usage' - - fig = go.Figure( - data=[go.Scatter(x=list(range(0, end - start + 1)), - y=task_plot, - name=name1, - ), - go.Scatter(x=list(range(0, end - start + 1)), - y=[total] * (end - start + 1), - name=name2, - ) - ], - layout=go.Layout(xaxis=dict(autorange=True, - title='Time (seconds)'), - yaxis=dict(title=yaxis), - title=title)) - return plot(fig, show_link=False, output_type="div", include_plotlyjs=False) - except Exception as e: - print(e) - return "The resource efficiency 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a/parsl/parsl/monitoring/visualization/static/parsl-monitor.css +++ /dev/null @@ -1,21 +0,0 @@ -.task{ - border: 2px solid; - font-size: 10px; - padding: 5px; - margin-right: 3px; -} - -.task-complete{ - border-radius: 50%!important; - border-color: #006600; -} - -.task-running{ - border-radius: 50%!important; - border: 3px dashed; - border-color: #66ff66; -} - -.task-failed{ - border-color: red; -} \ No newline at end of file diff --git a/parsl/parsl/monitoring/visualization/templates/app.html b/parsl/parsl/monitoring/visualization/templates/app.html deleted file mode 100644 index ab63b9b4..00000000 --- a/parsl/parsl/monitoring/visualization/templates/app.html +++ /dev/null @@ -1,53 +0,0 @@ -{% extends "layout.html" %} - -{% block content %} - -
      -

      {{ app_name }}

      -
      - - -       - - - - - - - - - - - - - - {% for t in task_summary %} - - - - - - - {% endfor %} - -
      NameTask IDDependenciesCompleted
      {{ t.task_func_name }}{{ t['task_id'] }} - {% if t['task_depends'] %} - {% for id in t['task_depends'].split(",") %} - {{ id }} - {% endfor %} - {% else %} - None - {% endif %} - {{ t['task_time_returned'] | timeformat }}
      - -{% endblock %} diff --git a/parsl/parsl/monitoring/visualization/templates/dag.html b/parsl/parsl/monitoring/visualization/templates/dag.html deleted file mode 100644 index 3fdc4b4a..00000000 --- a/parsl/parsl/monitoring/visualization/templates/dag.html +++ /dev/null @@ -1,34 +0,0 @@ -{% extends "layout.html" %} - -{% block content %} - -
      -

      {{ workflow_details['workflow_name'] }}

      - -
      - -
        -
      • Started: {{ workflow_details['time_began'] | timeformat }}
        • -
      • Completed: {{ workflow_details['time_completed'] | timeformat }}
        • -
      • Workflow duration: {{ (workflow_details['time_began'], workflow_details['time_completed']) | durationformat }}
        • -
      • Owner: {{ workflow_details['user'] }}
        • -
      • Host: {{ workflow_details['host'] }}
        • -
      • Run directory: {{ workflow_details['rundir'] }}
        • -
      • Number of completed tasks: {{ workflow_details['tasks_completed_count'] }}
        • -
      • Number of failed tasks: {{ workflow_details['tasks_failed_count'] }}
        • -
      - -{% if not group_by_apps %} -       View workflow DAG -- colored by apps -{% else %} - View workflow DAG -- colored by task states -{% endif%} -
      View workflow task summary - -
      -
      - {{ workflow_dag_plot | safe }} -
      -
      - -{% endblock %} diff --git a/parsl/parsl/monitoring/visualization/templates/error.html b/parsl/parsl/monitoring/visualization/templates/error.html deleted file mode 100644 index fbf54c39..00000000 --- a/parsl/parsl/monitoring/visualization/templates/error.html +++ /dev/null @@ -1,9 +0,0 @@ -{% extends "layout.html" %} - -{% block content %} - -
      -

      {{ message }}

      - - -{% endblock %} diff --git a/parsl/parsl/monitoring/visualization/templates/layout.html b/parsl/parsl/monitoring/visualization/templates/layout.html deleted file mode 100644 index 0b25df42..00000000 --- a/parsl/parsl/monitoring/visualization/templates/layout.html +++ /dev/null @@ -1,54 +0,0 @@ - - - - - - - - - Parsl Monitoring Interface - - - - - - - - - - - - - - - - - - - -
      - - {% block content %}{% endblock %} - - -
      - - - - diff --git a/parsl/parsl/monitoring/visualization/templates/resource_usage.html b/parsl/parsl/monitoring/visualization/templates/resource_usage.html deleted file mode 100644 index fb97cb9f..00000000 --- a/parsl/parsl/monitoring/visualization/templates/resource_usage.html +++ /dev/null @@ -1,62 +0,0 @@ -{% extends "layout.html" %} - -{% block content %} - -
      -

      {{ workflow_details['workflow_name'] }}

      - -
      - -
        -
      • Started: {{ workflow_details['time_began'] | timeformat }}
        • -
      • Completed: {{ workflow_details['time_completed'] | timeformat }}
        • -
      • Workflow duration: {{ (workflow_details['time_began'], workflow_details['time_completed']) | durationformat }}
        • -
      • Owner: {{ workflow_details['user'] }}
        • -
      • Host: {{ workflow_details['host'] }}
        • -
      • Run directory: {{ workflow_details['rundir'] }}
        • -
      • Number of completed tasks: {{ workflow_details['tasks_completed_count'] }}
        • -
      • Number of failed tasks: {{ workflow_details['tasks_failed_count'] }}
        • -
      - -View workflow DAG -- colored by apps -
      View workflow DAG -- colored by task states -
      View workflow task summary - - -
      Worker usage
      -
      -{{ worker_efficiency|safe }} -
      - -
      CPU usage
      -
      -{{ cpu_efficiency|safe }} -
      - -
      CPU Usage
      -
      -
      -
      -{{ user_time_distribution_max_plot|safe }} -
      -
      -
      - -
      Memory usage
      -
      -{{ memory_efficiency|safe }} -
      - -
      Memory Usage
      -
      -
      -
      -{{ memory_usage_distribution_avg_plot|safe }} -
      -
      -{{ memory_usage_distribution_max_plot|safe }} -
      -
      -
      - -{% endblock %} diff --git a/parsl/parsl/monitoring/visualization/templates/task.html b/parsl/parsl/monitoring/visualization/templates/task.html deleted file mode 100644 index 3e763401..00000000 --- a/parsl/parsl/monitoring/visualization/templates/task.html +++ /dev/null @@ -1,80 +0,0 @@ -{% extends "layout.html" %} - -{% block content %} - -
      -

      {{ task_details['task_func_name'] }} ({{ task_details['task_id'] }})

      -
      -
      -
      -
      - -
      -
      -
      Task State
      - - - - - - - - - - {% for t in task_status %} - - - - - {% endfor %} - -
      TimeState
      {{ t.timestamp | timeformat }}{{ t.task_status_name }}
      -
      -
      -
      - -
      -
      -
      -{{ time_series_memory_resident|safe }} -
      -
      -
      - - -{% endblock %} diff --git a/parsl/parsl/monitoring/visualization/templates/workflow.html b/parsl/parsl/monitoring/visualization/templates/workflow.html deleted file mode 100644 index d8b78a18..00000000 --- a/parsl/parsl/monitoring/visualization/templates/workflow.html +++ /dev/null @@ -1,55 +0,0 @@ -{% extends "layout.html" %} - -{% block content %} - -
      -

      {{ workflow_details['workflow_name'] }}

      -
      - -
      -
      -
      -
      Workflow Summary
      - -
        -
      • Started: {{ workflow_details['time_began'] | timeformat}}
        • -
      • Completed: {{ workflow_details['time_completed'] | timeformat }}
        • -
      • Workflow duration: {{ (workflow_details['time_began'], workflow_details['time_completed']) | durationformat }}
        • -
      • Owner: {{ workflow_details['user'] }}
        • -
      • Host: {{ workflow_details['host'] }}
        • -
      • Run directory: {{ workflow_details['rundir'] }}
        • -
      • Number of completed tasks: {{ workflow_details['tasks_completed_count'] }}
        • -
      • Number of failed tasks: {{ workflow_details['tasks_failed_count'] }}
        • -
      -
      -
      -
      App Summary
      - - - - - - - - - - {% for (_, t) in task_summary.iterrows() %} - - - - - {% endfor %} - -
      NameCount
      {{ t['task_func_name'] }}{{ t.frequency }}
      -
      -
      -
      - -View workflow DAG -- colored by apps -
      View workflow DAG -- colored by task states -
      View workflow resource usage - -{{ task_gantt | safe }} -{{ task_per_app |safe }} - -{% endblock %} diff --git a/parsl/parsl/monitoring/visualization/templates/workflows_summary.html b/parsl/parsl/monitoring/visualization/templates/workflows_summary.html deleted file mode 100644 index 277e2fea..00000000 --- a/parsl/parsl/monitoring/visualization/templates/workflows_summary.html +++ /dev/null @@ -1,37 +0,0 @@ -{% extends "layout.html" %} - -{% block content %} - -
      -

      Workflows

      - - - - - - - - - - - - - - {% for w in workflows %} - - - - - - - - - {% endfor %} - -
      NameVersionOwnerStatusRuntime (s)Tasks
      {{ w.workflow_name }}{{ w.workflow_version }}{{ w.user }}{{ w.status }}{{ (w.time_began, w.time_completed) | durationformat }} - {{ w['tasks_completed_count'] }} - {{ w['tasks_failed_count'] }} - -
      - -{% endblock %} diff --git a/parsl/parsl/monitoring/visualization/utils.py b/parsl/parsl/monitoring/visualization/utils.py deleted file mode 100644 index a2438c5c..00000000 --- a/parsl/parsl/monitoring/visualization/utils.py +++ /dev/null @@ -1,15 +0,0 @@ -from datetime import datetime - -DB_DATE_FORMAT = '%Y-%m-%d %H:%M:%S' - - -def timestamp_to_float(time, format=DB_DATE_FORMAT): - return datetime.strptime(time, format).timestamp() - - -def timestamp_to_int(time): - return int(timestamp_to_float(time)) - - -def num_to_timestamp(n): - return datetime.fromtimestamp(n) diff --git a/parsl/parsl/monitoring/visualization/version.py b/parsl/parsl/monitoring/visualization/version.py deleted file mode 100644 index e7800716..00000000 --- a/parsl/parsl/monitoring/visualization/version.py +++ /dev/null @@ -1,6 +0,0 @@ -"""Set module version. - -..[alpha/beta/..] -Alphas will be numbered like this -> 0.4.0a0 -""" -VERSION = '0.1' diff --git a/parsl/parsl/monitoring/visualization/views.py b/parsl/parsl/monitoring/visualization/views.py deleted file mode 100644 index 02e7b04a..00000000 --- a/parsl/parsl/monitoring/visualization/views.py +++ /dev/null @@ -1,187 +0,0 @@ -import pandas as pd -import sqlalchemy -from flask import current_app as app -from flask import render_template - -import parsl.monitoring.queries.pandas as queries -from parsl.monitoring.visualization.models import Status, Task, Workflow, db -from parsl.monitoring.visualization.plots.default.task_plots import ( - time_series_memory_per_task_plot, -) -from parsl.monitoring.visualization.plots.default.workflow_plots import ( - task_gantt_plot, - task_per_app_plot, - workflow_dag_plot, -) -from parsl.monitoring.visualization.plots.default.workflow_resource_plots import ( - resource_distribution_plot, - resource_efficiency, - worker_efficiency, -) - -dummy = True - -import datetime - - -def format_time(value): - if value is None: - return value - elif isinstance(value, float): - return str(datetime.timedelta(seconds=round(value))) - elif isinstance(value, datetime.datetime): - return value.replace(microsecond=0) - elif isinstance(value, datetime.timedelta): - rounded_timedelta = datetime.timedelta(days=value.days, seconds=value.seconds) - return rounded_timedelta - else: - return "Incorrect time format found (neither float nor datetime.datetime object)" - - -def format_duration(value): - (start, end) = value - if start and end: - return format_time(end - start) - else: - return "-" - - -app.jinja_env.filters['timeformat'] = format_time -app.jinja_env.filters['durationformat'] = format_duration - - -@app.route('/') -def index(): - workflows = Workflow.query.all() - for workflow in workflows: - workflow.status = 'Running' - if workflow.time_completed is not None: - workflow.status = 'Completed' - return render_template('workflows_summary.html', workflows=workflows) - - -@app.route('/workflow//') -def workflow(workflow_id): - workflow_details = Workflow.query.filter_by(run_id=workflow_id).first() - - if workflow_details is None: - return render_template('error.html', message="Workflow %s could not be found" % workflow_id) - - df_status = queries.status_for_workflow(workflow_id, db.engine) - df_task = queries.completion_times_for_workflow(workflow_id, db.engine) - df_task_tries = queries.tries_for_workflow(workflow_id, db.engine) - task_summary = queries.app_counts_for_workflow(workflow_id, db.engine) - - return render_template('workflow.html', - workflow_details=workflow_details, - task_summary=task_summary, - task_gantt=task_gantt_plot(df_task, df_status, time_completed=workflow_details.time_completed), - task_per_app=task_per_app_plot(df_task_tries, df_status, time_completed=workflow_details.time_completed)) - - -@app.route('/workflow//app/') -def parsl_app(workflow_id, app_name): - workflow_details = Workflow.query.filter_by(run_id=workflow_id).first() - - if workflow_details is None: - return render_template('error.html', message="Workflow %s could not be found" % workflow_id) - - task_summary = Task.query.filter_by( - run_id=workflow_id, task_func_name=app_name) - return render_template('app.html', - app_name=app_name, - workflow_details=workflow_details, - task_summary=task_summary) - - -@app.route('/workflow//task/') -def parsl_apps(workflow_id): - workflow_details = Workflow.query.filter_by(run_id=workflow_id).first() - - if workflow_details is None: - return render_template('error.html', message="Workflow %s could not be found" % workflow_id) - - task_summary = Task.query.filter_by(run_id=workflow_id) - return render_template('app.html', - app_name="All Apps", - workflow_details=workflow_details, - task_summary=task_summary) - - -@app.route('/workflow//task/') -def task(workflow_id, task_id): - workflow_details = Workflow.query.filter_by(run_id=workflow_id).first() - - if workflow_details is None: - return render_template('error.html', message="Workflow %s could not be found" % workflow_id) - - task_details = Task.query.filter_by( - run_id=workflow_id, task_id=task_id).first() - task_status = Status.query.filter_by( - run_id=workflow_id, task_id=task_id).order_by(Status.timestamp) - - df_resources = queries.resources_for_task(workflow_id, task_id, db.engine) - - return render_template('task.html', - workflow_details=workflow_details, - task_details=task_details, - task_status=task_status, - time_series_memory_resident=time_series_memory_per_task_plot( - df_resources, 'psutil_process_memory_resident', 'Memory Usage'), - ) - - -@app.route('/workflow//dag_') -def workflow_dag_details(workflow_id, path): - assert path == "group_by_apps" or path == "group_by_states" - - workflow_details = Workflow.query.filter_by(run_id=workflow_id).first() - query = """SELECT task.task_id, task.task_func_name, task.task_depends, status.task_status_name - FROM task LEFT JOIN status - ON task.task_id = status.task_id - AND task.run_id = status.run_id - AND status.timestamp = (SELECT MAX(status.timestamp) - FROM status - WHERE status.task_id = task.task_id and status.run_id = task.run_id - ) - WHERE task.run_id=:run_id""" - - df_tasks = pd.read_sql_query(sqlalchemy.text(query), db.engine, params={"run_id": workflow_id}) - - group_by_apps = (path == "group_by_apps") - return render_template('dag.html', - workflow_details=workflow_details, - group_by_apps=group_by_apps, - workflow_dag_plot=workflow_dag_plot(df_tasks, group_by_apps=group_by_apps)) - - -@app.route('/workflow//resource_usage') -def workflow_resources(workflow_id): - workflow_details = Workflow.query.filter_by(run_id=workflow_id).first() - if workflow_details is None: - return render_template('error.html', message="Workflow %s could not be found" % workflow_id) - - df_resources = queries.resources_for_workflow(workflow_id, db.engine) - if df_resources.empty: - return render_template('error.html', - message="Workflow %s does not have any resource usage records." % workflow_id) - - df_task = queries.tasks_for_workflow(workflow_id, db.engine) - - query = """SELECT task.task_id, task_func_name, - task_try_time_launched, task_try_time_running, task_try_time_returned from task, try - WHERE task.task_id = try.task_id AND task.run_id=:run_id and try.run_id=task.run_id""" - df_task_tries = pd.read_sql_query(query, db.engine, params={"run_id": workflow_id}) - df_node = queries.nodes_for_workflow(workflow_id, db.engine) - - return render_template('resource_usage.html', workflow_details=workflow_details, - user_time_distribution_max_plot=resource_distribution_plot( - df_resources, df_task, type='psutil_process_time_user', label='CPU Time Distribution', option='max'), - memory_usage_distribution_avg_plot=resource_distribution_plot( - df_resources, df_task, type='psutil_process_memory_resident', label='Memory Distribution', option='avg'), - memory_usage_distribution_max_plot=resource_distribution_plot( - df_resources, df_task, type='psutil_process_memory_resident', label='Memory Distribution', option='max'), - cpu_efficiency=resource_efficiency(df_resources, df_node, label='CPU'), - memory_efficiency=resource_efficiency(df_resources, df_node, label='mem'), - worker_efficiency=worker_efficiency(df_task_tries, df_node), - ) diff --git a/parsl/parsl/multiprocessing.py b/parsl/parsl/multiprocessing.py deleted file mode 100644 index d055f9da..00000000 --- a/parsl/parsl/multiprocessing.py +++ /dev/null @@ -1,60 +0,0 @@ -"""Helpers for cross-platform multiprocessing support. -""" - -import logging -import multiprocessing -import multiprocessing.queues -from multiprocessing.context import SpawnProcess as SpawnProcessType -import platform -from typing import Callable - -logger = logging.getLogger(__name__) - -if platform.system() != 'Windows': - from multiprocessing.context import ForkProcess as ForkProcessType - ForkContext = multiprocessing.get_context("fork") - ForkProcess: Callable[..., ForkProcessType] = ForkContext.Process -else: - ForkContext = None - ForkProcess = None - -SpawnContext = multiprocessing.get_context("spawn") -SpawnProcess: Callable[..., SpawnProcessType] = SpawnContext.Process - -SpawnEvent = SpawnContext.Event -SpawnQueue = SpawnContext.Queue - - -def join_terminate_close_proc(process: SpawnProcessType, *, timeout: int = 30) -> None: - """Increasingly aggressively terminate a process. - - This function assumes that the process is likely to exit before - the join timeout, driven by some other means, such as the - MonitoringHub router_exit_event. If the process does not exit, then - first terminate() and then kill() will be used to end the process. - - In the case of a very mis-behaving process, this function might take - up to 3*timeout to exhaust all termination methods and return. - """ - logger.debug("Joining process") - process.join(timeout) - - # run a sequence of increasingly aggressive steps to shut down the process. - if process.is_alive(): - logger.error("Process did not join. Terminating.") - process.terminate() - process.join(timeout) - if process.is_alive(): - logger.error("Process did not join after terminate. Killing.") - process.kill() - process.join(timeout) - # This kill should not be caught by any signal handlers so it is - # unlikely that this join will timeout. If it does, there isn't - # anything further to do except log an error in the next if-block. - - if process.is_alive(): - logger.error("Process failed to end") - # don't call close if the process hasn't ended: - # process.close() doesn't work on a running process. - else: - process.close() diff --git a/parsl/parsl/process_loggers.py b/parsl/parsl/process_loggers.py deleted file mode 100644 index a7352de7..00000000 --- a/parsl/parsl/process_loggers.py +++ /dev/null @@ -1,38 +0,0 @@ -import functools -import logging -import threading -from typing import Callable, Optional - - -def wrap_with_logs(fn: Optional[Callable] = None, target: str = __name__) -> Callable: - """Calls the supplied function, and logs whether that - function raised an exception or terminated normally. - - This is intended to be used around the top level functions of - processes and threads, where exceptions would normally not - go to a log. - """ - - def decorator(func): - - @functools.wraps(func) - def wrapped(*args, **kwargs): - assert func is not None - thread = threading.current_thread() - name = f"{func.__name__} on thread {thread.name}" - logger = logging.getLogger(target) - - try: - r = func(*args, **kwargs) - logger.debug("Normal ending for {}".format(name)) - return r - except Exception: - logger.error("Exceptional ending for {}".format(name), exc_info=True) - raise - - return wrapped - - if fn is not None: - return decorator(fn) - else: - return decorator diff --git a/parsl/parsl/providers/__init__.py b/parsl/parsl/providers/__init__.py deleted file mode 100644 index 6855915b..00000000 --- a/parsl/parsl/providers/__init__.py +++ /dev/null @@ -1,26 +0,0 @@ -# Cloud Providers -from parsl.providers.aws.aws import AWSProvider -from parsl.providers.azure.azure import AzureProvider -from parsl.providers.condor.condor import CondorProvider -from parsl.providers.googlecloud.googlecloud import GoogleCloudProvider -from parsl.providers.grid_engine.grid_engine import GridEngineProvider - -# Kubernetes -from parsl.providers.kubernetes.kube import KubernetesProvider -from parsl.providers.local.local import LocalProvider -from parsl.providers.lsf.lsf import LSFProvider -from parsl.providers.pbspro.pbspro import PBSProProvider -from parsl.providers.slurm.slurm import SlurmProvider -from parsl.providers.torque.torque import TorqueProvider - -__all__ = ['LocalProvider', - 'CondorProvider', - 'GridEngineProvider', - 'SlurmProvider', - 'TorqueProvider', - 'LSFProvider', - 'PBSProProvider', - 'AWSProvider', - 'GoogleCloudProvider', - 'KubernetesProvider', - 'AzureProvider'] diff --git a/parsl/parsl/providers/aws/__init__.py b/parsl/parsl/providers/aws/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/providers/aws/aws.py b/parsl/parsl/providers/aws/aws.py deleted file mode 100644 index 62af6553..00000000 --- a/parsl/parsl/providers/aws/aws.py +++ /dev/null @@ -1,790 +0,0 @@ -import json -import logging -import os -import time -from string import Template - -from parsl.errors import ConfigurationError, OptionalModuleMissing -from parsl.jobs.states import JobState, JobStatus -from parsl.launchers import SingleNodeLauncher -from parsl.providers.aws.template import template_string -from parsl.providers.base import ExecutionProvider -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - -try: - import boto3 - from botocore.exceptions import ClientError - -except ImportError: - _boto_enabled = False -else: - _boto_enabled = True - -translate_table = { - 'pending': JobState.PENDING, - 'running': JobState.RUNNING, - 'terminated': JobState.COMPLETED, - 'shutting-down': JobState.COMPLETED, # (configuring), - 'stopping': JobState.COMPLETED, # We shouldn't really see this state - 'stopped': JobState.COMPLETED, # We shouldn't really see this state -} - - -class AWSProvider(ExecutionProvider, RepresentationMixin): - """A provider for using Amazon Elastic Compute Cloud (EC2) resources. - - One of 3 methods are required to authenticate: keyfile, profile or environment - variables. If neither keyfile or profile are set, the following environment - variables must be set: ``AWS_ACCESS_KEY_ID`` (the access key for your AWS account), - ``AWS_SECRET_ACCESS_KEY`` (the secret key for your AWS account), and (optionaly) the - ``AWS_SESSION_TOKEN`` (the session key for your AWS account). - - Parameters - ---------- - image_id : str - Identification of the Amazon Machine Image (AMI). - worker_init : str - String to append to the Userdata script executed in the cloudinit phase of - instance initialization. - walltime : str - Walltime requested per block in HH:MM:SS. - key_file : str - Path to json file that contains 'AWSAccessKeyId' and 'AWSSecretKey'. - nodes_per_block : int - This is always 1 for ec2. Nodes to provision per block. - profile : str - Profile to be used from the standard aws config file ~/.aws/config. - nodes_per_block : int - Nodes to provision per block. Default is 1. - init_blocks : int - Number of blocks to provision at the start of the run. Default is 1. - min_blocks : int - Minimum number of blocks to maintain. Default is 0. - max_blocks : int - Maximum number of blocks to maintain. Default is 10. - instance_type : str - EC2 instance type. Instance types comprise varying combinations of CPU, memory, - storage, and networking capacity For more information on possible instance types, - see `here `_. Default is 't2.small'. - region : str - Amazon Web Service (AWS) region to launch machines. Default is 'us-east-2'. - key_name : str - Name of the AWS private key (.pem file) that is usually generated on the console - to allow SSH access to the EC2 instances. This is mostly used for debugging. - spot_max_bid : float - Maximum bid price (if requesting spot market machines). - iam_instance_profile_arn : str - Launch instance with a specific role. - state_file : str - Path to the state file from a previous run to re-use. - walltime : str - Walltime requested per block in HH:MM:SS. This option is not currently honored by this provider. - launcher : Launcher - Launcher for this provider. With AWS, usually the default - :class:`~parsl.launchers.SingleNodeLauncher` will be appropriate. - linger : Bool - When set to True, the workers will not ``halt``. The user is responsible for shutting - down the node. - """ - - def __init__(self, - image_id, - key_name, - init_blocks=1, - min_blocks=0, - max_blocks=10, - nodes_per_block=1, - parallelism=1, - - worker_init='', - instance_type='t2.small', - region='us-east-2', - spot_max_bid=0, - - key_file=None, - profile=None, - iam_instance_profile_arn='', - - state_file=None, - walltime="01:00:00", - linger=False, - launcher=SingleNodeLauncher()): - if not _boto_enabled: - raise OptionalModuleMissing(['boto3'], "AWS Provider requires the boto3 module.") - - self.image_id = image_id - self._label = 'ec2' - self.init_blocks = init_blocks - self.min_blocks = min_blocks - self.max_blocks = max_blocks - self.nodes_per_block = nodes_per_block - self.max_nodes = max_blocks * nodes_per_block - self.parallelism = parallelism - - self.worker_init = worker_init - self.instance_type = instance_type - self.region = region - self.spot_max_bid = spot_max_bid - - self.key_name = key_name - self.key_file = key_file - self.profile = profile - self.iam_instance_profile_arn = iam_instance_profile_arn - - self.walltime = walltime - self.launcher = launcher - self.linger = linger - self.resources = {} - self.script_dir = None - self.state_file = state_file if state_file is not None else 'awsproviderstate.json' - - env_specified = os.getenv("AWS_ACCESS_KEY_ID") is not None and os.getenv("AWS_SECRET_ACCESS_KEY") is not None - if profile is None and key_file is None and not env_specified: - raise ConfigurationError("Must specify either profile', 'key_file', or " - "'AWS_ACCESS_KEY_ID' and 'AWS_SECRET_ACCESS_KEY' environment variables.") - - try: - self.initialize_boto_client() - except Exception as e: - logger.error("{} failed to initialize.".format(self)) - raise e - - state_file_exists = False - try: - self.read_state_file(self.state_file) - state_file_exists = True - except Exception: - logger.info("No state file found. Cannot load previous options. Creating new infrastructure.") - - if not state_file_exists: - try: - self.create_vpc().id - except Exception as e: - logger.info("Failed to create ec2 infrastructure: {0}".format(e)) - raise - else: - self.write_state_file() - - def initialize_boto_client(self): - """Initialize the boto client.""" - - self.session = self.create_session() - self.client = self.session.client('ec2') - self.ec2 = self.session.resource('ec2') - self.instances = [] - self.instance_states = {} - self.vpc_id = 0 - self.sg_id = 0 - self.sn_ids = [] - - def read_state_file(self, state_file): - """Read the state file, if it exists. - - If this script has been run previously, resource IDs will have been written to a - state file. On starting a run, a state file will be looked for before creating new - infrastructure. Information on VPCs, security groups, and subnets are saved, as - well as running instances and their states. - - AWS has a maximum number of VPCs per region per account, so we do not want to - clutter users' AWS accounts with security groups and VPCs that will be used only - once. - """ - try: - fh = open(state_file, 'r') - state = json.load(fh) - self.vpc_id = state['vpcID'] - self.sg_id = state['sgID'] - self.sn_ids = state['snIDs'] - self.instances = state['instances'] - except Exception as e: - logger.debug("Caught exception while reading state file: {0}".format(e)) - raise e - logger.debug("Done reading state from the local state file.") - - def write_state_file(self): - """Save information that must persist to a file. - - We do not want to create a new VPC and new identical security groups, so we save - information about them in a file between runs. - """ - fh = open(self.state_file, 'w') - state = {} - state['vpcID'] = self.vpc_id - state['sgID'] = self.sg_id - state['snIDs'] = self.sn_ids - state['instances'] = self.instances - state["instanceState"] = self.instance_states - fh.write(json.dumps(state, indent=4)) - - def create_session(self): - """Create a session. - - First we look in self.key_file for a path to a json file with the - credentials. The key file should have 'AWSAccessKeyId' and 'AWSSecretKey'. - - Next we look at self.profile for a profile name and try - to use the Session call to automatically pick up the keys for the profile from - the user default keys file ~/.aws/config. - - Finally, boto3 will look for the keys in environment variables: - AWS_ACCESS_KEY_ID: The access key for your AWS account. - AWS_SECRET_ACCESS_KEY: The secret key for your AWS account. - AWS_SESSION_TOKEN: The session key for your AWS account. - This is only needed when you are using temporary credentials. - The AWS_SECURITY_TOKEN environment variable can also be used, - but is only supported for backwards compatibility purposes. - AWS_SESSION_TOKEN is supported by multiple AWS SDKs besides python. - """ - - session = None - - if self.key_file is not None: - credfile = os.path.expandvars(os.path.expanduser(self.key_file)) - - try: - with open(credfile, 'r') as f: - creds = json.load(f) - except json.JSONDecodeError as e: - logger.error( - "EC2Provider '{}': json decode error in credential file {}".format(self.label, credfile) - ) - raise e - - except Exception as e: - logger.debug( - "EC2Provider '{0}' caught exception while reading credential file: {1}".format( - self.label, credfile - ) - ) - raise e - - logger.debug("EC2Provider '{}': Using credential file to create session".format(self.label)) - session = boto3.session.Session(region_name=self.region, **creds) - elif self.profile is not None: - logger.debug("EC2Provider '{}': Using profile name to create session".format(self.label)) - session = boto3.session.Session( - profile_name=self.profile, region_name=self.region - ) - else: - logger.debug("EC2Provider '{}': Using environment variables to create session".format(self.label)) - session = boto3.session.Session(region_name=self.region) - - return session - - def create_vpc(self): - """Create and configure VPC - - We create a VPC with CIDR 10.0.0.0/16, which provides up to 64,000 instances. - - We attach a subnet for each availability zone within the region specified in the - config. We give each subnet an ip range like 10.0.X.0/20, which is large enough - for approx. 4000 instances. - - Security groups are configured in function security_group. - """ - - vpc_name = self.generate_aws_id() - - try: - tag_spec = self.create_name_tag_spec('vpc', vpc_name) - - # We use a large VPC so that the cluster can get large - vpc = self.ec2.create_vpc( - CidrBlock='10.0.0.0/16', - AmazonProvidedIpv6CidrBlock=False, - TagSpecifications=tag_spec, - ) - except Exception as e: - # This failure will cause a full abort - logger.error("{}\n".format(e)) - raise e - - # Attach internet gateway so that our cluster can - # talk to the outside internet - internet_gateway = self.ec2.create_internet_gateway() - internet_gateway.attach_to_vpc(VpcId=vpc.vpc_id) # Returns None - self.internet_gateway = internet_gateway.id - - # Create and configure route table to allow proper traffic - route_table = self.config_route_table(vpc, internet_gateway) - self.route_table = route_table.id - - # Get all avaliability zones - availability_zones = self.client.describe_availability_zones() - - # go through AZs and set up a subnet per - for num, zone in enumerate(availability_zones['AvailabilityZones']): - if zone['State'] == "available": - zone_name = zone['ZoneName'] - tag_spec = self.create_name_tag_spec('subnet', '{0}.{1}'.format(vpc_name, zone_name)) - - # Create a large subnet (4000 max nodes) - subnet = vpc.create_subnet( - CidrBlock='10.0.{}.0/20'.format(16 * num), - AvailabilityZone=zone_name, - TagSpecifications=tag_spec, - ) - - # Make subnet accessible - subnet.meta.client.modify_subnet_attribute( - SubnetId=subnet.id, MapPublicIpOnLaunch={"Value": True} - ) - - route_table.associate_with_subnet(SubnetId=subnet.id) - self.sn_ids.append(subnet.id) - else: - logger.info("{} unavailable".format(zone['ZoneName'])) - # Security groups - security_group_iterator = vpc.security_groups.all() - for security_group in security_group_iterator: - # this can only be the default security group for this VPC, - # since no other security groups have been created yet - security_group.create_tags( - Tags=[{'Key': 'Name', 'Value': '{0}.default'.format(vpc_name)}], - ) - - self.security_group(vpc, vpc_name) - self.vpc_id = vpc.id - return vpc - - def security_group(self, vpc, name): - """Create and configure a new security group. - - Allows all ICMP in, all TCP and UDP in within VPC. - - This security group is very open. It allows all incoming ping requests on all - ports. It also allows all outgoing traffic on all ports. This can be limited by - changing the allowed port ranges. - - Parameters - ---------- - vpc : VPC instance - VPC in which to set up security group. - name : str - Name tag for the newly created security group. - """ - - tag_spec = self.create_name_tag_spec('security-group', name) - - sg = vpc.create_security_group( - GroupName="private-subnet", - Description="security group for remote executors", - TagSpecifications=tag_spec, - ) - - ip_ranges = [{'CidrIp': '10.0.0.0/16'}] - - # Allows all ICMP in, all TCP and UDP in within VPC - in_permissions = [ - { - 'IpProtocol': 'TCP', - 'FromPort': 0, - 'ToPort': 65535, - 'IpRanges': ip_ranges, - }, { - 'IpProtocol': 'UDP', - 'FromPort': 0, - 'ToPort': 65535, - 'IpRanges': ip_ranges, - }, { - 'IpProtocol': 'ICMP', - 'FromPort': -1, - 'ToPort': -1, - 'IpRanges': [{ - 'CidrIp': '0.0.0.0/0' - }], - }, { - 'IpProtocol': 'TCP', - 'FromPort': 22, - 'ToPort': 22, - 'IpRanges': [{ - 'CidrIp': '0.0.0.0/0' - }], - } - ] - - # Allows all TCP out, all TCP and UDP out within VPC - out_permissions = [ - { - 'IpProtocol': 'TCP', - 'FromPort': 0, - 'ToPort': 65535, - 'IpRanges': [{ - 'CidrIp': '0.0.0.0/0' - }], - }, - { - 'IpProtocol': 'TCP', - 'FromPort': 0, - 'ToPort': 65535, - 'IpRanges': ip_ranges, - }, - { - 'IpProtocol': 'UDP', - 'FromPort': 0, - 'ToPort': 65535, - 'IpRanges': ip_ranges, - }, - ] - - sg.authorize_ingress(IpPermissions=in_permissions) - sg.authorize_egress(IpPermissions=out_permissions) - self.sg_id = sg.id - return sg - - def config_route_table(self, vpc, internet_gateway): - """Configure route table for Virtual Private Cloud (VPC). - - Parameters - ---------- - vpc : dict - Representation of the VPC (created by create_vpc()). - internet_gateway : dict - Representation of the internet gateway (created by create_vpc()). - """ - route_table = vpc.create_route_table() - route_table.create_route( - DestinationCidrBlock='0.0.0.0/0', GatewayId=internet_gateway.internet_gateway_id - ) - return route_table - - def xstr(self, s): - return '' if s is None else s - - def spin_up_instance(self, command, job_name): - """Start an instance in the VPC in the first available subnet. - - N instances will be started if nodes_per_block > 1. - Not supported. We only do 1 node per block. - - Parameters - ---------- - command : str - Command string to execute on the node. - job_name : str - Name associated with the instances. - """ - - command = Template(template_string).substitute(jobname=job_name, - user_script=command, - linger=str(self.linger).lower(), - worker_init=self.worker_init) - instance_type = self.instance_type - ami_id = self.image_id - total_instances = len(self.instances) - - # not all availability zones support all instance types, - # so we need to check which subnets can actually be used given the - # user-provided instance type - - # this finds the availability zones in a region that support - # the user-provided instance type - az_offerings = self.client.describe_instance_type_offerings( - LocationType='availability-zone', - Filters=[ - { - 'Name': 'instance-type', - 'Values': [ - instance_type, - ] - }, - ], - ) - - allowed_azs = [] - for az in az_offerings['InstanceTypeOfferings']: - allowed_azs.append(az['Location']) - - # this is used to retrieve the availability zone of each subnet - subnets = self.client.describe_subnets( - SubnetIds=list(self.sn_ids), - ) - - subnet = None - for sn in subnets['Subnets']: - sn_id = sn['SubnetId'] - az = sn['AvailabilityZone'] - # a subnet has been found with an availability zone - # that is supported for the instance type, so we should use - # this subnet - if az in allowed_azs: - subnet = sn_id - break - - if subnet is None: - logger.error("No subnet in region {0} supports instance type {1}".format(self.region, instance_type)) - return [None] - - if float(self.spot_max_bid) > 0: - spot_options = { - 'MarketType': 'spot', - 'SpotOptions': { - 'MaxPrice': str(self.spot_max_bid), - 'SpotInstanceType': 'one-time', - 'InstanceInterruptionBehavior': 'terminate' - } - } - else: - spot_options = {} - - if total_instances > self.max_nodes: - logger.warning("Exceeded instance limit ({}). Cannot continue\n".format(self.max_nodes)) - return [None] - try: - tag_spec = self.create_name_tag_spec('instance', job_name) - - instance = self.ec2.create_instances( - MinCount=1, - MaxCount=1, - InstanceType=instance_type, - ImageId=ami_id, - KeyName=self.key_name, - SubnetId=subnet, - SecurityGroupIds=[self.sg_id], - TagSpecifications=tag_spec, - InstanceMarketOptions=spot_options, - InstanceInitiatedShutdownBehavior='terminate', - IamInstanceProfile={'Arn': self.iam_instance_profile_arn}, - UserData=command, - ) - except ClientError as e: - print(e) - logger.error(e.response) - return [None] - - except Exception as e: - logger.error("Request for EC2 resources failed : {0}".format(e)) - return [None] - - self.instances.append(instance[0].id) - logger.info( - "Started up 1 instance {}. Instance type: {}".format(instance[0].id, instance_type) - ) - return instance - - def shut_down_instance(self, instances=None): - """Shut down a list of instances, if provided. - - If no instance is provided, the last instance started up will be shut down. - """ - - if instances and len(self.instances) > 0: - print(instances) - try: - print([i.id for i in instances]) - except Exception as e: - print(e) - term = self.client.terminate_instances(InstanceIds=instances) - logger.info("Shut down {} instances (ids:{}".format(len(instances), str(instances))) - elif len(self.instances) > 0: - instance = self.instances.pop() - term = self.client.terminate_instances(InstanceIds=[instance]) - logger.info("Shut down 1 instance (id:{})".format(instance)) - else: - logger.warning("No Instances to shut down.\n") - return -1 - self.get_instance_state() - return term - - def get_instance_state(self, instances=None): - """Get states of all instances on EC2 which were started by this file.""" - if instances: - desc = self.client.describe_instances(InstanceIds=instances) - else: - desc = self.client.describe_instances(InstanceIds=self.instances) - # pprint.pprint(desc['Reservations'],indent=4) - for i in range(len(desc['Reservations'])): - instance = desc['Reservations'][i]['Instances'][0] - self.instance_states[instance['InstanceId']] = instance['State']['Name'] - return self.instance_states - - def status(self, job_ids): - """Get the status of a list of jobs identified by their ids. - - Parameters - ---------- - job_ids : list of str - Identifiers for the jobs. - - Returns - ------- - list of int - The status codes of the requsted jobs. - """ - - all_states = [] - - status = self.client.describe_instances(InstanceIds=list(job_ids)) - for r in status['Reservations']: - for i in r['Instances']: - instance_id = i['InstanceId'] - instance_state = translate_table.get(i['State']['Name'], JobState.UNKNOWN) - instance_status = JobStatus(instance_state) - self.resources[instance_id]['status'] = instance_status - all_states.extend([instance_status]) - - return all_states - - def submit(self, command='sleep 1', tasks_per_node=1, job_name="parsl.aws"): - """Submit the command onto a freshly instantiated AWS EC2 instance. - - Submit returns an ID that corresponds to the task that was just submitted. - - Parameters - ---------- - command : str - Command to be invoked on the remote side. - tasks_per_node : int (default=1) - Number of command invocations to be launched per node - job_name : str - Prefix for the job name. - - Returns - ------- - None or str - If at capacity, None will be returned. Otherwise, the job identifier will be returned. - """ - - job_name = self.generate_aws_id() - wrapped_cmd = self.launcher(command, - tasks_per_node, - self.nodes_per_block, - self.script_dir) - [instance, *rest] = self.spin_up_instance(command=wrapped_cmd, job_name=job_name) - - if not instance: - logger.error("Failed to submit request to EC2") - return None - - logger.debug("Started instance_id: {0}".format(instance.instance_id)) - - state = translate_table.get(instance.state['Name'], JobState.PENDING) - - self.resources[instance.instance_id] = { - "job_id": instance.instance_id, - "instance": instance, - "status": JobStatus(state) - } - - return instance.instance_id - - def cancel(self, job_ids): - """Cancel the jobs specified by a list of job ids. - - Parameters - ---------- - job_ids : list of str - List of of job identifiers - - Returns - ------- - list of bool - Each entry in the list will contain False if the operation fails. Otherwise, the entry will be True. - """ - - if self.linger is True: - logger.debug("Ignoring cancel requests due to linger mode") - return [False for x in job_ids] - - try: - self.client.terminate_instances(InstanceIds=list(job_ids)) - except Exception as e: - logger.error("Caught error while attempting to remove instances: {0}".format(job_ids)) - raise e - else: - logger.debug("Removed the instances: {0}".format(job_ids)) - - for job_id in job_ids: - self.resources[job_id]["status"] = JobStatus(JobState.COMPLETED) - - for job_id in job_ids: - self.instances.remove(job_id) - - return [True for x in job_ids] - - def show_summary(self): - """Print human readable summary of current AWS state to log and to console.""" - self.get_instance_state() - status_string = "EC2 Summary:\n\tVPC IDs: {}\n\tSubnet IDs: \ -{}\n\tSecurity Group ID: {}\n\tRunning Instance IDs: {}\n".format( - self.vpc_id, self.sn_ids, self.sg_id, self.instances - ) - status_string += "\tInstance States:\n\t\t" - self.get_instance_state() - for state in self.instance_states.keys(): - status_string += "Instance ID: {} State: {}\n\t\t".format( - state, self.instance_states[state] - ) - status_string += "\n" - logger.info(status_string) - return status_string - - def teardown(self): - """Teardown the EC2 infastructure. - - Terminate all EC2 instances, delete all subnets, delete security group, delete VPC, - and reset all instance variables. - """ - - self.shut_down_instance(self.instances) - self.instances = [] - try: - self.client.delete_internet_gateway(InternetGatewayId=self.internet_gateway) - self.internet_gateway = None - self.client.delete_route_table(RouteTableId=self.route_table) - self.route_table = None - for subnet in list(self.sn_ids): - # Cast to list ensures that this is a copy - # Which is important because it means that - # the length of the list won't change during iteration - self.client.delete_subnet(SubnetId=subnet) - self.sn_ids.remove(subnet) - self.client.delete_security_group(GroupId=self.sg_id) - self.sg_id = None - self.client.delete_vpc(VpcId=self.vpc_id) - self.vpc_id = None - except Exception as e: - logger.error("{}".format(e)) - raise e - self.show_summary() - os.remove(self.config['state_file_path']) - - def generate_aws_id(self): - """Generate a new ID for AWS resources. - - Returns - ------- - str - An ID of the form 'parsl.aws.123456.789' for giving resources unique identifiers. - """ - return "parsl.aws.{0}".format(time.time()) - - def create_name_tag_spec(self, resource_type, name): - """Create a new tag specification for a resource name. - - Parameters - ---------- - resource_type : str - The AWS resource type - name : str - The name to assign to the resource - - Returns - ------- - record - A TagSpecifications record to be passed into the creation of a new AWS resource. - """ - return [{"ResourceType": resource_type, "Tags": [{'Key': 'Name', 'Value': name}]}] - - @property - def label(self): - return self._label - - def goodbye(self): - self.teardown() - - @property - def status_polling_interval(self): - return 60 diff --git a/parsl/parsl/providers/aws/template.py b/parsl/parsl/providers/aws/template.py deleted file mode 100644 index 41332dd0..00000000 --- a/parsl/parsl/providers/aws/template.py +++ /dev/null @@ -1,17 +0,0 @@ -template_string = """#!/bin/bash -cd ~ -export DEBIAN_FRONTEND=noninteractive -apt-get update -y -apt-get install -y python3 python3-pip libffi-dev g++ libssl-dev -pip3 install numpy scipy parsl -$worker_init - -$user_script - -# Shutdown the instance as soon as the worker scripts exits -# or times out to avoid EC2 costs. -if ! $linger -then - halt -fi -""" diff --git a/parsl/parsl/providers/azure/__init__.py b/parsl/parsl/providers/azure/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/providers/azure/azure.py b/parsl/parsl/providers/azure/azure.py deleted file mode 100644 index ee4391a5..00000000 --- a/parsl/parsl/providers/azure/azure.py +++ /dev/null @@ -1,488 +0,0 @@ -import json -import logging -import os -import time -from string import Template - -from parsl.errors import ConfigurationError, OptionalModuleMissing -from parsl.jobs.states import JobState, JobStatus -from parsl.launchers import SingleNodeLauncher -from parsl.providers.azure.template import template_string -from parsl.providers.base import ExecutionProvider -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - -try: - from azure.common.credentials import ServicePrincipalCredentials - from azure.mgmt.compute import ComputeManagementClient - from azure.mgmt.compute.models import DiskCreateOption - from azure.mgmt.network import NetworkManagementClient - from azure.mgmt.resource import ResourceManagementClient - from msrestazure.azure_exceptions import CloudError - - _api_enabled = True - -except ImportError: - _api_enabled = False -else: - _api_enabled = True - -translate_table = { - 'VM pending': JobState.PENDING, - 'VM running': JobState.RUNNING, - 'VM deallocated': JobState.COMPLETED, - 'VM stopping': JobState.COMPLETED, # We shouldn't really see this state - 'VM stopped': JobState.COMPLETED, # We shouldn't really see this state -} - - -class AzureProvider(ExecutionProvider, RepresentationMixin): - """ - A Provider for using Microsoft Azure Resources - - One of 2 methods are required to authenticate: `key_file`, or environment - variables. If `key_file` is not set, the following environment - variables must be set: `AZURE_CLIENT_ID` (the access key for - your azure account), - `AZURE_CLIENT_SECRET` (the secret key for your azure account), - `AZURE_TENANT_ID` (the session key for your azure account), and - `AZURE_SUBSCRIPTION_ID`. - - The tenant ID is also known as the directory ID here: - in https://portal.azure.com/#blade/Microsoft_AAD_IAM/ActiveDirectoryMenuBlade/Properties - A tenant ID is a GUID. - - A client ID and secret can be created by following these instructions: - https://docs.microsoft.com/en-us/azure/active-directory/develop/howto-create-service-principal-portal - A client ID is a GUID. - - The subscription ID can be found here: - https://portal.azure.com/#blade/Microsoft_Azure_Billing/SubscriptionsBlade - - Parameters - ---------- - vm_reference : dict - Dictionary describing the parameters of the Azure VM. - - Required structure: - { - 'publisher': VM OS publisher - 'offer': VM OS offer - 'sku': VM OS SKU - 'version': VM OS version - 'vm_size': VM Size, analogous to instance type for AWS - 'disk_size_gb': (int) size of VM disk in gb - 'admin_username': (str) admin username of VM instances, - 'password': (str) admin password for VM instances - } - - VM Publisher, author, SKU, and Version can be found in the Azure marketplace. - One way to do so is to use the CLI, as described - [here](https://docs.microsoft.com/en-us/azure/virtual-machines/linux/cli-ps-findimage) - - Another way is to look in the marketplace's GUI, - [here](https://azuremarketplace.microsoft.com/en-us/marketplace/apps?filters=virtual-machine-images%3Blinux&page=1). - - worker_init : str - String to append to the Userdata script executed in the cloudinit phase of - instance initialization. - key_file : str - Path to JSON file that contains 'Azure keys' - The structure of the key file is as follows: - { - "AZURE_CLIENT_ID": (str) azure client id [from account principal], - "AZURE_CLIENT_SECRET":(str) azure client secret [from account principal], - "AZURE_TENANT_ID": (str) azure tenant [account] id, - "AZURE_SUBSCRIPTION_ID": (str) azure subscription id - } - init_blocks : int - Number of blocks to provision at the start of the run. Default is 1. - min_blocks : int - Minimum number of blocks to maintain. Default is 0. - max_blocks : int - Maximum number of blocks to maintain. Default is 10. - region : str - Azure region to launch machines. Default is 'westus'. - key_name : str - Name of the Azure private key (.pem file) that is usually generated on the console - to allow SSH access to the Azure instances. This is mostly used for debugging. - launcher : Launcher - Launcher for this provider. Possible launchers include - :class:`~parsl.launchers.SingleNodeLauncher` (the default), - :class:`~parsl.launchers.SrunLauncher`, or - :class:`~parsl.launchers.AprunLauncher` - linger : Bool - When set to True, the workers will not `halt`. The user is responsible for shutting - down the nodes. - """ - - def __init__(self, - vm_reference, - init_blocks=1, - min_blocks=0, - max_blocks=10, - parallelism=1, - worker_init='', - location='westus', - group_name='parsl.group', - key_name=None, - key_file=None, - vnet_name="parsl.vnet", - linger=False, - launcher=SingleNodeLauncher()): - if not _api_enabled: - raise OptionalModuleMissing( - ['azure', 'msrestazure'], "Azure Provider requires the azure module.") - - self._label = 'azure' - self.init_blocks = init_blocks - self.min_blocks = min_blocks - self.max_blocks = max_blocks - self.max_nodes = max_blocks - self.parallelism = parallelism - self.nodes_per_block = 1 - - self.worker_init = worker_init - self.vm_reference = vm_reference - self.region = location - self.vnet_name = vnet_name - - self.key_name = key_name - self.key_file = key_file - self.location = location - self.group_name = group_name - - self.launcher = launcher - self.linger = linger - self.resources = {} - self.script_dir = None - self.instances = [] - - env_specified = os.getenv("AZURE_CLIENT_ID") is not None and os.getenv( - "AZURE_CLIENT_SECRET") is not None and os.getenv( - "AZURE_TENANT_ID") is not None and os.getenv("AZURE_SUBSCRIPTION_ID") is not None - - if key_file is None and not env_specified: - raise ConfigurationError(("Must specify either: 'key_file', or " - "`AZURE_CLIENT_ID`, `AZURE_CLIENT_SECRET`, " - "and `AZURE_TENANT_ID` environment variables.")) - - if key_file is None: - self.clientid = os.getenv("AZURE_CLIENT_ID") - self.clientsecret = os.getenv("AZURE_CLIENT_SECRET") - self.tenantid = os.getenv("AZURE_TENANT_ID") - self.subid = os.getenv("AZURE_SUBSCRIPTION_ID") - else: - with open(key_file) as fh: - keys = json.load(fh) - self.clientid = keys.get("AZURE_CLIENT_ID") - self.clientsecret = keys.get("AZURE_CLIENT_SECRET") - self.tenantid = keys.get("AZURE_TENANT_ID") - self.subid = keys.get("AZURE_SUBSCRIPTION_ID") - - self.get_clients() - - def get_clients(self): - """ - Set up access to Azure API clients - """ - credentials, subscription_id = self.get_credentials() - self.resource_client = ResourceManagementClient( - credentials, subscription_id) - self.compute_client = ComputeManagementClient(credentials, - subscription_id) - self.network_client = NetworkManagementClient(credentials, - subscription_id) - - def get_credentials(self): - """ - Authenticate to the Azure API - """ - subscription_id = self.subid - credentials = ServicePrincipalCredentials( - client_id=self.clientid, - secret=self.clientsecret, - tenant=self.tenantid) - return credentials, subscription_id - - def submit(self, - command='sleep 1', - tasks_per_node=1, - job_name="parsl.azure"): - """ - Submit the command onto a freshly instantiated Azure VM - Submit returns an ID that corresponds to the task that was just submitted. - Parameters - ---------- - command : str - Command to be invoked on the remote side. - tasks_per_node : int (default=1) - Number of command invocations to be launched per node - job_name : str - Prefix for the job name. - Returns - ------- - None or str - If at capacity, None will be returned. Otherwise, the job identifier will be returned. - """ - self.resource_client.resource_groups.create_or_update( - self.group_name, {'location': self.location}) - self.resources["group"] = self.group_name - - logger.info('Creating NIC') - nic = self.create_nic(self.network_client) - - wrapped_cmd = self.launcher(command, tasks_per_node, 1, self.script_dir) - - cmd_str = Template(template_string).substitute(jobname=job_name, - user_script=wrapped_cmd, - linger=str(self.linger).lower(), - worker_init=self.worker_init) - - logger.info('Creating Linux Virtual Machine') - vm_parameters = self.create_vm_parameters(nic.id, - self.vm_reference) - - # Uniqueness strategy from AWS provider - job_name = "{0}-parsl-azure".format(str(time.time()).replace(".", "")) - - async_vm_creation = self.compute_client.\ - virtual_machines.create_or_update( - self.vnet_name, job_name, vm_parameters) - - vm_info = async_vm_creation.result() - self.instances.append(vm_info.name) - - try: - - logger.debug("Started instance_id: {0}".format(vm_info.id)) - disk, d_name = self.create_disk() - - self.resources[vm_info.id] = { - "job_id": vm_info.id, - "instance": vm_info, - "status": JobStatus(JobState.PENDING) - } - - vm_info.storage_profile.data_disks.append({ - 'lun': - 12, - 'name': - d_name, - 'create_option': - DiskCreateOption.attach, - 'managed_disk': { - 'id': disk.id - } - }) - async_disk_attach = self.\ - compute_client.virtual_machines.create_or_update( - self.group_name, vm_info.name, vm_info) - async_disk_attach.wait() - - async_vm_start = self.compute_client.virtual_machines.start( - self.group_name, job_name) - async_vm_start.wait() - - logger.debug("attempting to connect instance to Parsl master") - run_command_parameters = { - 'command_id': 'RunShellScript', - 'script': cmd_str.split("\n") - } - self.compute_client.virtual_machines.run_command( - self.group_name, - vm_info.name, - run_command_parameters) - except KeyboardInterrupt: - self.cancel([vm_info.name]) - raise KeyboardInterrupt - - return vm_info.name - - def status(self, job_ids): - """Get the status of a list of jobs identified by their ids. - Parameters - ---------- - job_ids : list of str - Identifiers for the jobs. - Returns - ------- - list of int - The status codes of the requsted jobs. - """ - statuses = [] - logger.info('List VMs in resource group') - for job_id in job_ids: - try: - vm = self.compute_client.virtual_machines.get( - self.group_name, job_id, expand='instanceView') - status = vm.instance_view.statuses[1].display_status - statuses.append(JobStatus(translate_table.get(status, JobState.UNKNOWN))) - # This only happens when it is in ProvisionState/Pending - except IndexError: - statuses.append(JobStatus(JobState.PENDING)) - return statuses - - def cancel(self, job_ids): - """Cancel the jobs specified by a list of job ids. - Parameters - ---------- - job_ids : list of str - List of of job identifiers - Returns - ------- - list of bool - Each entry in the list will contain False if the operation fails. Otherwise, the entry will be True. - """ - - return_vals = [] - - if self.linger: - logger.debug("Ignoring cancel requests due to linger mode") - return [False for x in job_ids] - - for job_id in job_ids: - try: - logger.debug('Delete VM {}'.format(job_id)) - async_vm_delete = self.compute_client.virtual_machines.delete( - self.group_name, job_id) - async_vm_delete.wait() - self.instances.remove(job_id) - return_vals.append(True) - except Exception: - return_vals.append(False) - - return return_vals - - @property - def label(self): - return self._label - - def create_nic(self, network_client): - """Create (or update, if it exists already) a Network Interface for a VM. - - Also ensures that there's a virtual network available. - - We create a VPC with CIDR 10.0.0.0/16, which provides up to 64,000 instances. - - We attach a subnet for each availability zone within the region specified in the - config. We give each subnet an ip range like 10.0.X.0/20, which is large enough - for approx. 4000 instances. - - - """ - try: - logger.info('Creating (or updating) Vnet') - async_vnet_creation = self.network_client.virtual_networks.\ - create_or_update( - self.group_name, self.vnet_name, { - 'location': self.location, - 'address_space': { - 'address_prefixes': ['10.0.0.0/16'] - } - }) - vnet_info = async_vnet_creation.result() - self.resources["vnet"] = vnet_info - - except CloudError as e: - - if "InUse" in str(e): - logger.info('Found Existing Vnet. Proceeding.') - else: - raise e - - if not self.resources.get("subnets", None): - self.resources["subnets"] = {} - - try: - logger.info('Creating (or updating) Subnet') - async_subnet_creation = self.network_client.subnets.create_or_update( - self.group_name, self.vnet_name, "{}.subnet".format( - self.group_name), {'address_prefix': '10.0.0.0/20'}) - subnet_info = async_subnet_creation.result() - - self.resources["subnets"][subnet_info.id] = subnet_info - - except CloudError as e: - if "InUse" in str(e): - subnet_info = self.network_client.subnets.get(self.group_name, self.vnet_name, "{}.subnet".format( - self.group_name)) - self.resources["subnets"][subnet_info.id] = subnet_info - logger.info('Found Existing Subnet. Proceeding.') - else: - raise e - - logger.info('Creating (or updating) NIC') - async_nic_creation = self.network_client.network_interfaces.\ - create_or_update( - self.group_name, - "{}.{}.nic".format(self.group_name, time.time()), { - 'location': - self.location, - 'ip_configurations': [{ - 'name': - "{}.ip.config".format(self.group_name), - 'subnet': { - 'id': subnet_info.id - } - }] - }) - - nic_info = async_nic_creation.result() - - if not self.resources.get("nics", None): - self.resources["nics"] = {} - - self.resources["nics"][nic_info.id] = nic_info - - return nic_info - - def create_vm_parameters(self, nic_id, vm_reference): - """Create the VM parameters structure. - """ - return { - 'location': self.region, - 'os_profile': { - 'computer_name': "{}.{}".format(self.vnet_name, time.time()), - 'admin_username': self.vm_reference["admin_username"], - 'admin_password': self.vm_reference["password"] - }, - 'hardware_profile': { - 'vm_size': vm_reference["vm_size"] - }, - 'storage_profile': { - 'image_reference': { - 'publisher': vm_reference['publisher'], - 'offer': vm_reference['offer'], - 'sku': vm_reference['sku'], - 'version': vm_reference['version'] - }, - }, - 'network_profile': { - 'network_interfaces': [{ - 'id': nic_id, - }] - } - } - - def create_disk(self): - """Create a managed data disk of size specified in config. - - Each instance gets one disk""" - logger.info('Create (empty) managed Data Disk') - name = '{}.{}'.format(self.group_name, time.time()) - async_disk_creation = self.compute_client.disks.create_or_update( - self.group_name, name, { - 'location': self.location, - 'disk_size_gb': self.vm_reference["disk_size_gb"], - 'creation_data': { - 'create_option': DiskCreateOption.empty - } - }) - data_disk = async_disk_creation.result() - return data_disk, name - - @property - def status_polling_interval(self): - return 60 diff --git a/parsl/parsl/providers/azure/template.py b/parsl/parsl/providers/azure/template.py deleted file mode 100644 index 92efc65d..00000000 --- a/parsl/parsl/providers/azure/template.py +++ /dev/null @@ -1,15 +0,0 @@ -template_string = """#!/bin/bash -cd ~ -export DEBIAN_FRONTEND=noninteractive -apt-get update -y -apt-get install -y python3 python3-pip libffi-dev g++ libssl-dev -pip3 install numpy scipy parsl -$worker_init -$user_script -# Shutdown the instance as soon as the worker scripts exits -# or times out to avoid Azure costs. -if ! $linger -then - halt -fi -""" diff --git a/parsl/parsl/providers/base.py b/parsl/parsl/providers/base.py deleted file mode 100644 index 66c58f83..00000000 --- a/parsl/parsl/providers/base.py +++ /dev/null @@ -1,142 +0,0 @@ -import logging -from abc import ABCMeta, abstractmethod, abstractproperty -from typing import Any, Dict, Optional, Sequence - -from parsl.jobs.states import JobStatus - -logger = logging.getLogger(__name__) - - -class ExecutionProvider(metaclass=ABCMeta): - """Execution providers are responsible for managing execution resources - that have a Local Resource Manager (LRM). For instance, campus clusters - and supercomputers generally have LRMs (schedulers) such as Slurm, - Torque/PBS, and Condor. Clouds, on the other hand, have API - interfaces that allow much more fine-grained composition of an execution - environment. An execution provider abstracts these types of resources and - provides a single uniform interface to them. - - The providers abstract away the interfaces provided by various systems to - request, monitor, and cancel compute resources. - - .. code:: python - - +------------------ - | - script_string ------->| submit - id <--------|---+ - | - [ ids ] ------->| status - [statuses] <--------|----+ - | - [ ids ] ------->| cancel - [cancel] <--------|----+ - | - +------------------- - - In addition to the listed methods, an ExecutionProvider instance must always - have these attributes, which both default to `None`: - - mem_per_node: Real memory to provision per node in GB. - - Providers which set this attribute should ask for mem_per_node of memory - when provisioning resources, and set the corresponding environment - variable PARSL_MEMORY_GB before executing submitted commands. - - If this attribute is set, executors may use it to calculate how many tasks can - run concurrently per node. - - cores_per_node: Number of cores to provision per node. - - Providers which set this attribute should ask for cores_per_node cores - when provisioning resources, and set the corresponding environment - variable PARSL_CORES before executing submitted commands. - - If this attribute is set, executors may use it to calculate how many tasks can - run concurrently per node. - """ - - @abstractmethod - def __init__(self) -> None: - self.min_blocks: int - self.max_blocks: int - self.init_blocks: int - self.nodes_per_block: int - self.script_dir: Optional[str] - self.parallelism: float - self.resources: Dict[object, Any] - self.cores_per_node: Optional[int] = None - self.mem_per_node: Optional[float] = None - pass - - @abstractmethod - def submit(self, command: str, tasks_per_node: int, job_name: str = "parsl.auto") -> object: - ''' The submit method takes the command string to be executed upon - instantiation of a resource most often to start a pilot (such as for - HighThroughputExecutor or WorkQueueExecutor). - - Args : - - command (str) : The bash command string to be executed - - tasks_per_node (int) : command invocations to be launched per node - - KWargs: - - job_name (str) : Human friendly name to be assigned to the job request - - Returns: - - A job identifier, this could be an integer, string etc - or None or any other object that evaluates to boolean false - if submission failed but an exception isn't thrown. - - Raises: - - ExecutionProviderException or its subclasses - ''' - - pass - - @abstractmethod - def status(self, job_ids: Sequence[object]) -> Sequence[JobStatus]: - ''' Get the status of a list of jobs identified by the job identifiers - returned from the submit request. - - Args: - - job_ids (list) : A list of job identifiers - - Returns: - - A list of JobStatus objects corresponding to each job_id in the job_ids list. - - Raises: - - ExecutionProviderException or its subclasses - - ''' - - pass - - @abstractmethod - def cancel(self, job_ids: Sequence[object]) -> Sequence[bool]: - ''' Cancels the resources identified by the job_ids provided by the user. - - Args: - - job_ids (list): A list of job identifiers - - Returns: - - A list of status from cancelling the job which can be True, False - - Raises: - - ExecutionProviderException or its subclasses - ''' - - pass - - @abstractproperty - def label(self) -> str: - ''' Provides the label for this provider ''' - pass - - @property - @abstractmethod - def status_polling_interval(self) -> int: - """Returns the interval, in seconds, at which the status method should be called. - - :return: the number of seconds to wait between calls to status() - """ - pass diff --git a/parsl/parsl/providers/cluster_provider.py b/parsl/parsl/providers/cluster_provider.py deleted file mode 100644 index db7ef9ea..00000000 --- a/parsl/parsl/providers/cluster_provider.py +++ /dev/null @@ -1,138 +0,0 @@ -import logging -from abc import abstractmethod -from string import Template - -from parsl.launchers.base import Launcher -from parsl.launchers.errors import BadLauncher -from parsl.providers.base import ExecutionProvider -from parsl.providers.errors import SchedulerMissingArgs, ScriptPathError -from parsl.utils import execute_wait - -logger = logging.getLogger(__name__) - - -class ClusterProvider(ExecutionProvider): - """ This class defines behavior common to all cluster/supercompute-style scheduler systems. - - Parameters - ---------- - label : str - Label for this provider. - walltime : str - Walltime requested per block in HH:MM:SS. - launcher : Launcher - Launcher for this provider. - cmd_timeout : int - Timeout for commands made to the scheduler in seconds - - .. code:: python - - +------------------ - | - script_string ------->| submit - id <--------|---+ - | - [ ids ] ------->| status - [statuses] <--------|----+ - | - [ ids ] ------->| cancel - [cancel] <--------|----+ - | - +------------------- - """ - - def __init__(self, - label, - nodes_per_block, - init_blocks, - min_blocks, - max_blocks, - parallelism, - walltime, - launcher, - cmd_timeout=10): - - self._label = label - self.nodes_per_block = nodes_per_block - self.init_blocks = init_blocks - self.min_blocks = min_blocks - self.max_blocks = max_blocks - self.parallelism = parallelism - self.launcher = launcher - self.walltime = walltime - self.cmd_timeout = cmd_timeout - if not isinstance(self.launcher, Launcher): - raise BadLauncher(self.launcher) - - self.script_dir = None - - # Dictionary that keeps track of jobs, keyed on job_id - self.resources = {} - - def execute_wait(self, cmd, timeout=None): - t = self.cmd_timeout - if timeout is not None: - t = timeout - return execute_wait(cmd, t) - - def _write_submit_script(self, template, script_filename, job_name, configs): - """Generate submit script and write it to a file. - - Args: - - template (string) : The template string to be used for the writing submit script - - script_filename (string) : Name of the submit script - - job_name (string) : job name - - configs (dict) : configs that get pushed into the template - - Returns: - - None - - Raises: - SchedulerMissingArgs : If template is missing args - ScriptPathError : Unable to write submit script out - """ - - try: - submit_script = Template(template).substitute(jobname=job_name, **configs) - with open(script_filename, 'w') as f: - f.write(submit_script) - - except KeyError as e: - logger.error("Missing keys for submit script : %s", e) - raise SchedulerMissingArgs(e.args, self.label) - - except IOError as e: - logger.error("Failed writing to submit script: %s", script_filename) - raise ScriptPathError(script_filename, e) - except Exception as e: - print("Template : ", template) - print("Args : ", job_name) - print("Kwargs : ", configs) - logger.error("Uncategorized error: %s", e) - raise e - - @abstractmethod - def _status(self): - pass - - def status(self, job_ids): - """ Get the status of a list of jobs identified by the job identifiers - returned from the submit request. - - Args: - - job_ids (list) : A list of job identifiers - - Returns: - - A list of JobStatus objects corresponding to each job_id in the job_ids list. - - Raises: - - ExecutionProviderException or its subclasses - - """ - if job_ids: - self._status() - return [self.resources[jid]['status'] for jid in job_ids] - - @property - def label(self): - return self._label diff --git a/parsl/parsl/providers/condor/__init__.py b/parsl/parsl/providers/condor/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/providers/condor/condor.py b/parsl/parsl/providers/condor/condor.py deleted file mode 100644 index e92e0920..00000000 --- a/parsl/parsl/providers/condor/condor.py +++ /dev/null @@ -1,330 +0,0 @@ -import logging -import os -import re -import time - -import typeguard - -from parsl.jobs.states import JobState, JobStatus -from parsl.launchers import SingleNodeLauncher -from parsl.launchers.base import Launcher -from parsl.providers.cluster_provider import ClusterProvider -from parsl.providers.condor.template import template_string -from parsl.providers.errors import ScaleOutFailed -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - -from typing import Dict, List, Optional - -# See http://pages.cs.wisc.edu/~adesmet/status.html -translate_table = { - '1': JobState.PENDING, - '2': JobState.RUNNING, - '3': JobState.CANCELLED, - '4': JobState.COMPLETED, - '5': JobState.FAILED, - '6': JobState.FAILED, -} - - -class CondorProvider(RepresentationMixin, ClusterProvider): - """HTCondor Execution Provider. - - Parameters - ---------- - nodes_per_block : int - Nodes to provision per block. - cores_per_slot : int - Specify the number of cores to provision per slot. If set to None, executors - will assume all cores on the node are available for computation. Default is None. - mem_per_slot : float - Specify the real memory to provision per slot in GB. If set to None, no - explicit request to the scheduler will be made. Default is None. - init_blocks : int - Number of blocks to provision at time of initialization - min_blocks : int - Minimum number of blocks to maintain - max_blocks : int - Maximum number of blocks to maintain. - parallelism : float - Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive - scaling where as many resources as possible are used; parallelism close to 0 represents - the opposite situation in which as few resources as possible (i.e., min_blocks) are used. - environment : dict of str - A dictionary of environmant variable name and value pairs which will be set before - running a task. - project : str - Project which the job will be charged against - scheduler_options : str - String to add specific condor attributes to the HTCondor submit script. - transfer_input_files : list(str) - List of strings of paths to additional files or directories to transfer to the job - worker_init : str - Command to be run before starting a worker. - requirements : str - Condor requirements. - launcher : Launcher - Launcher for this provider. Possible launchers include - :class:`~parsl.launchers.SingleNodeLauncher` (the default), - cmd_timeout : int - Timeout for commands made to the scheduler in seconds. - cmd_chunk_size : int - Calls to the scheduler will be made for chunks of blocks with this size. - """ - @typeguard.typechecked - def __init__(self, - nodes_per_block: int = 1, - cores_per_slot: Optional[int] = None, - mem_per_slot: Optional[float] = None, - init_blocks: int = 1, - min_blocks: int = 0, - max_blocks: int = 1, - parallelism: float = 1, - environment: Optional[Dict[str, str]] = None, - project: str = '', - scheduler_options: str = '', - transfer_input_files: List[str] = [], - walltime: str = "00:10:00", - worker_init: str = '', - launcher: Launcher = SingleNodeLauncher(), - requirements: str = '', - cmd_timeout: int = 60, - cmd_chunk_size: int = 100) -> None: - - label = 'condor' - super().__init__(label, - nodes_per_block, - init_blocks, - min_blocks, - max_blocks, - parallelism, - walltime, - launcher, - cmd_timeout=cmd_timeout) - self.cores_per_slot = cores_per_slot - self.mem_per_slot = mem_per_slot - self.cmd_chunk_size = cmd_chunk_size - - # To Parsl, Condor slots should be treated equivalently to nodes - self.cores_per_node = cores_per_slot - self.mem_per_node = mem_per_slot - - self.environment = environment if environment is not None else {} - for key, value in self.environment.items(): - # To escape literal quote marks, double them - # See: http://research.cs.wisc.edu/htcondor/manual/v8.6/condor_submit.html - try: - self.environment[key] = "'{}'".format(value.replace("'", '"').replace('"', '""')) - except AttributeError: - pass - - self.project = project - self.scheduler_options = scheduler_options + '\n' - self.worker_init = worker_init + '\n' - self.requirements = requirements - self.transfer_input_files = transfer_input_files - - def _status(self): - """Update the resource dictionary with job statuses.""" - - for job_id_chunk in _chunker(self.resources.keys(), self.cmd_chunk_size): - job_id_list = ' '.join(job_id_chunk) - cmd = "condor_q {0} -af:jr JobStatus".format(job_id_list) - retcode, stdout, stderr = self.execute_wait(cmd) - """ - Example output: - - $ condor_q 34524642.0 34524643.0 -af:jr JobStatus - 34524642.0 2 - 34524643.0 1 - """ - - for line in stdout.splitlines(): - parts = line.strip().split() - job_id = parts[0] - state = translate_table.get(parts[1], JobState.UNKNOWN) - if job_id in self.resources: - self.resources[job_id]['status'] = JobStatus(state) - - def status(self, job_ids): - """Get the status of a list of jobs identified by their ids. - - Parameters - ---------- - job_ids : list of int - Identifiers of jobs for which the status will be returned. - - Returns - ------- - List of int - Status codes for the requested jobs. - - """ - self._status() - return [self.resources[jid]['status'] for jid in job_ids] - - def submit(self, command, tasks_per_node, job_name="parsl.condor"): - """Submits the command onto an Local Resource Manager job. - - example file with the complex case of multiple submits per job: - Universe =vanilla - output = out.$(Cluster).$(Process) - error = err.$(Cluster).$(Process) - log = log.$(Cluster) - leave_in_queue = true - executable = test.sh - queue 5 - executable = foo - queue 1 - - $ condor_submit test.sub - Submitting job(s)...... - 5 job(s) submitted to cluster 118907. - 1 job(s) submitted to cluster 118908. - - Parameters - ---------- - command : str - Command to execute - job_name : str - Job name prefix. - tasks_per_node : int - command invocations to be launched per node - Returns - ------- - None or str - None if at capacity and cannot provision more; otherwise the identifier for the job. - """ - - logger.debug("Attempting to launch") - - job_name = "parsl.{0}.{1}".format(job_name, time.time()) - - scheduler_options = self.scheduler_options - worker_init = self.worker_init - if self.mem_per_slot is not None: - scheduler_options += 'RequestMemory = {}\n'.format(self.mem_per_slot * 1024) - worker_init += 'export PARSL_MEMORY_GB={}\n'.format(self.mem_per_slot) - if self.cores_per_slot is not None: - scheduler_options += 'RequestCpus = {}\n'.format(self.cores_per_slot) - worker_init += 'export PARSL_CORES={}\n'.format(self.cores_per_slot) - - script_path = "{0}/{1}.submit".format(self.script_dir, job_name) - script_path = os.path.abspath(script_path) - userscript_path = "{0}/{1}.script".format(self.script_dir, job_name) - userscript_path = os.path.abspath(userscript_path) - - self.environment["JOBNAME"] = "'{}'".format(job_name) - - job_config = {} - job_config["job_name"] = job_name - job_config["submit_script_dir"] = self.script_dir - job_config["project"] = self.project - job_config["nodes"] = self.nodes_per_block - job_config["scheduler_options"] = scheduler_options - job_config["worker_init"] = worker_init - job_config["user_script"] = command - job_config["tasks_per_node"] = tasks_per_node - job_config["requirements"] = self.requirements - job_config["environment"] = ' '.join(['{}={}'.format(key, value) for key, value in self.environment.items()]) - - # Move the user script - # This is where the command should be wrapped by the launchers. - wrapped_command = self.launcher(command, - tasks_per_node, - self.nodes_per_block, - self.script_dir) - - with open(userscript_path, 'w') as f: - f.write(job_config["worker_init"] + '\n' + wrapped_command) - - the_input_files = [userscript_path] + self.transfer_input_files - job_config["input_files"] = ','.join(the_input_files) - job_config["job_script"] = os.path.basename(userscript_path) - - # Construct and move the submit script - self._write_submit_script(template_string, script_path, job_name, job_config) - - cmd = "condor_submit {0}".format(script_path) - try: - retcode, stdout, stderr = self.execute_wait(cmd) - except Exception as e: - raise ScaleOutFailed(self.label, str(e)) - - job_id = [] - - if retcode == 0: - for line in stdout.split('\n'): - if re.match('^[0-9]', line) is not None: - cluster = line.split(" ")[5] - # We know the first job id ("process" in condor terms) within a - # cluster is 0 and we know the total number of jobs from - # condor_submit, so we use some list comprehensions to expand - # the condor_submit output into job IDs - # e.g., ['118907.0', '118907.1', '118907.2', '118907.3', '118907.4', '118908.0'] - processes = [str(x) for x in range(0, int(line[0]))] - job_id += [cluster + process for process in processes] - - self._add_resource(job_id) - return job_id[0] - else: - message = "Command '{}' failed with return code {}".format(cmd, retcode) - message += " and standard output '{}'".format(stdout.strip()) if stdout is not None else '' - message += " and standard error '{}'".format(stderr.strip()) if stderr is not None else '' - raise ScaleOutFailed(self.label, message) - - def cancel(self, job_ids): - """Cancels the jobs specified by a list of job IDs. - - Parameters - ---------- - job_ids : list of str - The job IDs to cancel. - - Returns - ------- - list of bool - Each entry in the list will be True if the job is cancelled succesfully, otherwise False. - """ - rets = [] - for job_id_chunk in _chunker(job_ids, self.cmd_chunk_size): - job_id_list = ' '.join(job_id_chunk) - cmd = "condor_rm {0}; condor_rm -forcex {0}".format(job_id_list) - logger.debug("Attempting removal of jobs : {0}".format(cmd)) - retcode, stdout, stderr = self.execute_wait(cmd) - if retcode == 0: - for jid in job_id_chunk: - if jid in self.resources: - self.resources[jid]['status'] = JobStatus(JobState.CANCELLED) - rets.extend([True for i in job_id_chunk]) - else: - rets.extend([False for i in job_id_chunk]) - - return rets - - def _add_resource(self, job_id): - for jid in job_id: - self.resources[jid] = {'status': JobStatus(JobState.PENDING), 'size': 1} - return True - - @property - def status_polling_interval(self): - return 60 - - -# see https://stackoverflow.com/a/18243990 -def _chunker(seq, size): - res = [] - for el in seq: - res.append(el) - if len(res) == size: - yield res - res = [] - if res: - yield res - - -if __name__ == "__main__": - - print("None") diff --git a/parsl/parsl/providers/condor/template.py b/parsl/parsl/providers/condor/template.py deleted file mode 100644 index a31b29d5..00000000 --- a/parsl/parsl/providers/condor/template.py +++ /dev/null @@ -1,28 +0,0 @@ -template_string = ''' -universe = vanilla -should_transfer_files = YES -when_to_transfer_output = ON_EXIT_OR_EVICT -Transfer_Executable = false -transfer_input_files = ${input_files} -machine_count = ${nodes} -output = ${submit_script_dir}/${job_name}.stdout -error = ${submit_script_dir}/${job_name}.stderr -log = ${submit_script_dir}/${job_name}.log -executable = /bin/bash -arguments = ${job_script} -requirements = ${requirements} -+projectname = ${project} -leave_in_queue = TRUE -environment = "${environment}" -${scheduler_options} - -queue - -''' - -# for later, -# if we want to remove on preemption, this might work: -# PERIODIC_REMOVE = (NumJobstarts > 1) -# or if the pilot can trap signals, then we can send a special exit code on -# sigterm/sigkill and remove that way. but then we still need to be careful in -# cases where the worker dies, for example-- no signal is sent diff --git a/parsl/parsl/providers/errors.py b/parsl/parsl/providers/errors.py deleted file mode 100644 index c3aa7e59..00000000 --- a/parsl/parsl/providers/errors.py +++ /dev/null @@ -1,68 +0,0 @@ -import warnings - -from parsl.errors import ParslError - - -class ExecutionProviderException(ParslError): - """ Base class for all exceptions - Only to be invoked when only a more specific error is not available. - - """ - pass - - -class ScaleOutFailed(ExecutionProviderException): - ''' Scale out failed in the submit phase on the provider side - ''' - - def __init__(self, provider, reason): - self.provider = provider - self.reason = reason - - def __str__(self): - return "Unable to scale out {} provider: {}".format(self.provider, self.reason) - - -class SchedulerMissingArgs(ExecutionProviderException): - ''' Error raised when the template used to compose the submit script to the local resource manager is missing required arguments - ''' - - def __init__(self, missing_keywords, sitename): - self.missing_keywords = missing_keywords - self.sitename = sitename - - def __str__(self): - return "SchedulerMissingArgs: Pool:{0} Arg:{1}".format(self.sitename, self.missing_keywords) - - -class ScriptPathError(ExecutionProviderException): - ''' Error raised when the template used to compose the submit script to the local resource manager is missing required arguments - ''' - - def __init__(self, script_path, reason): - self.script_path = script_path - self.reason = reason - - def __str__(self): - return "Unable to write submit script:{0} Reason:{1}".format(self.script_path, self.reason) - - -class SubmitException(ExecutionProviderException): - '''Raised by the submit() method of a provider if there is an error in launching a job. - ''' - - def __init__(self, job_name, message, stdout=None, stderr=None, retcode=None): - self.job_name = job_name - self.message = message - self.stdout = stdout - self.stderr = stderr - self.retcode = retcode - - @property - def task_name(self) -> str: - warnings.warn("task_name is deprecated; use .job_name instead. This will be removed after 2024-06.", DeprecationWarning) - return self.job_name - - def __str__(self) -> str: - # TODO: make this more user-friendly - return f"Cannot launch job {self.job_name}: {self.message}; recode={self.retcode}, stdout={self.stdout}, stderr={self.stderr}" diff --git a/parsl/parsl/providers/googlecloud/__init__.py b/parsl/parsl/providers/googlecloud/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/providers/googlecloud/googlecloud.py b/parsl/parsl/providers/googlecloud/googlecloud.py deleted file mode 100644 index 1b860b76..00000000 --- a/parsl/parsl/providers/googlecloud/googlecloud.py +++ /dev/null @@ -1,238 +0,0 @@ -import atexit -import logging -import os - -from parsl.jobs.states import JobState, JobStatus -from parsl.launchers import SingleNodeLauncher - -logger = logging.getLogger(__name__) - -try: - import googleapiclient.discovery - -except ImportError: - _google_enabled = False -else: - _google_enabled = True - -translate_table = { - 'PENDING': JobState.PENDING, - 'PROVISIONING': JobState.PENDING, - "STAGING": JobState.PENDING, - 'RUNNING': JobState.RUNNING, - 'DONE': JobState.COMPLETED, - 'STOPPING': JobState.COMPLETED, - 'STOPPED': JobState.COMPLETED, - 'TERMINATED': JobState.COMPLETED, - 'SUSPENDING': JobState.COMPLETED, - 'SUSPENDED': JobState.COMPLETED, -} - - -class GoogleCloudProvider(): - """A provider for using resources from the Google Compute Engine. - - Parameters - ---------- - project_id : str - Project ID from Google compute engine. - key_file : str - Path to authorization private key json file. This is required for auth. - A new one can be generated here: https://console.cloud.google.com/apis/credentials - region : str - Region in which to start instances - os_project : str - OS project code for Google compute engine. - os_family : str - OS family to request. - google_version : str - Google compute engine version to use. Possibilies include 'v1' (default) or 'beta'. - instance_type: str - 'n1-standard-1', - init_blocks : int - Number of blocks to provision immediately. Default is 1. - min_blocks : int - Minimum number of blocks to maintain. Default is 0. - max_blocks : int - Maximum number of blocks to maintain. Default is 10. - parallelism : float - Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive - scaling where as many resources as possible are used; parallelism close to 0 represents - the opposite situation in which as few resources as possible (i.e., min_blocks) are used. - - """ - - def __init__(self, - project_id, - key_file, - region, - os_project, - os_family, - google_version='v1', - instance_type='n1-standard-1', - init_blocks=1, - min_blocks=0, - max_blocks=10, - launcher=SingleNodeLauncher(), - parallelism=1): - self.project_id = project_id - os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = key_file - self.zone = self.get_zone(region) - self.os_project = os_project - self.os_family = os_family - self.label = 'google_cloud' - self.client = googleapiclient.discovery.build('compute', google_version) - self.instance_type = instance_type - self.init_blocks = init_blocks - self.min_blocks = min_blocks - self.max_blocks = max_blocks - self.parallelism = parallelism - self.num_instances = 0 - self.launcher = launcher - self.script_dir = None - - # Dictionary that keeps track of jobs, keyed on job_id - self.resources = {} - atexit.register(self.bye) - - def submit(self, command, tasks_per_node, job_name="parsl.gcs"): - ''' The submit method takes the command string to be executed upon - instantiation of a resource most often to start a pilot. - - Args : - - command (str) : The bash command string to be executed. - - tasks_per_node (int) : command invocations to be launched per node - - KWargs: - - job_name (str) : Human friendly name to be assigned to the job request - - Returns: - - A job identifier, this could be an integer, string etc - - Raises: - - ExecutionProviderException or its subclasses - ''' - wrapped_cmd = self.launcher(command, - tasks_per_node, - 1, - self.script_dir) - - instance, name = self.create_instance(command=wrapped_cmd) - self.resources[name] = {"job_id": name, "status": JobStatus(translate_table[instance['status']])} - return name - - def status(self, job_ids): - ''' Get the status of a list of jobs identified by the job identifiers - returned from the submit request. - - Args: - - job_ids (list) : A list of job identifiers - - Returns: - - A list of JobStatus objects corresponding to each job_id in the job_ids list. - - Raises: - - ExecutionProviderException or its subclasses - - ''' - statuses = [] - for job_id in job_ids: - instance = self.client.instances().get(instance=job_id, project=self.project_id, zone=self.zone).execute() - job_status = JobStatus(translate_table[instance['status']]) - self.resources[job_id]['status'] = job_status - statuses.append(job_status) - return statuses - - def cancel(self, job_ids): - ''' Cancels the resources identified by the job_ids provided by the user. - - Args: - - job_ids (list): A list of job identifiers - - Returns: - - A list of status from cancelling the job which can be True, False - - Raises: - - ExecutionProviderException or its subclasses - ''' - statuses = [] - for job_id in job_ids: - try: - self.delete_instance(job_id) - statuses.append(True) - except Exception: - statuses.append(False) - return statuses - - def bye(self): - self.cancel([i for i in list(self.resources)]) - - def create_instance(self, command=""): - name = "parslauto{}".format(self.num_instances) - self.num_instances += 1 - compute = self.client - project = self.project_id - image_response = compute.images().getFromFamily( - project=self.os_project, family=self.os_family).execute() - source_disk_image = image_response['selfLink'] - - # Configure the machine - machine_type = "zones/{}/machineTypes/{}".format(self.zone, self.instance_type) - startup_script = command - - config = { - 'name': name, - 'machineType': machine_type, - - # Specify the boot disk and the image to use as a source. - 'disks': [{ - 'boot': True, - 'autoDelete': True, - 'initializeParams': { - 'sourceImage': source_disk_image, - } - }], - 'networkInterfaces': [{ - 'network': 'global/networks/default', - 'accessConfigs': [{ - 'type': 'ONE_TO_ONE_NAT', - 'name': 'External NAT' - }] - }], - 'serviceAccounts': [{ - 'email': - 'default', - 'scopes': [ - 'https://www.googleapis.com/auth/devstorage.read_write', - 'https://www.googleapis.com/auth/logging.write' - ] - }], - 'metadata': { - 'items': [{ - # Startup script is automatically executed by the - # instance upon startup. - 'key': 'startup-script', - 'value': startup_script - }] - } - } - - return compute.instances().insert(project=project, zone=self.zone, body=config).execute(), name - - def get_zone(self, region): - res = self.client.zones().list(project=self.project_id).execute() - for zone in res['items']: - if region in zone['name'] and zone['status'] == "UP": - return zone["name"] - - def delete_instance(self, name): - - compute = self.client - project = self.project_id - zone = self.zone - - return compute.instances().delete(project=project, zone=zone, instance=name).execute() - - @property - def status_polling_interval(self): - return 60 diff --git a/parsl/parsl/providers/grid_engine/__init__.py b/parsl/parsl/providers/grid_engine/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/providers/grid_engine/grid_engine.py b/parsl/parsl/providers/grid_engine/grid_engine.py deleted file mode 100644 index afb3e583..00000000 --- a/parsl/parsl/providers/grid_engine/grid_engine.py +++ /dev/null @@ -1,223 +0,0 @@ -import logging -import os -import time - -from parsl.jobs.states import JobState, JobStatus -from parsl.launchers import SingleNodeLauncher -from parsl.providers.cluster_provider import ClusterProvider -from parsl.providers.grid_engine.template import template_string -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - -translate_table = { - 'qw': JobState.PENDING, - 'hqw': JobState.PENDING, - 'hrwq': JobState.PENDING, - 'r': JobState.RUNNING, - 's': JobState.FAILED, # obsuspended - 'ts': JobState.FAILED, - 't': JobState.FAILED, # Suspended by alarm - 'eqw': JobState.FAILED, # Error states - 'ehqw': JobState.FAILED, # .. - 'ehrqw': JobState.FAILED, # .. - 'd': JobState.COMPLETED, - 'dr': JobState.COMPLETED, - 'dt': JobState.COMPLETED, - 'drt': JobState.COMPLETED, - 'ds': JobState.COMPLETED, - 'drs': JobState.COMPLETED, -} - - -class GridEngineProvider(ClusterProvider, RepresentationMixin): - """A provider for the Grid Engine scheduler. - - Parameters - ---------- - nodes_per_block : int - Nodes to provision per block. - min_blocks : int - Minimum number of blocks to maintain. - max_blocks : int - Maximum number of blocks to maintain. - parallelism : float - Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive - scaling where as many resources as possible are used; parallelism close to 0 represents - the opposite situation in which as few resources as possible (i.e., min_blocks) are used. - walltime : str - Walltime requested per block in HH:MM:SS. - scheduler_options : str - String to prepend to the #$$ blocks in the submit script to the scheduler. - worker_init : str - Command to be run before starting a worker, such as 'module load Anaconda; source activate env'. - launcher : Launcher - Launcher for this provider. Possible launchers include - :class:`~parsl.launchers.SingleNodeLauncher` (the default), - cmd_timeout : int - Timeout for commands made to the scheduler in seconds - """ - - def __init__(self, - nodes_per_block=1, - init_blocks=1, - min_blocks=0, - max_blocks=1, - parallelism=1, - walltime="00:10:00", - scheduler_options='', - worker_init='', - launcher=SingleNodeLauncher(), - cmd_timeout: int = 60, - queue=None): - label = 'grid_engine' - super().__init__(label, - nodes_per_block, - init_blocks, - min_blocks, - max_blocks, - parallelism, - walltime, - launcher, - cmd_timeout=cmd_timeout) - self.scheduler_options = scheduler_options - self.worker_init = worker_init - self.queue = queue - - if launcher in ['srun', 'srun_mpi']: - logger.warning("Use of {} launcher is usually appropriate for Slurm providers. " - "Recommended options include 'single_node' or 'aprun'.".format(launcher)) - - def get_configs(self, command, tasks_per_node): - """Compose a dictionary with information for writing the submit script.""" - - logger.debug("Requesting one block with {} nodes per block and {} tasks per node".format( - self.nodes_per_block, tasks_per_node)) - - job_config = {} - job_config["submit_script_dir"] = self.script_dir - job_config["nodes"] = self.nodes_per_block - job_config["walltime"] = self.walltime - job_config["scheduler_options"] = self.scheduler_options - job_config["worker_init"] = self.worker_init - job_config["user_script"] = command - - job_config["user_script"] = self.launcher(command, - tasks_per_node, - self.nodes_per_block, - self.script_dir) - return job_config - - def submit(self, command, tasks_per_node, job_name="parsl.sge"): - ''' The submit method takes the command string to be executed upon - instantiation of a resource most often to start a pilot. - - Args : - - command (str) : The bash command string to be executed. - - tasks_per_node (int) : command invocations to be launched per node - - KWargs: - - job_name (str) : Human friendly name to be assigned to the job request - - Returns: - - A job identifier, this could be an integer, string etc - - Raises: - - ExecutionProviderException or its subclasses - ''' - - # Set job name - job_name = "{0}.{1}".format(job_name, time.time()) - - # Set script path - script_path = "{0}/{1}.submit".format(self.script_dir, job_name) - script_path = os.path.abspath(script_path) - - job_config = self.get_configs(command, tasks_per_node) - - logger.debug("Writing submit script") - self._write_submit_script(template_string, script_path, job_name, job_config) - - if self.queue is not None: - cmd = "qsub -q {0} -terse {1}".format(self.queue, script_path) - else: - cmd = "qsub -terse {0}".format(script_path) - retcode, stdout, stderr = self.execute_wait(cmd) - - if retcode == 0: - for line in stdout.split('\n'): - job_id = line.strip() - if not job_id: - continue - self.resources[job_id] = {'job_id': job_id, 'status': JobStatus(JobState.PENDING)} - return job_id - else: - logger.error("Submit command failed") - logger.error("Retcode:%s STDOUT:%s STDERR:%s", retcode, stdout.strip(), stderr.strip()) - - def _status(self): - ''' Get the status of a list of jobs identified by the job identifiers - returned from the submit request. - - Returns: - - A list of JobStatus objects corresponding to each job_id in the job_ids list. - - Raises: - - ExecutionProviderException or its subclasses - - ''' - - cmd = "qstat" - - retcode, stdout, stderr = self.execute_wait(cmd) - - # Execute_wait failed. Do no update - if retcode != 0: - return - - jobs_missing = list(self.resources.keys()) - for line in stdout.split('\n'): - parts = line.split() - if parts and parts[0].lower().lower() != 'job-id' \ - and not parts[0].startswith('----'): - job_id = parts[0] - state = translate_table.get(parts[4].lower(), JobState.UNKNOWN) - if job_id in self.resources: - self.resources[job_id]['status'] = JobStatus(state) - jobs_missing.remove(job_id) - - # Filling in missing blanks for jobs that might have gone missing - # we might lose some information about why the jobs failed. - for missing_job in jobs_missing: - self.resources[missing_job]['status'] = JobStatus(JobState.COMPLETED) - - def cancel(self, job_ids): - ''' Cancels the resources identified by the job_ids provided by the user. - - Args: - - job_ids (list): A list of job identifiers - - Returns: - - A list of status from cancelling the job which can be True, False - - Raises: - - ExecutionProviderException or its subclasses - ''' - - job_id_list = ' '.join(job_ids) - cmd = "qdel {}".format(job_id_list) - retcode, stdout, stderr = self.execute_wait(cmd) - - rets = None - if retcode == 0: - for jid in job_ids: - self.resources[jid]['status'] = JobStatus(JobState.COMPLETED) - rets = [True for i in job_ids] - else: - rets = [False for i in job_ids] - - return rets - - @property - def status_polling_interval(self): - return 60 diff --git a/parsl/parsl/providers/grid_engine/template.py b/parsl/parsl/providers/grid_engine/template.py deleted file mode 100644 index 6ba32100..00000000 --- a/parsl/parsl/providers/grid_engine/template.py +++ /dev/null @@ -1,14 +0,0 @@ -template_string = """#!/bin/bash -#$$ -S /bin/bash -#$$ -o ${submit_script_dir}/${jobname}.submit.stdout -#$$ -e ${submit_script_dir}/${jobname}.submit.stderr -#$$ -cwd -#$$ -l h_rt=${walltime} -${scheduler_options} - -${worker_init} - -export JOBNAME="${jobname}" - -$user_script -""" diff --git a/parsl/parsl/providers/kubernetes/__init__.py b/parsl/parsl/providers/kubernetes/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/providers/kubernetes/kube.py b/parsl/parsl/providers/kubernetes/kube.py deleted file mode 100644 index 2f70d2ad..00000000 --- a/parsl/parsl/providers/kubernetes/kube.py +++ /dev/null @@ -1,366 +0,0 @@ -import logging -import uuid -from typing import Any, Dict, List, Optional, Tuple - -import typeguard - -from parsl.errors import OptionalModuleMissing -from parsl.jobs.states import JobState, JobStatus -from parsl.providers.base import ExecutionProvider -from parsl.providers.kubernetes.template import template_string -from parsl.utils import RepresentationMixin, sanitize_dns_subdomain_rfc1123 - -try: - from kubernetes import client, config - _kubernetes_enabled = True -except (ImportError, NameError, FileNotFoundError): - _kubernetes_enabled = False - -logger = logging.getLogger(__name__) - -translate_table = { - 'Running': JobState.RUNNING, - 'Pending': JobState.PENDING, - 'Succeeded': JobState.COMPLETED, - 'Failed': JobState.FAILED, - 'Unknown': JobState.UNKNOWN, -} - - -class KubernetesProvider(ExecutionProvider, RepresentationMixin): - """ Kubernetes execution provider - - Parameters - ---------- - - namespace : str - Kubernetes namespace to create deployments. - image : str - Docker image to use in the deployment. - nodes_per_block : int - Nodes to provision per block. - init_blocks : int - Number of blocks to provision at the start of the run. Default is 1. - min_blocks : int - Minimum number of blocks to maintain. - max_blocks : int - Maximum number of blocks to maintain. - max_cpu : float - CPU limits of the blocks (pods), in cpu units. - This is the cpu "limits" option for resource specification. - Check kubernetes docs for more details. Default is 2. - max_mem : str - Memory limits of the blocks (pods), in Mi or Gi. - This is the memory "limits" option for resource specification on kubernetes. - Check kubernetes docs for more details. Default is 500Mi. - init_cpu : float - CPU limits of the blocks (pods), in cpu units. - This is the cpu "requests" option for resource specification. - Check kubernetes docs for more details. Default is 1. - init_mem : str - Memory limits of the blocks (pods), in Mi or Gi. - This is the memory "requests" option for resource specification on kubernetes. - Check kubernetes docs for more details. Default is 250Mi. - extra_requests: Dict[str, str] - Extra resource requests of the blocks (pods). Check kubernetes docs for more details. - extra_limits: Dict[str, str] - Extra resource limits of the blocks (pods). Check kubernetes docs for more details. - parallelism : float - Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive - scaling where as many resources as possible are used; parallelism close to 0 represents - the opposite situation in which as few resources as possible (i.e., min_blocks) are used. - worker_init : str - Command to be run first for the workers, such as ``python start.py``. - secret : str - The Kubernetes ImagePullSecret secret to use to pull images - pod_name : str - The name for the pod, will be appended with a timestamp. - Default is None, meaning parsl automatically names the pod. - user_id : str - Unix user id to run the container as. - group_id : str - Unix group id to run the container as. - run_as_non_root : bool - Run as non-root (True) or run as root (False). - persistent_volumes: list[(str, str)] - List of tuples describing persistent volumes to be mounted in the pod. - The tuples consist of (PVC Name, Mount Directory). - service_account_name: str - Name of the service account to run the pod as. - annotations: Dict[str, str] - Annotations to set on the pod. - """ - @typeguard.typechecked - def __init__(self, - image: str, - namespace: str = 'default', - nodes_per_block: int = 1, - init_blocks: int = 4, - min_blocks: int = 0, - max_blocks: int = 10, - max_cpu: float = 2, - max_mem: str = "500Mi", - init_cpu: float = 1, - init_mem: str = "250Mi", - extra_requests: Optional[Dict[str, str]] = None, - extra_limits: Optional[Dict[str, str]] = None, - parallelism: float = 1, - worker_init: str = "", - pod_name: Optional[str] = None, - user_id: Optional[str] = None, - group_id: Optional[str] = None, - run_as_non_root: bool = False, - secret: Optional[str] = None, - persistent_volumes: List[Tuple[str, str]] = [], - service_account_name: Optional[str] = None, - annotations: Optional[Dict[str, str]] = None) -> None: - if not _kubernetes_enabled: - raise OptionalModuleMissing(['kubernetes'], - "Kubernetes provider requires kubernetes module and config.") - try: - config.load_kube_config() - except config.config_exception.ConfigException: - # `load_kube_config` assumes a local kube-config file, and fails if not - # present, raising: - # - # kubernetes.config.config_exception.ConfigException: Invalid - # kube-config file. No configuration found. - # - # Since running a parsl driver script on a kubernetes cluster is a common - # pattern to enable worker-interchange communication, this enables an - # in-cluster config to be loaded if a kube-config file isn't found. - # - # Based on: https://github.com/kubernetes-client/python/issues/1005 - try: - config.load_incluster_config() - except config.config_exception.ConfigException: - raise config.config_exception.ConfigException( - "Failed to load both kube-config file and in-cluster configuration." - ) - - self.namespace = namespace - self.image = image - self.nodes_per_block = nodes_per_block - self.init_blocks = init_blocks - self.min_blocks = min_blocks - self.max_blocks = max_blocks - self.max_cpu = max_cpu - self.max_mem = max_mem - self.init_cpu = init_cpu - self.init_mem = init_mem - self.extra_requests = extra_requests if extra_requests else {} - self.extra_limits = extra_limits if extra_limits else {} - self.parallelism = parallelism - self.worker_init = worker_init - self.secret = secret - self.pod_name = pod_name - self.user_id = user_id - self.group_id = group_id - self.run_as_non_root = run_as_non_root - self.persistent_volumes = persistent_volumes - self.service_account_name = service_account_name - self.annotations = annotations - - self.kube_client = client.CoreV1Api() - - # Dictionary that keeps track of jobs, keyed on job_id - self.resources: Dict[object, Dict[str, Any]] - self.resources = {} - - def submit(self, cmd_string: str, tasks_per_node: int, job_name: str = "parsl.kube"): - """ Submit a job - Args: - - cmd_string :(String) - Name of the container to initiate - - tasks_per_node (int) : command invocations to be launched per node - - Kwargs: - - job_name (String): Name for job - - Returns: - - job_id: (string) Identifier for the job - """ - job_id = uuid.uuid4().hex[:8] - - pod_name = self.pod_name or job_name - try: - pod_name = sanitize_dns_subdomain_rfc1123(pod_name) - except ValueError: - logger.warning( - f"Invalid pod name '{pod_name}' for job '{job_id}', falling back to 'parsl.kube'" - ) - pod_name = "parsl.kube" - pod_name = pod_name[:253 - 1 - len(job_id)] # Leave room for the job ID - pod_name = pod_name.rstrip(".-") # Remove trailing dot or hyphen after trim - pod_name = f"{pod_name}.{job_id}" - - formatted_cmd = template_string.format(command=cmd_string, - worker_init=self.worker_init) - - logger.debug("Pod name: %s", pod_name) - self._create_pod(image=self.image, - pod_name=pod_name, - job_id=job_id, - cmd_string=formatted_cmd, - volumes=self.persistent_volumes, - service_account_name=self.service_account_name, - annotations=self.annotations) - self.resources[job_id] = {'status': JobStatus(JobState.RUNNING), 'pod_name': pod_name} - - return job_id - - def status(self, job_ids): - """ Get the status of a list of jobs identified by the job identifiers - returned from the submit request. - Args: - - job_ids (list) : A list of job identifiers - Returns: - - A list of JobStatus objects corresponding to each job_id in the job_ids list. - Raises: - - ExecutionProviderExceptions or its subclasses - """ - if job_ids: - self._status() - return [self.resources[jid]['status'] for jid in job_ids] - - def _get_pod_name(self, job_id: str) -> str: - return self.resources[job_id]['pod_name'] - - def cancel(self, job_ids): - """ Cancels the jobs specified by a list of job ids - Args: - job_ids : [ ...] - Returns : - [True/False...] : If the cancel operation fails the entire list will be False. - """ - for job in job_ids: - logger.debug("Terminating job/pod: {0}".format(job)) - pod_name = self._get_pod_name(job) - self._delete_pod(pod_name) - - self.resources[job]['status'] = JobStatus(JobState.CANCELLED) - rets = [True for i in job_ids] - - return rets - - def _status(self): - """Returns the status list for a list of job_ids - Args: - self - Returns: - [status...] : Status list of all jobs - """ - - job_ids = list(self.resources.keys()) - to_poll_job_ids = [jid for jid in job_ids if not self.resources[jid]['status'].terminal] - logger.debug("Polling Kubernetes pod status: {}".format(to_poll_job_ids)) - for jid in to_poll_job_ids: - phase = None - try: - pod_name = self._get_pod_name(jid) - pod = self.kube_client.read_namespaced_pod(name=pod_name, namespace=self.namespace) - except Exception: - logger.exception("Failed to poll pod {} status, most likely because pod was terminated".format(jid)) - if self.resources[jid]['status'] is JobStatus(JobState.RUNNING): - phase = 'Unknown' - else: - phase = pod.status.phase - if phase: - status = translate_table.get(phase, JobState.UNKNOWN) - logger.debug("Updating pod {} with status {} to parsl status {}".format(jid, - phase, - status)) - self.resources[jid]['status'] = JobStatus(status) - - def _create_pod(self, - image: str, - pod_name: str, - job_id: str, - port: int = 80, - cmd_string=None, - volumes=[], - service_account_name=None, - annotations=None): - """ Create a kubernetes pod for the job. - Args: - - image (string) : Docker image to launch - - pod_name (string) : Name of the pod - - job_id (string) : Job ID - KWargs: - - port (integer) : Container port - Returns: - - None - """ - - security_context = None - if self.user_id and self.group_id: - security_context = client.V1SecurityContext(run_as_group=int(self.group_id), - run_as_user=int(self.user_id), - run_as_non_root=self.run_as_non_root) - - # Create the environment variables and command to initiate IPP - environment_vars = client.V1EnvVar(name="TEST", value="SOME DATA") - - launch_args = ["-c", "{0}".format(cmd_string)] - - volume_mounts = [] - # Create mount paths for the volumes - for volume in volumes: - volume_mounts.append(client.V1VolumeMount(mount_path=volume[1], - name=volume[0])) - resources = client.V1ResourceRequirements(limits={'cpu': str(self.max_cpu), - 'memory': self.max_mem} | self.extra_limits, - requests={'cpu': str(self.init_cpu), - 'memory': self.init_mem} | self.extra_requests, - ) - # Configure Pod template container - container = client.V1Container( - name=job_id, - image=image, - resources=resources, - ports=[client.V1ContainerPort(container_port=port)], - volume_mounts=volume_mounts, - command=['/bin/bash'], - args=launch_args, - env=[environment_vars], - security_context=security_context) - - # Create a secret to enable pulling images from secure repositories - secrets = None - if self.secret: - secrets = [client.V1LocalObjectReference(name=self.secret)] - - # Create list of volumes from (pvc, mount) tuples - volume_defs = [] - for volume in volumes: - volume_defs.append(client.V1Volume(name=volume[0], - persistent_volume_claim=client.V1PersistentVolumeClaimVolumeSource( - claim_name=volume[0]))) - - metadata = client.V1ObjectMeta(name=pod_name, - labels={"parsl-job-id": job_id}, - annotations=annotations) - spec = client.V1PodSpec(containers=[container], - image_pull_secrets=secrets, - volumes=volume_defs, - service_account_name=service_account_name) - - pod = client.V1Pod(spec=spec, metadata=metadata) - api_response = self.kube_client.create_namespaced_pod(namespace=self.namespace, - body=pod) - logger.debug("Pod created. status='{0}'".format(str(api_response.status))) - - def _delete_pod(self, pod_name): - """Delete a pod""" - - api_response = self.kube_client.delete_namespaced_pod(name=pod_name, - namespace=self.namespace, - body=client.V1DeleteOptions()) - logger.debug("Pod deleted. status='{0}'".format(str(api_response.status))) - - @property - def label(self): - return "kubernetes" - - @property - def status_polling_interval(self): - return 60 diff --git a/parsl/parsl/providers/kubernetes/template.py b/parsl/parsl/providers/kubernetes/template.py deleted file mode 100644 index 6693b1ba..00000000 --- a/parsl/parsl/providers/kubernetes/template.py +++ /dev/null @@ -1,4 +0,0 @@ -template_string = """{worker_init} - -{command} -""" diff --git a/parsl/parsl/providers/local/__init__.py b/parsl/parsl/providers/local/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/providers/local/local.py b/parsl/parsl/providers/local/local.py deleted file mode 100644 index eaab4087..00000000 --- a/parsl/parsl/providers/local/local.py +++ /dev/null @@ -1,287 +0,0 @@ -import logging -import os -import platform -import textwrap -import time - -import psutil - -from parsl.jobs.states import JobState, JobStatus -from parsl.launchers import SingleNodeLauncher -from parsl.providers.base import ExecutionProvider -from parsl.providers.errors import ( - SchedulerMissingArgs, - ScriptPathError, - SubmitException, -) -from parsl.utils import RepresentationMixin, execute_wait - -logger = logging.getLogger(__name__) - - -class LocalProvider(ExecutionProvider, RepresentationMixin): - """ Local Execution Provider - - This provider is used to provide execution resources from the localhost. - - Parameters - ---------- - - min_blocks : int - Minimum number of blocks to maintain. - max_blocks : int - Maximum number of blocks to maintain. - parallelism : float - Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive - scaling where as many resources as possible are used; parallelism close to 0 represents - the opposite situation in which as few resources as possible (i.e., min_blocks) are used. - worker_init : str - Command to be run before starting a worker, such as 'module load Anaconda; source activate env'. - """ - - def __init__(self, - nodes_per_block=1, - launcher=SingleNodeLauncher(), - init_blocks=1, - min_blocks=0, - max_blocks=1, - worker_init='', - cmd_timeout=30, - parallelism=1): - self._label = 'local' - self.nodes_per_block = nodes_per_block - self.launcher = launcher - self.worker_init = worker_init - self.init_blocks = init_blocks - self.min_blocks = min_blocks - self.max_blocks = max_blocks - self.parallelism = parallelism - self.script_dir = None - self.cmd_timeout = cmd_timeout - - # Dictionary that keeps track of jobs, keyed on job_id - self.resources = {} - - def status(self, job_ids): - ''' Get the status of a list of jobs identified by their ids. - - Args: - - job_ids (List of ids) : List of identifiers for the jobs - - Returns: - - List of status codes. - - ''' - for job_id in self.resources: - # This job dict should really be a class on its own - job_dict = self.resources[job_id] - if job_dict['status'] and job_dict['status'].terminal: - # We already checked this and it can't change after that - continue - script_path = job_dict['script_path'] - - alive = self._is_alive(job_dict) - str_ec = self._read_job_file(script_path, '.ec').strip() - - status = None - if str_ec == '-': - if alive: - status = JobStatus(JobState.RUNNING) - else: - # not alive but didn't get to write an exit code - if 'cancelled' in job_dict: - # because we cancelled it - status = JobStatus(JobState.CANCELLED) - else: - # we didn't cancel it, so it must have been killed by something outside - # parsl; we don't have a state for this, but we'll use CANCELLED with - # a specific message - status = JobStatus(JobState.CANCELLED, message='Killed') - else: - try: - # TODO: ensure that these files are only read once and clean them - ec = int(str_ec) - stdout_path = self._job_file_path(script_path, '.out') - stderr_path = self._job_file_path(script_path, '.err') - if ec == 0: - state = JobState.COMPLETED - else: - state = JobState.FAILED - status = JobStatus(state, exit_code=ec, - stdout_path=stdout_path, stderr_path=stderr_path) - except Exception: - status = JobStatus(JobState.FAILED, - 'Cannot parse exit code: {}'.format(str_ec)) - - job_dict['status'] = status - - return [self.resources[jid]['status'] for jid in job_ids] - - @staticmethod - def _is_alive(job_dict) -> bool: - return psutil.pid_exists(job_dict['remote_pid']) - - def _job_file_path(self, script_path: str, suffix: str) -> str: - path = '{0}{1}'.format(script_path, suffix) - return path - - def _read_job_file(self, script_path: str, suffix: str) -> str: - path = self._job_file_path(script_path, suffix) - - with open(path, 'r') as f: - return f.read() - - def _write_submit_script(self, script_string, script_filename): - ''' - Load the template string with config values and write the generated submit script to - a submit script file. - - Args: - - template_string (string) : The template string to be used for the writing submit script - - script_filename (string) : Name of the submit script - - Returns: - - True: on success - - Raises: - SchedulerMissingArgs : If template is missing args - ScriptPathError : Unable to write submit script out - ''' - - try: - with open(script_filename, 'w') as f: - f.write(script_string) - - except KeyError as e: - logger.error("Missing keys for submit script: %s", e) - raise SchedulerMissingArgs(e.args, self.label) - - except IOError as e: - logger.error("Failed writing to submit script: %s", script_filename) - raise ScriptPathError(script_filename, e) - - return True - - def submit(self, command, tasks_per_node, job_name="parsl.localprovider"): - ''' Submits the command onto an Local Resource Manager job. - Submit returns an ID that corresponds to the task that was just submitted. - - If tasks_per_node < 1: - 1/tasks_per_node is provisioned - - If tasks_per_node == 1: - A single node is provisioned - - If tasks_per_node > 1 : - tasks_per_node nodes are provisioned. - - Args: - - command :(String) Commandline invocation to be made on the remote side. - - tasks_per_node (int) : command invocations to be launched per node - - Kwargs: - - job_name (String): Name for job, must be unique - - Returns: - - None: At capacity, cannot provision more - - job_id: (string) Identifier for the job - - ''' - job_name = "{0}.{1}".format(job_name, time.time()) - - if platform.system() == 'Windows': - script_path = "{0}/{1}.ps1".format(self.script_dir, job_name) - jobname_cmd = f'\n$env:JOBNAME = "{job_name}"\n' - else: - script_path = "{0}/{1}.sh".format(self.script_dir, job_name) - jobname_cmd = f'\nexport JOBNAME={job_name}\n' - script_path = os.path.abspath(script_path) - wrap_command = (self.worker_init + jobname_cmd + - self.launcher(command, tasks_per_node, self.nodes_per_block, self.script_dir)) - self._write_submit_script(wrap_command, script_path) - job_id = None - remote_pid = None - - logger.debug("Launching") - # We need to capture the exit code and the streams, so we put them in files. We also write - # '-' to the exit code file to isolate potential problems with writing to files in the - # script directory - # - # The basic flow is: - # 1. write "-" to the exit code file. If this fails, exit - # 2. Launch the following sequence in the background: - # a. the command to run - # b. write the exit code of the command from (a) to the exit code file - # 3. Write the PID of the background sequence on stdout. The PID is needed if we want to - # cancel the task later. - # - # We need to do the >/dev/null 2>&1 so that bash closes stdout, otherwise - # execute_wait hangs reading the process stdout until all the - # background commands complete. - if platform.system() == 'Windows': - cmd = ( - f'powershell.exe -ExecutionPolicy Bypass -Command "' - f"Set-Content -Path '{script_path}.ec' -Value '-'; " - f"$p = Start-Process powershell.exe -WindowStyle Hidden -ArgumentList '-ExecutionPolicy', 'Bypass', '-Command', " - f"'$proc = Start-Process powershell.exe -WindowStyle Hidden -ArgumentList ''-ExecutionPolicy'', ''Bypass'', ''-File'', ''{script_path}'' " - f"-RedirectStandardOutput ''{script_path}.out'' -RedirectStandardError ''{script_path}.err'' -PassThru -Wait; " - f"Set-Content -Path ''{script_path}.ec'' -Value $proc.ExitCode' -PassThru; " - f"Write-Host ('PID:' + $p.Id)" - f'"' - ) - else: - cmd = textwrap.dedent( - f''' - /bin/bash -c 'echo - >{script_path}.ec && - {{ {{ - bash {script_path} 1>{script_path}.out 2>{script_path}.err ; - echo $? > {script_path}.ec ; - }} >/dev/null 2>&1 & echo "PID:$!" ; }}' - ''') - retcode, stdout, stderr = execute_wait(cmd, self.cmd_timeout) - if retcode != 0: - raise SubmitException(job_name, "Launch command exited with code {0}".format(retcode), - stdout, stderr) - for line in stdout.split('\n'): - if line.startswith("PID:"): - job_id = line.split("PID:")[1].strip() - remote_pid = int(job_id) - break - if job_id is None: - raise SubmitException(job_name, "Channel failed to start remote command/retrieve PID") - - self.resources[job_id] = {'job_id': job_id, 'status': JobStatus(JobState.RUNNING), - 'remote_pid': remote_pid, 'script_path': script_path} - - return job_id - - def cancel(self, job_ids): - ''' Cancels the jobs specified by a list of job ids - - Args: - job_ids : [ ...] - - Returns: [True] Always returns true for every job_id, regardless of - whether an individual cancel failed (unless an - exception is raised) - ''' - for job in job_ids: - job_dict = self.resources[job] - job_dict['cancelled'] = True - logger.debug("Terminating job/process ID: {0}".format(job)) - cmd = "kill -- -$(ps -o pgid= {} | grep -o '[0-9]*')".format(job_dict['remote_pid']) - retcode, stdout, stderr = execute_wait(cmd, self.cmd_timeout) - if retcode != 0: - logger.warning("Failed to kill PID: {} and child processes on {}".format(job_dict['remote_pid'], - self.label)) - - rets = [True for i in job_ids] - return rets - - @property - def label(self): - return self._label - - @property - def status_polling_interval(self): - return 5 diff --git a/parsl/parsl/providers/lsf/__init__.py b/parsl/parsl/providers/lsf/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/providers/lsf/lsf.py b/parsl/parsl/providers/lsf/lsf.py deleted file mode 100644 index 4c76ef6d..00000000 --- a/parsl/parsl/providers/lsf/lsf.py +++ /dev/null @@ -1,267 +0,0 @@ -import logging -import math -import os -import time - -from parsl.jobs.states import JobState, JobStatus -from parsl.launchers import SingleNodeLauncher -from parsl.providers.cluster_provider import ClusterProvider -from parsl.providers.lsf.template import template_string -from parsl.utils import RepresentationMixin, wtime_to_minutes - -logger = logging.getLogger(__name__) - -translate_table = { - 'PEND': JobState.PENDING, - 'RUN': JobState.RUNNING, - 'DONE': JobState.COMPLETED, - 'EXIT': JobState.FAILED, # (failed), - 'PSUSP': JobState.CANCELLED, - 'USUSP': JobState.CANCELLED, - 'SSUSP': JobState.CANCELLED, -} - - -class LSFProvider(ClusterProvider, RepresentationMixin): - """LSF Execution Provider - - This provider uses bsub to submit, bjobs for status and bkill to cancel - jobs. The bsub script to be used is created from a template file in this - same module. - - Parameters - ---------- - nodes_per_block : int - Nodes to provision per block. - When request_by_nodes is False, it is computed by cores_per_block / cores_per_node. - cores_per_block : int - Cores to provision per block. Enabled only when request_by_nodes is False. - cores_per_node: int - Cores to provision per node. Enabled only when request_by_nodes is False. - init_blocks : int - Number of blocks to request at the start of the run. - min_blocks : int - Minimum number of blocks to maintain. - max_blocks : int - Maximum number of blocks to maintain. - parallelism : float - Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive - scaling where as many resources as possible are used; parallelism close to 0 represents - the opposite situation in which as few resources as possible (i.e., min_blocks) are used. - walltime : str - Walltime requested per block in HH:MM:SS. - project : str - Project to which the resources must be charged - queue : str - Queue to which to submit the job request - scheduler_options : str - String to prepend to the #BSUB blocks in the submit script to the scheduler. - worker_init : str - Command to be run before starting a worker, such as 'module load Anaconda; source activate env'. - cmd_timeout : int - Seconds after which requests to the scheduler will timeout. Default: 120s - launcher : Launcher - Launcher for this provider. Possible launchers include - :class:`~parsl.launchers.SingleNodeLauncher` (the default), - :class:`~parsl.launchers.SrunLauncher`, or - :class:`~parsl.launchers.AprunLauncher` - bsub_redirection: Bool - Should a redirection symbol "<" be included when submitting jobs, i.e., Bsub < job_script. - request_by_nodes: Bool - Request by nodes or request by cores per block. - When this is set to false, nodes_per_block is computed by cores_per_block / cores_per_node. - Default is True. - """ - - def __init__(self, - nodes_per_block=1, - cores_per_block=None, - cores_per_node=None, - init_blocks=1, - min_blocks=0, - max_blocks=1, - parallelism=1, - walltime="00:10:00", - scheduler_options='', - worker_init='', - project=None, - queue=None, - cmd_timeout=120, - bsub_redirection=False, - request_by_nodes=True, - launcher=SingleNodeLauncher()): - label = 'LSF' - super().__init__(label, - nodes_per_block, - init_blocks, - min_blocks, - max_blocks, - parallelism, - walltime, - cmd_timeout=cmd_timeout, - launcher=launcher) - - self.project = project - self.queue = queue - self.cores_per_block = cores_per_block - self.cores_per_node = cores_per_node - self.bsub_redirection = bsub_redirection - self.request_by_nodes = request_by_nodes - - # Update scheduler options - self.scheduler_options = scheduler_options + "\n" - if project: - self.scheduler_options += "#BSUB -P {}\n".format(project) - if queue: - self.scheduler_options += "#BSUB -q {}\n".format(queue) - if request_by_nodes: - self.scheduler_options += "#BSUB -nnodes {}\n".format(nodes_per_block) - else: - assert cores_per_block is not None and cores_per_node is not None, \ - "Requesting resources by the number of cores. " \ - "Need to specify cores_per_block and cores_per_node in the LSF provider." - - self.scheduler_options += "#BSUB -n {}\n".format(cores_per_block) - self.scheduler_options += '#BSUB -R "span[ptile={}]"\n'.format(cores_per_node) - - # Set nodes_per_block manually for Parsl strategy - assert cores_per_node != 0, "Need to specify a non-zero cores_per_node." - self.nodes_per_block = int(math.ceil(cores_per_block / cores_per_node)) - - self.worker_init = worker_init - - def _status(self): - '''Returns the status list for a list of job_ids - - Args: - self - - Returns: - [status...] : Status list of all jobs - ''' - logger.debug(f"Resources: {self.resources}") - job_id_list = [jid for jid, job in self.resources.items() if not job['status'].terminal] - if not job_id_list: - logger.debug('No active jobs, skipping status update') - return - logger.debug(f"job_id_list: {job_id_list}") - # only request the JOBID and STAT columns from LSF - cmd = f"bjobs -noheader -o 'jobid stat' {' '.join(job_id_list)}" - logger.debug(f"Executing command: {cmd}") - retcode, stdout, stderr = self.execute_wait(cmd) - logger.debug(f"bjobs returned:\nstdout=\n{stdout}stderr=\n{stderr}") - # Execute_wait failed. Do no update - if retcode != 0: - logger.warning(f"bjobs failed with non-zero exit code: {retcode}") - return - - jobs_missing = set(job_id_list) - bjobs_lines = stdout.rstrip('\n').split('\n') - - for line in bjobs_lines: - line_list = line.split() - if len(line_list) != 2: - logger.debug(f"{line_list} length not equal to 2, skipping") - continue - job_id, lsf_state = line_list - if job_id not in job_id_list: - logger.debug(f"job_id {job_id} not in job_id_list, skipping") - continue - if lsf_state not in translate_table: - logger.warning(f"LSF status {lsf_state} is not recognized") - state = translate_table.get(lsf_state, JobState.UNKNOWN) - logger.debug(f"Updating job {job_id} with LSF status {lsf_state} " - f"to parsl state {state}") - self.resources[job_id]['status'] = JobStatus(state) - jobs_missing.remove(job_id) - - # bjobs does not report on jobs that are not running. So we are filling in the - # blanks for missing jobs, we might lose some information about why the jobs failed. - for missing_job in jobs_missing: - logger.debug(f"Updating missing job {missing_job} to completed status") - self.resources[missing_job]['status'] = JobStatus(JobState.COMPLETED) - - def submit(self, command, tasks_per_node, job_name="parsl.lsf"): - """Submit the command as an LSF job. - - Parameters - ---------- - command : str - Command to be made on the remote side. - tasks_per_node : int - Command invocations to be launched per node - job_name : str - Name for the job (must be unique). - Returns - ------- - None or str - If at capacity, returns None; otherwise, a string identifier for the job - """ - - job_name = "{0}.{1}".format(job_name, time.time()) - - script_path = "{0}/{1}.submit".format(self.script_dir, job_name) - script_path = os.path.abspath(script_path) - - logger.debug("Requesting one block with {} nodes".format(self.nodes_per_block)) - - job_config = {} - job_config["submit_script_dir"] = self.script_dir - job_config["nodes"] = self.nodes_per_block - job_config["tasks_per_node"] = tasks_per_node - job_config["walltime"] = wtime_to_minutes(self.walltime) - job_config["scheduler_options"] = self.scheduler_options - job_config["worker_init"] = self.worker_init - job_config["user_script"] = command - - # Wrap the command - job_config["user_script"] = self.launcher(command, - tasks_per_node, - self.nodes_per_block, - self.script_dir) - - logger.debug("Writing submit script") - self._write_submit_script(template_string, script_path, job_name, job_config) - - if self.bsub_redirection: - cmd = "bsub < {0}".format(script_path) - else: - cmd = "bsub {0}".format(script_path) - retcode, stdout, stderr = super().execute_wait(cmd) - - job_id = None - if retcode == 0: - for line in stdout.split('\n'): - if line.lower().startswith("job") and "is submitted to" in line.lower(): - job_id = line.split()[1].strip('<>') - self.resources[job_id] = {'job_id': job_id, 'status': JobStatus(JobState.PENDING)} - else: - logger.warning("Submit command failed") - logger.error("Retcode:%s STDOUT:%s STDERR:%s", retcode, stdout.strip(), stderr.strip()) - return job_id - - def cancel(self, job_ids): - ''' Cancels the jobs specified by a list of job ids - - Args: - job_ids : [ ...] - - Returns : - [True/False...] : If the cancel operation fails the entire list will be False. - ''' - - job_id_list = ' '.join(job_ids) - retcode, stdout, stderr = self.execute_wait("bkill {0}".format(job_id_list)) - rets = None - if retcode == 0: - for jid in job_ids: - self.resources[jid]['status'] = JobStatus(JobState.CANCELLED) # Setting state to cancelled - rets = [True for i in job_ids] - else: - rets = [False for i in job_ids] - - return rets - - @property - def status_polling_interval(self): - return 60 diff --git a/parsl/parsl/providers/lsf/template.py b/parsl/parsl/providers/lsf/template.py deleted file mode 100644 index c3db6ac5..00000000 --- a/parsl/parsl/providers/lsf/template.py +++ /dev/null @@ -1,14 +0,0 @@ -template_string = '''#!/bin/bash - -#BSUB -W ${walltime} -#BSUB -J ${jobname} -#BSUB -o ${submit_script_dir}/${jobname}.submit.stdout -#BSUB -e ${submit_script_dir}/${jobname}.submit.stderr -${scheduler_options} - -${worker_init} - -export JOBNAME="${jobname}" - -$user_script -''' diff --git a/parsl/parsl/providers/pbspro/__init__.py b/parsl/parsl/providers/pbspro/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/providers/pbspro/pbspro.py b/parsl/parsl/providers/pbspro/pbspro.py deleted file mode 100644 index 5d614412..00000000 --- a/parsl/parsl/providers/pbspro/pbspro.py +++ /dev/null @@ -1,229 +0,0 @@ -import json -import logging -import os -import time - -from parsl.jobs.states import JobState, JobStatus -from parsl.launchers import SingleNodeLauncher -from parsl.providers.errors import SubmitException -from parsl.providers.pbspro.template import template_string -from parsl.providers.torque.torque import TorqueProvider, translate_table - -logger = logging.getLogger(__name__) - - -class PBSProProvider(TorqueProvider): - """PBS Pro Execution Provider - - Parameters - ---------- - account : str - Account the job will be charged against. - queue : str - Queue to request blocks from. - nodes_per_block : int - Nodes to provision per block. - cpus_per_node : int - CPUs to provision per node. - init_blocks : int - Number of blocks to provision at the start of the run. Default is 1. - min_blocks : int - Minimum number of blocks to maintain. Default is 0. - max_blocks : int - Maximum number of blocks to maintain. - parallelism : float - Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive - scaling where as many resources as possible are used; parallelism close to 0 represents - the opposite situation in which as few resources as possible (i.e., min_blocks) are used. - walltime : str - Walltime requested per block in HH:MM:SS. - scheduler_options : str - String to prepend to the #PBS blocks in the submit script to the scheduler. - select_options : str - String to append to the #PBS -l select block in the submit script to the scheduler. This can be used to - specify ngpus. - worker_init : str - Command to be run before starting a worker, such as 'module load Anaconda; source activate env'. - launcher : Launcher - Launcher for this provider. The default is - :class:`~parsl.launchers.SingleNodeLauncher`. - """ - def __init__(self, - account=None, - queue=None, - scheduler_options='', - select_options='', - worker_init='', - nodes_per_block=1, - cpus_per_node=1, - init_blocks=1, - min_blocks=0, - max_blocks=1, - parallelism=1, - launcher=SingleNodeLauncher(), - walltime="00:20:00", - cmd_timeout=120): - super().__init__(account, - queue, - scheduler_options, - worker_init, - nodes_per_block, - init_blocks, - min_blocks, - max_blocks, - parallelism, - launcher, - walltime, - cmd_timeout=cmd_timeout) - - self.template_string = template_string - self._label = 'pbspro' - self.cpus_per_node = cpus_per_node - self.select_options = select_options - - def _status(self): - '''Returns the status list for a list of job_ids - - Args: - self - - Returns: - [status...] : Status list of all jobs - ''' - - job_ids = list(self.resources.keys()) - job_id_list = ' '.join(self.resources.keys()) - - jobs_missing = list(self.resources.keys()) - - retcode, stdout, stderr = self.execute_wait("qstat -x -f -F json {0}".format(job_id_list)) - - # If qstat failed do not update job state - if retcode != 0: - logger.warning("qstat failed with retcode:%s STDOUT:%s STDERR:%s", - retcode, - stdout.strip(), - stderr.strip()) - return - - job_statuses = json.loads(stdout) - - if 'Jobs' in job_statuses: - for job_id, job in job_statuses['Jobs'].items(): - for long_job_id in job_ids: - if long_job_id.startswith(job_id): - logger.debug('coerced job_id %s -> %s', job_id, long_job_id) - job_id = long_job_id - break - - job_state = job.get('job_state', JobState.UNKNOWN) - state = translate_table.get(job_state, JobState.UNKNOWN) - self.resources[job_id]['status'] = JobStatus(state, - stdout_path=self.resources[job_id]['job_stdout_path'], - stderr_path=self.resources[job_id]['job_stderr_path']) - jobs_missing.remove(job_id) - - # squeue does not report on jobs that are not running. So we are filling in the - # blanks for missing jobs, we might lose some information about why the jobs failed. - for missing_job in jobs_missing: - self.resources[missing_job]['status'] = JobStatus(JobState.COMPLETED, - stdout_path=self.resources[missing_job]['job_stdout_path'], - stderr_path=self.resources[missing_job]['job_stderr_path']) - - def submit(self, command, tasks_per_node, job_name="parsl"): - """Submits the command job. - - Parameters - ---------- - command : str - Command to be executed on the remote side. - tasks_per_node : int - Command invocations to be launched per node. - job_name : str - Identifier for job. - - Returns - ------- - None - If at capacity and cannot provision more - job_id : str - Identifier for the job - """ - - job_name = "{0}.{1}".format(job_name, time.time()) - - assert self.script_dir, "Expected script_dir to be set" - script_path = os.path.join(self.script_dir, job_name) - script_path = os.path.abspath(script_path) - job_stdout_path = script_path + ".stdout" - job_stderr_path = script_path + ".stderr" - - logger.debug("Requesting {} nodes_per_block, {} tasks_per_node".format( - self.nodes_per_block, tasks_per_node) - ) - - job_config = {} - job_config["submit_script_dir"] = self.script_dir - job_config["nodes_per_block"] = self.nodes_per_block - job_config["ncpus"] = self.cpus_per_node - job_config["walltime"] = self.walltime - job_config["scheduler_options"] = self.scheduler_options - job_config["worker_init"] = self.worker_init - job_config["user_script"] = command - job_config["job_stdout_path"] = job_stdout_path - job_config["job_stderr_path"] = job_stderr_path - - # Add a colon to select_options if one isn't included - if self.select_options and not self.select_options.startswith(":"): - self.select_options = ":" + self.select_options - - job_config["select_options"] = self.select_options - - # Wrap the command - job_config["user_script"] = self.launcher(command, - tasks_per_node, - self.nodes_per_block, - self.script_dir) - - logger.debug("Writing submit script") - self._write_submit_script(self.template_string, script_path, job_name, job_config) - - submit_options = '' - if self.queue is not None: - submit_options = '{0} -q {1}'.format(submit_options, self.queue) - if self.account is not None: - submit_options = '{0} -A {1}'.format(submit_options, self.account) - - launch_cmd = "qsub {0} {1}".format(submit_options, script_path) - retcode, stdout, stderr = self.execute_wait(launch_cmd) - - job_id = None - if retcode == 0: - for line in stdout.split('\n'): - if line.strip(): - job_id = line.strip() - self.resources[job_id] = {'job_id': job_id, - 'status': JobStatus(JobState.PENDING), - 'job_stdout_path': job_stdout_path, - 'job_stderr_path': job_stderr_path, - } - else: - message = f"Submit command '{launch_cmd}' failed" - logger.error( - f"{message}\n" - f" Return code: {retcode}\n" - f" STDOUT: {stdout.strip()}\n" - f" STDERR: {stderr.strip()}" - ) - raise SubmitException( - job_name=job_name, - message=message, - stdout=stdout, - stderr=stderr, - ) - - return job_id - - @property - def status_polling_interval(self): - return 60 diff --git a/parsl/parsl/providers/pbspro/template.py b/parsl/parsl/providers/pbspro/template.py deleted file mode 100644 index 1bccad8f..00000000 --- a/parsl/parsl/providers/pbspro/template.py +++ /dev/null @@ -1,18 +0,0 @@ -template_string = '''#!/bin/bash - -#PBS -S /bin/bash -#PBS -N ${jobname} -#PBS -m n -#PBS -l walltime=$walltime -#PBS -l select=${nodes_per_block}:ncpus=${ncpus}${select_options} -#PBS -o ${job_stdout_path} -#PBS -e ${job_stderr_path} -${scheduler_options} - -${worker_init} - -export JOBNAME="${jobname}" - -${user_script} - -''' diff --git a/parsl/parsl/providers/slurm/__init__.py b/parsl/parsl/providers/slurm/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/providers/slurm/slurm.py b/parsl/parsl/providers/slurm/slurm.py deleted file mode 100644 index 3086e3e7..00000000 --- a/parsl/parsl/providers/slurm/slurm.py +++ /dev/null @@ -1,414 +0,0 @@ -import logging -import math -import os -import re -import sys -import time -from typing import Any, Dict, Optional - -import typeguard - -from parsl.jobs.states import JobState, JobStatus -from parsl.launchers import SingleNodeLauncher -from parsl.launchers.base import Launcher -from parsl.providers.cluster_provider import ClusterProvider -from parsl.providers.errors import SubmitException -from parsl.providers.slurm.template import template_string -from parsl.utils import RepresentationMixin, wtime_to_minutes - -logger = logging.getLogger(__name__) - -# From https://slurm.schedmd.com/sacct.html#SECTION_JOB-STATE-CODES -sacct_translate_table = { - 'PENDING': JobState.PENDING, - 'RUNNING': JobState.RUNNING, - 'CANCELLED': JobState.CANCELLED, - 'COMPLETED': JobState.COMPLETED, - 'FAILED': JobState.FAILED, - 'NODE_FAIL': JobState.FAILED, - 'BOOT_FAIL': JobState.FAILED, - 'DEADLINE': JobState.TIMEOUT, - 'TIMEOUT': JobState.TIMEOUT, - 'REVOKED': JobState.FAILED, - 'OUT_OF_MEMORY': JobState.FAILED, - 'SUSPENDED': JobState.HELD, - 'PREEMPTED': JobState.TIMEOUT, - 'REQUEUED': JobState.PENDING -} - -squeue_translate_table = { - 'PD': JobState.PENDING, - 'R': JobState.RUNNING, - 'CA': JobState.CANCELLED, - 'CF': JobState.PENDING, # (configuring), - 'CG': JobState.RUNNING, # (completing), - 'CD': JobState.COMPLETED, - 'F': JobState.FAILED, # (failed), - 'TO': JobState.TIMEOUT, # (timeout), - 'NF': JobState.FAILED, # (node failure), - 'RV': JobState.FAILED, # (revoked) and - 'SE': JobState.FAILED # (special exit state) -} - - -if sys.version_info < (3, 12): - from itertools import islice - from typing import Iterable - - def batched( - iterable: Iterable[tuple[object, Any]], n: int, *, strict: bool = False - ): - """Batched - Turns a list into a batch of size n. This code is from - https://docs.python.org/3.12/library/itertools.html#itertools.batched - and in versions 3.12+ this can be replaced with - itertools.batched - """ - if n < 1: - raise ValueError("n must be at least one") - iterator = iter(iterable) - while batch := tuple(islice(iterator, n)): - yield batch -else: - from itertools import batched - - -class SlurmProvider(ClusterProvider, RepresentationMixin): - """Slurm Execution Provider - - This provider uses sbatch to submit, sacct for status and scancel to cancel - jobs. The sbatch script to be used is created from a template file in this - same module. - - Parameters - ---------- - partition : str - Slurm partition to request blocks from. If unspecified or ``None``, no partition slurm directive will be specified. - account : str - Slurm account to which to charge resources used by the job. If unspecified or ``None``, the job will use the - user's default account. - qos : str - Slurm queue to place job in. If unspecified or ``None``, no queue slurm directive will be specified. - constraint : str - Slurm job constraint, often used to choose cpu or gpu type. If unspecified or ``None``, no constraint slurm directive will be added. - clusters : str - Slurm cluster name, or comma seperated cluster list, used to choose between different clusters in a federated Slurm instance. - If unspecified or ``None``, no slurm directive for clusters will be added. - nodes_per_block : int - Nodes to provision per block. - cores_per_node : int - Specify the number of cores to provision per node. If set to None, executors - will assume all cores on the node are available for computation. Default is None. - mem_per_node : int - Specify the real memory to provision per node in GB. If set to None, no - explicit request to the scheduler will be made. Default is None. - gpus_per_node: int - Specify the number of GPUs to provision per node. If set to None, executors - request no gpus. Default: None - gres: str - Slurm GRES options to request arbitrary resources. - `Refer to slurm docs `_ - init_blocks : int - Number of blocks to provision at the start of the run. Default is 1. - min_blocks : int - Minimum number of blocks to maintain. - max_blocks : int - Maximum number of blocks to maintain. - parallelism : float - Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive - scaling where as many resources as possible are used; parallelism close to 0 represents - the opposite situation in which as few resources as possible (i.e., min_blocks) are used. - walltime : str - Walltime requested per block in HH:MM:SS. - scheduler_options : str - String to prepend to the #SBATCH blocks in the submit script to the scheduler. - regex_job_id : str - The regular expression used to extract the job ID from the ``sbatch`` standard output. - The default is ``r"Submitted batch job (?P\\S*)"``, where ``id`` is the regular expression - symbolic group for the job ID. - worker_init : str - Command to be run before starting a worker, such as 'module load Anaconda; source activate env'. - cmd_timeout : int (Default = 10) - Number of seconds to wait for slurm commands to finish. For schedulers with many this - may need to be increased to wait longer for scheduler information. - status_batch_size: int (Default = 50) - Number of jobs to batch together in calls to the scheduler status. For schedulers - with many jobs this may need to be decreased to get jobs in smaller batches. - exclusive : bool (Default = True) - Requests nodes which are not shared with other running jobs. - launcher : Launcher - Launcher for this provider. Possible launchers include - :class:`~parsl.launchers.SingleNodeLauncher` (the default), - :class:`~parsl.launchers.SrunLauncher`, or - :class:`~parsl.launchers.AprunLauncher` - """ - - @typeguard.typechecked - def __init__(self, - partition: Optional[str] = None, - account: Optional[str] = None, - qos: Optional[str] = None, - constraint: Optional[str] = None, - clusters: Optional[str] = None, - nodes_per_block: int = 1, - cores_per_node: Optional[int] = None, - mem_per_node: Optional[int] = None, - gpus_per_node: Optional[int] = None, - gres: Optional[str] = None, - init_blocks: int = 1, - min_blocks: int = 0, - max_blocks: int = 1, - parallelism: float = 1, - walltime: str = "00:10:00", - scheduler_options: str = '', - regex_job_id: str = r"Submitted batch job (?P\S*)", - worker_init: str = '', - cmd_timeout: int = 10, - status_batch_size: int = 50, - exclusive: bool = True, - launcher: Launcher = SingleNodeLauncher()): - label = 'slurm' - super().__init__(label, - nodes_per_block, - init_blocks, - min_blocks, - max_blocks, - parallelism, - walltime, - cmd_timeout=cmd_timeout, - launcher=launcher) - - self.partition = partition - self.cores_per_node = cores_per_node - self.mem_per_node = mem_per_node - self.gpus_per_node = gpus_per_node - self.gres = gres - self.exclusive = exclusive - self.account = account - self.qos = qos - self.constraint = constraint - self.clusters = clusters - # Used to batch requests to sacct/squeue for long jobs lists - self.status_batch_size = status_batch_size - self.scheduler_options = scheduler_options + '\n' - if exclusive: - self.scheduler_options += "#SBATCH --exclusive\n" - if partition: - self.scheduler_options += "#SBATCH --partition={}\n".format(partition) - if account: - self.scheduler_options += "#SBATCH --account={}\n".format(account) - if qos: - self.scheduler_options += "#SBATCH --qos={}\n".format(qos) - if constraint: - self.scheduler_options += "#SBATCH --constraint={}\n".format(constraint) - if clusters: - self.scheduler_options += "#SBATCH --clusters={}\n".format(clusters) - if gpus_per_node: - self.scheduler_options += "#SBATCH --gpus-per-node={}\n".format(gpus_per_node) - if gres: - self.scheduler_options += "#SBATCH --gres={}\n".format(gres) - - self.regex_job_id = regex_job_id - self.worker_init = worker_init + '\n' - # Check if sacct works and if not fall back to squeue - cmd = "sacct -X" - logger.debug("Executing %s", cmd) - retcode, stdout, stderr = self.execute_wait(cmd) - # If sacct fails it should return retcode=1 stderr="Slurm accounting storage is disabled" - logger.debug(f"sacct returned retcode={retcode} stderr={stderr}") - if retcode == 0: - logger.debug("using sacct to get job status") - _cmd = "sacct" - # Add clusters option to sacct if provided - if self.clusters: - _cmd += f" --clusters={self.clusters}" - # Using state%20 to get enough characters to not truncate output - # of the state. Without output can look like " CANCELLED+" - self._cmd = _cmd + " -X --noheader --format=jobid,state%20 --job '{0}'" - self._translate_table = sacct_translate_table - else: - logger.debug(f"sacct failed with retcode={retcode}") - logger.debug("falling back to using squeue to get job status") - _cmd = "squeue" - # Add clusters option to squeue if provided - if self.clusters: - _cmd += f" --clusters={self.clusters}" - self._cmd = _cmd + " --noheader --format='%i %t' --job '{0}'" - self._translate_table = squeue_translate_table - - def _status(self): - '''Returns the status list for a list of job_ids - - Args: - self - - Returns: - [status...] : Status list of all jobs - ''' - active_jobs = {jid: job for jid, job in self.resources.items() if not job["status"].terminal} - if len(active_jobs) == 0: - logger.debug("No active jobs, skipping status update") - return - - stdout = "" - for job_batch in batched(active_jobs.items(), self.status_batch_size): - job_id_list = ",".join(jid for jid, job in job_batch if not job["status"].terminal) - cmd = self._cmd.format(job_id_list) - logger.debug("Executing %s", cmd) - retcode, batch_stdout, batch_stderr = self.execute_wait(cmd) - logger.debug(f"sacct/squeue returned {retcode} {batch_stdout} {batch_stderr}") - stdout += batch_stdout - # Execute_wait failed. Do no update - if retcode != 0: - logger.warning("sacct/squeue failed with non-zero exit code {}".format(retcode)) - return - - jobs_missing = set(self.resources.keys()) - for line in stdout.split('\n'): - if not line: - # Blank line - continue - # Sacct includes extra information in some outputs - # For example " CANCELLED by " - # This splits and ignores anything past the first two unpacked values - job_id, slurm_state, *ignore = line.split() - if slurm_state not in self._translate_table: - logger.warning(f"Slurm status {slurm_state} is not recognized") - status = self._translate_table.get(slurm_state, JobState.UNKNOWN) - logger.debug("Updating job {} with slurm status {} to parsl state {!s}".format(job_id, slurm_state, status)) - self.resources[job_id]['status'] = JobStatus(status, - stdout_path=self.resources[job_id]['job_stdout_path'], - stderr_path=self.resources[job_id]['job_stderr_path']) - jobs_missing.remove(job_id) - - # sacct can get job info after jobs have completed so this path shouldn't be hit - # squeue does not report on jobs that are not running. So we are filling in the - # blanks for missing jobs, we might lose some information about why the jobs failed. - for missing_job in jobs_missing: - logger.debug("Updating missing job {} to completed status".format(missing_job)) - self.resources[missing_job]['status'] = JobStatus( - JobState.COMPLETED, stdout_path=self.resources[missing_job]['job_stdout_path'], - stderr_path=self.resources[missing_job]['job_stderr_path']) - - def submit(self, command: str, tasks_per_node: int, job_name="parsl.slurm") -> str: - """Submit the command as a slurm job. - - Parameters - ---------- - command : str - Command to be made on the remote side. - tasks_per_node : int - Command invocations to be launched per node - job_name : str - Name for the job - Returns - ------- - job id : str - A string identifier for the job - """ - - scheduler_options = self.scheduler_options - worker_init = self.worker_init - if self.mem_per_node is not None: - scheduler_options += '#SBATCH --mem={}g\n'.format(self.mem_per_node) - worker_init += 'export PARSL_MEMORY_GB={}\n'.format(self.mem_per_node) - if self.cores_per_node is not None: - cpus_per_task = math.floor(self.cores_per_node / tasks_per_node) - scheduler_options += '#SBATCH --cpus-per-task={}'.format(cpus_per_task) - worker_init += 'export PARSL_CORES={}\n'.format(cpus_per_task) - - job_name = "{0}.{1}".format(job_name, time.time()) - - assert self.script_dir, "Expected script_dir to be set" - script_path = os.path.join(self.script_dir, job_name) - script_path = os.path.abspath(script_path) - job_stdout_path = script_path + ".stdout" - job_stderr_path = script_path + ".stderr" - - logger.debug("Requesting one block with {} nodes".format(self.nodes_per_block)) - - job_config: Dict[str, Any] = {} - job_config["submit_script_dir"] = self.script_dir - job_config["nodes"] = self.nodes_per_block - job_config["tasks_per_node"] = tasks_per_node - job_config["walltime"] = wtime_to_minutes(self.walltime) - job_config["scheduler_options"] = scheduler_options - job_config["worker_init"] = worker_init - job_config["user_script"] = command - job_config["job_stdout_path"] = job_stdout_path - job_config["job_stderr_path"] = job_stderr_path - - # Wrap the command - job_config["user_script"] = self.launcher(command, - tasks_per_node, - self.nodes_per_block, - self.script_dir) - - logger.debug("Writing submit script") - self._write_submit_script(template_string, script_path, job_name, job_config) - - retcode, stdout, stderr = self.execute_wait("sbatch {0}".format(script_path)) - - if retcode == 0: - for line in stdout.split('\n'): - match = re.match(self.regex_job_id, line) - if match: - job_id = match.group("id") - self.resources[job_id] = {'job_id': job_id, - 'status': JobStatus(JobState.PENDING), - 'job_stdout_path': job_stdout_path, - 'job_stderr_path': job_stderr_path, - } - return job_id - else: - logger.error("Could not read job ID from submit command standard output.") - logger.error("Retcode:%s STDOUT:%s STDERR:%s", retcode, stdout.strip(), stderr.strip()) - raise SubmitException( - job_name, - "Could not read job ID from submit command standard output", - stdout=stdout, - stderr=stderr, - retcode=retcode - ) - else: - logger.error("Submit command failed") - logger.error("Retcode:%s STDOUT:%s STDERR:%s", retcode, stdout.strip(), stderr.strip()) - raise SubmitException( - job_name, "Could not read job ID from submit command standard output", - stdout=stdout, - stderr=stderr, - retcode=retcode - ) - - def cancel(self, job_ids): - ''' Cancels the jobs specified by a list of job ids - - Args: - job_ids : [ ...] - - Returns : - [True/False...] : If the cancel operation fails the entire list will be False. - ''' - - job_id_list = ' '.join(job_ids) - - # Make the command to cancel jobs - _cmd = "scancel" - if self.clusters: - _cmd += f" --clusters={self.clusters}" - _cmd += " {0}" - - retcode, stdout, stderr = self.execute_wait(_cmd.format(job_id_list)) - rets = None - if retcode == 0: - for jid in job_ids: - self.resources[jid]['status'] = JobStatus(JobState.CANCELLED) # Setting state to cancelled - rets = [True for i in job_ids] - else: - rets = [False for i in job_ids] - - return rets - - @property - def status_polling_interval(self): - return 60 diff --git a/parsl/parsl/providers/slurm/template.py b/parsl/parsl/providers/slurm/template.py deleted file mode 100644 index 3b66d274..00000000 --- a/parsl/parsl/providers/slurm/template.py +++ /dev/null @@ -1,16 +0,0 @@ -template_string = '''#!/bin/bash - -#SBATCH --job-name=${jobname} -#SBATCH --output=${job_stdout_path} -#SBATCH --error=${job_stderr_path} -#SBATCH --nodes=${nodes} -#SBATCH --time=${walltime} -#SBATCH --ntasks-per-node=${tasks_per_node} -${scheduler_options} - -${worker_init} - -export JOBNAME="${jobname}" - -$user_script -''' diff --git a/parsl/parsl/providers/torque/__init__.py b/parsl/parsl/providers/torque/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/providers/torque/template.py b/parsl/parsl/providers/torque/template.py deleted file mode 100644 index c01f86e6..00000000 --- a/parsl/parsl/providers/torque/template.py +++ /dev/null @@ -1,18 +0,0 @@ -template_string = '''#!/bin/bash - -${scheduler_options} -#PBS -S /bin/bash -#PBS -N ${jobname} -#PBS -m n -#PBS -l walltime=$walltime -#PBS -l nodes=${nodes_per_block}:ppn=${tasks_per_node} -#PBS -o ${submit_script_dir}/${jobname}.submit.stdout -#PBS -e ${submit_script_dir}/${jobname}.submit.stderr - -${worker_init} - -export JOBNAME="${jobname}" - -${user_script} - -''' diff --git a/parsl/parsl/providers/torque/torque.py b/parsl/parsl/providers/torque/torque.py deleted file mode 100644 index 0bbf28ec..00000000 --- a/parsl/parsl/providers/torque/torque.py +++ /dev/null @@ -1,238 +0,0 @@ -import logging -import os -import time - -from parsl.jobs.states import JobState, JobStatus -from parsl.launchers import AprunLauncher -from parsl.providers.cluster_provider import ClusterProvider -from parsl.providers.torque.template import template_string -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - -# From the man pages for qstat for PBS/Torque systems -translate_table = { - 'B': JobState.RUNNING, # This state is returned for running array jobs - 'R': JobState.RUNNING, - 'C': JobState.COMPLETED, # Completed after having run (TORQUE) - 'E': JobState.COMPLETED, # Exiting after having run - 'F': JobState.COMPLETED, # Finished (PBS Pro) - 'H': JobState.HELD, # Held - 'Q': JobState.PENDING, # Queued, and eligible to run - 'W': JobState.PENDING, # Job is waiting for it's execution time (-a option) to be reached - 'S': JobState.HELD, # Suspended - 'T': JobState.PENDING, # Transiting (PBS Pro) - 'U': JobState.HELD, # User-suspended (PBS Pro) - 'X': JobState.COMPLETED, # Subjob finished (PBS Pro) -} - - -class TorqueProvider(ClusterProvider, RepresentationMixin): - """Torque Execution Provider - - This provider uses qsub to submit, qstat for status, and qdel to cancel - jobs. The qsub script to be used is created from a template file in this - same module. - - Parameters - ---------- - account : str - Account the job will be charged against. - queue : str - Torque queue to request blocks from. - nodes_per_block : int - Nodes to provision per block. - init_blocks : int - Number of blocks to provision at the start of the run. Default is 1. - min_blocks : int - Minimum number of blocks to maintain. Default is 0. - max_blocks : int - Maximum number of blocks to maintain. - parallelism : float - Ratio of provisioned task slots to active tasks. A parallelism value of 1 represents aggressive - scaling where as many resources as possible are used; parallelism close to 0 represents - the opposite situation in which as few resources as possible (i.e., min_blocks) are used. - walltime : str - Walltime requested per block in HH:MM:SS. - scheduler_options : str - String to prepend to the #PBS blocks in the submit script to the scheduler. - WARNING: scheduler_options should only be given #PBS strings, and should not have trailing newlines. - worker_init : str - Command to be run before starting a worker, such as 'module load Anaconda; source activate env'. - launcher : Launcher - Launcher for this provider. Possible launchers include - :class:`~parsl.launchers.AprunLauncher` (the default), or - :class:`~parsl.launchers.SingleNodeLauncher`, - - """ - def __init__(self, - account=None, - queue=None, - scheduler_options='', - worker_init='', - nodes_per_block=1, - init_blocks=1, - min_blocks=0, - max_blocks=1, - parallelism=1, - launcher=AprunLauncher(), - walltime="00:20:00", - cmd_timeout=120): - label = 'torque' - super().__init__(label, - nodes_per_block, - init_blocks, - min_blocks, - max_blocks, - parallelism, - walltime, - launcher, - cmd_timeout=cmd_timeout) - - self.account = account - self.queue = queue - self.scheduler_options = scheduler_options - self.worker_init = worker_init - self.template_string = template_string - - # Dictionary that keeps track of jobs, keyed on job_id - self.resources = {} - - def _status(self): - '''Returns the status list for a list of job_ids - - Args: - self - - Returns: - [status...] : Status list of all jobs - ''' - - job_ids = list(self.resources.keys()) - job_id_list = ' '.join(self.resources.keys()) - - jobs_missing = list(self.resources.keys()) - - retcode, stdout, stderr = self.execute_wait("qstat {0}".format(job_id_list)) - for line in stdout.split('\n'): - parts = line.split() - if not parts or parts[0].upper().startswith('JOB') or parts[0].startswith('---'): - continue - job_id = parts[0] # likely truncated - for long_job_id in job_ids: - if long_job_id.startswith(job_id): - logger.debug('coerced job_id %s -> %s', job_id, long_job_id) - job_id = long_job_id - break - state = translate_table.get(parts[4], JobState.UNKNOWN) - self.resources[job_id]['status'] = JobStatus(state) - jobs_missing.remove(job_id) - - # squeue does not report on jobs that are not running. So we are filling in the - # blanks for missing jobs, we might lose some information about why the jobs failed. - for missing_job in jobs_missing: - self.resources[missing_job]['status'] = JobStatus(JobState.COMPLETED) - - def submit(self, command, tasks_per_node, job_name="parsl.torque"): - ''' Submits the command onto an Local Resource Manager job. - Submit returns an ID that corresponds to the task that was just submitted. - - If tasks_per_node < 1 : ! This is illegal. tasks_per_node should be integer - - If tasks_per_node == 1: - A single node is provisioned - - If tasks_per_node > 1 : - tasks_per_node number of nodes are provisioned. - - Args: - - command :(String) Commandline invocation to be made on the remote side. - - tasks_per_node (int) : command invocations to be launched per node - - Kwargs: - - job_name (String): Name for job, must be unique - - Returns: - - None: At capacity, cannot provision more - - job_id: (string) Identifier for the job - - ''' - - # Set job name - job_name = "parsl.{0}.{1}".format(job_name, time.time()) - - # Set script path - script_path = "{0}/{1}.submit".format(self.script_dir, job_name) - script_path = os.path.abspath(script_path) - - logger.debug("Requesting nodes_per_block:%s tasks_per_node:%s", self.nodes_per_block, - tasks_per_node) - - job_config = {} - job_config["submit_script_dir"] = self.script_dir - job_config["nodes"] = self.nodes_per_block - job_config["task_blocks"] = self.nodes_per_block * tasks_per_node - job_config["nodes_per_block"] = self.nodes_per_block - job_config["tasks_per_node"] = tasks_per_node - job_config["walltime"] = self.walltime - job_config["scheduler_options"] = self.scheduler_options - job_config["worker_init"] = self.worker_init - job_config["user_script"] = command - - # Wrap the command - job_config["user_script"] = self.launcher(command, - tasks_per_node, - self.nodes_per_block, - self.script_dir) - - logger.debug("Writing submit script") - self._write_submit_script(self.template_string, script_path, job_name, job_config) - - submit_options = '' - if self.queue is not None: - submit_options = '{0} -q {1}'.format(submit_options, self.queue) - if self.account is not None: - submit_options = '{0} -A {1}'.format(submit_options, self.account) - - launch_cmd = "qsub {0} {1}".format(submit_options, script_path) - retcode, stdout, stderr = self.execute_wait(launch_cmd) - - job_id = None - if retcode == 0: - for line in stdout.split('\n'): - if line.strip(): - job_id = line.strip() - self.resources[job_id] = {'job_id': job_id, 'status': JobStatus(JobState.PENDING)} - else: - message = "Command '{}' failed with return code {}".format(launch_cmd, retcode) - if (stdout is not None) and (stderr is not None): - message += "\nstderr:{}\nstdout{}".format(stderr.strip(), stdout.strip()) - logger.error(message) - - return job_id - - def cancel(self, job_ids): - ''' Cancels the jobs specified by a list of job ids - - Args: - job_ids : [ ...] - - Returns : - [True/False...] : If the cancel operation fails the entire list will be False. - ''' - - job_id_list = ' '.join(job_ids) - retcode, stdout, stderr = self.execute_wait("qdel {0}".format(job_id_list)) - rets = None - if retcode == 0: - for jid in job_ids: - self.resources[jid]['status'] = JobStatus(JobState.COMPLETED) # Setting state to exiting - rets = [True for i in job_ids] - else: - rets = [False for i in job_ids] - - return rets - - @property - def status_polling_interval(self): - return 60 diff --git a/parsl/parsl/py.typed b/parsl/parsl/py.typed deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/serialize/__init__.py b/parsl/parsl/serialize/__init__.py deleted file mode 100644 index 9f8f6c9c..00000000 --- a/parsl/parsl/serialize/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -from parsl.serialize.facade import ( - deserialize, - pack_apply_message, - serialize, - unpack_apply_message, -) - -__all__ = ( - "serialize", - "deserialize", - "pack_apply_message", - "unpack_apply_message", -) diff --git a/parsl/parsl/serialize/base.py b/parsl/parsl/serialize/base.py deleted file mode 100644 index 188c68db..00000000 --- a/parsl/parsl/serialize/base.py +++ /dev/null @@ -1,38 +0,0 @@ -import logging -from abc import abstractmethod -from functools import cached_property -from typing import Any - -logger = logging.getLogger(__name__) - - -class SerializerBase: - """ Adds shared functionality for all serializer implementations - """ - - @cached_property - def identifier(self) -> bytes: - """Compute identifier used in serialization header. - This will be used to indicate in byte streams that this class should - be used for deserialization. - - Serializers that use identifiers that don't align with the way this is - computed (such as the default concretes.py implementations) should - override this property with their own identifier. - - Returns - ------- - identifier : bytes - """ - t = type(self) - m = bytes(t.__module__, encoding="utf-8") - c = bytes(t.__name__, encoding="utf-8") - return m + b' ' + c - - @abstractmethod - def serialize(self, data: Any) -> bytes: - pass - - @abstractmethod - def deserialize(self, payload: bytes) -> Any: - pass diff --git a/parsl/parsl/serialize/concretes.py b/parsl/parsl/serialize/concretes.py deleted file mode 100644 index 40ad08be..00000000 --- a/parsl/parsl/serialize/concretes.py +++ /dev/null @@ -1,65 +0,0 @@ -import functools -import logging -import pickle - -import dill - -logger = logging.getLogger(__name__) -from typing import Any - -from parsl.serialize.base import SerializerBase - - -class PickleSerializer(SerializerBase): - """ Pickle serialization covers most python objects, with some notable exceptions: - - * functions defined in a interpreter/notebook - * classes defined in local context and not importable using a fully qualified name - * closures, generators and coroutines - """ - - identifier = b'01' - - def serialize(self, data: Any) -> bytes: - return pickle.dumps(data) - - def deserialize(self, body: bytes) -> Any: - return pickle.loads(body) - - -class DillSerializer(SerializerBase): - """ Dill serialization works on a superset of object including the ones covered by pickle. - However for most cases pickle is faster. For most callable objects the additional overhead - of dill can be amortized with an lru_cache. Here's items that dill handles that pickle - doesn't: - - * functions defined in a interpreter/notebook - * classes defined in local context and not importable using a fully qualified name - * functions that are wrapped/decorated by other functions/classes - * closures - """ - - identifier = b'02' - - def serialize(self, data: Any) -> bytes: - return dill.dumps(data) - - def deserialize(self, body: bytes) -> Any: - return dill.loads(body) - - -class DillCallableSerializer(SerializerBase): - """This serializer is a variant of the DillSerializer that will - serialize and deserialize callables using an lru_cache, under the - assumption that callables are immutable and so can be cached. - """ - - identifier = b'C2' - - @functools.lru_cache - def serialize(self, data: Any) -> bytes: - return dill.dumps(data) - - @functools.lru_cache - def deserialize(self, body: bytes) -> Any: - return dill.loads(body) diff --git a/parsl/parsl/serialize/errors.py b/parsl/parsl/serialize/errors.py deleted file mode 100644 index 21cfeefb..00000000 --- a/parsl/parsl/serialize/errors.py +++ /dev/null @@ -1,35 +0,0 @@ -from parsl.errors import ParslError - - -class DeserializationError(ParslError): - """Failure at the deserialization of results/exceptions from remote workers. - """ - - def __init__(self, reason: str) -> None: - self.reason = reason - - def __str__(self) -> str: - return f"Failed to deserialize objects. Reason: {self.reason}" - - -class SerializationError(ParslError): - """Failure to serialize task objects. - """ - - def __init__(self, fname: str) -> None: - self.fname = fname - self.troubleshooting = "https://parsl.readthedocs.io/en/latest/faq.html#addressing-serializationerror" - - def __str__(self) -> str: - return f"Failed to serialize objects for an invocation of function {self.fname}. Refer {self.troubleshooting}" - - -class DeserializerPluginError(ParslError): - """Failure to dynamically load a deserializer plugin. - """ - - def __init__(self, header: bytes) -> None: - self.header = header - - def __str__(self) -> str: - return f"Failed to load deserializer plugin for header {self.header!r}" diff --git a/parsl/parsl/serialize/facade.py b/parsl/parsl/serialize/facade.py deleted file mode 100644 index 87165795..00000000 --- a/parsl/parsl/serialize/facade.py +++ /dev/null @@ -1,180 +0,0 @@ -import importlib -import logging -from typing import Any, Dict, List, Union - -import parsl.serialize.concretes as concretes -from parsl.serialize.base import SerializerBase -from parsl.serialize.errors import DeserializerPluginError - -logger = logging.getLogger(__name__) - - -methods_for_code: Dict[bytes, SerializerBase] = {} - - -def register_method_for_code(s: SerializerBase) -> None: - methods_for_code[s.identifier] = s - - -register_method_for_code(concretes.DillCallableSerializer()) - - -methods_for_data: Dict[bytes, SerializerBase] = {} - - -def register_method_for_data(s: SerializerBase) -> None: - methods_for_data[s.identifier] = s - - -register_method_for_data(concretes.DillSerializer()) - - -# When deserialize dynamically loads a deserializer, it will be stored here, -# rather than in the methods_for_* dictionaries, so that loading does not -# cause it to be used for future serializations. -additional_methods_for_deserialization: Dict[bytes, SerializerBase] = {} - - -def pack_apply_message(func: Any, args: Any, kwargs: Any, buffer_threshold: int = int(128 * 1e6)) -> bytes: - """Serialize and pack function and parameters - - Parameters - ---------- - - func: Function - A function to ship - - args: Tuple/list of objects - positional parameters as a list - - kwargs: Dict - Dict containing named parameters - - buffer_threshold: int - Limits buffer to specified size in bytes. Exceeding this limit would give you - a warning in the log. Default is 128MB. - """ - b_func = serialize(func, buffer_threshold=buffer_threshold) - b_args = serialize(args, buffer_threshold=buffer_threshold) - b_kwargs = serialize(kwargs, buffer_threshold=buffer_threshold) - packed_buffer = pack_buffers([b_func, b_args, b_kwargs]) - return packed_buffer - - -def unpack_apply_message(packed_buffer: bytes) -> List[Any]: - """ Unpack and deserialize function and parameters - """ - return [deserialize(buf) for buf in unpack_buffers(packed_buffer)] - - -def serialize(obj: Any, buffer_threshold: int = int(1e6)) -> bytes: - """ Try available serialization methods one at a time - - Individual serialization methods might raise a TypeError (eg. if objects are non serializable) - This method will raise the exception from the last method that was tried, if all methods fail. - """ - result: Union[bytes, Exception] - if callable(obj): - methods = methods_for_code - else: - methods = methods_for_data - - for method in methods.values(): - try: - result = method.identifier + b'\n' + method.serialize(obj) - except Exception as e: - result = e - continue - else: - break - - if isinstance(result, BaseException): - raise result - else: - if len(result) > buffer_threshold: - logger.warning(f"Serialized object exceeds buffer threshold of {buffer_threshold} bytes, this could cause overflows") - return result - - -def deserialize(payload: bytes) -> Any: - """ - Parameters - ---------- - payload : str - Payload object to be deserialized - - """ - header, body = payload.split(b'\n', 1) - - if header in methods_for_code: - deserializer = methods_for_code[header] - elif header in methods_for_data: - deserializer = methods_for_data[header] - elif header in additional_methods_for_deserialization: - deserializer = additional_methods_for_deserialization[header] - else: - logger.info("Trying to dynamically load deserializer: {!r}".format(header)) - # This is a user plugin point, so expect exceptions to happen. - try: - module_name, class_name = header.split(b' ', 1) - decoded_module_name = module_name.decode('utf-8') - module = importlib.import_module(decoded_module_name) - deserializer_class = getattr(module, class_name.decode('utf-8')) - deserializer = deserializer_class() - additional_methods_for_deserialization[header] = deserializer - except Exception as e: - raise DeserializerPluginError(header) from e - - result = deserializer.deserialize(body) - - return result - - -def pack_buffers(buffers: List[bytes]) -> bytes: - """ - Parameters - ---------- - buffers: list of byte strings - """ - packed = b'' - for buf in buffers: - s_length = bytes(str(len(buf)) + '\n', 'utf-8') - packed += s_length + buf - - return packed - - -def unpack_buffers(packed_buffer: bytes) -> List[bytes]: - """ - Parameters - ---------- - packed_buffers : packed buffer as byte sequence - """ - unpacked = [] - while packed_buffer: - s_length, buf = packed_buffer.split(b'\n', 1) - i_length = int(s_length.decode('utf-8')) - current, packed_buffer = buf[:i_length], buf[i_length:] - unpacked.extend([current]) - - return unpacked - - -def unpack_and_deserialize(packed_buffer: bytes) -> Any: - """ Unpacks a packed buffer of 3 byte sequences and returns the - deserialized contents for use in function application. - Parameters - ---------- - packed_buffers : packed buffer of 3 byte sequences - """ - unpacked = [] - while packed_buffer: - s_length, buf = packed_buffer.split(b'\n', 1) - i_length = int(s_length.decode('utf-8')) - current, packed_buffer = buf[:i_length], buf[i_length:] - deserialized = deserialize(current) - unpacked.extend([deserialized]) - - assert len(unpacked) == 3, "Unpack expects 3 buffers, got {}".format(len(unpacked)) - - return unpacked diff --git a/parsl/parsl/serialize/proxystore.py b/parsl/parsl/serialize/proxystore.py deleted file mode 100644 index fb1d77e0..00000000 --- a/parsl/parsl/serialize/proxystore.py +++ /dev/null @@ -1,50 +0,0 @@ -import io -import typing as t - -import dill -from proxystore.store import Store - -from parsl.serialize.base import SerializerBase - - -class ProxyStoreDeepPickler(dill.Pickler): - """This class extends dill so that certain objects will be stored into - ProxyStore rather than serialized directly. The selection of objects is - made by a user-specified policy. - """ - - def __init__(self, *args: t.Any, should_proxy: t.Callable[[t.Any], bool], store: Store, **kwargs: t.Any) -> None: - super().__init__(*args, **kwargs) - self._store = store - self._should_proxy = should_proxy - - def reducer_override(self, o: t.Any) -> t.Any: - if self._should_proxy(o): - proxy = self._store.proxy(o) - return proxy.__reduce__() - else: - # fall through to dill - return NotImplemented - - -class ProxyStoreSerializer(SerializerBase): - - def __init__(self, *, should_proxy: t.Optional[t.Callable[[t.Any], bool]] = None, store: t.Optional[Store] = None) -> None: - self._store = store - self._should_proxy = should_proxy - - def serialize(self, data: t.Any) -> bytes: - assert self._store is not None - assert self._should_proxy is not None - - assert data is not None - - f = io.BytesIO() - pickler = ProxyStoreDeepPickler(file=f, store=self._store, should_proxy=self._should_proxy) - pickler.dump(data) - return f.getvalue() - - def deserialize(self, body: bytes) -> t.Any: - # because we aren't customising deserialization, use regular - # dill for deserialization - return dill.loads(body) diff --git a/parsl/parsl/tests/__init__.py b/parsl/parsl/tests/__init__.py deleted file mode 100644 index 7018ff6a..00000000 --- a/parsl/parsl/tests/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from parsl.data_provider.files import File -from parsl.dataflow.memoization import id_for_memo - - -@id_for_memo.register(File) -def id_for_memo_file(file: File, output_ref: bool = False): - return file.url diff --git a/parsl/parsl/tests/callables_helper.py b/parsl/parsl/tests/callables_helper.py deleted file mode 100644 index c7a859de..00000000 --- a/parsl/parsl/tests/callables_helper.py +++ /dev/null @@ -1,2 +0,0 @@ -def some_aux_func(): - pass diff --git a/parsl/parsl/tests/cleanup b/parsl/parsl/tests/cleanup deleted file mode 100755 index 2b7556f4..00000000 --- a/parsl/parsl/tests/cleanup +++ /dev/null @@ -1,11 +0,0 @@ -rm -rf *~ *txt *out *err outputs* data runinfo __pycache__ - -for dir in */ - do - echo "Cleaning dir: $dir" - cd $dir - rm -rf *txt *out *err outputs* *~ runinfo* ipengine.json __pycache__ - cd .. - done - -find . -path '*/runinfo*' -type d | xargs rm -rf diff --git a/parsl/parsl/tests/configs/__init__.py b/parsl/parsl/tests/configs/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/configs/azure_single_node.py b/parsl/parsl/tests/configs/azure_single_node.py deleted file mode 100644 index 32b6a5d3..00000000 --- a/parsl/parsl/tests/configs/azure_single_node.py +++ /dev/null @@ -1,46 +0,0 @@ -"""Config for Azure""" -import getpass - -from parsl.config import Config -from parsl.data_provider.ftp import FTPInTaskStaging -from parsl.data_provider.http import HTTPInTaskStaging -from parsl.data_provider.rsync import RSyncStaging -from parsl.executors import HighThroughputExecutor -from parsl.providers import AzureProvider - -# If you are a developer running tests, make sure to update parsl/tests/configs/user_opts.py -# If you are a user copying-and-pasting this as an example, make sure to either -# 1) create a local `user_opts.py`, or -# 2) delete the user_opts import below and replace all appearances of `user_opts` with the literal value -# (i.e., user_opts['swan']['username'] -> 'your_username') -from parsl.tests.configs.user_opts import user_opts - -vm_reference = { - - # these fields gave me key errors unless I specified them: - "admin_username": user_opts['azure']['admin_username'], - "password": user_opts['azure']['password'], - "vm_size": user_opts['azure']['vm_size'], - "disk_size_gb": user_opts['azure']['disk_size_gb'], - "publisher": user_opts['azure']['publisher'], - "offer": user_opts['azure']['offer'], - "sku": user_opts['azure']['sku'], - "version": user_opts['azure']['version'] -} - -provider = AzureProvider( - vm_reference=vm_reference, - key_file='azure_key_file.json', - ) - -config = Config( - executors=[ - HighThroughputExecutor( - storage_access=[HTTPInTaskStaging(), FTPInTaskStaging(), RSyncStaging(getpass.getuser() + "@" + user_opts['public_ip'])], - label='azure_single_node', - address=user_opts['public_ip'], - encrypted=True, - provider=provider - ) - ] -) diff --git a/parsl/parsl/tests/configs/bluewaters.py b/parsl/parsl/tests/configs/bluewaters.py deleted file mode 100644 index 67d21e18..00000000 --- a/parsl/parsl/tests/configs/bluewaters.py +++ /dev/null @@ -1,40 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import AprunLauncher -from parsl.providers import TorqueProvider - -from .user_opts import user_opts - - -def fresh_config(): - config = Config( - executors=[ - HighThroughputExecutor( - label="bw_htex", - cores_per_worker=1, - worker_debug=False, - max_workers_per_node=1, - encrypted=True, - provider=TorqueProvider( - queue='normal', - launcher=AprunLauncher(overrides="-b -- bwpy-environ --"), - # string to prepend to #SBATCH blocks in the submit - # script to the scheduler eg: '#SBATCH --constraint=knl,quad,cache' - scheduler_options='', - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init=user_opts['bluewaters']['worker_init'], - init_blocks=1, - max_blocks=1, - min_blocks=1, - nodes_per_block=2, - walltime='00:30:00', - cmd_timeout=120, - ), - ) - ], - ) - return config - - -config = fresh_config() diff --git a/parsl/parsl/tests/configs/bridges.py b/parsl/parsl/tests/configs/bridges.py deleted file mode 100644 index 21feec19..00000000 --- a/parsl/parsl/tests/configs/bridges.py +++ /dev/null @@ -1,40 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SrunLauncher -from parsl.providers import SlurmProvider - -from .user_opts import user_opts - - -def fresh_config(): - return Config( - executors=[ - HighThroughputExecutor( - label='Bridges_HTEX_multinode', - # This is the network interface on the login node to - # which compute nodes can communicate - # address=address_by_interface('bond0.144'), - max_workers_per_node=1, - encrypted=True, - provider=SlurmProvider( - user_opts['bridges']['partition'], # Partition / QOS - nodes_per_block=2, - init_blocks=1, - # string to prepend to #SBATCH blocks in the submit - # script to the scheduler eg: '#SBATCH --constraint=knl,quad,cache' - scheduler_options=user_opts['bridges']['scheduler_options'], - - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init=user_opts['bridges']['worker_init'], - - # We request all hyperthreads on a node. - launcher=SrunLauncher(), - walltime='00:10:00', - # Slurm scheduler on Cori can be slow at times, - # increase the command timeouts - cmd_timeout=120, - ), - ) - ] - ) diff --git a/parsl/parsl/tests/configs/cc_in2p3.py b/parsl/parsl/tests/configs/cc_in2p3.py deleted file mode 100644 index 5d9edf9d..00000000 --- a/parsl/parsl/tests/configs/cc_in2p3.py +++ /dev/null @@ -1,28 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.providers import GridEngineProvider - -from .user_opts import user_opts - - -def fresh_config(): - return Config( - executors=[ - HighThroughputExecutor( - label='cc_in2p3_htex', - max_workers_per_node=1, - encrypted=True, - provider=GridEngineProvider( - nodes_per_block=2, - init_blocks=2, - max_blocks=2, - walltime="00:20:00", - scheduler_options=user_opts['cc_in2p3']['scheduler_options'], - worker_init=user_opts['cc_in2p3']['worker_init'], - ), - ) - ], - ) - - -config = fresh_config() diff --git a/parsl/parsl/tests/configs/comet.py b/parsl/parsl/tests/configs/comet.py deleted file mode 100644 index 3a946b5c..00000000 --- a/parsl/parsl/tests/configs/comet.py +++ /dev/null @@ -1,36 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SrunLauncher -from parsl.providers import SlurmProvider - -from .user_opts import user_opts - - -def fresh_config(): - return Config( - executors=[ - HighThroughputExecutor( - label='Comet_HTEX_multinode', - max_workers_per_node=1, - encrypted=True, - provider=SlurmProvider( - 'debug', - launcher=SrunLauncher(), - # string to prepend to #SBATCH blocks in the submit - # script to the scheduler - scheduler_options=user_opts['comet']['scheduler_options'], - - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init=user_opts['comet']['worker_init'], - walltime='00:10:00', - init_blocks=1, - max_blocks=1, - nodes_per_block=2, - ), - ) - ] - ) - - -config = fresh_config() diff --git a/parsl/parsl/tests/configs/ec2_single_node.py b/parsl/parsl/tests/configs/ec2_single_node.py deleted file mode 100644 index 6222dbfa..00000000 --- a/parsl/parsl/tests/configs/ec2_single_node.py +++ /dev/null @@ -1,45 +0,0 @@ -"""Config for EC2. - -Block {Min:0, init:1, Max:1} -================== -| ++++++++++++++ | -| | Node | | -| | | | -| | Task Task | | -| | | | -| ++++++++++++++ | -================== - -""" -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.providers import AWSProvider - -# If you are a developer running tests, make sure to update parsl/tests/configs/user_opts.py -# If you are a user copying-and-pasting this as an example, make sure to either -# 1) create a local `user_opts.py`, or -# 2) delete the user_opts import below and replace all appearances of `user_opts` with the literal value -# (i.e., user_opts['swan']['username'] -> 'your_username') -from parsl.tests.configs.user_opts import user_opts - -config = Config( - executors=[ - HighThroughputExecutor( - label='ec2_single_node', - address=user_opts['public_ip'], - encrypted=True, - provider=AWSProvider( - user_opts['ec2']['image_id'], - region=user_opts['ec2']['region'], - key_name=user_opts['ec2']['key_name'], - profile="default", - state_file='awsproviderstate.json', - nodes_per_block=1, - init_blocks=1, - max_blocks=1, - min_blocks=0, - walltime='01:00:00', - ), - ) - ] -) diff --git a/parsl/parsl/tests/configs/ec2_spot.py b/parsl/parsl/tests/configs/ec2_spot.py deleted file mode 100644 index 56084a91..00000000 --- a/parsl/parsl/tests/configs/ec2_spot.py +++ /dev/null @@ -1,33 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.providers import AWSProvider - -# If you are a developer running tests, make sure to update parsl/tests/configs/user_opts.py -# If you are a user copying-and-pasting this as an example, make sure to either -# 1) create a local `user_opts.py`, or -# 2) delete the user_opts import below and replace all appearances of `user_opts` with the literal value -# (i.e., user_opts['swan']['username'] -> 'your_username') -from parsl.tests.configs.user_opts import user_opts - -config = Config( - executors=[ - HighThroughputExecutor( - label='ec2_single_node', - address=user_opts['public_ip'], - encrypted=True, - provider=AWSProvider( - user_opts['ec2']['image_id'], - region=user_opts['ec2']['region'], - key_name=user_opts['ec2']['key_name'], - spot_max_bid='1.0', - profile="default", - state_file='awsproviderstate.json', - nodes_per_block=1, - init_blocks=1, - max_blocks=1, - min_blocks=0, - walltime='01:00:00', - ), - ) - ] -) diff --git a/parsl/parsl/tests/configs/flux_local.py b/parsl/parsl/tests/configs/flux_local.py deleted file mode 100644 index 203dd590..00000000 --- a/parsl/parsl/tests/configs/flux_local.py +++ /dev/null @@ -1,11 +0,0 @@ -from parsl.config import Config -from parsl.executors import FluxExecutor - - -def fresh_config(): - return Config( - executors=[FluxExecutor()], - ) - - -config = fresh_config() diff --git a/parsl/parsl/tests/configs/frontera.py b/parsl/parsl/tests/configs/frontera.py deleted file mode 100644 index 41e2985e..00000000 --- a/parsl/parsl/tests/configs/frontera.py +++ /dev/null @@ -1,42 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SrunLauncher -from parsl.providers import SlurmProvider - -from .user_opts import user_opts - -""" This config assumes that it is used to launch parsl tasks from the login nodes -of Frontera at TACC. Each job submitted to the scheduler will request 2 nodes for 10 minutes. -""" - - -def fresh_config(): - return Config( - executors=[ - HighThroughputExecutor( - label="frontera_htex", - max_workers_per_node=1, - encrypted=True, - provider=SlurmProvider( - cmd_timeout=60, # Add extra time for slow scheduler responses - nodes_per_block=2, - init_blocks=1, - min_blocks=1, - max_blocks=1, - partition='development', # Replace with partition name - scheduler_options=user_opts['frontera']['scheduler_options'], - - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init=user_opts['frontera']['worker_init'], - - # Ideally we set the walltime to the longest supported walltime. - walltime='00:10:00', - launcher=SrunLauncher(), - ), - ) - ], - ) - - -config = fresh_config() diff --git a/parsl/parsl/tests/configs/globus_compute.py b/parsl/parsl/tests/configs/globus_compute.py deleted file mode 100644 index 19f90b8c..00000000 --- a/parsl/parsl/tests/configs/globus_compute.py +++ /dev/null @@ -1,20 +0,0 @@ -import os - -from globus_compute_sdk import Executor - -from parsl.config import Config -from parsl.executors import GlobusComputeExecutor - - -def fresh_config(): - - endpoint_id = os.environ["GLOBUS_COMPUTE_ENDPOINT"] - - return Config( - executors=[ - GlobusComputeExecutor( - executor=Executor(endpoint_id=endpoint_id), - label="globus_compute", - ) - ] - ) diff --git a/parsl/parsl/tests/configs/htcondor_ci.py b/parsl/parsl/tests/configs/htcondor_ci.py deleted file mode 100644 index ef296f82..00000000 --- a/parsl/parsl/tests/configs/htcondor_ci.py +++ /dev/null @@ -1,22 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SimpleLauncher -from parsl.providers import CondorProvider - - -def fresh_config(): - return Config( - executors=[ - HighThroughputExecutor( - label="docker_htcondor", - provider=CondorProvider( - worker_init=". ~/venv/bin/activate", - nodes_per_block=1, - init_blocks=1, - min_blocks=1, - max_blocks=1, - launcher=SimpleLauncher(), - ), - ) - ], - ) diff --git a/parsl/parsl/tests/configs/htex_local.py b/parsl/parsl/tests/configs/htex_local.py deleted file mode 100644 index 712570b6..00000000 --- a/parsl/parsl/tests/configs/htex_local.py +++ /dev/null @@ -1,24 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SimpleLauncher -from parsl.providers import LocalProvider - - -def fresh_config(): - return Config( - executors=[ - HighThroughputExecutor( - label="htex_local", - loopback_address="::1", - worker_debug=True, - cores_per_worker=1, - encrypted=True, - provider=LocalProvider( - init_blocks=1, - max_blocks=1, - launcher=SimpleLauncher(), - ), - ) - ], - strategy='none', - ) diff --git a/parsl/parsl/tests/configs/htex_local_alternate.py b/parsl/parsl/tests/configs/htex_local_alternate.py deleted file mode 100644 index 80d55cef..00000000 --- a/parsl/parsl/tests/configs/htex_local_alternate.py +++ /dev/null @@ -1,73 +0,0 @@ -""" -The aim of this configuration is to run a local htex -in a similar manner to htex_local.py, but with lots of -options different and more complicated than in that -configuration, so that more code paths are executed -than when testing only with htex_local. - -It does not matter too much *what* is different in this -configuration; what matters is that the differences -cause significantly different pieces of parsl code to be -run - for example, by turning on monitoring, by allowing -blocks to be started by a strategy, by using a different -set of staging providers, by using timing parameters that -will cause substantially different behaviour on whatever -those timing parameters control. -""" - -import os - -from parsl.config import Config -from parsl.data_provider.file_noop import NoOpFileStaging -from parsl.data_provider.ftp import FTPInTaskStaging -from parsl.data_provider.http import HTTPInTaskStaging -from parsl.data_provider.zip import ZipFileStaging -from parsl.dataflow.memoization import BasicMemoizer -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SingleNodeLauncher -from parsl.logconfigs.json import JSONLogging -from parsl.monitoring import MonitoringHub -from parsl.providers import LocalProvider - -working_dir = os.getcwd() + "/" + "test_htex_alternate" - - -def fresh_config(): - return Config( - executors=[ - HighThroughputExecutor( - address="127.0.0.1", - label="htex_Local", - working_dir=working_dir, - storage_access=[ZipFileStaging(), FTPInTaskStaging(), HTTPInTaskStaging(), NoOpFileStaging()], - worker_debug=True, - cores_per_worker=1, - heartbeat_period=2, - heartbeat_threshold=5, - poll_period=100, - encrypted=True, - provider=LocalProvider( - init_blocks=0, - min_blocks=0, - max_blocks=5, - launcher=SingleNodeLauncher(), - ), - block_error_handler=False - ) - ], - strategy='simple', - memoizer=BasicMemoizer(memoize=True, checkpoint_mode='task_exit'), - retries=2, - monitoring=MonitoringHub( - monitoring_debug=False, - resource_monitoring_interval=1, - ), - usage_tracking=3, - project_name="parsl htex_local_alternate test configuration", - - # level 1 is even more debuggy than logging.DEBUG - initialize_logging=JSONLogging(level=1) - ) - - -config = fresh_config() diff --git a/parsl/parsl/tests/configs/htex_local_intask_staging.py b/parsl/parsl/tests/configs/htex_local_intask_staging.py deleted file mode 100644 index 69649b2b..00000000 --- a/parsl/parsl/tests/configs/htex_local_intask_staging.py +++ /dev/null @@ -1,25 +0,0 @@ -from parsl.config import Config -from parsl.data_provider.file_noop import NoOpFileStaging -from parsl.data_provider.ftp import FTPInTaskStaging -from parsl.data_provider.http import HTTPInTaskStaging -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SimpleLauncher -from parsl.providers import LocalProvider - -config = Config( - executors=[ - HighThroughputExecutor( - label="htex_Local", - worker_debug=True, - cores_per_worker=1, - encrypted=True, - provider=LocalProvider( - init_blocks=1, - max_blocks=1, - launcher=SimpleLauncher(), - ), - storage_access=[HTTPInTaskStaging(), FTPInTaskStaging(), NoOpFileStaging()] - ) - ], - strategy='none', -) diff --git a/parsl/parsl/tests/configs/htex_local_rsync_staging.py b/parsl/parsl/tests/configs/htex_local_rsync_staging.py deleted file mode 100644 index d24b1da6..00000000 --- a/parsl/parsl/tests/configs/htex_local_rsync_staging.py +++ /dev/null @@ -1,26 +0,0 @@ -from parsl.config import Config -from parsl.data_provider.ftp import FTPInTaskStaging -from parsl.data_provider.http import HTTPInTaskStaging -from parsl.data_provider.rsync import RSyncStaging -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SimpleLauncher -from parsl.providers import LocalProvider - -config = Config( - executors=[ - HighThroughputExecutor( - label="htex_Local", - worker_debug=True, - cores_per_worker=1, - working_dir="./rsync-workdir/", - encrypted=True, - provider=LocalProvider( - init_blocks=1, - max_blocks=1, - launcher=SimpleLauncher(), - ), - storage_access=[HTTPInTaskStaging(), FTPInTaskStaging(), RSyncStaging("fitzroy.cqx.ltd.uk")] - ) - ], - strategy='none', -) diff --git a/parsl/parsl/tests/configs/local_radical.py b/parsl/parsl/tests/configs/local_radical.py deleted file mode 100644 index ec146f50..00000000 --- a/parsl/parsl/tests/configs/local_radical.py +++ /dev/null @@ -1,17 +0,0 @@ -import os - -from parsl.config import Config -from parsl.executors.radical import RadicalPilotExecutor, ResourceConfig - -rpex_cfg = ResourceConfig() - - -def fresh_config(): - - return Config( - executors=[ - RadicalPilotExecutor( - label='RPEXBulk', - rpex_cfg=rpex_cfg, - resource='local.localhost', - runtime=30, cores=4)]) diff --git a/parsl/parsl/tests/configs/local_radical_mpi.py b/parsl/parsl/tests/configs/local_radical_mpi.py deleted file mode 100644 index 582f406e..00000000 --- a/parsl/parsl/tests/configs/local_radical_mpi.py +++ /dev/null @@ -1,20 +0,0 @@ -import os - -from parsl.config import Config - - -def fresh_config(): - from parsl.executors.radical import RadicalPilotExecutor, ResourceConfig - - rpex_cfg = ResourceConfig() - rpex_cfg.worker_type = "MPI" - rpex_cfg.worker_cores_per_node = 7 - - return Config( - executors=[ - RadicalPilotExecutor( - label='RPEXMPI', - rpex_cfg=rpex_cfg, - bulk_mode=True, - resource='local.localhost', - runtime=30, cores=8)]) diff --git a/parsl/parsl/tests/configs/local_threads.py b/parsl/parsl/tests/configs/local_threads.py deleted file mode 100644 index 4de2a7bd..00000000 --- a/parsl/parsl/tests/configs/local_threads.py +++ /dev/null @@ -1,11 +0,0 @@ -from parsl.config import Config -from parsl.executors.threads import ThreadPoolExecutor - - -def fresh_config(): - return Config( - executors=[ThreadPoolExecutor()], - ) - - -config = fresh_config() diff --git a/parsl/parsl/tests/configs/local_threads_checkpoint_dfk_exit.py b/parsl/parsl/tests/configs/local_threads_checkpoint_dfk_exit.py deleted file mode 100644 index 63cf96b5..00000000 --- a/parsl/parsl/tests/configs/local_threads_checkpoint_dfk_exit.py +++ /dev/null @@ -1,12 +0,0 @@ -from parsl.config import Config -from parsl.dataflow.memoization import BasicMemoizer -from parsl.executors.threads import ThreadPoolExecutor - -config = Config( - executors=[ - ThreadPoolExecutor( - label='local_threads_checkpoint_dfk_exit', - ) - ], - memoizer=BasicMemoizer(checkpoint_mode='dfk_exit') -) diff --git a/parsl/parsl/tests/configs/local_threads_checkpoint_task_exit.py b/parsl/parsl/tests/configs/local_threads_checkpoint_task_exit.py deleted file mode 100644 index f5b36e06..00000000 --- a/parsl/parsl/tests/configs/local_threads_checkpoint_task_exit.py +++ /dev/null @@ -1,12 +0,0 @@ -from parsl.config import Config -from parsl.dataflow.memoization import BasicMemoizer -from parsl.executors.threads import ThreadPoolExecutor - -config = Config( - executors=[ - ThreadPoolExecutor( - label='local_threads_checkpoint_task_exit', - ) - ], - memoizer=BasicMemoizer(checkpoint_mode='task_exit') -) diff --git a/parsl/parsl/tests/configs/local_threads_ftp_in_task.py b/parsl/parsl/tests/configs/local_threads_ftp_in_task.py deleted file mode 100644 index 833465f6..00000000 --- a/parsl/parsl/tests/configs/local_threads_ftp_in_task.py +++ /dev/null @@ -1,15 +0,0 @@ -from parsl.config import Config -from parsl.data_provider.file_noop import NoOpFileStaging -from parsl.data_provider.ftp import FTPInTaskStaging -from parsl.executors.threads import ThreadPoolExecutor - - -def fresh_config(): - return Config( - executors=[ - ThreadPoolExecutor( - label='local_threads_http_in_task', - storage_access=[FTPInTaskStaging(), NoOpFileStaging()] - ) - ] - ) diff --git a/parsl/parsl/tests/configs/local_threads_globus.py b/parsl/parsl/tests/configs/local_threads_globus.py deleted file mode 100644 index 2a09e9a9..00000000 --- a/parsl/parsl/tests/configs/local_threads_globus.py +++ /dev/null @@ -1,32 +0,0 @@ -from parsl.config import Config -from parsl.data_provider.data_manager import default_staging -from parsl.executors.threads import ThreadPoolExecutor - -# If you are a developer running tests, make sure to update parsl/tests/configs/user_opts.py -# If you are a user copying-and-pasting this as an example, make sure to either -# 1) create a local `user_opts.py`, or -# 2) delete the user_opts import below and replace all appearances of `user_opts` with the literal value -# (i.e., user_opts['swan']['username'] -> 'your_username') -from .user_opts import user_opts - - -def fresh_config(): - from parsl.data_provider.globus import GlobusStaging - - storage_access = default_staging + [GlobusStaging( - endpoint_uuid=user_opts['globus']['endpoint'], - endpoint_path=user_opts['globus']['path'] - )] - - return Config( - executors=[ - ThreadPoolExecutor( - label='local_threads_globus', - working_dir=user_opts['globus']['path'], - storage_access=storage_access - ) - ] - ) - - -remote_writeable = user_opts['globus']['remote_writeable'] diff --git a/parsl/parsl/tests/configs/local_threads_http_in_task.py b/parsl/parsl/tests/configs/local_threads_http_in_task.py deleted file mode 100644 index 71f718a5..00000000 --- a/parsl/parsl/tests/configs/local_threads_http_in_task.py +++ /dev/null @@ -1,15 +0,0 @@ -from parsl.config import Config -from parsl.data_provider.file_noop import NoOpFileStaging -from parsl.data_provider.http import HTTPInTaskStaging -from parsl.executors.threads import ThreadPoolExecutor - - -def fresh_config(): - return Config( - executors=[ - ThreadPoolExecutor( - label='local_threads_http_in_task', - storage_access=[HTTPInTaskStaging(), NoOpFileStaging()] - ) - ] - ) diff --git a/parsl/parsl/tests/configs/midway.py b/parsl/parsl/tests/configs/midway.py deleted file mode 100644 index 01cc49fd..00000000 --- a/parsl/parsl/tests/configs/midway.py +++ /dev/null @@ -1,39 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SrunLauncher -from parsl.providers import SlurmProvider - -from .user_opts import user_opts - - -def fresh_config(): - config = Config( - executors=[ - HighThroughputExecutor( - label='Midway_HTEX_multinode', - worker_debug=False, - max_workers_per_node=1, - encrypted=True, - provider=SlurmProvider( - 'broadwl', # Partition name, e.g 'broadwl' - launcher=SrunLauncher(), - nodes_per_block=2, - init_blocks=1, - min_blocks=1, - max_blocks=1, - # string to prepend to #SBATCH blocks in the submit - # script to the scheduler eg: '#SBATCH --constraint=knl,quad,cache' - scheduler_options='', - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init=user_opts['midway']['worker_init'], - walltime='00:30:00', - cmd_timeout=120, - ), - ) - ], - ) - return config - - -config = fresh_config() diff --git a/parsl/parsl/tests/configs/nscc_singapore.py b/parsl/parsl/tests/configs/nscc_singapore.py deleted file mode 100644 index 8260fbb6..00000000 --- a/parsl/parsl/tests/configs/nscc_singapore.py +++ /dev/null @@ -1,43 +0,0 @@ -from parsl.addresses import address_by_interface -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import MpiRunLauncher -from parsl.providers import PBSProProvider - -from .user_opts import user_opts - - -def fresh_config(): - return Config( - executors=[ - HighThroughputExecutor( - label="htex", - heartbeat_period=15, - heartbeat_threshold=120, - worker_debug=False, - max_workers_per_node=1, - address=address_by_interface('ib0'), - encrypted=True, - provider=PBSProProvider( - launcher=MpiRunLauncher(), - # string to prepend to #PBS blocks in the submit - # script to the scheduler - # E.g., project name - scheduler_options=user_opts['nscc']['scheduler_options'], - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init=user_opts['nscc']['worker_init'], - nodes_per_block=2, - min_blocks=1, - max_blocks=1, - cpus_per_node=24, - walltime="00:20:00", - cmd_timeout=300, - ), - ), - ], - strategy='simple', - ) - - -config = fresh_config() diff --git a/parsl/parsl/tests/configs/osg_htex.py b/parsl/parsl/tests/configs/osg_htex.py deleted file mode 100644 index 3e0658b3..00000000 --- a/parsl/parsl/tests/configs/osg_htex.py +++ /dev/null @@ -1,32 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.providers import CondorProvider - -# If you are a developer running tests, make sure to update parsl/tests/configs/user_opts.py -# If you are a user copying-and-pasting this as an example, make sure to either -# 1) create a local `user_opts.py`, or -# 2) delete the user_opts import below and replace all appearances of `user_opts` with the literal value -# (i.e., user_opts['swan']['username'] -> 'your_username') -from .user_opts import user_opts - -config = Config( - executors=[ - HighThroughputExecutor( - label='OSG_HTEX', - max_workers_per_node=1, - encrypted=True, - provider=CondorProvider( - nodes_per_block=1, - init_blocks=4, - max_blocks=4, - # This scheduler option string ensures that the compute nodes provisioned - # will have modules - scheduler_options='Requirements = OSGVO_OS_STRING == "RHEL 6" && Arch == "X86_64" && HAS_MODULES == True', - # Command to be run before starting a worker, such as: - # 'module load Anaconda; source activate parsl_env'. - worker_init=user_opts['osg']['worker_init'], - walltime="00:20:00", - ), - ) - ] -) diff --git a/parsl/parsl/tests/configs/petrelkube.py b/parsl/parsl/tests/configs/petrelkube.py deleted file mode 100644 index 7fe35854..00000000 --- a/parsl/parsl/tests/configs/petrelkube.py +++ /dev/null @@ -1,54 +0,0 @@ -import os - -from parsl.addresses import address_by_route -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.providers import KubernetesProvider - -from .user_opts import user_opts - - -def fresh_config(): - worker_init_file = user_opts['petrelkube']['worker_init'] - with open(os.path.expanduser(worker_init_file)) as f: - lines = f.readlines() - worker_init = ';'.join([line.strip() for line in lines]) - - config = Config( - executors=[ - HighThroughputExecutor( - label='kube-htex', - cores_per_worker=1, - max_workers_per_node=1, - worker_logdir_root='.', - - # Address for the pod worker to connect back - address=address_by_route(), - encrypted=True, - provider=KubernetesProvider( - namespace="dlhub-privileged", - - # Docker image url to use for pods - image='python:3.7', - - # Command to be run upon pod start, such as: - # 'module load Anaconda; source activate parsl_env'. - # or 'pip install parsl' - worker_init=worker_init, - - # The secret key to download the image - secret="ryan-kube-secret", - - # Should follow the Kubernetes naming rules - pod_name='parsl-site-test-pod', - - nodes_per_block=1, - init_blocks=1, - # Maximum number of pods to scale up - max_blocks=2, - ), - ), - ] - ) - - return config diff --git a/parsl/parsl/tests/configs/slurm_local.py b/parsl/parsl/tests/configs/slurm_local.py deleted file mode 100644 index c0281cf3..00000000 --- a/parsl/parsl/tests/configs/slurm_local.py +++ /dev/null @@ -1,24 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SrunLauncher -from parsl.providers import SlurmProvider - - -def fresh_config(): - return Config( - executors=[ - HighThroughputExecutor( - label="docker_slurm", - encrypted=True, - provider=SlurmProvider( - cmd_timeout=60, # Add extra time for slow scheduler responses - nodes_per_block=1, - init_blocks=1, - min_blocks=1, - max_blocks=1, - walltime='00:10:00', - launcher=SrunLauncher(), - ), - ) - ], - ) diff --git a/parsl/parsl/tests/configs/summit.py b/parsl/parsl/tests/configs/summit.py deleted file mode 100644 index 69487f78..00000000 --- a/parsl/parsl/tests/configs/summit.py +++ /dev/null @@ -1,38 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import JsrunLauncher -from parsl.providers import LSFProvider - -from .user_opts import user_opts - -""" This config assumes that it is used to launch parsl tasks from the login nodes -of Frontera at TACC. Each job submitted to the scheduler will request 2 nodes for 10 minutes. -""" - - -def fresh_config(): - return Config( - executors=[ - HighThroughputExecutor( - label='Summit_HTEX', - # On Summit ensure that the working dir is writeable from the compute nodes, - # for eg. paths below /gpfs/alpine/world-shared/ - # working_dir='', - - # address=address_by_interface('ib0'), # This assumes Parsl is running on login node - worker_port_range=(50000, 55000), - max_workers_per_node=1, - encrypted=True, - provider=LSFProvider( - launcher=JsrunLauncher(), - walltime="00:10:00", - nodes_per_block=2, - init_blocks=1, - max_blocks=1, - worker_init=user_opts['summit']['worker_init'], - project=user_opts['summit']['project'], - cmd_timeout=60 - ), - ) - ], - ) diff --git a/parsl/parsl/tests/configs/taskvine_ex.py b/parsl/parsl/tests/configs/taskvine_ex.py deleted file mode 100644 index ee09dc70..00000000 --- a/parsl/parsl/tests/configs/taskvine_ex.py +++ /dev/null @@ -1,10 +0,0 @@ -from parsl.config import Config -from parsl.data_provider.file_noop import NoOpFileStaging -from parsl.data_provider.ftp import FTPInTaskStaging -from parsl.data_provider.http import HTTPInTaskStaging -from parsl.executors.taskvine import TaskVineExecutor, TaskVineManagerConfig - - -def fresh_config(): - return Config(executors=[TaskVineExecutor(manager_config=TaskVineManagerConfig(port=9000), - worker_launch_method='provider')]) diff --git a/parsl/parsl/tests/configs/user_opts.py b/parsl/parsl/tests/configs/user_opts.py deleted file mode 100644 index 540d13ea..00000000 --- a/parsl/parsl/tests/configs/user_opts.py +++ /dev/null @@ -1,154 +0,0 @@ -""" -Specification of user-specific configuration options. - -The fields must be configured separately for each user. To disable any associated configurations, comment -out the entry. - -User specific overrides that should not go in version control can be set by creating a -file called local_user_opts.py, which declares a dictionary local_user_opts. Top level -keys in that dictionary will replace entries in the below user opts file, so it should -be safe to cut-and-paste entries from this file into that file. -""" -from typing import Any, Dict - -# PUBLIC_IP = "52.86.208.63" # "128.135.250.229" -# MIDWAY_USERNAME = "yadunand" -# OSG_USERNAME = "yadunand" -# SWAN_USERNAME = "p02509" -# CORI_USERNAME = "yadunand" -# ALCF_USERNAME = "yadunand" -# ALCF_ALLOCATION = "CSC249ADCD01" -# COMET_USERNAME = "yadunand" - -user_opts = { - 'frontera': { - 'worker_init': 'source ~/setup_parsl_test_env.sh;', - }, - 'nscc': { - 'worker_init': 'source ~/setup_parsl_test_env.sh;', - }, - 'theta': { - 'worker_init': 'source ~/setup_parsl_test_env.sh;', - }, - 'summit': { - 'worker_init': 'source ~/setup_parsl_test_env.sh;', - }, - 'bluewaters': { - 'worker_init': 'source ~/setup_parsl_test_env.sh;', - }, - 'midway': { - 'worker_init': 'source ~/setup_parsl_test_env.sh;', - }, - 'petrelkube': { - 'worker_init': '~/setup_parsl_test_env.sh', - }, - # 'comet': { - # 'username': COMET_USERNAME, - # 'script_dir': '/home/{}/parsl_scripts'.format(COMET_USERNAME), - # 'scheduler_options': "", - # 'worker_init': 'export PATH:/home/{}/anaconda3/bin/:$PATH; source activate parsl_0.5.0_py3.6;'.format(COMET_USERNAME), - # }, - # 'midway': { - # 'username': MIDWAY_USERNAME, - # 'script_dir': '/scratch/midway2/{}/parsl_scripts'.format(MIDWAY_USERNAME), - # 'scheduler_options': "", - # 'worker_init': 'cd /scratch/midway2/{}/parsl_scripts; ' - # 'module load Anaconda3/5.1.0; source activate parsl_testing;' - # .format(MIDWAY_USERNAME), - # }, - # 'osg': { - # 'username': OSG_USERNAME, - # 'script_dir': '/home/{}/parsl_scripts'.format(OSG_USERNAME), - # 'scheduler_options': "", - # 'worker_init' : 'module load python/3.5.2; python3 -m venv parsl_env; - # source parsl_env/bin/activate; python3 -m pip install parsl==0.5.2' - # }, - # 'swan': { - # 'username': SWAN_USERNAME, - # 'script_dir' : "/home/users/{}/parsl_scripts".format(SWAN_USERNAME), - # 'scheduler_options': "", - # 'worker_init': "module load cray-python/3.6.1.1; source parsl_env/bin/activate" - # }, - # 'cooley': { - # 'username': ALCF_USERNAME, - # "account": ALCF_ALLOCATION, - # 'scheduler_options': "", - # "worker_init": "source /home/{}/setup_cooley_env.sh".format(ALCF_USERNAME), - # # Once you log onto Cooley, get the ip address of the login machine - # # by running >> ip addr show | grep -o 10.236.1.[0-9]* - # 'public_ip': '10.236.1.193' - # }, - # }, - # 'ec2': { - # "region": "us-east-2", - # "image_id": 'ami-82f4dae7', - # "key_name": "parsl.test", - # # Name of the profile used to identify credentials stored in ~/.aws/config - # "profile_name": "parsl", - # }, - # - # 'azure': { - # - # # Specifies a username/password which can be used to log into Azure VMs - # # These must be specified but are not used by parsl to access the VMs. - # 'admin_username': 'anyuser', - # 'password': 'mypassword1234567!', - # - # # Characteristics of the VMs to be started: - # 'vm_size': 'Standard_D1', - # 'disk_size_gb': '10', - # - # # Details of the image to be started on each VM. - # # Values can be found using, for example, the `az` command line tool: - # # az vm image list --publisher Debian - # 'publisher': 'Debian', - # 'offer': 'debian-10', - # 'sku': '10', - # 'version': 'latest' - # }, - # 'theta': { - # 'username': ALCF_USERNAME, - # "account": ALCF_ALLOCATION, - # 'scheduler_options': "", - # "worker_init": "source /home/{}/setup_theta_env.sh".format(ALCF_USERNAME), - # # Once you log onto theta, get the ip address of the login machine - # # by running >> ip addr show | grep -o 10.236.1.[0-9]* - # 'public_ip': '10.236.1.193' - # }, - # 'beagle': { - # 'username': 'fixme', - # "script_dir": "fixme", - # "scheduler_options": "#SBATCH --constraint=haswell", - # "worker_init": """module load python/3.5-anaconda ; source activate parsl_env_3.5""" - # }, - # 'cc_in2p3': { - # 'script_dir': "~/parsl_scripts", - # 'scheduler_options': "", - # "worker_init": """export PATH=/pbs/throng/lsst/software/anaconda/anaconda3-5.0.1/bin:$PATH; source activate parsl_env_3.5""" - # }, - # 'globus': { - # 'endpoint': 'fixme', - # 'path': 'fixme', - # - # # remote_writeable should specify a directory on a globus endpoint somewhere else, - # # where files can be staged out to via globus during globus staging tests. - # # For example: - # 'remote_writeable': 'globus://af7bda53-6d04-11e5-ba46-22000b92c6ec/home/bzc/' - # }, - # -} # type: Dict[str, Any] - -# This block attempts to import local_user_opts.py, which -# can provide local overrides to the version-controlled -# user_opts. -# Users can add their own overrides into local_user_opts -# in local_user_opts.py, which should not exist in a -# pristine parsl source tree, and which should help avoid -# accidentally committing secrets and other per-user -# config into version control. -try: - from .local_user_opts import local_user_opts - user_opts.update(local_user_opts) - -except ImportError: - pass diff --git a/parsl/parsl/tests/configs/workqueue_ex.py b/parsl/parsl/tests/configs/workqueue_ex.py deleted file mode 100644 index 7e9a6967..00000000 --- a/parsl/parsl/tests/configs/workqueue_ex.py +++ /dev/null @@ -1,10 +0,0 @@ -from parsl.config import Config -from parsl.data_provider.file_noop import NoOpFileStaging -from parsl.data_provider.ftp import FTPInTaskStaging -from parsl.data_provider.http import HTTPInTaskStaging -from parsl.executors import WorkQueueExecutor - - -def fresh_config(): - return Config(executors=[WorkQueueExecutor(port=9000, - coprocess=True)]) diff --git a/parsl/parsl/tests/conftest.py b/parsl/parsl/tests/conftest.py deleted file mode 100644 index d992bcd9..00000000 --- a/parsl/parsl/tests/conftest.py +++ /dev/null @@ -1,494 +0,0 @@ -import importlib.util -import itertools -import logging -import os -import pathlib -import random -import re -import shutil -import signal -import string -import sys -import tempfile -import threading -import time -import traceback -import types -import typing as t -from datetime import datetime -from glob import glob -from itertools import chain - -import _pytest.runner as runner -import pytest - -import parsl -from parsl.dataflow.dflow import DataFlowKernelLoader -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - - -def dumpstacks(sig, frame): - s = '' - try: - thread_names = {thread.ident: thread.name for thread in threading.enumerate()} - tf = sys._current_frames() - for thread_id, frame in tf.items(): - s += '\n\nThread: %s (%d)' % (thread_names[thread_id], thread_id) - s += ''.join(traceback.format_stack(frame)) - except Exception: - s = traceback.format_exc() - with open(os.getenv('HOME') + '/parsl_stack_dump.txt', 'w') as f: - f.write(s) - print(s) - - -def pytest_sessionstart(session): - signal.signal(signal.SIGUSR1, dumpstacks) - - -@pytest.fixture(scope="session") -def tmpd_cwd_session(pytestconfig): - def _chmod(path: pathlib.Path, mode: int): - try: - path.lchmod(mode) # support BSD and derivatives - except NotImplementedError: - path.chmod(mode) - - config = re.sub(r"[^A-z0-9_-]+", "_", pytestconfig.getoption('config')[0]) - cwd = pathlib.Path(os.getcwd()) - pytest_dir = cwd / "pytest-parsl" - pytest_dir.mkdir(mode=0o700, parents=True, exist_ok=True) - - test_dir_prefix = "parsltest-" - link = pytest_dir / f"{test_dir_prefix}current" - link.unlink(missing_ok=True) - n = datetime.now().strftime('%Y%m%d.%H%M%S') - tmpd = tempfile.mkdtemp(dir=pytest_dir, prefix=f"{test_dir_prefix}{n}-{config}-") - tmpd = pathlib.Path(tmpd) - link.symlink_to(tmpd.name) - yield link - - try: - preserve = int(os.getenv("PARSL_TEST_PRESERVE_NUM_RUNS", "3")) - except Exception: - preserve = 3 - - test_runs = sorted( - d for d in pytest_dir.glob(f"{test_dir_prefix}*") - if d.is_dir() and not d.is_symlink() - ) - for run_to_remove in test_runs[:-preserve]: - run_to_remove.chmod(0o700) - for root, subdirnames, fnames in os.walk(run_to_remove): - rpath = pathlib.Path(root) - for d in subdirnames: - _chmod(rpath / d, 0o700) - for f in fnames: - _chmod(rpath / f, 0o600) - shutil.rmtree(run_to_remove) - - -@pytest.fixture -def tmpd_cwd(tmpd_cwd_session, request: pytest.FixtureRequest): - node_name = re.sub(r'[^\w\s-]', '_', request.node.name) - prefix = f"{node_name}-" - tmpd = tempfile.mkdtemp(dir=tmpd_cwd_session, prefix=prefix) - yield pathlib.Path(tmpd) - - -def pytest_addoption(parser): - """Add parsl-specific command-line options to pytest. - """ - parser.addoption( - '--config', - action='store', - metavar='CONFIG', - type=str, - nargs=1, - required=True, - help="run with parsl CONFIG; use 'local' to run locally-defined config" - ) - - parser.addoption( - '--show-filtered-tests', - action='store_true', - default=False, - help="Report filtered tests as skipped instead of deselecting them" - ) - - -def pytest_configure(config): - """Configure help for parsl-specific pytest decorators. - - This help is returned by `pytest --markers`. - """ - config.addinivalue_line( - 'markers', - 'whitelist(config1, config2, ..., reason=None): mark test to run only on named configs. ' - 'Wildcards (*) are accepted. If `reason` is supplied, it will be included in the report.' - ) - config.addinivalue_line( - 'markers', - 'blacklist(config1, config2, ..., reason=None): mark test to skip named configs. ' - 'Wildcards (*) are accepted. If `reason` is supplied, it will be included in the report.' - ) - config.addinivalue_line( - 'markers', - 'local: mark test to only run locally-defined config.' - ) - config.addinivalue_line( - 'markers', - 'local(reason): mark test to only run locally-defined config; report will include supplied reason.' - ) - config.addinivalue_line( - 'markers', - 'cleannet: Enable tests that require a clean network connection (such as for testing FTP)' - ) - config.addinivalue_line( - 'markers', - 'staging_required: Marks tests that require a staging provider, when there is no sharedFS' - ) - config.addinivalue_line( - 'markers', - 'multiple_cores_required: Marks tests that require multiple cores, such as htex affinity' - ) - config.addinivalue_line( - 'markers', - 'unix_filesystem_permissions_required: Marks tests that require unix-level filesystem permission enforcement' - ) - config.addinivalue_line( - 'markers', - 'issue3328: Marks tests broken by issue #3328' - ) - config.addinivalue_line( - 'markers', - 'executor_supports_std_stream_tuples: Marks tests that require tuple support for stdout/stderr' - ) - config.addinivalue_line( - 'markers', - 'shared_fs: Marks tests that require a shared_fs between the workers are the test client' - ) - config.addinivalue_line( - 'markers', - 'issue_3620: Marks tests that do not work correctly on GlobusComputeExecutor (ref: issue 3620)' - ) - config.addinivalue_line( - 'markers', - 'workqueue: Marks local tests that require a working Work Queue installation' - ) - config.addinivalue_line( - 'markers', - 'taskvine: Marks local tests that require a working Task Vine installation' - ) - config.addinivalue_line( - 'markers', - 'globus_compute: tests that require the Globus Compute SDK' - ) - config.addinivalue_line( - 'markers', - 'radical: tests that require the Radical framework' - ) - - -@pytest.fixture(autouse=True, scope='session') -def load_dfk_session(request, pytestconfig, tmpd_cwd_session): - """Load a dfk around entire test suite, except in local mode. - - The special path `local` indicates that configuration will not come - from a pytest managed configuration file; in that case, see - load_dfk_local_module for module-level configuration management. - """ - - RepresentationMixin._validate_repr = True - - config = pytestconfig.getoption('config')[0] - - if config != 'local': - spec = importlib.util.spec_from_file_location('', config) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - - if DataFlowKernelLoader._dfk is not None: - raise RuntimeError("DFK didn't start as None - there was a DFK from somewhere already") - - if hasattr(module, 'config'): - parsl_conf = module.config - elif hasattr(module, 'fresh_config'): - parsl_conf = module.fresh_config() - else: - raise RuntimeError("Config module does not define config or fresh_config") - - if parsl_conf.run_dir == "runinfo": # the default - parsl_conf.run_dir = str(tmpd_cwd_session / parsl_conf.run_dir) - dfk = parsl.load(parsl_conf) - - for ex in dfk.executors.values(): - ex.working_dir = tmpd_cwd_session - - yield - - if parsl.dfk() != dfk: - raise RuntimeError("DFK changed unexpectedly during test") - dfk.cleanup() - assert DataFlowKernelLoader._dfk is None - else: - yield - - -@pytest.fixture(autouse=True, scope='module') -def load_dfk_local_module(request, pytestconfig, tmpd_cwd_session): - """Load the dfk around test modules, in local mode. - - If local_config is specified in the test module, it will be loaded using - parsl.load. It should be a Callable that returns a parsl Config object. - - If local_setup and/or local_teardown are callables (such as functions) in - the test module, they will be invoked before/after the tests. This can - be used to perform more interesting DFK initialisation not possible with - local_config. - """ - RepresentationMixin._validate_repr = True - - config = pytestconfig.getoption('config')[0] - - if config == 'local': - local_setup = getattr(request.module, "local_setup", None) - local_teardown = getattr(request.module, "local_teardown", None) - local_config = getattr(request.module, "local_config", None) - - if local_config: - assert callable(local_config) - c = local_config() - assert isinstance(c, parsl.Config) - - if c.run_dir == "runinfo": # the default - c.run_dir = str(tmpd_cwd_session / c.run_dir) - - dfk = parsl.load(c) - - for ex in dfk.executors.values(): - ex.working_dir = tmpd_cwd_session - - if callable(local_setup): - local_setup() - - yield - - if callable(local_teardown): - local_teardown() - assert DataFlowKernelLoader._dfk is None, "Expected teardown to clear DFK" - - if local_config: - if parsl.dfk() != dfk: - raise RuntimeError("DFK changed unexpectedly during test") - dfk.cleanup() - assert DataFlowKernelLoader._dfk is None - - else: - yield - - -@pytest.hookimpl(hookwrapper=True) -def pytest_collection_modifyitems(items: list[pytest.Item], config): - """Apply whitelist, blacklist, and local markers. - - Ensure that if a whitelist decorator is applied to a test, configs which are - not in the whitelist are skipped. Similarly, configs in a blacklist are skipped, - and configs which are not `local` are skipped if the `local` decorator is applied. - """ - - yield - - show_filtered = config.getoption('--show-filtered-tests') - - parsl_test_config = config.getoption('config')[0] - conf_path = os.path.abspath(parsl_test_config) - - whitelist_reason = f"config '{parsl_test_config}' not in whitelist" - blacklist_reason = f"config '{parsl_test_config}' is in blacklist" - local_reason = "intended for local config" - explicit_reason = "intended for explicit config" - - filtered_tests = [] - i = len(items) - while i > 0: - i -= 1 - t_fn = items[i] - if (m := t_fn.get_closest_marker('whitelist')) is not None: - if conf_path not in chain.from_iterable(glob(x) for x in m.args): - reason = whitelist_reason - if 'reason' in m.kwargs: - reason = m.kwargs['reason'] - filtered_tests.append((i, reason)) - - elif (m := t_fn.get_closest_marker('blacklist')) is not None: - if conf_path in chain.from_iterable(glob(x) for x in m.args): - reason = blacklist_reason - if 'reason' in m.kwargs: - reason = m.kwargs['reason'] - filtered_tests.append((i, reason)) - - elif (m := t_fn.get_closest_marker('local')) is not None: - if parsl_test_config != 'local': - reason = local_reason - if len(m.args) > 0: - reason = str(m.args[0]) - filtered_tests.append((i, reason)) - - elif parsl_test_config == 'local': - filtered_tests.append((i, explicit_reason)) - - for t_idx, reason in filtered_tests: - if show_filtered: - t_fn = items[t_idx] - t_fn.add_marker(pytest.mark.skip(reason=reason)) - else: - del items[t_idx] - - -@pytest.fixture -def setup_data(tmpd_cwd): - data_dir = tmpd_cwd / "data" - data_dir.mkdir() - - (data_dir / "test1.txt").write_text("1\n") - (data_dir / "test2.txt").write_text("2\n") - return data_dir - - -@pytest.fixture(autouse=True, scope='function') -def assert_no_outstanding_tasks(pytestconfig): - """If we're in a config-file based mode, wait for task completion between - each test. This will detect early on (by hanging) if particular test - tasks are not finishing, rather than silently falling off the end of - the test run with tasks still in progress. - In local mode, this fixture does nothing, as there isn't anything - reasonable to assume about DFK behaviour here. - """ - config = pytestconfig.getoption('config')[0] - yield - if config != 'local': - logger.info("Checking no outstanding tasks") - for task_record in parsl.dfk().tasks.values(): - fut = task_record['app_fu'] - assert fut.done(), f"Incomplete task found, task id {task_record['id']}" - logger.info("No outstanding tasks found") - - -def pytest_make_collect_report(collector): - call = runner.CallInfo.from_call(lambda: list(collector.collect()), 'collect') - longrepr = None - if not call.excinfo: - outcome = "passed" - else: - from _pytest.outcomes import Skipped - skip_exceptions = (Skipped,) - if call.excinfo.errisinstance(KeyError): - outcome = "skipped" - r = collector._repr_failure_py(call.excinfo, "line").reprcrash - message = "{} not configured in user_opts.py".format(r.message.split()[-1]) - longrepr = (str(r.path), r.lineno, message) - elif call.excinfo.errisinstance(skip_exceptions): - outcome = "skipped" - r = collector._repr_failure_py(call.excinfo, "line").reprcrash - longrepr = (str(r.path), r.lineno, r.message) - else: - outcome = "failed" - errorinfo = collector.repr_failure(call.excinfo) - if not hasattr(errorinfo, "toterminal"): - errorinfo = runner.CollectErrorRepr(errorinfo) - longrepr = errorinfo - rep = runner.CollectReport(collector.nodeid, outcome, longrepr, getattr(call, 'result', None)) - rep.call = call # see collect_one_node - return rep - - -def pytest_ignore_collect(path): - if 'integration' in path.strpath: - return True - elif 'manual_tests' in path.strpath: - return True - elif 'scaling_tests/test_scale' in path.strpath: - return True - else: - return False - - -def create_traceback(start: int = 0) -> t.Optional[types.TracebackType]: - """ - Dynamically create a traceback. - - Builds a traceback from the top of the stack (the currently executing frame) on - down to the root frame. Optionally, use start to build from an earlier stack - frame. - - N.B. uses `sys._getframe`, which I only know to exist in CPython. - """ - tb = None - for depth in itertools.count(start + 1, 1): - try: - frame = sys._getframe(depth) - tb = types.TracebackType(tb, frame, frame.f_lasti, frame.f_lineno) - except ValueError: - break - return tb - - -@pytest.fixture -def try_assert(): - def _impl( - test_func: t.Callable[[], bool], - fail_msg: str = "", - timeout_ms: float = 5000, - attempts: int = 0, - check_period_ms: int = 20, - factor: float = 2, - ): - tb = create_traceback(start=1) - check_period_s = abs(check_period_ms) / 1000.0 - if attempts > 0: - for _attempt_no in range(attempts): - fraction = random.random() - time.sleep(fraction * check_period_s) # jitter - check_period_s *= factor ** fraction - if test_func(): - return - else: - att_fail = ( - f"\n (Still failing after attempt limit [{attempts}], testing" - f" every {check_period_ms}ms)" - ) - exc = AssertionError(f"{str(fail_msg)}{att_fail}".strip()) - raise exc.with_traceback(tb) - - elif timeout_ms > 0: - timeout_s = abs(timeout_ms) / 1000.0 - end = time.monotonic() + timeout_s - while time.monotonic() < end: - fraction = random.random() - wait_for = fraction * check_period_s # jitter - time.sleep(min(wait_for, end - time.monotonic())) - check_period_s *= factor ** fraction - if test_func(): - return - att_fail = ( - f"\n (Still failing after timeout [{timeout_ms}ms], with attempts " - f"every {check_period_ms}ms)" - ) - exc = AssertionError(f"{str(fail_msg)}{att_fail}".strip()) - raise exc.with_traceback(tb) - - else: - raise AssertionError("Bad assert call: no attempts or timeout period") - - yield _impl - - -@pytest.fixture -def randomstring(): - def func(length=5, alphabet=string.ascii_letters): - return "".join(random.choice(alphabet) for _ in range(length)) - - return func diff --git a/parsl/parsl/tests/integration/__init__.py b/parsl/parsl/tests/integration/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/integration/latency.py b/parsl/parsl/tests/integration/latency.py deleted file mode 100644 index eb76b4bf..00000000 --- a/parsl/parsl/tests/integration/latency.py +++ /dev/null @@ -1,131 +0,0 @@ -""" -What is this test -================= - -This simple test measures the time taken to launch single tasks and receive results from an executor. - -We will call get the result back after launch as ``latency`` and the combined measure of time to launch -and the time to get the result back as ``roundtrip time``. - - -How do we measure -================= - -1. We start the executor -2. Run 100 tasks and wait for them to prime the executors and the ensure that workers are online -3. Run the actual measurements: - 1) Time to launch the app - 2) Time to receive result. - -4. Take the min, max, and mean of ``latency`` and ``roundtrip``. - - -Preliminary results -================== - -Results from running on Midway with IPP executor. - -Latency | Min:0.005968570709228516 Max:0.011006593704223633 Average:0.0065019774436950685 -Roundtrip | Min:0.00716400146484375 Max:0.012288331985473633 Average:0.007741005420684815 -""" -import time - -import parsl - - -@parsl.python_app -def python_app(): - import platform - return "Hello from {0}".format(platform.uname()) - - -@parsl.python_app -def python_app_slow(duration): - import platform - import time - time.sleep(duration) - return "Hello from {0}".format(platform.uname()) - - -@parsl.python_app -def python_noop(): - return - - -@parsl.bash_app -def bash_app(stdout=None, stderr=None): - return 'echo "Hello from $(uname -a)" ; sleep 2' - - -def test_python_remote(count=2): - """ Run with no delay. - """ - fus = [] - for i in range(0, count): - fu = python_app_slow(0) - fus.extend([fu]) - - for fu in fus: - print(fu.result()) - - -def test_python_remote_slow(count=2): - fus = [] - for i in range(0, count): - fu = python_app_slow(count) - fus.extend([fu]) - - for fu in fus: - print(fu.result()) - - -def average(x): - return sum(x) / len(x) - - -def test_python(count): - results = {} - - print("Priming the system") - items = [] - for i in range(0, 100): - items.extend([python_app()]) - print("Launched primer application") - for i in items: - i.result() - print("Primer done") - - latency = [] - rtt = [] - for i in range(0, 100): - pre = time.time() - results[i] = python_noop() - post = time.time() - results[i].result() - final = time.time() - latency.append(final - post) - rtt.append(final - pre) - - min_latency = min(latency) * 1000 - max_latency = max(latency) * 1000 - avg_latency = average(latency) * 1000 - print("Latency | Min:{0:0.3}ms Max:{1:0.3}ms Average:{2:0.3}ms".format(min_latency, - max_latency, - avg_latency)) - - min_rtt = min(rtt) * 1000 - max_rtt = max(rtt) * 1000 - avg_rtt = average(rtt) * 1000 - - print("Roundtrip | Min:{0:0.3}ms Max:{1:0.3}ms Average:{2:0.3}ms".format(min_rtt, - max_rtt, - avg_rtt)) - - -def test_bash(): - import os - fname = os.path.basename(__file__) - - x = bash_app(stdout="{0}.out".format(fname)) - print("Waiting ....") - print(x.result()) diff --git a/parsl/parsl/tests/integration/test_apps/__init__.py b/parsl/parsl/tests/integration/test_apps/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/integration/test_integration/test_ssh/__init__.py b/parsl/parsl/tests/integration/test_integration/test_ssh/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/integration/test_parsl_load_default_config.py b/parsl/parsl/tests/integration/test_parsl_load_default_config.py deleted file mode 100644 index bb9446c4..00000000 --- a/parsl/parsl/tests/integration/test_parsl_load_default_config.py +++ /dev/null @@ -1,23 +0,0 @@ -import parsl -from parsl import python_app - - -@python_app -def cpu_stress(inputs=[], outputs=[]): - s = 0 - for i in range(10**8): - s += i - return s - - -def test_parsl_load_default_config(): - dfk = parsl.load() - a1, b1 = [cpu_stress(), - cpu_stress()] - a1.result() - b1.result() - dfk.cleanup() - - -if __name__ == '__main__': - test_parsl_load_default_config() diff --git a/parsl/parsl/tests/integration/test_providers/ec2/__init__.py b/parsl/parsl/tests/integration/test_providers/ec2/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/integration/test_stress/README.rst b/parsl/parsl/tests/integration/test_stress/README.rst deleted file mode 100644 index fcd4178f..00000000 --- a/parsl/parsl/tests/integration/test_stress/README.rst +++ /dev/null @@ -1,17 +0,0 @@ -Profiling How-To ----------------- - -There's a heavy performance penalty when using conda vs standard python -distributions. So, these tests are preferably done with python installations -that come with the system. - - -To dump the profiling information -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -> python3 -m cProfile -s cumtime -o -c 1000 - -To view the performance data : -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -> pyprof2calltree -k -i diff --git a/parsl/parsl/tests/integration/test_stress/__init__.py b/parsl/parsl/tests/integration/test_stress/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/integration/test_stress/test_python_simple.py b/parsl/parsl/tests/integration/test_stress/test_python_simple.py deleted file mode 100644 index 7d104ba7..00000000 --- a/parsl/parsl/tests/integration/test_stress/test_python_simple.py +++ /dev/null @@ -1,43 +0,0 @@ -import argparse -import time - -import parsl -from parsl import python_app -from parsl.configs.htex_local import config - - -@python_app -def increment(x): - return x + 1 - - -def test_stress(count=1000): - """Threaded app RTT stress test""" - - start = time.time() - x = [] - for i in range(count): - f = increment(i) - x.append(f) - end = time.time() - print("Launched {0} tasks in {1:.2f} s".format(count, end - start)) - - [f.result() for f in x] - end = time.time() - print("Completed {0} tasks in {1:.2f} s".format(count, end - start)) - - -if __name__ == '__main__': - parsl.load(config) - - parser = argparse.ArgumentParser() - parser.add_argument("-c", "--count", type=int, default=1000, - help="width of the pipeline") - parser.add_argument("-d", "--debug", action='store_true', - help="Enable stream logging") - args = parser.parse_args() - - if args.debug: - parsl.set_stream_logger() - - test_stress(count=args.count) diff --git a/parsl/parsl/tests/integration/test_stress/test_python_threads.py b/parsl/parsl/tests/integration/test_stress/test_python_threads.py deleted file mode 100644 index 30f6ff9b..00000000 --- a/parsl/parsl/tests/integration/test_stress/test_python_threads.py +++ /dev/null @@ -1,40 +0,0 @@ -''' Testing bash apps -''' -import argparse -import time - -import parsl -from parsl import python_app -from parsl.tests.configs.local_threads import config - -local_config = config - - -@python_app -def increment(x): - return x + 1 - - -def test_stress(count=1000): - """Threaded app launch stress test""" - start = time.time() - x = {} - for i in range(count): - x[i] = increment(i) - end = time.time() - print("Launched {0} tasks in {1} s".format(count, end - start)) - - -if __name__ == '__main__': - - parser = argparse.ArgumentParser() - parser.add_argument("-c", "--count", default="100", - help="width of the pipeline") - parser.add_argument("-d", "--debug", action='store_true', - help="Count of apps to launch") - args = parser.parse_args() - - if args.debug: - parsl.set_stream_logger() - - test_stress(count=int(args.count)) diff --git a/parsl/parsl/tests/lint-inits.sh b/parsl/parsl/tests/lint-inits.sh deleted file mode 100755 index 8e72b17c..00000000 --- a/parsl/parsl/tests/lint-inits.sh +++ /dev/null @@ -1,18 +0,0 @@ -#!/bin/bash -e - -# Check that each directory with python source in it has a __init__.py -# file in it too. Often code will work in most situations without an -# __init__.py but some corner cases can arise that are awkward to debug; -# so the policy is that every directory must have __init__.py - -find parsl -name \*.py \ - | while read fn; do - dirname $fn - done \ - | sort | uniq \ - | while read dir; do - if [ -f ${dir}/__init__.py ] ; - then true; - else echo FAILURE: $dir does not have an __init__.py ; exit 1; - fi; - done diff --git a/parsl/parsl/tests/manual_tests/__init__.py b/parsl/parsl/tests/manual_tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/manual_tests/htex_local.py b/parsl/parsl/tests/manual_tests/htex_local.py deleted file mode 100644 index e85da878..00000000 --- a/parsl/parsl/tests/manual_tests/htex_local.py +++ /dev/null @@ -1,25 +0,0 @@ -from parsl.config import Config -from parsl.executors import HighThroughputExecutor - -# from parsl.launchers import SimpleLauncher -from parsl.launchers import SingleNodeLauncher -from parsl.providers import LocalProvider - -config = Config( - executors=[ - HighThroughputExecutor( - poll_period=1, - label="htex_local", - # worker_debug=True, - cores_per_worker=1, - encrypted=True, - provider=LocalProvider( - init_blocks=1, - max_blocks=1, - # tasks_per_node=1, # For HighThroughputExecutor, this option should in most cases be 1 - launcher=SingleNodeLauncher(), - ), - ) - ], - strategy='none', -) diff --git a/parsl/parsl/tests/manual_tests/site_testing_matrix.rst b/parsl/parsl/tests/manual_tests/site_testing_matrix.rst deleted file mode 100644 index c5b93ea4..00000000 --- a/parsl/parsl/tests/manual_tests/site_testing_matrix.rst +++ /dev/null @@ -1,54 +0,0 @@ -Site Testing -============ - -Sites that we will test in this exercise: - -* Local IPP -* Midway RCC at UChicago -* Open Science Grid (OSG) -* Swan at Cray -* Cori at NERSC -* Cooley (ALCF) -* Theta (ALCF) -* CC_in2p3 French grid -* AWS EC2 -* Comet (SDSC) - -Test Progress -============= - -+------------------------+------------+------------+------------+------------+ -|Site |Channel |Provider |Launcher |Test Status | -+========================+============+============+============+============+ -| Local |Local |Local |SingleNode | PASSED | -+------------------------+------------+------------+------------+------------+ -| Midway (RCC) |Ssh |Slurm |SingleNode | PASSED | -+------------------------+------------+------------+------------+------------+ -| Midway (RCC) |Ssh |Slurm |Srun | PASSED | -+------------------------+------------+------------+------------+------------+ -| Open Science Grid(OSG) |Ssh |Condor |SingleNode | PASSED | -+------------------------+------------+------------+------------+------------+ -| Swan (Cray) |Ssh |Torque |Aprun | PASSED | -+------------------------+------------+------------+------------+------------+ -| Cori (NERSC) |Ssh |Slurm |SingleNode | PASSED | -+------------------------+------------+------------+------------+------------+ -| Cori (NERSC) |Ssh |Slurm |Srun | PASSED | -+------------------------+------------+------------+------------+------------+ -| Cooley (ALCF) |SshIL |Cobalt |SingleNode | PASSED | -+------------------------+------------+------------+------------+------------+ -| Theta (ALCF) |Local |Cobalt |Aprun | PASSED | -+------------------------+------------+------------+------------+------------+ -| CC_IN2P3 (French grid) |Local |GridEngine |SingleNode | PASSED | -+------------------------+------------+------------+------------+------------+ -| AWS EC2 |None |AWS |SingleNode | PASSED | -+------------------------+------------+------------+------------+------------+ -| AWS EC2 (spot) |None |AWS |SingleNode | PASSED | -+------------------------+------------+------------+------------+------------+ -| Azure |None |Azure |SingleNode | DEFERRED | -+------------------------+------------+------------+------------+------------+ -| Nova/Jetstream |None |Jetstream |SingleNode | DEFERRED | -+------------------------+------------+------------+------------+------------+ -| Google Cloud |None |GoogleCloud |SingleNode | DEFERRED | -+------------------------+------------+------------+------------+------------+ -| Comet SDSC |Ssh |Slurm |Srun | PASSED | -+------------------------+------------+------------+------------+------------+ diff --git a/parsl/parsl/tests/manual_tests/test_basic.py b/parsl/parsl/tests/manual_tests/test_basic.py deleted file mode 100644 index 2a9fe93e..00000000 --- a/parsl/parsl/tests/manual_tests/test_basic.py +++ /dev/null @@ -1,155 +0,0 @@ -import argparse -import time - -import parsl - -# Tested. Confirmed. Local X Local X SingleNodeLauncher -# from parsl.tests.configs.local_ipp import config - -# Tested. Confirmed. ssh X Slurm X SingleNodeLauncher -# from parsl.tests.configs.midway_ipp import config - -# Tested. Confirmed. ssh X Slurm X SingelNodeLauncher -# from parsl.tests.configs.midway_ipp_multicore import config - -# Tested. Confirmed. ssh X Slurm X SrunLauncher -# from parsl.tests.configs.midway_ipp_multinode import config - -# OSG requires python3.5 for testing. This test is inconsitent, -# breaks often depending on where the test lands -# Tested. Confirmed. ssh X Condor X single_node -# from parsl.tests.configs.osg_ipp_multinode import config - -# Tested. Confirmed. ssh X Torque X AprunLauncher -# from parsl.tests.configs.swan_ipp import config - -# Tested. Confirmed. ssh X Torque X AprunLauncher -# from parsl.tests.configs.swan_ipp_multinode import config - -# from parsl.tests.configs.cooley_ssh_il_single_node import config - -# Tested. Confirmed. local X GridEngine X singleNode -# from parsl.tests.configs.cc_in2p3_local_single_node import config - -# from parsl.tests.configs.comet_ipp_multinode import config - -# from parsl.tests.configs.htex_local import config -parsl.set_stream_logger() - -# from htex_midway import config -# from htex_swan import config - - -from parsl.app.app import python_app # , bash_app - - -@python_app -def double(x): - return x * 2 - - -@python_app -def echo(x, string, stdout=None): - print(string) - return x * 5 - - -@python_app -def import_echo(x, string, stdout=None): - import time - time.sleep(0) - print(string) - return x * 5 - - -@python_app -def platform(sleep=10, stdout=None): - import platform - import time - time.sleep(sleep) - return platform.uname() - - -def test_simple(n=2): - start = time.time() - x = double(n) - print("Result : ", x.result()) - assert x.result() == n * \ - 2, "Expected double to return:{0} instead got:{1}".format( - n * 2, x.result()) - print("Duration : {0}s".format(time.time() - start)) - print("[TEST STATUS] test_parallel_for [SUCCESS]") - return True - - -def test_imports(n=2): - start = time.time() - x = import_echo(n, "hello world") - print("Result : ", x.result()) - assert x.result() == n * \ - 5, "Expected double to return:{0} instead got:{1}".format( - n * 2, x.result()) - print("Duration : {0}s".format(time.time() - start)) - print("[TEST STATUS] test_parallel_for [SUCCESS]") - return True - - -def test_platform(n=2): - # sync - x = platform(sleep=0) - print(x.result()) - - d = [] - for i in range(0, n): - x = platform(sleep=5) - d.append(x) - - print(set([i.result()for i in d])) - - return True - - -def test_parallel_for(n=2): - d = {} - start = time.time() - for i in range(0, n): - d[i] = double(i) - # time.sleep(0.01) - - assert len( - d.keys()) == n, "Only {0}/{1} keys in dict".format(len(d.keys()), n) - - [d[i].result() for i in d] - print("Duration : {0}s".format(time.time() - start)) - print("[TEST STATUS] test_parallel_for [SUCCESS]") - return d - - -if __name__ == '__main__': - - parser = argparse.ArgumentParser() - parser.add_argument("-s", "--sitespec", default=None) - parser.add_argument("-c", "--count", default="10", - help="Count of apps to launch") - parser.add_argument("-d", "--debug", action='store_true', - help="Count of apps to launch") - args = parser.parse_args() - - if args.sitespec: - c = None - try: - exec("import parsl; from {} import config".format(args.sitespec)) - parsl.load(c) - except Exception: - print("Failed to load the requested config : ", args.sitespec) - exit(0) - - if args.debug: - parsl.set_stream_logger() - - x = test_simple(int(args.count)) - # x = test_imports() - # x = test_parallel_for() - # x = test_parallel_for(int(args.count)) - # x = test_stdout() - x = test_platform() diff --git a/parsl/parsl/tests/manual_tests/test_log_filter.py b/parsl/parsl/tests/manual_tests/test_log_filter.py deleted file mode 100644 index ff620633..00000000 --- a/parsl/parsl/tests/manual_tests/test_log_filter.py +++ /dev/null @@ -1,79 +0,0 @@ -import argparse -import logging - -import parsl - -parsl.load() - -from parsl import python_app - - -@python_app -def platform(sleep=10, stdout=None): - import platform - import time - time.sleep(sleep) - return platform.uname() - - -class SkipTasksFilter(logging.Filter): - - def __init__(self, avoid_string): - self.avoid = avoid_string - - def filter(self, record): - return self.avoid not in record.getMessage() - - -def test_platform(n=2): - # sync - logger = logging.getLogger("parsl.dataflow.dflow") - skip_tags = ['Task', 'dependencies'] - for skip in skip_tags: - skip_filter = SkipTasksFilter(skip) - logger.addFilter(skip_filter) - - x = platform(sleep=0) - print(x.result()) - - d = [] - for i in range(0, n): - x = platform(sleep=5) - d.append(x) - - print(set([i.result()for i in d])) - - dfk = parsl.dfk() - dfk.cleanup() - - with open("{}/parsl.log".format(dfk.run_dir)) as f: - - for line in f.readlines(): - if any(skip in line for skip in skip_tags): - raise Exception("Logline {} contains a skip tag".format(line)) - return True - - -if __name__ == '__main__': - - parser = argparse.ArgumentParser() - parser.add_argument("-s", "--sitespec", default=None) - parser.add_argument("-c", "--count", default="10", - help="Count of apps to launch") - parser.add_argument("-d", "--debug", action='store_true', - help="Count of apps to launch") - args = parser.parse_args() - - if args.sitespec: - c = None - try: - exec("import parsl; from {} import config".format(args.sitespec)) - parsl.load(c) - except Exception: - print("Failed to load the requested config : ", args.sitespec) - exit(0) - - if args.debug: - parsl.set_stream_logger() - - x = test_platform() diff --git a/parsl/parsl/tests/manual_tests/test_memory_limits.py b/parsl/parsl/tests/manual_tests/test_memory_limits.py deleted file mode 100644 index 2f923a8b..00000000 --- a/parsl/parsl/tests/manual_tests/test_memory_limits.py +++ /dev/null @@ -1,82 +0,0 @@ -import argparse -import multiprocessing - -import psutil - -import parsl -from parsl.app.app import python_app # , bash_app -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SingleNodeLauncher -from parsl.providers import LocalProvider - - -@python_app -def double(x): - return x * 2 - - -def test_simple(mem_per_worker): - - config = Config( - executors=[ - HighThroughputExecutor( - poll_period=1, - label="htex_local", - worker_debug=True, - mem_per_worker=mem_per_worker, - cores_per_worker=0.1, - suppress_failure=True, - encrypted=True, - provider=LocalProvider( - init_blocks=1, - max_blocks=1, - launcher=SingleNodeLauncher(), - ), - ) - ], - strategy='none', - ) - parsl.load(config) - - print("Configuration requests:") - print("cores_per_worker: ", config.executors[0].cores_per_worker) - print("mem_per_worker: ", config.executors[0].mem_per_worker) - - available_mem_on_node = round(psutil.virtual_memory().available / (2**30), 1) - expected_workers = multiprocessing.cpu_count() / config.executors[0].cores_per_worker - if mem_per_worker: - expected_workers = int(available_mem_on_node / config.executors[0].mem_per_worker) - - print("Available memory: ", available_mem_on_node) - print("Expected workers: ", expected_workers) - # Prime a worker - double(5).result() - dfk = parsl.dfk() - connected = dfk.executors['htex_local'].connected_workers() - print("Connected : ", connected) - assert expected_workers == connected, "Expected {} workers, instead got {} workers".format(expected_workers, - connected) - parsl.clear() - return True - - -if __name__ == '__main__': - - parser = argparse.ArgumentParser() - parser.add_argument("-s", "--sleep", default="4") - parser.add_argument("-c", "--count", default="10", - help="Count of apps to launch") - parser.add_argument("-d", "--debug", action='store_true', - help="Count of apps to launch") - args = parser.parse_args() - - if args.debug: - parsl.set_stream_logger() - - available_mem_on_node = round(psutil.virtual_memory().available / (2**30), 1) - - x = test_simple(None) - x = test_simple(0.8) - x = test_simple(1) - x = test_simple(available_mem_on_node - 2) diff --git a/parsl/parsl/tests/manual_tests/test_regression_220.py b/parsl/parsl/tests/manual_tests/test_regression_220.py deleted file mode 100644 index 7221c624..00000000 --- a/parsl/parsl/tests/manual_tests/test_regression_220.py +++ /dev/null @@ -1,32 +0,0 @@ -import time - -import pytest - -from parsl import DataFlowKernel, set_stream_logger -from parsl.tests.configs.local_threads import config - - -@pytest.mark.skip('meant to be tested by hand') -def test_220(): - """Test async usage_tracking behavior for issue #220 """ - - print("This test assumes /etc/resolv being misconfigured") - with open("/etc/resolv.conf", 'r') as f: - for line in f.readlines(): - line = line.strip() - print("Line: [{}]".format(line)) - if line.startswith("nameserver") and line != "nameserver 2.2.2.2": - assert False, "/etc/resolv.conf should be misconfigured" - - start = time.time() - set_stream_logger() - dfk = DataFlowKernel(config=config) - delta = time.time() - start - print("Time taken : ", delta) - assert delta < 1, "DFK took too much time to start, delta:{}".format(delta) - dfk.cleanup() - - -if __name__ == "__main__": - - test_220() diff --git a/parsl/parsl/tests/manual_tests/test_worker_count.py b/parsl/parsl/tests/manual_tests/test_worker_count.py deleted file mode 100644 index 4ff32cc9..00000000 --- a/parsl/parsl/tests/manual_tests/test_worker_count.py +++ /dev/null @@ -1,75 +0,0 @@ -import argparse -import math -import multiprocessing -import time - -import parsl - -CORES = multiprocessing.cpu_count() -CORES_PER_WORKER = 1 -EXPECTED_WORKERS = math.floor(CORES / CORES_PER_WORKER) - - -from parsl.executors import HighThroughputExecutor - -# from parsl.tests.configs.htex_local import config -from parsl.tests.manual_tests.htex_local import config - -assert isinstance(config.executors[0], HighThroughputExecutor) -assert config.executors[0].provider is not None, "htex always has a provider" -config.executors[0].cores_per_worker = CORES_PER_WORKER -config.executors[0].provider.init_blocks = 1 - -# from htex_midway import config -# from htex_swan import config - -local_config = config - -from parsl.app.app import python_app # , bash_app - - -@python_app -def slow_pid(sleep=1): - import os - import time - time.sleep(sleep) - return os.getppid(), os.getpid() - - -def test_worker(n=2, sleep=0): - d = {} - start = time.time() - for i in range(0, n): - d[i] = slow_pid(sleep=sleep) - - foo = [d[i].result() for i in d] - manager_ids = set([f[0] for f in foo]) - worker_ids = set([f[1] for f in foo]) - - print("Got workers : {}".format(worker_ids)) - assert len(manager_ids) == 1, "Expected only 1 manager id, got ids : {}".format( - manager_ids) - assert len(worker_ids) == EXPECTED_WORKERS, "Expected {} workers, instead got {}".format(EXPECTED_WORKERS, - len(worker_ids)) - - print("Duration : {0}s".format(time.time() - start)) - print("[TEST STATUS] test_parallel_for [SUCCESS]") - return d - - -if __name__ == '__main__': - - parser = argparse.ArgumentParser() - parser.add_argument("-s", "--sleep", default="0") - parser.add_argument("-c", "--count", default="10", - help="Count of apps to launch") - parser.add_argument("-d", "--debug", action='store_true', - help="Count of apps to launch") - args = parser.parse_args() - - if args.debug: - parsl.set_stream_logger() - - parsl.load(local_config) - - x = test_worker(n=int(args.count), sleep=float(args.sleep)) diff --git a/parsl/parsl/tests/pytest.ini b/parsl/parsl/tests/pytest.ini deleted file mode 100644 index 28856c87..00000000 --- a/parsl/parsl/tests/pytest.ini +++ /dev/null @@ -1,2 +0,0 @@ -[pytest] -addopts = -x -rs diff --git a/parsl/parsl/tests/setup_path.sh b/parsl/parsl/tests/setup_path.sh deleted file mode 100755 index 854bd896..00000000 --- a/parsl/parsl/tests/setup_path.sh +++ /dev/null @@ -1,3 +0,0 @@ -export PATH=$PWD:$PATH -PARSL=$PWD/../../ -export PYTHONPATH=$PARSL:$PYTHONPATH diff --git a/parsl/parsl/tests/site_tests/README.rst b/parsl/parsl/tests/site_tests/README.rst deleted file mode 100644 index 85420c3b..00000000 --- a/parsl/parsl/tests/site_tests/README.rst +++ /dev/null @@ -1,73 +0,0 @@ -Site Testing -============ - -This doc will cover two items: - -1. Running the site tests on a site -2. How to prep a new site for site testing - - -The site tests are setup with the assumption that there is a conda installation -available either through modules available on the system, or from a user's personal -installation. - -* `conda_setup.sh` will create a unique conda env mapped to `parsl/parsl_` and - leave a script `~/setup_parsl_test_env.sh` that will activate this env. - -* `site_config_selector.py` is a facade that checks the submit node's hostname and picks a matching - configuration from `parsl/parsl/tests/configs`. - -* The tests in this folder are designed to use the site_config_selector and runs test against - that config. - - -Running tests -------------- - -Assuming the site tests are supported on your site, here's how you run tests:: - - 1. Checkout parsl into a *shared filesystem* with the right git tag/hash for testing - * If you are running tests on a system where the $HOME is not mounted on compute nodes - checkout to $WORK / $SCRATCH or the equivalent. For eg, Comet and Stampede2 - 2. `cd parsl/tests/site_tests` - 3. `bash conda_setup.sh` - 4. Update parsl/parsl/tests/configs/local_user_opts.py with user specific options. - 5. Now check if the setup script is available at `~/setup_parsl_test_env.sh` - 6. Go back to the parsl root dir - 7. Reinstall parsl, if you've made config changes in step 3 or 4. - >>> pip install . - 8. Run tests with `make site_test` - - -Adding a new site ------------------ - -* We want to pick a python environment that a user is most likely to use on the site. - 1. Specialized python builds for the system (for eg, Summit) - 2. Anaconda available via modules - 3. User's conda installation -* Add a new block to ``conda_setup.sh`` that installs a fresh environment and writes out - the activation commands to ``~/setup_parsl_test_env.sh`` -* Add a site config to ``parsl/tests/configs/`` and add your local user options - to ``parsl/tests/configs/local_user_opts.py``. For example, - `here's mine`_ - Make sure that the site config uses the ``fresh_config`` pattern. - Please ensure that the site config uses: - * ``max_workers_per_node = 1`` - * ``init_blocks = 1`` - * ``min_blocks = 0`` - -* Add this site config to ``parsl/tests/site_tests/site_config_selector.py`` -* Reinstall parsl, using ``pip install .`` -* Test a single test: ``python3 test_site.py -d`` to confirm that the site works correctly. -* Once tests are passing run the whole site_test with ``make site_test`` - - -Shared filesystem option ------------------------- - -There is a new env variable ``SHARED_FS_OPTIONS`` to pass markers to pytest to skip certain tests. - -When there's a shared-FS, the default NoOpStaging works. However, when there's no shared-FS some tests -that uses File objects require a staging provider (eg. rsync). These tests can be turned off with -``-k "not staging_required"`` diff --git a/parsl/parsl/tests/site_tests/__init__.py b/parsl/parsl/tests/site_tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/site_tests/conda_setup.sh b/parsl/parsl/tests/site_tests/conda_setup.sh deleted file mode 100644 index 644bda72..00000000 --- a/parsl/parsl/tests/site_tests/conda_setup.sh +++ /dev/null @@ -1,186 +0,0 @@ -#!/bin/bash - -SCRIPTPATH="$( cd "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P )" -echo $SCRIPTPATH -PARSL_ROOT=$(dirname $(dirname $(dirname $SCRIPTPATH))) -PARSL_GITHASH=$(git rev-parse --short HEAD) - -CONDA_TARGET=parsl_$PARSL_GITHASH.py3.7 -export CONDA_TARGET - -if [[ "$CONDA_TARGET" == "$CONDA_DEFAULT_ENV" ]] -then - echo "Conda target env $CONDA_TARGET loaded" - exit 0 -fi - - -create_conda() { - pushd . - cd $PARSL_ROOT - - if [[ "$(hostname)" =~ .*thetalogin.* ]] - then - echo "On theta" - module load miniconda-3/latest - conda create -p $CONDA_TARGET --clone $CONDA_PREFIX --yes --force - conda activate $CONDA_TARGET - # Theta is weird, we do explicit install - pip install -r test-requirements.txt - conda install pip psutil --yes - python3 setup.py install - echo "module load miniconda-3/latest;" > ~/setup_parsl_test_env.sh - echo "conda activate $PWD/$CONDA_TARGET" >> ~/setup_parsl_test_env.sh - return - - elif [[ "$(hostname)" =~ .*frontera.* ]] - then - echo "On Frontera" - if [[ -d ~/anaconda3 ]] - then - echo "Loading anaconda3 from ~/anaconda3" - source ~/anaconda3/bin/activate - else - echo "Please install conda to your home dir at ~/anaconda3" - fi - conda create -p $CONDA_TARGET python=3.7 --yes --force - conda activate $CONDA_TARGET - echo "source ~/anaconda3/bin/activate;" > ~/setup_parsl_test_env.sh - echo "conda activate $PWD/$CONDA_TARGET" >> ~/setup_parsl_test_env.sh - - elif [[ "$(hostname)" =~ .*nscc.* ]] - then - echo "On NSCC Singapore" - if [[ -d ~/anaconda3 ]] - then - echo "Loading anaconda3 from ~/anaconda3" - source ~/anaconda3/bin/activate - else - echo "Please install conda to your home dir at ~/anaconda3" - fi - conda create -p $CONDA_TARGET python=3.7 --yes --force - conda activate $CONDA_TARGET/ - echo "source ~/anaconda3/bin/activate;" > ~/setup_parsl_test_env.sh - echo "conda activate $PWD/$CONDA_TARGET/" >> ~/setup_parsl_test_env.sh - echo "module load openmpi/intel/1.10.2;" >> ~/setup_parsl_test_env.sh - - elif [[ "$(hostname -f)" =~ .*summit.* ]] - then - echo "On Summit" - module load ibm-wml-ce - conda create -p $CONDA_TARGET --yes --force - conda activate $PWD/$CONDA_TARGET - conda install paramiko>=2.7.1 pip numpy psutil pandas --yes - # conda install --file requirements.txt --yes - echo "module load ibm-wml-ce" > ~/setup_parsl_test_env.sh - echo "conda activate $PWD/$CONDA_TARGET" >> ~/setup_parsl_test_env.sh - - elif [[ "$(hostname)" =~ .*stampede2.* ]] - then - echo "On Stampede2" - if [[ -d ~/anaconda3 ]] - then - echo "Loading anaconda3 from ~/anaconda3" - source ~/anaconda3/bin/activate - else - echo "Please install conda to your home dir at ~/anaconda3" - fi - conda create -p $CONDA_TARGET python=3.7 --yes --force - conda activate $PWD/$CONDA_TARGET - echo "source ~/anaconda3/bin/activate;" > ~/setup_parsl_test_env.sh - echo "conda activate $PWD/$CONDA_TARGET" >> ~/setup_parsl_test_env.sh - - elif [[ "$(hostname)" =~ .*comet.* ]] - then - echo "On Comet" - if [[ -d ~/anaconda3 ]] - then - echo "Loading anaconda3 from ~/anaconda3" - source ~/anaconda3/bin/activate - else - echo "Please install conda to your home dir at ~/anaconda3" - fi - conda create -p $CONDA_TARGET python=3.7 --yes --force - conda activate $PWD/$CONDA_TARGET - echo "source ~/anaconda3/bin/activate;" > ~/setup_parsl_test_env.sh - echo "conda activate $PWD/$CONDA_TARGET" >> ~/setup_parsl_test_env.sh - - elif [[ "$(hostname)" =~ .*midway.* ]] - then - echo "On Midway" - module unload python - module load Anaconda3/5.3.0 - conda create -p $CONDA_TARGET python=3.7 --yes --force - source /software/Anaconda3-5.3.0-el7-x86_64/bin/activate - conda activate $PWD/$CONDA_TARGET - echo "module unload python" > ~/setup_parsl_test_env.sh - echo "module load Anaconda3/5.3.0" >> ~/setup_parsl_test_env.sh - echo "source /software/Anaconda3-5.3.0-el7-x86_64/bin/activate" >> ~/setup_parsl_test_env.sh - echo "conda activate $PWD/$CONDA_TARGET" >> ~/setup_parsl_test_env.sh - - elif [[ "$(hostname -f)" =~ .*in2p3.* ]] - then - echo "On CC-IN2P3" - export PATH=/pbs/throng/lsst/software/anaconda/anaconda3-5.0.1/bin:$PATH - conda create -p $CONDA_TARGET python=3.7 --yes --force - source activate $PWD/$CONDA_TARGET - conda install pip --yes - cat <<'EOF' > ~/setup_parsl_test_env.sh -export PATH=/pbs/throng/lsst/software/anaconda/anaconda3-5.0.1/bin:$PATH -export SHARED_FS_OPTIONS='-k "not issue363"' -EOF - echo "source activate $PWD/$CONDA_TARGET" >> ~/setup_parsl_test_env.sh - - elif [[ "$(hostname)" =~ .*cooley.* ]] - then - echo "On Cooley, testing Kubernetes on PetrelKube" - if [[ -d ~/anaconda3 ]] - then - echo "Loading anaconda3 from ~/anaconda3" - source ~/anaconda3/bin/activate - else - echo "Please install conda to your home dir at ~/anaconda3" - fi - conda create -p $CONDA_TARGET python=3.7 --yes --force - conda activate $PWD/$CONDA_TARGET - pip install kubernetes - echo "pip install git+https://github.com/Parsl/parsl@$PARSL_GITHASH" > ~/setup_parsl_test_env.sh - echo 'pip install pytest==4.6' >> ~/setup_parsl_test_env.sh - - elif [[ "$(hostname -f)" =~ .*h2ologin.* ]] - then - echo "On Blue Waters" - module load bwpy - python_version=3.6 - export EPYTHON="python$python_version" - mkdir -p parsl_$PARSL_GITHASH.py$python_version - virtualenv --system-site-packages $PWD/parsl_$PARSL_GITHASH.py$python_version - source $PWD/parsl_$PARSL_GITHASH.py$python_version/bin/activate - pip install --upgrade pip - echo "module load bwpy;" > ~/setup_parsl_test_env.sh - echo "cd $PWD;" >> ~/setup_parsl_test_env.sh - echo "source $PWD/parsl_$PARSL_GITHASH.py$python_version/bin/activate;" >> ~/setup_parsl_test_env.sh - - elif [[ "$(hostname -f)" =~ .*bridges.* ]] - then - echo "On bridges" - module load anaconda3 - conda create -p $CONDA_TARGET python=3.7 --yes --force - source activate $PWD/$CONDA_TARGET - echo "module load anaconda3" > ~/setup_parsl_test_env.sh - echo "source activate $PWD/$CONDA_TARGET" >> ~/setup_parsl_test_env.sh - - else - echo "Unknown site" - exit -1 - fi - - echo "Installing parsl from $PARSL_ROOT" - python3 -m pip install . - python3 -m pip install -r test-requirements.txt - - popd - -} - -create_conda diff --git a/parsl/parsl/tests/site_tests/site_config_selector.py b/parsl/parsl/tests/site_tests/site_config_selector.py deleted file mode 100644 index 921df197..00000000 --- a/parsl/parsl/tests/site_tests/site_config_selector.py +++ /dev/null @@ -1,58 +0,0 @@ -import os -import platform -import socket - - -def fresh_config(): - hostname = os.getenv('PARSL_HOSTNAME', platform.uname().node) - print("Loading config for {}".format(hostname)) - - if 'frontera' in hostname: - print("Loading Frontera config") - from parsl.tests.configs.frontera import fresh_config - config = fresh_config() - - elif 'nscc' in hostname: - print("Loading NSCC Singapore config") - from parsl.tests.configs.nscc_singapore import fresh_config - config = fresh_config() - - elif 'summit' in socket.getfqdn(): - print("Loading Frontera config") - from parsl.tests.configs.summit import fresh_config - config = fresh_config() - - elif 'comet' in hostname: - print("Loading Comet config") - from parsl.tests.configs.comet import fresh_config - config = fresh_config() - - elif 'midway' in hostname: - print("Loading Midway config") - from parsl.tests.configs.midway import fresh_config - config = fresh_config() - - elif 'h2ologin' in hostname: - print("Loading Blue Waters config") - from parsl.tests.configs.bluewaters import fresh_config - config = fresh_config() - - elif 'in2p3' in socket.getfqdn(): - print("Loading CC-IN2P3 config") - from parsl.tests.configs.cc_in2p3 import fresh_config - config = fresh_config() - - elif 'bridges' in socket.getfqdn(): - print("Loading bridges config") - from parsl.tests.configs.bridges import fresh_config - config = fresh_config() - - elif 'cooley' in hostname: - print("on Cooley, loading PetrelKube config") - from parsl.tests.configs.petrelkube import fresh_config - config = fresh_config() - - else: - raise RuntimeError("This site cannot be identified") - - return config diff --git a/parsl/parsl/tests/sites/CC-IN2P3.rst b/parsl/parsl/tests/sites/CC-IN2P3.rst deleted file mode 100644 index 5ff3f045..00000000 --- a/parsl/parsl/tests/sites/CC-IN2P3.rst +++ /dev/null @@ -1,57 +0,0 @@ -Environment setup on CC-IN2P3 -============================= - -For the DESC folks using CC-IN2P3 resources under the LSST allocation, there's an anaconda installation -on the shared project space that can be used. If you are not under this allocation, please follow -instructions to install anaconda to your $HOME directory from `here `_. - -For DESC users : - -.. code-block:: bash - - # Add this to your $HOME/.bashrc file - export PATH=/pbs/throng/lsst/software/anaconda/anaconda3-5.0.1/bin:$PATH - - # Create your conda env for Parsl, this will by default install to your $HOME/.conda directory - conda create --name parsl_env_3.6 python=3.6 - - # Activate the env - source activate parsl_env_3.6 - - # Install Parsl - pip install parsl - -Running tests -============= - -In order to run tests make sure you have Parsl and the test requirements installed. - -.. code-block:: bash - - # Clone parsl - git clone https://github.com/Parsl/parsl.git - cd parsl - - # Activate the env from the previous section - source activate parsl_env_3.6 - - # Install the test-requirements - python3 -m pip install -r test-requirements.txt - - # Run the CC-IN2P3 tests - cd parsl/tests/test_sites/test_CC-IN2P3/ - nosetests . - -Important Notes -=============== - -The GridEngine is designed for the Grid, a loose collection with cores being -the compute unit which is quite different from HPC systems and Clouds. -As a result resource requests are made in terms of cores, and multinode -mpi-launch capabilities with "mpirun/gerun". - -Please refer `here`_ for -the config option supported by gridEngine. - - - diff --git a/parsl/parsl/tests/sites/__init__.py b/parsl/parsl/tests/sites/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/sites/swan.rst b/parsl/parsl/tests/sites/swan.rst deleted file mode 100644 index c0149949..00000000 --- a/parsl/parsl/tests/sites/swan.rst +++ /dev/null @@ -1,12 +0,0 @@ -Swan notes -========== - -Environment -=========== -Swan has a module cray-python/3.6.1.1 which can be used readily to setup and virtual -env:: - - module load cray-python/3.6.1.1 - python3 -m venv parsl_env - source parsl_env/bin/activate - pip install parsl diff --git a/parsl/parsl/tests/sites/test_concurrent.py b/parsl/parsl/tests/sites/test_concurrent.py deleted file mode 100644 index ee91a58d..00000000 --- a/parsl/parsl/tests/sites/test_concurrent.py +++ /dev/null @@ -1,96 +0,0 @@ -"""Tests of the interfaces to Python's concurrent library""" -from pytest import mark, raises, warns - -from parsl import Config, HighThroughputExecutor, load, python_app -from parsl.concurrent import ParslPoolExecutor - - -def f(x): - return x + 1 - - -def g(x): - return 2 * x - - -@python_app -def is_odd(x): - if x % 2 == 1: - return 1 - else: - return 0 - - -def make_config(): - return Config( - executors=[ - HighThroughputExecutor( - label='test_executor', - address="127.0.0.1", - max_workers_per_node=2, - heartbeat_period=2, - heartbeat_threshold=4, - encrypted=False, - ) - ], - strategy='none', - ) - - -@mark.local -def test_init_errors(): - with load(make_config()) as dfk, raises(ValueError, match='Specify only one of config or dfk'): - ParslPoolExecutor(config=make_config(), dfk=dfk) - - with raises(ValueError, match='Must specify one of config or dfk'): - ParslPoolExecutor() - - -@mark.local -def test_executor(): - my_config = make_config() - - with ParslPoolExecutor(my_config) as exc: - # Test a single submit - future = exc.submit(f, 1) - assert future.result() == 2 - - # Make sure the map works - results = list(exc.map(f, [1, 2, 3])) - assert results == [2, 3, 4] - - # Make sure map works with a timeout - results = list(exc.map(f, [1, 2, 3], timeout=5)) - assert results == [2, 3, 4] - - # Make sure only one function was registered - assert exc.app_count == 1 - - with raises(RuntimeError, match='shut down'): - exc.submit(f, 1) - - with warns(UserWarning): - ParslPoolExecutor(make_config()).shutdown(False, cancel_futures=True) - - -@mark.local -def test_with_dfk(): - config = make_config() - - with load(config) as dfk, ParslPoolExecutor(dfk=dfk, executors=['test_executor']) as exc: - future = exc.submit(f, 1) - assert future.result() == 2 - assert exc.get_app(f).executors == ['test_executor'] - - -@mark.local -def test_chaining(): - """Make sure the executor functions can be chained together""" - config = make_config() - - with ParslPoolExecutor(config) as exc: - future_odd = exc.submit(f, 10) - assert is_odd(future_odd).result() - - future_even = exc.submit(g, future_odd) - assert not is_odd(future_even).result() diff --git a/parsl/parsl/tests/sites/test_dynamic_executor.py b/parsl/parsl/tests/sites/test_dynamic_executor.py deleted file mode 100644 index f1e44f78..00000000 --- a/parsl/parsl/tests/sites/test_dynamic_executor.py +++ /dev/null @@ -1,98 +0,0 @@ -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.executors import HighThroughputExecutor -from parsl.executors.threads import ThreadPoolExecutor -from parsl.providers import LocalProvider - - -@python_app(executors=['threads']) -def sleeper(dur=0): - import time - time.sleep(dur) - - -@python_app(executors=['threads2']) -def cpu_stress(dur=0.01): - import time - s = 0 - start = time.time() - for i in range(10**8): - s += i - if time.time() - start >= dur: - break - return s - - -@python_app(executors=['htex_local']) -def add(dur=0.01): - import time - s = 0 - start = time.time() - for i in range(10**8): - s += i - if time.time() - start >= dur: - break - return s - - -@pytest.mark.local -def test_dynamic_executor(caplog): - dfk = parsl.load() - - expected_executors = {('threads', 'parsl.executors.threads.ThreadPoolExecutor')} - - def check_expected_logs(): - seen_executors = {(r.__dict__['parsl.executor.label'], - r.__dict__['parsl.executor.type']) - for r in caplog.records - if 'parsl.executor.label' in r.__dict__ and - 'parsl.executor.type' in r.__dict__} - assert expected_executors <= seen_executors - - check_expected_logs() - - tasks = [sleeper() for i in range(5)] - results = [i.result() for i in tasks] - print("Done with initial test. The results are", results) - - # Here we add a new executor to an active DFK - thread_executors = [ThreadPoolExecutor( - label='threads2', - max_threads=4) - ] - dfk.add_executors(executors=thread_executors) - - expected_executors = {('threads2', 'parsl.executors.threads.ThreadPoolExecutor')} - check_expected_logs() - - tasks = [cpu_stress() for i in range(8)] - results = [i.result() for i in tasks] - print("Successfully added thread executor and ran with it. The results are", results) - - # We add a htex executor to an active DFK - executors = [ - HighThroughputExecutor( - label='htex_local', - cores_per_worker=1, - max_workers_per_node=5, - encrypted=True, - provider=LocalProvider( - init_blocks=1, - max_blocks=1, - ), - ) - ] - dfk.add_executors(executors=executors) - - expected_executors.add(('htex_local', 'parsl.executors.high_throughput.executor.HighThroughputExecutor')) - check_expected_logs() - - tasks = [add() for i in range(10)] - results = [i.result() for i in tasks] - print("Successfully added htex executor and ran with it. The results are", results) - - print("Done testing") - - dfk.cleanup() diff --git a/parsl/parsl/tests/sites/test_ec2.py b/parsl/parsl/tests/sites/test_ec2.py deleted file mode 100644 index 7cb089a0..00000000 --- a/parsl/parsl/tests/sites/test_ec2.py +++ /dev/null @@ -1,79 +0,0 @@ -import logging - -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.tests.configs.ec2_single_node import config - -logger = logging.getLogger(__name__) - -local_config = config - - -@python_app(executors=['ec2_single_node']) -def python_app_2(): - import os - import threading - import time - time.sleep(1) - return "Hello from PID[{}] TID[{}]".format(os.getpid(), threading.current_thread()) - - -@python_app(executors=['ec2_single_node']) -def python_app_1(): - import os - import threading - import time - time.sleep(1) - return "Hello from PID[{}] TID[{}]".format(os.getpid(), threading.current_thread()) - - -@parsl.bash_app -def bash_app(stdout=None, stderr=None): - return 'echo "Hello from $(uname -a)" ; sleep 2' - - -@pytest.mark.local -def test_python(N=2): - """Testing basic python functionality.""" - - r1 = {} - r2 = {} - for i in range(0, N): - r1[i] = python_app_1() - r2[i] = python_app_2() - print("Waiting ....") - - for x in r1: - print("python_app_1 : ", r1[x].result()) - for x in r2: - print("python_app_2 : ", r2[x].result()) - - return - - -@pytest.mark.local -def test_bash(): - """Testing basic bash functionality.""" - - import os - fname = os.path.basename(__file__) - - x = bash_app(stdout="{0}.out".format(fname)) - print("Waiting ....") - print(x.result()) - - -if __name__ == "__main__": - parsl.set_stream_logger() - - # this config is imported inside the main block for the same - # reason that it has local standup/teardown code at the top - # of this file. For that same reason, set_stream_logger - # is called before this import. - - from parsl.tests.configs.ec2_single_node import config - parsl.load(config) - test_python() - test_bash() diff --git a/parsl/parsl/tests/sites/test_mpi/__init__.py b/parsl/parsl/tests/sites/test_mpi/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/sites/test_mpi/mpi_hello b/parsl/parsl/tests/sites/test_mpi/mpi_hello deleted file mode 120000 index 3c9ece2f..00000000 --- a/parsl/parsl/tests/sites/test_mpi/mpi_hello +++ /dev/null @@ -1 +0,0 @@ -mpi_test/mpi_hello \ No newline at end of file diff --git a/parsl/parsl/tests/sites/test_mpi/mpi_test/Makefile b/parsl/parsl/tests/sites/test_mpi/mpi_test/Makefile deleted file mode 100644 index 344f60d7..00000000 --- a/parsl/parsl/tests/sites/test_mpi/mpi_test/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -CC=mpicc - -CFLAGS = -Wall -g -lm -fopenmp -DDEBUG - -SRC = $(wildcard *.c) - -DEPS = $(wildcard *.h) - -OBJS = $(SRC:.c=.o) - -EXEC=mpi_hello - -%.o: %.c $(DEPS) - $(CC) -c -o $@ $< $(CFLAGS) - -$(EXEC) : $(OBJS) - $(CC) -o $@ $^ $(CFLAGS) - -clean: - rm -f $(OBJS) $(EXEC) *~ *out *err diff --git a/parsl/parsl/tests/sites/test_mpi/mpi_test/README.md b/parsl/parsl/tests/sites/test_mpi/mpi_test/README.md deleted file mode 100644 index 71f64711..00000000 --- a/parsl/parsl/tests/sites/test_mpi/mpi_test/README.md +++ /dev/null @@ -1,26 +0,0 @@ -Running the MPI Test -==================== - -This simple MPI test is designed to give you some very basic information: - 1. What are the ranks running on ? - 2. Args passed by a launch mechanism printed as argv[1], and argv[2] - - -Compile -======= - -1. Load the appropriate MPI modules for your system -2. Compile the code with make: - - make clean; make - -Running the app -=============== - -Make sure the right MPI modules is loaded. Run the app as a simple executable: - - ./mpi_hello - -Or launch it with N ranks with an mpi launcher like mpirun: - - mpirun -n 8 mpi_hello \ No newline at end of file diff --git a/parsl/parsl/tests/sites/test_mpi/mpi_test/debug.h b/parsl/parsl/tests/sites/test_mpi/mpi_test/debug.h deleted file mode 100644 index d6be5538..00000000 --- a/parsl/parsl/tests/sites/test_mpi/mpi_test/debug.h +++ /dev/null @@ -1,14 +0,0 @@ -#ifndef DEBUG_H -#define DEBUG_H - -extern int GLOB_DEBUG; - -#ifdef DEBUG - #define debug(format, ...) if (GLOB_DEBUG == 1) fprintf(stdout, format , __VA_ARGS__) -#else - #define debug(format, ...) -#endif - -#define error(format, ...) fprintf(stderr, format , __VA_ARGS__) - -#endif diff --git a/parsl/parsl/tests/sites/test_mpi/mpi_test/mpi_hello.c b/parsl/parsl/tests/sites/test_mpi/mpi_test/mpi_hello.c deleted file mode 100644 index 815f0bb8..00000000 --- a/parsl/parsl/tests/sites/test_mpi/mpi_test/mpi_hello.c +++ /dev/null @@ -1,43 +0,0 @@ -#include -#include -#include -#include -#include - - -void usage (void) -{ - fprintf(stderr, - "Usage : ./mpi_hello [options]" - "-d Debug" - ); -} - -int main(int argc, char *argv[]) -{ - int repeat = 1; - char ch; - int myrank; - int nprocs; - int ticks; - int type = 1; - char processor_name[200]; - int namelen; - int bs = 4; // Default block size - int stat; - MPI_Init(&argc, &argv); - - stat = MPI_Comm_size(MPI_COMM_WORLD, &nprocs); - if ( stat != 0 ) error ("MPI_Comm_size returned an error code : %d", stat); - - stat = MPI_Comm_rank(MPI_COMM_WORLD, &myrank); - if ( stat != 0 ) error ("MPI_Comm_rank returned an error code : %d", stat); - - MPI_Get_processor_name(processor_name, &namelen); - - fprintf(stderr, "[Rank:%d] Processor : %s\n", myrank, processor_name); - fprintf(stdout, "[Rank:%d] %s %s\n", myrank, argv[1], argv[2]); - MPI_Finalize(); - - return 0; -} diff --git a/parsl/parsl/tests/sites/test_mpi/setup.sh b/parsl/parsl/tests/sites/test_mpi/setup.sh deleted file mode 100644 index e165ae45..00000000 --- a/parsl/parsl/tests/sites/test_mpi/setup.sh +++ /dev/null @@ -1,6 +0,0 @@ -module load mvapich2 - -cd mpi_test -make clean ; make -cd .. -ln -s mpi_test/mpi_hello . diff --git a/parsl/parsl/tests/slurm-entrypoint.sh b/parsl/parsl/tests/slurm-entrypoint.sh deleted file mode 100755 index 418db841..00000000 --- a/parsl/parsl/tests/slurm-entrypoint.sh +++ /dev/null @@ -1,16 +0,0 @@ -#!/bin/bash -set -e - -echo "---> Starting the MUNGE Authentication service (munged) ..." -gosu munge /usr/sbin/munged - -echo "---> Starting the slurmctld ..." -exec gosu slurm /usr/sbin/slurmctld -i -Dvvv & - -echo "---> Waiting for slurmctld to become active before starting slurmd..." - -echo "---> Starting the Slurm Node Daemon (slurmd) ..." -exec /usr/sbin/slurmd -Dvvv & - -echo "---> Running user command '${@}'" -exec "$@" diff --git a/parsl/parsl/tests/test-viz.sh b/parsl/parsl/tests/test-viz.sh deleted file mode 100755 index 0fdccac6..00000000 --- a/parsl/parsl/tests/test-viz.sh +++ /dev/null @@ -1,24 +0,0 @@ -#!/bin/bash -ex - -# pass any argument to make this script generate a fresh monitoring database -# using pytest; otherwise, by default, it will test against an existing -# monitoring.db - -killall --wait parsl-visualize || echo No previous parsl-visualize to kill - -if [ -n "$1" ]; then - rm -f runinfo/monitoring.db - pytest parsl/tests/ -k "not cleannet" --config parsl/tests/configs/htex_local_alternate.py --cov=parsl --cov-append --cov-report= --random-order -fi - -parsl-visualize --debug & - -mkdir -p test-parsl-visualize.tmp -cd test-parsl-visualize.tmp - -# now wait for this to become responsive to connections -wget http://127.0.0.1:8080/ --retry-connrefused --tries 30 --waitretry=1 -# wget will return a failure code if any of the requested URLs don't return an HTTP 200 result -wget http://127.0.0.1:8080/ --recursive --no-verbose --page-requisites --level=inf -e robots=off -killall --wait parsl-visualize - diff --git a/parsl/parsl/tests/test_bash_apps/__init__.py b/parsl/parsl/tests/test_bash_apps/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_bash_apps/test_apptimeout.py b/parsl/parsl/tests/test_bash_apps/test_apptimeout.py deleted file mode 100644 index 1c8eebaf..00000000 --- a/parsl/parsl/tests/test_bash_apps/test_apptimeout.py +++ /dev/null @@ -1,24 +0,0 @@ -import pytest - -import parsl -from parsl.app.app import bash_app -from parsl.app.errors import AppTimeout -from parsl.tests.configs.local_threads import config - - -@bash_app -def echo_to_file(inputs=(), outputs=(), walltime=0.01): - return """echo "sleeping"; sleep 0.05""" - - -def test_walltime(): - """Testing walltime exceeded exception """ - x = echo_to_file() - with pytest.raises(AppTimeout): - x.result() - - -def test_walltime_longer(): - """Test that an app that runs in less than walltime will succeed.""" - y = echo_to_file(walltime=0.2) - y.result() diff --git a/parsl/parsl/tests/test_bash_apps/test_basic.py b/parsl/parsl/tests/test_bash_apps/test_basic.py deleted file mode 100644 index 0eea6d4d..00000000 --- a/parsl/parsl/tests/test_bash_apps/test_basic.py +++ /dev/null @@ -1,90 +0,0 @@ -import logging -import os -import random -import re - -import pytest - -import parsl -from parsl import File -from parsl.app.app import bash_app - - -@bash_app -def echo_to_file(inputs=(), outputs=(), stderr=None, stdout=None): - res = "" - for o in outputs: - for i in inputs: - res += "echo {} >& {}".format(i, o) - return res - - -@bash_app -def foo(x, y, z=10, stdout=None, label=None): - return f"echo {x} {y} {z}" - - -@pytest.mark.shared_fs -def test_command_format_1(tmpd_cwd): - """Testing command format for BashApps""" - - outdir = tmpd_cwd / "outputs" - outdir.mkdir() - stdout = outdir / "foo-std.out" - - foo_future = foo(1, 4, stdout=str(stdout)) - assert foo_future.result() == 0, "BashApp had non-zero exit code" - - so_content = stdout.read_text().strip() - assert so_content == "1 4 10" - - -@pytest.mark.shared_fs -def test_auto_log_filename_format(caplog): - """Testing auto log filename format for BashApps - """ - app_label = "label_test_auto_log_filename_format" - rand_int = random.randint(1000, 1000000000) - - foo_future = foo(1, rand_int, stdout=parsl.AUTO_LOGNAME, label=app_label) - - assert foo_future.result() == 0, "BashApp exited with an error code : {0}".format( - foo_future.result()) - - log_fpath = foo_future.stdout - assert isinstance(log_fpath, str) - - log_pattern = fr".*/task_\d+_foo_{app_label}" - assert re.match(log_pattern, log_fpath), 'Output file "{0}" does not match pattern "{1}"'.format( - log_fpath, log_pattern) - assert os.path.exists(log_fpath), 'Output file does not exist "{0}"'.format(log_fpath) - with open(log_fpath, 'r') as stdout_f: - contents = stdout_f.read() - - assert contents == '1 {0} 10\n'.format(rand_int), \ - 'Output does not match expected string "1 {0} 10", Got: "{1}"'.format(rand_int, contents) - - for record in caplog.records: - assert record.levelno < logging.ERROR - - -@pytest.mark.shared_fs -def test_parallel_for(tmpd_cwd, n=3): - """Testing a simple parallel for loop""" - outdir = tmpd_cwd / "outputs/test_parallel" - outdir.mkdir(parents=True) - futs = [ - echo_to_file( - inputs=[f"Hello World {i}"], - outputs=[File(str(outdir / f"out.{i}.txt"))], - stdout=str(outdir / f"std.{i}.out"), - stderr=str(outdir / f"std.{i}.err"), - ) - for i in range(n) - ] - - for f in futs: - f.result() - - stdout_file_count = len(list(outdir.glob("*.out"))) - assert stdout_file_count == n, sorted(outdir.iterdir()) diff --git a/parsl/parsl/tests/test_bash_apps/test_error_codes.py b/parsl/parsl/tests/test_bash_apps/test_error_codes.py deleted file mode 100644 index 96f4140f..00000000 --- a/parsl/parsl/tests/test_bash_apps/test_error_codes.py +++ /dev/null @@ -1,57 +0,0 @@ -import pytest - -import parsl.app.errors as pe -from parsl.app.app import bash_app -from parsl.tests.configs.local_threads import fresh_config as local_config - - -@pytest.fixture -def test_fn(tmpd_cwd): - def _wrapper(parsl_app): - serr = str(tmpd_cwd / "std.err") - sout = str(tmpd_cwd / "std.out") - return parsl_app(stdout=sout, stderr=serr) - - return _wrapper - - -@bash_app -def command_not_found(stderr='std.err', stdout='std.out'): - cmd_line = 'catdogcat' - return cmd_line - - -@bash_app -def bash_misuse(stderr='std.err', stdout='std.out'): - cmd_line = 'exit 15' - return cmd_line - - -@bash_app -def div_0(stderr='std.err', stdout='std.out'): - cmd_line = '$((5/0))' - return cmd_line - - -@bash_app -def not_executable(stderr='std.err', stdout='std.out'): - cmd_line = '/dev/null' - return cmd_line - - -exit_codes = { - div_0: 1, - bash_misuse: 15, - not_executable: 126, - command_not_found: 127, -} - - -@pytest.mark.shared_fs -@pytest.mark.parametrize("app", (div_0, bash_misuse, not_executable, command_not_found)) -def test_failing_bash_app(test_fn, app): - err_code = exit_codes[app] - with pytest.raises(pe.BashExitFailure) as pyt_e: - test_fn(app).result() - e: pe.BashExitFailure = pyt_e.value - assert e.exitcode == err_code, f"[{e}] {app.__name__} expected {err_code=} but got {e.exitcode}" diff --git a/parsl/parsl/tests/test_bash_apps/test_inputs_default.py b/parsl/parsl/tests/test_bash_apps/test_inputs_default.py deleted file mode 100644 index 9b6d7a18..00000000 --- a/parsl/parsl/tests/test_bash_apps/test_inputs_default.py +++ /dev/null @@ -1,25 +0,0 @@ -import pytest - -from parsl import AUTO_LOGNAME, Config, bash_app, python_app -from parsl.executors import ThreadPoolExecutor - - -def local_config(): - return Config(executors=[ThreadPoolExecutor()]) - - -@pytest.mark.local -def test_default_inputs(): - @python_app - def identity(inp): - return inp - - @bash_app - def sum_inputs(inputs=[identity(1), identity(2)], stdout=AUTO_LOGNAME): - calc = sum(inputs) - return f"echo {calc}" - - fut = sum_inputs() - fut.result() - with open(fut.stdout, 'r') as f: - assert int(f.read()) == 3 diff --git a/parsl/parsl/tests/test_bash_apps/test_keyword_overlaps.py b/parsl/parsl/tests/test_bash_apps/test_keyword_overlaps.py deleted file mode 100644 index 41a9ba74..00000000 --- a/parsl/parsl/tests/test_bash_apps/test_keyword_overlaps.py +++ /dev/null @@ -1,15 +0,0 @@ -import parsl - - -@parsl.bash_app -def my_app(cache=7): - assert type(cache) is int - return "true" - - -def test_default_value(): - my_app().result() - - -def test_specified_value(): - my_app(cache=8).result() diff --git a/parsl/parsl/tests/test_bash_apps/test_kwarg_storage.py b/parsl/parsl/tests/test_bash_apps/test_kwarg_storage.py deleted file mode 100644 index e88a4c29..00000000 --- a/parsl/parsl/tests/test_bash_apps/test_kwarg_storage.py +++ /dev/null @@ -1,33 +0,0 @@ -import pytest - -from parsl.app.app import bash_app - - -@bash_app -def foo(z=2, stdout=None): - return f"echo {z}" - - -@pytest.mark.shared_fs -def test_command_format_1(tmpd_cwd): - """Testing command format for BashApps - """ - - stdout = tmpd_cwd / "std.out" - for exp_value, z in ( - ("3", 3), - ("4", 4), - ("5", 5), - ): - app_fu = foo(z=z, stdout=str(stdout)) - assert app_fu.result() == 0, "BashApp had non-zero exit" - - so_content = stdout.read_text().strip() - assert so_content == exp_value - stdout.unlink() - - app_fu = foo(stdout=str(stdout)) - assert app_fu.result() == 0, "BashApp had non-zero exit" - - so_content = stdout.read_text().strip() - assert so_content == "2" diff --git a/parsl/parsl/tests/test_bash_apps/test_memoize.py b/parsl/parsl/tests/test_bash_apps/test_memoize.py deleted file mode 100644 index 387837f4..00000000 --- a/parsl/parsl/tests/test_bash_apps/test_memoize.py +++ /dev/null @@ -1,42 +0,0 @@ -import pytest - -from parsl import File -from parsl.app.app import bash_app - - -@bash_app(cache=True) -def fail_on_presence(outputs=()): - return 'if [ -f {0} ] ; then exit 1 ; else touch {0}; fi'.format(outputs[0]) - - -@pytest.mark.shared_fs -def test_bash_memoization(tmpd_cwd, n=2): - """Testing bash memoization - """ - mpath = tmpd_cwd / "test.memoization.tmp" - temp_file = File(str(mpath)) - fail_on_presence(outputs=[temp_file]).result() - - futs = [fail_on_presence(outputs=[temp_file]) for _ in range(n)] - for f in futs: - assert f.exception() is None - - -@bash_app(cache=True) -def fail_on_presence_kw(outputs=(), foo=None): - return 'if [ -f {0} ] ; then exit 1 ; else touch {0}; fi'.format(outputs[0]) - - -@pytest.mark.shared_fs -def test_bash_memoization_keywords(tmpd_cwd, n=2): - """Testing bash memoization - """ - mpath = tmpd_cwd / "test.memoization.tmp" - temp_file = File(str(mpath)) - - foo = {"a": 1, "b": 2} - fail_on_presence_kw(outputs=[temp_file], foo=foo).result() - - futs = [fail_on_presence_kw(outputs=[temp_file], foo=foo) for _ in range(n)] - for f in futs: - assert f.exception() is None diff --git a/parsl/parsl/tests/test_bash_apps/test_memoize_ignore_args.py b/parsl/parsl/tests/test_bash_apps/test_memoize_ignore_args.py deleted file mode 100644 index 16cb919f..00000000 --- a/parsl/parsl/tests/test_bash_apps/test_memoize_ignore_args.py +++ /dev/null @@ -1,43 +0,0 @@ -import os - -import pytest - -import parsl -from parsl.app.app import bash_app - - -@bash_app(cache=True, ignore_for_cache=['x']) -def oneshot_app(x): - if x != 0: - raise RuntimeError("App was executed when it should not have been") - return "true" - - -def test_memo_same_at_definition(): - oneshot_app(x=0).result() # this should be executed - oneshot_app(x=1).result() # this should be memoized, and will raise a RuntimeError if actually executed - - -@bash_app(cache=True, ignore_for_cache=['stdout']) -def no_checkpoint_stdout_app_ignore_args(stdout=None): - return "echo X" - - -@pytest.mark.shared_fs -def test_memo_stdout(tmpd_cwd): - path_x = tmpd_cwd / "test.memo.stdout.x" - - # this should run and create a file named after path_x - no_checkpoint_stdout_app_ignore_args(stdout=str(path_x)).result() - assert path_x.exists() - - # this should be memoized, so should not get created - path_y = tmpd_cwd / "test.memo.stdout.y" - - no_checkpoint_stdout_app_ignore_args(stdout=path_y).result() - assert not path_y.exists(), "For memoization, expected NO file written" - - # this should also be memoized, so not create an arbitrary name - z_fut = no_checkpoint_stdout_app_ignore_args(stdout=parsl.AUTO_LOGNAME) - z_fut.result() - assert not os.path.exists(z_fut.stdout) diff --git a/parsl/parsl/tests/test_bash_apps/test_memoize_ignore_args_regr.py b/parsl/parsl/tests/test_bash_apps/test_memoize_ignore_args_regr.py deleted file mode 100644 index e1a9465f..00000000 --- a/parsl/parsl/tests/test_bash_apps/test_memoize_ignore_args_regr.py +++ /dev/null @@ -1,48 +0,0 @@ -import copy -from typing import List - -import pytest - -import parsl -from parsl.app.app import bash_app - -const_list_x: List[str] = [] -const_list_x_arg: List[str] = copy.deepcopy(const_list_x) - -assert const_list_x == const_list_x_arg - - -@bash_app(cache=True, ignore_for_cache=['x']) -def oneshot_app(x): - if x != 0: - raise RuntimeError("App was executed when it should not have been") - return "true" - - -def test_memo_same_at_definition(): - oneshot_app(x=0).result() # this should be executed - oneshot_app(x=1).result() # this should be memoized, and will raise a RuntimeError if actually executed - - -@bash_app(cache=True, ignore_for_cache=const_list_x_arg) -def no_checkpoint_stdout_app(stdout=None): - return "echo X" - - -@pytest.mark.shared_fs -def test_memo_stdout(tmpd_cwd): - assert const_list_x == const_list_x_arg - - path_x = tmpd_cwd / "test.memo.stdout.x" - - # this should run and create a file named after path_x - no_checkpoint_stdout_app(stdout=str(path_x)).result() - path_x.unlink(missing_ok=False) - - no_checkpoint_stdout_app(stdout=str(path_x)).result() - assert not path_x.exists(), "For memoization, expected NO file written" - - # this should also be memoized, so not create an arbitrary name - z_fut = no_checkpoint_stdout_app(stdout=parsl.AUTO_LOGNAME) - z_fut.result() - assert const_list_x == const_list_x_arg diff --git a/parsl/parsl/tests/test_bash_apps/test_multiline.py b/parsl/parsl/tests/test_bash_apps/test_multiline.py deleted file mode 100644 index cfdb674e..00000000 --- a/parsl/parsl/tests/test_bash_apps/test_multiline.py +++ /dev/null @@ -1,37 +0,0 @@ -import pytest - -from parsl import File -from parsl.app.app import bash_app - - -@bash_app -def multiline(inputs=(), outputs=(), stderr=None, stdout=None): - return """echo {inputs[0]} &> {outputs[0]} - echo {inputs[1]} &> {outputs[1]} - echo {inputs[2]} &> {outputs[2]} - echo "Testing STDOUT" - echo "Testing STDERR" 1>&2 - """.format(inputs=inputs, outputs=outputs) - - -@pytest.mark.shared_fs -def test_multiline(tmpd_cwd): - so, se = tmpd_cwd / "std.out", tmpd_cwd / "std.err" - f = multiline( - inputs=["Hello", "This is", "Cat!"], - outputs=[ - File(str(tmpd_cwd / "hello.txt")), - File(str(tmpd_cwd / "this.txt")), - File(str(tmpd_cwd / "cat.txt")), - ], - stdout=str(so), - stderr=str(se), - ) - f.result() - - flist = list(map(str, (f.name for f in tmpd_cwd.iterdir()))) - assert 'hello.txt' in flist, "hello.txt is missing" - assert 'this.txt' in flist, "this.txt is missing" - assert 'cat.txt' in flist, "cat.txt is missing" - assert "Testing STDOUT" in so.read_text() - assert "Testing STDERR" in se.read_text() diff --git a/parsl/parsl/tests/test_bash_apps/test_pipeline.py b/parsl/parsl/tests/test_bash_apps/test_pipeline.py deleted file mode 100644 index 75f16237..00000000 --- a/parsl/parsl/tests/test_bash_apps/test_pipeline.py +++ /dev/null @@ -1,84 +0,0 @@ -import pytest - -from parsl.app.app import bash_app -from parsl.app.futures import DataFuture -from parsl.data_provider.files import File - - -@bash_app -def increment(inputs=(), outputs=(), stdout=None, stderr=None): - cmd_line = """ - if ! [ -f {inputs[0]} ] ; then exit 43 ; fi - x=$(cat {inputs[0]}) - echo $(($x+1)) > {outputs[0]} - """.format(inputs=inputs, outputs=outputs) - return cmd_line - - -@bash_app -def slow_increment(dur, inputs=(), outputs=(), stdout=None, stderr=None): - cmd_line = """ - x=$(cat {inputs[0]}) - echo $(($x+1)) > {outputs[0]} - sleep {0} - """.format(dur, inputs=inputs, outputs=outputs) - return cmd_line - - -@pytest.mark.staging_required -def test_increment(tmpd_cwd, depth=5): - """Test simple pipeline A->B...->N - """ - fpath = tmpd_cwd / "test0.txt" - fpath.write_text("0\n") - - prev = [File(str(fpath))] - futs = [] - for i in range(1, depth): - assert isinstance(prev[0], (DataFuture, File)) - output = File(str(tmpd_cwd / f"test{i}.txt")) - f = increment( - inputs=prev, - outputs=[output], - stdout=str(tmpd_cwd / f"incr{i}.out"), - stderr=str(tmpd_cwd / f"incr{i}.err"), - ) - prev = f.outputs - futs.append((i, prev[0])) - assert isinstance(prev[0], DataFuture) - - for key, f in futs: - file = f.result() - expected = str(tmpd_cwd / f"test{key}.txt") - - assert file.local_path is None, "File on local side has overridden local_path, file: {}".format(repr(file)) - assert file.filepath == expected, "Submit side filepath has not been preserved over execution" - data = open(file.filepath).read().strip() - assert data == str(key) - - -@pytest.mark.staging_required -def test_increment_slow(tmpd_cwd, depth=5, dur=0.01): - """Test simple pipeline slow (sleep.5) A->B...->N - """ - - fpath = tmpd_cwd / "test0.txt" - fpath.write_text("0\n") - - prev = [File(str(fpath))] - futs = [] - for i in range(1, depth): - output = File(str(tmpd_cwd / f"test{i}.txt")) - f = slow_increment( - dur, - inputs=prev, - outputs=[output], - stdout=str(tmpd_cwd / f"incr{i}.out"), - stderr=str(tmpd_cwd / f"incr{i}.err"), - ) - prev = f.outputs - futs.append((i, prev[0])) - - for key, f in futs: - data = open(f.result().filepath).read().strip() - assert data == str(key) diff --git a/parsl/parsl/tests/test_bash_apps/test_std_uri.py b/parsl/parsl/tests/test_bash_apps/test_std_uri.py deleted file mode 100644 index 3da1fd0d..00000000 --- a/parsl/parsl/tests/test_bash_apps/test_std_uri.py +++ /dev/null @@ -1,123 +0,0 @@ -import logging -import zipfile -from functools import partial - -import pytest - -import parsl -from parsl.app.futures import DataFuture -from parsl.data_provider.files import File -from parsl.executors import ThreadPoolExecutor - - -@parsl.bash_app -def app_stdout(stdout=parsl.AUTO_LOGNAME): - return "echo hello" - - -def const_str(cpath, task_record, err_or_out): - return cpath - - -def const_with_cpath(autopath_specifier, content_path, caplog): - with parsl.load(parsl.Config(std_autopath=partial(const_str, autopath_specifier))): - fut = app_stdout() - - # we don't have to wait for a result to check this attributes - assert fut.stdout is autopath_specifier - - # there is no DataFuture to wait for in the str case: the model is that - # the stdout will be immediately available on task completion. - fut.result() - - with open(content_path, "r") as file: - assert file.readlines() == ["hello\n"] - - for record in caplog.records: - assert record.levelno < logging.ERROR - - -@pytest.mark.local -def test_std_autopath_const_str(caplog, tmpd_cwd): - """Tests str and tuple mode autopaths with constant autopath, which should - all be passed through unmodified. - """ - cpath = str(tmpd_cwd / "CONST") - const_with_cpath(cpath, cpath, caplog) - - -@pytest.mark.local -def test_std_autopath_const_pathlike(caplog, tmpd_cwd): - cpath = tmpd_cwd / "CONST" - const_with_cpath(cpath, cpath, caplog) - - -@pytest.mark.local -def test_std_autopath_const_tuples(caplog, tmpd_cwd): - file = tmpd_cwd / "CONST" - cpath = (file, "w") - const_with_cpath(cpath, file, caplog) - - -class URIFailError(Exception): - pass - - -def fail_uri(task_record, err_or_out): - raise URIFailError("Deliberate failure in std stream filename generation") - - -@pytest.mark.local -def test_std_autopath_fail(caplog): - with parsl.load(parsl.Config(std_autopath=fail_uri)): - with pytest.raises(URIFailError): - app_stdout() - - -@parsl.bash_app -def app_both(stdout=parsl.AUTO_LOGNAME, stderr=parsl.AUTO_LOGNAME): - return "echo hello; echo goodbye >&2" - - -def zip_uri(base, task_record, err_or_out): - """Should generate Files in base.zip like app_both.0.out or app_both.123.err""" - zip_path = base / "base.zip" - file = f"{task_record['func_name']}.{task_record['id']}.{task_record['try_id']}.{err_or_out}" - return File(f"zip:{zip_path}/{file}") - - -@pytest.mark.local -def test_std_autopath_zip(caplog, tmpd_cwd): - with parsl.load(parsl.Config(run_dir=str(tmpd_cwd), - executors=[ThreadPoolExecutor(working_dir=str(tmpd_cwd))], - std_autopath=partial(zip_uri, tmpd_cwd))): - futs = [] - - for _ in range(10): - fut = app_both() - - # assertions that should hold after submission - assert isinstance(fut.stdout, DataFuture) - assert fut.stdout.file_obj.url.startswith("zip") - - futs.append(fut) - - # Barrier for all the stageouts to complete so that we can - # poke at the zip file. - [(fut.stdout.result(), fut.stderr.result()) for fut in futs] - - with zipfile.ZipFile(tmpd_cwd / "base.zip") as z: - for fut in futs: - - assert fut.done(), "AppFuture should be done if stageout is done" - - stdout_relative_path = f"app_both.{fut.tid}.0.stdout" - with z.open(stdout_relative_path) as f: - assert f.readlines() == [b'hello\n'] - - stderr_relative_path = f"app_both.{fut.tid}.0.stderr" - with z.open(stderr_relative_path) as f: - assert f.readlines()[-1] == b'goodbye\n' - - for record in caplog.records: - assert record.levelno < logging.ERROR diff --git a/parsl/parsl/tests/test_bash_apps/test_stdout.py b/parsl/parsl/tests/test_bash_apps/test_stdout.py deleted file mode 100644 index c935def7..00000000 --- a/parsl/parsl/tests/test_bash_apps/test_stdout.py +++ /dev/null @@ -1,130 +0,0 @@ -import logging -import os - -import pytest -import typeguard - -import parsl.app.errors as perror -from parsl.app.app import bash_app - - -@bash_app -def echo_to_streams(msg, stderr=None, stdout=None): - return 'echo "{0}"; echo "{0}" >&2'.format(msg) - - -whitelist = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'configs', '*threads*') - -speclist = ( - ['t3.out', 'w'], - ('t4.out', None), - (42, 'w'), - ('t6.out', 'w', 42), - ('t7.out',), - ('t8.out', 'badmode') -) - -testids = [ - 'list_not_tuple', - 'null_mode', - 'not_a_string', - '3tuple', - '1tuple', - 'bad_mode' -] - - -@pytest.mark.parametrize('spec', speclist, ids=testids) -def test_bad_stdout_specs(spec): - """Testing bad stdout spec cases""" - - fn = echo_to_streams("Hello world", stdout=spec, stderr='t.err') - - try: - fn.result() - except Exception as e: - # This tests for TypeCheckError by string matching on the type name - # because that class does not exist in typeguard 2.x - it is new in - # typeguard 4.x. When typeguard 2.x support is dropped, this test can - # become an isinstance check. - assert "TypeCheckError" in str(type(e)) or isinstance(e, TypeError) or isinstance(e, perror.BadStdStreamFile), "Exception is wrong type" - else: - assert False, "Did not raise expected exception" - - -@pytest.mark.issue3328 -@pytest.mark.unix_filesystem_permissions_required -def test_bad_stdout_file(): - """Testing bad stderr file""" - - o = "/bad/dir/t2.out" - - fn = echo_to_streams("Hello world", stdout=o, stderr='t.err') - - try: - fn.result() - except perror.BadStdStreamFile: - pass - else: - assert False, "Did not raise expected exception BadStdStreamFile" - - return - - -@pytest.mark.issue3328 -@pytest.mark.unix_filesystem_permissions_required -def test_bad_stderr_file(): - """Testing bad stderr file""" - - err = "/bad/dir/t2.err" - - fn = echo_to_streams("Hello world", stderr=err) - - try: - fn.result() - except perror.BadStdStreamFile: - pass - else: - assert False, "Did not raise expected exception BadStdStreamFile" - - return - - -@pytest.mark.executor_supports_std_stream_tuples -@pytest.mark.shared_fs -def test_stdout_truncate(tmpd_cwd, caplog): - """Testing truncation of prior content of stdout""" - - out = (str(tmpd_cwd / 't1.out'), 'w') - err = str(tmpd_cwd / 't1.err') - - echo_to_streams('hi', stdout=out, stderr=err).result() - len1 = len(open(out[0]).readlines()) - - echo_to_streams('hi', stdout=out, stderr=err).result() - len2 = len(open(out[0]).readlines()) - - assert len1 == 1 - assert len1 == len2 - - for record in caplog.records: - assert record.levelno < logging.ERROR - - -@pytest.mark.shared_fs -def test_stdout_append(tmpd_cwd, caplog): - """Testing appending to prior content of stdout (default open() mode)""" - - out = str(tmpd_cwd / 't1.out') - err = str(tmpd_cwd / 't1.err') - - echo_to_streams('hi', stdout=out, stderr=err).result() - len1 = len(open(out).readlines()) - - echo_to_streams('hi', stdout=out, stderr=err).result() - len2 = len(open(out).readlines()) - - assert len1 == 1 and len2 == 2 - - for record in caplog.records: - assert record.levelno < logging.ERROR diff --git a/parsl/parsl/tests/test_callables.py b/parsl/parsl/tests/test_callables.py deleted file mode 100644 index 14b533d9..00000000 --- a/parsl/parsl/tests/test_callables.py +++ /dev/null @@ -1,71 +0,0 @@ -# this file contains some tests of passing complicated -# callables as parameters to apps, mainly to check that -# they are serialised correctly with serialization based -# executors. - -import importlib -import pathlib -from functools import partial - -import parsl - - -@parsl.python_app -def app(x): - return True - - -def test_check_base_app(): - app(0).result() - - -def somefunc(*args): - pass - - -def test_check_this_module_function(): - app(somefunc).result() - - -def test_check_this_module_function_partial(): - app(partial(somefunc, 1)).result() - - -def test_check_import_module_function(): - from parsl.tests.callables_helper import some_aux_func - app(some_aux_func).result() - - -def test_check_import_module_function_partial(): - from parsl.tests.callables_helper import some_aux_func - app(partial(some_aux_func, 1)).result() - - -def test_check_importlib_file_function(): - helper_path = pathlib.Path(__file__).parent / "callables_helper.py" - spec = importlib.util.spec_from_file_location("dynamically_loaded_module", helper_path) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - some_aux_func = module.some_aux_func - app(some_aux_func).result() - - -def test_check_importlib_file_function_partial(): - helper_path = pathlib.Path(__file__).parent / "callables_helper.py" - spec = importlib.util.spec_from_file_location("dynamically_loaded_module", helper_path) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - some_aux_func = module.some_aux_func - app(partial(some_aux_func, 1)).result() - - -def test_check_importlib_module_function(): - module = importlib.import_module("parsl.tests.callables_helper") - some_aux_func = module.some_aux_func - app(some_aux_func).result() - - -def test_check_importlib_module_partial(): - module = importlib.import_module("parsl.tests.callables_helper") - some_aux_func = module.some_aux_func - app(partial(some_aux_func, 1)).result() diff --git a/parsl/parsl/tests/test_checkpointing/__init__.py b/parsl/parsl/tests/test_checkpointing/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_checkpointing/test_get_all_checkpoints.py b/parsl/parsl/tests/test_checkpointing/test_get_all_checkpoints.py deleted file mode 100644 index 15ea75a9..00000000 --- a/parsl/parsl/tests/test_checkpointing/test_get_all_checkpoints.py +++ /dev/null @@ -1,44 +0,0 @@ -import contextlib -import os - -import pytest - -import parsl -from parsl import python_app -from parsl.config import Config -from parsl.dataflow.memoization import BasicMemoizer -from parsl.executors.threads import ThreadPoolExecutor - - -def parsl_configured(run_dir, memoizer): - return parsl.load(Config( - run_dir=str(run_dir), - executors=[ - ThreadPoolExecutor( - label='local_threads_checkpoint', - ) - ], - memoizer=memoizer - )) - - -@python_app(cache=True) -def uuid_app(x): - import uuid - return uuid.uuid4() - - -@pytest.mark.local -def test_loading_checkpoint(tmpd_cwd): - """Tests default loading of all checkpoint files from previous runs. - """ - import uuid - param = str(uuid.uuid4()) - - with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit")): - result = uuid_app(param).result() - - with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit")): - relaunched = uuid_app(param).result() - - assert result == relaunched, "Expected following call to uuid_app to return cached uuid" diff --git a/parsl/parsl/tests/test_checkpointing/test_get_last_checkpoint.py b/parsl/parsl/tests/test_checkpointing/test_get_last_checkpoint.py deleted file mode 100644 index 9938cfa3..00000000 --- a/parsl/parsl/tests/test_checkpointing/test_get_last_checkpoint.py +++ /dev/null @@ -1,107 +0,0 @@ -import contextlib -import os - -import pytest - -import parsl -from parsl import python_app -from parsl.config import Config -from parsl.dataflow.memoization import BasicMemoizer -from parsl.executors.threads import ThreadPoolExecutor -from parsl.utils import get_last_checkpoint - - -def parsl_configured(run_dir, memoizer): - return parsl.load(Config( - run_dir=str(run_dir), - executors=[ - ThreadPoolExecutor( - label='local_threads_checkpoint', - ) - ], - memoizer=memoizer - )) - - -@python_app(cache=True) -def uuid_app(x): - import uuid - return uuid.uuid4() - - -@pytest.mark.local -def test_loading_checkpoint(tmpd_cwd): - """Tests that get_last_checkpoint finds checkpoints from prior run - """ - import uuid - param = str(uuid.uuid4()) - - with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit", checkpoint_files=[])): - result = uuid_app(param).result() - - with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit", checkpoint_files=get_last_checkpoint(rundir=str(tmpd_cwd)))): - relaunched = uuid_app(param).result() - - assert result == relaunched, "Expected following call to uuid_app to return cached uuid" - - # check that we don't lose the result just because it came from two runs ago, - # by running the same test again. - with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit", checkpoint_files=get_last_checkpoint(rundir=str(tmpd_cwd)))): - relaunched = uuid_app(param).result() - - assert result == relaunched, "Expected following call to uuid_app to return cached uuid" - - -@pytest.mark.local -def test_loading_checkpoint_other_files(tmpd_cwd): - """Tests that get_last_checkpoint ignores other files. - - This is a regression test for a bug where the alphabetically last - per-run directory was being selected, and then ignored if it was - not a checkpoint directory, for example when the monitoring.db - file is also present (and alphabetically after any numbered - run directories). - """ - import uuid - param = str(uuid.uuid4()) - - with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit", checkpoint_files=[])): - result = uuid_app(param).result() - - with open(tmpd_cwd / "X", "w"): - # don't need to write anything, as we only need the directory entry - # which is made by the open call. - pass - - with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit", checkpoint_files=get_last_checkpoint(rundir=str(tmpd_cwd)))): - relaunched = uuid_app(param).result() - - assert result == relaunched, "Expected following call to uuid_app to return cached uuid" - - -@pytest.mark.local -def test_high_rundir(tmpd_cwd): - """Tests that get_last_checkpoint respects rundirs of more than 3 digits. - """ - import uuid - param = str(uuid.uuid4()) - - with open(tmpd_cwd / "998", "w"): - pass - - # this will have rundir "999" - with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit", checkpoint_files=[])): - assert "999" in parsl.dfk().run_dir - result_999 = uuid_app(param).result() - - # this will have rundir "1000", lexically before 999 - with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit", checkpoint_files=[])): - assert "1000" in parsl.dfk().run_dir - result_1000 = uuid_app(param).result() - - assert result_999 != result_1000, "Should have not used previous result" - - with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit", checkpoint_files=get_last_checkpoint(rundir=str(tmpd_cwd)))): - relaunched = uuid_app(param).result() - - assert result_1000 == relaunched, "Expected restore to be from 1000 rundir" diff --git a/parsl/parsl/tests/test_checkpointing/test_manual.py b/parsl/parsl/tests/test_checkpointing/test_manual.py deleted file mode 100644 index a51ee8d0..00000000 --- a/parsl/parsl/tests/test_checkpointing/test_manual.py +++ /dev/null @@ -1,33 +0,0 @@ -import os -from pathlib import Path - -import pytest - -import parsl -from parsl import python_app -from parsl.config import Config -from parsl.dataflow.memoization import BasicMemoizer - - -@python_app(cache=True) -def uuid_app(): - import uuid - return uuid.uuid4() - - -@pytest.mark.local -def test_manual_checkpoint() -> None: - """Launch a single app, write manually, check file exists. - """ - memoizer = BasicMemoizer(checkpoint_mode="manual") - - with parsl.load(Config(memoizer=memoizer)): - uuid_app().result() - - cpt_dir = Path(parsl.dfk().run_dir) / 'checkpoint' - - cptpath = cpt_dir / 'tasks.pkl' - - assert not os.path.exists(cptpath), f"Tasks checkpoint should not exist yet: {cptpath}" - memoizer.checkpoint() - assert os.path.exists(cptpath), f"Tasks checkpoint should exist now: {cptpath}" diff --git a/parsl/parsl/tests/test_checkpointing/test_periodic.py b/parsl/parsl/tests/test_checkpointing/test_periodic.py deleted file mode 100644 index 9336f7a5..00000000 --- a/parsl/parsl/tests/test_checkpointing/test_periodic.py +++ /dev/null @@ -1,59 +0,0 @@ -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.config import Config -from parsl.dataflow.memoization import BasicMemoizer -from parsl.executors.threads import ThreadPoolExecutor - - -def fresh_config(*, config_run_dir): - tpe = ThreadPoolExecutor(label='local_threads_checkpoint_periodic', max_threads=1) - return Config( - run_dir=config_run_dir, - executors=[tpe], - memoizer=BasicMemoizer( - checkpoint_mode='periodic', - checkpoint_period='00:00:02' - ) - ) - - -@python_app(cache=True) -def slow_double(x, sleep_dur=1): - import time - time.sleep(sleep_dur) - return x * 2 - - -def tstamp_to_seconds(line): - f = line.partition(" ")[0] - return float(f) - - -@pytest.mark.local -def test_periodic(tmpd_cwd): - """Test checkpointing with task_periodic behavior - """ - with parsl.load(fresh_config(config_run_dir=str(tmpd_cwd))): - memoizer = parsl.dfk().memoizer - h, m, s = map(int, memoizer.checkpoint_period.split(":")) - assert h == 0, "Verify test setup" - assert m == 0, "Verify test setup" - assert s > 0, "Verify test setup" - sleep_for = s + 1 - futs = [slow_double(sleep_for) for _ in range(4)] - [f.result() for f in futs] - run_dir = parsl.dfk().run_dir - - # Here we will check if the loglines came back with 5 seconds deltas - with open("{}/parsl.log".format(run_dir)) as f: - log_lines = f.readlines() - expected_msg = " Done checkpointing" - expected_msg2 = " No tasks checkpointed in this pass" - - lines = [line for line in log_lines if expected_msg in line or expected_msg2 in line] - assert len(lines) >= 3, "Insufficient checkpoint lines in logfile" - deltas = [tstamp_to_seconds(line) for line in lines] - assert deltas[1] - deltas[0] < 5.5, "Delta between checkpoints exceeded period" - assert deltas[2] - deltas[1] < 5.5, "Delta between checkpoints exceeded period" diff --git a/parsl/parsl/tests/test_checkpointing/test_regression_232.py b/parsl/parsl/tests/test_checkpointing/test_regression_232.py deleted file mode 100644 index c0167df4..00000000 --- a/parsl/parsl/tests/test_checkpointing/test_regression_232.py +++ /dev/null @@ -1,86 +0,0 @@ -import os -import pickle -import signal -import subprocess -import time - -import pytest - - -def kill(): - my_pid = os.getpid() - os.kill(my_pid, signal.SIGINT) - print("Killing self") - - -@pytest.mark.local -@pytest.mark.skip('fails on Travis in pytest') -def test_regress_232_task_exit(count=2): - """Recovering from a run that was SIGINT'ed with task_exit checkpointing - """ - cwd = os.path.dirname(os.path.realpath(__file__)) - checkpoint_file = os.path.join(cwd, 'checkpointed.py') - - if os.path.exists("test.txt"): - os.remove("test.txt") - - proc = subprocess.Popen("python3 {} -n {} -m task_exit".format(checkpoint_file, count), - shell=True) - - # Poll for at least 3 seconds - for i in range(30): - if os.path.exists("test.txt"): - break - time.sleep(0.1) - - proc.send_signal(signal.SIGINT) - proc.wait() - # We need to wait after the signal to make sure files were closed and such - last = os.path.abspath( - 'runinfo/{0}/checkpoint'.format(sorted(os.listdir('runinfo/'))[-1])) - checkpoint_file = "{}/tasks.pkl".format(last) - - with open(checkpoint_file, 'rb') as f: - tasks = [] - try: - while f: - tasks.append(pickle.load(f)) - print - except EOFError: - print("Caught error") - pass - print("Tasks from cache : ", tasks) - assert len(tasks) == count, "Expected {} checkpoint events, got {}".format( - 1, len(tasks)) - - -@pytest.mark.local -@pytest.mark.skip('fails on Travis in pytest') -def test_regress_232_dfk_exit(count=2): - """Recovering from a run that was SIGINT'ed with dfk_exit checkpointing - """ - cwd = os.path.dirname(os.path.realpath(__file__)) - checkpoint_file = os.path.join(cwd, 'checkpointed.py') - - proc = subprocess.Popen("python3 {} -n {} -m dkf_exit".format(checkpoint_file, count), - shell=True) - proc.send_signal(signal.SIGINT) - # We need to wait after the signal to make sure files were closed and such - proc.wait() - - last = os.path.abspath( - 'runinfo/{0}/checkpoint'.format(sorted(os.listdir('runinfo/'))[-1])) - checkpoint_file = "{}/tasks.pkl".format(last) - - print(checkpoint_file, "now exists") - with open(checkpoint_file, 'rb') as f: - tasks = [] - try: - while f: - tasks.append(pickle.load(f)) - print - except EOFError: - pass - print("Tasks from cache : ", tasks) - assert len(tasks) == count, "Expected {} checkpoint events, got {}".format( - 1, len(tasks)) diff --git a/parsl/parsl/tests/test_checkpointing/test_regression_233.py b/parsl/parsl/tests/test_checkpointing/test_regression_233.py deleted file mode 100644 index 2abd55ab..00000000 --- a/parsl/parsl/tests/test_checkpointing/test_regression_233.py +++ /dev/null @@ -1,75 +0,0 @@ -import pytest - -from parsl.app.app import python_app -from parsl.config import Config -from parsl.dataflow.dflow import DataFlowKernel -from parsl.dataflow.memoization import BasicMemoizer - - -def run_checkpointed(checkpoints): - config = Config(memoizer=BasicMemoizer(checkpoint_files=checkpoints, checkpoint_mode='task_exit')) - dfk = DataFlowKernel(config=config) - - @python_app(data_flow_kernel=dfk, cache=True) - def cached_rand(x): - import random - return random.randint(0, 10000) - - items = [] - for i in range(0, 5): - x = cached_rand(i) - items.append(x) - - # wait for all of the results - results = [i.result() for i in items] - - dfk.cleanup() - return results, dfk.run_dir - - -def run_race(sleep_dur): - - from parsl.tests.configs.local_threads_checkpoint_dfk_exit import config - dfk = DataFlowKernel(config=config) - - @python_app(data_flow_kernel=dfk, cache=True) - def cached_rand(x, sleep_dur=0): - import random - import time - time.sleep(sleep_dur) - return random.randint(0, 10000) - - items = [] - for i in range(0, 5): - x = cached_rand(i, sleep_dur=sleep_dur) - items.append(x) - - dfk.cleanup() - return [i.result for i in items] - - -@pytest.mark.local -def test_regress_234(): - """Test task_exit checkpointing with fast tasks""" - run_race(0) - - -@pytest.mark.local -def test_slower_apps(): - """Test task_exit tests with slow apps""" - run_race(0.5) - - -@pytest.mark.local -def test_checkpoint_availability(): - import os - - original, run_dir = run_checkpointed([]) - last_checkpoint = os.path.join(run_dir, 'checkpoint') - print(last_checkpoint) - cached, _ = run_checkpointed([last_checkpoint]) - - print(cached) - print(original) - - assert cached == original, "All tasks were not cached" diff --git a/parsl/parsl/tests/test_checkpointing/test_task_exit.py b/parsl/parsl/tests/test_checkpointing/test_task_exit.py deleted file mode 100644 index cc1ffd84..00000000 --- a/parsl/parsl/tests/test_checkpointing/test_task_exit.py +++ /dev/null @@ -1,65 +0,0 @@ -import argparse -import pickle - -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.tests.configs.local_threads_checkpoint_task_exit import config -from parsl.utils import time_limited_open - - -def local_setup(): - global dfk - dfk = parsl.load(config) - - -def local_teardown(): - parsl.dfk().cleanup() - - -@python_app(cache=True) -def slow_double(x, sleep_dur=1): - import time - time.sleep(sleep_dur) - return x * 2 - - -@pytest.mark.local -def test_at_task_exit(n=2): - """Test checkpointing at task_exit behavior - """ - - d = {} - - print("Launching: ", n) - for i in range(0, n): - d[i] = slow_double(i) - print("Done launching") - - for i in range(0, n): - d[i].result() - - # There are two potential race conditions here which - # might be useful to be aware of if debugging this test. - - # i) .result() returning does not necessarily mean that - # a checkpoint that has been written: it means that the - # AppFuture has had its result written. In the DFK - # implementation at time of writing, .result() returning - # does not indicate that a checkpoint has been written, - # it seems like. - - # ii) time_limited_open has a specific time limit in it. - # While this limit might seem generous at time of writing, - # it should be remembered that this is still a race. - - with time_limited_open("{}/checkpoint/tasks.pkl".format(dfk.run_dir), 'rb', seconds=5) as f: - tasks = [] - try: - while f: - tasks.append(pickle.load(f)) - except EOFError: - pass - - assert len(tasks) == n, "Expected {} checkpoint events, got {}".format(n, len(tasks)) diff --git a/parsl/parsl/tests/test_checkpointing/test_task_exit_roundtrip.py b/parsl/parsl/tests/test_checkpointing/test_task_exit_roundtrip.py deleted file mode 100644 index e976c6eb..00000000 --- a/parsl/parsl/tests/test_checkpointing/test_task_exit_roundtrip.py +++ /dev/null @@ -1,65 +0,0 @@ -import contextlib -import os - -import pytest - -import parsl -from parsl import python_app -from parsl.config import Config -from parsl.dataflow.memoization import BasicMemoizer -from parsl.executors.threads import ThreadPoolExecutor - - -def parsl_configured(run_dir, memoizer): - return parsl.load(Config( - run_dir=str(run_dir), - executors=[ - ThreadPoolExecutor( - label='local_threads_checkpoint', - ) - ], - memoizer=memoizer - )) - - -@python_app(cache=True) -def uuid_app(): - import uuid - return uuid.uuid4() - - -@python_app(cache=True) -def uuid_in_exception_app(): - import uuid - raise RuntimeError(uuid.uuid4()) - - -@pytest.mark.local -def test_loading_checkpoint(tmpd_cwd): - """Load memoization table from previous checkpoint - """ - with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit")): - checkpoint_files = [os.path.join(parsl.dfk().run_dir, "checkpoint")] - result = uuid_app().result() - - with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_files=checkpoint_files)): - relaunched = uuid_app().result() - - assert result == relaunched, "Expected following call to uuid_app to return cached uuid" - - -@pytest.mark.local -def test_regression_239(tmpd_cwd): - """Check that a failing app is run again in a subsequent run. - - This doesn't check that the app is not recorded in the checkpoint database, - only that its exception is not re-used at the task execution level. - """ - with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_mode="task_exit")): - checkpoint_files = [os.path.join(parsl.dfk().run_dir, "checkpoint")] - result = uuid_in_exception_app().exception() - - with parsl_configured(tmpd_cwd, BasicMemoizer(checkpoint_files=checkpoint_files)): - relaunched = uuid_in_exception_app().exception() - - assert result.args[0] != relaunched.args[0], "RuntimeError UUIDs should be different" diff --git a/parsl/parsl/tests/test_curvezmq.py b/parsl/parsl/tests/test_curvezmq.py deleted file mode 100644 index bce5ca6f..00000000 --- a/parsl/parsl/tests/test_curvezmq.py +++ /dev/null @@ -1,421 +0,0 @@ -import os -import pathlib -from typing import Union -from unittest import mock - -import pytest -import zmq -import zmq.auth -from zmq.auth.thread import ThreadAuthenticator - -from parsl import curvezmq - -ADDR = "tcp://127.0.0.1" - - -def get_server_socket(ctx: curvezmq.ServerContext): - sock = ctx.socket(zmq.PULL) - sock.setsockopt(zmq.RCVTIMEO, 200) - sock.setsockopt(zmq.LINGER, 0) - port = sock.bind_to_random_port(ADDR) - return sock, port - - -def get_client_socket(ctx: curvezmq.ClientContext, port: int): - sock = ctx.socket(zmq.PUSH) - sock.setsockopt(zmq.SNDTIMEO, 200) - sock.setsockopt(zmq.LINGER, 0) - sock.connect(f"{ADDR}:{port}") - return sock - - -def get_external_server_socket( - ctx: Union[curvezmq.ServerContext, zmq.Context], secret_key: bytes -): - sock = ctx.socket(zmq.PULL) - sock.setsockopt(zmq.RCVTIMEO, 200) - sock.setsockopt(zmq.LINGER, 0) - sock.setsockopt(zmq.CURVE_SECRETKEY, secret_key) - sock.setsockopt(zmq.CURVE_SERVER, True) - port = sock.bind_to_random_port(ADDR) - return sock, port - - -def get_external_client_socket( - ctx: Union[curvezmq.ClientContext, zmq.Context], - public_key: bytes, - secret_key: bytes, - server_key: bytes, - port: int, -): - sock = ctx.socket(zmq.PUSH) - sock.setsockopt(zmq.LINGER, 0) - sock.setsockopt(zmq.CURVE_PUBLICKEY, public_key) - sock.setsockopt(zmq.CURVE_SECRETKEY, secret_key) - sock.setsockopt(zmq.CURVE_SERVERKEY, server_key) - sock.connect(f"{ADDR}:{port}") - return sock - - -@pytest.fixture -def encrypted(request: pytest.FixtureRequest): - if hasattr(request, "param"): - return request.param - return True - - -@pytest.fixture -def cert_dir(encrypted: bool, tmpd_cwd: pathlib.Path): - if not encrypted: - return None - return curvezmq.create_certificates(tmpd_cwd) - - -@pytest.fixture -def server_ctx(cert_dir: Union[str, None]): - ctx = curvezmq.ServerContext(cert_dir) - yield ctx - ctx.destroy() - - -@pytest.fixture -def client_ctx(cert_dir: Union[str, None]): - ctx = curvezmq.ClientContext(cert_dir) - yield ctx - ctx.destroy() - - -@pytest.fixture -def zmq_ctx(): - ctx = zmq.Context() - yield ctx - ctx.destroy() - - -@pytest.mark.local -@pytest.mark.parametrize("encrypted", (True, False), indirect=True) -def test_client_context_init(cert_dir: Union[str, None]): - ctx = curvezmq.ClientContext(cert_dir=cert_dir) - - assert ctx.cert_dir == cert_dir - if cert_dir is None: - assert not ctx.encrypted - else: - assert ctx.encrypted - - ctx.destroy() - - -@pytest.mark.local -@pytest.mark.parametrize("encrypted", (True, False), indirect=True) -def test_server_context_init(cert_dir: Union[str, None]): - ctx = curvezmq.ServerContext(cert_dir=cert_dir) - - assert ctx.cert_dir == cert_dir - if cert_dir is None: - assert not ctx.encrypted - assert not ctx.auth_thread - else: - assert ctx.encrypted - assert isinstance(ctx.auth_thread, ThreadAuthenticator) - - ctx.destroy() - - -@pytest.mark.local -def test_create_certificates(tmpd_cwd: pathlib.Path): - cert_dir = tmpd_cwd / "certificates" - assert not os.path.exists(cert_dir) - - ret = curvezmq.create_certificates(tmpd_cwd) - - assert str(cert_dir) == ret - assert os.path.exists(cert_dir) - assert os.stat(cert_dir).st_mode & 0o777 == 0o700 - assert len(os.listdir(cert_dir)) == 4 - - -@pytest.mark.local -def test_create_certificates_overwrite(tmpd_cwd: pathlib.Path): - cert_dir = curvezmq.create_certificates(tmpd_cwd) - client_pub_1, client_sec_1 = curvezmq._load_certificate(cert_dir, name="client") - server_pub_1, server_sec_1 = curvezmq._load_certificate(cert_dir, name="server") - - curvezmq.create_certificates(tmpd_cwd) - client_pub_2, client_sec_2 = curvezmq._load_certificate(cert_dir, name="client") - server_pub_2, server_sec_2 = curvezmq._load_certificate(cert_dir, name="server") - - assert client_pub_1 != client_pub_2 - assert client_sec_1 != client_sec_2 - assert server_pub_1 != server_pub_2 - assert server_sec_1 != server_sec_2 - - -@pytest.mark.local -def test_cert_dir_not_private(tmpd_cwd: pathlib.Path): - cert_dir = tmpd_cwd / "certificates" - os.makedirs(cert_dir, mode=0o777) - client_ctx = curvezmq.ClientContext(cert_dir) - server_ctx = curvezmq.ServerContext(cert_dir) - - err_msg = "directory must be private" - - with pytest.raises(OSError) as pyt_e: - client_ctx.socket(zmq.REQ) - assert err_msg in str(pyt_e.value) - - with pytest.raises(OSError) as pyt_e: - server_ctx.socket(zmq.REP) - assert err_msg in str(pyt_e.value) - - client_ctx.destroy() - server_ctx.destroy() - - -@pytest.mark.local -def test_missing_cert_dir(): - cert_dir = "/bad/cert/dir" - client_ctx = curvezmq.ClientContext(cert_dir) - server_ctx = curvezmq.ServerContext(cert_dir) - - err_msg = "No such file or directory" - - with pytest.raises(FileNotFoundError) as pyt_e: - client_ctx.socket(zmq.REQ) - assert err_msg in str(pyt_e.value) - - with pytest.raises(FileNotFoundError) as pyt_e: - server_ctx.socket(zmq.REP) - assert err_msg in str(pyt_e.value) - - client_ctx.destroy() - server_ctx.destroy() - - -@pytest.mark.local -def test_missing_secret_file(tmpd_cwd: pathlib.Path): - cert_dir = tmpd_cwd / "certificates" - os.makedirs(cert_dir, mode=0o700) - - client_ctx = curvezmq.ClientContext(cert_dir) - server_ctx = curvezmq.ServerContext(cert_dir) - - err_msg = "Invalid certificate file" - - with pytest.raises(OSError) as pyt_e: - client_ctx.socket(zmq.REQ) - assert err_msg in str(pyt_e.value) - - with pytest.raises(OSError) as pyt_e: - server_ctx.socket(zmq.REP) - assert err_msg in str(pyt_e.value) - - client_ctx.destroy() - server_ctx.destroy() - - -@pytest.mark.local -def test_bad_secret_file(tmpd_cwd: pathlib.Path): - client_sec = tmpd_cwd / "client.key_secret" - server_sec = tmpd_cwd / "server.key_secret" - client_sec.write_text("bad") - server_sec.write_text("boy") - - client_ctx = curvezmq.ClientContext(tmpd_cwd) - server_ctx = curvezmq.ServerContext(tmpd_cwd) - - err_msg = "No public key found" - - with pytest.raises(ValueError) as pyt_e: - client_ctx.socket(zmq.REQ) - assert err_msg in str(pyt_e.value) - - with pytest.raises(ValueError) as pyt_e: - server_ctx.socket(zmq.REP) - assert err_msg in str(pyt_e.value) - - client_ctx.destroy() - server_ctx.destroy() - - -@pytest.mark.local -@pytest.mark.parametrize("encrypted", (True, False), indirect=True) -def test_client_context_term(client_ctx: curvezmq.ClientContext): - assert not client_ctx.closed - - client_ctx.term() - - assert client_ctx.closed - - -@pytest.mark.local -@pytest.mark.parametrize("encrypted", (True, False), indirect=True) -def test_server_context_term(server_ctx: curvezmq.ServerContext, encrypted: bool): - assert not server_ctx.closed - auth_thread = None - if encrypted: - auth_thread = server_ctx.auth_thread - assert auth_thread - assert auth_thread.pipe - - server_ctx.term() - - assert server_ctx.closed - if encrypted: - assert server_ctx.auth_thread is None - assert auth_thread is not None, "Pulled from ctx before term()" - assert not auth_thread.is_alive() - assert not auth_thread.pipe - - -@pytest.mark.local -@pytest.mark.parametrize("encrypted", (True, False), indirect=True) -def test_client_context_destroy(client_ctx: curvezmq.ClientContext): - sock = client_ctx.socket(zmq.REP) - - assert not client_ctx.closed - - client_ctx.destroy() - - assert sock.closed - assert client_ctx.closed - - -@pytest.mark.local -@pytest.mark.parametrize("encrypted", (True, False), indirect=True) -def test_server_context_destroy(server_ctx: curvezmq.ServerContext, encrypted: bool): - sock = server_ctx.socket(zmq.REP) - - assert not server_ctx.closed - auth_thread = None - if encrypted: - auth_thread = server_ctx.auth_thread - assert auth_thread - assert auth_thread.pipe - - server_ctx.destroy() - - assert sock.closed - assert server_ctx.closed - if encrypted: - assert server_ctx.auth_thread is None - assert auth_thread is not None, "Pulled from ctx before destroy()" - assert not auth_thread.is_alive() - assert not auth_thread.pipe - - -@pytest.mark.local -@pytest.mark.parametrize("encrypted", (True, False), indirect=True) -def test_connection( - server_ctx: curvezmq.ServerContext, client_ctx: curvezmq.ClientContext -): - server_socket, port = get_server_socket(server_ctx) - client_socket = get_client_socket(client_ctx, port) - - msg = b"howdy" - client_socket.send(msg) - recv = server_socket.recv() - - assert recv == msg - - -@pytest.mark.local -@mock.patch.object(curvezmq, "_load_certificate") -def test_invalid_key_format( - mock_load_cert: mock.MagicMock, - server_ctx: curvezmq.ServerContext, - client_ctx: curvezmq.ClientContext, -): - mock_load_cert.return_value = (b"badkey", b"badkey") - - with pytest.raises(ValueError) as e1_info: - server_ctx.socket(zmq.REP) - with pytest.raises(ValueError) as e2_info: - client_ctx.socket(zmq.REQ) - e1, e2 = e1_info.exconly, e2_info.exconly - - assert str(e1) == str(e2) - assert "Invalid CurveZMQ key format" in str(e1) - - -@pytest.mark.local -def test_invalid_client_keys(server_ctx: curvezmq.ServerContext, zmq_ctx: zmq.Context): - server_socket, port = get_server_socket(server_ctx) - - cert_dir = server_ctx.cert_dir - assert cert_dir # For mypy - public_key, secret_key = curvezmq._load_certificate(cert_dir, "client") - server_key, _ = curvezmq._load_certificate(cert_dir, "server") - - BAD_PUB_KEY, BAD_SEC_KEY = zmq.curve_keypair() - msg = b"howdy" - - client_socket = get_external_client_socket( - zmq_ctx, - public_key, - secret_key, - server_key, - port, - ) - client_socket.send(msg) - assert server_socket.recv() == msg - - client_socket = get_external_client_socket( - zmq_ctx, - BAD_PUB_KEY, - BAD_SEC_KEY, - server_key, - port, - ) - client_socket.send(msg) - with pytest.raises(zmq.Again): - server_socket.recv() - - client_socket = get_external_client_socket( - zmq_ctx, - public_key, - secret_key, - BAD_PUB_KEY, - port, - ) - client_socket.send(msg) - with pytest.raises(zmq.Again): - server_socket.recv() - - # Ensure sockets are operational - client_socket = get_external_client_socket( - zmq_ctx, - public_key, - secret_key, - server_key, - port, - ) - client_socket.send(msg) - assert server_socket.recv() == msg - - -@pytest.mark.local -def test_invalid_server_key(client_ctx: curvezmq.ClientContext, zmq_ctx: zmq.Context): - cert_dir = client_ctx.cert_dir - assert cert_dir # For mypy - _, secret_key = curvezmq._load_certificate(cert_dir, "server") - - _, BAD_SEC_KEY = zmq.curve_keypair() - msg = b"howdy" - - server_socket, port = get_external_server_socket(zmq_ctx, secret_key) - client_socket = get_client_socket(client_ctx, port) - client_socket.send(msg) - assert server_socket.recv() == msg - - server_socket, port = get_external_server_socket(zmq_ctx, BAD_SEC_KEY) - client_socket = get_client_socket(client_ctx, port) - client_socket.send(msg) - with pytest.raises(zmq.Again): - server_socket.recv() - - # Ensure sockets are operational - server_socket, port = get_external_server_socket(zmq_ctx, secret_key) - client_socket = get_client_socket(client_ctx, port) - client_socket.send(msg) - assert server_socket.recv() == msg diff --git a/parsl/parsl/tests/test_docs/__init__.py b/parsl/parsl/tests/test_docs/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_docs/test_from_slides.py b/parsl/parsl/tests/test_docs/test_from_slides.py deleted file mode 100644 index b3242e81..00000000 --- a/parsl/parsl/tests/test_docs/test_from_slides.py +++ /dev/null @@ -1,33 +0,0 @@ -import os - -import pytest - -from parsl.app.app import bash_app, python_app -from parsl.data_provider.files import File - - -@bash_app -def echo(message, outputs=[]): - return 'echo {m} &> {o}'.format(m=message, o=outputs[0]) - - -@python_app -def cat(inputs=[]): - with open(inputs[0].filepath) as f: - return f.readlines() - - -@pytest.mark.staging_required -def test_slides(): - """Testing code snippet from slides """ - - if os.path.exists('hello1.txt'): - os.remove('hello1.txt') - - hello = echo("Hello World!", outputs=[File('hello1.txt')]) - - message = cat(inputs=[hello.outputs[0]]) - - # Waits. This need not be in the slides. - print(hello.result()) - print(message.result()) diff --git a/parsl/parsl/tests/test_docs/test_kwargs.py b/parsl/parsl/tests/test_docs/test_kwargs.py deleted file mode 100644 index 80907ebe..00000000 --- a/parsl/parsl/tests/test_docs/test_kwargs.py +++ /dev/null @@ -1,40 +0,0 @@ -"""Functions used to explain kwargs""" -from pathlib import Path - -import pytest - -from parsl import File, python_app - - -def test_inputs(): - @python_app() - def map_app(x): - return x * 2 - - @python_app() - def reduce_app(inputs=()): - return sum(inputs) - - map_futures = [map_app(x) for x in range(3)] - reduce_future = reduce_app(inputs=map_futures) - - assert reduce_future.result() == 6 - - -@pytest.mark.shared_fs -def test_outputs(tmpd_cwd): - @python_app() - def write_app(message, outputs=()): - """Write a single message to every file in outputs""" - for path in outputs: - with open(path, 'w') as fp: - print(message, file=fp) - - to_write = [ - File(Path(tmpd_cwd) / 'output-0.txt'), - File(Path(tmpd_cwd) / 'output-1.txt') - ] - write_app('Hello!', outputs=to_write).result() - for path in to_write: - with open(path) as fp: - assert fp.read() == 'Hello!\n' diff --git a/parsl/parsl/tests/test_docs/test_workflow1.py b/parsl/parsl/tests/test_docs/test_workflow1.py deleted file mode 100644 index 271baab4..00000000 --- a/parsl/parsl/tests/test_docs/test_workflow1.py +++ /dev/null @@ -1,41 +0,0 @@ -import os - -import pytest - -import parsl -from parsl.app.app import bash_app, python_app -from parsl.data_provider.files import File -from parsl.tests.configs.local_threads import config - -# parsl.set_stream_logger() - - -@python_app -def generate(limit): - from random import randint - """Generate a random integer and return it""" - return randint(1, limit) - - -@bash_app -def save(message, outputs=[]): - return 'echo {m} &> {o}'.format(m=message, o=outputs[0]) - - -@pytest.mark.staging_required -def test_procedural(N=2): - """Procedural workflow example from docs on - Composing a workflow - """ - - if os.path.exists('output.txt'): - os.remove('output.txt') - - message = generate(N) - - saved = save(message, outputs=[File('output.txt')]) - - with open(saved.outputs[0].result().filepath, 'r') as f: - item = int(f.read().strip()) - assert item <= N, "Expected file to contain int <= N" - assert item >= 1, "Expected file to contain int >= 1" diff --git a/parsl/parsl/tests/test_docs/test_workflow4.py b/parsl/parsl/tests/test_docs/test_workflow4.py deleted file mode 100644 index 05a2a6f8..00000000 --- a/parsl/parsl/tests/test_docs/test_workflow4.py +++ /dev/null @@ -1,43 +0,0 @@ -import pytest - -from parsl.app.app import bash_app, python_app -from parsl.data_provider.files import File - - -@bash_app -def generate(outputs=()): - return "echo 1 &> {o}".format(o=outputs[0]) - - -@bash_app -def concat(inputs=(), outputs=(), stdout=None, stderr=None): - return "cat {0} >> {1}".format(" ".join(map(lambda x: x.filepath, inputs)), outputs[0]) - - -@python_app -def total(inputs=()): - with open(inputs[0].filepath, "r") as f: - return sum(int(line) for line in f) - - -@pytest.mark.staging_required -@pytest.mark.parametrize("width", (5, 10, 15)) -def test_parallel_dataflow(tmpd_cwd, width): - """Test parallel dataflow from docs on Composing workflows - """ - - # create 5 files with random numbers - output_files = [ - generate(outputs=[File(str(tmpd_cwd / f"random-{i}.txt"))]) - for i in range(width) - ] - - # concatenate the files into a single file - cc = concat( - inputs=[i.outputs[0] for i in output_files], - outputs=[File(str(tmpd_cwd / "all.txt"))] - ) - - # calculate the average of the random numbers - totals = total(inputs=[cc.outputs[0]]) - assert totals.result() == len(output_files) diff --git a/parsl/parsl/tests/test_error_handling/__init__.py b/parsl/parsl/tests/test_error_handling/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_error_handling/test_fail.py b/parsl/parsl/tests/test_error_handling/test_fail.py deleted file mode 100644 index 27d5b005..00000000 --- a/parsl/parsl/tests/test_error_handling/test_fail.py +++ /dev/null @@ -1,14 +0,0 @@ -import pytest - -from parsl.app.app import python_app - - -@python_app -def always_fail(): - raise ValueError("This ValueError should propagate to the app caller in fut.result()") - - -def test_simple(): - with pytest.raises(ValueError): - fut = always_fail() - fut.result() diff --git a/parsl/parsl/tests/test_error_handling/test_python_walltime.py b/parsl/parsl/tests/test_error_handling/test_python_walltime.py deleted file mode 100644 index 7e570c8c..00000000 --- a/parsl/parsl/tests/test_error_handling/test_python_walltime.py +++ /dev/null @@ -1,42 +0,0 @@ -import pytest - -import parsl -from parsl.app.errors import AppTimeout - - -@parsl.python_app -def my_app(t=0.0, walltime=0.1): - import time - end = time.monotonic() + t - while time.monotonic() < end: - time.sleep(0.01) # work with timeout(); allow access to thread - return 7 - - -def test_python_walltime(): - f = my_app(2) - with pytest.raises(AppTimeout): - f.result() - - -def test_python_longer_walltime_at_invocation(): - f = my_app(0.01, walltime=6) - assert f.result() == 7 - - -def test_python_walltime_wrapped_names(): - f = my_app(0.01, walltime=6) - assert f.result() == 7 - assert f.task_record['func'].__name__ == "my_app" - assert f.task_record['func_name'] == "my_app" - - -def test_python_bad_decorator_args(): - - with pytest.raises(TypeError): - @pytest.mark.local - @parsl.python_app(walltime=1) - def my_app_2(): - import time - time.sleep(1.2) - return True diff --git a/parsl/parsl/tests/test_error_handling/test_resource_spec.py b/parsl/parsl/tests/test_error_handling/test_resource_spec.py deleted file mode 100644 index 5adb27b6..00000000 --- a/parsl/parsl/tests/test_error_handling/test_resource_spec.py +++ /dev/null @@ -1,46 +0,0 @@ -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.executors import WorkQueueExecutor -from parsl.executors.errors import InvalidResourceSpecification -from parsl.executors.high_throughput.executor import HighThroughputExecutor -from parsl.executors.threads import ThreadPoolExecutor - - -@python_app -def double(x, parsl_resource_specification={}): - return x * 2 - - -@pytest.mark.issue_3620 -def test_resource(n=2): - executors = parsl.dfk().executors - executor = None - for label in executors: - if label != '_parsl_internal': - executor = executors[label] - break - - # Specify incorrect number of resources - spec = {'cores': 1, 'memory': 1} - fut = double(n, parsl_resource_specification=spec) - try: - fut.result() - except InvalidResourceSpecification: - assert ( - isinstance(executor, HighThroughputExecutor) or - isinstance(executor, WorkQueueExecutor) or - isinstance(executor, ThreadPoolExecutor)) - - # Specify resources with wrong types - # 'cpus' is incorrect, should be 'cores' - spec = {'cpus': 1, 'memory': 1, 'disk': 1} - fut = double(n, parsl_resource_specification=spec) - try: - fut.result() - except InvalidResourceSpecification: - assert ( - isinstance(executor, HighThroughputExecutor) or - isinstance(executor, WorkQueueExecutor) or - isinstance(executor, ThreadPoolExecutor)) diff --git a/parsl/parsl/tests/test_error_handling/test_retries.py b/parsl/parsl/tests/test_error_handling/test_retries.py deleted file mode 100644 index 06ae8170..00000000 --- a/parsl/parsl/tests/test_error_handling/test_retries.py +++ /dev/null @@ -1,101 +0,0 @@ -import os - -import pytest - -from parsl import bash_app, python_app -from parsl.tests.configs.local_threads import fresh_config - - -def local_config(): - c = fresh_config() - c.retries = 2 - return c - - -@python_app -def sleep_then_fail(inputs=[], sleep_dur=0.1): - import math - import time - time.sleep(sleep_dur) - math.ceil("Trigger TypeError") - return 0 - - -@bash_app -def succeed_on_retry(filename, success_on=1, stdout="succeed.out"): - """If the input file does not exist it creates it. - Then, if the file contains success_on lines it exits with 0 - """ - - return """if [[ ! -e {filename} ]]; then touch {filename}; fi; - tries=`wc -l {filename} | cut -f1 -d' '` - echo $tries >> {filename} - - if [[ "$tries" -eq "{success_on}" ]] - then - echo "Match. Success" - else - echo "Tries != success_on , exiting with error" - exit 5 - fi - """.format(filename=filename, success_on=success_on) - - -@python_app -def sleep(sleep_dur=0.1): - import time - time.sleep(sleep_dur) - return 0 - - -@pytest.mark.local -def test_fail_nowait(numtasks=10): - """Test retries on tasks with no dependencies. - """ - fus = [] - for i in range(0, numtasks): - fu = sleep_then_fail(sleep_dur=0.1) - fus.extend([fu]) - - # wait for all tasks to complete before ending this test - [x.exception() for x in fus] - - try: - [x.result() for x in fus] - except Exception as e: - assert isinstance( - e, TypeError), "Expected a TypeError, got {}".format(e) - - -@pytest.mark.local -def test_fail_delayed(numtasks=10): - """Test retries on tasks with dependencies. - - This is testing retry behavior when AppFutures are created - with no parent. - """ - - x = sleep() - fus = [] - for i in range(0, numtasks): - fu = sleep_then_fail(inputs=[x], sleep_dur=0.5) - fus.extend([fu]) - - # wait for all tasks to complete before ending this test - [x.exception() for x in fus] - - try: - [x.result() for x in fus] - except Exception as e: - assert isinstance( - e, TypeError), "Expected a TypeError, got {}".format(e) - - -@pytest.mark.local -def test_retry(tmpd_cwd): - """Test retries via app that succeeds on the Nth retry. - """ - - fpath = tmpd_cwd / "retry.out" - sout = str(tmpd_cwd / "stdout") - succeed_on_retry(str(fpath), stdout=sout).result() diff --git a/parsl/parsl/tests/test_error_handling/test_retry_handler.py b/parsl/parsl/tests/test_error_handling/test_retry_handler.py deleted file mode 100644 index 788c6063..00000000 --- a/parsl/parsl/tests/test_error_handling/test_retry_handler.py +++ /dev/null @@ -1,65 +0,0 @@ -import os - -import pytest - -import parsl -from parsl import bash_app -from parsl.tests.configs.local_threads import fresh_config - - -def half_handler(*args): - """Cost 0.5 for each retry, not the default of 1""" - return 0.5 - - -def local_config(): - c = fresh_config() - c.retries = 2 - c.retry_handler = half_handler - return c - - -@bash_app -def succeed_on_retry(filename, success_on=1, stdout="succeed.out"): - """If the input file does not exist it creates it. - Then, if the file contains success_on lines it exits with 0 - """ - - return """if [[ ! -e {filename} ]]; then touch {filename}; fi; - tries=`wc -l {filename} | cut -f1 -d' '` - echo $tries >> {filename} - - if [[ "$tries" -eq "{success_on}" ]] - then - echo "Match. Success" - else - echo "Tries != success_on , exiting with error" - exit 5 - fi - """.format(filename=filename, success_on=success_on) - - -@pytest.mark.local -def test_retry(): - """Test retries via app that succeeds on the Nth retry. - """ - - fname = "retry.out" - try: - os.remove(fname) - except OSError: - pass - fu = succeed_on_retry(fname, success_on=4) - - fu.result() - - try: - os.remove(fname) - except OSError: - pass - fu = succeed_on_retry(fname, success_on=5) - - with pytest.raises(parsl.app.errors.BashExitFailure): - fu.result() - - assert fu.exception().exitcode == 5 diff --git a/parsl/parsl/tests/test_error_handling/test_retry_handler_failure.py b/parsl/parsl/tests/test_error_handling/test_retry_handler_failure.py deleted file mode 100644 index 8d5406bb..00000000 --- a/parsl/parsl/tests/test_error_handling/test_retry_handler_failure.py +++ /dev/null @@ -1,24 +0,0 @@ -import pytest - -import parsl - - -@parsl.python_app -def always_fails(): - raise ValueError("always_fails deliberate exception") - - -def retry_handler_raises(exc, task_record): - raise RuntimeError("retry_handler_raises deliberate exception") - - -def local_config(): - return parsl.config.Config(retry_handler=retry_handler_raises) - - -@pytest.mark.local -def test_retry_handler_exception(): - fut = always_fails() - with pytest.raises(RuntimeError): - fut.result() - assert fut.exception().args[0] == "retry_handler_raises deliberate exception" diff --git a/parsl/parsl/tests/test_error_handling/test_serialization_fail.py b/parsl/parsl/tests/test_error_handling/test_serialization_fail.py deleted file mode 100644 index 60b54afb..00000000 --- a/parsl/parsl/tests/test_error_handling/test_serialization_fail.py +++ /dev/null @@ -1,33 +0,0 @@ -import pytest - -from parsl import python_app -from parsl.serialize.errors import SerializationError -from parsl.tests.configs.htex_local import fresh_config - - -def local_config(): - config = fresh_config() - config.retries = 2 - return config - - -@python_app -def fail_pickling(x): - return next(x) - - -def generator(): - num = 0 - while True: - yield num - num += 1 - - -@pytest.mark.local -def test_serialization_error(): - - gen = generator() - x = fail_pickling(gen) - - assert x.exception() is not None, "Should have got an exception, actually got: " + repr(x.result()) - assert isinstance(x.exception(), SerializationError) diff --git a/parsl/parsl/tests/test_error_handling/test_wrap_with_logs.py b/parsl/parsl/tests/test_error_handling/test_wrap_with_logs.py deleted file mode 100644 index 28200b65..00000000 --- a/parsl/parsl/tests/test_error_handling/test_wrap_with_logs.py +++ /dev/null @@ -1,34 +0,0 @@ -import logging - -import pytest - -from parsl.process_loggers import wrap_with_logs - - -@wrap_with_logs -def somefunc_ok(): - return 5 - - -@wrap_with_logs -def somefunc_exception(): - raise RuntimeError("Deliberate failure") - - -@pytest.mark.local -def test_wrap_with_logs_ok(caplog): - caplog.set_level(logging.DEBUG) - - # check that return value is passed back through wrap_with_logs - x = somefunc_ok() - assert x == 5 - assert 'Normal ending' in caplog.text - - -@pytest.mark.local -def test_wrap_with_logs_exception(caplog): - caplog.set_level(logging.ERROR) - # check that exception is passed back through wrap_with_logs - with pytest.raises(RuntimeError): - somefunc_exception() - assert 'Exceptional ending' in caplog.text diff --git a/parsl/parsl/tests/test_execute_task.py b/parsl/parsl/tests/test_execute_task.py deleted file mode 100644 index de45d632..00000000 --- a/parsl/parsl/tests/test_execute_task.py +++ /dev/null @@ -1,20 +0,0 @@ -import os - -import pytest - -from parsl.executors.execute_task import execute_task -from parsl.serialize.facade import pack_apply_message - - -def addemup(*args: int, name: str = "apples"): - total = sum(args) - return f"{total} {name}" - - -@pytest.mark.local -def test_execute_task(): - args = (1, 2, 3) - kwargs = {"name": "boots"} - buff = pack_apply_message(addemup, args, kwargs) - res = execute_task(buff) - assert res == addemup(*args, **kwargs) diff --git a/parsl/parsl/tests/test_flux.py b/parsl/parsl/tests/test_flux.py deleted file mode 100644 index 993d8702..00000000 --- a/parsl/parsl/tests/test_flux.py +++ /dev/null @@ -1,170 +0,0 @@ -import concurrent.futures as cf -import os - -import pytest - -from parsl.app.errors import AppException -from parsl.executors.flux.executor import ( - FluxExecutor, - FluxFutureWrapper, - _complete_future, -) - -try: - import flux.job.executor # noqa: F401 -except ImportError: - FLUX_AVAIL = False -else: - FLUX_AVAIL = True - -require_flux = pytest.mark.skipif( - not FLUX_AVAIL, reason="Flux not available, test will fail" -) -ERRMSG = "Some error message" - - -def multiply(x, y): - return x * y - - -def bad_foo(): - raise ValueError(ERRMSG) - - -@require_flux -@pytest.mark.local -def test_multiply(): - with FluxExecutor() as executor: - executor.start() - futures = [executor.submit(multiply, {}, i, 7) for i in range(5)] - for i, future in enumerate(futures): - assert future.result() == i * 7 - assert future.done() - assert future.exception() is None - assert isinstance(future, FluxFutureWrapper) - - -@require_flux -@pytest.mark.local -def test_except(): - with FluxExecutor() as executor: - executor.start() - future = executor.submit(bad_foo, {}) - with pytest.raises(ValueError, match=ERRMSG): - future.result() - assert isinstance(future.exception(), ValueError) - - -@require_flux -@pytest.mark.local -@pytest.mark.skipif( - not hasattr(os, "sched_getaffinity") or len(os.sched_getaffinity(0)) < 2, - reason="Not Linux or too few CPUs", -) -def test_affinity(): - with FluxExecutor() as executor: - executor.start() - future = executor.submit(os.sched_getaffinity, {"cores_per_task": 2}, 0) - assert len(future.result()) > 1 - - -@require_flux -@pytest.mark.local -def test_cancel(): - with FluxExecutor() as executor: - executor.start() - futures = [executor.submit(multiply, {}, i, 9) for i in range(3)] - for i, future in enumerate(futures): - if future.cancel(): - assert future.cancelled() - assert future.done() - with pytest.raises(cf.CancelledError): - future.exception() - with pytest.raises(cf.CancelledError): - future.result() - else: - assert future.running() - assert future.done() - assert not future.cancelled() - assert future.result() == i * 9 - - -@pytest.mark.local -def test_future_cancel(): - underlying_future = cf.Future() - wrapper_future = FluxFutureWrapper() - wrapper_future._flux_future = underlying_future - assert not wrapper_future.done() - assert not wrapper_future.running() - assert not wrapper_future.cancelled() - assert wrapper_future.cancel() # should cancel underlying future - assert wrapper_future.cancel() # try again for good measure - assert wrapper_future.cancelled() - assert wrapper_future.done() - assert underlying_future.cancelled() - assert underlying_future.done() - - -@pytest.mark.local -def test_future_running(): - underlying_future = cf.Future() - wrapper_future = FluxFutureWrapper() - assert not underlying_future.running() - assert underlying_future.set_running_or_notify_cancel() - assert underlying_future.running() - assert not wrapper_future.running() - wrapper_future._flux_future = underlying_future - assert wrapper_future.running() - - -@pytest.mark.local -def test_future_callback_returncode(): - testfile = ".fluxexecutortest.txt" - returncode = 1 - underlying_future = cf.Future() - wrapper_future = FluxFutureWrapper() - wrapper_future._flux_future = underlying_future - underlying_future.add_done_callback( - lambda fut: _complete_future(testfile, wrapper_future, fut) - ) - underlying_future.set_result(returncode) - assert wrapper_future.done() - assert isinstance(wrapper_future.exception(), AppException) - - -@pytest.mark.local -def test_future_callback_nofile(): - testfile = ".fluxexecutortest.txt" - returncode = 0 - if os.path.isfile(testfile): - os.remove(testfile) - underlying_future = cf.Future() - wrapper_future = FluxFutureWrapper() - wrapper_future._flux_future = underlying_future - underlying_future.add_done_callback( - lambda fut: _complete_future(testfile, wrapper_future, fut) - ) - underlying_future.set_result(returncode) - assert wrapper_future.done() - assert isinstance(wrapper_future.exception(), FileNotFoundError) - - -@pytest.mark.local -def test_future_callback_flux_exception(): - underlying_future = cf.Future() - wrapper_future = FluxFutureWrapper() - wrapper_future._flux_future = underlying_future - underlying_future.add_done_callback( - lambda fut: _complete_future(".fluxexecutortest.txt", wrapper_future, fut) - ) - underlying_future.set_exception(ValueError()) - assert wrapper_future.done() - assert isinstance(wrapper_future.exception(), ValueError) - - -@pytest.mark.local -def test_future_cancel_no_underlying_future(): - wrapper_future = FluxFutureWrapper() - assert wrapper_future.cancel() - assert wrapper_future.cancelled() - assert not wrapper_future.running() diff --git a/parsl/parsl/tests/test_htex/__init__.py b/parsl/parsl/tests/test_htex/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_htex/mpi/__init__.py b/parsl/parsl/tests/test_htex/mpi/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_htex/mpi/mocks/pbs_nodefile b/parsl/parsl/tests/test_htex/mpi/mocks/pbs_nodefile deleted file mode 100644 index 42e3ab71..00000000 --- a/parsl/parsl/tests/test_htex/mpi/mocks/pbs_nodefile +++ /dev/null @@ -1,4 +0,0 @@ -node1 -node2 -node3 -node4 diff --git a/parsl/parsl/tests/test_htex/mpi/mocks/pbs_nodefile.8 b/parsl/parsl/tests/test_htex/mpi/mocks/pbs_nodefile.8 deleted file mode 100644 index 643d60f3..00000000 --- a/parsl/parsl/tests/test_htex/mpi/mocks/pbs_nodefile.8 +++ /dev/null @@ -1,8 +0,0 @@ -NODE001 -NODE002 -NODE003 -NODE004 -NODE005 -NODE006 -NODE007 -NODE008 diff --git a/parsl/parsl/tests/test_htex/mpi/test_bad_mpi_config.py b/parsl/parsl/tests/test_htex/mpi/test_bad_mpi_config.py deleted file mode 100644 index d7d928f7..00000000 --- a/parsl/parsl/tests/test_htex/mpi/test_bad_mpi_config.py +++ /dev/null @@ -1,50 +0,0 @@ -import pytest - -from parsl import Config -from parsl.executors import MPIExecutor -from parsl.launchers import AprunLauncher, SimpleLauncher, SrunLauncher -from parsl.providers import SlurmProvider - - -@pytest.mark.local -def test_bad_launcher(): - """TypeError if a launcher other than SimpleLauncher is supplied""" - - for launcher in [SrunLauncher(), AprunLauncher()]: - with pytest.raises(TypeError): - Config(executors=[ - MPIExecutor( - provider=SlurmProvider(launcher=launcher), - ) - ]) - - -@pytest.mark.local -def test_bad_mpi_launcher(): - """ValueError if an unsupported mpi_launcher is specified""" - - with pytest.raises(ValueError): - Config(executors=[ - MPIExecutor( - mpi_launcher="bad_launcher", - provider=SlurmProvider(launcher=SimpleLauncher()), - ) - ]) - - -@pytest.mark.local -@pytest.mark.parametrize( - "mpi_launcher", - ["srun", "aprun", "mpiexec"] -) -def test_correct_launcher_with_mpi_mode(mpi_launcher: str): - """Confirm that SimpleLauncher works with mpi_mode""" - - executor = MPIExecutor( - mpi_launcher=mpi_launcher, - provider=SlurmProvider(launcher=SimpleLauncher()), - ) - - assert executor.provider is not None, "htex always has a provider" - assert hasattr(executor.provider, 'launcher'), "supplied provider has a launcher attribute" - assert isinstance(executor.provider.launcher, SimpleLauncher) diff --git a/parsl/parsl/tests/test_htex/mpi/test_mpi_mode_enabled.py b/parsl/parsl/tests/test_htex/mpi/test_mpi_mode_enabled.py deleted file mode 100644 index 63005a8d..00000000 --- a/parsl/parsl/tests/test_htex/mpi/test_mpi_mode_enabled.py +++ /dev/null @@ -1,188 +0,0 @@ -import logging -import os -import random -from typing import Dict - -import pytest - -import parsl -from parsl import Config, bash_app, python_app -from parsl.executors import MPIExecutor -from parsl.executors.errors import InvalidResourceSpecification -from parsl.launchers import SimpleLauncher -from parsl.providers import LocalProvider - -EXECUTOR_LABEL = "MPI_TEST" - - -def local_setup(): - - cwd = os.path.abspath(os.path.dirname(__file__)) - pbs_nodefile = os.path.join(cwd, "mocks", "pbs_nodefile") - - config = Config( - executors=[ - MPIExecutor( - label=EXECUTOR_LABEL, - max_workers_per_block=2, - mpi_launcher="mpiexec", - provider=LocalProvider( - worker_init=f"export PBS_NODEFILE={pbs_nodefile}", - launcher=SimpleLauncher() - ) - ) - ]) - - parsl.load(config) - - -def local_teardown(): - parsl.dfk().cleanup() - - -@python_app -def get_env_vars(parsl_resource_specification: Dict = {}) -> Dict: - import os - - parsl_vars = {} - for key in os.environ: - if key.startswith("PARSL_"): - parsl_vars[key] = os.environ[key] - return parsl_vars - - -@pytest.mark.local -def test_only_resource_specs_set(): - """Confirm that resource_spec env vars are set while launch prefixes are not - when enable_mpi_mode = False""" - resource_spec = { - "num_nodes": 2, - "ranks_per_node": 2, - } - - future = get_env_vars(parsl_resource_specification=resource_spec) - - result = future.result() - assert isinstance(result, Dict) - logging.warning(f"Got table: {result}") - assert "PARSL_MPI_PREFIX" in result - assert "PARSL_MPIEXEC_PREFIX" in result - assert result["PARSL_MPI_PREFIX"] == result["PARSL_MPIEXEC_PREFIX"] - assert result["PARSL_NUM_NODES"] == str(resource_spec["num_nodes"]) - assert result["PARSL_RANKS_PER_NODE"] == str(resource_spec["ranks_per_node"]) - assert result["PARSL_NUM_RANKS"] == str(resource_spec["ranks_per_node"] * resource_spec["num_nodes"]) - - -@bash_app -def echo_launch_cmd( - parsl_resource_specification: Dict, - stdout=parsl.AUTO_LOGNAME, - stderr=parsl.AUTO_LOGNAME, -): - return 'echo "$PARSL_MPI_PREFIX hostname"' - - -@pytest.mark.local -def test_bash_default_prefix_set(): - """Confirm that resource_spec env vars are set while launch prefixes are not - when enable_mpi_mode = False""" - resource_spec = { - "num_nodes": 2, - "ranks_per_node": 2, - } - - future = echo_launch_cmd(parsl_resource_specification=resource_spec) - - result = future.result() - assert result == 0 - with open(future.stdout) as f: - output = f.readlines() - assert output[0].startswith("mpiexec") - logging.warning(f"output : {output}") - - -@pytest.mark.local -def test_bash_multiple_set(): - """Confirm that multiple apps can run without blocking each other out - when enable_mpi_mode = False""" - resource_spec = { - "num_nodes": 2, - "num_ranks": 4, - } - futures = [] - for i in range(4): - resource_spec["num_nodes"] = i + 1 - future = echo_launch_cmd(parsl_resource_specification=resource_spec) - futures.append(future) - - for future in futures: - result = future.result() - assert result == 0 - with open(future.stdout) as f: - output = f.readlines() - assert output[0].startswith("mpiexec") - - -@bash_app -def bash_resource_spec(parsl_resource_specification=None, stdout=parsl.AUTO_LOGNAME): - total_ranks = ( - parsl_resource_specification["ranks_per_node"] * parsl_resource_specification["num_nodes"] - ) - return f'echo "{total_ranks}"' - - -@pytest.mark.local -def test_bash_app_using_resource_spec(): - resource_spec = { - "num_nodes": 2, - "ranks_per_node": 2, - } - future = bash_resource_spec(parsl_resource_specification=resource_spec) - assert future.result() == 0 - with open(future.stdout) as f: - output = f.readlines() - total_ranks = resource_spec["num_nodes"] * resource_spec["ranks_per_node"] - assert int(output[0].strip()) == total_ranks - - -@python_app -def mock_app(sleep_dur: float = 0.0, parsl_resource_specification: Dict = {}): - import os - import time - time.sleep(sleep_dur) - - total_ranks = int(os.environ["PARSL_NUM_NODES"]) * int(os.environ["PARSL_RANKS_PER_NODE"]) - nodes = os.environ["PARSL_MPI_NODELIST"].split(',') - - return total_ranks, nodes - - -@pytest.mark.local -def test_simulated_load(rounds: int = 100): - - node_choices = (1, 2, 4) - sleep_choices = (0, 0.01, 0.02, 0.04) - ranks_per_node = (4, 8) - - futures = {} - for i in range(rounds): - resource_spec = { - "num_nodes": random.choice(node_choices), - "ranks_per_node": random.choice(ranks_per_node), - } - future = mock_app(sleep_dur=random.choice(sleep_choices), - parsl_resource_specification=resource_spec) - futures[future] = resource_spec - - for future in futures: - total_ranks, nodes = future.result(timeout=10) - assert len(nodes) == futures[future]["num_nodes"] - assert total_ranks == futures[future]["num_nodes"] * futures[future]["ranks_per_node"] - - -@pytest.mark.local -def test_missing_resource_spec(): - - with pytest.raises(InvalidResourceSpecification): - future = mock_app(sleep_dur=0.4) - future.result(timeout=10) diff --git a/parsl/parsl/tests/test_htex/mpi/test_mpi_prefix.py b/parsl/parsl/tests/test_htex/mpi/test_mpi_prefix.py deleted file mode 100644 index 48740950..00000000 --- a/parsl/parsl/tests/test_htex/mpi/test_mpi_prefix.py +++ /dev/null @@ -1,71 +0,0 @@ -import logging - -import pytest - -from parsl.executors.high_throughput.mpi_prefix_composer import ( - compose_all, - compose_aprun_launch_cmd, - compose_mpiexec_launch_cmd, - compose_srun_launch_cmd, -) -from parsl.executors.high_throughput.mpi_resource_management import Scheduler - -resource_spec = {"num_nodes": 2, - "num_ranks": 8, - "ranks_per_node": 4} - - -@pytest.mark.local -def test_srun_launch_cmd(): - prefix_name, composed_prefix = compose_srun_launch_cmd( - resource_spec=resource_spec, node_hostnames=["node1", "node2"] - ) - assert prefix_name == "PARSL_SRUN_PREFIX" - logging.warning(composed_prefix) - - assert "None" not in composed_prefix - - -@pytest.mark.local -def test_aprun_launch_cmd(): - prefix_name, composed_prefix = compose_aprun_launch_cmd( - resource_spec=resource_spec, node_hostnames=["node1", "node2"] - ) - logging.warning(composed_prefix) - assert prefix_name == "PARSL_APRUN_PREFIX" - assert "None" not in composed_prefix - - -@pytest.mark.local -def test_mpiexec_launch_cmd(): - prefix_name, composed_prefix = compose_mpiexec_launch_cmd( - resource_spec=resource_spec, node_hostnames=["node1", "node2"] - ) - logging.warning(composed_prefix) - assert prefix_name == "PARSL_MPIEXEC_PREFIX" - assert "None" not in composed_prefix - - -@pytest.mark.local -def test_slurm_launch_cmd(): - table = compose_all( - mpi_launcher="srun", - resource_spec=resource_spec, - node_hostnames=["NODE001", "NODE002"], - ) - - assert "PARSL_MPI_PREFIX" in table - assert "PARSL_SRUN_PREFIX" in table - - -@pytest.mark.local -def test_default_launch_cmd(): - table = compose_all( - mpi_launcher="srun", - resource_spec=resource_spec, - node_hostnames=["NODE001", "NODE002"], - ) - - assert "PARSL_MPI_PREFIX" in table - assert "PARSL_SRUN_PREFIX" in table - assert table["PARSL_MPI_PREFIX"] == table["PARSL_SRUN_PREFIX"] diff --git a/parsl/parsl/tests/test_htex/mpi/test_mpi_scheduler.py b/parsl/parsl/tests/test_htex/mpi/test_mpi_scheduler.py deleted file mode 100644 index 38a0420e..00000000 --- a/parsl/parsl/tests/test_htex/mpi/test_mpi_scheduler.py +++ /dev/null @@ -1,163 +0,0 @@ -import os -import pickle -from unittest import mock - -import pytest - -from parsl.executors.high_throughput.mpi_resource_management import ( - MPITaskScheduler, - TaskScheduler, -) -from parsl.multiprocessing import SpawnContext -from parsl.serialize import pack_apply_message - -mock_task_buffer = pack_apply_message("func", "args", "kwargs") - - -@pytest.fixture(autouse=True) -def set_pbs_nodefile_envvars(): - cwd = os.path.abspath(os.path.dirname(__file__)) - pbs_nodefile = os.path.join(cwd, "mocks", "pbs_nodefile.8") - with mock.patch.dict(os.environ, {"PBS_NODEFILE": pbs_nodefile}): - yield - - -@pytest.mark.local -def test_NoopScheduler(): - task_q, result_q = SpawnContext.Queue(), SpawnContext.Queue() - scheduler = TaskScheduler(task_q, result_q) - - scheduler.put_task("TaskFoo") - assert task_q.get() == "TaskFoo" - - result_q.put("Result1") - assert scheduler.get_result(True, 1) == "Result1" - - -@pytest.mark.local -def test_MPISched_put_task(): - task_q, result_q = SpawnContext.Queue(), SpawnContext.Queue() - scheduler = MPITaskScheduler(task_q, result_q) - - assert scheduler.available_nodes - assert len(scheduler.available_nodes) == 8 - assert scheduler._free_node_counter.value == 8 - - ctxt = {"resource_spec": {"num_nodes": 2, "ranks_per_node": 2}} - task_package = {"task_id": 1, "buffer": mock_task_buffer, "context": ctxt} - scheduler.put_task(task_package) - - assert scheduler._free_node_counter.value == 6 - - -@pytest.mark.local -def test_MPISched_get_result(): - task_q, result_q = SpawnContext.Queue(), SpawnContext.Queue() - scheduler = MPITaskScheduler(task_q, result_q) - - assert scheduler.available_nodes - assert len(scheduler.available_nodes) == 8 - assert scheduler._free_node_counter.value == 8 - - nodes = [scheduler.nodes_q.get() for _ in range(4)] - scheduler._free_node_counter.value = 4 - scheduler._map_tasks_to_nodes[1] = nodes - - result_package = pickle.dumps({"task_id": 1, "type": "result", "buffer": "Foo"}) - result_q.put(result_package) - result_received = scheduler.get_result(block=True, timeout=1) - assert result_received == result_package - - assert scheduler._free_node_counter.value == 8 - - -@pytest.mark.local -def test_MPISched_roundtrip(): - task_q, result_q = SpawnContext.Queue(), SpawnContext.Queue() - scheduler = MPITaskScheduler(task_q, result_q) - - assert scheduler.available_nodes - assert len(scheduler.available_nodes) == 8 - - for trip in range(1, 9): - assert scheduler._free_node_counter.value == 8 - - ctxt = {"resource_spec": {"num_nodes": trip, "ranks_per_node": 2}} - task_package = {"task_id": trip, "buffer": mock_task_buffer, "context": ctxt} - scheduler.put_task(task_package) - - assert scheduler._free_node_counter.value == 8 - trip - - # Pop in a mock result - result_pkl = pickle.dumps({"task_id": trip, "type": "result", "buffer": "RESULT BUF"}) - result_q.put(result_pkl) - - got_result = scheduler.get_result(True, 1) - assert got_result == result_pkl - - -@pytest.mark.local -def test_MPISched_contention(): - """Second task has to wait for the first task due to insufficient resources""" - task_q, result_q = SpawnContext.Queue(), SpawnContext.Queue() - scheduler = MPITaskScheduler(task_q, result_q) - - assert scheduler.available_nodes - assert len(scheduler.available_nodes) == 8 - - assert scheduler._free_node_counter.value == 8 - - ctxt_1 = {"resource_spec": {"num_nodes": 8, "ranks_per_node": 2}} - task_package = {"task_id": 1, "buffer": mock_task_buffer, "context": ctxt_1} - scheduler.put_task(task_package) - - assert scheduler._free_node_counter.value == 0 - assert scheduler._backlog_queue.empty() - - ctxt_2 = {"resource_spec": {"num_nodes": 8, "ranks_per_node": 2}} - task_package = {"task_id": 2, "buffer": mock_task_buffer, "context": ctxt_2} - scheduler.put_task(task_package) - - # Second task should now be in the backlog_queue - assert not scheduler._backlog_queue.empty() - - # Confirm that the first task is available and has all 8 nodes provisioned - task_on_worker_side = task_q.get() - assert task_on_worker_side['task_id'] == 1 - assert len(ctxt_1["resource_spec"]["MPI_NODELIST"].split(",")) == 8 - assert task_q.empty() # Confirm that task 2 is not yet scheduled - - # Simulate worker returning result and the scheduler picking up result - result_pkl = pickle.dumps({"task_id": 1, "type": "result", "buffer": "RESULT BUF"}) - result_q.put(result_pkl) - got_result = scheduler.get_result(True, 1) - assert got_result == result_pkl - - # Now task2 must be scheduled - assert scheduler._backlog_queue.empty() - - # Pop in a mock result - task_on_worker_side = task_q.get() - assert task_on_worker_side['task_id'] == 2 - assert len(ctxt_2["resource_spec"]["MPI_NODELIST"].split(",")) == 8 - - -@pytest.mark.local -def test_hashable_backlog_queue(): - """Run multiple large tasks that to force entry into backlog_queue - where queue.PriorityQueue expects hashability/comparability - """ - - task_q, result_q = SpawnContext.Queue(), SpawnContext.Queue() - scheduler = MPITaskScheduler(task_q, result_q) - - assert scheduler.available_nodes - assert len(scheduler.available_nodes) == 8 - - assert scheduler._free_node_counter.value == 8 - - for i in range(3): - ctxt = {"resource_spec": {"num_nodes": 8, "ranks_per_node": 2}} - task_package = {"task_id": i, "buffer": mock_task_buffer, "context": ctxt} - scheduler.put_task(task_package) - assert scheduler._backlog_queue.qsize() == 2, "Expected 2 backlogged tasks" diff --git a/parsl/parsl/tests/test_htex/mpi/test_mpiex.py b/parsl/parsl/tests/test_htex/mpi/test_mpiex.py deleted file mode 100644 index e52eccd9..00000000 --- a/parsl/parsl/tests/test_htex/mpi/test_mpiex.py +++ /dev/null @@ -1,64 +0,0 @@ -"""Tests for the wrapper class""" -from inspect import signature -from pathlib import Path - -import pytest - -from parsl import Config, HighThroughputExecutor -from parsl.executors.high_throughput.mpi_executor import MPIExecutor -from parsl.launchers import SimpleLauncher -from parsl.providers import LocalProvider - -from .test_mpi_mode_enabled import get_env_vars - -cwd = Path(__file__).parent.absolute() -pbs_nodefile = cwd.joinpath("mocks", "pbs_nodefile") - - -def local_config(): - return Config( - executors=[ - MPIExecutor( - max_workers_per_block=1, - provider=LocalProvider( - worker_init=f"export PBS_NODEFILE={pbs_nodefile}", - launcher=SimpleLauncher() - ) - ) - ] - ) - - -@pytest.mark.local -def test_docstring(): - """Ensure the old kwargs are copied over into the new class""" - assert 'label' in MPIExecutor.__doc__ - assert 'max_workers_per_block' in MPIExecutor.__doc__ - assert 'available_accelerators' not in MPIExecutor.__doc__ - - -@pytest.mark.local -def test_init(): - """Ensure all relevant kwargs are copied over from HTEx""" - - new_kwargs = {'max_workers_per_block', 'mpi_launcher'} - excluded_kwargs = {'available_accelerators', 'cores_per_worker', 'max_workers_per_node', - 'mem_per_worker', 'cpu_affinity', 'manager_selector'} - - # Get the kwargs from both HTEx and MPIEx - htex_kwargs = set(signature(HighThroughputExecutor.__init__).parameters) - mpix_kwargs = set(signature(MPIExecutor.__init__).parameters) - - assert mpix_kwargs.difference(htex_kwargs) == new_kwargs - assert len(mpix_kwargs.intersection(excluded_kwargs)) == 0 - assert mpix_kwargs.union(excluded_kwargs).difference(new_kwargs) == htex_kwargs - - -@pytest.mark.local -def test_get_env(): - future = get_env_vars(parsl_resource_specification={ - "num_nodes": 3, - "ranks_per_node": 5, - }) - env_vars = future.result() - assert env_vars['PARSL_NUM_RANKS'] == '15' diff --git a/parsl/parsl/tests/test_htex/mpi/test_resource_spec.py b/parsl/parsl/tests/test_htex/mpi/test_resource_spec.py deleted file mode 100644 index 058a7ef4..00000000 --- a/parsl/parsl/tests/test_htex/mpi/test_resource_spec.py +++ /dev/null @@ -1,138 +0,0 @@ -import contextlib -import logging -import os -import queue -import typing -import unittest -from typing import Dict -from unittest import mock - -import pytest - -from parsl.app.app import python_app -from parsl.executors.errors import InvalidResourceSpecification -from parsl.executors.high_throughput.executor import HighThroughputExecutor -from parsl.executors.high_throughput.mpi_executor import MPIExecutor -from parsl.executors.high_throughput.mpi_resource_management import ( - get_nodes_in_batchjob, - get_pbs_hosts_list, - get_slurm_hosts_list, - identify_scheduler, -) -from parsl.launchers import SimpleLauncher -from parsl.providers import LocalProvider -from parsl.tests.configs.htex_local import fresh_config - -EXECUTOR_LABEL = "MPI_TEST" - - -def local_config(): - config = fresh_config() - config.executors[0].label = EXECUTOR_LABEL - config.executors[0].max_workers_per_node = 1 - return config - - -@python_app -def double(x, resource_spec=None): - return x * 2 - - -@python_app -def get_env_vars(parsl_resource_specification: Dict = {}) -> Dict: - import os - - parsl_vars = {} - for key in os.environ: - if key.startswith("PARSL_"): - parsl_vars[key] = os.environ[key] - return parsl_vars - - -@pytest.mark.local -@unittest.mock.patch("subprocess.check_output", return_value=b"c203-031\nc203-032\n") -def test_slurm_mocked_mpi_fetch(subprocess_check): - nodeinfo = get_slurm_hosts_list() - assert isinstance(nodeinfo, list) - assert len(nodeinfo) == 2 - - -@contextlib.contextmanager -def add_to_path(path: os.PathLike) -> typing.Generator[None, None, None]: - old_path = os.environ["PATH"] - try: - os.environ["PATH"] += str(path) - yield - finally: - os.environ["PATH"] = old_path - - -@contextlib.contextmanager -def mock_pbs_nodefile(nodefile: str = "pbs_nodefile") -> typing.Generator[None, None, None]: - cwd = os.path.abspath(os.path.dirname(__file__)) - filename = os.path.join(cwd, "mocks", nodefile) - try: - os.environ["PBS_NODEFILE"] = filename - yield - finally: - del os.environ["PBS_NODEFILE"] - - -@pytest.mark.local -def test_get_pbs_hosts_list(): - with mock_pbs_nodefile(): - nodelist = get_pbs_hosts_list() - assert nodelist - assert len(nodelist) == 4 - - -@pytest.mark.local -def test_top_level(): - with mock_pbs_nodefile(): - scheduler = identify_scheduler() - nodelist = get_nodes_in_batchjob(scheduler) - assert len(nodelist) > 0 - - -@pytest.mark.local -@pytest.mark.parametrize( - "resource_spec, exception", - ( - - ({"num_nodes": 2, "ranks_per_node": 1}, None), - ({"launcher_options": "--debug_foo"}, None), - ({"num_nodes": 2, "BAD_OPT": 1}, InvalidResourceSpecification), - ({}, InvalidResourceSpecification), - ) -) -def test_mpi_resource_spec(resource_spec: Dict, exception): - """Test validation of resource_specification in MPIExecutor""" - - mpi_ex = MPIExecutor(provider=LocalProvider(launcher=SimpleLauncher())) - mpi_ex.outgoing_q = mock.Mock(spec=queue.Queue) - - if exception: - with pytest.raises(exception): - mpi_ex.validate_resource_spec(resource_spec) - else: - result = mpi_ex.validate_resource_spec(resource_spec) - assert result is None - - -@pytest.mark.local -@pytest.mark.parametrize( - "resource_spec", - ( - {"num_nodes": 2, "ranks_per_node": 1}, - {"launcher_options": "--debug_foo"}, - {"BAD_OPT": 1}, - ) -) -def test_mpi_resource_spec_passed_to_htex(resource_spec: dict): - """HTEX should reject every resource_spec""" - - htex = HighThroughputExecutor() - htex.outgoing_q = mock.Mock(spec=queue.Queue) - - with pytest.raises(InvalidResourceSpecification): - htex.validate_resource_spec(resource_spec) diff --git a/parsl/parsl/tests/test_htex/test_affinity.py b/parsl/parsl/tests/test_htex/test_affinity.py deleted file mode 100644 index 792d490e..00000000 --- a/parsl/parsl/tests/test_htex/test_affinity.py +++ /dev/null @@ -1,51 +0,0 @@ -"""Tests related to assigning workers to specific compute units""" - -import os - -import pytest - -from parsl import python_app -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.providers import LocalProvider - - -def local_config(): - return Config( - executors=[ - HighThroughputExecutor( - label="htex_Local", - worker_debug=True, - max_workers_per_node=2, - cpu_affinity='block', - available_accelerators=2, - encrypted=True, - provider=LocalProvider( - init_blocks=1, - max_blocks=1, - ), - ) - ], - strategy='none', - ) - - -@python_app -def get_worker_info(): - import os - from time import sleep - rank = int(os.environ['PARSL_WORKER_RANK']) - aff = os.sched_getaffinity(0) - device = os.environ.get('CUDA_VISIBLE_DEVICES') - sleep(1.0) - return rank, (aff, device) - - -@pytest.mark.local -@pytest.mark.skipif('sched_getaffinity' not in dir(os), reason='System does not support sched_setaffinity') -@pytest.mark.skipif(os.cpu_count() == 1, reason='Must have a more than one CPU') -def test_htex(): - worker_info = [get_worker_info() for _ in range(4)] - worker_affinity = dict([r.result() for r in worker_info]) - assert worker_affinity[0] != worker_affinity[1] - assert worker_affinity[0][1] == "0" # Make sure it is pinned to the correct CUDA device diff --git a/parsl/parsl/tests/test_htex/test_basic.py b/parsl/parsl/tests/test_htex/test_basic.py deleted file mode 100644 index feb8404e..00000000 --- a/parsl/parsl/tests/test_htex/test_basic.py +++ /dev/null @@ -1,27 +0,0 @@ -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.tests.configs.htex_local import fresh_config - - -def local_setup(): - config = fresh_config() - config.executors[0].poll_period = 1 - config.executors[0].max_workers_per_node = 1 - parsl.load(config) - - -def local_teardown(): - parsl.dfk().cleanup() - - -@python_app -def dummy(): - pass - - -@pytest.mark.local -def test_app(): - x = dummy() - assert x.result() is None diff --git a/parsl/parsl/tests/test_htex/test_block_manager_selector_unit.py b/parsl/parsl/tests/test_htex/test_block_manager_selector_unit.py deleted file mode 100644 index 46b2bb1e..00000000 --- a/parsl/parsl/tests/test_htex/test_block_manager_selector_unit.py +++ /dev/null @@ -1,20 +0,0 @@ -import pytest - -from parsl.executors.high_throughput.manager_record import ManagerRecord -from parsl.executors.high_throughput.manager_selector import BlockIdManagerSelector - - -@pytest.mark.local -def test_sort_managers(): - ready_managers = { - b'manager1': {'block_id': 1}, - b'manager2': {'block_id': None}, - b'manager3': {'block_id': 3}, - b'manager4': {'block_id': 2} - } - - manager_list = {b'manager1', b'manager2', b'manager3', b'manager4'} - expected_sorted_list = [b'manager2', b'manager1', b'manager4', b'manager3'] - manager_selector = BlockIdManagerSelector() - sorted_managers = manager_selector.sort_managers(ready_managers, manager_list) - assert sorted_managers == expected_sorted_list diff --git a/parsl/parsl/tests/test_htex/test_command_client_timeout.py b/parsl/parsl/tests/test_htex/test_command_client_timeout.py deleted file mode 100644 index f003950a..00000000 --- a/parsl/parsl/tests/test_htex/test_command_client_timeout.py +++ /dev/null @@ -1,66 +0,0 @@ -import threading -import time - -import pytest -import zmq - -from parsl import curvezmq -from parsl.executors.high_throughput.errors import ( - CommandClientBadError, - CommandClientTimeoutError, -) -from parsl.executors.high_throughput.zmq_pipes import CommandClient - -# Time constant used for timeout tests: various delays and -# timeouts will be appropriate multiples of this, but the -# value of T itself should not matter too much as long as -# it is big enough for zmq connections to happen successfully. -T = 0.25 - - -@pytest.mark.local -def test_command_not_sent() -> None: - """Tests timeout on command send. - """ - # RFC6335 ephemeral port range - cc = CommandClient("127.0.0.1", (49152, 65535)) - - # cc will now wait for a connection, but we won't do anything to make the - # other side of the connection exist, so any command given to cc should - # timeout. - - with pytest.raises(CommandClientTimeoutError): - cc.run("SOMECOMMAND", timeout_s=T) - - cc.close() - - -@pytest.mark.local -def test_command_ignored() -> None: - """Tests timeout on command response. - Tests that we timeout after a response and that the command client - sets itself into a bad state. - - This only tests sequential access to the command client, even though - htex makes multithreaded use of the command client: see issue #3376 about - that lack of thread safety. - """ - # RFC6335 ephemeral port range - cc = CommandClient("127.0.0.1", (49152, 65535)) - - ic_ctx = curvezmq.ServerContext(None) - ic_channel = ic_ctx.socket(zmq.REP) - ic_channel.connect(f"tcp://127.0.0.1:{cc.port}") - - with pytest.raises(CommandClientTimeoutError): - cc.run("SLOW_COMMAND", timeout_s=T) - - req = ic_channel.recv_pyobj() - assert req == "SLOW_COMMAND", "Should have received command on interchange side" - assert not cc.ok, "CommandClient should have set itself to bad" - - with pytest.raises(CommandClientBadError): - cc.run("ANOTHER_COMMAND") - - cc.close() - ic_channel.close() diff --git a/parsl/parsl/tests/test_htex/test_command_concurrency_regression_1321.py b/parsl/parsl/tests/test_htex/test_command_concurrency_regression_1321.py deleted file mode 100644 index f319789c..00000000 --- a/parsl/parsl/tests/test_htex/test_command_concurrency_regression_1321.py +++ /dev/null @@ -1,53 +0,0 @@ -import time -from threading import Event, Thread - -import pytest - -import parsl -from parsl.tests.configs.htex_local import fresh_config as local_config - -N_THREADS = 50 -DURATION_S = 10 - - -@pytest.mark.local -def test_concurrency_blast(): - """Blast interchange command channel from many threads. - """ - - cc = parsl.dfk().executors['htex_local'].command_client - - threads = [] - - ok_so_far = True - - for _ in range(N_THREADS): - - # This event will be set if the thread reaches the end of its body. - event = Event() - - thread = Thread(target=blast, args=(cc, event)) - threads.append((thread, event)) - - for thread, event in threads: - thread.start() - - for thread, event in threads: - thread.join() - if not event.is_set(): - ok_so_far = False - - assert ok_so_far, "at least one thread did not exit normally" - - -def blast(cc, e): - target_end = time.monotonic() + DURATION_S - - while time.monotonic() < target_end: - cc.run("MANGERS_PACKAGES") - cc.run("CONNECTED_BLOCKS") - cc.run("WORKER_BINDS") - - # If any of the preceeding cc.run calls raises an exception, the thread - # will not set its successful completion event. - e.set() diff --git a/parsl/parsl/tests/test_htex/test_connected_blocks.py b/parsl/parsl/tests/test_htex/test_connected_blocks.py deleted file mode 100644 index 94f36b35..00000000 --- a/parsl/parsl/tests/test_htex/test_connected_blocks.py +++ /dev/null @@ -1,62 +0,0 @@ -import pytest - -import parsl -from parsl import Config -from parsl.executors import HighThroughputExecutor -from parsl.providers import LocalProvider - - -def local_config(): - return Config( - executors=[ - HighThroughputExecutor( - label="HTEX", - heartbeat_period=1, - heartbeat_threshold=2, - poll_period=100, - address="127.0.0.1", - max_workers_per_node=1, - provider=LocalProvider( - init_blocks=0, - max_blocks=2, - min_blocks=0, - ), - ) - ], - max_idletime=0.5, - strategy='htex_auto_scale', - ) - - -@parsl.python_app -def double(x): - return x * 2 - - -@pytest.mark.local -def test_get_connected_blocks(): - """Test reporting of connected blocks from HTEX""" - dfk = parsl.dfk() - executor = dfk.executors["HTEX"] - - connected_blocks = executor.connected_blocks() - assert not connected_blocks, "Expected 0 blocks" - - blocking_task = double(5).result() - assert blocking_task == 10 - - connected_blocks = executor.connected_blocks() - assert len(connected_blocks) == 1, "Expected 1 block" - - executor.scale_in(1) - - connected_blocks = executor.connected_blocks() - assert len(connected_blocks) == 1, "Expected 1 block" - - blocking_task = double(5).result() - assert blocking_task == 10 - - # With the first block scaled_in there should be reporting - # 2 blocks including the older one. - connected_blocks = executor.connected_blocks() - assert len(connected_blocks) == 2, "Expected 2 blocks" diff --git a/parsl/parsl/tests/test_htex/test_cpu_affinity_explicit.py b/parsl/parsl/tests/test_htex/test_cpu_affinity_explicit.py deleted file mode 100644 index 0dfef432..00000000 --- a/parsl/parsl/tests/test_htex/test_cpu_affinity_explicit.py +++ /dev/null @@ -1,48 +0,0 @@ -import logging -import os -import random - -import pytest - -import parsl -from parsl.tests.configs.htex_local import fresh_config - -logger = logging.getLogger(__name__) - - -@parsl.python_app -def my_affinity(): - """an app that returns the affinity of the unix process it is currently in. - """ - import os - return os.sched_getaffinity(0) - - -@pytest.mark.local -@pytest.mark.multiple_cores_required -@pytest.mark.skipif('sched_getaffinity' not in dir(os), reason='System does not support sched_setaffinity') -def test_cpu_affinity_explicit(): - available_cores = os.sched_getaffinity(0) - - logger.debug(f"Got these cores: {available_cores}") - - assert len(available_cores) >= 2, "This test requires multiple cores. Run with '-k not multiple_cores' to skip" - - cores_as_list = list(available_cores) - - single_core = random.choice(cores_as_list) - affinity = f"list:{single_core}" - - logger.debug(f"Will test with affinity for one worker, one core: {affinity}") - - config = fresh_config() - config.executors[0].cpu_affinity = affinity - config.executors[0].max_workers_per_node = 1 - - logger.debug(f"config: {config}") - - with parsl.load(config): - worker_affinity = my_affinity().result() - logger.debug(f"worker reported this affinity: {worker_affinity}") - assert len(worker_affinity) == 1 - assert worker_affinity == set((single_core,)) diff --git a/parsl/parsl/tests/test_htex/test_disconnected_blocks.py b/parsl/parsl/tests/test_htex/test_disconnected_blocks.py deleted file mode 100644 index 2fc6c627..00000000 --- a/parsl/parsl/tests/test_htex/test_disconnected_blocks.py +++ /dev/null @@ -1,65 +0,0 @@ -import logging - -import pytest - -import parsl -from parsl import Config -from parsl.executors import HighThroughputExecutor -from parsl.executors.errors import BadStateException -from parsl.jobs.states import JobState, JobStatus -from parsl.providers import LocalProvider - - -def local_config(): - """Config to simulate failing blocks without connecting""" - return Config( - executors=[ - HighThroughputExecutor( - label="HTEX", - heartbeat_period=1, - heartbeat_threshold=2, - poll_period=100, - max_workers_per_node=1, - provider=LocalProvider( - worker_init="conda deactivate; export PATH=''; which python; exit 0", - init_blocks=0, - max_blocks=2, - min_blocks=0, - ), - ) - ], - max_idletime=0.5, - strategy='htex_auto_scale', - ) - - -@parsl.python_app -def double(x): - return x * 2 - - -@pytest.mark.local -def test_disconnected_blocks(): - """Test reporting of blocks that fail to connect from HTEX""" - dfk = parsl.dfk() - executor = dfk.executors["HTEX"] - - connected_blocks = executor.connected_blocks() - assert not connected_blocks, "Expected 0 blocks" - - future = double(5) - with pytest.raises(BadStateException): - future.result() - - assert isinstance(future.exception(), BadStateException) - exception_body = str(future.exception()) - assert "EXIT CODE: 0" in exception_body - - status_dict = executor.status() - assert len(status_dict) == 1, "Expected only 1 block" - for status in status_dict.values(): - assert isinstance(status, JobStatus) - assert status.state == JobState.MISSING - - connected_blocks = executor.connected_blocks() - assert not connected_blocks, "Expected 0 blocks" diff --git a/parsl/parsl/tests/test_htex/test_disconnected_blocks_failing_provider.py b/parsl/parsl/tests/test_htex/test_disconnected_blocks_failing_provider.py deleted file mode 100644 index b2fa507a..00000000 --- a/parsl/parsl/tests/test_htex/test_disconnected_blocks_failing_provider.py +++ /dev/null @@ -1,71 +0,0 @@ -import logging - -import pytest - -import parsl -from parsl import Config -from parsl.executors import HighThroughputExecutor -from parsl.executors.errors import BadStateException -from parsl.jobs.states import JobState, JobStatus -from parsl.providers import LocalProvider - - -class FailingProvider(LocalProvider): - def submit(*args, **kwargs): - raise RuntimeError("Deliberate failure of provider.submit") - - -def local_config(): - """Config to simulate failing blocks without connecting""" - return Config( - executors=[ - HighThroughputExecutor( - label="HTEX", - heartbeat_period=1, - heartbeat_threshold=2, - poll_period=100, - max_workers_per_node=1, - provider=FailingProvider( - init_blocks=0, - max_blocks=2, - min_blocks=0, - ), - ) - ], - max_idletime=0.5, - strategy='htex_auto_scale', - strategy_period=0.1 - # this strategy period needs to be a few times smaller than the - # status_polling_interval of FailingProvider, which is 5s at - # time of writing - ) - - -@parsl.python_app -def double(x): - return x * 2 - - -@pytest.mark.local -def test_disconnected_blocks(): - """Test reporting of blocks that fail to connect from HTEX""" - dfk = parsl.dfk() - executor = dfk.executors["HTEX"] - - connected_blocks = executor.connected_blocks() - assert not connected_blocks, "Expected 0 blocks" - - future = double(5) - with pytest.raises(BadStateException): - future.result() - - assert isinstance(future.exception(), BadStateException) - - status_dict = executor.status() - assert len(status_dict) == 1, "Expected exactly 1 block" - for status in status_dict.values(): - assert isinstance(status, JobStatus) - assert status.state == JobState.MISSING - - connected_blocks = executor.connected_blocks() - assert connected_blocks == [], "Expected exactly 0 connected blocks" diff --git a/parsl/parsl/tests/test_htex/test_drain.py b/parsl/parsl/tests/test_htex/test_drain.py deleted file mode 100644 index 663978f4..00000000 --- a/parsl/parsl/tests/test_htex/test_drain.py +++ /dev/null @@ -1,79 +0,0 @@ -import time - -import pytest - -import parsl -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SimpleLauncher -from parsl.providers import LocalProvider - -# this constant is used to scale some durations that happen -# based around the expected drain period: the drain period -# is TIME_CONST seconds, and the single executed task will -# last twice that many number of seconds. -TIME_CONST = 4 - -CONNECTED_MANAGERS_POLL_MS = 100 - - -def local_config(): - return Config( - executors=[ - HighThroughputExecutor( - label="htex_local", - drain_period=TIME_CONST, - worker_debug=True, - cores_per_worker=1, - encrypted=True, - provider=LocalProvider( - init_blocks=1, - min_blocks=0, - max_blocks=0, - launcher=SimpleLauncher(), - ), - ) - ], - strategy='none', - strategy_period=0.1 - ) - - -@parsl.python_app -def f(n): - import time - time.sleep(n) - - -@pytest.mark.local -def test_drain(try_assert): - - htex = parsl.dfk().executors['htex_local'] - - # wait till we have a block running... - - try_assert(lambda: len(htex.connected_managers()) == 1, check_period_ms=CONNECTED_MANAGERS_POLL_MS) - - managers = htex.connected_managers() - assert managers[0]['active'], "The manager should be active" - assert not managers[0]['draining'], "The manager should not be draining" - - fut = f(TIME_CONST * 2) - - time.sleep(TIME_CONST) - - # this assert should happen *very fast* after the above delay... - try_assert(lambda: htex.connected_managers()[0]['draining'], timeout_ms=500, check_period_ms=CONNECTED_MANAGERS_POLL_MS) - - # and the test task should still be running... - assert not fut.done(), "The test task should still be running" - - fut.result() - - # and now we should see the manager disappear... - # ... with strategy='none', this should be coming from draining but - # that information isn't immediately obvious from the absence in - # connected managers. - # As with the above draining assert, this should happen very fast after - # the task ends. - try_assert(lambda: len(htex.connected_managers()) == 0, timeout_ms=500, check_period_ms=CONNECTED_MANAGERS_POLL_MS) diff --git a/parsl/parsl/tests/test_htex/test_htex.py b/parsl/parsl/tests/test_htex/test_htex.py deleted file mode 100644 index a8d9e86b..00000000 --- a/parsl/parsl/tests/test_htex/test_htex.py +++ /dev/null @@ -1,185 +0,0 @@ -import logging -import pathlib -from subprocess import Popen, TimeoutExpired -from typing import Optional, Sequence -from unittest import mock - -import pytest - -from parsl import HighThroughputExecutor, curvezmq -from parsl.serialize.facade import pack_apply_message, unpack_apply_message - -_MOCK_BASE = "parsl.executors.high_throughput.executor" - - -@pytest.fixture -def encrypted(request: pytest.FixtureRequest): - if hasattr(request, "param"): - return request.param - return True - - -@pytest.fixture -def htex(encrypted: bool, tmpd_cwd): - htex = HighThroughputExecutor(encrypted=encrypted) - htex.max_workers_per_node = 1 - htex.run_dir = tmpd_cwd - - assert htex.provider is not None, "htex always has a provider" - htex.provider.script_dir = tmpd_cwd - - yield htex - - if hasattr(htex, "outgoing_q"): - htex.scale_in(blocks=1000) - htex.shutdown() - - -@pytest.mark.local -@pytest.mark.parametrize("encrypted", (True, False), indirect=True) -@pytest.mark.parametrize("cert_dir_provided", (True, False)) -def test_htex_start_encrypted( - encrypted: bool, - cert_dir_provided: bool, - htex: HighThroughputExecutor, - tmpd_cwd: pathlib.Path, -): - htex.run_dir = str(tmpd_cwd) - if cert_dir_provided: - provided_base_dir = tmpd_cwd / "provided" - provided_base_dir.mkdir() - cert_dir = curvezmq.create_certificates(provided_base_dir) - htex.cert_dir = cert_dir - else: - cert_dir = curvezmq.create_certificates(htex.logdir) - - if not encrypted and cert_dir_provided: - with pytest.raises(AttributeError) as pyt_e: - htex.start() - assert "change cert_dir to None" in str(pyt_e.value) - return - - htex.start() - - assert htex.encrypted is encrypted - if encrypted: - assert htex.cert_dir == cert_dir - assert htex.outgoing_q.zmq_context.cert_dir == cert_dir - assert htex.incoming_q.zmq_context.cert_dir == cert_dir - assert htex.command_client.zmq_context.cert_dir == cert_dir - assert isinstance(htex.outgoing_q.zmq_context, curvezmq.ClientContext) - assert isinstance(htex.incoming_q.zmq_context, curvezmq.ClientContext) - assert isinstance(htex.command_client.zmq_context, curvezmq.ClientContext) - else: - assert htex.cert_dir is None - assert htex.outgoing_q.zmq_context.cert_dir is None - assert htex.incoming_q.zmq_context.cert_dir is None - assert htex.command_client.zmq_context.cert_dir is None - - -@pytest.mark.local -@pytest.mark.parametrize("started", (True, False)) -@pytest.mark.parametrize("timeout_expires", (True, False)) -def test_htex_shutdown( - started: bool, - timeout_expires: bool, - htex: HighThroughputExecutor, - caplog -): - mock_ix_proc = mock.Mock(spec=Popen) - - if started: - htex.interchange_proc = mock_ix_proc - - # This will, in the absence of any exit trigger, block forever if - # no timeout is given and if the interchange does not terminate. - # Raise an exception to report that, rather than actually block, - # and hope that nothing is catching that exception. - - # this function implements the behaviour if the interchange has - # not received a termination call - def proc_wait_alive(timeout): - if timeout: - raise TimeoutExpired(cmd="mock-interchange", timeout=timeout) - else: - raise RuntimeError("This wait call would hang forever") - - def proc_wait_terminated(timeout): - return 0 - - mock_ix_proc.wait.side_effect = proc_wait_alive - - if not timeout_expires: - # Simulate termination of the Interchange process - def kill_interchange(*args, **kwargs): - mock_ix_proc.wait.side_effect = proc_wait_terminated - - mock_ix_proc.terminate.side_effect = kill_interchange - - with caplog.at_level(logging.INFO): - htex.shutdown() - - if started: - assert mock_ix_proc.terminate.called - assert mock_ix_proc.wait.called - assert {"timeout": 10} == mock_ix_proc.wait.call_args[1] - if timeout_expires: - assert "Unable to terminate Interchange" in caplog.text - assert mock_ix_proc.kill.called - assert "Attempting HighThroughputExecutor shutdown" in caplog.text - assert "Finished HighThroughputExecutor shutdown" in caplog.text - else: - assert not mock_ix_proc.terminate.called - assert not mock_ix_proc.wait.called - assert "HighThroughputExecutor has not started" in caplog.text - - -@pytest.mark.local -@pytest.mark.parametrize("cmd", (None, "custom-launch-cmd")) -def test_htex_worker_pool_launch_cmd(cmd: Optional[str]): - if cmd: - htex = HighThroughputExecutor(launch_cmd=cmd) - assert htex.launch_cmd == cmd - else: - htex = HighThroughputExecutor() - assert htex.launch_cmd.startswith("process_worker_pool.py") - - -@pytest.mark.local -@pytest.mark.parametrize("cmd", (None, ["custom", "launch", "cmd"])) -def test_htex_interchange_launch_cmd(cmd: Optional[Sequence[str]]): - if cmd: - htex = HighThroughputExecutor(interchange_launch_cmd=cmd) - assert htex.interchange_launch_cmd == cmd - else: - htex = HighThroughputExecutor() - assert htex.interchange_launch_cmd == ["interchange.py"] - - -def dyn_exec(buf, *vec_y): - f, a, _ = unpack_apply_message(buf) - custom_args = [a, vec_y] - return f(*custom_args) - - -@pytest.mark.local -def test_worker_dynamic_import(htex: HighThroughputExecutor): - def _dot_prod(vec_x, vec_y): - return sum(x * y for x, y in zip(vec_x, vec_y)) - - htex.start() - htex.scale_out_facade(1) - - num_array = tuple(range(10)) - - fn_buf = pack_apply_message(_dot_prod, num_array, {}) - ctxt = { - "task_executor": { - "f": f"{dyn_exec.__module__}.{dyn_exec.__name__}", - "a": num_array, # prove "custom" dyn_exec - } - } - val = htex.submit_payload(ctxt, fn_buf).result() - exp_val = _dot_prod(num_array, num_array) - - assert val == exp_val diff --git a/parsl/parsl/tests/test_htex/test_interchange_exit_bad_registration.py b/parsl/parsl/tests/test_htex/test_interchange_exit_bad_registration.py deleted file mode 100644 index 196c24ca..00000000 --- a/parsl/parsl/tests/test_htex/test_interchange_exit_bad_registration.py +++ /dev/null @@ -1,241 +0,0 @@ -import logging -import os -import pickle -import platform -import subprocess -import sys -import time - -import psutil -import pytest -import zmq - -import parsl.executors.high_throughput.zmq_pipes as zmq_pipes -from parsl.executors.high_throughput.executor import DEFAULT_INTERCHANGE_LAUNCH_CMD -from parsl.executors.high_throughput.manager_selector import RandomManagerSelector -from parsl.version import VERSION as PARSL_VERSION - -P_ms = 10 - - -@pytest.mark.local -def test_exit_with_bad_registration(tmpd_cwd, try_assert): - """Test that the interchange exits when it receives a bad registration message. - This complements parsl/tests/test_scaling/test_worker_interchange_bad_messages_3262.py - which tests that the interchange is resistent to other forms of bad message. - """ - - outgoing_q = zmq_pipes.TasksOutgoing( - "127.0.0.1", (49152, 65535), None - ) - incoming_q = zmq_pipes.ResultsIncoming( - "127.0.0.1", (49152, 65535), None - ) - command_client = zmq_pipes.CommandClient( - "127.0.0.1", (49152, 65535), None - ) - - interchange_config = {"client_address": "127.0.0.1", - "client_ports": (outgoing_q.port, - incoming_q.port, - command_client.port), - "interchange_address": "127.0.0.1", - "worker_port": None, - "worker_port_range": (50000, 60000), - "hub_address": None, - "hub_zmq_port": None, - "logdir": tmpd_cwd, - "log_config": None, - "heartbeat_threshold": 120, - "poll_period": P_ms, - "logging_level": logging.DEBUG, - "cert_dir": None, - "manager_selector": RandomManagerSelector(), - "run_id": "test", - "_check_python_mismatch": True, - } - - config_pickle = pickle.dumps(interchange_config) - - interchange_proc = subprocess.Popen(DEFAULT_INTERCHANGE_LAUNCH_CMD, stdin=subprocess.PIPE) - stdin = interchange_proc.stdin - assert stdin is not None, "Popen should have created an IO object (vs default None) because of PIPE mode" - - stdin.write(config_pickle) - stdin.flush() - stdin.close() - - # wait for interchange to be alive, by waiting for the command thread to become - # responsive. if the interchange process didn't start enough to get the command - # thread running, this will time out. - - worker_port = command_client.run("WORKER_BINDS", timeout_s=120) - - # now we'll assume that if the interchange command thread is responding, - # then the worker polling code is also running and that the interchange has - # started successfully. - - # send bad registration message as if from a new worker pool. The badness here - # is that the Python version does not match the real Python version - which - # unlike some other bad interchange messages, should cause the interchange - # to shut down. - - msg = {'type': 'registration', - 'parsl_v': PARSL_VERSION, - 'python_v': "1.1.1", # this is the bad bit - 'worker_count': 1, - 'uid': 'testuid', - 'block_id': 0, - 'start_time': time.time(), - 'drain_time': float('inf'), - 'prefetch_capacity': 0, - 'max_capacity': 1, - 'os': platform.system(), - 'hostname': platform.node(), - 'dir': os.getcwd(), - 'cpu_count': psutil.cpu_count(logical=False), - 'total_memory': psutil.virtual_memory().total, - } - - # connect to worker port and send this message. - - context = zmq.Context() - channel_timeout = 10000 # in milliseconds - task_channel = context.socket(zmq.DEALER) - task_channel.setsockopt(zmq.LINGER, 0) - task_channel.setsockopt(zmq.IDENTITY, b'testid') - - task_channel.set_hwm(0) - task_channel.setsockopt(zmq.SNDTIMEO, channel_timeout) - task_channel.connect(f"tcp://127.0.0.1:{worker_port}") - - task_channel.send(pickle.dumps(msg)) - - # check that the interchange exits within some reasonable time - try_assert(lambda: interchange_proc.poll() is not None, "Interchange did not exit after killing watched client process", timeout_ms=5000) - - # See issue #3697 - ideally the interchange would exit cleanly, but it does not. - # assert interchange_proc.poll() == 0, "Interchange exited with an error code, not 0" - - task_channel.close() - context.term() - - -@pytest.mark.local -@pytest.mark.parametrize( - "worker_version_info", [ - {"parsl_v": PARSL_VERSION, - "python_v": "0.0.0", # python_v mismatch - }, - {"parsl_v": "0.0.0", # parsl_v mismatch - "python_v": "{}.{}.{}".format(sys.version_info.major, - sys.version_info.minor, - sys.version_info.micro) - }, - {"parsl_v": "0.0.0", # parsl_v mismatch and - "python_v": "0.0.0", # python_v mismatch - }] -) -def test_ignore_version_check_at_registration(tmpd_cwd, try_assert, worker_version_info): - """Test that the interchange will ignore version checks - - Uses _check_python_version flag to indicate that version mismatches should - be ignored. - """ - - outgoing_q = zmq_pipes.TasksOutgoing( - "127.0.0.1", (49152, 65535), None - ) - incoming_q = zmq_pipes.ResultsIncoming( - "127.0.0.1", (49152, 65535), None - ) - command_client = zmq_pipes.CommandClient( - "127.0.0.1", (49152, 65535), None - ) - - interchange_config = {"client_address": "127.0.0.1", - "client_ports": (outgoing_q.port, - incoming_q.port, - command_client.port), - "interchange_address": "127.0.0.1", - "worker_port": None, - "worker_port_range": (50000, 60000), - "hub_address": None, - "hub_zmq_port": None, - "logdir": tmpd_cwd, - "log_config": None, - "heartbeat_threshold": 120, - "poll_period": P_ms, - "logging_level": logging.DEBUG, - "cert_dir": None, - "manager_selector": RandomManagerSelector(), - "run_id": "test", - "_check_python_mismatch": False, - } - - config_pickle = pickle.dumps(interchange_config) - - interchange_proc = subprocess.Popen(DEFAULT_INTERCHANGE_LAUNCH_CMD, stdin=subprocess.PIPE) - stdin = interchange_proc.stdin - assert stdin is not None, "Popen should have created an IO object (vs default None) because of PIPE mode" - - stdin.write(config_pickle) - stdin.flush() - stdin.close() - - # wait for interchange to be alive, by waiting for the command thread to become - # responsive. if the interchange process didn't start enough to get the command - # thread running, this will time out. - - worker_port = command_client.run("WORKER_BINDS", timeout_s=120) - - # now we'll assume that if the interchange command thread is responding, - # then the worker polling code is also running and that the interchange has - # started successfully. - - # send bad registration message as if from a new worker pool. The badness here - # is that the Python version does not match the real Python version - which - # unlike some other bad interchange messages, should cause the interchange - # to shut down. - - msg = {'type': 'registration', - 'worker_count': 1, - 'uid': 'testuid', - 'block_id': 0, - 'start_time': time.time(), - 'drain_time': float('inf'), - 'prefetch_capacity': 0, - 'max_capacity': 1, - 'os': platform.system(), - 'hostname': platform.node(), - 'dir': os.getcwd(), - 'cpu_count': psutil.cpu_count(logical=False), - 'total_memory': psutil.virtual_memory().total, - **worker_version_info, - } - - # connect to worker port and send this message. - - context = zmq.Context() - channel_timeout = 10000 # in milliseconds - task_channel = context.socket(zmq.DEALER) - task_channel.setsockopt(zmq.LINGER, 0) - task_channel.setsockopt(zmq.IDENTITY, b'testid') - - task_channel.set_hwm(0) - task_channel.setsockopt(zmq.SNDTIMEO, channel_timeout) - task_channel.connect(f"tcp://127.0.0.1:{worker_port}") - - task_channel.send(pickle.dumps(msg)) - - # Run a command against the interchange to confirm that the interchange has - # not failed, this call will hang if the interchange exits early - try_assert( - lambda: len(command_client.run("MANAGERS", max_retries=0, timeout_s=1)) == 1, - "Interchange did not accept the version-mismatched registration", - timeout_ms=5000, - ) - - interchange_proc.terminate() - task_channel.close() - context.term() diff --git a/parsl/parsl/tests/test_htex/test_log_config.py b/parsl/parsl/tests/test_htex/test_log_config.py deleted file mode 100644 index 8a9ac727..00000000 --- a/parsl/parsl/tests/test_htex/test_log_config.py +++ /dev/null @@ -1,60 +0,0 @@ -import pathlib - -import pytest - -import parsl -from parsl.executors.high_throughput.executor import HighThroughputExecutor -from parsl.logconfigs.file import FileLogging - - -@parsl.python_app -def noop_app(): - pass - - -@pytest.mark.local -def test_interchange_log_default(tmpd_cwd): - with parsl.load(parsl.Config(run_dir=str(tmpd_cwd), - executors=[HighThroughputExecutor(label="htex")])): - assert (pathlib.Path(parsl.dfk().run_dir) / "htex" / "interchange.log").exists(), \ - "By default, interchange.log should exist in executor directory after startup" - - -@pytest.mark.local -def test_log_fileconfig(tmpd_cwd): - # this arbitrary token should flow via the format_string parameter into the - # resulting text-format log file, and the test will check it arrives there. - token = "XXXXYYYYY" - msg = f"{token} %(message)s" - with parsl.load(parsl.Config(initialize_logging=FileLogging(format_string=msg), - run_dir=str(tmpd_cwd), - executors=[HighThroughputExecutor(label="htex", max_workers_per_node=1)])): - # ensure that enough has started to run a task - so that includes everything - # all the way to a worker, on the task execution path - noop_app().result() - - interchange_logfile = pathlib.Path(parsl.dfk().run_dir) / "htex" / "interchange.log" - - # assume in this configuration that there is only one subdirectory of block-0, - # that it is the directory for a started worker pool, and that the worker that completed - # the above app invocation is worker 0 (the only worker). - pool_dir = next((pathlib.Path(parsl.dfk().run_dir) / "htex" / "block-0").iterdir()) - manager_logfile = pool_dir / "manager.log" - worker_logfile = pool_dir / "worker_0.log" - - assert interchange_logfile.exists(), \ - "interchange.log should exist after startup" - assert manager_logfile.exists(), \ - "a manager log file should exist after startup" - assert worker_logfile.exists(), \ - "a worker log file should exist after startup" - - def check_file(logfile: pathlib.Path): - with open(logfile, "r") as f: - ls = f.readlines() - assert len(ls) >= 1, f"There should be at least one log entry in {logfile}" - assert ls[0].startswith(token), f"Log entry in {logfile} should be formatted with token from format_string parameter" - - check_file(interchange_logfile) - check_file(manager_logfile) - check_file(worker_logfile) diff --git a/parsl/parsl/tests/test_htex/test_manager_failure.py b/parsl/parsl/tests/test_htex/test_manager_failure.py deleted file mode 100644 index b07b62d3..00000000 --- a/parsl/parsl/tests/test_htex/test_manager_failure.py +++ /dev/null @@ -1,51 +0,0 @@ -import os -import signal -import time - -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.tests.configs.htex_local import fresh_config - - -@pytest.fixture(autouse=True, scope="function") -def load_config(): - config = fresh_config() - config.executors[0].poll_period = 1 - config.executors[0].max_workers_per_node = 1 - config.executors[0].heartbeat_period = 1 - - parsl.load(config) - yield - - parsl.dfk().cleanup() - - -@python_app -def get_worker_pid(): - import os - return os.getpid() - - -@python_app -def kill_manager(sig: int): - import os - os.kill(os.getppid(), sig) - - -@pytest.mark.local -@pytest.mark.parametrize("sig", [signal.SIGTERM, signal.SIGKILL]) -def test_htex_manager_failure_worker_shutdown(sig: int): - """Ensure that HTEX workers shut down when the Manager process dies.""" - worker_pid = get_worker_pid().result() - - kill_manager(sig) - - with pytest.raises(OSError): - end = time.monotonic() + 5 - while time.monotonic() < end: - # Raises an exception if the process - # does not exist - os.kill(worker_pid, 0) - time.sleep(.1) diff --git a/parsl/parsl/tests/test_htex/test_manager_selector_by_block.py b/parsl/parsl/tests/test_htex/test_manager_selector_by_block.py deleted file mode 100644 index 1b2a4ee1..00000000 --- a/parsl/parsl/tests/test_htex/test_manager_selector_by_block.py +++ /dev/null @@ -1,51 +0,0 @@ -import time - -import pytest - -import parsl -from parsl.app.app import bash_app, python_app -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.executors.high_throughput.manager_selector import ( - BlockIdManagerSelector, - ManagerSelector, -) -from parsl.launchers import WrappedLauncher -from parsl.providers import LocalProvider -from parsl.usage_tracking.levels import LEVEL_1 - -BLOCK_COUNT = 2 - - -@parsl.python_app -def get_worker_pid(): - import os - return os.environ.get('PARSL_WORKER_BLOCK_ID') - - -@pytest.mark.local -def test_block_id_selection(try_assert): - htex = HighThroughputExecutor( - label="htex_local", - max_workers_per_node=1, - manager_selector=BlockIdManagerSelector(), - provider=LocalProvider( - init_blocks=BLOCK_COUNT, - max_blocks=BLOCK_COUNT, - min_blocks=BLOCK_COUNT, - ), - ) - - config = Config( - executors=[htex], - usage_tracking=LEVEL_1, - ) - - with parsl.load(config): - blockids = [] - try_assert(lambda: len(htex.connected_managers()) == BLOCK_COUNT, timeout_ms=20000) - for i in range(10): - future = get_worker_pid() - blockids.append(future.result()) - - assert all(blockid == "1" for blockid in blockids) diff --git a/parsl/parsl/tests/test_htex/test_managers_command.py b/parsl/parsl/tests/test_htex/test_managers_command.py deleted file mode 100644 index 5d60213a..00000000 --- a/parsl/parsl/tests/test_htex/test_managers_command.py +++ /dev/null @@ -1,54 +0,0 @@ -import sys - -import packaging.version -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.tests.configs.htex_local import fresh_config - - -def local_config(): - config = fresh_config() - config.executors[0].poll_period = 1 - config.executors[0].max_workers_per_node = 1 - return config - - -@python_app -def dummy(): - pass - - -@pytest.mark.local -def test_connected_managers(): - - # Run dummy function to ensure a manager is online - x = dummy() - assert x.result() is None - executor = parsl.dfk().executors['htex_local'] - manager_info_list = executor.connected_managers() - assert len(manager_info_list) == 1 - manager_info = manager_info_list[0] - assert 'python_version' in manager_info - assert 'parsl_version' in manager_info - assert manager_info['parsl_version'] == parsl.__version__ - assert manager_info['python_version'] == f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}" - - -@pytest.mark.local -def test_connected_managers_packages(): - # Run dummy function to ensure a manager is online - f = dummy() - assert f.result() is None - - htex = parsl.dfk().executors['htex_local'] - assert isinstance(htex, parsl.HighThroughputExecutor) - managers_info_list = htex.connected_managers() - managers_packages = htex.connected_managers_packages() - - assert len(managers_packages) == len(managers_info_list) == 1 - manager_id, packages = list(managers_packages.items())[0] - assert manager_id == managers_info_list[0]['manager'] - normalized_version = str(packaging.version.parse(parsl.__version__)) - assert packages['parsl'] == normalized_version diff --git a/parsl/parsl/tests/test_htex/test_maxworker_computation_executor.py b/parsl/parsl/tests/test_htex/test_maxworker_computation_executor.py deleted file mode 100644 index 5c9f26e4..00000000 --- a/parsl/parsl/tests/test_htex/test_maxworker_computation_executor.py +++ /dev/null @@ -1,77 +0,0 @@ -import random - -import pytest - -import parsl -from parsl.config import Config -from parsl.executors.high_throughput.executor import HighThroughputExecutor -from parsl.providers import LocalProvider - - -@pytest.mark.local -def test_max_workers_per_node(): - - w = random.randint(1, 8) - h = HighThroughputExecutor(max_workers_per_node=w, - provider=LocalProvider(max_blocks=0, init_blocks=0, min_blocks=0) - ) - c = Config(executors=[h]) - - with parsl.load(c): - assert h.max_workers_per_node == w, "max_workers_per_node should be wired all the way through" - assert h.workers_per_node == w, "max_workers_per_node is chosen in the absence of other restrictions" - - -@pytest.mark.local -def test_cores(): - - p = LocalProvider(max_blocks=0, init_blocks=0, min_blocks=0) - p.cores_per_node = 8 - - h = HighThroughputExecutor(max_workers_per_node=16, - provider=p, - cores_per_worker=4 - ) - c = Config(executors=[h]) - - with parsl.load(c): - assert h.cores_per_worker == 4, "cores_per_worker should be wired all the way through" - assert h.workers_per_node == 2, "8 cores per node, 4 cores per worker => 2 workers" - - -@pytest.mark.local -def test_mem(): - - p = LocalProvider(max_blocks=0, init_blocks=0, min_blocks=0) - p.cores_per_node = 8 - p.mem_per_node = 16 - - h = HighThroughputExecutor(max_workers_per_node=16, - provider=p, - cores_per_worker=4, - mem_per_worker=16 - ) - c = Config(executors=[h]) - - with parsl.load(c): - assert h.mem_per_worker == 16, "mem_per_worker should be wired all the way through" - assert h.workers_per_node == 1, "one worker gets all the memory" - - -@pytest.mark.local -def test_accelerators(): - - p = LocalProvider(max_blocks=0, init_blocks=0, min_blocks=0) - p.cores_per_node = 16 - p.mem_per_node = 16 - - h = HighThroughputExecutor(max_workers_per_node=16, - provider=p, - cores_per_worker=4, - mem_per_worker=2, - available_accelerators=3 - ) - c = Config(executors=[h]) - - with parsl.load(c): - assert h.workers_per_node == 3, "number of accelerators is worker constraining limit here" diff --git a/parsl/parsl/tests/test_htex/test_missing_worker.py b/parsl/parsl/tests/test_htex/test_missing_worker.py deleted file mode 100644 index f74ab0c3..00000000 --- a/parsl/parsl/tests/test_htex/test_missing_worker.py +++ /dev/null @@ -1,33 +0,0 @@ -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.tests.configs.htex_local import fresh_config - - -def local_config(): - config = fresh_config() - config.executors[0].poll_period = 1 - config.executors[0].max_workers_per_node = 1 - config.executors[0].launch_cmd = "executable_that_hopefully_does_not_exist_1030509.py" - return config - - -@python_app -def dummy(): - pass - - -@pytest.mark.local -def test_that_it_fails(): - x = dummy() - failed = False - try: - x.result() - except Exception as ex: - print(ex) - failed = True - if not failed: - raise Exception("The app somehow ran without a valid worker") - - assert parsl.dfk().config.executors[0]._executor_bad_state.is_set() diff --git a/parsl/parsl/tests/test_htex/test_multiple_disconnected_blocks.py b/parsl/parsl/tests/test_htex/test_multiple_disconnected_blocks.py deleted file mode 100644 index 4168f41b..00000000 --- a/parsl/parsl/tests/test_htex/test_multiple_disconnected_blocks.py +++ /dev/null @@ -1,67 +0,0 @@ -import logging - -import pytest - -import parsl -from parsl import Config -from parsl.executors import HighThroughputExecutor -from parsl.executors.errors import BadStateException -from parsl.jobs.states import JobState, JobStatus -from parsl.providers import LocalProvider - - -def local_config(): - """Config to simulate failing blocks without connecting""" - return Config( - executors=[ - HighThroughputExecutor( - label="HTEX", - heartbeat_period=1, - heartbeat_threshold=2, - poll_period=100, - max_workers_per_node=1, - provider=LocalProvider( - worker_init="exit 0", - init_blocks=2 - ), - ) - ], - run_dir="/tmp/test_htex", - max_idletime=0.5, - strategy='none', - ) - - -@parsl.python_app -def double(x): - return x * 2 - - -@pytest.mark.local -def test_multiple_disconnected_blocks(): - """Test reporting of blocks that fail to connect from HTEX - When init_blocks == N, error handling expects N failures before - the run is cancelled - """ - dfk = parsl.dfk() - executor = dfk.executors["HTEX"] - - connected_blocks = executor.connected_blocks() - assert not connected_blocks, "Expected 0 blocks" - - future = double(5) - with pytest.raises(BadStateException): - future.result() - - assert isinstance(future.exception(), BadStateException) - exception_body = str(future.exception()) - assert "EXIT CODE: 0" in exception_body - - status_dict = executor.status() - assert len(status_dict) == 2, "Expected 2 blocks" - for status in status_dict.values(): - assert isinstance(status, JobStatus) - assert status.state == JobState.MISSING - - connected_blocks = executor.connected_blocks() - assert not connected_blocks, "Expected 0 blocks" diff --git a/parsl/parsl/tests/test_htex/test_priority_queue.py b/parsl/parsl/tests/test_htex/test_priority_queue.py deleted file mode 100644 index 91a6d458..00000000 --- a/parsl/parsl/tests/test_htex/test_priority_queue.py +++ /dev/null @@ -1,93 +0,0 @@ -from random import randint - -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.executors.high_throughput.manager_selector import RandomManagerSelector -from parsl.providers import LocalProvider -from parsl.usage_tracking.levels import LEVEL_1 - - -@python_app -def fake_task(parsl_resource_specification=None): - import time - return time.time() - - -@pytest.mark.local -def test_priority_queue(try_assert): - provider = LocalProvider( - init_blocks=0, - max_blocks=0, - min_blocks=0, - ) - - htex = HighThroughputExecutor( - label="htex_local", - max_workers_per_node=1, - manager_selector=RandomManagerSelector(), - provider=provider, - worker_debug=True, # needed to instrospect interchange logs - ) - - config = Config( - executors=[htex], - strategy="htex_auto_scale", - usage_tracking=LEVEL_1, - ) - - with parsl.load(config): - futures = {} - - # Submit tasks with mixed priorities - # Test fallback behavior with a guaranteed-unsorted priorities - priorities = [randint(2, 9) for _ in range(randint(1, 10))] - priorities.insert(0, 10) - priorities.extend((1, 10, 1)) - for i, priority in enumerate(priorities): - spec = {'priority': priority} - futures[(priority, i)] = fake_task(parsl_resource_specification=spec) - - # wait for the interchange to have received all tasks - # (which happens asynchronously to the main thread, and is otherwise - # a race condition which can cause this test to fail) - - n = len(priorities) - - def interchange_logs_task_count(): - with open(htex.worker_logdir + "/interchange.log", "r") as f: - lines = f.readlines() - for line in lines: - if f"Put task {n} onto pending_task_queue" in line: - return True - return False - - try_assert(interchange_logs_task_count) - - provider.max_blocks = 1 - htex.scale_out_facade(1) # don't wait for the JSP to catch up - - # Wait for completion - results = { - key: future.result() for key, future in futures.items() - } - - # Sort by finish time - sorted_by_completion = sorted(results.items(), key=lambda item: item[1]) - execution_order = [key for key, _ in sorted_by_completion] - - # check priority queue functionality - priorities_only = [p for (p, i) in execution_order] - assert priorities_only == sorted(priorities_only), "Priority execution order failed" - - # check FIFO fallback - from collections import defaultdict - seen = defaultdict(list) - for (priority, idx) in execution_order: - seen[priority].append(idx) - - for priority, indices in seen.items(): - assert indices == sorted(indices), f"FIFO fallback violated for priority {priority}" diff --git a/parsl/parsl/tests/test_htex/test_resource_spec_validation.py b/parsl/parsl/tests/test_htex/test_resource_spec_validation.py deleted file mode 100644 index 4cf717b2..00000000 --- a/parsl/parsl/tests/test_htex/test_resource_spec_validation.py +++ /dev/null @@ -1,45 +0,0 @@ -import queue -from unittest import mock - -import pytest - -from parsl.executors import HighThroughputExecutor -from parsl.executors.errors import InvalidResourceSpecification - - -def double(x): - return x * 2 - - -@pytest.mark.local -def test_submit_calls_validate(): - - htex = HighThroughputExecutor() - htex.outgoing_q = mock.Mock(spec=queue.Queue) - htex.validate_resource_spec = mock.Mock(spec=htex.validate_resource_spec) - - res_spec = {} - htex.submit(double, res_spec, (5,), {}) - htex.validate_resource_spec.assert_called() - - -@pytest.mark.local -def test_resource_spec_validation(): - htex = HighThroughputExecutor() - ret_val = htex.validate_resource_spec({}) - assert ret_val is None - - -@pytest.mark.local -def test_resource_spec_validation_bad_keys(): - htex = HighThroughputExecutor() - - with pytest.raises(InvalidResourceSpecification): - htex.validate_resource_spec({"num_nodes": 2}) - - -@pytest.mark.local -def test_resource_spec_validation_one_key(): - htex = HighThroughputExecutor() - ret_val = htex.validate_resource_spec({"priority": 2}) - assert ret_val is None diff --git a/parsl/parsl/tests/test_htex/test_worker_failure.py b/parsl/parsl/tests/test_htex/test_worker_failure.py deleted file mode 100644 index ee24fc7c..00000000 --- a/parsl/parsl/tests/test_htex/test_worker_failure.py +++ /dev/null @@ -1,26 +0,0 @@ -import pytest - -from parsl.app.app import python_app -from parsl.executors.high_throughput.errors import WorkerLost - - -def local_config(): - from parsl.tests.configs.htex_local import fresh_config - config = fresh_config() - config.executors[0].poll_period = 1 - config.executors[0].max_workers_per_node = 1 - config.executors[0].heartbeat_period = 1 - return config - - -@python_app -def kill_worker(): - raise SystemExit(2) - - -@pytest.mark.local -def test_htex_worker_failures(): - for _ in range(3): - with pytest.raises(WorkerLost): - f = kill_worker() - f.result() diff --git a/parsl/parsl/tests/test_htex/test_worker_info.py b/parsl/parsl/tests/test_htex/test_worker_info.py deleted file mode 100644 index e55064a5..00000000 --- a/parsl/parsl/tests/test_htex/test_worker_info.py +++ /dev/null @@ -1,44 +0,0 @@ -"""Tests related to Parsl workers being able to access their worker ID""" - -import pytest - -from parsl import python_app -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.providers import LocalProvider - - -def local_config(): - return Config( - executors=[ - HighThroughputExecutor( - label="htex_Local", - worker_debug=True, - max_workers_per_node=4, - encrypted=True, - provider=LocalProvider( - init_blocks=1, - max_blocks=1, - ), - ) - ], - strategy='none', - ) - - -@python_app -def get_worker_info(): - import os - rank = int(os.environ['PARSL_WORKER_RANK']) - size = int(os.environ['PARSL_WORKER_COUNT']) - pool_id = os.environ['PARSL_WORKER_POOL_ID'] - return rank, size, pool_id - - -@pytest.mark.local -def test_htex(): - worker_info = [get_worker_info() for _ in range(4)] - worker_ids, worker_size, pool_info = zip(*[r.result() for r in worker_info]) - assert len(set(worker_info)) > 1 # Tasks should run on >1 worker - assert len(set(worker_size)) == 1 # All workers have same pool size - assert len(set(pool_info)) == 1 # All from the same pool diff --git a/parsl/parsl/tests/test_htex/test_worker_interchange_bad_messages_3262.py b/parsl/parsl/tests/test_htex/test_worker_interchange_bad_messages_3262.py deleted file mode 100644 index 9a613e2c..00000000 --- a/parsl/parsl/tests/test_htex/test_worker_interchange_bad_messages_3262.py +++ /dev/null @@ -1,86 +0,0 @@ -import pytest -import zmq - -import parsl -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SimpleLauncher -from parsl.providers import LocalProvider - -T_s = 1 - - -def fresh_config(): - htex = HighThroughputExecutor( - heartbeat_period=1 * T_s, - heartbeat_threshold=3 * T_s, - label="htex_local", - worker_debug=True, - cores_per_worker=1, - encrypted=False, - provider=LocalProvider( - init_blocks=0, - min_blocks=0, - max_blocks=0, - launcher=SimpleLauncher(), - ), - ) - c = Config( - executors=[htex], - strategy='none', - strategy_period=0.5, - ) - return c, htex - - -@parsl.python_app -def app(): - return 7 - - -@pytest.mark.local -@pytest.mark.parametrize("msg", - (b'FuzzyByte\rSTREAM', # not JSON - b'{}', # missing fields - b'{"type":"heartbeat"}', # regression test #3262 - ) - ) -def test_bad_messages(try_assert, msg): - """This tests that the interchange is resilient to a few different bad - messages: malformed messages caused by implementation errors, and - heartbeat messages from managers that are not registered. - - The heartbeat test is a regression test for issues #3262, #3632 - """ - - c, htex = fresh_config() - - with parsl.load(c): - - # send a bad message into the interchange on the worker_sock worker - # channel, and then check that the interchange is still alive enough - # that we can scale out a block and run a task. - - worker_port = htex.command_client.run("WORKER_BINDS") - - context = zmq.Context() - channel_timeout = 10000 # in milliseconds - task_channel = context.socket(zmq.DEALER) - task_channel.setsockopt(zmq.LINGER, 0) - task_channel.setsockopt(zmq.IDENTITY, b'testid') - - task_channel.set_hwm(0) - task_channel.setsockopt(zmq.SNDTIMEO, channel_timeout) - task_channel.connect(f"tcp://localhost:{worker_port}") - - task_channel.send(msg) - - # If the interchange exits, it's likely that this test will hang rather - # than raise an error, because the interchange interaction code - # assumes the interchange is always there. - # In the case of issue #3262, an exception message goes to stderr, and - # no error goes to the interchange log file. - htex.scale_out_facade(1) - try_assert(lambda: len(htex.connected_managers()) == 1, timeout_ms=10000) - - assert app().result() == 7 diff --git a/parsl/parsl/tests/test_htex/test_zmq_binding.py b/parsl/parsl/tests/test_htex/test_zmq_binding.py deleted file mode 100644 index 562341fd..00000000 --- a/parsl/parsl/tests/test_htex/test_zmq_binding.py +++ /dev/null @@ -1,118 +0,0 @@ -import logging -import pathlib -from typing import Optional -from unittest import mock - -import psutil -import pytest -import zmq - -from parsl import curvezmq -from parsl.executors.high_throughput.interchange import Interchange -from parsl.executors.high_throughput.manager_selector import RandomManagerSelector - - -def make_interchange(*, - interchange_address: Optional[str], - cert_dir: Optional[str], - worker_port: Optional[int] = None) -> Interchange: - return Interchange(interchange_address=interchange_address, - cert_dir=cert_dir, - client_address="127.0.0.1", - client_ports=(50055, 50056, 50057), - worker_port=worker_port, - worker_port_range=(54000, 55000), - hub_address=None, - hub_zmq_port=None, - heartbeat_threshold=60, - logdir=".", - logging_level=logging.INFO, - log_config=None, - manager_selector=RandomManagerSelector(), - poll_period=10, - run_id="test_run_id", - _check_python_mismatch=True) - - -@pytest.fixture -def encrypted(request: pytest.FixtureRequest): - if hasattr(request, "param"): - return request.param - return True - - -@pytest.fixture -def cert_dir(encrypted: bool, tmpd_cwd: pathlib.Path): - if not encrypted: - return None - return curvezmq.create_certificates(tmpd_cwd) - - -@pytest.mark.local -@pytest.mark.parametrize("encrypted", (True, False), indirect=True) -@mock.patch.object(curvezmq.ServerContext, "socket", return_value=mock.MagicMock()) -def test_interchange_curvezmq_sockets( - mock_socket: mock.MagicMock, cert_dir: Optional[str], encrypted: bool -): - address = "127.0.0.1" - ix = make_interchange(interchange_address=address, cert_dir=cert_dir) - assert isinstance(ix.zmq_context, curvezmq.ServerContext) - assert ix.zmq_context.encrypted is encrypted - assert mock_socket.call_count == 4 - - -@pytest.mark.local -@pytest.mark.parametrize("encrypted", (True, False), indirect=True) -def test_interchange_binding_no_address(cert_dir: Optional[str]): - ix = make_interchange(interchange_address=None, cert_dir=cert_dir) - assert ix.interchange_address == "*" - - -@pytest.mark.local -@pytest.mark.parametrize("encrypted", (True, False), indirect=True) -def test_interchange_binding_with_address(cert_dir: Optional[str]): - # Using loopback address - address = "127.0.0.1" - ix = make_interchange(interchange_address=address, cert_dir=cert_dir) - assert ix.interchange_address == address - - -@pytest.mark.skip("This behaviour is possibly unexpected. See issue #3037") -@pytest.mark.local -@pytest.mark.parametrize("encrypted", (True, False), indirect=True) -def test_interchange_binding_with_non_ipv4_address(cert_dir: Optional[str]): - # Confirm that a ipv4 address is required - address = "localhost" - with pytest.raises(zmq.error.ZMQError): - make_interchange(interchange_address=address, cert_dir=cert_dir) - - -@pytest.mark.local -@pytest.mark.parametrize("encrypted", (True, False), indirect=True) -def test_interchange_binding_bad_address(cert_dir: Optional[str]): - """Confirm that we raise a ZMQError when a bad address is supplied""" - address = "550.0.0.0" - with pytest.raises(ValueError): - make_interchange(interchange_address=address, cert_dir=cert_dir) - - -@pytest.mark.local -@pytest.mark.parametrize("encrypted", (True, False), indirect=True) -def test_limited_interface_binding(cert_dir: Optional[str]): - """When address is specified the worker_port would be bound to it rather than to 0.0.0.0""" - address = "127.0.0.1" - ix = make_interchange(interchange_address=address, cert_dir=cert_dir) - proc = psutil.Process() - conns = proc.connections(kind="tcp") - - matched_conns = [conn for conn in conns if conn.laddr.port == ix.worker_port] - assert len(matched_conns) == 1 - # laddr.ip can return ::ffff:127.0.0.1 when using IPv6 - assert address in matched_conns[0].laddr.ip - - -@pytest.mark.local -@pytest.mark.parametrize("encrypted", (True, False), indirect=True) -def test_fixed_ports(cert_dir: Optional[str]): - ix = make_interchange(interchange_address=None, cert_dir=cert_dir, worker_port=51117) - assert ix.interchange_address == "*" diff --git a/parsl/parsl/tests/test_logging/__init__.py b/parsl/parsl/tests/test_logging/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_logging/test_dfk_executor_binds.py b/parsl/parsl/tests/test_logging/test_dfk_executor_binds.py deleted file mode 100644 index 961d22b6..00000000 --- a/parsl/parsl/tests/test_logging/test_dfk_executor_binds.py +++ /dev/null @@ -1,45 +0,0 @@ -import pytest - -import parsl - - -@parsl.python_app -def noop(): - pass - - -@pytest.mark.local -def test_executor_binding_threads(caplog): - """Tests executor binding for executor with no task IDs""" - with parsl.load(): - fut = noop() - fut.result() - - bindings = [e for e in caplog.records - if hasattr(e, 'parsl.task') and - hasattr(e, 'parsl.executor') and - e.__dict__['parsl.task'] == fut.tid and - e.__dict__['parsl.executor'] == 'threads' - ] - - assert len(bindings) == 1, "expected exactly one task/executor binding" - - -@pytest.mark.local -def test_executor_binding_htex(caplog): - """Tests executor binding for executor with task IDs""" - LABEL = "htex_log_test" - with parsl.load(parsl.Config(executors=[parsl.HighThroughputExecutor(label=LABEL)])): - fut = noop() - fut.result() - - bindings = [e for e in caplog.records - if hasattr(e, 'parsl.task') and - hasattr(e, 'parsl.executor') and - hasattr(e, 'parsl.executor_task') and - e.__dict__['parsl.task'] == fut.tid and - e.__dict__['parsl.executor'] == LABEL and - e.__dict__['parsl.executor_task'] == 1 # HTEX task IDs start at 1 - ] - - assert len(bindings) == 1, "expected exactly one task/executor binding" diff --git a/parsl/parsl/tests/test_logging/test_dfk_states.py b/parsl/parsl/tests/test_logging/test_dfk_states.py deleted file mode 100644 index 8446c631..00000000 --- a/parsl/parsl/tests/test_logging/test_dfk_states.py +++ /dev/null @@ -1,50 +0,0 @@ -import pytest - -import parsl - - -@parsl.python_app -def noop(): - pass - - -@parsl.python_app -def fail(): - raise RuntimeError() - - -@pytest.mark.local -def test_state_logs_success(caplog): - with parsl.load(): - noop().result() - - # from the DFK logs, we wouldn't expect to see running or - # running_done states as those exist only in the worker. - - expected_states = {"pending", "launched", "exec_done"} - - seen_states = {r.__dict__['parsl.task_state'] - for r in caplog.records - if 'parsl.task_state' in r.__dict__} - - assert expected_states <= seen_states, "every expected state should have been seen" - - for s in expected_states: - assert s in caplog.text, f"Expected state {s} to be logged as text" - - -@pytest.mark.local -def test_state_logs_fail(caplog): - with parsl.load(): - fail().exception() - - expected_states = {"pending", "launched", "failed"} - - seen_states = {r.__dict__['parsl.task_state'] - for r in caplog.records - if 'parsl.task_state' in r.__dict__} - - assert expected_states <= seen_states, "every expected state should have been seen" - - for s in expected_states: - assert s in caplog.text, f"Expected state {s} to be logged as text" diff --git a/parsl/parsl/tests/test_logging/test_initialize_logging.py b/parsl/parsl/tests/test_logging/test_initialize_logging.py deleted file mode 100644 index 64fc2d71..00000000 --- a/parsl/parsl/tests/test_logging/test_initialize_logging.py +++ /dev/null @@ -1,80 +0,0 @@ -import json -import logging -import pathlib - -import pytest - -import parsl -from parsl.logconfigs.file import FileLogging -from parsl.logconfigs.json import JSONLogging -from parsl.logconfigs.noop import NoopLogging - - -@pytest.mark.local -def test_parsl_log_exists(tmpd_cwd): - with parsl.load(parsl.Config(run_dir=str(tmpd_cwd))): - assert (pathlib.Path(parsl.dfk().run_dir) / "parsl.log").exists(), \ - "By default, parsl.log should exist in rundir after startup" - - -@pytest.mark.local -def test_parsl_log_not_exists(tmpd_cwd): - with parsl.load(parsl.Config(run_dir=str(tmpd_cwd), initialize_logging=False)): - assert not (pathlib.Path(parsl.dfk().run_dir) / "parsl.log").exists(), \ - "With initialize_logging=False, parsl.log should not exist in rundir after startup" - - -@pytest.mark.local -def test_parsl_log_Noop_not_exists(tmpd_cwd): - with parsl.load(parsl.Config(run_dir=str(tmpd_cwd), initialize_logging=NoopLogging())): - assert not (pathlib.Path(parsl.dfk().run_dir) / "parsl.log").exists(), \ - "With NoopLogging, parsl.log should not exist in rundir after startup" - - -@pytest.mark.local -def test_parsl_log_FileLogging(tmpd_cwd): - with parsl.load(parsl.Config(run_dir=str(tmpd_cwd), initialize_logging=FileLogging())): - assert (pathlib.Path(parsl.dfk().run_dir) / "parsl.log").exists(), \ - "With FileLogging, parsl.log should exist in rundir after startup" - - -@pytest.mark.local -def test_parsl_log_FileLogging_format(tmpd_cwd): - msg = "XXXYYY %(message)s" - with parsl.load(parsl.Config(run_dir=str(tmpd_cwd), initialize_logging=FileLogging(format_string=msg))): - logfile = pathlib.Path(parsl.dfk().run_dir) / "parsl.log" - assert logfile.exists(), \ - "With FileLogging, parsl.log should exist in rundir after startup" - with open(logfile, "r") as f: - ls = f.readlines() - assert len(ls) >= 1, "logfile should contain lines" - assert ls[0].startswith("XXXYYY "), "logged lines should match format string" - - -@pytest.mark.local -def test_parsl_log_JSONLogging(tmpd_cwd): - with parsl.load(parsl.Config(run_dir=str(tmpd_cwd), initialize_logging=JSONLogging())): - logger = logging.getLogger(__name__) - msg1 = "Test without extra" - msg2 = "Test with extra" - - logger.info(msg1) - logger.info("Test with extra", extra={"parsl.somekey": "vv"}) - - logfile = pathlib.Path(parsl.dfk().run_dir) / "parsl.jsonlog" - assert logfile.exists(), \ - "With JSONLogging, parsl.jsonlog should exist in rundir after startup" - - # close down the DFK before attempt to read log file, so that there aren't any partial - # writes. - with open(logfile, "r") as f: - lines = f.readlines() - - # we should parse every line as JSON, without Exceptions - parsed = [json.loads(line) for line in lines] - - assert len([None for p in parsed if p['msg'] == msg1]) == 1, "msg1 should appear once" - - msgs2 = [p for p in parsed if p['msg'] == msg2] - assert len(msgs2) == 1, "msg2 should appear once" - assert msgs2[0]['parsl.somekey'] == "vv", "extra key should have been stored in record" diff --git a/parsl/parsl/tests/test_logging/test_oneshot.py b/parsl/parsl/tests/test_logging/test_oneshot.py deleted file mode 100644 index b6fce295..00000000 --- a/parsl/parsl/tests/test_logging/test_oneshot.py +++ /dev/null @@ -1,145 +0,0 @@ -import pickle -import unittest -import uuid -from unittest.mock import Mock - -import pytest - -import parsl.logconfigs.base as b - - -@pytest.mark.local -def test_simple(tmpd_cwd): - lc = Mock(spec=b.LogConfig) - lc.uuid = uuid.uuid4() - - assert b._initialized_log_contexts == {}, "precondition" - - lc.initialize_logging.assert_not_called() - b.oneshot_initialize_logging(log_config=lc, log_dir=tmpd_cwd, log_name="test_simple") - lc.initialize_logging.assert_called() - - cb = b._initialized_log_contexts[lc.uuid].uninit_callback - - cb.assert_not_called() - b.oneshot_uninitialize_logging(log_config=lc) - cb.assert_called() - - assert b._initialized_log_contexts == {}, "back to satisfying the precondition" - - -@pytest.mark.local -def test_nested(tmpd_cwd): - lc = Mock(spec=b.LogConfig) - lc.uuid = uuid.uuid4() - - assert b._initialized_log_contexts == {}, "precondition" - - b.oneshot_initialize_logging(log_config=lc, log_dir=tmpd_cwd, log_name="test_nested_1") - lc.initialize_logging.assert_called_once() - - b.oneshot_initialize_logging(log_config=lc, log_dir=tmpd_cwd, log_name="test_nested_2") - lc.initialize_logging.assert_called_once() - - cb = b._initialized_log_contexts[lc.uuid].uninit_callback - - cb.assert_not_called() - b.oneshot_uninitialize_logging(log_config=lc) - cb.assert_not_called() - b.oneshot_uninitialize_logging(log_config=lc) - cb.assert_called() - - assert b._initialized_log_contexts == {}, "back to satisfying the precondition" - - -@pytest.mark.local -def test_sequential(tmpd_cwd): - """Test that a log context can be released completely, and then reinitialized.""" - lc = Mock(spec=b.LogConfig) - lc.uuid = uuid.uuid4() - - assert b._initialized_log_contexts == {}, "precondition" - - b.oneshot_initialize_logging(log_config=lc, log_dir=tmpd_cwd, log_name="test_nested_1") - lc.initialize_logging.assert_called_once() - cb = b._initialized_log_contexts[lc.uuid].uninit_callback - cb.assert_not_called() - b.oneshot_uninitialize_logging(log_config=lc) - cb.assert_called() - - assert b._initialized_log_contexts == {}, "back to satisfying the precondition, between uses" - - b.oneshot_initialize_logging(log_config=lc, log_dir=tmpd_cwd, log_name="test_nested_2") - assert lc.initialize_logging.call_count == 2, "Should have been initialized once per block" - - cb2 = b._initialized_log_contexts[lc.uuid].uninit_callback - - # There is no requirement that cb2 be a fresh callback (mock) or be re-used. - # When writing this test, cb2 is a reused mock, `cb2 is cb`, but that isn't - # a requirement of the API. - c = cb2.call_count - b.oneshot_uninitialize_logging(log_config=lc) - assert cb2.call_count == c + 1 - - assert b._initialized_log_contexts == {}, "back to satisfying the precondition" - - -class NestedPickleTestConfig(b.LogConfig): - def initialize_logging(self, *args, **kwargs): - def de(): - pass - return de - - -@pytest.mark.local -def test_nested_pickle(tmpd_cwd): - - # this config will never be exposed to the log system except after passing - # through pickle via two different paths, to generate different "remote-like" - # objects. - lc_orig = NestedPickleTestConfig() - - p = pickle.dumps(lc_orig) - - assert lc_orig.uuid not in b._initialized_log_contexts - lc_1 = pickle.loads(p) - b.oneshot_initialize_logging(log_config=lc_1, log_dir=tmpd_cwd, log_name="test_nested_1") - - assert lc_orig.uuid in b._initialized_log_contexts - lc_2 = pickle.loads(p) - b.oneshot_initialize_logging(log_config=lc_2, log_dir=tmpd_cwd, log_name="test_nested_2") - - assert lc_orig.uuid in b._initialized_log_contexts - - b.oneshot_uninitialize_logging(log_config=lc_2) - assert lc_orig.uuid in b._initialized_log_contexts - - b.oneshot_uninitialize_logging(log_config=lc_1) - assert lc_orig.uuid not in b._initialized_log_contexts - - -@pytest.mark.local -def test_overlap_pickle(tmpd_cwd): - - # this config will never be exposed to the log system except after passing - # through pickle via two different paths, to generate different "remote-like" - # objects. - lc_orig = NestedPickleTestConfig() - - p = pickle.dumps(lc_orig) - - assert lc_orig.uuid not in b._initialized_log_contexts - lc_1 = pickle.loads(p) - b.oneshot_initialize_logging(log_config=lc_1, log_dir=tmpd_cwd, log_name="test_nested_1") - - assert lc_orig.uuid in b._initialized_log_contexts - lc_2 = pickle.loads(p) - b.oneshot_initialize_logging(log_config=lc_2, log_dir=tmpd_cwd, log_name="test_nested_2") - - assert lc_orig.uuid in b._initialized_log_contexts - - b.oneshot_uninitialize_logging(log_config=lc_2) - assert lc_orig.uuid in b._initialized_log_contexts - - b.oneshot_uninitialize_logging(log_config=lc_1) - assert lc_orig.uuid not in b._initialized_log_contexts diff --git a/parsl/parsl/tests/test_monitoring/__init__.py b/parsl/parsl/tests/test_monitoring/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_monitoring/test_app_names.py b/parsl/parsl/tests/test_monitoring/test_app_names.py deleted file mode 100644 index fdc08370..00000000 --- a/parsl/parsl/tests/test_monitoring/test_app_names.py +++ /dev/null @@ -1,86 +0,0 @@ -"""Tests monitoring records app name under various decoration patterns. -""" - -import os -import time - -import pytest - -import parsl -from parsl.tests.configs.htex_local_alternate import fresh_config - - -@parsl.python_app -def regular_decorated_app(): - return 5 - - -@pytest.mark.local -def get_regular_decorated_app(): - return regular_decorated_app - - -def for_decoration_later(): - return 77 - - -def get_for_decoration_later(): - return parsl.python_app(for_decoration_later) - - -def get_decorated_closure(): - - r = 53 - - @parsl.python_app - def decorated_closure(): - return r - - return decorated_closure - - -@pytest.mark.local -@pytest.mark.parametrize("get_app,expected_name,expected_result", - [(get_regular_decorated_app, "regular_decorated_app", 5), - (get_for_decoration_later, "for_decoration_later", 77), - (get_decorated_closure, "decorated_closure", 53) - ]) -def test_app_name(get_app, expected_name, expected_result, tmpd_cwd): - - # this is imported here rather than at module level because - # it isn't available in a plain parsl install, so this module - # would otherwise fail to import and break even a basic test - # run. - import sqlalchemy - - c = fresh_config() - c.run_dir = str(tmpd_cwd) - c.monitoring.logging_endpoint = f"sqlite:///{tmpd_cwd}/monitoring.db" - parsl.load(c) - - app = get_app() - assert app().result() == expected_result - - parsl.dfk().cleanup() - - engine = sqlalchemy.create_engine(c.monitoring.logging_endpoint) - with engine.begin() as connection: - - def count_rows(table: str): - result = connection.execute(sqlalchemy.text(f"SELECT COUNT(*) FROM {table}")) - (c, ) = result.first() - return c - - # one workflow... - assert count_rows("workflow") == 1 - - # ... with one task ... - assert count_rows("task") == 1 - - # ... that was tried once ... - assert count_rows("try") == 1 - - # ... and has the expected name. - result = connection.execute(sqlalchemy.text("SELECT task_func_name FROM task")) - (c, ) = result.first() - assert c == expected_name diff --git a/parsl/parsl/tests/test_monitoring/test_basic.py b/parsl/parsl/tests/test_monitoring/test_basic.py deleted file mode 100644 index 73bceb32..00000000 --- a/parsl/parsl/tests/test_monitoring/test_basic.py +++ /dev/null @@ -1,169 +0,0 @@ -import os -import time - -import pytest - -import parsl -from parsl import HighThroughputExecutor, ThreadPoolExecutor -from parsl.config import Config -from parsl.executors.status_handling import BlockProviderExecutor -from parsl.monitoring import MonitoringHub -from parsl.monitoring.radios.filesystem import FilesystemRadio -from parsl.monitoring.radios.htex import HTEXRadio -from parsl.monitoring.radios.udp import UDPRadio - - -@parsl.python_app -def this_app(): - return 5 - - -# The below fresh configs are for use in parametrization, and should return -# a configuration that is suitably configured for monitoring. - -def thread_config(): - c = Config(executors=[ThreadPoolExecutor(remote_monitoring_radio=UDPRadio(address="localhost", atexit_timeout=0))], - monitoring=MonitoringHub(resource_monitoring_interval=0)) - return c - - -def htex_config(): - """This config will use htex's default htex-specific monitoring radio mode""" - from parsl.tests.configs.htex_local_alternate import fresh_config - return fresh_config() - - -def htex_udp_config(): - """This config will force UDP""" - from parsl.tests.configs.htex_local_alternate import fresh_config - c = fresh_config() - assert len(c.executors) == 1 - ex = c.executors[0] - - assert isinstance(ex.remote_monitoring_radio, HTEXRadio), "precondition: htex is configured for the HTEXRadio" - ex.remote_monitoring_radio = UDPRadio(address="localhost", atexit_timeout=0) - - return c - - -def htex_filesystem_config(): - """This config will force filesystem radio""" - from parsl.tests.configs.htex_local_alternate import fresh_config - c = fresh_config() - assert len(c.executors) == 1 - ex = c.executors[0] - - assert isinstance(ex.remote_monitoring_radio, HTEXRadio), "precondition: htex is configured for the HTEXRadio" - ex.remote_monitoring_radio = FilesystemRadio() - - return c - - -def workqueue_config(): - from parsl.tests.configs.workqueue_ex import fresh_config - c = fresh_config() - c.monitoring = MonitoringHub( - resource_monitoring_interval=1) - return c - - -def taskvine_config(): - from parsl.executors.taskvine import TaskVineExecutor, TaskVineManagerConfig - c = Config(executors=[TaskVineExecutor(manager_config=TaskVineManagerConfig(port=9000), - worker_launch_method='provider')], - strategy_period=0.5, - - monitoring=MonitoringHub(resource_monitoring_interval=1)) - return c - - -def row_counts_parametrized(tmpd_cwd, fresh_config): - # this is imported here rather than at module level because - # it isn't available in a plain parsl install, so this module - # would otherwise fail to import and break even a basic test - # run. - import sqlalchemy - from sqlalchemy import text - - db_url = f"sqlite:///{tmpd_cwd}/monitoring.db" - - config = fresh_config() - config.run_dir = str(tmpd_cwd) - config.monitoring.logging_endpoint = db_url - - with parsl.load(config): - assert this_app().result() == 5 - - # at this point, we should find one row in the monitoring database. - - engine = sqlalchemy.create_engine(db_url) - with engine.begin() as connection: - - result = connection.execute(text("SELECT COUNT(*) FROM workflow")) - (c, ) = result.first() - assert c == 1 - - result = connection.execute(text("SELECT COUNT(*) FROM task")) - (c, ) = result.first() - assert c == 1 - - result = connection.execute(text("SELECT COUNT(*) FROM try")) - (c, ) = result.first() - assert c == 1 - - result = connection.execute(text("SELECT COUNT(*) FROM status, try " - "WHERE status.task_id = try.task_id " - "AND status.task_status_name='exec_done' " - "AND task_try_time_running is NULL")) - (c, ) = result.first() - assert c == 0 - - result = connection.execute(text("SELECT COUNT(*) FROM workflow WHERE time_began IS NOT NULL AND time_completed IS NOT NULL")) - (c, ) = result.first() - assert c == 1, "start/end times should be populated" - - if isinstance(config.executors[0], HighThroughputExecutor): - # The node table is specific to the HighThroughputExecutor - # Two entries: one showing manager active, one inactive - result = connection.execute(text("SELECT COUNT(*) FROM node")) - (c, ) = result.first() - assert c == 4 - - if isinstance(config.executors[0], BlockProviderExecutor): - # This case assumes that a BlockProviderExecutor is actually being - # used with blocks. It might not be (for example, Work Queue and - # Task Vine can be configured to launch their own workers; and it - # is a valid (although occasional) use of htex to launch executors - # manually. - # If you just added test cases like that and are wondering why this - # assert is failing, that might be why. - result = connection.execute(text("SELECT COUNT(*) FROM block")) - (c, ) = result.first() - assert c >= 2, "There should be at least two block statuses from a BlockProviderExecutor" - - result = connection.execute(text("SELECT COUNT(*) FROM resource")) - (c, ) = result.first() - if isinstance(config.executors[0], ThreadPoolExecutor): - assert c == 0, "Thread pool executors should not be recording resources" - else: - assert c >= 1, "Task execution should have created some resource records" - - -@pytest.mark.local -@pytest.mark.parametrize("fresh_config", [thread_config, htex_config, htex_filesystem_config, htex_udp_config]) -def test_row_counts_base(tmpd_cwd, fresh_config): - row_counts_parametrized(tmpd_cwd, fresh_config) - - -@pytest.mark.workqueue -@pytest.mark.local -@pytest.mark.parametrize("fresh_config", [workqueue_config]) -def test_row_counts_wq(tmpd_cwd, fresh_config): - row_counts_parametrized(tmpd_cwd, fresh_config) - - -@pytest.mark.taskvine -@pytest.mark.local -@pytest.mark.parametrize("fresh_config", [taskvine_config]) -def test_row_counts_tv(tmpd_cwd, fresh_config): - row_counts_parametrized(tmpd_cwd, fresh_config) diff --git a/parsl/parsl/tests/test_monitoring/test_db_locks.py b/parsl/parsl/tests/test_monitoring/test_db_locks.py deleted file mode 100644 index e1d821a4..00000000 --- a/parsl/parsl/tests/test_monitoring/test_db_locks.py +++ /dev/null @@ -1,88 +0,0 @@ -import logging -import os -import time - -import pytest - -import parsl - -logger = logging.getLogger(__name__) - - -@parsl.python_app -def this_app(): - return 5 - - -@pytest.mark.local -def test_row_counts(): - import sqlalchemy - from sqlalchemy import text - - from parsl.tests.configs.htex_local_alternate import fresh_config - if os.path.exists("runinfo/monitoring.db"): - logger.info("Monitoring database already exists - deleting") - os.remove("runinfo/monitoring.db") - - engine = sqlalchemy.create_engine("sqlite:///runinfo/monitoring.db") - - logger.info("loading parsl") - parsl.load(fresh_config()) - - # parsl.load() returns before all initialisation of monitoring - # is complete, which means it isn't safe to take a read lock on - # the database yet. This delay tries to work around that - some - # better async behaviour might be nice, but what? - # - # Taking a read lock before monitoring is initialized will cause - # a failure in the part of monitoring which creates tables, and - # which is not protected against read locks at the time this test - # was written. - time.sleep(10) - - # to get an sqlite3 read lock that is held over a controllable - # long time, create a transaction and perform a SELECT in it. - # The lock will be held until the end of the transaction. - # (see bottom of https://sqlite.org/lockingv3.html) - - logger.info("Getting a read lock on the monitoring database") - with engine.begin() as readlock_connection: - readlock_connection.execute(text("BEGIN TRANSACTION")) - result = readlock_connection.execute(text("SELECT COUNT(*) FROM workflow")) - (c, ) = result.first() - assert c == 1 - # now readlock_connection should have a read lock that will - # stay locked until the transaction is ended, or the with - # block ends. - - logger.info("invoking and waiting for result") - assert this_app().result() == 5 - - # there is going to be some raciness here making sure that - # the database manager actually tries to write while the - # read lock is held. I'm not sure if there is a better way - # to detect this other than a hopefully long-enough sleep. - time.sleep(10) - - logger.info("cleaning up parsl") - parsl.dfk().cleanup() - - # at this point, we should find data consistent with executing one - # task in the database. - - logger.info("checking database content") - with engine.begin() as connection: - - result = connection.execute(text("SELECT COUNT(*) FROM workflow")) - (c, ) = result.first() - assert c == 1 - - result = connection.execute(text("SELECT COUNT(*) FROM task")) - (c, ) = result.first() - assert c == 1 - - result = connection.execute(text("SELECT COUNT(*) FROM try")) - (c, ) = result.first() - assert c == 1 - - logger.info("all done") diff --git a/parsl/parsl/tests/test_monitoring/test_exit_helper.py b/parsl/parsl/tests/test_monitoring/test_exit_helper.py deleted file mode 100644 index 0e12e595..00000000 --- a/parsl/parsl/tests/test_monitoring/test_exit_helper.py +++ /dev/null @@ -1,54 +0,0 @@ -import multiprocessing -import signal - -import psutil -import pytest - -from parsl.multiprocessing import SpawnEvent, SpawnProcess, join_terminate_close_proc - - -def noop(): - pass - - -@pytest.mark.local -def test_end_process_already_exited(): - p = SpawnProcess(target=noop) - p.start() - p.join() - join_terminate_close_proc(p) - - -def hang(): - while True: - pass - - -@pytest.mark.local -def test_end_hung_process(): - """Test calling against a process that will not exit itself.""" - p = SpawnProcess(target=hang) - p.start() - pid = p.pid - join_terminate_close_proc(p, timeout=1) - assert not psutil.pid_exists(pid), "process should not exist any more" - - -def hang_no_sigint(e): - def s(*args, **kwargs): - e.set() - signal.signal(signal.SIGTERM, s) - while True: - pass - - -@pytest.mark.local -def test_end_hung_process_no_sigint(): - """Test calling against a process that will not exit itself.""" - e = SpawnEvent() - p = SpawnProcess(target=hang_no_sigint, args=(e,)) - p.start() - pid = p.pid - join_terminate_close_proc(p, timeout=2) - assert not psutil.pid_exists(pid), "process should not exist any more" - assert e.is_set(), "hung process should have set event on signal" diff --git a/parsl/parsl/tests/test_monitoring/test_htex_fuzz_zmq.py b/parsl/parsl/tests/test_monitoring/test_htex_fuzz_zmq.py deleted file mode 100644 index e35702ed..00000000 --- a/parsl/parsl/tests/test_monitoring/test_htex_fuzz_zmq.py +++ /dev/null @@ -1,113 +0,0 @@ -import logging -import os -import socket -import time - -import pytest -import zmq - -import parsl - -logger = logging.getLogger(__name__) - - -@parsl.python_app -def this_app(): - return 5 - - -@pytest.mark.local -def test_fuzz(): - """This test sends fuzz into the ZMQ radio receiver that HTEX starts - for receiving monitoring messages from the interchange, and checks - that monitoring still records things ok. - """ - - import sqlalchemy - from sqlalchemy import text - - from parsl.tests.configs.htex_local_alternate import fresh_config - - if os.path.exists("runinfo/monitoring.db"): - logger.info("Monitoring database already exists - deleting") - os.remove("runinfo/monitoring.db") - - logger.info("loading parsl") - parsl.load(fresh_config()) - - logger.info("invoking apps and waiting for result") - - assert this_app().result() == 5 - assert this_app().result() == 5 - - # now we've run some apps, send fuzz into the monitoring ZMQ - # socket, before trying to run some more tests. - - # there are different kinds of fuzz: - # could send ZMQ messages that are weird - # could send random bytes to the TCP socket - # the latter is what i'm most suspicious of in my present investigation - - # dig out the interchange port... - hub_address = parsl.dfk().executors["htex_Local"].loopback_address - hub_zmq_port = parsl.dfk().executors["htex_Local"].hub_zmq_port - - # this will send a string to a new socket connection - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - s.connect((hub_address, hub_zmq_port)) - s.sendall(b'fuzzing\r') - - context = zmq.Context() - channel_timeout = 10000 # in milliseconds - hub_channel = context.socket(zmq.DEALER) - hub_channel.setsockopt(zmq.LINGER, 0) - hub_channel.set_hwm(0) - hub_channel.setsockopt(zmq.SNDTIMEO, channel_timeout) - hub_channel.connect("tcp://{}:{}".format(hub_address, hub_zmq_port)) - - # this will send a non-object down the DFK's existing ZMQ connection - hub_channel.send(b'FuzzyByte\rSTREAM') - - # This following attack is commented out, because monitoring is not resilient - # to this. - # In practice, it works some of the time but in some circumstances, - # it would still abandon writing multiple unrelated records to the database, - # causing ongoing monitoring data loss. - - # This will send an unusual python object down the - # DFK's existing ZMQ connection. this doesn't break the router, - # but breaks the db_manager in a way that isn't reported until - # the very end of the run, and database writing is abandoned - # rather than completing, in this case. - # I'm unclear if this is a case we should be trying to handle. - # parsl.dfk().monitoring._dfk_channel.send_pyobj("FUZZ3") - - # hopefully long enough for any breakage to happen - # before attempting to run more tasks - time.sleep(5) - - assert this_app().result() == 5 - assert this_app().result() == 5 - - logger.info("cleaning up parsl") - parsl.dfk().cleanup() - - # at this point, we should find one row in the monitoring database. - - logger.info("checking database content") - engine = sqlalchemy.create_engine("sqlite:///runinfo/monitoring.db") - with engine.begin() as connection: - - result = connection.execute(text("SELECT COUNT(*) FROM workflow")) - (c, ) = result.first() - assert c == 1 - - result = connection.execute(text("SELECT COUNT(*) FROM task")) - (c, ) = result.first() - assert c == 4 - - result = connection.execute(text("SELECT COUNT(*) FROM try")) - (c, ) = result.first() - assert c == 4 - - logger.info("all done") diff --git a/parsl/parsl/tests/test_monitoring/test_htex_init_blocks_vs_monitoring.py b/parsl/parsl/tests/test_monitoring/test_htex_init_blocks_vs_monitoring.py deleted file mode 100644 index 73daaaad..00000000 --- a/parsl/parsl/tests/test_monitoring/test_htex_init_blocks_vs_monitoring.py +++ /dev/null @@ -1,79 +0,0 @@ -import logging -import os -import time - -import pytest - -import parsl -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SimpleLauncher -from parsl.monitoring import MonitoringHub -from parsl.providers import LocalProvider - - -def fresh_config(run_dir, strategy, db_url): - return Config( - run_dir=os.fspath(run_dir), - executors=[ - HighThroughputExecutor( - label="htex_local", - cores_per_worker=1, - encrypted=True, - provider=LocalProvider( - init_blocks=1, - # min and max are set to 0 to ensure that we don't get - # a block from ongoing strategy scaling, only from - # init_blocks - min_blocks=0, - max_blocks=0, - launcher=SimpleLauncher(), - ), - ) - ], - strategy=strategy, - strategy_period=0.1, - monitoring=MonitoringHub( - logging_endpoint=db_url - ) - ) - - -@parsl.python_app -def this_app(): - pass - - -@pytest.mark.local -@pytest.mark.parametrize("strategy", ('none', 'simple', 'htex_auto_scale')) -def test_row_counts(tmpd_cwd, strategy): - # this is imported here rather than at module level because - # it isn't available in a plain parsl install, so this module - # would otherwise fail to import and break even a basic test - # run. - import sqlalchemy - from sqlalchemy import text - - db_url = f"sqlite:///{tmpd_cwd}/monitoring.db" - with parsl.load(fresh_config(tmpd_cwd, strategy, db_url)): - dfk = parsl.dfk() - run_id = dfk.run_id - - this_app().result() - - engine = sqlalchemy.create_engine(db_url) - with engine.begin() as connection: - - binds = {"run_id": run_id} - - result = connection.execute(text("SELECT COUNT(DISTINCT block_id) FROM block WHERE run_id = :run_id"), binds) - (c, ) = result.first() - assert c == 1, "We should see a single block in this database" - - result = connection.execute(text("SELECT COUNT(*) FROM block WHERE block_id = 0 AND status = 'PENDING' AND run_id = :run_id"), binds) - (c, ) = result.first() - assert c == 1, "There should be a single pending status" - - result = connection.execute(text("SELECT COUNT(*) FROM block WHERE block_id = 0 AND status = 'SCALED_IN' AND run_id = :run_id"), binds) - (c, ) = result.first() - assert c == 1, "There should be a single cancelled status" diff --git a/parsl/parsl/tests/test_monitoring/test_incomplete_futures.py b/parsl/parsl/tests/test_monitoring/test_incomplete_futures.py deleted file mode 100644 index 237e92a3..00000000 --- a/parsl/parsl/tests/test_monitoring/test_incomplete_futures.py +++ /dev/null @@ -1,66 +0,0 @@ -import logging -import os -import random -from concurrent.futures import Future - -import pytest - -import parsl - - -@parsl.python_app -def this_app(inputs=()): - return inputs[0] - - -@pytest.mark.local -def test_future_representation(tmpd_cwd): - import sqlalchemy - from sqlalchemy import text - - from parsl.tests.configs.htex_local_alternate import fresh_config - - monitoring_db = str(tmpd_cwd / "monitoring.db") - monitoring_url = "sqlite:///" + monitoring_db - - c = fresh_config() - c.monitoring.logging_endpoint = monitoring_url - c.run_dir = str(tmpd_cwd) - - parsl.load(c) - - # we're going to pass this TOKEN into an app via a pre-requisite Future, - # and then expect to see it appear in the monitoring database. - TOKEN = random.randint(0, 1000000) - - # make a Future that has no result yet - # invoke a task that depends on it - # inspect and insert something about the monitoring recorded value of that Future - # make the Future complete - # inspect and insert something about the monitoring recorded value of that Future - - f1 = Future() - - f2 = this_app(inputs=[f1]) - - f1.set_result(TOKEN) - - assert f2.result() == TOKEN - - # this cleanup gives a barrier that allows the monitoring code to store - # everything it has in the database - without this, there's a race - # condition that the task will not have arrived in the database yet. - # A different approach for this test might be to poll the DB for a few - # seconds, with the assumption "data will arrive in the DB within - # 30 seconds, but probably much sooner". - parsl.dfk().cleanup() - - engine = sqlalchemy.create_engine(monitoring_url) - with engine.begin() as connection: - result = connection.execute(text("SELECT COUNT(*) FROM task")) - (task_count, ) = result.first() - assert task_count == 1 - - result = connection.execute(text("SELECT task_inputs FROM task")) - (task_inputs, ) = result.first() - assert task_inputs == "[" + repr(TOKEN) + "]" diff --git a/parsl/parsl/tests/test_monitoring/test_log_config.py b/parsl/parsl/tests/test_monitoring/test_log_config.py deleted file mode 100644 index 80ec10de..00000000 --- a/parsl/parsl/tests/test_monitoring/test_log_config.py +++ /dev/null @@ -1,36 +0,0 @@ -import pathlib - -import pytest - -import parsl -from parsl.config import Config -from parsl.executors import ThreadPoolExecutor -from parsl.logconfigs.json import JSONLogging -from parsl.monitoring import MonitoringHub - - -def config(*, run_dir): - c = Config(executors=[ThreadPoolExecutor()], - monitoring=MonitoringHub(), - run_dir=str(run_dir)) - return c - - -def config_log(*, run_dir, log_config): - c = Config(executors=[ThreadPoolExecutor()], - monitoring=MonitoringHub(), - run_dir=str(run_dir), - initialize_logging=log_config) - return c - - -@pytest.mark.local -def test_log_default_config(tmpd_cwd, try_assert): - with parsl.load(config(run_dir=tmpd_cwd)): - try_assert(lambda: (pathlib.Path(parsl.dfk().run_dir) / "database_manager.log").exists()) - - -@pytest.mark.local -def test_log_json_config(tmpd_cwd, try_assert): - with parsl.load(config_log(run_dir=tmpd_cwd, log_config=JSONLogging())): - try_assert(lambda: (pathlib.Path(parsl.dfk().run_dir) / "database_manager.jsonlog").exists()) diff --git a/parsl/parsl/tests/test_monitoring/test_memoization_representation.py b/parsl/parsl/tests/test_monitoring/test_memoization_representation.py deleted file mode 100644 index bb673872..00000000 --- a/parsl/parsl/tests/test_monitoring/test_memoization_representation.py +++ /dev/null @@ -1,96 +0,0 @@ - -import logging -import os - -import pytest - -import parsl -from parsl.config import Config -from parsl.executors import ThreadPoolExecutor -from parsl.monitoring import MonitoringHub -from parsl.monitoring.radios.udp import UDPRadio - -logger = logging.getLogger(__name__) - - -@parsl.python_app(cache=True) -def this_app(x): - return x + 1 - - -def fresh_config(*, config_run_dir: str): - c = Config(run_dir=config_run_dir, - executors=[ThreadPoolExecutor(remote_monitoring_radio=UDPRadio(address="localhost", atexit_timeout=0))], - monitoring=MonitoringHub(resource_monitoring_interval=0)) - return c - - -@pytest.mark.local -def test_hashsum(tmpd_cwd): - import sqlalchemy - from sqlalchemy import text - - if os.path.exists("runinfo/monitoring.db"): - logger.info("Monitoring database already exists - deleting") - os.remove("runinfo/monitoring.db") - - logger.info("loading parsl") - parsl.load(fresh_config(config_run_dir=str(tmpd_cwd))) - - logger.info("invoking and waiting for result (1/4)") - f1 = this_app(4) - assert f1.result() == 5 - - logger.info("invoking and waiting for result (2/4)") - f2 = this_app(17) - assert f2.result() == 18 - - logger.info("invoking and waiting for result (3/4)") - f3 = this_app(4) - assert f3.result() == 5 - - logger.info("invoking and waiting for result (4/4)") - f4 = this_app(4) - assert f4.result() == 5 - - assert f1.task_record['hashsum'] == f3.task_record['hashsum'] - assert f1.task_record['hashsum'] == f4.task_record['hashsum'] - assert f1.task_record['hashsum'] != f2.task_record['hashsum'] - - logger.info("cleaning up parsl") - parsl.dfk().cleanup() - - # at this point, we should find one row in the monitoring database. - - logger.info("checking database content") - - monitoring_db = str(tmpd_cwd / "monitoring.db") - monitoring_url = "sqlite:///" + monitoring_db - - engine = sqlalchemy.create_engine(monitoring_url) - with engine.begin() as connection: - - # we should have three tasks, but with only two tries, because the - # memo try should be missing - result = connection.execute(text("SELECT COUNT(*) FROM task")) - (task_count, ) = result.first() - assert task_count == 4 - - # this will check that the number of task rows for each hashsum matches the above app invocations - result = connection.execute(text(f"SELECT COUNT(task_hashsum) FROM task WHERE task_hashsum='{f1.task_record['hashsum']}'")) - (hashsum_count, ) = result.first() - assert hashsum_count == 3 - - result = connection.execute(text(f"SELECT COUNT(task_hashsum) FROM task WHERE task_hashsum='{f2.task_record['hashsum']}'")) - (hashsum_count, ) = result.first() - assert hashsum_count == 1 - - result = connection.execute(text("SELECT COUNT(*) FROM status WHERE task_status_name='exec_done'")) - (memo_count, ) = result.first() - assert memo_count == 2 - - result = connection.execute(text("SELECT COUNT(*) FROM status WHERE task_status_name='memo_done'")) - (memo_count, ) = result.first() - assert memo_count == 2 - - logger.info("all done") diff --git a/parsl/parsl/tests/test_monitoring/test_radio_filesystem.py b/parsl/parsl/tests/test_monitoring/test_radio_filesystem.py deleted file mode 100644 index c9fbdcca..00000000 --- a/parsl/parsl/tests/test_monitoring/test_radio_filesystem.py +++ /dev/null @@ -1,48 +0,0 @@ -import pytest - -from parsl.monitoring.message_type import MessageType -from parsl.monitoring.radios.filesystem import FilesystemRadio -from parsl.multiprocessing import SpawnQueue - - -@pytest.mark.local -def test_filesystem(tmpd_cwd): - """Test filesystem radio/receiver pair. - This test checks that the pair can be started up locally, that a message - is conveyed from radio to receiver, and that the receiver process goes - away at shutdown. - """ - - resource_msgs = SpawnQueue() - - radio_config = FilesystemRadio() - - # start receiver - receiver = radio_config.create_receiver(run_dir=str(tmpd_cwd), - resource_msgs=resource_msgs) - - # make radio - - radio_sender = radio_config.create_sender() - - # send message into radio - - message = (MessageType.RESOURCE_INFO, {}) - - radio_sender.send(message) - - # verify it comes out of the receiver - - m = resource_msgs.get() - - assert m == message, "The sent message should appear in the queue" - - # shut down router - - receiver.shutdown() - - # we can't inspect the process if it has been closed properly, but - # we can verify that it raises the expected ValueError the closed - # processes raise on access. - with pytest.raises(ValueError): - receiver.process.exitcode diff --git a/parsl/parsl/tests/test_monitoring/test_radio_multiprocessing.py b/parsl/parsl/tests/test_monitoring/test_radio_multiprocessing.py deleted file mode 100644 index fca75783..00000000 --- a/parsl/parsl/tests/test_monitoring/test_radio_multiprocessing.py +++ /dev/null @@ -1,44 +0,0 @@ -import pytest - -from parsl.monitoring.message_type import MessageType -from parsl.monitoring.radios.multiprocessing import MultiprocessingQueueRadio -from parsl.multiprocessing import SpawnQueue - - -@pytest.mark.local -def test_radio(tmpd_cwd): - """Test filesystem radio/receiver pair. - This test checks that the pair can be started up locally, that a message - is conveyed from radio to receiver, and that the receiver process goes - away at shutdown. - """ - - resource_msgs = SpawnQueue() - - radio_config = MultiprocessingQueueRadio() - - # start receiver - receiver = radio_config.create_receiver(run_dir=str(tmpd_cwd), - resource_msgs=resource_msgs) - - # make radio - - radio_sender = radio_config.create_sender() - - # send message into radio - - message = (MessageType.RESOURCE_INFO, {}) - - radio_sender.send(message) - - # verify it comes out of the receiver - - m = resource_msgs.get() - - assert m == message, "The sent message should appear in the queue" - - # Shut down router. - # In the multiprocessing radio, nothing happens at shutdown, so this - # validates that the call executes without raising an exception, but - # not much else. - receiver.shutdown() diff --git a/parsl/parsl/tests/test_monitoring/test_radio_udp.py b/parsl/parsl/tests/test_monitoring/test_radio_udp.py deleted file mode 100644 index 2c4f8f14..00000000 --- a/parsl/parsl/tests/test_monitoring/test_radio_udp.py +++ /dev/null @@ -1,204 +0,0 @@ -import socket -import time - -import pytest - -from parsl.monitoring.message_type import MessageType -from parsl.monitoring.radios.udp import UDPRadio -from parsl.multiprocessing import SpawnQueue - - -@pytest.mark.local -def test_udp(tmpd_cwd): - """Test UDP radio/receiver pair. - This test checks that the pair can be started up locally, that a message - is conveyed from radio to receiver, and that the receiver process goes - away at shutdown. - """ - - resource_msgs = SpawnQueue() - - radio_config = UDPRadio(address="localhost", atexit_timeout=0) - - # start receiver - udp_receiver = radio_config.create_receiver(run_dir=str(tmpd_cwd), - resource_msgs=resource_msgs) - - # check hash properties - - assert len(radio_config.hmac_key) == 64, "With default hash, should expect 64 byte key" - - # make radio - - radio_sender = radio_config.create_sender() - - # send message into radio - - message = (MessageType.RESOURCE_INFO, {}) - - radio_sender.send(message) - - # verify it comes out of the receiver - - m = resource_msgs.get() - - assert m == message, "The sent message should appear in the queue" - - # shut down router - - udp_receiver.shutdown() - - # we can't inspect the process if it has been closed properly, but - # we can verify that it raises the expected ValueError the closed - # processes raise on access. - with pytest.raises(ValueError): - udp_receiver.process.exitcode - - -@pytest.mark.local -def test_bad_hmac(tmpd_cwd, caplog): - """Test when HMAC does not match. - """ - - resource_msgs = SpawnQueue() - - radio_config = UDPRadio(address="localhost", atexit_timeout=0) - - # start receiver - udp_receiver = radio_config.create_receiver(run_dir=str(tmpd_cwd), - resource_msgs=resource_msgs) - - # check the hmac is configured in the right place, - # then change it to something different (by prepending a new byte) - assert radio_config.hmac_key is not None - radio_config.hmac_key += b'x' - - # make radio, after changing the HMAC key - - radio_sender = radio_config.create_sender() - - # send message into radio - - message = (MessageType.RESOURCE_INFO, {}) - - radio_sender.send(message) - - # We should expect no message from the UDP side. That's hard to - # state in this scenario because UDP doesn't have any delivery - # guarantees for the test-failing case. - # So sleep a while to allow that test to misdeliver and fail. - time.sleep(1) - - assert resource_msgs.empty(), "receiving queue should be empty" - assert udp_receiver.process.is_alive(), "UDP router process should still be alive" - - with open(f"{tmpd_cwd}/monitoring_udp_router.log", "r") as logfile: - assert "ERROR" in logfile.read(), "Router log file should contain an error" - - # shut down router - - udp_receiver.shutdown() - - # we can't inspect the process if it has been closed properly, but - # we can verify that it raises the expected ValueError the closed - # processes raise on access. - with pytest.raises(ValueError): - udp_receiver.process.exitcode - - -@pytest.mark.local -def test_wrong_digest(tmpd_cwd, caplog): - """Test when HMAC does not match. - """ - - resource_msgs = SpawnQueue() - - radio_config = UDPRadio(address="localhost", atexit_timeout=0) - - # start receiver - udp_receiver = radio_config.create_receiver(run_dir=str(tmpd_cwd), - resource_msgs=resource_msgs) - - # check the hmac is configured in the right place, - # then change it to a different digest. The choice of different - # digest is arbitrary. - assert radio_config.hmac_digest is not None - radio_config.hmac_digest = "sha3_224" - - # make radio, after changing the HMAC digest - - radio_sender = radio_config.create_sender() - - # send message into radio - - message = (MessageType.RESOURCE_INFO, {}) - - radio_sender.send(message) - - # We should expect no message from the UDP side. That's hard to - # state in this scenario because UDP doesn't have any delivery - # guarantees for the test-failing case. - # So sleep a while to allow that test to misdeliver and fail. - time.sleep(1) - - assert resource_msgs.empty(), "receiving queue should be empty" - assert udp_receiver.process.is_alive(), "UDP router process should still be alive" - - with open(f"{tmpd_cwd}/monitoring_udp_router.log", "r") as logfile: - assert "ERROR" in logfile.read(), "Router log file should contain an error" - - # shut down router - - udp_receiver.shutdown() - - # we can't inspect the process if it has been closed properly, but - # we can verify that it raises the expected ValueError the closed - # processes raise on access. - with pytest.raises(ValueError): - udp_receiver.process.exitcode - - -@pytest.mark.local -def test_short_message(tmpd_cwd, caplog): - """Test when UDP message is so short it can't even be parsed into - HMAC + the rest. - """ - - resource_msgs = SpawnQueue() - - radio_config = UDPRadio(address="localhost", atexit_timeout=0) - - # start receiver - udp_receiver = radio_config.create_receiver(run_dir=str(tmpd_cwd), - resource_msgs=resource_msgs) - - # now send a bad UDP message, rather than using the sender mechanism. - - sock = socket.socket(socket.AF_INET, - socket.SOCK_DGRAM, - socket.IPPROTO_UDP) - - sock.sendto(b'', (radio_config.address, radio_config.port)) - sock.close() - - # We should expect no message from the UDP side. That's hard to - # state in this scenario because UDP doesn't have any delivery - # guarantees for the test-failing case. - # So sleep a while to allow that test to misdeliver and fail. - time.sleep(1) - - assert resource_msgs.empty(), "receiving queue should be empty" - assert udp_receiver.process.is_alive(), "UDP router process should still be alive" - - with open(f"{tmpd_cwd}/monitoring_udp_router.log", "r") as logfile: - assert "ERROR" in logfile.read(), "Router log file should contain an error" - - # shut down router - - udp_receiver.shutdown() - - # we can't inspect the process if it has been closed properly, but - # we can verify that it raises the expected ValueError the closed - # processes raise on access. - with pytest.raises(ValueError): - udp_receiver.process.exitcode diff --git a/parsl/parsl/tests/test_monitoring/test_radio_zmq.py b/parsl/parsl/tests/test_monitoring/test_radio_zmq.py deleted file mode 100644 index 92e43c44..00000000 --- a/parsl/parsl/tests/test_monitoring/test_radio_zmq.py +++ /dev/null @@ -1,36 +0,0 @@ -import pytest - -import parsl.curvezmq as curvezmq -from parsl.monitoring.radios.zmq import ZMQRadioSender -from parsl.monitoring.radios.zmq_router import start_zmq_receiver -from parsl.multiprocessing import SpawnQueue - - -@pytest.mark.local -@pytest.mark.parametrize("encrypted", (False, True)) -def test_send_recv_message(tmpd_cwd, try_assert, encrypted): - - if encrypted: - cert_dir = curvezmq.create_certificates(tmpd_cwd) - else: - cert_dir = None - - q = SpawnQueue() - loopback = "127.0.0.1" - r = start_zmq_receiver(monitoring_messages=q, - loopback_address=loopback, - port_range=(49152, 65535), - logdir=str(tmpd_cwd), - worker_debug=False, - cert_dir=cert_dir) - - s = ZMQRadioSender(loopback, r.port, cert_dir=cert_dir) - - test_msg = ("test", {}) - s.send(test_msg) - - assert q.get() == test_msg - - assert r.process.is_alive() - r.exit_event.set() - try_assert(lambda: not r.process.is_alive()) diff --git a/parsl/parsl/tests/test_monitoring/test_stdouterr.py b/parsl/parsl/tests/test_monitoring/test_stdouterr.py deleted file mode 100644 index 3bb4b195..00000000 --- a/parsl/parsl/tests/test_monitoring/test_stdouterr.py +++ /dev/null @@ -1,135 +0,0 @@ -"""Tests monitoring records app name under various decoration patterns. -""" - -import logging -import os -import re -import time -from typing import Union - -import pytest - -import parsl -from parsl.config import Config -from parsl.data_provider.data_manager import default_staging -from parsl.data_provider.files import File -from parsl.data_provider.staging import Staging -from parsl.executors import HighThroughputExecutor -from parsl.monitoring import MonitoringHub -from parsl.providers import LocalProvider - - -def fresh_config(run_dir): - return Config( - run_dir=str(run_dir), - executors=[ - HighThroughputExecutor( - address="127.0.0.1", - label="htex_Local", - provider=LocalProvider( - init_blocks=1, - min_blocks=1, - max_blocks=1, - ) - ) - ], - strategy='simple', - strategy_period=0.1, - monitoring=MonitoringHub(), - ) - - -@parsl.python_app -def stdapp(stdout=None, stderr=None): - pass - - -class ArbitraryPathLike(os.PathLike): - def __init__(self, path: Union[str, bytes]) -> None: - self.path = path - - def __fspath__(self) -> Union[str, bytes]: - return self.path - - -class ArbitraryStaging(Staging): - """This staging provider will not actually do any staging, but will - accept arbitrary: scheme URLs. That's enough for this monitoring test - which doesn't need any actual stage out action to happen. - """ - def can_stage_out(self, file): - return file.scheme == "arbitrary" - - -@pytest.mark.local -@pytest.mark.parametrize('stdx,expected_stdx', - [('hello.txt', 'hello.txt'), - (None, ''), - (('tuple.txt', 'w'), 'tuple.txt'), - (ArbitraryPathLike('pl.txt'), 'pl.txt'), - (ArbitraryPathLike(b'pl2.txt'), 'pl2.txt'), - ((ArbitraryPathLike('pl3.txt'), 'w'), 'pl3.txt'), - ((ArbitraryPathLike(b'pl4.txt'), 'w'), 'pl4.txt'), - (parsl.AUTO_LOGNAME, - lambda p: - isinstance(p, str) and - os.path.isabs(p) and - re.match("^.*/task_0000_stdapp\\.std...$", p)), - (File("arbitrary:abc123"), "arbitrary:abc123"), - (File("file:///tmp/pl5"), "file:///tmp/pl5"), - ]) -@pytest.mark.parametrize('stream', ['stdout', 'stderr']) -def test_stdstream_to_monitoring(stdx, expected_stdx, stream, tmpd_cwd, caplog): - """This tests that various forms of stdout/err specification are - represented in monitoring correctly. The stderr and stdout codepaths - are generally duplicated, rather than factorised, and so this test - runs the same tests on both stdout and stderr. - """ - - # this is imported here rather than at module level because - # it isn't available in a plain parsl install, so this module - # would otherwise fail to import and break even a basic test - # run. - import sqlalchemy - - c = fresh_config(tmpd_cwd) - c.monitoring.logging_endpoint = f"sqlite:///{tmpd_cwd}/monitoring.db" - c.executors[0].storage_access = default_staging + [ArbitraryStaging()] - - with parsl.load(c): - kwargs = {stream: stdx} - stdapp(**kwargs).result() - - for record in caplog.records: - assert record.levelno < logging.ERROR - - engine = sqlalchemy.create_engine(c.monitoring.logging_endpoint) - with engine.begin() as connection: - - def count_rows(table: str): - result = connection.execute(sqlalchemy.text(f"SELECT COUNT(*) FROM {table}")) - (c, ) = result.first() - return c - - # one workflow... - assert count_rows("workflow") == 1 - - # ... with one task ... - assert count_rows("task") == 1 - - # ... that was tried once ... - assert count_rows("try") == 1 - - # ... and has the expected name. - result = connection.execute(sqlalchemy.text(f"SELECT task_{stream} FROM task")) - (c, ) = result.first() - - if isinstance(expected_stdx, str): - assert c == expected_stdx - elif callable(expected_stdx): - assert expected_stdx(c) - else: - raise RuntimeError("Bad expected_stdx value") - - for record in caplog.records: - assert record.levelno < logging.ERROR diff --git a/parsl/parsl/tests/test_monitoring/test_viz_colouring.py b/parsl/parsl/tests/test_monitoring/test_viz_colouring.py deleted file mode 100644 index 5d7d82f9..00000000 --- a/parsl/parsl/tests/test_monitoring/test_viz_colouring.py +++ /dev/null @@ -1,18 +0,0 @@ -import pytest - -from parsl.dataflow.states import States - - -@pytest.mark.local -def test_all_states_colored() -> None: - """This checks that the coloring tables in parsl-visualize contain - a color for each state defined in the task state enumeration. - """ - - # imports inside test because viz can't be imported in an environment - # with no monitoring installed - import parsl.monitoring.visualization.plots.default.workflow_plots as workflow_plots - - for s in States: - assert s.name in workflow_plots.gantt_colors - assert s.name in workflow_plots.dag_state_colors diff --git a/parsl/parsl/tests/test_providers/__init__.py b/parsl/parsl/tests/test_providers/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_providers/test_kubernetes_provider.py b/parsl/parsl/tests/test_providers/test_kubernetes_provider.py deleted file mode 100644 index 453dc574..00000000 --- a/parsl/parsl/tests/test_providers/test_kubernetes_provider.py +++ /dev/null @@ -1,102 +0,0 @@ -import re -from unittest import mock - -import pytest - -from parsl.providers.kubernetes.kube import KubernetesProvider -from parsl.tests.test_utils.test_sanitize_dns import DNS_SUBDOMAIN_REGEX - -_MOCK_BASE = "parsl.providers.kubernetes.kube" - - -@pytest.fixture(autouse=True) -def mock_kube_config(): - with mock.patch(f"{_MOCK_BASE}.config") as mock_config: - mock_config.load_kube_config.return_value = None - yield mock_config - - -@pytest.fixture -def mock_kube_client(): - mock_client = mock.MagicMock() - with mock.patch(f"{_MOCK_BASE}.client.CoreV1Api") as mock_api: - mock_api.return_value = mock_client - yield mock_client - - -@pytest.mark.local -def test_submit_happy_path(mock_kube_client: mock.MagicMock): - image = "test-image" - namespace = "test-namespace" - cmd_string = "test-command" - volumes = [("test-volume", "test-mount-path")] - service_account_name = "test-service-account" - annotations = {"test-annotation": "test-value"} - max_cpu = 2 - max_mem = "2Gi" - init_cpu = 1 - init_mem = "1Gi" - provider = KubernetesProvider( - image=image, - persistent_volumes=volumes, - namespace=namespace, - service_account_name=service_account_name, - annotations=annotations, - max_cpu=max_cpu, - max_mem=max_mem, - init_cpu=init_cpu, - init_mem=init_mem, - ) - - job_name = "test.job.name" - job_id = provider.submit(cmd_string=cmd_string, tasks_per_node=1, job_name=job_name) - - assert job_id in provider.resources - assert mock_kube_client.create_namespaced_pod.call_count == 1 - - call_args = mock_kube_client.create_namespaced_pod.call_args[1] - pod = call_args["body"] - container = pod.spec.containers[0] - volume = container.volume_mounts[0] - - assert image == container.image - assert namespace == call_args["namespace"] - assert any(cmd_string in arg for arg in container.args) - assert volumes[0] == (volume.name, volume.mount_path) - assert service_account_name == pod.spec.service_account_name - assert annotations == pod.metadata.annotations - assert str(max_cpu) == container.resources.limits["cpu"] - assert max_mem == container.resources.limits["memory"] - assert str(init_cpu) == container.resources.requests["cpu"] - assert init_mem == container.resources.requests["memory"] - assert job_id == pod.metadata.labels["parsl-job-id"] - assert job_id == container.name - assert f"{job_name}.{job_id}" == pod.metadata.name - - -@pytest.mark.local -@mock.patch(f"{_MOCK_BASE}.KubernetesProvider._create_pod") -@pytest.mark.parametrize("char", (".", "-")) -def test_submit_pod_name_includes_job_id(mock_create_pod: mock.MagicMock, char: str): - provider = KubernetesProvider(image="test-image") - - job_name = "a." * 121 + f"a{char}" + "a" * 9 - assert len(job_name) == 253 # Max length for pod name - job_id = provider.submit(cmd_string="test-command", tasks_per_node=1, job_name=job_name) - - expected_pod_name = job_name[:253 - len(job_id) - 2] + f".{job_id}" - actual_pod_name = mock_create_pod.call_args[1]["pod_name"] - assert re.match(DNS_SUBDOMAIN_REGEX, actual_pod_name) - assert expected_pod_name == actual_pod_name - - -@pytest.mark.local -@mock.patch(f"{_MOCK_BASE}.KubernetesProvider._create_pod") -@mock.patch(f"{_MOCK_BASE}.logger") -@pytest.mark.parametrize("job_name", ("", ".", "-", "a.-.a", "$$$")) -def test_submit_invalid_job_name(mock_logger: mock.MagicMock, mock_create_pod: mock.MagicMock, job_name: str): - provider = KubernetesProvider(image="test-image") - job_id = provider.submit(cmd_string="test-command", tasks_per_node=1, job_name=job_name) - assert mock_logger.warning.call_count == 1 - assert f"Invalid pod name '{job_name}' for job '{job_id}'" in mock_logger.warning.call_args[0][0] - assert f"parsl.kube.{job_id}" == mock_create_pod.call_args[1]["pod_name"] diff --git a/parsl/parsl/tests/test_providers/test_local_provider.py b/parsl/parsl/tests/test_providers/test_local_provider.py deleted file mode 100644 index c7508144..00000000 --- a/parsl/parsl/tests/test_providers/test_local_provider.py +++ /dev/null @@ -1,80 +0,0 @@ -import logging -import os -import pathlib -import random -import shutil -import socket -import subprocess -import tempfile -import threading -import time - -import pytest - -from parsl.jobs.states import JobState -from parsl.launchers import SingleNodeLauncher -from parsl.providers import LocalProvider - -logger = logging.getLogger(__name__) - - -def _run_tests(p: LocalProvider): - status = _run(p, '/bin/true') - assert status.message is None - assert status.state == JobState.COMPLETED - assert status.exit_code == 0 - assert status.stdout == '' - assert status.stderr == '' - - status = _run(p, '/bin/true', np=2) - assert status.message is None - assert status.state == JobState.COMPLETED - assert status.exit_code == 0 - assert status.stdout == '' - assert status.stderr == '' - - status = _run(p, '/bin/false') - assert status.state == JobState.FAILED - assert status.exit_code != 0 - assert status.stdout == '' - assert status.stderr == '' - - status = _run(p, '/bin/false', np=2) - assert status.state == JobState.FAILED - assert status.exit_code != 0 - assert status.stdout == '' - assert status.stderr == '' - - status = _run(p, '/bin/echo -n magic') - assert status.state == JobState.COMPLETED - assert status.exit_code == 0 - assert status.stdout == 'magic' - assert status.stderr == '' - - status = _run(p, '/bin/echo -n magic 1>&2') - assert status.state == JobState.COMPLETED - assert status.exit_code == 0 - assert status.stdout == '' - assert status.stderr == 'magic' - - -@pytest.mark.local -def test_local_channel(): - with tempfile.TemporaryDirectory() as script_dir: - script_dir = tempfile.mkdtemp() - p = LocalProvider(launcher=SingleNodeLauncher(debug=False)) - p.script_dir = script_dir - _run_tests(p) - - -def _run(p: LocalProvider, command: str, np: int = 1): - id = p.submit(command, np) - return _wait(p, id) - - -def _wait(p: LocalProvider, id: object): - status = p.status([id])[0] - while not status.terminal: - time.sleep(0.1) - status = p.status([id])[0] - return status diff --git a/parsl/parsl/tests/test_providers/test_pbspro_template.py b/parsl/parsl/tests/test_providers/test_pbspro_template.py deleted file mode 100644 index 12647311..00000000 --- a/parsl/parsl/tests/test_providers/test_pbspro_template.py +++ /dev/null @@ -1,27 +0,0 @@ -import random -from unittest import mock - -import pytest - -from parsl.providers import PBSProProvider - - -@pytest.mark.local -def test_submit_script_basic(tmp_path): - """Test slurm resources table""" - - provider = PBSProProvider( - queue="debug" - ) - provider.script_dir = tmp_path - job_id = str(random.randint(55000, 59000)) - provider.execute_wait = mock.Mock(spec=PBSProProvider.execute_wait) - provider.execute_wait.return_value = (0, job_id, "") - result_job_id = provider.submit("test", tasks_per_node=1) - assert job_id == result_job_id - provider.execute_wait.assert_called() - assert job_id in provider.resources - - job_info = provider.resources[job_id] - assert "job_stdout_path" in job_info - assert "job_stderr_path" in job_info diff --git a/parsl/parsl/tests/test_providers/test_slurm_instantiate.py b/parsl/parsl/tests/test_providers/test_slurm_instantiate.py deleted file mode 100644 index 1fed0f18..00000000 --- a/parsl/parsl/tests/test_providers/test_slurm_instantiate.py +++ /dev/null @@ -1,16 +0,0 @@ -import pytest - -from parsl.providers import SlurmProvider - - -@pytest.mark.local -def test_slurm_instantiate_regression_2994(): - """This test checks that repr can be executed on SlurmProvider. - This does not need a SLURM installation around. - - Because of the behaviour of RepresentationMixin, it's a relatively - common problem to break repr when adding new parameters to the - SlurmProvider, and this tests that repr does not raise an exception. - """ - p = SlurmProvider() - repr(p) diff --git a/parsl/parsl/tests/test_providers/test_slurm_template.py b/parsl/parsl/tests/test_providers/test_slurm_template.py deleted file mode 100644 index 55074fef..00000000 --- a/parsl/parsl/tests/test_providers/test_slurm_template.py +++ /dev/null @@ -1,28 +0,0 @@ -import logging -import random -from unittest import mock - -import pytest - -from parsl.providers import SlurmProvider - - -@pytest.mark.local -def test_submit_script_basic(tmp_path): - """Test slurm resources table""" - - provider = SlurmProvider( - partition="debug" - ) - provider.script_dir = tmp_path - job_id = str(random.randint(55000, 59000)) - provider.execute_wait = mock.MagicMock(spec=SlurmProvider.execute_wait) - provider.execute_wait.return_value = (0, f"Submitted batch job {job_id}", "") - result_job_id = provider.submit("test", tasks_per_node=1) - assert job_id == result_job_id - provider.execute_wait.assert_called() - assert job_id in provider.resources - - job_info = provider.resources[job_id] - assert "job_stdout_path" in job_info - assert "job_stderr_path" in job_info diff --git a/parsl/parsl/tests/test_providers/test_submiterror_deprecation.py b/parsl/parsl/tests/test_providers/test_submiterror_deprecation.py deleted file mode 100644 index 352e6844..00000000 --- a/parsl/parsl/tests/test_providers/test_submiterror_deprecation.py +++ /dev/null @@ -1,22 +0,0 @@ -import random -import string - -import pytest - -from parsl.providers.errors import SubmitException - - -@pytest.mark.local -def test_submit_exception_task_name_deprecation(): - """This tests the deprecation warning of task_name in SubmitException - """ - j = "the_name-" + "".join(random.sample(string.ascii_lowercase, 10)) - - ex = SubmitException(j, "m") - - # the new behaviour - assert ex.job_name == j - - # the old behaviour - with pytest.deprecated_call(): - assert ex.task_name == j diff --git a/parsl/parsl/tests/test_python_apps/__init__.py b/parsl/parsl/tests/test_python_apps/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_python_apps/test_arg_input_types.py b/parsl/parsl/tests/test_python_apps/test_arg_input_types.py deleted file mode 100644 index 1149705e..00000000 --- a/parsl/parsl/tests/test_python_apps/test_arg_input_types.py +++ /dev/null @@ -1,34 +0,0 @@ -""" -These tests check that inputs[] can be passed from any type, -and that they can be used as regular objects unless they are -Files. -""" -from parsl import python_app - - -@python_app -def take_a_value(inputs=[]): - return str(inputs[0]) - - -@python_app -def add_two_values(inputs=[]): - return inputs[0] + inputs[1] - - -def test_input_str(): - f = take_a_value(inputs=["hi"]) - assert f.result() == "hi" - - -def test_input_num(): - f = take_a_value(inputs=[3.14]) - assert f.result() == "3.14" - - f = add_two_values(inputs=[5, 7, 3]) - assert f.result() == 12 - - -def test_input_list(): - f = take_a_value(inputs=[["foo", 7]]) - assert f.result() == "['foo', 7]" diff --git a/parsl/parsl/tests/test_python_apps/test_basic.py b/parsl/parsl/tests/test_python_apps/test_basic.py deleted file mode 100644 index 0e67c3d5..00000000 --- a/parsl/parsl/tests/test_python_apps/test_basic.py +++ /dev/null @@ -1,46 +0,0 @@ -import pytest - -from parsl.app.app import python_app - - -@python_app -def double(x): - return x * 2 - - -@python_app -def import_square(x): - import math - return math.pow(x, 2) - - -def test_simple(n=2): - x = double(n) - assert x.result() == n * 2 - - -def test_log(n=2, *, caplog): - x = double(n) - assert x.result() == n * 2 - ks = [e for e in caplog.records - if hasattr(e, "parsl.task") and - e.__dict__["parsl.task"] == x.tid and - hasattr(e, "parsl.app_name") and - e.__dict__["parsl.app_name"] == "double" - ] - assert len(ks) > 0, "should have got at least one binding log record" - - -@pytest.mark.parametrize("n", (-2, -1, 0, 1, 2, 3)) -def test_imports(n): - x = import_square(n) - assert x.result() == n * n - - -@pytest.mark.parametrize("n", (0, 1, 2, 3, 5, 8, 13, 21)) -def test_parallel_for(n): - d = {i: double(i) for i in range(n)} - assert len(d.keys()) == n - - for i in d: - assert d[i].result() == 2 * i diff --git a/parsl/parsl/tests/test_python_apps/test_context_manager.py b/parsl/parsl/tests/test_python_apps/test_context_manager.py deleted file mode 100644 index 6d3b020b..00000000 --- a/parsl/parsl/tests/test_python_apps/test_context_manager.py +++ /dev/null @@ -1,128 +0,0 @@ -from concurrent.futures import Future -from threading import Event - -import pytest - -import parsl -from parsl.config import Config -from parsl.dataflow.dflow import DataFlowKernel, DataFlowKernelLoader -from parsl.errors import NoDataFlowKernelError -from parsl.tests.configs.local_threads import fresh_config - - -@parsl.python_app -def square(x): - return x * x - - -@parsl.bash_app -def foo(x, stdout='foo.stdout'): - return f"echo {x + 1}" - - -@parsl.python_app -def wait_for_event(ev: Event): - ev.wait() - - -@parsl.python_app -def raise_app(): - raise RuntimeError("raise_app deliberate failure") - - -@pytest.mark.local -def test_within_context_manger(tmpd_cwd): - config = fresh_config() - with parsl.load(config=config) as dfk: - assert isinstance(dfk, DataFlowKernel) - - bash_future = foo(1, stdout=tmpd_cwd / 'foo.stdout') - assert bash_future.result() == 0 - - with open(tmpd_cwd / 'foo.stdout', 'r') as f: - assert f.read() == "2\n" - - with pytest.raises(NoDataFlowKernelError) as excinfo: - square(2).result() - assert str(excinfo.value) == "Must first load config" - - -@pytest.mark.local -def test_exit_skip(): - config = fresh_config() - config.exit_mode = "skip" - - with parsl.load(config) as dfk: - ev = Event() - fut = wait_for_event(ev) - # deliberately don't wait for this to finish, so that the context - # manager can exit - - assert parsl.dfk() is dfk, "global dfk should be left in place by skip mode" - - assert not fut.done(), "wait_for_event should not be done yet" - ev.set() - - # now we can wait for that result... - fut.result() - assert fut.done(), "wait_for_event should complete outside of context manager in 'skip' mode" - - # now cleanup the DFK that the above `with` block - # deliberately avoided doing... - dfk.cleanup() - - -# 'wait' mode has two cases to test: -# 1. that we wait when there is no exception -# 2. that we do not wait when there is an exception -@pytest.mark.local -def test_exit_wait_no_exception(): - config = fresh_config() - config.exit_mode = "wait" - - with parsl.load(config) as dfk: - fut = square(1) - # deliberately don't wait for this to finish, so that the context - # manager can exit - - assert fut.done(), "This future should be marked as done before the context manager exits" - - assert dfk.cleanup_called, "The DFK should have been cleaned up by the context manager" - assert DataFlowKernelLoader._dfk is None, "The global DFK should have been removed" - - -@pytest.mark.local -def test_exit_wait_exception(): - config = fresh_config() - config.exit_mode = "wait" - - with pytest.raises(RuntimeError): - with parsl.load(config) as dfk: - # we'll never fire this future - fut_never = Future() - - fut_raise = raise_app() - - fut_depend = square(fut_never) - - # this should cause an exception, which should cause the context - # manager to exit, without waiting for fut_depend to finish. - fut_raise.result() - - assert dfk.cleanup_called, "The DFK should have been cleaned up by the context manager" - assert DataFlowKernelLoader._dfk is None, "The global DFK should have been removed" - assert fut_raise.exception() is not None, "fut_raise should contain an exception" - assert not fut_depend.done(), "fut_depend should have been left un-done (due to dependency failure)" - - -@pytest.mark.local -def test_exit_wrong_mode(): - - with pytest.raises(Exception) as ex: - Config(exit_mode="wrongmode") - - # with typeguard 4.x this is TypeCheckError, - # with typeguard 2.x this is TypeError - # we can't instantiate TypeCheckError if we're in typeguard 2.x environment - # because it does not exist... so check name using strings. - assert ex.type.__name__ == "TypeCheckError" or ex.type.__name__ == "TypeError" diff --git a/parsl/parsl/tests/test_python_apps/test_dep_standard_futures.py b/parsl/parsl/tests/test_python_apps/test_dep_standard_futures.py deleted file mode 100644 index 29bb6b27..00000000 --- a/parsl/parsl/tests/test_python_apps/test_dep_standard_futures.py +++ /dev/null @@ -1,48 +0,0 @@ -from concurrent.futures import Future - -import parsl -from parsl.dataflow.errors import DependencyError - - -@parsl.python_app -def copy_app(v): - return v - - -def test_future_result_dependency(): - - plain_fut = Future() - - parsl_fut = copy_app(plain_fut) - - assert not parsl_fut.done() - - message = "Test" - - plain_fut.set_result(message) - - assert parsl_fut.result() == message - - -def test_future_fail_dependency(): - - plain_fut = Future() - - parsl_fut = copy_app(plain_fut) - - assert not parsl_fut.done() - - plain_fut.set_exception(ValueError("Plain failure")) - - ex = parsl_fut.exception() - - # check that what we got is a dependency error... - assert isinstance(ex, DependencyError) - - # and that the dependency error string mentions the dependency - # Future, plain_fut, somewhere in its str - - assert repr(plain_fut) in str(ex) - assert len(ex.dependent_exceptions_tids) == 1 - assert isinstance(ex.dependent_exceptions_tids[0][0], ValueError) - assert ex.dependent_exceptions_tids[0][1].startswith("140 causes Parsl to hang -N = 100 - -# MAX_STACK is the maximum Python stack depth allowed for either -# task submission to an executor or execution of a task. -# With mid-2024 Parsl, 2-3 stack entries will be used per -# recursively launched parsl task. So this should be smaller than -# 2*N, but big enough to allow regular pytest+parsl stuff to -# happen. -MAX_STACK = 50 - - -def local_config(): - return parsl.Config(executors=[ImmediateExecutor()]) - - -class ImmediateExecutor(ParslExecutor): - def start(self): - pass - - def shutdown(self): - pass - - def submit(self, func: Callable, resource_specification: Dict[str, Any], *args: Any, **kwargs: Any) -> Future: - stack_depth = len(inspect.stack()) - assert stack_depth < MAX_STACK, "tasks should not be launched deep in the Python stack" - fut: Future[None] = Future() - res = func(*args, **kwargs) - fut.set_result(res) - return fut - - -@parsl.python_app -def chain(upstream): - stack_depth = len(inspect.stack()) - assert stack_depth < MAX_STACK, "chained dependencies should not be launched deep in the Python stack" - - -@pytest.mark.local -def test_deep_dependency_stack_depth(): - - fut = Future() - here = fut - - for _ in range(N): - here = chain(here) - - fut.set_result(None) - here.result() diff --git a/parsl/parsl/tests/test_python_apps/test_depfail_propagation.py b/parsl/parsl/tests/test_python_apps/test_depfail_propagation.py deleted file mode 100644 index 23758a11..00000000 --- a/parsl/parsl/tests/test_python_apps/test_depfail_propagation.py +++ /dev/null @@ -1,64 +0,0 @@ -import parsl -from parsl import python_app -from parsl.dataflow.errors import DependencyError -from parsl.dataflow.states import States - - -@python_app -def fails(): - raise ValueError("Deliberate failure") - - -@python_app -def depends(parent): - return 1 - - -def test_depfail_once(): - """Test the simplest dependency failure case""" - start_dep_fail_count = parsl.dfk().task_state_counts[States.dep_fail] - f1 = fails() - f2 = depends(f1) - - assert isinstance(f1.exception(), Exception) - assert not isinstance(f1.exception(), DependencyError) - assert isinstance(f2.exception(), DependencyError) - - # check that the task ID of the failing task is mentioned - # in the DependencyError message - assert ("task " + str(f1.task_record['id'])) in str(f2.exception()) - - assert parsl.dfk().task_state_counts[States.dep_fail] == start_dep_fail_count + 1 - - -def test_depfail_chain(): - """Test that dependency failures chain""" - start_dep_fail_count = parsl.dfk().task_state_counts[States.dep_fail] - f1 = fails() - f2 = depends(f1) - f3 = depends(f2) - f4 = depends(f3) - - assert isinstance(f1.exception(), Exception) - assert not isinstance(f1.exception(), DependencyError) - assert isinstance(f2.exception(), DependencyError) - assert isinstance(f3.exception(), DependencyError) - assert isinstance(f4.exception(), DependencyError) - - assert parsl.dfk().task_state_counts[States.dep_fail] == start_dep_fail_count + 3 - - -def test_depfail_branches(): - """Test that dependency failures propagate in the - presence of multiple downstream tasks.""" - start_dep_fail_count = parsl.dfk().task_state_counts[States.dep_fail] - f1 = fails() - f2 = depends(f1) - f3 = depends(f1) - - assert isinstance(f1.exception(), Exception) - assert not isinstance(f1.exception(), DependencyError) - assert isinstance(f2.exception(), DependencyError) - assert isinstance(f3.exception(), DependencyError) - - assert parsl.dfk().task_state_counts[States.dep_fail] == start_dep_fail_count + 2 diff --git a/parsl/parsl/tests/test_python_apps/test_exception.py b/parsl/parsl/tests/test_python_apps/test_exception.py deleted file mode 100644 index 52d4dc74..00000000 --- a/parsl/parsl/tests/test_python_apps/test_exception.py +++ /dev/null @@ -1,19 +0,0 @@ -import pytest - -from parsl.app.app import python_app - - -class CustomException(Exception): - pass - - -@python_app -def custom_exception(): - from parsl.tests.test_python_apps.test_exception import CustomException - raise CustomException('foobar') - - -def test_custom_exception(): - x = custom_exception() - with pytest.raises(CustomException): - x.result() diff --git a/parsl/parsl/tests/test_python_apps/test_fail.py b/parsl/parsl/tests/test_python_apps/test_fail.py deleted file mode 100644 index 8f6616f1..00000000 --- a/parsl/parsl/tests/test_python_apps/test_fail.py +++ /dev/null @@ -1,58 +0,0 @@ -import pytest - -from parsl.app.app import python_app -from parsl.dataflow.errors import DependencyError - - -class ManufacturedTestFailure(Exception): - pass - - -@python_app -def random_fail(fail_prob: float, inputs=()): - import random - if random.random() < fail_prob: - raise ManufacturedTestFailure("App failure") - - -def test_no_deps(): - """Test basic error handling, with no dependent failures - """ - futs = [random_fail(1), random_fail(0), random_fail(0)] - - for f in futs: - try: - f.result() - except ManufacturedTestFailure: - pass - - -def test_fail_sequence_first(): - t1 = random_fail(fail_prob=1) - t2 = random_fail(fail_prob=0, inputs=[t1]) - t_final = random_fail(fail_prob=0, inputs=[t2]) - - with pytest.raises(DependencyError): - t_final.result() - - assert len(t_final.exception().dependent_exceptions_tids) == 1 - assert isinstance(t_final.exception().dependent_exceptions_tids[0][0], DependencyError) - assert t_final.exception().dependent_exceptions_tids[0][1].startswith("task ") - - assert hasattr(t_final.exception(), '__cause__') - assert t_final.exception().__cause__ == t1.exception() - - -def test_fail_sequence_middle(): - t1 = random_fail(fail_prob=0) - t2 = random_fail(fail_prob=1, inputs=[t1]) - t_final = random_fail(fail_prob=0, inputs=[t2]) - - with pytest.raises(DependencyError): - t_final.result() - - assert len(t_final.exception().dependent_exceptions_tids) == 1 - assert isinstance(t_final.exception().dependent_exceptions_tids[0][0], ManufacturedTestFailure) - - assert hasattr(t_final.exception(), '__cause__') - assert t_final.exception().__cause__ == t2.exception() diff --git a/parsl/parsl/tests/test_python_apps/test_fibonacci_iterative.py b/parsl/parsl/tests/test_python_apps/test_fibonacci_iterative.py deleted file mode 100644 index 10372a8d..00000000 --- a/parsl/parsl/tests/test_python_apps/test_fibonacci_iterative.py +++ /dev/null @@ -1,25 +0,0 @@ -import argparse - -import parsl -from parsl.app.app import python_app -from parsl.tests.configs.local_threads import config - - -@python_app -def get_num(first, second): - return first + second - - -def test_fibonacci(num=3): - counter = 0 - results = [] - results.append(0) - results.append(1) - while counter < num - 2: - counter += 1 - results.append(get_num(results[counter - 1], results[counter])) - for i in range(len(results)): - if isinstance(results[i], int): - print(results[i]) - else: - print(results[i].result()) diff --git a/parsl/parsl/tests/test_python_apps/test_fibonacci_recursive.py b/parsl/parsl/tests/test_python_apps/test_fibonacci_recursive.py deleted file mode 100644 index 077cd5ae..00000000 --- a/parsl/parsl/tests/test_python_apps/test_fibonacci_recursive.py +++ /dev/null @@ -1,28 +0,0 @@ -from parsl.app.app import join_app, python_app - - -@python_app -def add(*args): - """Add all of the arguments together. If no arguments, then - zero is returned (the neutral element of +) - """ - accumulator = 0 - for v in args: - accumulator += v - return accumulator - - -@join_app -def fibonacci(n): - if n == 0: - return add() - elif n == 1: - return add(1) - else: - return add(fibonacci(n - 1), fibonacci(n - 2)) - - -def test_fibonacci(): - assert fibonacci(0).result() == 0 - assert fibonacci(4).result() == 3 - assert fibonacci(6).result() == 8 diff --git a/parsl/parsl/tests/test_python_apps/test_futures.py b/parsl/parsl/tests/test_python_apps/test_futures.py deleted file mode 100644 index 500845f7..00000000 --- a/parsl/parsl/tests/test_python_apps/test_futures.py +++ /dev/null @@ -1,88 +0,0 @@ -"""Testing behavior of futures - -We have the following cases for AppFutures: - -1. App launched immmediately on call -2. App launch was delayed (due to unresolved dep(s)) - -Same applies to datafutures, and we need to know the behavior wrt. - -1. result() called on 1, vs 2 -2. done() called on 1, vs 2 - -""" -from os.path import basename - -import pytest - -from parsl.app.app import python_app -from parsl.data_provider.files import File - - -@python_app -def delay_incr(x, delay=0.0, outputs=()): - import time - if outputs: - with open(outputs[0].filepath, 'w') as outs: - outs.write(str(x + 1)) - time.sleep(delay) - return x + 1 - - -def get_contents(filename): - with open(filename, 'r') as f: - return f.read() - - -def test_fut_case_1(): - """Testing the behavior of AppFutures where there are no dependencies - """ - - app_fu = delay_incr(1, delay=0.01) - assert app_fu.result() == 2 - - -@pytest.mark.staging_required -def test_fut_case_2(tmpd_cwd): - """Testing the behavior of DataFutures where there are no dependencies - """ - output_f = tmpd_cwd / 'test_fut_case_2.txt' - app_fu = delay_incr(1, delay=0.01, outputs=[File(str(output_f))]) - data_fu = app_fu.outputs[0] - - result = data_fu.result().filepath - assert basename(result) == output_f.name, "DataFuture did not return filename" - - contents = get_contents(result) - assert contents == '2' - - -def test_fut_case_3(): - """Testing the behavior of AppFutures where there are dependencies - - The first call has a delay, and the second call depends on the first - """ - - app_1 = delay_incr(1, delay=0.01) - app_2 = delay_incr(app_1) - - assert app_2.result() == 3 - - -@pytest.mark.staging_required -def test_fut_case_4(tmpd_cwd): - """Testing the behavior of DataFutures where there are dependencies - - The first call has a delay, and the second call depends on the first - """ - output_f1 = tmpd_cwd / 'test_fut_case_4_f1.txt' - output_f2 = tmpd_cwd / 'test_fut_case_4_f2.txt' - app_1 = delay_incr(1, delay=0.01, outputs=[File(str(output_f1))]) - app_2 = delay_incr(app_1, delay=0.01, outputs=[File(str(output_f2))]) - data_2 = app_2.outputs[0] - - result = data_2.result().filepath - assert basename(result) == output_f2.name, "DataFuture did not return the filename" - - contents = get_contents(result) - assert contents == '3' diff --git a/parsl/parsl/tests/test_python_apps/test_garbage_collect.py b/parsl/parsl/tests/test_python_apps/test_garbage_collect.py deleted file mode 100644 index 6dc17e24..00000000 --- a/parsl/parsl/tests/test_python_apps/test_garbage_collect.py +++ /dev/null @@ -1,30 +0,0 @@ -import threading -import time - -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.tests.configs.local_threads import fresh_config as local_config - - -@python_app -def slow_double(x, may_continue: threading.Event): - may_continue.wait() - return x * 2 - - -@pytest.mark.local -def test_garbage_collect(): - """ Launches an app with a dependency and waits till it's done and asserts that - the internal refs were wiped - """ - evt = threading.Event() - x = slow_double(10, evt) - x = slow_double(x, evt) - - assert parsl.dfk().tasks[x.tid]['app_fu'] == x, "Tasks table should have app_fu ref before done" - - evt.set() - assert x.result() == 10 * 4 - assert x.tid not in parsl.dfk().tasks, "Task record should be wiped after task completion" diff --git a/parsl/parsl/tests/test_python_apps/test_import_fail.py b/parsl/parsl/tests/test_python_apps/test_import_fail.py deleted file mode 100644 index 654247cd..00000000 --- a/parsl/parsl/tests/test_python_apps/test_import_fail.py +++ /dev/null @@ -1,41 +0,0 @@ -import parsl -from parsl.app.app import python_app - - -@python_app -def platform_name(): - return platform.platform() - - -def test_name_error(n=2): - """Catch NameError for missing name - """ - - p = platform_name() - - try: - p.result() - except NameError: - print("Caught NameError") - else: - assert False, "Raise the wrong Error" - - -@python_app -def bad_import(): - import non_existent - return non_existent.foo() - - -def test_import_error(n=2): - """Catch ImportError for missing name - """ - - p = bad_import() - - try: - p.result() - except ImportError: - print("Caught ImportError") - else: - assert False, "Raise the wrong Error" diff --git a/parsl/parsl/tests/test_python_apps/test_inputs_default.py b/parsl/parsl/tests/test_python_apps/test_inputs_default.py deleted file mode 100644 index cf77c1a8..00000000 --- a/parsl/parsl/tests/test_python_apps/test_inputs_default.py +++ /dev/null @@ -1,22 +0,0 @@ -import pytest - -import parsl -from parsl import python_app -from parsl.executors.threads import ThreadPoolExecutor - - -def local_config(): - return parsl.Config(executors=[ThreadPoolExecutor()]) - - -@pytest.mark.local -def test_default_inputs(): - @python_app - def identity(inp): - return inp - - @python_app - def add_inputs(inputs=[identity(1), identity(2)]): - return sum(inputs) - - assert add_inputs().result() == 3 diff --git a/parsl/parsl/tests/test_python_apps/test_join.py b/parsl/parsl/tests/test_python_apps/test_join.py deleted file mode 100644 index 926d90bf..00000000 --- a/parsl/parsl/tests/test_python_apps/test_join.py +++ /dev/null @@ -1,145 +0,0 @@ -import pytest - -from parsl import join_app, python_app -from parsl.dataflow.errors import JoinError - -RESULT_CONSTANT = 3 - - -@python_app -def inner_app(): - return RESULT_CONSTANT - - -@join_app -def outer_app(): - fut = inner_app() - return fut - - -@python_app -def add_one(n): - return n + 1 - - -@python_app -def combine(*args): - """Wait for an arbitrary list of futures and return them as a list""" - return list(args) - - -@join_app -def outer_make_a_dag_combine(n): - return combine(*(inner_app() for _ in range(n))) - - -@join_app -def outer_make_a_dag_multi(n): - return [inner_app() for _ in range(n)] - - -def test_result_flow(): - f = outer_app() - assert f.result() == RESULT_CONSTANT - - -@join_app -def join_wrong_type_app(): - return 3 - - -def test_wrong_type(): - f = join_wrong_type_app() - with pytest.raises(TypeError): - f.result() - - -def test_dependency_on_joined(): - g = add_one(outer_app()) - assert g.result() == RESULT_CONSTANT + 1 - - -def test_combine(): - f = outer_make_a_dag_combine(inner_app()) - assert f.result() == [RESULT_CONSTANT] * RESULT_CONSTANT - - -def test_multiple_return(): - f = outer_make_a_dag_multi(inner_app()) - assert f.result() == [RESULT_CONSTANT] * RESULT_CONSTANT - - -class InnerError(RuntimeError): - pass - - -@python_app -def inner_error(s="X"): - raise InnerError("Error " + s) - - -@join_app -def outer_error(): - return inner_error() - - -@join_app -def outer_two_errors(): - return [inner_error("A"), inner_error("B")] - - -@join_app -def outer_one_error_one_result(): - return [inner_error("A"), inner_app()] - - -def test_error(): - f = outer_error() - e = f.exception() - assert isinstance(e, JoinError) - - assert len(e.dependent_exceptions_tids) == 1 - assert isinstance(e.dependent_exceptions_tids[0][0], InnerError) - assert e.dependent_exceptions_tids[0][1].startswith("task ") - - -def test_two_errors(): - f = outer_two_errors() - e = f.exception() - assert isinstance(e, JoinError) - assert len(e.dependent_exceptions_tids) == 2 - - de0 = e.dependent_exceptions_tids[0][0] - assert isinstance(de0, InnerError) - assert de0.args[0] == "Error A" - assert e.dependent_exceptions_tids[0][1].startswith("task ") - - de1 = e.dependent_exceptions_tids[1][0] - assert isinstance(de1, InnerError) - assert de1.args[0] == "Error B" - assert e.dependent_exceptions_tids[1][1].startswith("task ") - - -def test_one_error_one_result(): - f = outer_one_error_one_result() - e = f.exception() - - assert isinstance(e, JoinError) - assert len(e.dependent_exceptions_tids) == 1 - - de0 = e.dependent_exceptions_tids[0][0] - assert isinstance(de0, InnerError) - assert de0.args[0] == "Error A" - assert e.dependent_exceptions_tids[0][1].startswith("task ") - - -@join_app -def app_no_futures(): - return [] - - -def test_no_futures(): - # tests that a list of futures that contains no futures will - # complete - regression test for issue #2792 - f = app_no_futures() - assert f.result() == [] diff --git a/parsl/parsl/tests/test_python_apps/test_lifted.py b/parsl/parsl/tests/test_python_apps/test_lifted.py deleted file mode 100644 index 77084ac0..00000000 --- a/parsl/parsl/tests/test_python_apps/test_lifted.py +++ /dev/null @@ -1,142 +0,0 @@ -from concurrent.futures import Future -from typing import TypeVar - -import pytest - -from parsl import python_app - -T = TypeVar('T') - - -@python_app -def returns_a_dict() -> dict: - return {"a": "X", "b": "Y"} - - -@python_app -def returns_a_list() -> list: - return ["X", "Y"] - - -@python_app -def returns_a_tuple() -> tuple: - return ("X", "Y") - - -@python_app -def returns_a_class() -> type: - from dataclasses import dataclass - - @dataclass - class MyClass: - a: str = "X" - b: str = "Y" - - return MyClass - - -class MyOuterClass(): - def __init__(self): - self.q = "A" - self.r = "B" - - -@python_app -def returns_a_class_instance() -> object: - return MyOuterClass() - - -def test_returns_a_dict(): - - # precondition that returns_a_dict behaves - # correctly - assert returns_a_dict().result()["a"] == "X" - - # check that the deferred __getitem__ functionality works, - # allowing [] to be used on an AppFuture - assert returns_a_dict()["a"].result() == "X" - - -def test_returns_a_list(): - - # precondition that returns_a_list behaves - # correctly - assert returns_a_list().result()[0] == "X" - - # check that the deferred __getitem__ functionality works, - # allowing [] to be used on an AppFuture - assert returns_a_list()[0].result() == "X" - - -def test_returns_a_tuple(): - - # precondition that returns_a_tuple behaves - # correctly - assert returns_a_tuple().result()[0] == "X" - - # check that the deferred __getitem__ functionality works, - # allowing [] to be used on an AppFuture - assert returns_a_tuple()[0].result() == "X" - - -def test_lifted_getitem_on_dict_bad_key(): - assert isinstance(returns_a_dict()["invalid"].exception(), KeyError) - - -def test_returns_a_class_instance(): - # precondition - assert returns_a_class_instance().result().q == "A" - - # test of commuting . and result() - assert returns_a_class_instance().q.result() == "A" - - -def test_returns_a_class_instance_no_underscores(): - # test that _underscore attribute references are not lifted - f = returns_a_class_instance() - with pytest.raises(AttributeError): - f._nosuchattribute.result() - f.exception() # wait for f to complete before the test ends - - -def test_returns_a_class(): - - # precondition that returns_a_class behaves - # correctly - assert returns_a_class().result().a == "X" - - # check that the deferred __getattr__ functionality works, - # allowing [] to be used on an AppFuture - assert returns_a_class().a.result() == "X" - - # when the result is not indexable, a sensible error should - # appear in the appropriate future - - -@python_app -def passthrough(v: T) -> T: - return v - - -def test_lifted_getitem_ordering(): - # this should test that lifting getitem has the correct execution - # order: that it does not defer the execution of following code - - f_prereq = Future() - - f_top = passthrough(f_prereq) - - f_a = f_top['a'] - - # lifted ['a'] should not defer execution here (so it should not - # implicitly call result() on f_top). If it does, this test will - # hang at this point, waiting for f_top to get a value, which - # will not happen until f_prereq gets a value.. - # which doesn't happen until: - - f_prereq.set_result({"a": "X"}) - - # now at this point it should be safe to wait for f_a to get a result - # while passthrough and lifted getitem run... - - assert f_a.result() == "X" diff --git a/parsl/parsl/tests/test_python_apps/test_mapred.py b/parsl/parsl/tests/test_python_apps/test_mapred.py deleted file mode 100644 index 1c22334c..00000000 --- a/parsl/parsl/tests/test_python_apps/test_mapred.py +++ /dev/null @@ -1,34 +0,0 @@ -import pytest - -from parsl.app.app import python_app - - -@python_app -def times_two(x): - return x * 2 - - -@python_app -def accumulate(inputs=()): - return sum(inputs) - - -@python_app -def accumulate_t(*args): - return sum(args) - - -@pytest.mark.parametrize("width", (2, 3, 5)) -def test_mapred_type1(width): - """MapReduce test with the reduce stage taking futures in inputs=[]""" - futs = [times_two(i) for i in range(width)] - red = accumulate(inputs=futs) - assert red.result() == 2 * sum(range(width)) - - -@pytest.mark.parametrize("width", (2, 3, 5)) -def test_mapred_type2(width): - """MapReduce test with the reduce stage taking futures on the args""" - futs = [times_two(i) for i in range(width)] - red = accumulate_t(*futs) - assert red.result() == 2 * sum(range(width)) diff --git a/parsl/parsl/tests/test_python_apps/test_memoize_1.py b/parsl/parsl/tests/test_python_apps/test_memoize_1.py deleted file mode 100644 index 63076701..00000000 --- a/parsl/parsl/tests/test_python_apps/test_memoize_1.py +++ /dev/null @@ -1,21 +0,0 @@ -import argparse - -import parsl -from parsl.app.app import python_app - - -@python_app(cache=True) -def random_uuid(x, cache=True): - import uuid - return str(uuid.uuid4()) - - -def test_python_memoization(n=2): - """Testing python memoization.""" - x = random_uuid(0) - print(x.result()) - - for i in range(0, n): - foo = random_uuid(0) - print(foo.result()) - assert foo.result() == x.result(), "Memoized results were not used" diff --git a/parsl/parsl/tests/test_python_apps/test_memoize_2.py b/parsl/parsl/tests/test_python_apps/test_memoize_2.py deleted file mode 100644 index fe418b1d..00000000 --- a/parsl/parsl/tests/test_python_apps/test_memoize_2.py +++ /dev/null @@ -1,43 +0,0 @@ -import argparse - -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.config import Config -from parsl.dataflow.memoization import BasicMemoizer -from parsl.executors.threads import ThreadPoolExecutor - - -def local_config(): - return Config( - executors=[ - ThreadPoolExecutor(max_threads=4), - ], - memoizer=BasicMemoizer(memoize=False) - ) - - -@python_app(cache=True) -def random_uuid(x): - import uuid - return str(uuid.uuid4()) - - -@pytest.mark.local -def test_python_memoization(n=2): - """Testing python memoization disable via DFK call - """ - x = random_uuid(0) - - # Force x to completion before running other tests that will - # potentially re-use (or not-re-use) the result of x. Otherwise, - # results might not be reused because x is not completed, rather - # than because app-caching is disabled. - - x.result() - - for i in range(0, n): - foo = random_uuid(0) - assert foo.result() != x.result( - ), "Memoized results were used when memoization was disabled" diff --git a/parsl/parsl/tests/test_python_apps/test_memoize_4.py b/parsl/parsl/tests/test_python_apps/test_memoize_4.py deleted file mode 100644 index 92706ce4..00000000 --- a/parsl/parsl/tests/test_python_apps/test_memoize_4.py +++ /dev/null @@ -1,26 +0,0 @@ -import argparse - -import parsl -from parsl.app.app import python_app -from parsl.tests.configs.local_threads import config - - -def test_python_memoization(n=2): - """Testing python memoization when func bodies differ - """ - @python_app - def random_uuid(x): - import uuid - return str(uuid.uuid4()) - - x = random_uuid(0) - print(x.result()) - - @python_app - def random_uuid(x): - import uuid - print("hi") - return str(uuid.uuid4()) - - y = random_uuid(0) - assert x.result() != y.result(), "Memoized results were not used" diff --git a/parsl/parsl/tests/test_python_apps/test_memoize_bad_id_for_memo.py b/parsl/parsl/tests/test_python_apps/test_memoize_bad_id_for_memo.py deleted file mode 100644 index d4235457..00000000 --- a/parsl/parsl/tests/test_python_apps/test_memoize_bad_id_for_memo.py +++ /dev/null @@ -1,64 +0,0 @@ -import pytest - -from parsl import python_app -from parsl.dataflow.memoization import id_for_memo - - -# this class should not have a memoizer registered for it -class Unmemoizable: - pass - - -# this class should have a memoizer that always raises an -# exception -class FailingMemoizable: - pass - - -class FailingMemoizerTestError(ValueError): - pass - - -@id_for_memo.register(FailingMemoizable) -def failing_memoizer(v, output_ref=False): - raise FailingMemoizerTestError("Deliberate memoizer failure") - - -@python_app(cache=True) -def noop_app(x, inputs=[], cache=True): - return None - - -@python_app -def some_func(_t): - pass - - -def test_python_unmemoizable(): - """Testing behaviour when an unmemoizable parameter is used - """ - fut = noop_app(Unmemoizable()) - with pytest.raises(ValueError): - fut.result() - - -def test_python_failing_memoizer(): - """Testing behaviour when id_for_memo raises an exception - """ - fut = noop_app(FailingMemoizable()) - with pytest.raises(FailingMemoizerTestError): - fut.result() - - -def test_python_unmemoizable_after_dep(): - memoizable_fut = some_func(1) - fut = noop_app(Unmemoizable(), inputs=[memoizable_fut]) - with pytest.raises(ValueError): - fut.result() - - -def test_python_failing_memoizer_afer_dep(): - memoizable_fut = some_func(1) - fut = noop_app(FailingMemoizable(), inputs=[memoizable_fut]) - with pytest.raises(ValueError): - fut.result() diff --git a/parsl/parsl/tests/test_python_apps/test_memoize_exception.py b/parsl/parsl/tests/test_python_apps/test_memoize_exception.py deleted file mode 100644 index 28a53685..00000000 --- a/parsl/parsl/tests/test_python_apps/test_memoize_exception.py +++ /dev/null @@ -1,55 +0,0 @@ -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.config import Config -from parsl.dataflow.memoization import BasicMemoizer -from parsl.executors.threads import ThreadPoolExecutor - - -def local_config(): - return Config( - executors=[ThreadPoolExecutor()], - memoizer=BasicMemoizer() - ) - - -@python_app(cache=True) -def raise_exception_cache(x, cache=True): - raise RuntimeError("exception from raise_exception_cache") - - -@python_app(cache=False) -def raise_exception_nocache(x, cache=True): - raise RuntimeError("exception from raise_exception_nocache") - - -@pytest.mark.local -def test_python_memoization(n=2): - """Test BasicMemoizer memoization of exceptions, with cache=True""" - x = raise_exception_cache(0) - - # wait for x to be done - x.exception() - - for i in range(0, n): - fut = raise_exception_cache(0) - - # check that we get back the same exception object, rather than - # a new one from a second invocation of raise_exception(). - assert fut.exception() is x.exception(), "Memoized exception should have been memoized" - - -@pytest.mark.local -def test_python_no_memoization(n=2): - """Test BasicMemoizer non-memoization of exceptions, with cache=False""" - x = raise_exception_nocache(0) - - # wait for x to be done - x.exception() - - for i in range(0, n): - fut = raise_exception_nocache(0) - - # check that we get back a different exception object each time - assert fut.exception() is not x.exception(), "Memoized exception should have been memoized" diff --git a/parsl/parsl/tests/test_python_apps/test_memoize_ignore_args.py b/parsl/parsl/tests/test_python_apps/test_memoize_ignore_args.py deleted file mode 100644 index 03ebff57..00000000 --- a/parsl/parsl/tests/test_python_apps/test_memoize_ignore_args.py +++ /dev/null @@ -1,30 +0,0 @@ -from parsl.app.app import python_app - - -@python_app(cache=True, ignore_for_cache=[]) -def random_uuid(x): - import uuid - return str(uuid.uuid4()) - - -@python_app(cache=True, ignore_for_cache=['x']) -def random_uuid_def(x): - import uuid - return str(uuid.uuid4()) - - -def test_memo_different(): - # explicitly call result() before launching next app so that - # the executions are serialized and the x result will be in - # memo table - x = random_uuid(x=0).result() - y = random_uuid(x=1).result() - - assert x != y, "Memoized results were used incorrectly" - - -def test_memo_same_at_definition(): - x = random_uuid_def(x=0).result() - y = random_uuid_def(x=1).result() - - assert x == y, "Memoized results were not used" diff --git a/parsl/parsl/tests/test_python_apps/test_memoize_joinapp.py b/parsl/parsl/tests/test_python_apps/test_memoize_joinapp.py deleted file mode 100644 index 42c8e043..00000000 --- a/parsl/parsl/tests/test_python_apps/test_memoize_joinapp.py +++ /dev/null @@ -1,23 +0,0 @@ -from parsl.app.app import join_app, python_app - - -@join_app(cache=True) -def random_uuid(x): - import uuid - return const(str(uuid.uuid4())) - - -@python_app -def const(x): - return x - - -def test_join_memoization(): - """Testing python memoization disable - """ - x = random_uuid(0) - x.result() - - for i in range(0, 2): - foo = random_uuid(0) - assert foo.result() == x.result(), "Memoized results were not used" diff --git a/parsl/parsl/tests/test_python_apps/test_memoize_plugin.py b/parsl/parsl/tests/test_python_apps/test_memoize_plugin.py deleted file mode 100644 index f983f37d..00000000 --- a/parsl/parsl/tests/test_python_apps/test_memoize_plugin.py +++ /dev/null @@ -1,49 +0,0 @@ -import argparse - -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.config import Config -from parsl.dataflow.memoization import BasicMemoizer -from parsl.dataflow.taskrecord import TaskRecord - - -class DontReuseSevenMemoizer(BasicMemoizer): - def check_memo(self, task_record: TaskRecord): - if task_record['args'][0] == 7: - return None # we didn't find a suitable memo record... - else: - return super().check_memo(task_record) - - -def local_config(): - return Config(memoizer=DontReuseSevenMemoizer()) - - -@python_app(cache=True) -def random_uuid(x, cache=True): - import uuid - return str(uuid.uuid4()) - - -@pytest.mark.local -@pytest.mark.parametrize("n", (10,)) -def test_python_memoization(n: int): - """Testing using an alternate memoization plugin. - - The plugin under test memoizes except for functions with the parameter 7, - and that behaviour should be observable in uuid app results. - """ - - x = random_uuid(0).result() - - for i in range(0, n): - foo = random_uuid(0) - assert foo.result() == x, "Memoized results were incorrectly not used" - - y = random_uuid(7).result() - - for i in range(0, n): - foo = random_uuid(7) - assert foo.result() != y, "Memoized results were incorrectly used" diff --git a/parsl/parsl/tests/test_python_apps/test_outputs.py b/parsl/parsl/tests/test_python_apps/test_outputs.py deleted file mode 100644 index b4273cf2..00000000 --- a/parsl/parsl/tests/test_python_apps/test_outputs.py +++ /dev/null @@ -1,28 +0,0 @@ -import os -from concurrent.futures import wait - -import pytest - -from parsl import File, python_app - - -@python_app -def double(x, outputs=[]): - with open(outputs[0], 'w') as f: - f.write(x * 5) - return x * 5 - - -whitelist = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'configs', '*threads*') - - -@pytest.mark.shared_fs -def test_launch_apps(tmpd_cwd, n=2): - outdir = tmpd_cwd / "outputs" - outdir.mkdir() - - futs = [double(i, outputs=[File(str(outdir / f"{i}.txt"))]) for i in range(n)] - wait(futs) - - stdout_file_count = len(list(outdir.glob("*.txt"))) - assert stdout_file_count == n, sorted(outdir.glob("*.txt")) diff --git a/parsl/parsl/tests/test_python_apps/test_overview.py b/parsl/parsl/tests/test_python_apps/test_overview.py deleted file mode 100644 index 63e205ef..00000000 --- a/parsl/parsl/tests/test_python_apps/test_overview.py +++ /dev/null @@ -1,23 +0,0 @@ -from parsl.app.app import python_app - - -@python_app -def app_double(x): - return x * 2 - - -@python_app -def app_sum(inputs=()): - return sum(inputs) - - -def test_1(N=10): - """Testing code snippet from the documentation - """ - - # Create a list of integers, then apply *app* function to each - items = range(N) - mapped_results = list(map(app_double, items)) - - total = app_sum(inputs=mapped_results) - assert total.result() == 2 * sum(items) diff --git a/parsl/parsl/tests/test_python_apps/test_pipeline.py b/parsl/parsl/tests/test_python_apps/test_pipeline.py deleted file mode 100644 index ceb2d11e..00000000 --- a/parsl/parsl/tests/test_python_apps/test_pipeline.py +++ /dev/null @@ -1,35 +0,0 @@ -import argparse - -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.tests.configs.local_threads import config - - -@python_app -def increment(x): - return x + 1 - - -@python_app -def slow_increment(x): - import time - time.sleep(0.001) - return x + 1 - - -@pytest.mark.parametrize("depth", (2, 3)) -def test_increment(depth): - """Test simple pipeline A->B...->N""" - futs = [increment(0)] - futs.extend(increment(futs[i - 1]) for i in range(1, depth)) - assert sum(f.result() for f in futs) == sum(range(1, depth + 1)) - - -@pytest.mark.parametrize("depth", (2, 3)) -def test_increment_slow(depth): - """Test simple pipeline A->B...->N with delay""" - futs = [slow_increment(0)] - futs.extend(slow_increment(futs[i - 1]) for i in range(1, depth)) - assert sum(f.result() for f in futs) == sum(range(1, depth + 1)) diff --git a/parsl/parsl/tests/test_python_apps/test_pluggable_future_resolution.py b/parsl/parsl/tests/test_python_apps/test_pluggable_future_resolution.py deleted file mode 100644 index 4793c0ac..00000000 --- a/parsl/parsl/tests/test_python_apps/test_pluggable_future_resolution.py +++ /dev/null @@ -1,161 +0,0 @@ -from concurrent.futures import Future -from pathlib import Path -from threading import Event -from typing import Sequence - -import pytest - -import parsl -from parsl.config import Config -from parsl.dataflow.dependency_resolvers import DEEP_DEPENDENCY_RESOLVER -from parsl.dataflow.errors import DependencyError - - -def local_config(): - return Config(dependency_resolver=DEEP_DEPENDENCY_RESOLVER) - - -@parsl.python_app -def a(event): - event.wait() - return 7 - - -@parsl.python_app -def b(x: int): - return x + 1 - - -@pytest.mark.local -def test_simple_pos_arg(): - e = Event() - s = a(e) - f_b = b(s) - e.set() - - assert f_b.result() == 8 - - -@parsl.python_app -def b_first(x: Sequence[int]): - return x[0] + 1 - - -@pytest.mark.local -def test_tuple_pos_arg(): - e = Event() - s = (a(e),) - f_b = b_first(s) - e.set() - assert f_b.result() == 8 - - -@pytest.mark.local -def test_list_exception(): - a = Future() - a.set_exception(RuntimeError("artificial error")) - f_b = b([a]) - assert isinstance(f_b.exception(), DependencyError) - - -@parsl.python_app -def make_path(s: str): - return Path(s) - - -@parsl.python_app -def append_paths(iterable, end_str: str = "end"): - type_ = type(iterable) - return type_([Path(s, end_str) for s in iterable]) - - -@pytest.mark.local -@pytest.mark.parametrize( - "type_", - [ - tuple, - list, - set, - ], -) -def test_resolving_iterables(type_): - output1 = make_path("test1") - output2 = make_path("test2") - output3 = append_paths(type_([output1, output2]), end_str="end") - assert output3.result() == type_([Path("test1", "end"), Path("test2", "end")]) - - -@parsl.python_app -def append_paths_dict(iterable: dict, end_str: str = "end"): - return {Path(k, end_str): Path(v, end_str) for k, v in iterable.items()} - - -@pytest.mark.local -def test_resolving_dict(): - output1 = make_path("test1") - output2 = make_path("test2") - output3 = append_paths_dict({output1: output2}, end_str="end") - assert output3.result() == {Path("test1", "end"): Path("test2", "end")} - - -@parsl.python_app -def extract_deep(struct: list): - return struct[0][0][0][0][0] - - -@pytest.mark.local -def test_deeper_list(): - f = Future() - f.set_result(7) - f_b = extract_deep([[[[[f]]]]]) - - assert f_b.result() == 7 - - -@pytest.mark.local -def test_deeper_list_and_tuple(): - f = Future() - f.set_result(7) - f_b = extract_deep([([([f],)],)]) - - assert f_b.result() == 7 - - -@parsl.python_app -def dictionary_checker(d): - assert d["a"] == [30, 10] - assert d["b"] == 20 - - -@pytest.mark.local -def test_dictionary(): - k1 = Future() - k1.set_result("a") - k2 = Future() - k2.set_result("b") - v1 = Future() - v1.set_result(10) - - # this .result() will fail if the asserts fail - dictionary_checker({k1: [30, v1], k2: 20}).result() - - -@pytest.mark.local -def test_dictionary_later(): - k1 = Future() - k2 = Future() - v1 = Future() - - f1 = dictionary_checker({k1: [30, v1], k2: 20}) - - assert not f1.done() - k1.set_result("a") - k2.set_result("b") - v1.set_result(10) - - # having set the results, f1 should fairly rapidly complete (but not - # instantly) so we can't assert on done() here... but we can - # check that f1 does "eventually" complete... meaning that - # none of the assertions inside test_dictionary have failed - - assert f1.result() is None diff --git a/parsl/parsl/tests/test_python_apps/test_simple.py b/parsl/parsl/tests/test_python_apps/test_simple.py deleted file mode 100644 index 007db7b9..00000000 --- a/parsl/parsl/tests/test_python_apps/test_simple.py +++ /dev/null @@ -1,32 +0,0 @@ -from parsl.app.app import python_app - - -@python_app -def increment(x): - return x + 1 - - -@python_app -def slow_increment(x, dur): - import time - time.sleep(dur) - return x + 1 - - -def test_increment(depth=5): - futs = {0: 0} - for i in range(1, depth): - futs[i] = increment(futs[i - 1]) - - x = sum([futs[i].result() for i in futs if not isinstance(futs[i], int)]) - assert x == sum(range(1, depth)), "[TEST] increment [FAILED]" - - -def test_slow_increment(depth=5): - futs = {0: 0} - for i in range(1, depth): - futs[i] = slow_increment(futs[i - 1], 0.01) - - x = sum([futs[i].result() for i in futs if not isinstance(futs[i], int)]) - - assert x == sum(range(1, depth)), "[TEST] slow_increment [FAILED]" diff --git a/parsl/parsl/tests/test_python_apps/test_timeout.py b/parsl/parsl/tests/test_python_apps/test_timeout.py deleted file mode 100644 index 1b051332..00000000 --- a/parsl/parsl/tests/test_python_apps/test_timeout.py +++ /dev/null @@ -1,40 +0,0 @@ -import time - -import pytest - -from parsl.app.errors import AppTimeout -from parsl.app.python import timeout - - -class TimeoutTestSpecificException(Exception): - pass - - -def kernel(fail=False): - if fail: - raise TimeoutTestSpecificException() - - -def test_timeout(): - timeout_dur = 0.1 - try: - timeout(kernel, timeout_dur)() # nominally returns "instantly" - except AppTimeout: - pytest.skip("Pre-condition failed: result not received in timely fashion") - assert False - - try: - time.sleep(timeout_dur + 0.01) # this is the verification - except AppTimeout: - assert False, "Timer was not cancelled!" - - -@pytest.mark.parametrize("timeout_dur", (0.005, 0.01)) -def test_timeout_after_exception(timeout_dur): - with pytest.raises(TimeoutTestSpecificException): - timeout(kernel, timeout_dur)(True) - - try: - time.sleep(timeout_dur + 0.01) # this is the verification - except AppTimeout: - assert False, "Timer was not cancelled!" diff --git a/parsl/parsl/tests/test_python_apps/test_type5.py b/parsl/parsl/tests/test_python_apps/test_type5.py deleted file mode 100644 index a58a97f4..00000000 --- a/parsl/parsl/tests/test_python_apps/test_type5.py +++ /dev/null @@ -1,31 +0,0 @@ -import pytest - -from parsl.app.app import python_app - - -@python_app -def map_x2(x): - return x * 2 - - -@python_app -def add_xy(x, y): - return x + y - - -def test_func_linked_to_previous_result(depth=2): - futs = (map_x2(i) for i in range(1, depth + 1)) - for _ in range(1, depth - 1): - futs = (map_x2(fut) for fut in futs) - futs = [map_x2(fut) for fut in futs] - - assert sum(f.result() for f in futs) == (2 ** depth) * sum(range(1, depth + 1)) - - -def test_func_linked_to_multiple_previous_results(width=4): - futs = (map_x2(i) for i in range(1, width + 1)) - f_it = iter(futs) - - # Requires that len(futs) % 2 == 0 (even number of items) - futs = [add_xy(f, next(f_it)) for f in f_it] - assert sum(f.result() for f in futs) == 2 * sum(range(1, width + 1)) diff --git a/parsl/parsl/tests/test_radical/__init__.py b/parsl/parsl/tests/test_radical/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_radical/test_mpi_funcs.py b/parsl/parsl/tests/test_radical/test_mpi_funcs.py deleted file mode 100644 index 851328da..00000000 --- a/parsl/parsl/tests/test_radical/test_mpi_funcs.py +++ /dev/null @@ -1,34 +0,0 @@ -import os - -import pytest - -import parsl -from parsl.tests.configs.local_radical_mpi import fresh_config as local_config - - -@parsl.python_app -def some_mpi_func(msg, sleep, comm=None, parsl_resource_specification={}): - import time - msg = 'hello %d/%d: %s' % (comm.rank, comm.size, msg) - time.sleep(sleep) - print(msg) - return comm.size - - -apps = [] - - -@pytest.mark.local -@pytest.mark.radical -@pytest.mark.skipif( - os.environ.get("RADICAL_CI") != "1", - reason="Only runs in Radical CI workflow" -) -def test_radical_mpi(n=7): - # rank size should be > 1 for the - # radical runtime system to run this function in MPI env - for i in range(2, n): - spec = {'ranks': i} - t = some_mpi_func(msg='mpi.func.%06d' % i, sleep=1, comm=None, parsl_resource_specification=spec) - apps.append(t) - assert [len(app.result()) for app in apps] == list(range(2, n)) diff --git a/parsl/parsl/tests/test_regression/__init__.py b/parsl/parsl/tests/test_regression/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_regression/test_1480.py b/parsl/parsl/tests/test_regression/test_1480.py deleted file mode 100644 index a52a1f3e..00000000 --- a/parsl/parsl/tests/test_regression/test_1480.py +++ /dev/null @@ -1,20 +0,0 @@ -import pytest - -from parsl import python_app -from parsl.tests.configs.htex_local import fresh_config as local_config - - -@python_app -def compute_descript(size=1000): - import numpy as np - x = np.array(list(range(0, size)), dtype=complex).astype(np.float32) - return x - - -@pytest.mark.local -def test_1480(size=10**6): - x = compute_descript(size=size) - - # Before PR#1841 this would have raised a TypeError - # Now, with the threshold increased this should not trigger any error - assert len(x.result()) == size, "Lengths do not match" diff --git a/parsl/parsl/tests/test_regression/test_1606_wait_for_current_tasks.py b/parsl/parsl/tests/test_regression/test_1606_wait_for_current_tasks.py deleted file mode 100644 index 5cced409..00000000 --- a/parsl/parsl/tests/test_regression/test_1606_wait_for_current_tasks.py +++ /dev/null @@ -1,42 +0,0 @@ -import threading -import time - -import pytest - -import parsl -from parsl.tests.configs.local_threads import fresh_config as local_config - - -@parsl.python_app -def slow_app(evt: threading.Event): - evt.wait() - - -@pytest.mark.local -def test_wait_for_tasks(): - """ - gh#1606 reported that wait_for_current_tasks fails due to tasks being removed - from the DFK tasks dict as they complete; bug introduced in #1543. - """ - def test_kernel(may_wait: threading.Event): - e1, e2 = threading.Event(), threading.Event() - - # app_slow is in *middle* of internal DFK data structure - app_fast1, app_slow, app_fast2 = slow_app(e1), slow_app(e2), slow_app(e1) - - may_wait.set() # initiated wait in outer test - time.sleep(0.01) - - e1.set() - - while not all(f.done() for f in (app_fast1, app_fast2)): - time.sleep(0.01) - - e2.set() - app_slow.result() - - may_continue = threading.Event() - threading.Thread(target=test_kernel, daemon=True, args=(may_continue,)).start() - - may_continue.wait() - parsl.dfk().wait_for_current_tasks() # per sleeps, waits for all 3 tasks diff --git a/parsl/parsl/tests/test_regression/test_1653.py b/parsl/parsl/tests/test_regression/test_1653.py deleted file mode 100644 index 61ab1bd4..00000000 --- a/parsl/parsl/tests/test_regression/test_1653.py +++ /dev/null @@ -1,22 +0,0 @@ -import pytest - -from parsl import python_app -from parsl.tests.configs.htex_local import fresh_config as local_config - - -@python_app -def compute_descript(size=1000): - import numpy as np - x = np.array(list(range(0, size)), dtype=complex).astype(np.float32) - return x - - -@pytest.mark.local -def test_1653(): - """ Check if #1653 works correctly - """ - x = compute_descript(size=100).result() - assert x.shape == (100,), "Got incorrect numpy shape" - - x = compute_descript(size=1000).result() - assert x.shape == (1000,), "Got incorrect numpy shape" diff --git a/parsl/parsl/tests/test_regression/test_221.py b/parsl/parsl/tests/test_regression/test_221.py deleted file mode 100644 index d9b96b58..00000000 --- a/parsl/parsl/tests/test_regression/test_221.py +++ /dev/null @@ -1,29 +0,0 @@ -import pytest - -from parsl.app.app import python_app -from parsl.tests.configs.htex_local import fresh_config as local_config - - -@python_app -def slow_double(x, dur=0.1): - import time - time.sleep(dur) - return x * 5 - - -@pytest.mark.local -def test_cleanup_behavior_221(): - """ A1 A2 A3 -> cleanup - B1 B2 B3 - - """ - - round_1 = [] - for i in range(0, 2): - f = slow_double(i) - round_1.append(f) - - round_2 = [] - for i in round_1: - f = slow_double(i) - round_2.append(f) diff --git a/parsl/parsl/tests/test_regression/test_226.py b/parsl/parsl/tests/test_regression/test_226.py deleted file mode 100644 index 8babf4cb..00000000 --- a/parsl/parsl/tests/test_regression/test_226.py +++ /dev/null @@ -1,63 +0,0 @@ -''' -Regression test for #226. -''' -import os - -import pandas as pd -import pytest - -from parsl.app.app import bash_app, python_app - - -class Foo: - def __init__(self, x): - self.x = x - - def __eq__(self, value): - raise NotImplementedError - - -bar = Foo(1) - - -@python_app -def get_foo_x(a, b=bar, c=None): - return b.x - - -@bash_app -def get_foo_x_bash(a, b=bar, c=None): - return "echo {}".format(b.x) - - -data = pd.DataFrame({'x': [None, 2, [3]]}) - - -@python_app -def get_dataframe(d=data): - return d - - -@bash_app -def echo(msg, postfix='there', stdout='std.out'): - return 'echo {} {}'.format(msg, postfix) - - -def test_no_eq(): - res = get_foo_x('foo').result() - assert res == 1, 'Expected 1, returned {}'.format(res) - - -def test_get_dataframe(): - res = get_dataframe().result() - assert res.equals(data), 'Unexpected dataframe' - - -@pytest.mark.shared_fs -def test_bash_default_arg(): - if os.path.exists('std.out'): - os.remove('std.out') - - echo('hello').result() - with open('std.out', 'r') as f: - assert f.read().strip() == 'hello there', "Output should be 'hello there'" diff --git a/parsl/parsl/tests/test_regression/test_2652.py b/parsl/parsl/tests/test_regression/test_2652.py deleted file mode 100644 index 42cdae9c..00000000 --- a/parsl/parsl/tests/test_regression/test_2652.py +++ /dev/null @@ -1,28 +0,0 @@ -import pytest - -from parsl.jobs.states import JobState - - -@pytest.mark.local -def test_JobStatus_repr(): - # in Python 3.11, the behavior of enums changed a bit, and so repr - # (inherited from a superclass) raised an exception rather than - # returning a string. - - # This test checks that repr returns some string value - it does - # not validate that that string is some expected value. - - j = JobState.COMPLETED - - # this repr call started raising exceptions with python 3.11 - # prior to the #2652 bugfix. - r = repr(j) - assert isinstance(r, str) - - s = str(j) - assert isinstance(s, str) - - # IntEnum changed behaviour from python 3.10 -> 3.10 so as to return - # an integer. This checks that the JobState enum implementation - # returns a name string on all tested platforms. - assert s == "JobState.COMPLETED" diff --git a/parsl/parsl/tests/test_regression/test_3874.py b/parsl/parsl/tests/test_regression/test_3874.py deleted file mode 100644 index 00390ed5..00000000 --- a/parsl/parsl/tests/test_regression/test_3874.py +++ /dev/null @@ -1,47 +0,0 @@ -import shutil - -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.config import Config -from parsl.executors import HighThroughputExecutor - - -@python_app -def noop(): - pass - - -@pytest.mark.local -def test_regression_3874(tmpd_cwd_session): - # HTEX run 1 - - rundir_1 = str(tmpd_cwd_session / "1") - - config = Config(executors=[HighThroughputExecutor()], strategy_period=0.5) - config.run_dir = rundir_1 - - with parsl.load(config): - noop().result() - - # It is necessary to delete this rundir to exercise the bug. Otherwise, - # the next run will be able to continue looking at this directory - the - # bug manifests when it cannot. - - shutil.rmtree(rundir_1) - - # HTEX run 2 - # In the case of issue 3874, this run hangs (rather than failing) as the - # JobStatusPoller fails to collect status of all of its managed tasks - # every iteration, without converging towards failure. - - rundir_2 = str(tmpd_cwd_session / "2") - - config = Config(executors=[HighThroughputExecutor()], strategy_period=0.5) - config.run_dir = rundir_2 - - with parsl.load(config): - noop().result() - - shutil.rmtree(rundir_2) diff --git a/parsl/parsl/tests/test_regression/test_97_parallelism_0.py b/parsl/parsl/tests/test_regression/test_97_parallelism_0.py deleted file mode 100644 index a83a5b79..00000000 --- a/parsl/parsl/tests/test_regression/test_97_parallelism_0.py +++ /dev/null @@ -1,54 +0,0 @@ -import pytest - -import parsl -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SimpleLauncher -from parsl.providers import LocalProvider - - -def local_config() -> Config: - return Config( - executors=[ - HighThroughputExecutor( - label="htex_local", - worker_debug=True, - cores_per_worker=1, - encrypted=True, - provider=LocalProvider( - init_blocks=0, - min_blocks=0, - max_blocks=1, - parallelism=0, - launcher=SimpleLauncher(), - ), - ) - ], - strategy='simple', - ) - - -@parsl.python_app -def python_app(): - return 7 - - -@pytest.mark.local -def test_parallism_0(): - """This is a regression test for parsl issue #97, - https://github.com/Parsl/parsl/issues/97 - - In that issue, if parallelism is set to 0, then no blocks would be - provisioned by the simple scaling strategy, as the number of blocks - to be scaled out would include a parallelism factor of 0 and so would - always be 0. With 0 blocks, no tasks would execute. - - Commit 2828a779e00bf0188ec82454c9aa3004480b5809 adds in an additional - scaling strategy case to make one block be scaled out when there are - active tasks, even if the parallelism-based path says 0 blocks are - needed. - - This test runs a single app to completion. This test would hang in the - case of a regression. - """ - assert python_app().result() == 7 diff --git a/parsl/parsl/tests/test_regression/test_98.py b/parsl/parsl/tests/test_regression/test_98.py deleted file mode 100644 index 486e33f1..00000000 --- a/parsl/parsl/tests/test_regression/test_98.py +++ /dev/null @@ -1,23 +0,0 @@ -''' -Regression test for issue #98 -''' -import argparse - -import pytest - -import parsl -from parsl.dataflow.dflow import DataFlowKernel -from parsl.tests.configs.local_threads import config - - -@pytest.mark.local -def test_immutable_config(n=2): - """Regression test for immutable config #98 - """ - - original = str(config) - dfk = DataFlowKernel(config=config) - after = str(config) - - dfk.cleanup() - assert original == after, "Config modified" diff --git a/parsl/parsl/tests/test_scaling/__init__.py b/parsl/parsl/tests/test_scaling/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_scaling/test_block_error_handler.py b/parsl/parsl/tests/test_scaling/test_block_error_handler.py deleted file mode 100644 index 3a7beb9f..00000000 --- a/parsl/parsl/tests/test_scaling/test_block_error_handler.py +++ /dev/null @@ -1,174 +0,0 @@ -from functools import partial -from unittest.mock import Mock - -import pytest - -from parsl.executors import HighThroughputExecutor -from parsl.jobs.error_handlers import ( - noop_error_handler, - simple_error_handler, - windowed_error_handler, -) -from parsl.jobs.states import JobState, JobStatus -from parsl.providers import LocalProvider - - -@pytest.mark.local -def test_block_error_handler_false(): - mock = Mock() - htex = HighThroughputExecutor(block_error_handler=False, encrypted=True) - assert htex.block_error_handler is noop_error_handler - htex.set_bad_state_and_fail_all = mock - - bad_jobs = {'1': JobStatus(JobState.FAILED), - '2': JobStatus(JobState.FAILED), - '3': JobStatus(JobState.FAILED), - '4': JobStatus(JobState.FAILED)} - - htex.handle_errors(bad_jobs) - mock.assert_not_called() - - -@pytest.mark.local -def test_block_error_handler_mock(): - handler_mock = Mock() - htex = HighThroughputExecutor(block_error_handler=handler_mock, encrypted=True) - assert htex.block_error_handler is handler_mock - - bad_jobs = {'1': JobStatus(JobState.FAILED), - '2': JobStatus(JobState.FAILED), - '3': JobStatus(JobState.FAILED), - '4': JobStatus(JobState.FAILED)} - - htex.handle_errors(bad_jobs) - handler_mock.assert_called() - handler_mock.assert_called_with(htex, bad_jobs) - - -@pytest.mark.local -def test_simple_error_handler(): - htex = HighThroughputExecutor(block_error_handler=simple_error_handler, - encrypted=True, - provider=LocalProvider(init_blocks=3)) - - assert htex.block_error_handler is simple_error_handler - - bad_state_mock = Mock() - htex.set_bad_state_and_fail_all = bad_state_mock - - bad_jobs = {'1': JobStatus(JobState.FAILED), - '2': JobStatus(JobState.FAILED)} - htex.handle_errors(bad_jobs) - bad_state_mock.assert_not_called() - - # Check the bad behavior where if any job is not failed - # bad state won't be set - bad_jobs = {'1': JobStatus(JobState.COMPLETED), - '2': JobStatus(JobState.FAILED), - '3': JobStatus(JobState.FAILED), - '4': JobStatus(JobState.FAILED)} - - htex.handle_errors(bad_jobs) - bad_state_mock.assert_not_called() - - bad_jobs = {'1': JobStatus(JobState.FAILED), - '2': JobStatus(JobState.FAILED), - '3': JobStatus(JobState.FAILED), - '4': JobStatus(JobState.FAILED)} - - htex.handle_errors(bad_jobs) - bad_state_mock.assert_called() - - -@pytest.mark.local -def test_windowed_error_handler(): - htex = HighThroughputExecutor(block_error_handler=windowed_error_handler, encrypted=True) - assert htex.block_error_handler is windowed_error_handler - - bad_state_mock = Mock() - htex.set_bad_state_and_fail_all = bad_state_mock - - bad_jobs = {'1': JobStatus(JobState.FAILED), - '2': JobStatus(JobState.FAILED)} - htex.handle_errors(bad_jobs) - bad_state_mock.assert_not_called() - - bad_jobs = {'1': JobStatus(JobState.COMPLETED), - '2': JobStatus(JobState.FAILED), - '3': JobStatus(JobState.FAILED)} - htex.handle_errors(bad_jobs) - bad_state_mock.assert_not_called() - - bad_jobs = {'1': JobStatus(JobState.FAILED), - '2': JobStatus(JobState.FAILED), - '3': JobStatus(JobState.COMPLETED), - '4': JobStatus(JobState.FAILED)} - htex.handle_errors(bad_jobs) - bad_state_mock.assert_not_called() - - bad_jobs = {'1': JobStatus(JobState.COMPLETED), - '2': JobStatus(JobState.FAILED), - '3': JobStatus(JobState.FAILED), - '4': JobStatus(JobState.FAILED)} - htex.handle_errors(bad_jobs) - bad_state_mock.assert_called() - - -@pytest.mark.local -def test_windowed_error_handler_sorting(): - htex = HighThroughputExecutor(block_error_handler=windowed_error_handler, encrypted=True) - assert htex.block_error_handler is windowed_error_handler - - bad_state_mock = Mock() - htex.set_bad_state_and_fail_all = bad_state_mock - - bad_jobs = {'8': JobStatus(JobState.FAILED), - '9': JobStatus(JobState.FAILED), - '10': JobStatus(JobState.FAILED), - '11': JobStatus(JobState.COMPLETED), - '12': JobStatus(JobState.COMPLETED)} - htex.handle_errors(bad_jobs) - bad_state_mock.assert_not_called() - - bad_jobs = {'8': JobStatus(JobState.COMPLETED), - '9': JobStatus(JobState.FAILED), - '21': JobStatus(JobState.FAILED), - '22': JobStatus(JobState.FAILED), - '10': JobStatus(JobState.FAILED)} - htex.handle_errors(bad_jobs) - bad_state_mock.assert_called() - - -@pytest.mark.local -def test_windowed_error_handler_with_threshold(): - error_handler = partial(windowed_error_handler, threshold=2) - htex = HighThroughputExecutor(block_error_handler=error_handler, encrypted=True) - assert htex.block_error_handler is error_handler - - bad_state_mock = Mock() - htex.set_bad_state_and_fail_all = bad_state_mock - - bad_jobs = {'1': JobStatus(JobState.COMPLETED), - '2': JobStatus(JobState.FAILED)} - htex.handle_errors(bad_jobs) - bad_state_mock.assert_not_called() - - bad_jobs = {'1': JobStatus(JobState.COMPLETED), - '2': JobStatus(JobState.FAILED), - '3': JobStatus(JobState.COMPLETED)} - htex.handle_errors(bad_jobs) - bad_state_mock.assert_not_called() - - bad_jobs = {'1': JobStatus(JobState.COMPLETED), - '2': JobStatus(JobState.COMPLETED), - '3': JobStatus(JobState.COMPLETED), - '4': JobStatus(JobState.FAILED)} - htex.handle_errors(bad_jobs) - bad_state_mock.assert_not_called() - - bad_jobs = {'1': JobStatus(JobState.COMPLETED), - '2': JobStatus(JobState.COMPLETED), - '3': JobStatus(JobState.FAILED), - '4': JobStatus(JobState.FAILED)} - htex.handle_errors(bad_jobs) - bad_state_mock.assert_called() diff --git a/parsl/parsl/tests/test_scaling/test_regression_1621.py b/parsl/parsl/tests/test_scaling/test_regression_1621.py deleted file mode 100644 index ea7d8f26..00000000 --- a/parsl/parsl/tests/test_scaling/test_regression_1621.py +++ /dev/null @@ -1,70 +0,0 @@ -import threading - -import pytest - -import parsl -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SimpleLauncher -from parsl.providers import LocalProvider - -# Timing notes: -# The configured strategy_period must be much smaller than the delay in -# app() so that multiple iterations of the strategy have had a chance -# to (mis)behave. -# The status polling interval in OneShotLocalProvider must be much bigger -# than the above times, so that the job status cached from the provider -# will not be updated while the single invocation of app() runs. - - -@parsl.python_app -def app(): - import time - time.sleep(1) - - -class OneShotLocalProvider(LocalProvider): - def __init__(self, *args, **kwargs): - self.recorded_submits = 0 - super().__init__(*args, **kwargs) - - def submit(self, *args, **kwargs): - self.recorded_submits += 1 - return super().submit(*args, **kwargs) - - status_polling_interval = 600 - - -@pytest.mark.local -def test_one_block(tmpd_cwd): - """ - this test is intended to ensure that only one block is launched when only - one app is invoked. this is a regression test. - """ - oneshot_provider = OneShotLocalProvider( - init_blocks=0, - min_blocks=0, - max_blocks=10, - launcher=SimpleLauncher(), - ) - - config = Config( - executors=[ - HighThroughputExecutor( - label="htex_local", - address="127.0.0.1", - worker_debug=True, - cores_per_worker=1, - encrypted=True, - provider=oneshot_provider, - worker_logdir_root=str(tmpd_cwd) - ) - ], - strategy='simple', - strategy_period=0.1 - ) - - with parsl.load(config): - app().result() - - assert oneshot_provider.recorded_submits == 1 diff --git a/parsl/parsl/tests/test_scaling/test_regression_3568_scaledown_vs_MISSING.py b/parsl/parsl/tests/test_scaling/test_regression_3568_scaledown_vs_MISSING.py deleted file mode 100644 index 0c4a474b..00000000 --- a/parsl/parsl/tests/test_scaling/test_regression_3568_scaledown_vs_MISSING.py +++ /dev/null @@ -1,84 +0,0 @@ -import time - -import pytest - -import parsl -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import WrappedLauncher -from parsl.providers import LocalProvider - - -def local_config(): - # see the comments inside test_regression for reasoning about why each - # of these parameters is set why it is. - return Config( - max_idletime=1, - - strategy='htex_auto_scale', - strategy_period=1, - - executors=[ - HighThroughputExecutor( - label="htex_local", - encrypted=True, - provider=LocalProvider( - init_blocks=1, - min_blocks=0, - max_blocks=1, - launcher=WrappedLauncher(prepend="sleep inf ; "), - ), - ) - ], - ) - - -@parsl.python_app -def task(): - return 7 - - -@pytest.mark.local -def test_regression(try_assert): - # The above config means that we should start scaling out one initial - # block, but then scale it back in after a second or so if the executor - # is kept idle (which this test does using try_assert). - - # Because of 'sleep inf' in the WrappedLaucher, the block will not ever - # register. - - # The bug being tested is about mistreatment of blocks which are scaled in - # before they have a chance to register, and the above forces that to - # happen. - - # After that scaling in has happened, we should see that we have one block - # and it should be in a terminal state. The below try_assert waits for - # that to become true. - - # At that time, we should also see htex reporting no blocks registered - as - # mentioned above, that is a necessary part of the bug being tested here. - - # Give 10 strategy periods for the above to happen: each step of scale up, - # and scale down due to idleness isn't guaranteed to happen in exactly one - # scaling step. - - htex = parsl.dfk().executors['htex_local'] - - try_assert(lambda: len(htex.status_facade) == 1 and htex.status_facade['0'].terminal, - timeout_ms=10000) - - assert htex.connected_blocks() == [], "No block should have connected to interchange" - - # Now we can reconfigure the launcher to let subsequent blocks launch ok, - # and run a trivial task. That trivial task will scale up a new block and - # run the task successfully. - - # Prior to issue #3568, the bug was that the scale in of the first - # block earlier in the test case would have incorrectly been treated as a - # failure, and then the block error handler would have treated that failure - # as a permanent htex failure, and so the task execution below would raise - # a BadStateException rather than attempt to run the task. - - assert htex.provider.launcher.prepend != "", "Pre-req: prepend attribute should exist and be non-empty" - htex.provider.launcher.prepend = "" - assert task().result() == 7 diff --git a/parsl/parsl/tests/test_scaling/test_regression_3696_oscillation.py b/parsl/parsl/tests/test_scaling/test_regression_3696_oscillation.py deleted file mode 100644 index 1b221c27..00000000 --- a/parsl/parsl/tests/test_scaling/test_regression_3696_oscillation.py +++ /dev/null @@ -1,104 +0,0 @@ -import math -from unittest.mock import MagicMock - -import pytest - -from parsl.executors.high_throughput.executor import HighThroughputExecutor -from parsl.jobs.states import JobState, JobStatus -from parsl.jobs.strategy import Strategy - - -# the parameterize tuple consists of: -# Input: -# * number of tasks to mock the load as -# * number of workers per node -# Expected output: -# * the number of blocks we should expect to be launched -# in this situation -# -# This test will configure an executor, then run strategize -# a few times, asserting that it converges to the correct -# number of blocks without oscillating. -@pytest.mark.local -@pytest.mark.parametrize("ns", [(14, 48, 1), # values from issue #3696 - - (1, 1, 1), # one task needs one block - - (100, 1, 20), # many one-task blocks, hitting hard-coded max blocks - - (47, 48, 1), # some edge cases around #3696 values - (48, 48, 1), # " - (49, 48, 2), # " - (149, 50, 3)]) # " -def test_htex_strategy_does_not_oscillate(ns): - """Check for oscillations in htex scaling. - In issue 3696, with a large number of workers per block - and a smaller number of active tasks, the htex scaling - strategy oscillates between 0 and 1 active block, rather - than converging to 1 active block. - """ - - n_tasks, n_workers, n_blocks = ns - - s = Strategy(strategy='htex_auto_scale', max_idletime=0) - - provider = MagicMock() - executor = MagicMock(spec=HighThroughputExecutor) - - statuses = {} - - executor.provider = provider - executor.outstanding = lambda: n_tasks - executor.status_facade = statuses - executor.workers_per_node = n_workers - executor.bad_state_is_set = False - - provider.parallelism = 1 - provider.init_blocks = 0 - provider.min_blocks = 0 - provider.max_blocks = 20 - provider.nodes_per_block = 1 - - def scale_out(n): - for _ in range(n): - statuses[len(statuses)] = JobStatus(state=JobState.PENDING) - - executor.scale_out_facade.side_effect = scale_out - - def scale_in(n, max_idletime=None): - # find n PENDING jobs and set them to CANCELLED - for k in statuses: - if n == 0: - return - if statuses[k].state == JobState.PENDING: - statuses[k].state = JobState.CANCELLED - n -= 1 - - executor.scale_in_facade.side_effect = scale_in - - s.add_executors([executor]) - - # In issue #3696, this first strategise does initial and load based - # scale outs, because n_tasks > n_workers*0 - s.strategize([executor]) - - executor.scale_out_facade.assert_called() - assert len(statuses) == n_blocks, "Should have launched n_blocks" - assert len([k for k in statuses if statuses[k].state == JobState.PENDING]) == n_blocks - # there might be several calls to scale_out_facade inside strategy, - # but the end effect should be that exactly one block is scaled out. - - executor.scale_in_facade.assert_not_called() - - # In issue #3696, this second strategize does a scale in, because n_tasks < n_workers*1 - s.strategize([executor]) - - # assert that there should still be n_blocks pending blocks - assert len([k for k in statuses if statuses[k].state == JobState.PENDING]) == n_blocks - # this assert fails due to issue #3696 - - # Now check scale in happens with 0 load - executor.outstanding = lambda: 0 - s.strategize([executor]) - executor.scale_in_facade.assert_called() - assert len([k for k in statuses if statuses[k].state == JobState.PENDING]) == 0 diff --git a/parsl/parsl/tests/test_scaling/test_scale_down.py b/parsl/parsl/tests/test_scaling/test_scale_down.py deleted file mode 100644 index 057c3e6c..00000000 --- a/parsl/parsl/tests/test_scaling/test_scale_down.py +++ /dev/null @@ -1,97 +0,0 @@ -import logging -import time - -import pytest - -import parsl -from parsl import File, python_app -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SingleNodeLauncher -from parsl.providers import LocalProvider - -logger = logging.getLogger(__name__) - -_max_blocks = 5 -_min_blocks = 2 - - -def local_config(): - return Config( - executors=[ - HighThroughputExecutor( - heartbeat_period=1, - heartbeat_threshold=2, - poll_period=100, - label="htex_local", - address="127.0.0.1", - max_workers_per_node=1, - encrypted=True, - provider=LocalProvider( - init_blocks=0, - max_blocks=_max_blocks, - min_blocks=_min_blocks, - launcher=SingleNodeLauncher(), - ), - ) - ], - max_idletime=0.5, - strategy='simple', - strategy_period=0.5, - ) - - -@python_app -def waiting_app(ident: int, inputs=(), outputs=()): - import pathlib - import time - - # Approximate an Event by writing to files; the test logic will poll this file - with open(outputs[0], "a") as f: - f.write(f"Ready: {ident}\n") - - # Similarly, use Event approximation (file check!) by polling. - may_finish_file = pathlib.Path(inputs[0]) - while not may_finish_file.exists(): - time.sleep(0.01) - - -# see issue #1885 for details of failures of this test. -# at the time of issue #1885 this test was failing frequently -# in CI. -@pytest.mark.local -def test_scale_out(tmpd_cwd, try_assert): - dfk = parsl.dfk() - - num_managers = len(dfk.executors['htex_local'].connected_managers()) - - assert num_managers == 0, "Expected 0 managers at start" - assert dfk.executors['htex_local'].outstanding() == 0, "Expected 0 tasks at start" - - ntasks = 10 - ready_path = tmpd_cwd / "workers_ready" - finish_path = tmpd_cwd / "workers_may_continue" - ready_path.touch() - inputs = [File(finish_path)] - outputs = [File(ready_path)] - - futs = [waiting_app(i, outputs=outputs, inputs=inputs) for i in range(ntasks)] - - while ready_path.read_text().count("\n") < _max_blocks: - time.sleep(0.5) - - assert len(dfk.executors['htex_local'].connected_managers()) == _max_blocks - - finish_path.touch() # Approximation of Event, via files - [x.result() for x in futs] - - assert dfk.executors['htex_local'].outstanding() == 0 - - def assert_kernel(): - return len(dfk.executors['htex_local'].connected_managers()) == _min_blocks - - try_assert( - assert_kernel, - fail_msg=f"Expected {_min_blocks} managers when no tasks (min_blocks)", - timeout_ms=15000, - ) diff --git a/parsl/parsl/tests/test_scaling/test_scale_down_htex_auto_scale.py b/parsl/parsl/tests/test_scaling/test_scale_down_htex_auto_scale.py deleted file mode 100644 index 9ca91be1..00000000 --- a/parsl/parsl/tests/test_scaling/test_scale_down_htex_auto_scale.py +++ /dev/null @@ -1,147 +0,0 @@ -from threading import Event - -import pytest - -import parsl -from parsl import File, python_app -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SingleNodeLauncher -from parsl.providers import LocalProvider - -_max_blocks = 5 -_min_blocks = 0 - - -def local_config(): - return Config( - executors=[ - HighThroughputExecutor( - heartbeat_period=1, - heartbeat_threshold=2, - poll_period=100, - label="htex_local", - address="127.0.0.1", - max_workers_per_node=1, - encrypted=True, - provider=LocalProvider( - init_blocks=0, - max_blocks=_max_blocks, - min_blocks=_min_blocks, - launcher=SingleNodeLauncher(), - ), - ) - ], - max_idletime=0.5, - strategy='htex_auto_scale', - strategy_period=0.1 - ) - - -@python_app -def waiting_app(ident: int, outputs=(), inputs=()): - import pathlib - import time - - # Approximate an Event by writing to files; the test logic will poll this file - with open(outputs[0], "a") as f: - f.write(f"Ready: {ident}\n") - - # Similarly, use Event approximation (file check!) by polling. - may_finish_file = pathlib.Path(inputs[0]) - while not may_finish_file.exists(): - time.sleep(0.01) - - -# see issue #1885 for details of failures of this test. -# at the time of issue #1885 this test was failing frequently -# in CI. -@pytest.mark.local -def test_scale_out(tmpd_cwd, try_assert): - dfk = parsl.dfk() - - num_managers = len(dfk.executors['htex_local'].connected_managers()) - - assert num_managers == 0, "Expected 0 managers at start" - assert dfk.executors['htex_local'].outstanding() == 0, "Expected 0 tasks at start" - - ntasks = _max_blocks * 2 - ready_path = tmpd_cwd / "workers_ready" - finish_path = tmpd_cwd / "stage1_workers_may_continue" - ready_path.touch() - inputs = [File(finish_path)] - outputs = [File(ready_path)] - - futs = [waiting_app(i, outputs=outputs, inputs=inputs) for i in range(ntasks)] - - try_assert(lambda: ready_path.read_text().count("\n") == _max_blocks, "Wait for _max_blocks tasks to be running", timeout_ms=15000) - - # This should be true immediately, because the previous try_assert should - # wait until there are max_blocks tasks running, and his test should be - # configured to use 1 worker per block. - assert len(dfk.executors['htex_local'].connected_managers()) == _max_blocks - - finish_path.touch() # Approximation of Event, via files - [x.result() for x in futs] - - assert dfk.executors['htex_local'].outstanding() == 0 - - # now we can launch one "long" task - - # and what should happen is that the connected_managers count "eventually" (?) converges to 1 and stays there. - - finish_path = tmpd_cwd / "stage2_workers_may_continue" - - fut = waiting_app(0, outputs=outputs, inputs=[File(finish_path)]) - - def check_one_block(): - return len(dfk.executors['htex_local'].connected_managers()) == 1 - - try_assert( - check_one_block, - fail_msg="Expected 1 managers during a single long task", - ) - - # the task should not have finished by the time we end up with 1 manager - assert not fut.done() - - # This section wait for the strategy to run again, with the above single - # task outstanding, and check that the strategy has not scaled up or - # down more on those subsequent iterations. - - # It does this by hooking the callback of the job status poller, and - # waiting until it has run. - - old_cb = dfk.job_status_poller.callback - - strategy_iterated = Event() - - def hook_cb(*args, **kwargs): - r = old_cb(*args, **kwargs) - strategy_iterated.set() - return r - - dfk.job_status_poller.callback = hook_cb - - # hack strategies to run more frequently. this allo - # dfk.job_status_poller. - - try_assert( - strategy_iterated.is_set, - fail_msg="Expected strategy to have run within this period", - ) - - assert check_one_block() - - finish_path.touch() # now we can end the single stage-2 task - - fut.result() - - # now we should expect min_blocks scale down - - def check_min_blocks(): - return len(dfk.executors['htex_local'].connected_managers()) == _min_blocks - - try_assert( - check_min_blocks, - fail_msg=f"Expected {_min_blocks} managers when no tasks (min_blocks)", - ) diff --git a/parsl/parsl/tests/test_scaling/test_scale_down_htex_unregistered.py b/parsl/parsl/tests/test_scaling/test_scale_down_htex_unregistered.py deleted file mode 100644 index 90a9b9ff..00000000 --- a/parsl/parsl/tests/test_scaling/test_scale_down_htex_unregistered.py +++ /dev/null @@ -1,71 +0,0 @@ -import logging -import time - -import pytest - -import parsl -from parsl import File, python_app -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.jobs.states import TERMINAL_STATES, JobState -from parsl.launchers import SingleNodeLauncher -from parsl.providers import LocalProvider - -logger = logging.getLogger(__name__) - -_max_blocks = 1 -_min_blocks = 0 - - -def local_config(): - return Config( - executors=[ - HighThroughputExecutor( - heartbeat_period=1, - heartbeat_threshold=2, - poll_period=100, - label="htex_local", - address="127.0.0.1", - max_workers_per_node=1, - encrypted=True, - launch_cmd="sleep inf", - provider=LocalProvider( - init_blocks=1, - max_blocks=_max_blocks, - min_blocks=_min_blocks, - launcher=SingleNodeLauncher(), - ), - ) - ], - max_idletime=0.5, - strategy='htex_auto_scale', - strategy_period=0.1 - ) - - -# see issue #1885 for details of failures of this test. -# at the time of issue #1885 this test was failing frequently -# in CI. -@pytest.mark.local -def test_scaledown_with_register(try_assert): - dfk = parsl.dfk() - htex = dfk.executors['htex_local'] - - num_managers = len(htex.connected_managers()) - assert num_managers == 0, "Expected 0 managers at start" - - try_assert(lambda: len(htex.status()), - fail_msg="Expected 1 block at start") - - s = htex.status() - assert s['0'].state == JobState.RUNNING, "Expected block to be in RUNNING" - - def check_zero_blocks(): - s = htex.status() - return len(s) == 1 and s['0'].state in TERMINAL_STATES - - try_assert( - check_zero_blocks, - fail_msg="Expected 0 blocks after idle scaledown", - timeout_ms=15000, - ) diff --git a/parsl/parsl/tests/test_scaling/test_shutdown_scalein.py b/parsl/parsl/tests/test_scaling/test_shutdown_scalein.py deleted file mode 100644 index c46b770e..00000000 --- a/parsl/parsl/tests/test_scaling/test_shutdown_scalein.py +++ /dev/null @@ -1,76 +0,0 @@ -import random -import threading - -import pytest - -import parsl -from parsl.config import Config -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SimpleLauncher -from parsl.providers import LocalProvider - -# we need some blocks, but it doesn't matter too much how many, as long -# as they can all start up and get registered within the try_assert -# timeout later on. -BLOCK_COUNT = 3 - -# the try_assert timeout for the above number of blocks to get started -PERMITTED_STARTUP_TIME_S = 30 - - -class AccumulatingLocalProvider(LocalProvider): - def __init__(self, *args, **kwargs): - # Use a list for submitted job IDs because if there are multiple - # submissions returning the same job ID, this test should count - # those... - self.submit_job_ids = [] - - # ... but there's no requirement, I think, that cancel must be called - # only once per job id. What matters here is that each job ID is - # cancelled at least once. - self.cancel_job_ids = set() - - super().__init__(*args, **kwargs) - - def submit(self, *args, **kwargs): - job_id = super().submit(*args, **kwargs) - self.submit_job_ids.append(job_id) - return job_id - - def cancel(self, job_ids): - self.cancel_job_ids.update(job_ids) - return super().cancel(job_ids) - - -@pytest.mark.local -def test_shutdown_scalein_blocks(tmpd_cwd, try_assert): - """ - This test scales up several blocks, and then checks that they are all - scaled in at DFK shutdown. - """ - accumulating_provider = AccumulatingLocalProvider( - init_blocks=BLOCK_COUNT, - min_blocks=0, - max_blocks=0, - launcher=SimpleLauncher(), - ) - - htex = HighThroughputExecutor( - label="htex_local", - cores_per_worker=1, - provider=accumulating_provider - ) - - config = Config( - executors=[htex], - strategy='none', - strategy_period=0.1, - run_dir=str(tmpd_cwd) - ) - - with parsl.load(config): - # this will wait for everything to be scaled out fully - try_assert(lambda: len(htex.connected_managers()) == BLOCK_COUNT, timeout_ms=PERMITTED_STARTUP_TIME_S * 1000) - - assert len(accumulating_provider.submit_job_ids) == BLOCK_COUNT, f"Exactly {BLOCK_COUNT} blocks should have been launched" - assert len(accumulating_provider.cancel_job_ids) == BLOCK_COUNT, f"Exactly {BLOCK_COUNT} blocks should have been scaled in" diff --git a/parsl/parsl/tests/test_serialization/__init__.py b/parsl/parsl/tests/test_serialization/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_serialization/test_2555_caching_deserializer.py b/parsl/parsl/tests/test_serialization/test_2555_caching_deserializer.py deleted file mode 100644 index 7a46731e..00000000 --- a/parsl/parsl/tests/test_serialization/test_2555_caching_deserializer.py +++ /dev/null @@ -1,34 +0,0 @@ -import pytest - -import parsl -from parsl.tests.configs.htex_local import fresh_config as local_config - - -@parsl.python_app -def return_range(x): - return list(range(x)) - - -@pytest.mark.local -def test_range_identities(): - x = 3 - - fut1 = return_range(x) - res1 = fut1.result() - - fut2 = return_range(x) - res2 = fut2.result() - - # Check that the returned futures are different, by both usual - # Python equalities. - # This is not strictly part of the regression test for #2555 - # but will detect related unexpected Future caching. - - assert fut1 != fut2 - assert id(fut1) != id(fut2) - - # check that the two invocations returned the same value... - assert res1 == res2 - - # ... but in two different objects. - assert id(res1) != id(res2) diff --git a/parsl/parsl/tests/test_serialization/test_3495_deserialize_managerlost.py b/parsl/parsl/tests/test_serialization/test_3495_deserialize_managerlost.py deleted file mode 100644 index 8a6cebf1..00000000 --- a/parsl/parsl/tests/test_serialization/test_3495_deserialize_managerlost.py +++ /dev/null @@ -1,50 +0,0 @@ -import os -import signal - -import pytest - -import parsl -from parsl import Config, HighThroughputExecutor -from parsl.executors.high_throughput.errors import ManagerLost - - -@parsl.python_app -def get_manager_pgid(): - import os - return os.getpgid(os.getpid()) - - -@parsl.python_app -def lose_manager(): - import os - import signal - import time - - manager_pid = os.getppid() - os.kill(manager_pid, signal.SIGSTOP) - - time.sleep(5) # approximate "forever"; if not enough, something's awry - - -@pytest.mark.local -def test_manager_lost_system_failure(tmpd_cwd): - hte = HighThroughputExecutor( - label="htex_local", - address="127.0.0.1", - max_workers_per_node=2, - cores_per_worker=1, - worker_logdir_root=str(tmpd_cwd), - heartbeat_period=1, - heartbeat_threshold=3, - ) - c = Config(executors=[hte], strategy='simple', strategy_period=0.1) - - with parsl.load(c): - manager_pgid = get_manager_pgid().result() - try: - with pytest.raises(ManagerLost): - lose_manager().result() - finally: - # Allow process to clean itself up - os.killpg(manager_pgid, signal.SIGCONT) - os.killpg(manager_pgid, signal.SIGTERM) diff --git a/parsl/parsl/tests/test_serialization/test_basic.py b/parsl/parsl/tests/test_serialization/test_basic.py deleted file mode 100644 index 15f506fb..00000000 --- a/parsl/parsl/tests/test_serialization/test_basic.py +++ /dev/null @@ -1,25 +0,0 @@ -import pytest - -from parsl.serialize import deserialize, serialize -from parsl.serialize.concretes import DillSerializer, PickleSerializer - - -@pytest.mark.local -def test_serialize(): - assert deserialize(serialize(1)) == 1 - - -@pytest.mark.local -def test_pickle_wrapper(): - s = PickleSerializer() - d = s.serialize(1) - assert isinstance(d, bytes) - assert s.deserialize(d) == 1 - - -@pytest.mark.local -def test_dill_wrapper(): - s = DillSerializer() - d = s.serialize(1) - assert isinstance(d, bytes) - assert s.deserialize(d) == 1 diff --git a/parsl/parsl/tests/test_serialization/test_htex_code_cache.py b/parsl/parsl/tests/test_serialization/test_htex_code_cache.py deleted file mode 100644 index 65cb7252..00000000 --- a/parsl/parsl/tests/test_serialization/test_htex_code_cache.py +++ /dev/null @@ -1,56 +0,0 @@ -from typing import Any - -import pytest - -import parsl -from parsl.serialize.facade import methods_for_code -from parsl.tests.configs.htex_local import fresh_config as local_config - - -@parsl.python_app -def f(x): - return x + 1 - - -@pytest.mark.local -def test_caching() -> None: - # for future serializer devs: if this is failing because you added another - # code serializer, you'll also probably need to re-think what is being tested - # about serialization caching here. - assert len(methods_for_code) == 1 - - serializer = methods_for_code[b'C2'] - - # force type to Any here because a serializer method coming from - # methods_for_code doesn't statically have any cache management - # methods on itself such as cache_clear or cache_info. - serialize_method: Any = serializer.serialize - - serialize_method.cache_clear() - - assert serialize_method.cache_info().hits == 0 - assert serialize_method.cache_info().misses == 0 - assert serialize_method.cache_info().currsize == 0 - - assert f(7).result() == 8 - - # the code serializer cache should now contain only a (probably wrapped) f ... - assert serialize_method.cache_info().currsize == 1 - - # ... which was not already in the cache. - assert serialize_method.cache_info().misses == 1 - assert serialize_method.cache_info().hits == 0 - - assert f(100).result() == 101 - - # this time round, we should have got a single cache hit... - assert serialize_method.cache_info().hits == 1 - assert serialize_method.cache_info().misses == 1 - assert serialize_method.cache_info().currsize == 1 - - assert f(200).result() == 201 - - # this time round, we should have got another single cache hit... - assert serialize_method.cache_info().hits == 2 - assert serialize_method.cache_info().misses == 1 - assert serialize_method.cache_info().currsize == 1 diff --git a/parsl/parsl/tests/test_serialization/test_proxystore_configured.py b/parsl/parsl/tests/test_serialization/test_proxystore_configured.py deleted file mode 100644 index 90bea12a..00000000 --- a/parsl/parsl/tests/test_serialization/test_proxystore_configured.py +++ /dev/null @@ -1,86 +0,0 @@ -import logging -import uuid - -import pytest - -import parsl -from parsl.serialize.facade import ( - additional_methods_for_deserialization, - methods_for_data, - register_method_for_data, -) -from parsl.tests.configs.htex_local import fresh_config - -logger = logging.getLogger(__name__) - - -def local_setup(): - from proxystore.connectors.file import FileConnector - from proxystore.store import Store, register_store - - from parsl.serialize.proxystore import ProxyStoreSerializer - - parsl.load(fresh_config()) - - store = Store(name='parsl_store_' + str(uuid.uuid4()), connector=FileConnector(store_dir="/tmp")) - register_store(store) - - s = ProxyStoreSerializer(store=store, should_proxy=policy_example) - - global previous_methods - previous_methods = methods_for_data.copy() - - # get rid of all data serialization methods, in preparation for using only - # proxystore. put all the old methods as additional methods used only for - # deserialization, because those will be needed to deserialize the results, - # which will be serialized using the default serializer set. - additional_methods_for_deserialization.update(previous_methods) - methods_for_data.clear() - - register_method_for_data(s) - logger.info(f"BENC: methods for data: {methods_for_data}") - - -def local_teardown(): - parsl.dfk().cleanup() - - methods_for_data.clear() - methods_for_data.update(previous_methods) - - additional_methods_for_deserialization.clear() - - -@parsl.python_app -def identity(o): - return o - - -@parsl.python_app -def is_proxy(o) -> bool: - from proxystore.proxy import Proxy - return isinstance(o, Proxy) - - -def policy_example(o): - """Example policy will proxy only lists.""" - return isinstance(o, frozenset) - - -@pytest.mark.local -def test_proxystore_via_apps(): - from proxystore.proxy import Proxy - - # check roundtrip for an int, which should not be proxystored according - # to example_policy() - roundtripped_7 = identity(7).result() - assert roundtripped_7 == 7 - assert not isinstance(roundtripped_7, Proxy) - - # check roundtrip for a list, which should be proxystored according - # to example_policy() - v = frozenset([1, 2, 3]) - - assert is_proxy(v).result() - - roundtripped_v = identity(v).result() - assert roundtripped_v == v diff --git a/parsl/parsl/tests/test_serialization/test_proxystore_impl.py b/parsl/parsl/tests/test_serialization/test_proxystore_impl.py deleted file mode 100644 index 5a04af0f..00000000 --- a/parsl/parsl/tests/test_serialization/test_proxystore_impl.py +++ /dev/null @@ -1,40 +0,0 @@ -import uuid - -import pytest - - -def policy_example(o): - """Example policy will proxy only lists.""" - return isinstance(o, list) - - -@pytest.mark.local -def test_proxystore_nonglobal(): - """Check that values are roundtripped, for both proxied and non-proxied types. - """ - # import in function, because proxystore is not importable in base parsl - # installation. - from proxystore.connectors.file import FileConnector - from proxystore.proxy import Proxy - from proxystore.store import Store, register_store - - from parsl.serialize.proxystore import ProxyStoreSerializer - - store = Store(name='parsl_store_' + str(uuid.uuid4()), connector=FileConnector(store_dir="/tmp")) - register_store(store) - - s = ProxyStoreSerializer(store=store, should_proxy=policy_example) - - # check roundtrip for an int, which will not be proxystored - s_7 = s.serialize(7) - assert isinstance(s_7, bytes) - roundtripped_7 = s.deserialize(s_7) - assert roundtripped_7 == 7 - assert not isinstance(roundtripped_7, Proxy) - - v = [1, 2, 3] - s_v = s.serialize(v) - assert isinstance(s_7, bytes) - roundtripped_v = s.deserialize(s_v) - assert roundtripped_v == v - assert isinstance(roundtripped_v, Proxy) diff --git a/parsl/parsl/tests/test_shutdown/__init__.py b/parsl/parsl/tests/test_shutdown/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_shutdown/test_kill_monitoring.py b/parsl/parsl/tests/test_shutdown/test_kill_monitoring.py deleted file mode 100644 index a7d6ea3e..00000000 --- a/parsl/parsl/tests/test_shutdown/test_kill_monitoring.py +++ /dev/null @@ -1,64 +0,0 @@ -import os -import signal -import time - -import pytest - -import parsl -from parsl.tests.configs.htex_local_alternate import fresh_config - -# This is a very generous upper bound on expected shutdown time of target -# process after receiving a signal, measured in seconds. -PERMITTED_SHUTDOWN_TIME_S = 60 - - -@parsl.python_app -def simple_app(): - return True - - -@pytest.mark.local -def test_no_kills(): - """This tests that we can create a monitoring-enabled DFK and shut it down.""" - - parsl.load(fresh_config()) - - assert parsl.dfk().monitoring is not None, "This test requires monitoring" - - parsl.dfk().cleanup() - - -@pytest.mark.local -@pytest.mark.parametrize("sig", [signal.SIGINT, signal.SIGTERM, signal.SIGKILL, signal.SIGQUIT]) -@pytest.mark.parametrize("process_attr", ["dbm_proc"]) -def test_kill_monitoring_helper_process(sig, process_attr, try_assert): - """This tests that we can kill a monitoring process and still have successful shutdown. - SIGINT emulates some racy behaviour when ctrl-C is pressed: that - monitoring processes receive a ctrl-C too, and so the other processes - need to be tolerant to monitoring processes arbitrarily exiting. - """ - - parsl.load(fresh_config()) - - dfk = parsl.dfk() - - assert dfk.monitoring is not None, "Monitoring required" - - target_proc = getattr(dfk.monitoring, process_attr) - - assert target_proc is not None, "prereq: target process must exist" - assert target_proc.is_alive(), "prereq: target process must be alive" - - target_pid = target_proc.pid - assert target_pid is not None, "prereq: target process must have a pid" - - os.kill(target_pid, sig) - - try_assert(lambda: not target_proc.is_alive(), timeout_ms=PERMITTED_SHUTDOWN_TIME_S * 1000) - - # now we have broken one piece of the monitoring system, do some app - # execution and then shut down. - - simple_app().result() - - parsl.dfk().cleanup() diff --git a/parsl/parsl/tests/test_staging/README.rst b/parsl/parsl/tests/test_staging/README.rst deleted file mode 100644 index 5a360d36..00000000 --- a/parsl/parsl/tests/test_staging/README.rst +++ /dev/null @@ -1,18 +0,0 @@ -Setting up globus endpoint -========================== - -Setup a Globus personal endpoint or activate a shared endpoint, following instructions here: -`Globus docs `_ - -Once an endpoint is setup, get the endpoint UUID for the endpoint from `Globus.org `_. - -You can track the file staging event here: `transfer activity `_. - -Running the tests -================= - -1. Ensure that the endpoint is active. -2. Edit the ``user_opts.py`` script with the Globus endpoint. -3. Run the tests - ``pytest test_globus`` - diff --git a/parsl/parsl/tests/test_staging/__init__.py b/parsl/parsl/tests/test_staging/__init__.py deleted file mode 100644 index 8da4e9fa..00000000 --- a/parsl/parsl/tests/test_staging/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -def read_sort_write(in_path, out_path): - with open(in_path) as u: - strs = u.read().split() - strs.sort() - with open(out_path, 'w') as s: - for e in strs: - print(e, file=s) diff --git a/parsl/parsl/tests/test_staging/staging_provider.py b/parsl/parsl/tests/test_staging/staging_provider.py deleted file mode 100644 index d309e8d3..00000000 --- a/parsl/parsl/tests/test_staging/staging_provider.py +++ /dev/null @@ -1,89 +0,0 @@ -# this replaces the no-op file staging provider with a more -# complicated no-op provider that launches (empty) tasks -# for staging, and asserts specific behaviours do/do not happen. - -# the elaborate no-op provider is based around the structure -# of the globus staging provider - -import logging - -from parsl import python_app -from parsl.data_provider.staging import Staging -from parsl.utils import RepresentationMixin - -logger = logging.getLogger(__name__) - - -class NoOpTestingFileStaging(Staging, RepresentationMixin): - - def __init__(self, allow_stage_in=True, allow_stage_out=True): - """The NoOpTestingFileStaging provider can be configured to fail in - specific ways to ensure that certain things are not happening. - - The provider will always claim that it can stage "file" URLs, - but will raise an exception if staging is actually attempted - if allow_stage[in/out] is set to False. - """ - self.allow_stage_in = allow_stage_in - self.allow_stage_out = allow_stage_out - - def can_stage_in(self, file): - logger.info("NoOpTestingFileStaging checking file {} for stage-in".format(repr(file))) - b = file.scheme == 'file' - logger.info("b = {}".format(b)) - return b - - def can_stage_out(self, file): - logger.info("NoOpTestingFileStaging checking file {} for stage-out".format(repr(file))) - b = file.scheme == 'file' - logger.info("b = {}".format(b)) - return b - - def stage_in(self, dm, executor, file, parent_fut): - logger.info("Creating state-in app for file {}".format(file)) - if self.allow_stage_in: - stage_in_app = make_stage_in_app(executor=executor, dfk=dm.dfk) - app_fut = stage_in_app(outputs=[file], _parsl_staging_inhibit=True, parent_fut=parent_fut) - return app_fut._outputs[0] - else: - raise NoOpError("NoOpTestingFileStaging provider configured to prohibit stage in") - - def stage_out(self, dm, executor, file, app_fu): - logger.info("Creating state-out app for file {}".format(file)) - if self.allow_stage_out: - stage_out_app = make_stage_out_app(executor=executor, dfk=dm.dfk) - return stage_out_app(app_fu, inputs=[file], _parsl_staging_inhibit=True) - else: - raise NoOpError("NoOpTestingFileStaging provider configured to prohibit stage out") - - -def make_stage_out_app(executor, dfk): - return python_app(executors=[executor], data_flow_kernel=dfk)(stage_out_noop) - - -def stage_out_noop(app_fu, inputs=[], _parsl_staging_inhibit=True): - import logging - import time - logger = logging.getLogger(__name__) - logger.info("stage_out_noop") - time.sleep(1) - return None - - -def make_stage_in_app(executor, dfk): - return python_app(executors=[executor], data_flow_kernel=dfk)(stage_in_noop) - - -def stage_in_noop(parent_fut=None, outputs=[], _parsl_staging_inhibit=True): - import logging - import time - logger = logging.getLogger(__name__) - logger.info("stage_in_noop") - time.sleep(1) - return None - - -class NoOpError(RuntimeError): - """This should behave the same as a RuntimeError, but is a subclass so - that it can be checked for specifically in test cases.""" - pass diff --git a/parsl/parsl/tests/test_staging/test_1316.py b/parsl/parsl/tests/test_staging/test_1316.py deleted file mode 100644 index 9d5b7a33..00000000 --- a/parsl/parsl/tests/test_staging/test_1316.py +++ /dev/null @@ -1,85 +0,0 @@ -import time - -import pytest - -import parsl -from parsl import ThreadPoolExecutor, python_app -from parsl.config import Config -from parsl.data_provider.files import File -from parsl.data_provider.staging import Staging - - -@python_app -def observe_input_local_path(f): - """Returns the local_path that is seen by the app, - so that the test suite can also see that path. - """ - return f.local_path - - -@python_app -def wait_and_create(outputs=[]): - # for test purposes, this doesn't actually need to create the output - # file as nothing ever touches file content - the test only deals with - # names and Futures. - time.sleep(10) - - -class SP2(Staging): - def can_stage_in(self, file): - return file.scheme == 'sp2' - - def stage_in(self, dm, executor, file, parent_fut): - file.local_path = "./test1.tmp" - return None # specify no tasks inserted in graph so parsl will preserve stageout dependency - - def can_stage_out(self, file): - return file.scheme == 'sp2' - - def stage_out(self, dm, executor, file, app_fu): - file.local_path = "./test1.tmp" - return None # no tasks inserted so parsl should use app completion for DataFuture completion - - -@pytest.mark.local -def test_1316_local_path_on_execution_side_sp2(): - """This test demonstrates the ability of a StagingProvider to set the - local_path of a File on the execution side, but that the change does not - modify the local_path of the corresponding submit side File, even when - running in a single python process. - """ - - config = Config(executors=[ThreadPoolExecutor(storage_access=[SP2()])]) - - file = File("sp2://test") - - parsl.load(config) - p = observe_input_local_path(file).result() - - assert p == "./test1.tmp", "File object on the execution side gets the local_path set by the staging provider" - - assert not file.local_path, "The local_path on the submit side should not be set" - - parsl.dfk().cleanup() - - -@pytest.mark.local -def test_1316_local_path_setting_preserves_dependency_sp2(): - config = Config(executors=[ThreadPoolExecutor(storage_access=[SP2()])]) - - file = File("sp2://test") - - parsl.load(config) - - wc_app_future = wait_and_create(outputs=[file]) - data_future = wc_app_future.outputs[0] - - p = observe_input_local_path(data_future).result() - - assert wc_app_future.done(), "wait_and_create should finish before observe_input_local_path finishes" - - assert p == "./test1.tmp", "File object on the execution side gets the local_path set by the staging provider" - - assert not file.local_path, "The local_path on the submit side should not be set" - - parsl.dfk().cleanup() diff --git a/parsl/parsl/tests/test_staging/test_docs_1.py b/parsl/parsl/tests/test_staging/test_docs_1.py deleted file mode 100644 index c4f0e3b0..00000000 --- a/parsl/parsl/tests/test_staging/test_docs_1.py +++ /dev/null @@ -1,25 +0,0 @@ -import pytest - -from parsl import File, python_app - - -@python_app -def convert(inputs=[], outputs=[]): - with open(inputs[0].filepath, 'r') as inp: - content = inp.read() - with open(outputs[0].filepath, 'w') as out: - out.write(content.upper()) - - -@pytest.mark.cleannet -@pytest.mark.staging_required -def test(): - # create an remote Parsl file - inp = File('ftp://ftp.iana.org/pub/mirror/rirstats/arin/ARIN-STATS-FORMAT-CHANGE.txt') - - # create a local Parsl file - out = File('file:///tmp/ARIN-STATS-FORMAT-CHANGE.txt') - - # call the convert app with the Parsl file - f = convert(inputs=[inp], outputs=[out]) - f.result() diff --git a/parsl/parsl/tests/test_staging/test_docs_2.py b/parsl/parsl/tests/test_staging/test_docs_2.py deleted file mode 100644 index a985872b..00000000 --- a/parsl/parsl/tests/test_staging/test_docs_2.py +++ /dev/null @@ -1,21 +0,0 @@ -import pytest - -from parsl import File, bash_app -from parsl.tests.configs.local_threads import fresh_config as local_config - - -@bash_app -def cat(inputs=[], stdout='stdout.txt'): - return 'cat %s' % (inputs[0]) - - -@pytest.mark.local -def test(): - # create a test file - open('/tmp/test.txt', 'w').write('Hello\n') - - # create the Parsl file - parsl_file = File('file:///tmp/test.txt') - - # call the cat app with the Parsl file - cat(inputs=[parsl_file]) diff --git a/parsl/parsl/tests/test_staging/test_elaborate_noop_file.py b/parsl/parsl/tests/test_staging/test_elaborate_noop_file.py deleted file mode 100644 index 6f525360..00000000 --- a/parsl/parsl/tests/test_staging/test_elaborate_noop_file.py +++ /dev/null @@ -1,82 +0,0 @@ -# this replaces the no-op file staging provider with a more -# complicated no-op provider that launches (empty) tasks -# for staging - -# the elaborate no-op provider is based around the structure -# of the globus staging provider - -import logging - -import pytest - -import parsl -from parsl import bash_app, python_app -from parsl.config import Config -from parsl.data_provider.files import File -from parsl.executors.threads import ThreadPoolExecutor -from parsl.tests.test_staging.staging_provider import NoOpError, NoOpTestingFileStaging - -logger = logging.getLogger(__name__) - - -@bash_app -def touch(filename, outputs=()): - return f"touch {filename}" - - -@python_app -def app_test_in(file): - # does not need to do anything as this - # is just for exercising staging - pass - - -@pytest.fixture -def storage_access_parsl(): - def _setup_config(*args, **kwargs): - tpe = ThreadPoolExecutor( - label='local_threads', - storage_access=[NoOpTestingFileStaging(*args, **kwargs)] - ) - config = Config(executors=[tpe]) - parsl.load(config) - - yield _setup_config - - parsl.dfk().cleanup() - - -@pytest.mark.local -def test_regression_stage_out_does_not_stage_in(storage_access_parsl, tmpd_cwd): - storage_access_parsl(allow_stage_in=False) - - # Test that the helper app runs with no staging - touch(str(tmpd_cwd / "test.1"), outputs=[]).result() - - # Test with stage-out, checking that provider stage-in is never - # invoked. If stage-in is invoked, then the NoOpTestingFileStaging - # provider will raise an exception, which should propagate to - # .result() here. - fpath = tmpd_cwd / "test.2" - touch(str(fpath), outputs=[File(fpath)]).result() - - # Test that stage-in exceptions propagate out to user code. - with pytest.raises(NoOpError): - touch("test.3", inputs=[File("test.3")]).result() - - -@pytest.mark.local -def test_regression_stage_in_does_not_stage_out(storage_access_parsl, tmpd_cwd): - storage_access_parsl(allow_stage_out=False) - - fpath = tmpd_cwd / "test.4" - fpath.write_text("test") - - # Test that stage in does not invoke stage out. If stage out is - # attempted, then the NoOpTestingFileStaging provider will raise - # an exception which should propagate here. - app_test_in(File("test.4")).result() - - # Test that stage out exceptions propagate to user code. - with pytest.raises(NoOpError): - touch("test.5", outputs=[File("test.5")]).result() diff --git a/parsl/parsl/tests/test_staging/test_file.py b/parsl/parsl/tests/test_staging/test_file.py deleted file mode 100644 index d7897da1..00000000 --- a/parsl/parsl/tests/test_staging/test_file.py +++ /dev/null @@ -1,32 +0,0 @@ -import os - -import pytest - -from parsl.data_provider.files import File - - -@pytest.mark.local -def test_files(): - fp = os.path.abspath('test_file.py') - strings = [{'f': 'file:///test_file.py', 'scheme': 'file', 'path': '/test_file.py'}, - {'f': './test_file.py', 'scheme': 'file', 'path': './test_file.py'}, - {'f': fp, 'scheme': 'file', 'path': fp}, - ] - - for test in strings: - x = File(test['f']) - assert x.scheme == test['scheme'], "[TEST] Scheme error. Expected {0} Got {1}".format( - test['scheme'], x.scheme) - assert x.filepath == test['path'], "[TEST] Path error. Expected {0} Got {1}".format( - test['path'], x.path) - - -@pytest.mark.local -def test_open(tmpd_cwd): - fpath = tmpd_cwd / 'test-open.txt' - fpath.write_text('Hello') - - pfile = File(fpath) - - with open(pfile) as opfile: - assert (opfile.read() == 'Hello') diff --git a/parsl/parsl/tests/test_staging/test_file_apps.py b/parsl/parsl/tests/test_staging/test_file_apps.py deleted file mode 100644 index fa56bb54..00000000 --- a/parsl/parsl/tests/test_staging/test_file_apps.py +++ /dev/null @@ -1,58 +0,0 @@ -import pytest - -from parsl.app.app import bash_app -from parsl.data_provider.files import File - - -@bash_app -def cat(inputs=(), outputs=(), stdout=None, stderr=None): - infiles = " ".join(i.filepath for i in inputs) - return f"cat {infiles} &> {outputs[0]}" - - -@pytest.mark.staging_required -def test_files(setup_data): - fs = sorted(setup_data / f for f in setup_data.iterdir()) - fs = list(map(File, fs)) - x = cat( - inputs=fs, - outputs=[File(setup_data / "cat_out.txt")], - stdout=setup_data / "f_app.out", - stderr=setup_data / "f_app.err", - ) - x.result() - d_x = x.outputs[0] - data = open(d_x.filepath).read().strip() - assert "1\n2" == data, "Per setup_data fixture" - - -@bash_app -def increment(inputs=(), outputs=(), stdout=None, stderr=None): - return ( - f"x=$(cat {inputs[0]})\n" - f"echo $(($x+1)) > {outputs[0]}" - ) - - -@pytest.mark.staging_required -def test_increment(tmpd_cwd, depth=5): - """Test simple pipeline A->B...->N""" - # Test setup - first_fpath = tmpd_cwd / "test0.txt" - first_fpath.write_text("0\n") - - prev = [File(first_fpath)] - futs = [] - for i in range(1, depth): - f = increment( - inputs=prev, - outputs=[File(tmpd_cwd / f"test{i}.txt")], - stdout=tmpd_cwd / f"incr{i}.out", - stderr=tmpd_cwd / f"incr{i}.err", - ) - prev = f.outputs - futs.append((i, prev[0])) - - for i, f in futs: - data = open(f.result().filepath).read().strip() - assert data == str(i) diff --git a/parsl/parsl/tests/test_staging/test_file_staging.py b/parsl/parsl/tests/test_staging/test_file_staging.py deleted file mode 100644 index 4e3102c5..00000000 --- a/parsl/parsl/tests/test_staging/test_file_staging.py +++ /dev/null @@ -1,25 +0,0 @@ -import pytest - -from parsl.app.app import bash_app -from parsl.data_provider.files import File - - -@bash_app -def cat(inputs=(), outputs=(), stdout=None, stderr=None): - infiles = " ".join(i.filepath for i in inputs) - return f"cat {infiles} &> {outputs[0]}" - - -@pytest.mark.staging_required -def test_regression_200(tmpd_cwd): - """Regression test for #200. Pickleablility of Files""" - opath = tmpd_cwd / "test_output.txt" - fpath = tmpd_cwd / "test.txt" - - fpath.write_text("Hello World") - f = cat(inputs=[File(fpath)], outputs=[File(opath)]) - - f.result() - with open(f.outputs[0].filepath) as f: - data = f.read() - assert "Hello World" == data diff --git a/parsl/parsl/tests/test_staging/test_output_chain_filenames.py b/parsl/parsl/tests/test_staging/test_output_chain_filenames.py deleted file mode 100644 index 442bd6fd..00000000 --- a/parsl/parsl/tests/test_staging/test_output_chain_filenames.py +++ /dev/null @@ -1,39 +0,0 @@ -from concurrent.futures import Future - -import pytest - -from parsl import File -from parsl.app.app import bash_app - - -@bash_app -def app1(inputs=(), outputs=(), stdout=None, stderr=None, mock=False): - return f"echo 'test' > {outputs[0]}" - - -@bash_app -def app2(inputs=(), outputs=(), stdout=None, stderr=None, mock=False): - return f"echo '{inputs[0]}' > {outputs[0]}" - - -@pytest.mark.shared_fs -def test_behavior(tmpd_cwd): - expected_path = str(tmpd_cwd / "simple-out.txt") - app1_future = app1( - inputs=[], - outputs=[File(expected_path)] - ) - - o = app1_future.outputs[0] - assert isinstance(o, Future) - - app2_future = app2( - inputs=[o], - outputs=[File(str(tmpd_cwd / "simple-out2.txt"))] - ) - app2_future.result() - - with open(app2_future.outputs[0].filepath, 'r') as f: - name = f.read().strip() - - assert name == expected_path, "Filename mangled due to DataFuture handling" diff --git a/parsl/parsl/tests/test_staging/test_staging_ftp.py b/parsl/parsl/tests/test_staging/test_staging_ftp.py deleted file mode 100644 index a004f5a5..00000000 --- a/parsl/parsl/tests/test_staging/test_staging_ftp.py +++ /dev/null @@ -1,31 +0,0 @@ -import pytest - -from parsl.app.app import python_app -from parsl.data_provider.files import File - - -@python_app -def sort_strings(inputs=[], outputs=[]): - with open(inputs[0].filepath, 'r') as u: - strs = u.readlines() - strs.sort() - with open(outputs[0].filepath, 'w') as s: - for e in strs: - s.write(e) - - -@pytest.mark.cleannet -@pytest.mark.staging_required -def test_staging_ftp(): - """Test staging for an ftp file - - Create a remote input file (ftp) that points to file_test_cpt.txt. - """ - - unsorted_file = File('ftp://ftp.iana.org/pub/mirror/rirstats/arin/ARIN-STATS-FORMAT-CHANGE.txt') - - # Create a local file for output data - sorted_file = File('sorted.txt') - - f = sort_strings(inputs=[unsorted_file], outputs=[sorted_file]) - f.result() diff --git a/parsl/parsl/tests/test_staging/test_staging_ftp_in_task.py b/parsl/parsl/tests/test_staging/test_staging_ftp_in_task.py deleted file mode 100644 index da6a508b..00000000 --- a/parsl/parsl/tests/test_staging/test_staging_ftp_in_task.py +++ /dev/null @@ -1,32 +0,0 @@ -import pytest - -from parsl.app.app import python_app -from parsl.data_provider.files import File -from parsl.tests.configs.local_threads_ftp_in_task import fresh_config as local_config - - -@python_app -def sort_strings(inputs=[], outputs=[]): - with open(inputs[0].filepath, 'r') as u: - strs = u.readlines() - strs.sort() - with open(outputs[0].filepath, 'w') as s: - for e in strs: - s.write(e) - - -@pytest.mark.local -@pytest.mark.cleannet -def test_staging_ftp(): - """Test staging for an ftp file - - Create a remote input file (ftp) that points to file_test_cpt.txt. - """ - - unsorted_file = File('ftp://ftp.iana.org/pub/mirror/rirstats/arin/ARIN-STATS-FORMAT-CHANGE.txt') - - # Create a local file for output data - sorted_file = File('sorted.txt') - - f = sort_strings(inputs=[unsorted_file], outputs=[sorted_file]) - f.result() diff --git a/parsl/parsl/tests/test_staging/test_staging_globus.py b/parsl/parsl/tests/test_staging/test_staging_globus.py deleted file mode 100644 index 9bdb76c0..00000000 --- a/parsl/parsl/tests/test_staging/test_staging_globus.py +++ /dev/null @@ -1,71 +0,0 @@ -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.data_provider.files import File -from parsl.tests.configs.local_threads_globus import fresh_config, remote_writeable - -local_config = fresh_config - - -@python_app -def sort_strings(inputs=[], outputs=[]): - with open(inputs[0].filepath, 'r') as u: - strs = u.readlines() - strs.sort() - with open(outputs[0].filepath, 'w') as s: - for e in strs: - s.write(e) - - -@pytest.mark.local -def test_stage_in_globus(): - """Test stage-in for a file coming from a remote Globus endpoint - - Prerequisite: - unsorted.txt must already exist at the specified endpoint - """ - - unsorted_file = File('globus://03d7d06a-cb6b-11e8-8c6a-0a1d4c5c824a/unsorted.txt') - - # Create a local file for output data - sorted_file = File('sorted.txt') - - f = sort_strings(inputs=[unsorted_file], outputs=[sorted_file]) - - f.result() - - -@pytest.mark.local -def test_stage_in_out_globus(): - """Test stage-in then stage-out to/from Globus - - Prerequisite: - unsorted.txt must already exist at the specified endpoint - the specified output endpoint must be writeable - """ - - unsorted_file = File('globus://03d7d06a-cb6b-11e8-8c6a-0a1d4c5c824a/unsorted.txt') - - # Create a local file for output data - sorted_file = File(remote_writeable + "/sorted.txt") - - f = sort_strings(inputs=[unsorted_file], outputs=[sorted_file]) - - # wait for both the app to complete, and the stageout DataFuture to complete. - # It isn't clearly defined whether we need to wait for both, or whether - # waiting for one is sufficient, but at time of writing this test, - # neither is sufficient (!) - see issue #778 - and instead this test will - # sometimes pass even though stageout is not working. - - f.result() - - result_file = f.outputs[0].result() - - assert unsorted_file.local_path is None, "Input file on local side has overridden local_path, file: {}".format(repr(unsorted_file)) - - # use 'is' rather than '==' because specifically want to check - # object identity rather than value equality - assert sorted_file is result_file, "Result file is not the specified input-output file" - - assert result_file.local_path is None, "Result file on local side has overridden local_path, file: {}".format(repr(result_file)) diff --git a/parsl/parsl/tests/test_staging/test_staging_https.py b/parsl/parsl/tests/test_staging/test_staging_https.py deleted file mode 100644 index c9774722..00000000 --- a/parsl/parsl/tests/test_staging/test_staging_https.py +++ /dev/null @@ -1,106 +0,0 @@ -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.data_provider.files import File - -# This config is for the local test which will adding an executor. -# Most tests in this file should be non-local and use the configuration -# specificed with --config, not this one. -from parsl.tests.configs.htex_local import fresh_config as local_config - -""" -Test staging for an http file - -Create a remote input file (https) that points to unsorted.txt, then -a local file for output data (sorted.txt). -""" - -_unsorted_url = ( - 'https://gist.githubusercontent.com/yadudoc/7f21dd15e64a421990a46766bfa5359c/' - 'raw/7fe04978ea44f807088c349f6ecb0f6ee350ec49/unsorted.txt' -) -_exp_sorted = sorted(f"{i}\n" for i in range(1, 101))[:20] - - -@python_app -def sort_strings(inputs=(), outputs=()): - from parsl.tests.test_staging import read_sort_write - read_sort_write(inputs[0].filepath, outputs[0].filepath) - - -@python_app -def sort_strings_kw(*, x=None, outputs=()): - from parsl.tests.test_staging import read_sort_write - read_sort_write(x.filepath, outputs[0].filepath) - - -@python_app -def sort_strings_arg(x, /, outputs=()): - from parsl.tests.test_staging import read_sort_write - read_sort_write(x.filepath, outputs[0].filepath) - - -@python_app(executors=['other']) -def sort_strings_additional_executor(inputs=(), outputs=()): - from parsl.tests.test_staging import read_sort_write - read_sort_write(inputs[0].filepath, outputs[0].filepath) - - -@pytest.mark.cleannet -@pytest.mark.staging_required -def test_staging_https_cleannet(tmpd_cwd): - unsorted_file = File(_unsorted_url) - sorted_file = File(tmpd_cwd / 'sorted.txt') - - sort_strings(inputs=[unsorted_file], outputs=[sorted_file]).result() - with open(sorted_file) as f: - assert all(a == b for a, b in zip(f.readlines(), _exp_sorted)) - - -@pytest.mark.local -def test_staging_https_local(tmpd_cwd): - unsorted_file = File(_unsorted_url) - sorted_file = File(tmpd_cwd / 'sorted.txt') - - sort_strings(inputs=[unsorted_file], outputs=[sorted_file]).result() - with open(sorted_file) as f: - assert all(a == b for a, b in zip(f.readlines(), _exp_sorted)) - - -@pytest.mark.cleannet -@pytest.mark.staging_required -def test_staging_https_kwargs(tmpd_cwd): - unsorted_file = File(_unsorted_url) - sorted_file = File(tmpd_cwd / 'sorted.txt') - - sort_strings_kw(x=unsorted_file, outputs=[sorted_file]).result() - with open(sorted_file) as f: - assert all(a == b for a, b in zip(f.readlines(), _exp_sorted)) - - -@pytest.mark.cleannet -@pytest.mark.staging_required -def test_staging_https_args(tmpd_cwd): - unsorted_file = File(_unsorted_url) - sorted_file = File(tmpd_cwd / 'sorted.txt') - - sort_strings_arg(unsorted_file, outputs=[sorted_file]).result() - with open(sorted_file) as f: - assert all(a == b for a, b in zip(f.readlines(), _exp_sorted)) - - -@pytest.mark.cleannet -@pytest.mark.local -def test_staging_https_additional_executor(tmpd_cwd): - unsorted_file = File(_unsorted_url) - sorted_file = File(tmpd_cwd / 'sorted.txt') - - other_executor = parsl.ThreadPoolExecutor(label='other') - other_executor.working_dir = tmpd_cwd - - parsl.dfk().add_executors([other_executor]) - - sort_strings_additional_executor(inputs=[unsorted_file], outputs=[sorted_file]).result() - with open(sorted_file) as f: - assert all(a == b for a, b in zip(f.readlines(), _exp_sorted)) diff --git a/parsl/parsl/tests/test_staging/test_staging_stdout.py b/parsl/parsl/tests/test_staging/test_staging_stdout.py deleted file mode 100644 index 0eae6df7..00000000 --- a/parsl/parsl/tests/test_staging/test_staging_stdout.py +++ /dev/null @@ -1,64 +0,0 @@ -import logging -import os -import zipfile - -import pytest - -import parsl -from parsl.app.futures import DataFuture -from parsl.data_provider.files import File -from parsl.tests.configs.htex_local import fresh_config as local_config - - -@parsl.bash_app -def output_to_stds(*, stdout=parsl.AUTO_LOGNAME, stderr=parsl.AUTO_LOGNAME): - return "echo hello ; echo goodbye >&2" - - -@pytest.mark.staging_required -def test_stdout_staging_file(tmpd_cwd, caplog): - basename = str(tmpd_cwd) + "/stdout.txt" - stdout_file = File("file://" + basename) - - app_future = output_to_stds(stdout=stdout_file) - - assert isinstance(app_future.stdout, DataFuture) - app_future.stdout.result() - - assert os.path.exists(basename) - - for record in caplog.records: - assert record.levelno < logging.ERROR - - -@pytest.mark.staging_required -def test_stdout_stderr_staging_zip(tmpd_cwd, caplog): - zipfile_name = str(tmpd_cwd) + "/staging.zip" - stdout_relative_path = "somewhere/test-out.txt" - stdout_file = File("zip:" + zipfile_name + "/" + stdout_relative_path) - - stderr_relative_path = "somewhere/test-error.txt" - stderr_file = File("zip:" + zipfile_name + "/" + stderr_relative_path) - - app_future = output_to_stds(stdout=stdout_file, stderr=stderr_file) - - assert isinstance(app_future.stdout, DataFuture) - app_future.stdout.result() - - # check the file exists as soon as possible - assert os.path.exists(zipfile_name) - with zipfile.ZipFile(zipfile_name) as z: - with z.open(stdout_relative_path) as f: - assert f.readlines() == [b'hello\n'] - - assert isinstance(app_future.stderr, DataFuture) - app_future.stderr.result() - with zipfile.ZipFile(zipfile_name) as z: - with z.open(stderr_relative_path) as f: - # The last line of stderr should be goodbye, but Parsl will write - # other Parsl-specific into to stderr before that, so only assert - # the behaviour of the final line. - assert f.readlines()[-1] == b'goodbye\n' - - for record in caplog.records: - assert record.levelno < logging.ERROR diff --git a/parsl/parsl/tests/test_staging/test_zip_in.py b/parsl/parsl/tests/test_staging/test_zip_in.py deleted file mode 100644 index 9d43a4ab..00000000 --- a/parsl/parsl/tests/test_staging/test_zip_in.py +++ /dev/null @@ -1,39 +0,0 @@ -import random -import zipfile - -import pytest - -import parsl -from parsl.config import Config -from parsl.data_provider.files import File -from parsl.data_provider.zip import ZipAuthorityError, ZipFileStaging -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SimpleLauncher -from parsl.providers import LocalProvider -from parsl.tests.configs.htex_local import fresh_config as local_config - - -@parsl.python_app -def count_lines(file): - with open(file, "r") as f: - return len(f.readlines()) - - -@pytest.mark.local -def test_zip_in(tmpd_cwd): - # basic test of zip file stage-in - zip_path = tmpd_cwd / "container.zip" - file_base = "data.txt" - zip_file = File(f"zip:{zip_path / file_base}") - - # create a zip file containing one file with some abitrary number of lines - n_lines = random.randint(0, 1000) - - with zipfile.ZipFile(zip_path, mode='w') as z: - with z.open(file_base, mode='w') as f: - for _ in range(n_lines): - f.write(b'someline\n') - - app_future = count_lines(zip_file) - - assert app_future.result() == n_lines diff --git a/parsl/parsl/tests/test_staging/test_zip_out.py b/parsl/parsl/tests/test_staging/test_zip_out.py deleted file mode 100644 index 79fbb504..00000000 --- a/parsl/parsl/tests/test_staging/test_zip_out.py +++ /dev/null @@ -1,110 +0,0 @@ -import zipfile - -import pytest - -import parsl -from parsl.config import Config -from parsl.data_provider.data_manager import default_staging -from parsl.data_provider.files import File -from parsl.data_provider.zip import ZipAuthorityError, ZipFileStaging -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SimpleLauncher -from parsl.providers import LocalProvider -from parsl.tests.configs.htex_local import fresh_config as local_config - - -@pytest.mark.local -def test_zip_path_split(): - from parsl.data_provider.zip import zip_path_split - assert zip_path_split("/tmp/foo/this.zip/inside/here.txt") == ("/tmp/foo/this.zip", "inside/here.txt") - - -@parsl.bash_app -def output_something(outputs=()): - """This should output something into every specified output file: - the position in the output sequence will be written into the - corresponding output file. - """ - cmds = [] - for n in range(len(outputs)): - cmds.append(f"echo {n} > {outputs[n]}") - - return "; ".join(cmds) - - -@pytest.mark.local -def test_zip_out(tmpd_cwd): - # basic test of zip file stage-out - zip_path = tmpd_cwd / "container.zip" - file_base = "data.txt" - of = File(f"zip:{zip_path / file_base}") - - app_future = output_something(outputs=[of]) - output_file_future = app_future.outputs[0] - - app_future.result() - output_file_future.result() - - assert zipfile.is_zipfile(zip_path) - - with zipfile.ZipFile(zip_path) as z: - assert file_base in z.namelist() - assert len(z.namelist()) == 1 - with z.open(file_base) as f: - assert f.readlines() == [b'0\n'] - - -@pytest.mark.local -def test_zip_out_multi(tmpd_cwd): - # tests multiple files, multiple zip files and multiple - # sub-paths - - zip_path_1 = tmpd_cwd / "container1.zip" - zip_path_2 = tmpd_cwd / "container2.zip" - - relative_file_path_1 = "a/b/c/data.txt" - relative_file_path_2 = "something.txt" - relative_file_path_3 = "a/d/other.txt" - of1 = File(f"zip:{zip_path_1 / relative_file_path_1}") - of2 = File(f"zip:{zip_path_1 / relative_file_path_2}") - of3 = File(f"zip:{zip_path_2 / relative_file_path_3}") - - app_future = output_something(outputs=[of1, of2, of3]) - - for f in app_future.outputs: - f.result() - - app_future.result() - - assert zipfile.is_zipfile(zip_path_1) - - with zipfile.ZipFile(zip_path_1) as z: - assert relative_file_path_1 in z.namelist() - assert relative_file_path_2 in z.namelist() - assert len(z.namelist()) == 2 - with z.open(relative_file_path_1) as f: - assert f.readlines() == [b'0\n'] - with z.open(relative_file_path_2) as f: - assert f.readlines() == [b'1\n'] - - assert zipfile.is_zipfile(zip_path_2) - - with zipfile.ZipFile(zip_path_2) as z: - assert relative_file_path_3 in z.namelist() - assert len(z.namelist()) == 1 - with z.open(relative_file_path_3) as f: - assert f.readlines() == [b'2\n'] - - -@pytest.mark.local -def test_zip_bad_authority(tmpd_cwd): - # tests that there's an exception when staging a ZIP url with an authority - # section specified, rather than silently ignoring it. This simulates a - # user who misunderstands what that piece of what a zip: URL means. - - zip_path = tmpd_cwd / "container.zip" - file_base = "data.txt" - of = File(f"zip://someauthority/{zip_path / file_base}") - - with pytest.raises(ZipAuthorityError): - output_something(outputs=[of]) diff --git a/parsl/parsl/tests/test_staging/test_zip_to_zip.py b/parsl/parsl/tests/test_staging/test_zip_to_zip.py deleted file mode 100644 index 3fea4216..00000000 --- a/parsl/parsl/tests/test_staging/test_zip_to_zip.py +++ /dev/null @@ -1,41 +0,0 @@ -import random -import zipfile - -import pytest - -import parsl -from parsl.config import Config -from parsl.data_provider.files import File -from parsl.data_provider.zip import ZipAuthorityError, ZipFileStaging -from parsl.executors import HighThroughputExecutor -from parsl.launchers import SimpleLauncher -from parsl.providers import LocalProvider -from parsl.tests.configs.htex_local import fresh_config as local_config - - -@parsl.python_app -def generate_lines(n: int, *, outputs): - with open(outputs[0], "w") as f: - for x in range(n): - # write numbered lines - f.write(str(x) + "\n") - - -@parsl.python_app -def count_lines(file): - with open(file, "r") as f: - return len(f.readlines()) - - -@pytest.mark.local -def test_zip_pipeline(tmpd_cwd): - # basic test of zip file stage-in - zip_path = tmpd_cwd / "container.zip" - file_base = "data.txt" - zip_file = File(f"zip:{zip_path / file_base}") - - n_lines = random.randint(0, 1000) - generate_fut = generate_lines(n_lines, outputs=[zip_file]) - n_lines_out = count_lines(generate_fut.outputs[0]).result() - - assert n_lines == n_lines_out diff --git a/parsl/parsl/tests/test_summary.py b/parsl/parsl/tests/test_summary.py deleted file mode 100644 index e98f3939..00000000 --- a/parsl/parsl/tests/test_summary.py +++ /dev/null @@ -1,29 +0,0 @@ -import pytest - -import parsl -from parsl.tests.configs.local_threads import fresh_config - - -@parsl.python_app -def succeed(): - pass - - -@parsl.python_app -def fail(): - raise RuntimeError("Deliberate failure in fail() app") - - -@pytest.mark.local -def test_summary(caplog): - - parsl.load(fresh_config()) - - succeed().result() - fail().exception() - - parsl.dfk().cleanup() - - assert "Summary of tasks in DFK:" in caplog.text - assert "Tasks in state States.exec_done: 1" in caplog.text - assert "Tasks in state States.failed: 1" in caplog.text diff --git a/parsl/parsl/tests/test_threads/__init__.py b/parsl/parsl/tests/test_threads/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_threads/test_configs.py b/parsl/parsl/tests/test_threads/test_configs.py deleted file mode 100644 index 022f8825..00000000 --- a/parsl/parsl/tests/test_threads/test_configs.py +++ /dev/null @@ -1,33 +0,0 @@ -import pytest - -import parsl -from parsl.app.app import python_app -from parsl.tests.configs.local_threads import fresh_config - - -@python_app -def worker_identify(x, sleep_dur=0.2): - import os - import threading - import time - time.sleep(sleep_dur) - return {"pid": os.getpid(), - "tid": threading.current_thread()} - - -@pytest.mark.local -def test_parallel_for(): - config = fresh_config() - dfk = parsl.load(config) - d = [] - for i in range(0, config.executors[0].max_threads): - d.extend([worker_identify(i)]) - - [item.result() for item in d] - - thread_count = len(set([item.result()['tid'] for item in d])) - process_count = len(set([item.result()['pid'] for item in d])) - assert thread_count <= config.executors[0].max_threads, "More threads than allowed" - assert process_count == 1, "More processes than allowed" - dfk.cleanup() - return d diff --git a/parsl/parsl/tests/test_threads/test_lazy_errors.py b/parsl/parsl/tests/test_threads/test_lazy_errors.py deleted file mode 100644 index a070487e..00000000 --- a/parsl/parsl/tests/test_threads/test_lazy_errors.py +++ /dev/null @@ -1,28 +0,0 @@ -import pytest - -import parsl -from parsl import python_app -from parsl.tests.configs.local_threads import fresh_config - - -@pytest.mark.local -def test_lazy_behavior(): - """Testing that lazy errors work""" - - config = fresh_config() - parsl.load(config) - - @python_app - def divide(a, b): - return a / b - - futures = [] - for i in range(0, 10): - futures.append(divide(10, 0)) - - for f in futures: - assert isinstance(f.exception(), ZeroDivisionError) - assert f.done() - - parsl.dfk().cleanup() - return diff --git a/parsl/parsl/tests/test_utils/__init__.py b/parsl/parsl/tests/test_utils/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/test_utils/test_execute_wait.py b/parsl/parsl/tests/test_utils/test_execute_wait.py deleted file mode 100644 index 44488c23..00000000 --- a/parsl/parsl/tests/test_utils/test_execute_wait.py +++ /dev/null @@ -1,35 +0,0 @@ -import pytest - -from parsl.utils import execute_wait - - -@pytest.mark.local -def test_env(): - ''' Regression testing for issue #27 - ''' - - rc, stdout, stderr = execute_wait("env", 1) - - stdout = stdout.split('\n') - x = [s for s in stdout if s.startswith("PATH=")] - assert x, "PATH not found" - - x = [s for s in stdout if s.startswith("HOME=")] - assert x, "HOME not found" - - -@pytest.mark.local -def test_large_output_2210(): - """Regression test for #2210. - execute_wait was hanging if the specified command gave too - much output, due to a race condition between process exiting and - pipes filling up. - """ - - # this will output 128kb of stdout - execute_wait("yes | dd count=128 bs=1024", walltime=60) - - # if this test fails, execute_wait should raise a timeout - # exception. - - # The contents out the output is not verified by this test diff --git a/parsl/parsl/tests/test_utils/test_logutils.py b/parsl/parsl/tests/test_utils/test_logutils.py deleted file mode 100644 index e0aac778..00000000 --- a/parsl/parsl/tests/test_utils/test_logutils.py +++ /dev/null @@ -1,70 +0,0 @@ -import io -import logging - -import pytest - -from parsl.log_utils import set_file_logger, set_stream_logger - - -@pytest.mark.local -def test_stream_close(): - """Tests that set_stream_logger callback detaches log handler. - """ - - logger = logging.getLogger("parsl") - - s1 = io.StringIO() - close_callback_1 = set_stream_logger(stream=s1) - logger.info("AAA") - close_callback_1() - - s2 = io.StringIO() - close_callback_2 = set_stream_logger(stream=s2) - logger.info("BBB") - close_callback_2() - - logger.info("CCC") - - assert "AAA" in s1.getvalue() - assert "AAA" not in s2.getvalue() - - assert "BBB" not in s1.getvalue() - assert "BBB" in s2.getvalue() - - assert "CCC" not in s1.getvalue() - assert "CCC" not in s2.getvalue() - - -@pytest.mark.local -def test_file_close(tmpd_cwd): - """Tests that set_file_Logger callback detaches log handler. - """ - - logger = logging.getLogger("parsl") - - f1 = str(tmpd_cwd / "log1") - close_callback_1 = set_file_logger(filename=f1) - logger.info("AAA") - close_callback_1() - - f2 = str(tmpd_cwd / "log2") - close_callback_2 = set_file_logger(filename=f2) - logger.info("BBB") - close_callback_2() - - logger.info("CCC") - - with open(f1, "r") as f: - s1 = f.read() - - with open(f2, "r") as f: - s2 = f.read() - - assert "AAA" in s1 - assert "AAA" not in s2 - - assert "BBB" not in s1 - assert "BBB" in s2 - - assert "CCC" not in s1 - assert "CCC" not in s2 diff --git a/parsl/parsl/tests/test_utils/test_representation_mixin.py b/parsl/parsl/tests/test_utils/test_representation_mixin.py deleted file mode 100644 index 0f08a0b3..00000000 --- a/parsl/parsl/tests/test_utils/test_representation_mixin.py +++ /dev/null @@ -1,121 +0,0 @@ -import pytest - -from parsl.utils import RepresentationMixin - - -class GoodRepr(RepresentationMixin): - def __init__(self, x, y): - self.x = x - self.y = y - - -class GoodReprDefaults(RepresentationMixin): - def __init__(self, x, y="default 2"): - self.x = x - self.y = y - - -class GoodReprKeywordOnly(RepresentationMixin): - def __init__(self, *, x, y): - self.x = x - self.y = y - - -class BadRepr(RepresentationMixin): - """This class incorrectly subclasses RepresentationMixin. - It does not store the parameter x on self. - """ - def __init__(self, x, y): - self.y = y - - -@pytest.mark.local -def test_repr_good(): - p1 = "parameter 1" - p2 = "the second parameter" - - # repr should not raise an exception - r = repr(GoodRepr(p1, p2)) - - # representation should contain both values supplied - # at object creation. - assert p1 in r - assert p2 in r - - -@pytest.mark.local -def test_repr_good_defaults_overridden(): - p1 = "parameter 1" - p2 = "the second parameter" - - # repr should not raise an exception - r = repr(GoodReprDefaults(p1, p2)) - - # representation should contain both values supplied - # at object creation. - assert p1 in r - assert p2 in r - - -@pytest.mark.local -def test_repr_good_defaults_defaulted(): - p1 = "parameter 1" - - # repr should not raise an exception - r = repr(GoodReprDefaults(p1)) - - # representation should contain one value supplied - # at object creation, and the other defaulted. - assert p1 in r - assert "default 2" in r - - -@pytest.mark.local -def test_repr_good_keyword_only(): - p1 = "parameter 1" - p2 = "the second parameter" - - # repr should not raise an exception - r = repr(GoodReprKeywordOnly(x=p1, y=p2)) - - # representation should contain both values supplied - # at object creation. - assert p1 in r - assert p2 in r - - -@pytest.mark.local -def test_repr_bad(): - p1 = "parameter 1" - p2 = "the second parameter" - - # repr should raise an exception - with pytest.raises(AttributeError): - repr(BadRepr(p1, p2)) - - -class NonValidatingRepresentationMixin(RepresentationMixin): - """This will override the process level RepresentationMixin which can - be set to validating mode by pytest fixtures""" - _validate_repr = False - - -class BadReprNonValidating(NonValidatingRepresentationMixin): - """This class incorrectly subclasses RepresentationMixin. - It does not store the parameter x on self. - """ - def __init__(self, x, y): - self.y = y - - -@pytest.mark.local -def test_repr_bad_unvalidated(): - p1 = "parameter 1" - p2 = "the second parameter" - - # repr should not raise an exception - r = repr(BadReprNonValidating(p1, p2)) - # parameter 2 should be found in the representation, but not - # parameter 1 - assert p1 not in r - assert p2 in r diff --git a/parsl/parsl/tests/test_utils/test_sanitize_dns.py b/parsl/parsl/tests/test_utils/test_sanitize_dns.py deleted file mode 100644 index 17b80133..00000000 --- a/parsl/parsl/tests/test_utils/test_sanitize_dns.py +++ /dev/null @@ -1,76 +0,0 @@ -import random -import re - -import pytest - -from parsl.utils import sanitize_dns_label_rfc1123, sanitize_dns_subdomain_rfc1123 - -# Ref: https://datatracker.ietf.org/doc/html/rfc1123 -DNS_LABEL_REGEX = r'^[a-z0-9]([-a-z0-9]{0,61}[a-z0-9])?$' -DNS_SUBDOMAIN_REGEX = r'^[a-z0-9]([-a-z0-9]{0,61}[a-z0-9])?(\.[a-z0-9]([-a-z0-9]{0,61}[a-z0-9])?)*$' - -test_labels = [ - "example-label-123", # Valid label - "EXAMPLE", # Case sensitivity - "!@#example*", # Remove invalid characters - "--leading-and-trailing--", # Leading and trailing hyphens - "..leading.and.trailing..", # Leading and tailing dots - "multiple..dots", # Consecutive dots - "valid--label", # Consecutive hyphens - "a" * random.randint(64, 70), # Longer than 63 characters - f"{'a' * 62}-a", # Trailing hyphen at max length -] - - -def _generate_test_subdomains(num_subdomains: int): - subdomains = [] - for _ in range(num_subdomains): - num_labels = random.randint(1, 5) - labels = [test_labels[random.randint(0, num_labels - 1)] for _ in range(num_labels)] - subdomain = ".".join(labels) - subdomains.append(subdomain) - return subdomains - - -@pytest.mark.local -@pytest.mark.parametrize("raw_string", test_labels) -def test_sanitize_dns_label_rfc1123(raw_string: str): - print(sanitize_dns_label_rfc1123(raw_string)) - assert re.match(DNS_LABEL_REGEX, sanitize_dns_label_rfc1123(raw_string)) - - -@pytest.mark.local -@pytest.mark.parametrize("raw_string", ("", "-", "@", "$$$")) -def test_sanitize_dns_label_rfc1123_empty(raw_string: str): - with pytest.raises(ValueError) as e_info: - sanitize_dns_label_rfc1123(raw_string) - assert str(e_info.value) == f"Sanitized DNS label is empty for input '{raw_string}'" - - -@pytest.mark.local -@pytest.mark.parametrize("raw_string", _generate_test_subdomains(10)) -def test_sanitize_dns_subdomain_rfc1123(raw_string: str): - assert re.match(DNS_SUBDOMAIN_REGEX, sanitize_dns_subdomain_rfc1123(raw_string)) - - -@pytest.mark.local -@pytest.mark.parametrize("char", ("-", ".")) -def test_sanitize_dns_subdomain_rfc1123_trailing_non_alphanumeric_at_max_length(char: str): - raw_string = (f"{'a' * 61}." * 4) + f".aaaa{char}a" - assert re.match(DNS_SUBDOMAIN_REGEX, sanitize_dns_subdomain_rfc1123(raw_string)) - - -@pytest.mark.local -@pytest.mark.parametrize("raw_string", ("", ".", "...")) -def test_sanitize_dns_subdomain_rfc1123_empty(raw_string: str): - with pytest.raises(ValueError) as e_info: - sanitize_dns_subdomain_rfc1123(raw_string) - assert str(e_info.value) == f"Sanitized DNS subdomain is empty for input '{raw_string}'" - - -@pytest.mark.local -@pytest.mark.parametrize( - "raw_string", ("a" * 253, "a" * random.randint(254, 300)), ids=("254 chars", ">253 chars") -) -def test_sanitize_dns_subdomain_rfc1123_max_length(raw_string: str): - assert len(sanitize_dns_subdomain_rfc1123(raw_string)) <= 253 diff --git a/parsl/parsl/tests/unit/__init__.py b/parsl/parsl/tests/unit/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/unit/executors/high_throughput/__init__.py b/parsl/parsl/tests/unit/executors/high_throughput/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/tests/unit/executors/high_throughput/test_htex.py b/parsl/parsl/tests/unit/executors/high_throughput/test_htex.py deleted file mode 100644 index e1359163..00000000 --- a/parsl/parsl/tests/unit/executors/high_throughput/test_htex.py +++ /dev/null @@ -1,24 +0,0 @@ -from unittest import mock - -import pytest - -from parsl import HighThroughputExecutor -from parsl.executors.high_throughput import zmq_pipes - - -@pytest.mark.local -def test_submit_payload(): - htex = HighThroughputExecutor() - htex.outgoing_q = mock.Mock(spec=zmq_pipes.TasksOutgoing) - ctxt = {"some": "context"} - buf = b'some buffer (function) payload' - for task_num in range(1, 20): - htex.outgoing_q.reset_mock() - fut = htex.submit_payload(ctxt, buf) - (msg,), _ = htex.outgoing_q.put.call_args - - assert htex.tasks[fut.parsl_executor_task_id] is fut - assert fut.parsl_executor_task_id == task_num, "Expect monotonic increase" - assert msg["task_id"] == fut.parsl_executor_task_id - assert msg["context"] == ctxt, "Expect no modification" - assert msg["buffer"] == buf, "Expect no modification" diff --git a/parsl/parsl/tests/unit/executors/high_throughput/test_interchange.py b/parsl/parsl/tests/unit/executors/high_throughput/test_interchange.py deleted file mode 100644 index 964aed60..00000000 --- a/parsl/parsl/tests/unit/executors/high_throughput/test_interchange.py +++ /dev/null @@ -1,133 +0,0 @@ -import logging -import pickle -import threading -import time -import uuid -from typing import cast -from unittest import mock - -import pytest -import zmq - -from parsl import curvezmq -from parsl.executors.high_throughput.interchange import ( - PKL_SHUTDOWN_CODE, - Interchange, -) -from parsl.executors.high_throughput.manager_selector import RandomManagerSelector -from parsl.version import VERSION as PARSL_VERSION - - -@pytest.fixture -def ix(tmpd_cwd): - with mock.patch.object(curvezmq.ServerContext, "socket", side_effect=lambda *a, **kw: mock.MagicMock()): - yield Interchange(interchange_address="127.0.0.1", - cert_dir=None, - client_address="127.0.0.1", - client_ports=(50055, 50056, 50057), - worker_port=None, - worker_port_range=(54000, 55000), - hub_address=None, - hub_zmq_port=None, - heartbeat_threshold=60, - logdir=str(tmpd_cwd), - logging_level=logging.INFO, - log_config=None, - manager_selector=RandomManagerSelector(), - poll_period=10, - run_id="test_run_id", - _check_python_mismatch=True) - - -@pytest.fixture -def manager_id() -> bytes: - return f"manager-{uuid.uuid4()}".encode() - - -def send_from_manager(ix: Interchange, manager_id: bytes, msg: dict) -> None: - ix.socks = {ix.manager_sock: zmq.POLLIN} - mock_sock = cast(mock.MagicMock, ix.manager_sock) - mock_sock.recv_multipart.return_value = [manager_id, pickle.dumps(msg)] - ix.process_manager_socket_message(set(), None, threading.Event()) - - -def registration_msg(ix: Interchange, **kw) -> dict: - return {'type': 'registration', - 'parsl_v': PARSL_VERSION, - 'python_v': ix.current_platform['python_v'], - 'worker_count': 1, - 'uid': 'testuid', - 'block_id': '0', - 'start_time': time.time(), - 'prefetch_capacity': 0, - 'max_capacity': 1, - **kw} - - -def shutdown_sent(ix: Interchange, manager_id: bytes) -> bool: - mock_sock = cast(mock.MagicMock, ix.manager_sock) - return mock.call([manager_id, PKL_SHUTDOWN_CODE]) in mock_sock.send_multipart.call_args_list - - -@pytest.mark.local -def test_no_drain_before_drain_time(ix, manager_id): - reg_msg = registration_msg(ix, drain_time=time.time() + 3600) - send_from_manager(ix, manager_id, reg_msg) - assert manager_id in ix._ready_managers, "manager should have been registered" - - ix.expire_drained_managers(set(), None) - - assert not ix._ready_managers[manager_id]['draining'] - assert not shutdown_sent(ix, manager_id) - - -@pytest.mark.local -@pytest.mark.parametrize("is_interesting", (True, False)) -def test_idle_manager_shut_down_at_drain_time(ix, manager_id, is_interesting): - reg_msg = registration_msg(ix, drain_time=time.time()) - send_from_manager(ix, manager_id, reg_msg) - assert manager_id in ix._ready_managers, "manager should have been registered" - - interesting_managers = set([manager_id] if is_interesting else []) - ix.expire_drained_managers(interesting_managers, None) - - assert shutdown_sent(ix, manager_id) - assert manager_id not in ix._ready_managers - assert manager_id not in interesting_managers - - -@pytest.mark.local -def test_draining_manager_shut_down_after_last_result(ix, manager_id): - reg_msg = registration_msg(ix, drain_time=time.time()) - send_from_manager(ix, manager_id, reg_msg) - assert manager_id in ix._ready_managers, "manager should have been registered" - - ix._ready_managers[manager_id]['tasks'].append(1) - ix.expire_drained_managers(set(), None) - - assert ix._ready_managers[manager_id]['draining'] - assert not shutdown_sent(ix, manager_id) - - send_from_manager(ix, manager_id, {'type': 'result', 'task_id': 1}) - ix.expire_drained_managers(set(), None) - - assert shutdown_sent(ix, manager_id) - assert manager_id not in ix._ready_managers - - -@pytest.mark.local -def test_legacy_manager_without_drain_time(ix, manager_id): - # ensure new/post-drain_time interchanges work with old/pre-drain_time managers - reg_msg = registration_msg(ix) - assert 'drain_time' not in reg_msg, "test prerequisite" - send_from_manager(ix, manager_id, reg_msg) - assert manager_id in ix._ready_managers, "manager should have been registered" - - ix.expire_drained_managers(set(), None) - assert not shutdown_sent(ix, manager_id), "ix should wait for legacy manager to request shutdown" - - send_from_manager(ix, manager_id, {'type': 'drain'}) - ix.expire_drained_managers(set(), None) - - assert shutdown_sent(ix, manager_id) - assert manager_id not in ix._ready_managers diff --git a/parsl/parsl/tests/unit/executors/high_throughput/test_process_worker_pool.py b/parsl/parsl/tests/unit/executors/high_throughput/test_process_worker_pool.py deleted file mode 100644 index 672b29f1..00000000 --- a/parsl/parsl/tests/unit/executors/high_throughput/test_process_worker_pool.py +++ /dev/null @@ -1,195 +0,0 @@ -import os -import pickle -import sys -from argparse import ArgumentError -from typing import Any, Dict -from unittest import mock - -import pytest - -from parsl.app.errors import RemoteExceptionWrapper -from parsl.executors.high_throughput import process_worker_pool -from parsl.executors.high_throughput.process_worker_pool import worker -from parsl.multiprocessing import SpawnContext -from parsl.serialize.facade import deserialize - -if sys.version_info < (3, 12): - # exit_on_error bug; see https://github.com/python/cpython/issues/121018 - # "argparse.ArgumentParser.parses_args does not honor exit_on_error=False when - # given unrecognized arguments" - pytest.skip(allow_module_level=True, reason="exit_on_error argparse bug") - -# due to above pytest.skip, mypy on < Py312 thinks this is unreachable. :facepalm: -_known_required = ( # type: ignore[unreachable, unused-ignore] - "--cert_dir", - "--cpu-affinity", - "--port", - "--addresses", -) - - -@pytest.mark.local -def test_arg_parser_exits_on_error(): - p = process_worker_pool.get_arg_parser() - assert p.exit_on_error - - -@pytest.mark.local -def test_arg_parser_known_required(): - p = process_worker_pool.get_arg_parser() - reqd = [a for a in p._actions if a.required] - for a in reqd: - assert a.option_strings[-1] in _known_required, "Update _known_required?" - - -@pytest.mark.local -@pytest.mark.parametrize("req", _known_required) -def test_arg_parser_required(req): - p = process_worker_pool.get_arg_parser() - p.exit_on_error = False - with pytest.raises(ArgumentError) as pyt_exc: - p.parse_args([]) - - e_msg = pyt_exc.value.args[1] - assert req in e_msg - - -@pytest.mark.local -@pytest.mark.parametrize("valid,val", ( - (True, "NoNe"), - (True, "none"), - (True, "block"), - (True, "alternating"), - (True, "block-reverse"), - (True, "list"), - (False, "asdf"), - (False, ""), -)) -def test_arg_parser_validates_cpu_affinity(valid, val): - reqd_args = [] - reqd_args.extend(("--cert_dir", "/some/path")) - reqd_args.extend(("--port", "123")) - reqd_args.extend(("--addresses", "asdf")) - reqd_args.extend(("--cpu-affinity", val)) - - p = process_worker_pool.get_arg_parser() - p.exit_on_error = False - if valid: - p.parse_args(reqd_args) - else: - with pytest.raises(ArgumentError) as pyt_exc: - p.parse_args(reqd_args) - assert "must be one of" in pyt_exc.value.args[1] - - -def _always_raise(*a, **k): - raise ArithmeticError(f"{a=}\n{k=}") - - -@pytest.mark.local -def test_worker_dynamic_import_happy_path(tmpd_cwd) -> None: - import_str = f"{_always_raise.__module__}.{_always_raise.__name__}" - task_exec: Dict[str, Any] = { - "f": import_str, - "a": (1, 2), - "k": {"a": "b"}, - } - req = { - "task_id": 15, - "context": {"task_executor": task_exec}, - "buffer": b"some serialized value" - } - - try: - task_args = [req["buffer"]] - task_args.extend(task_exec["a"]) - _always_raise(*task_args, **task_exec["k"]) - except Exception as e: - exp_exc = e - else: - raise RuntimeError("Test failure; this branch should not run") - - q = mock.Mock(side_effect=(req, MemoryError("intentional test error"))) - q.get = q - - block_id = "bid" - worker_id = 1 - pool = 1 - (tmpd_cwd / f"block-{block_id}/{worker_id}").mkdir(parents=True) - with pytest.raises(MemoryError): - worker( - worker_id, - pool_id=str(pool), - pool_size=pool, - task_queue=q, - result_queue=q, - monitoring_queue=None, - ready_worker_count=SpawnContext.Value("i", 0), - tasks_in_progress={}, - cpu_affinity="none", - accelerator=None, - block_id=block_id, - task_queue_timeout=0, - manager_pid=os.getpid(), - logdir=str(tmpd_cwd), - log_config=None, - debug=True, - mpi_launcher="", - ) - (result_pkl,), _ = q.put.call_args - r = pickle.loads(result_pkl) - assert "exception" in r - wrapped_exc: RemoteExceptionWrapper = deserialize(r["exception"]) - exc = wrapped_exc.get_exception() - assert isinstance(exc, type(exp_exc)), "Approximate equality" - assert str(exp_exc) == str(exc), "Approximate equality; all args, kwargs conveyed" - - -@pytest.mark.local -def test_worker_bad_dynamic_import(tmpd_cwd) -> None: - req = { - "task_id": 15, - "context": { - "task_executor": { - "f": "parsl.some.not_existing.module.__nope", - "a": (1, 2), - "k": {"a": "b"}, - }, - }, - "buffer": b"some serialized value" - } - - q = mock.Mock(side_effect=(req, MemoryError("intentional test error"))) - q.get = q - - block_id = "bid" - worker_id = 1 - pool = 1 - (tmpd_cwd / f"block-{block_id}/{worker_id}").mkdir(parents=True) - with pytest.raises(MemoryError): - worker( - worker_id, - pool_id=str(pool), - pool_size=pool, - task_queue=q, - result_queue=q, - monitoring_queue=None, - ready_worker_count=SpawnContext.Value("i", 0), - tasks_in_progress={}, - cpu_affinity="none", - accelerator=None, - block_id=block_id, - task_queue_timeout=0, - manager_pid=os.getpid(), - logdir=str(tmpd_cwd), - log_config=None, - debug=True, - mpi_launcher="", - ) - (result_pkl,), _ = q.put.call_args - r = pickle.loads(result_pkl) - assert "exception" in r - wrapped_exc: RemoteExceptionWrapper = deserialize(r["exception"]) - exc = wrapped_exc.get_exception() - assert isinstance(exc, ModuleNotFoundError) - assert "No module named" in str(exc) diff --git a/parsl/parsl/tests/unit/test_address.py b/parsl/parsl/tests/unit/test_address.py deleted file mode 100644 index b579baa7..00000000 --- a/parsl/parsl/tests/unit/test_address.py +++ /dev/null @@ -1,21 +0,0 @@ -import pytest - -from parsl.addresses import tcp_url - - -@pytest.mark.local -@pytest.mark.parametrize("address, port,expected", [ - ("127.0.0.1", 55001, "tcp://127.0.0.1:55001"), - ("127.0.0.1", "55001", "tcp://127.0.0.1:55001"), - ("127.0.0.1", None, "tcp://127.0.0.1"), - ("::1", "55001", "tcp://[::1]:55001"), - ("::ffff:127.0.0.1", 55001, "tcp://[::ffff:127.0.0.1]:55001"), - ("::ffff:127.0.0.1", None, "tcp://::ffff:127.0.0.1"), - ("::ffff:127.0.0.1", None, "tcp://::ffff:127.0.0.1"), - ("*", None, "tcp://*"), - ("*", 55001, "tcp://*:55001") -]) -def test_tcp_url(address, port, expected): - """Confirm valid address generation""" - result = tcp_url(address, port) - assert result == expected diff --git a/parsl/parsl/tests/unit/test_file.py b/parsl/parsl/tests/unit/test_file.py deleted file mode 100644 index 87b3746c..00000000 --- a/parsl/parsl/tests/unit/test_file.py +++ /dev/null @@ -1,99 +0,0 @@ -import os -from unittest import mock - -import pytest - -from parsl import File - -_MOCK_BASE = "parsl.data_provider.files." - - -@pytest.mark.local -@pytest.mark.parametrize("scheme", ("http", "https", "ftp", "ftps", "asdfasdf")) -def test_file_init_scheme(scheme): - basename = "some_base_name" - path = f"/some/path/1/2/3/{basename}" - fqdn = "some.fqdn.example.com" - exp_url = f"{scheme}://{fqdn}{path}" - f = File(exp_url) - assert f.url == exp_url, "Expected given url to be stored" - assert f.scheme == scheme - assert f.netloc == fqdn - assert f.path == path - assert f.filename == basename - assert f.local_path is None, "Expect only set by API consumer, not constructor" - - -@pytest.mark.local -@pytest.mark.parametrize("url", ("some weird :// url", "", "a")) -def test_file_init_file_url_fallback(url): - exp_url = "some weird :// url" - f = File(exp_url) - assert f.url == exp_url - assert not f.netloc, "invalid host, should be no netloc" - assert f.path == exp_url, "Should fail to fully parse, so path is whole url" - assert f.filename == exp_url.rsplit("/", 1)[-1] - - assert f.scheme == "file" - - -@pytest.mark.local -def test_file_proxies_for_filepath(randomstring): - # verify (current) expected internal hookup - exp_filepath = randomstring() - with mock.patch( - f"{_MOCK_BASE}File.filepath", new_callable=mock.PropertyMock - ) as mock_fpath: - mock_fpath.return_value = exp_filepath - f = File("") - assert str(f) == exp_filepath - assert os.fspath(f) == exp_filepath - - -@pytest.mark.local -@pytest.mark.parametrize("scheme", ("file://", "")) -def test_file_filepath_local_path_is_priority(scheme, randomstring): - exp_path = "/some/local/path" - url = f"{scheme}{exp_path}" - f = File(url) - - f.local_path = randomstring() - assert f.filepath == f.local_path - - f.local_path = None - assert f.filepath == exp_path - - -@pytest.mark.local -def test_file_filepath_requires_local_accessible_path(): - with pytest.raises(ValueError) as pyt_exc: - _ = File("http://").filepath - - assert "No local_path" in str(pyt_exc.value), "Expected reason in exception" - - -@pytest.mark.local -@pytest.mark.parametrize("scheme", ("https", "ftps", "", "file", "asdfasdf")) -def test_file_repr(scheme): - netloc = "some.netloc" - filename = "some_file_name" - path = f"/some/path/{filename}" - if scheme: - url = f"{scheme}://{netloc}{path}" - else: - scheme = "file" - url = path - - f = File(url) - r = repr(f) - assert r.startswith("<") - assert r.endswith(">") - assert f"<{type(f).__name__} " in r - assert f" at 0x{id(f):x}" in r - assert f" url={url}" in r - assert f" scheme={scheme}" in r - assert f" path={path}" in r - assert f" filename={filename}" in r - - if scheme != "file": - assert f" netloc={netloc}" in r diff --git a/parsl/parsl/tests/unit/test_globus_compute_executor.py b/parsl/parsl/tests/unit/test_globus_compute_executor.py deleted file mode 100644 index 21e1da00..00000000 --- a/parsl/parsl/tests/unit/test_globus_compute_executor.py +++ /dev/null @@ -1,113 +0,0 @@ -import random -from unittest import mock - -import pytest - -from parsl.executors import GlobusComputeExecutor - - -@pytest.fixture -def mock_ex(): - # Not Parsl's job to test GC's Executor, although it - # still needs to be importable for these test cases. - from globus_compute_sdk import Executor - - yield mock.Mock(spec=Executor) - - -@pytest.mark.local -@pytest.mark.globus_compute -def test_gc_executor_mock_spec(mock_ex): - # a test of tests -- make sure we're using spec= in the mock - with pytest.raises(AttributeError): - mock_ex.aasdf() - - -@pytest.mark.local -@pytest.mark.globus_compute -def test_gc_executor_label_default(mock_ex): - gce = GlobusComputeExecutor(mock_ex) - assert gce.label == type(gce).__name__, "Expect reasonable default label" - - -@pytest.mark.local -@pytest.mark.globus_compute -def test_gc_executor_label(mock_ex, randomstring): - exp_label = randomstring() - gce = GlobusComputeExecutor(mock_ex, label=exp_label) - assert gce.label == exp_label - - -@pytest.mark.local -@pytest.mark.globus_compute -def test_gc_executor_resets_spec_after_submit(mock_ex, randomstring): - submit_res = {randomstring(): "some submit res"} - res = {"some": randomstring(), "spec": randomstring()} - mock_ex.resource_specification = res - mock_ex.user_endpoint_config = None - gce = GlobusComputeExecutor(mock_ex) - - fn = mock.Mock() - orig_res = mock_ex.resource_specification - orig_uep = mock_ex.user_endpoint_config - - def mock_submit(*a, **k): - assert mock_ex.resource_specification == submit_res, "Expect set for submission" - assert mock_ex.user_endpoint_config is None - mock_ex.submit.side_effect = mock_submit - - gce.submit(fn, resource_specification=submit_res) - - assert mock_ex.resource_specification == orig_res - assert mock_ex.user_endpoint_config is orig_uep - - -@pytest.mark.local -@pytest.mark.globus_compute -def test_gc_executor_resets_uep_after_submit(mock_ex, randomstring): - uep_conf = randomstring() - res = {"some": randomstring()} - gce = GlobusComputeExecutor(mock_ex) - - fn = mock.Mock() - orig_res = mock_ex.resource_specification - orig_uep = mock_ex.user_endpoint_config - - def mock_submit(*a, **k): - - assert mock_ex.resource_specification == res, "Expect set for submission" - assert mock_ex.user_endpoint_config == uep_conf, "Expect set for submission" - mock_ex.submit.side_effect = mock_submit - - gce.submit(fn, resource_specification={"user_endpoint_config": uep_conf, **res}) - - assert mock_ex.resource_specification == orig_res - assert mock_ex.user_endpoint_config is orig_uep - - -@pytest.mark.local -@pytest.mark.globus_compute -def test_gc_executor_happy_path(mock_ex, randomstring): - mock_fn = mock.Mock() - args = tuple(randomstring() for _ in range(random.randint(0, 3))) - kwargs = {randomstring(): randomstring() for _ in range(random.randint(0, 3))} - - gce = GlobusComputeExecutor(mock_ex) - gce.submit(mock_fn, {}, *args, **kwargs) - - assert mock_ex.submit.called, "Expect proxying of args to underlying executor" - found_a, found_k = mock_ex.submit.call_args - assert found_a[0] is mock_fn - assert found_a[1:] == args - assert found_k == kwargs - - -@pytest.mark.local -@pytest.mark.globus_compute -def test_gc_executor_shuts_down_asynchronously(mock_ex): - gce = GlobusComputeExecutor(mock_ex) - gce.shutdown() - assert mock_ex.shutdown.called - a, k = mock_ex.shutdown.call_args - assert k["wait"] is False - assert k["cancel_futures"] is True diff --git a/parsl/parsl/tests/unit/test_launchers.py b/parsl/parsl/tests/unit/test_launchers.py deleted file mode 100644 index 9f184479..00000000 --- a/parsl/parsl/tests/unit/test_launchers.py +++ /dev/null @@ -1,360 +0,0 @@ -import os -import subprocess -import tempfile -from pathlib import Path - -import pytest - -from parsl.launchers import ( - AprunLauncher, - GnuParallelLauncher, - JsrunLauncher, - MpiExecLauncher, - MpiRunLauncher, - SimpleLauncher, - SingleNodeLauncher, - SrunLauncher, - SrunMPILauncher, - WrappedLauncher, -) - - -def test_wrapped_launcher(caplog): - w = WrappedLauncher('docker') - assert w('test', 1, 1) == 'docker test' - - # Make sure it complains if you have > 1 node or task per node - w('test', 2, 2) - assert 'tasks per node' in caplog.text - assert 'nodes per block' in caplog.text - - -def create_mock_script(script_content: str, mock_dir: Path) -> str: - """Prepend mock function definitions to the launcher script.""" - - # Mock functions for HPC commands - mocks = f""" -# Mock HPC commands for testing -export PATH="{mock_dir}:$PATH" - -srun() {{ - echo "MOCK_SRUN called with: $@" >&2 - # Look for bash and execute the script that follows - while [[ $# -gt 0 ]]; do - if [[ "$1" == "bash" ]]; then - shift - bash "$@" - return $? - fi - shift - done - echo "ERROR: srun mock did not find bash command to execute" >&2 - return 1 -}} - -mpiexec() {{ - echo "MOCK_MPIEXEC called with: $@" >&2 - # Look for bash and execute the script that follows - while [[ $# -gt 0 ]]; do - if [[ "$1" == "bash" ]] || [[ "$1" == "/usr/bin/sh" ]]; then - shift - bash "$@" - return $? - fi - shift - done - echo "ERROR: mpiexec mock did not find bash command to execute" >&2 - return 1 -}} - -mpirun() {{ - echo "MOCK_MPIRUN called with: $@" >&2 - # Look for bash and execute the script that follows - while [[ $# -gt 0 ]]; do - if [[ "$1" == "bash" ]] || [[ "$1" == *"/bash" ]]; then - shift - bash "$@" - return $? - fi - shift - done - echo "ERROR: mpirun mock did not find bash command to execute" >&2 - return 1 -}} - -aprun() {{ - echo "MOCK_APRUN called with: $@" >&2 - # Look for bash and execute the script that follows - while [[ $# -gt 0 ]]; do - if [[ "$1" == "/bin/bash" ]] || [[ "$1" == "bash" ]]; then - shift - bash "$@" - return $? - fi - shift - done - echo "ERROR: aprun mock did not find bash command to execute" >&2 - return 1 -}} - -jsrun() {{ - echo "MOCK_JSRUN called with: $@" >&2 - # Look for bash and execute the script that follows - while [[ $# -gt 0 ]]; do - if [[ "$1" == "/bin/bash" ]] || [[ "$1" == "bash" ]]; then - shift - bash "$@" - return $? - fi - shift - done - echo "ERROR: jsrun mock did not find bash command to execute" >&2 - return 1 -}} - -parallel() {{ - echo "MOCK_PARALLEL called with: $@" >&2 - # For gnu parallel, read commands from stdin and execute them - while IFS= read -r line; do - eval "$line" - done -}} - -# Original script content follows -{script_content} -""" - return mocks - - -@pytest.mark.local -@pytest.mark.parametrize("launcher_class,creates_cmd_script", [ - (SimpleLauncher, False), - (SingleNodeLauncher, False), - (GnuParallelLauncher, True), - (MpiExecLauncher, True), - (MpiRunLauncher, True), - (SrunLauncher, True), - (SrunMPILauncher, True), - (AprunLauncher, True), - (JsrunLauncher, True), -]) -def test_launcher_script_execution(launcher_class, creates_cmd_script): - """Test that launcher scripts can be generated and executed with mocked commands.""" - - test_command = "echo 'Hello from worker'" - launcher = launcher_class() - name = launcher_class.__name__ - - with tempfile.TemporaryDirectory() as tmpdir: - tmppath = Path(tmpdir) - mock_bin = tmppath / "mock_bin" - mock_bin.mkdir() - - # Generate the launcher script - script_dir = tmppath / f"scripts_{name}" - script_dir.mkdir(exist_ok=True) - - # Call the launcher - launcher_script = launcher( - command=test_command, - tasks_per_node=2, - nodes_per_block=1, - script_dir=str(script_dir) - ) - - # Verify we got a string back - assert isinstance(launcher_script, str), f"{name} should return a string" - assert len(launcher_script) > 0, f"{name} should return non-empty script" - - # Create a test script with mocks - test_script = tmppath / f"test_{name}.sh" - full_script = create_mock_script(launcher_script, mock_bin) - test_script.write_text(full_script) - test_script.chmod(0o755) - - # Set environment variables that scripts might expect - env = os.environ.copy() - env['JOBNAME'] = f'test_job_{name}' - env['SLURM_JOB_NAME'] = f'test_job_{name}' - env['SLURM_CPUS_ON_NODE'] = '4' # Mock CPU count for SLURM launchers - env['SLURM_JOB_NUM_NODES'] = '2' # Mock node count for SLURM launchers - env['PBS_NODEFILE'] = '/dev/null' - - # Execute the script - result = subprocess.run( - ['bash', str(test_script)], - capture_output=True, - text=True, - timeout=10, - env=env, - cwd=tmpdir - ) - - # Check that script executed successfully - assert result.returncode == 0, ( - f"{name} script failed with returncode {result.returncode}\n" - f"STDOUT: {result.stdout}\n" - f"STDERR: {result.stderr}\n" - f"SCRIPT:\n{full_script}" - ) - - # Verify the command was actually executed by checking output - assert "Hello from worker" in result.stdout, ( - f"{name} command did not produce expected output\n" - f"Expected 'Hello from worker' in stdout\n" - f"STDOUT: {result.stdout}\n" - f"STDERR: {result.stderr}" - ) - - # Verify command script was created if launcher creates one - if creates_cmd_script: - cmd_scripts = list(script_dir.glob("cmd_*.sh")) - assert len(cmd_scripts) > 0, f"{name} should create a cmd_*.sh file in {script_dir}" - - -@pytest.mark.local -@pytest.mark.parametrize("launcher_class,uses_script_dir", [ - (GnuParallelLauncher, True), - (MpiExecLauncher, True), - (MpiRunLauncher, True), - (SrunLauncher, True), - (SrunMPILauncher, True), - (AprunLauncher, True), - (JsrunLauncher, True), -]) -def test_launcher_script_dir_parameter(launcher_class, uses_script_dir): - """Test that script_dir parameter is properly set in generated scripts.""" - - with tempfile.TemporaryDirectory() as tmpdir: - script_dir = Path(tmpdir) / "my_custom_scripts" - script_dir.mkdir() - - launcher = launcher_class() - script = launcher( - command="echo test", - tasks_per_node=1, - nodes_per_block=1, - script_dir=str(script_dir) - ) - - # Verify the exact SCRIPT_DIR assignment appears in the script - expected_line = f'SCRIPT_DIR="{script_dir}"' - assert expected_line in script, ( - f"{launcher_class.__name__} should set SCRIPT_DIR to the provided path\n" - f"Expected line: {expected_line}\n" - f"Generated script:\n{script}" - ) - - # Verify the directory check logic is present - assert "[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR=\"\"" in script, ( - f"{launcher_class.__name__} should include directory existence check" - ) - - # Verify the script uses the conditional expansion pattern - assert "${SCRIPT_DIR:+$SCRIPT_DIR/}" in script, ( - f"{launcher_class.__name__} should use bash parameter expansion for script_dir" - ) - - -@pytest.mark.local -@pytest.mark.parametrize("launcher_class,uses_script_dir", [ - (SimpleLauncher, False), - (SingleNodeLauncher, False), - (GnuParallelLauncher, True), - (MpiExecLauncher, True), - (MpiRunLauncher, True), - (SrunLauncher, True), - (SrunMPILauncher, True), - (AprunLauncher, True), - (JsrunLauncher, True), -]) -def test_launcher_with_empty_script_dir(launcher_class, uses_script_dir): - """Test that launchers handle empty script_dir gracefully.""" - - launcher = launcher_class() - script = launcher( - command="echo test", - tasks_per_node=1, - nodes_per_block=1, - script_dir="" - ) - - # Should still generate valid script - assert isinstance(script, str) - assert len(script) > 0 - - # Launchers that create command scripts should properly handle empty script_dir - if uses_script_dir: - # Verify SCRIPT_DIR is set to empty string - assert 'SCRIPT_DIR=""' in script, ( - f"{launcher_class.__name__} should set SCRIPT_DIR to empty string when script_dir is empty\n" - f"Generated script:\n{script}" - ) - - # Verify the directory check logic is present - assert "[[ -d $SCRIPT_DIR ]] || SCRIPT_DIR=\"\"" in script, ( - f"{launcher_class.__name__} should include directory existence check" - ) - - # Verify the parameter expansion pattern is used - assert "${SCRIPT_DIR:+$SCRIPT_DIR/}" in script, ( - f"{launcher_class.__name__} should use bash parameter expansion for script_dir" - ) - - -@pytest.mark.local -@pytest.mark.parametrize("launcher_class", [ - SimpleLauncher, - SingleNodeLauncher, - GnuParallelLauncher, - MpiExecLauncher, - MpiRunLauncher, - SrunLauncher, - SrunMPILauncher, - AprunLauncher, - JsrunLauncher, -]) -def test_launcher_bash_syntax(launcher_class): - """Test that generated launcher scripts have valid bash syntax.""" - - test_command = "echo test" - launcher = launcher_class() - script = launcher(test_command, 1, 1, "") - - # Check bash syntax without executing - result = subprocess.run( - ['bash', '-n'], # -n flag checks syntax only - input=script, - capture_output=True, - text=True - ) - - launcher_name = launcher_class.__name__ - assert result.returncode == 0, ( - f"Syntax error in {launcher_name}\n" - f"Error: {result.stderr}\n" - f"Script:\n{script}" - ) - - -@pytest.mark.local -def test_wrapped_launcher_bash_syntax(): - """Test that WrappedLauncher generates valid bash syntax.""" - - test_command = "echo test" - launcher = WrappedLauncher('echo') - script = launcher(test_command, 1, 1, "") - - # Check bash syntax without executing - result = subprocess.run( - ['bash', '-n'], # -n flag checks syntax only - input=script, - capture_output=True, - text=True - ) - - assert result.returncode == 0, ( - f"Syntax error in WrappedLauncher\n" - f"Error: {result.stderr}\n" - f"Script:\n{script}" - ) diff --git a/parsl/parsl/tests/unit/test_usage_tracking.py b/parsl/parsl/tests/unit/test_usage_tracking.py deleted file mode 100644 index 1581249c..00000000 --- a/parsl/parsl/tests/unit/test_usage_tracking.py +++ /dev/null @@ -1,66 +0,0 @@ -"""Test usage_tracking values.""" - -import pytest - -import parsl -from parsl.config import Config -from parsl.errors import ConfigurationError - - -@pytest.mark.local -def test_config_load(): - """Test loading a config with usage tracking.""" - with parsl.load(Config(usage_tracking=3)): - pass - parsl.clear() - - -@pytest.mark.local -@pytest.mark.parametrize("level", (0, 1, 2, 3, False, True)) -def test_valid(level): - """Test valid usage_tracking values.""" - Config(usage_tracking=level) - assert Config(usage_tracking=level).usage_tracking == level - - -@pytest.mark.local -@pytest.mark.parametrize("level", (12, 1000, -1)) -def test_invalid_values(level): - """Test invalid usage_tracking values.""" - with pytest.raises(ConfigurationError): - Config(usage_tracking=level) - - -@pytest.mark.local -@pytest.mark.parametrize("level", ("abcd", None, bytes(1), 1.0, 1j, object())) -def test_invalid_types(level): - """Test invalid usage_tracking types.""" - with pytest.raises(Exception) as ex: - Config(usage_tracking=level) - - # with typeguard 4.x this is TypeCheckError, - # with typeguard 2.x this is TypeError - # we can't instantiate TypeCheckError if we're in typeguard 2.x environment - # because it does not exist... so check name using strings. - assert ex.type.__name__ in ["TypeCheckError", "TypeError"] - - -@pytest.mark.local -def test_valid_project_name(): - """Test valid project_name.""" - assert ( - Config( - usage_tracking=3, - project_name="unit-test", - ).project_name == "unit-test" - ) - - -@pytest.mark.local -@pytest.mark.parametrize("name", (1, 1.0, True, object())) -def test_invalid_project_name(name): - """Test invalid project_name.""" - with pytest.raises(Exception) as ex: - Config(usage_tracking=3, project_name=name) - - assert ex.type.__name__ in ["TypeCheckError", "TypeError"] diff --git a/parsl/parsl/tests/utils.py b/parsl/parsl/tests/utils.py deleted file mode 100644 index 110758c2..00000000 --- a/parsl/parsl/tests/utils.py +++ /dev/null @@ -1,5 +0,0 @@ -import os - - -def get_config(filename): - return os.path.join(os.path.dirname(__file__), 'configs', filename) diff --git a/parsl/parsl/usage_tracking/__init__.py b/parsl/parsl/usage_tracking/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/parsl/parsl/usage_tracking/api.py b/parsl/parsl/usage_tracking/api.py deleted file mode 100644 index 2d8b0811..00000000 --- a/parsl/parsl/usage_tracking/api.py +++ /dev/null @@ -1,65 +0,0 @@ -import inspect -from abc import abstractmethod -from functools import singledispatch -from typing import Any, List, Sequence - -from parsl.utils import RepresentationMixin - -# Traverse the configuration hierarchy, returning a JSON component -# for each one. Configuration components which implement -# RepresentationMixin will be in the right form for inspecting -# object attributes. Configuration components which are lists or tuples -# are traversed in sequence. Other types default to reporting no -# usage information. - - -@singledispatch -def get_parsl_usage(obj) -> List[Any]: - return [] - - -@get_parsl_usage.register -def get_parsl_usage_representation_mixin(obj: RepresentationMixin) -> List[Any]: - t = type(obj) - qualified_name = t.__module__ + "." + t.__name__ - - # me can contain anything that can be rendered as JSON - me: List[Any] = [] - - if isinstance(obj, UsageInformation): - # report rich usage information for this component - attrs = {'c': qualified_name} - attrs.update(obj.get_usage_information()) - me = [attrs] - else: - # report the class name of this component - me = [qualified_name] - - # unwrap typeguard-style unwrapping - init: Any = type(obj).__init__ - if hasattr(init, '__wrapped__'): - init = init.__wrapped__ - - argspec = inspect.getfullargspec(init) - - for arg in argspec.args[1:]: # skip first arg, self - arg_value = getattr(obj, arg) - d = get_parsl_usage(arg_value) - me += d - - return me - - -@get_parsl_usage.register(list) -@get_parsl_usage.register(tuple) -def get_parsl_usage_sequence(obj: Sequence) -> List[Any]: - result = [] - for v in obj: - result += get_parsl_usage(v) - return result - - -class UsageInformation: - @abstractmethod - def get_usage_information(self) -> dict: - pass diff --git a/parsl/parsl/usage_tracking/levels.py b/parsl/parsl/usage_tracking/levels.py deleted file mode 100644 index a772220c..00000000 --- a/parsl/parsl/usage_tracking/levels.py +++ /dev/null @@ -1,6 +0,0 @@ -"""Module for defining the usage tracking levels.""" - -DISABLED = 0 # Tracking is disabled -LEVEL_1 = 1 # Share info about Parsl version, Python version, platform -LEVEL_2 = 2 # Share info about config + level 1 -LEVEL_3 = 3 # Share info about app count, app fails, execution time + level 2 diff --git a/parsl/parsl/usage_tracking/usage.py b/parsl/parsl/usage_tracking/usage.py deleted file mode 100644 index 5bf79268..00000000 --- a/parsl/parsl/usage_tracking/usage.py +++ /dev/null @@ -1,240 +0,0 @@ -import json -import logging -import platform -import socket -import sys -import time -import uuid - -from parsl.dataflow.states import States -from parsl.errors import ConfigurationError -from parsl.multiprocessing import SpawnProcess -from parsl.usage_tracking.api import get_parsl_usage -from parsl.usage_tracking.levels import DISABLED as USAGE_TRACKING_DISABLED -from parsl.usage_tracking.levels import LEVEL_3 as USAGE_TRACKING_LEVEL_3 -from parsl.utils import setproctitle -from parsl.version import VERSION as PARSL_VERSION - -logger = logging.getLogger(__name__) - -from typing import Callable - -from typing_extensions import ParamSpec - -# protocol version byte: when (for example) compression parameters are changed -# that cannot be inferred from the compressed message itself, this version -# ID needs to imply those parameters. - -# Earlier protocol versions: b'{' - the original pure-JSON protocol pre-March 2024 -PROTOCOL_VERSION = b'1' - -P = ParamSpec("P") - - -def start_udp_messenger_process(*args, **kwargs): - proc = SpawnProcess(target=udp_messenger, args=args, kwargs=kwargs, name="Usage-Tracking") - proc.start() - return proc - - -def udp_messenger(domain_name: str, UDP_PORT: int, sock_timeout: int, message: bytes) -> None: - """Send UDP messages to usage tracker asynchronously - - This multiprocessing based messenger was written to overcome the limitations - of signalling/terminating a thread that is blocked on a system call. - - Args: - - domain_name (str) : Domain name string - - UDP_PORT (int) : UDP port to send out on - - sock_timeout (int) : Socket timeout - """ - setproctitle("parsl: Usage tracking") - - try: - UDP_IP = socket.gethostbyname(domain_name) - - sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # UDP - sock.settimeout(sock_timeout) - sock.sendto(message, (UDP_IP, UDP_PORT)) - sock.close() - - except socket.timeout: - logger.debug("Failed to send usage tracking data: socket timeout") - except OSError as e: - logger.debug("Failed to send usage tracking data: OSError: {}".format(e)) - except Exception as e: - logger.debug("Failed to send usage tracking data: Exception: {}".format(e)) - - -class UsageTracker: - """Usage Tracking for Parsl. - - The server for this is here: https://github.com/Parsl/parsl_tracking - This issue captures the discussion that went into functionality - implemented here: https://github.com/Parsl/parsl/issues/34 - - """ - - def __init__(self, dfk, port=50077, - domain_name='tracking.parsl-project.org'): - """Initialize usage tracking unless the user has opted-out. - - We will try to resolve the hostname specified in kwarg:domain_name - and if that fails attempt to use the kwarg:ip. Determining the - IP and sending message happens in an asynchronous processs to avoid - slowing down DFK initialization. - - Tracks usage stats by inspecting the internal state of the dfk. - - Args: - - dfk (DFK object) : Data Flow Kernel object - - KWargs: - - port (int) : Port number, Default:50077 - - domain_name (string) : Domain name, will override IP - Default: tracking.parsl-project.org - """ - - self.domain_name = domain_name - # The sock timeout will only apply to UDP send and not domain resolution - self.sock_timeout = 5 - self.UDP_PORT = port - self.procs = [] - self.dfk = dfk - self.config = self.dfk.config - self.correlator_uuid = str(uuid.uuid4()) - self.parsl_version = PARSL_VERSION - self.python_version = "{}.{}.{}".format(sys.version_info.major, - sys.version_info.minor, - sys.version_info.micro) - self.tracking_level = self.check_tracking_level() - self.project_name = self.config.project_name - self.start_time = None - logger.debug("Tracking level: {}".format(self.tracking_level)) - - def check_tracking_level(self) -> int: - """Check if tracking is enabled and return level. - - Checks usage_tracking in Config - - Possible values: [True, False, 0, 1, 2, 3] - - True/False values are treated as Level 1/Level 0 respectively. - - Returns: int - - 0 : Tracking is disabled - - 1 : Tracking is enabled with level 1 - Share info about Parsl version, Python version, platform - - 2 : Tracking is enabled with level 2 - Share info about config + level 1 - - 3 : Tracking is enabled with level 3 - Share info about app count, app fails, execution time + level 2 - """ - if not USAGE_TRACKING_DISABLED <= self.config.usage_tracking <= USAGE_TRACKING_LEVEL_3: - raise ConfigurationError( - f"Usage Tracking values must be 0, 1, 2, or 3 and not {self.config.usage_tracking}" - ) - - return self.config.usage_tracking - - def construct_start_message(self) -> bytes: - """Collect preliminary run info at the start of the DFK. - - Returns : - - Message dict dumped as json string, ready for UDP - """ - message = {'correlator': self.correlator_uuid, - 'parsl_v': self.parsl_version, - 'python_v': self.python_version, - 'platform.system': platform.system(), - 'tracking_level': int(self.tracking_level)} - - if self.project_name: - message['project_name'] = self.project_name - - if self.tracking_level >= 2: - message['components'] = get_parsl_usage(self.dfk._config) - - if self.tracking_level == 3: - self.start_time = int(time.time()) - message['start'] = self.start_time - - logger.debug(f"Usage tracking start message: {message}") - - return self.encode_message(message) - - def construct_end_message(self) -> bytes: - """Collect the final run information at the time of DFK cleanup. - This is only called if tracking level is 3. - - Returns: - - Message dict dumped as json string, ready for UDP - """ - end_time = int(time.time()) - - app_count = self.dfk.task_count - - app_fails = self.dfk.task_state_counts[States.failed] + self.dfk.task_state_counts[States.dep_fail] - - # the DFK is tangled into this code as a god-object, so it is - # handled separately from the usual traversal code, but presenting - # the same protocol-level report. - dfk_component = {'c': type(self.dfk).__module__ + "." + type(self.dfk).__name__, - 'app_count': app_count, - 'app_fails': app_fails} - - message = {'correlator': self.correlator_uuid, - 'end': end_time, - 'execution_time': end_time - self.start_time, - 'components': [dfk_component] + get_parsl_usage(self.dfk._config)} - - if self.project_name: - message['project_name'] = self.project_name - - logger.debug(f"Usage tracking end message (unencoded): {message}") - - return self.encode_message(message) - - def encode_message(self, obj): - return PROTOCOL_VERSION + json.dumps(obj).encode() - - def send_UDP_message(self, message: bytes) -> None: - """Send UDP message.""" - try: - proc = start_udp_messenger_process( - self.domain_name, self.UDP_PORT, self.sock_timeout, message) - self.procs.append(proc) - except Exception as e: - logger.debug("Usage tracking failed: {}".format(e)) - - def send_start_message(self) -> None: - if self.tracking_level: - logger.info("Sending start message for usage tracking") - self.start_time = time.time() - message = self.construct_start_message() - self.send_UDP_message(message) - - def send_end_message(self) -> None: - if self.tracking_level == 3: - logger.info("Sending end message for usage tracking") - message = self.construct_end_message() - self.send_UDP_message(message) - - def close(self, timeout: float = 10.0) -> None: - """First give each process one timeout period to finish what it is - doing, then kill it (SIGKILL). There's no softer SIGTERM step, - because that adds one join period of delay for what is almost - definitely either: going to behave broadly the same as to SIGKILL, - or won't respond to SIGTERM. - """ - if self.tracking_level: - logger.info("Closing usage tracking") - - for proc in self.procs: - logger.debug("Joining usage tracking process %s", proc) - proc.join(timeout=timeout) - if proc.is_alive(): - logger.warning("Usage tracking process did not end itself; sending SIGKILL") - proc.kill() - proc.join(timeout=timeout) - - proc.close() diff --git a/parsl/parsl/utils.py b/parsl/parsl/utils.py deleted file mode 100644 index 0b727c19..00000000 --- a/parsl/parsl/utils.py +++ /dev/null @@ -1,522 +0,0 @@ -import inspect -import logging -import os -import re -import shlex -import subprocess -import threading -import time -from contextlib import contextmanager -import platform -from types import TracebackType -from typing import ( - IO, - Any, - Callable, - Dict, - Generator, - List, - Optional, - Sequence, - Tuple, - Union, -) - -import typeguard -from typing_extensions import Type - -import parsl -from parsl.app.errors import BadStdStreamFile -from parsl.version import VERSION - -try: - import setproctitle as setproctitle_module -except ImportError: - _setproctitle_enabled = False -else: - _setproctitle_enabled = True - - -logger = logging.getLogger(__name__) - - -@typeguard.typechecked -def get_version() -> str: - version = parsl.__version__ # type: str - work_tree = os.path.dirname(os.path.dirname(__file__)) - git_dir = os.path.join(work_tree, '.git') - if os.path.exists(git_dir): - env = {'GIT_WORK_TREE': work_tree, 'GIT_DIR': git_dir} - try: - cmd = shlex.split('git rev-parse --short HEAD') - head = subprocess.check_output(cmd, env=env).strip().decode('utf-8') - diff = subprocess.check_output(shlex.split('git diff HEAD'), env=env) - status = 'dirty' if diff else 'clean' - version = f'{VERSION}-{head}-{status}' - except Exception: - pass - - return version - - -@typeguard.typechecked -def get_all_checkpoints(rundir: str = "runinfo") -> Sequence[str]: - """Finds the checkpoints from all runs in the rundir. - - Kwargs: - - rundir(str) : Path to the runinfo directory - - Returns: - - a list suitable for the checkpoint_files parameter of `Config` - - """ - - if not os.path.isdir(rundir): - return [] - - dirs = sorted(os.listdir(rundir)) - - checkpoints = [] - - for runid in dirs: - - checkpoint = os.path.abspath(f'{rundir}/{runid}/checkpoint') - - if os.path.isdir(checkpoint): - checkpoints.append(checkpoint) - - return checkpoints - - -@typeguard.typechecked -def get_last_checkpoint(rundir: str = "runinfo") -> Sequence[str]: - """Finds the checkpoint from the last run, if one exists. - - Note that checkpoints are incremental, and this helper will not find - previous checkpoints from earlier than the most recent run. If you - want that behaviour, see `get_all_checkpoints`. - - Kwargs: - - rundir(str) : Path to the runinfo directory - - Returns: - - a list suitable for the checkpoint_files parameter of `Config`, - with 0 or 1 elements - - """ - if not os.path.isdir(rundir): - return [] - - dirs = os.listdir(rundir) - - logger.debug("Rundir candidates: %s", dirs) - - # dirs_parseable is a list of number-like directory names, - # which is the rundir subdirectories, excluding non-numerical - # files such as the monitoring database. - dirs_parseable = [d for d in dirs if d.isdigit()] - logger.debug("Parseable candidates: %s", dirs_parseable) - - # dirs_sorted will be a list containing the largest numbered directories - # first (parsed as an int, not lexically sorted, to accomodate different - # length run directory names that can happen after 1000 runs) - dirs_sorted = sorted(dirs_parseable, key=int, reverse=True) - - # use a generator so that the resulting co-routine will only do filesystem - # operations on paths until it finds the first one with a checkpoint - which - # is likely to be the first one. - last_path = next(([path] for d in dirs_sorted - if os.path.isdir(path := os.path.abspath(f'{rundir}/{d}/checkpoint'))), - []) - - logger.debug("Selected path: %s", last_path) - return last_path - - -@typeguard.typechecked -def get_std_fname_mode( - fdname: str, - stdfspec: Union[os.PathLike, str, Tuple[str, str], Tuple[os.PathLike, str]] -) -> Tuple[str, str]: - import parsl.app.errors as pe - if isinstance(stdfspec, (str, os.PathLike)): - fname = stdfspec - mode = 'a+' - elif isinstance(stdfspec, tuple): - if len(stdfspec) != 2: - # this is annotated as unreachable because the type annotation says - # it cannot be reached. Earlier versions of typeguard did not enforce - # that type annotation at runtime, though, and the parameters to this - # function come from the user. - # When typeguard lower bound is raised to around version 4, this - # unreachable can be removed. - msg = (f"std descriptor {fdname} has incorrect tuple length " # type: ignore[unreachable] - f"{len(stdfspec)}") - raise pe.BadStdStreamFile(msg) - fname, mode = stdfspec - - path = os.fspath(fname) - - if isinstance(path, str): - return path, mode - elif isinstance(path, bytes): - return path.decode(), mode - else: - raise BadStdStreamFile(f"fname has invalid type {type(path)}") - - -@contextmanager -def wait_for_file(path: str, seconds: int = 10) -> Generator[None, None, None]: - for _ in range(0, int(seconds * 100)): - time.sleep(seconds / 100.) - if os.path.exists(path): - break - yield - - -@contextmanager -def time_limited_open(path: str, mode: str, seconds: int = 1) -> Generator[IO, None, None]: - with wait_for_file(path, seconds): - logger.debug("wait_for_file yielded") - f = open(path, mode) - yield f - f.close() - - -def wtime_to_minutes(time_string: str) -> int: - ''' wtime_to_minutes - - Convert standard wallclock time string to minutes. - - Args: - - Time_string in HH:MM:SS format - - Returns: - (int) minutes - - ''' - hours, mins, seconds = time_string.split(':') - total_mins = int(hours) * 60 + int(mins) - if total_mins < 1: - msg = (f"Time string '{time_string}' parsed to {total_mins} minutes, " - f"less than 1") - logger.warning(msg) - return total_mins - - -class RepresentationMixin: - """A mixin class for adding a __repr__ method. - - The __repr__ method will return a string equivalent to the code used to instantiate - the child class, with any defaults included explicitly. The __max_width__ class variable - controls the maximum width of the representation string. If this width is exceeded, - the representation string will be split up, with one argument or keyword argument per line. - - Any arguments or keyword arguments in the constructor must be defined as attributes, or - an AttributeError will be raised. - - Examples - -------- - >>> from parsl.utils import RepresentationMixin - >>> class Foo(RepresentationMixin): - def __init__(self, first, second, third='three', fourth='fourth'): - self.first = first - self.second = second - self.third = third - self.fourth = fourth - >>> bar = Foo(1, 'two', fourth='baz') - >>> bar - Foo(1, 'two', third='three', fourth='baz') - """ - __max_width__ = 80 - - _validate_repr = False - - def __repr__(self) -> str: - init = self.__init__ # type: ignore[misc] - - # This test looks for a single layer of wrapping performed by - # functools.update_wrapper, commonly used in decorators. This will - # allow RepresentationMixin to see through a single such decorator - # applied to the __init__ method of a class, and find the underlying - # arguments. It will not see through multiple layers of such - # decorators, or cope with other decorators which do not use - # functools.update_wrapper. - - if hasattr(init, '__wrapped__'): - init = init.__wrapped__ - - argspec = inspect.getfullargspec(init) - if len(argspec.args) > 1 and argspec.defaults is not None: - defaults = dict(zip(reversed(argspec.args), reversed(argspec.defaults))) - else: - defaults = {} - - if self._validate_repr: - for arg in argspec.args[1:]: - if not hasattr(self, arg): - template = (f'class {self.__class__.__name__} uses {arg} in the' - f' constructor, but does not define it as an ' - f'attribute') - raise AttributeError(template) - - default = "" - - if len(defaults) != 0: - args = [getattr(self, a, default) for a in argspec.args[1:-len(defaults)]] - else: - args = [getattr(self, a, default) for a in argspec.args[1:]] - kwargs = {key: getattr(self, key, default) for key in defaults} - - kwonlyargs = {key: getattr(self, key, default) for key in argspec.kwonlyargs} - kwargs.update(kwonlyargs) - - def assemble_multiline(args: List[str], kwargs: Dict[str, object]) -> str: - def indent(text: str) -> str: - lines = text.splitlines() - if len(lines) <= 1: - return text - return "\n".join(" " + line for line in lines).strip() - args = [f"\n {indent(repr(a))}," for a in args] - kwargsl = [f"\n {k}={indent(repr(v))}" for k, v in - sorted(kwargs.items())] - - info = "".join(args) + ", ".join(kwargsl) - return self.__class__.__name__ + f"({info}\n)" - - def assemble_line(args: List[str], kwargs: Dict[str, object]) -> str: - kwargsl = [f'{k}={repr(v)}' for k, v in sorted(kwargs.items())] - - info = ", ".join([repr(a) for a in args] + kwargsl) - return self.__class__.__name__ + f"({info})" - - if len(assemble_line(args, kwargs)) <= self.__class__.__max_width__: - return assemble_line(args, kwargs) - else: - return assemble_multiline(args, kwargs) - - -class AtomicIDCounter: - """A class to allocate counter-style IDs, in a thread-safe way. - """ - - def __init__(self) -> None: - self.count = 0 - self.lock = threading.Lock() - - def get_id(self) -> int: - with self.lock: - new_id = self.count - self.count += 1 - return new_id - - -def setproctitle(title: str) -> None: - if _setproctitle_enabled: - setproctitle_module.setproctitle(title) - else: - logger.warn(f"setproctitle not enabled for process {title}") - - -class Timer: - """This class will make a callback periodically, with a period - specified by the interval parameter. - - This is based on the following logic : - - .. code-block:: none - - - BEGIN (INTERVAL, THRESHOLD, callback) : - start = current_time() - - while (current_time()-start < INTERVAL) : - wait() - break - - callback() - - """ - - def __init__(self, callback: Callable, *args: Any, interval: Union[float, int] = 5, name: Optional[str] = None) -> None: - """Initialize the Timer object. - We start the timer thread here - - KWargs: - - interval (int or float) : number of seconds between callback events - - name (str) : a base name to use when naming the started thread - """ - - self.interval = max(0, interval) - self.cb_args = args - self.callback = callback - - self._kill_event = threading.Event() - tname = f"Timer-Thread-{id(self)}" - if name: - tname = f"{name}-{tname}" - self._thread = threading.Thread( - target=self._wake_up_timer, name=tname, daemon=True - ) - self._thread.start() - - def _wake_up_timer(self) -> None: - self.make_callback() - - while not self._kill_event.wait(self.interval): - self.make_callback() - - def make_callback(self) -> None: - """Makes the callback and resets the timer. - """ - try: - self.callback(*self.cb_args) - except Exception: - logger.error("Callback threw an exception - logging and proceeding anyway", exc_info=True) - - def close(self, timeout: Optional[float] = None) -> None: - """Merge the threads and terminate. - """ - self._kill_event.set() - self._thread.join(timeout=timeout) - - -class AutoCancelTimer(threading.Timer): - """ - Extend threading.Timer for use as a context manager - - Example: - - with AutoCancelTimer(delay, your_callback): - some_func() - - If `some_func()` returns before the delay is up, the timer will - be cancelled. - """ - def __enter__(self) -> "AutoCancelTimer": - self.start() - return self - - def __exit__( - self, - exc_type: Optional[Type[BaseException]], - exc_val: Optional[BaseException], - exc_tb: Optional[TracebackType] - ) -> None: - self.cancel() - - -def sanitize_dns_label_rfc1123(raw_string: str) -> str: - """Convert input string to a valid RFC 1123 DNS label. - - Parameters - ---------- - raw_string : str - String to sanitize. - - Returns - ------- - str - Sanitized string. - - Raises - ------ - ValueError - If the string is empty after sanitization. - """ - # Convert to lowercase and replace non-alphanumeric characters with hyphen - sanitized = re.sub(r'[^a-z0-9]', '-', raw_string.lower()) - - # Remove consecutive hyphens - sanitized = re.sub(r'-+', '-', sanitized) - - # DNS label cannot exceed 63 characters - sanitized = sanitized[:63] - - # Strip after trimming to avoid trailing hyphens - sanitized = sanitized.strip("-") - - if not sanitized: - raise ValueError(f"Sanitized DNS label is empty for input '{raw_string}'") - - return sanitized - - -def sanitize_dns_subdomain_rfc1123(raw_string: str) -> str: - """Convert input string to a valid RFC 1123 DNS subdomain. - - Parameters - ---------- - raw_string : str - String to sanitize. - - Returns - ------- - str - Sanitized string. - - Raises - ------ - ValueError - If the string is empty after sanitization. - """ - segments = raw_string.split('.') - - sanitized_segments = [] - for segment in segments: - if not segment: - continue - sanitized_segment = sanitize_dns_label_rfc1123(segment) - sanitized_segments.append(sanitized_segment) - - sanitized = '.'.join(sanitized_segments) - - # DNS subdomain cannot exceed 253 characters - sanitized = sanitized[:253] - - # Strip after trimming to avoid trailing dots or hyphens - sanitized = sanitized.strip(".-") - - if not sanitized: - raise ValueError(f"Sanitized DNS subdomain is empty for input '{raw_string}'") - - return sanitized - - -def execute_wait(cmd: str, walltime: Optional[int] = None) -> Tuple[int, str, str]: - ''' Synchronously execute a commandline string on the shell. - - Args: - - cmd (string) : Commandline string to execute - - walltime (int) : walltime in seconds - - Returns: - - retcode : Return code from the execution - - stdout : stdout string - - stderr : stderr string - ''' - try: - logger.debug("Creating process with command '%s'", cmd) - if platform.system() == "Windows": - kwargs = dict(creationflags=subprocess.CREATE_NEW_PROCESS_GROUP) - else: - kwargs = dict(preexec_fn=os.setpgrp) - proc = subprocess.Popen( - cmd, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - shell=True, - **kwargs - ) - logger.debug("Created process with pid %s. Performing communicate", proc.pid) - (stdout, stderr) = proc.communicate(timeout=walltime) - retcode = proc.returncode - logger.debug("Process %s returned %s", proc.pid, proc.returncode) - - except Exception: - logger.exception(f"Execution of command failed:\n{cmd}") - raise - else: - logger.debug("Execution of command in process %s completed normally", proc.pid) - - return (retcode, stdout.decode("utf-8"), stderr.decode("utf-8")) diff --git a/parsl/parsl/version.py b/parsl/parsl/version.py deleted file mode 100644 index 0ba4a3c9..00000000 --- a/parsl/parsl/version.py +++ /dev/null @@ -1,3 +0,0 @@ -"""Set module version. -""" -VERSION = '1.3.0-dev' diff --git a/parsl/requirements.txt b/parsl/requirements.txt deleted file mode 100644 index 8d3832ed..00000000 --- a/parsl/requirements.txt +++ /dev/null @@ -1,17 +0,0 @@ -pyzmq>=17.1.2 - -# typeguard uses semantic versioning. -# major v3 was a shortlived version that was rapidly replaced by major v4, -# so this specification excludes it. -# v2 and v4 are both seen in the wild with users in complex deployments. -typeguard>=2.10,!=3.*,<5 - - -typing-extensions>=4.6,<5 -dill -tblib -requests -sortedcontainers -psutil>=5.5.1 -setproctitle -filelock>=3.13,<4 diff --git a/parsl/setup.py b/parsl/setup.py deleted file mode 100755 index 6c7eb06e..00000000 --- a/parsl/setup.py +++ /dev/null @@ -1,99 +0,0 @@ -from setuptools import find_packages, setup - -with open('parsl/version.py') as f: - exec(f.read()) - -with open('requirements.txt') as f: - install_requires = f.readlines() - -extras_require = { - 'monitoring' : [ - # sqlalchemy does not use semantic versioning. - # see https://github.com/sqlalchemy/sqlalchemy/discussions/11391#discussioncomment-9472033 - 'sqlalchemy>=2,<2.1' - ], - 'visualization' : [ - # this pydot bound is copied from networkx's pyproject.toml, - # version 3.2 (aa2de1adecea09f7b86ff6093b212ca86f22b3ef), - # because networkx[extra] installs quite a lot of extra stuff - # that needs more OS dependencies in addition to pydot. - 'pydot>=1.4.2', - - 'networkx>=3.2,<3.3', - 'Flask>=1.0.2', - 'flask_sqlalchemy', - - # pandas uses "loose semantic versioning" - # https://pandas.pydata.org/docs/development/policies.html#version-policy - 'pandas<3,>=2.2', - - # plotly doesn't explicitly use semantic versioning, but their - # breakage behaviour is as if they do - 'plotly>=5.19,<7', - - 'python-daemon' - ], - 'aws' : ['boto3'], - 'kubernetes' : ['kubernetes'], - 'docs' : [ - 'ipython<=8.6.0', - 'nbsphinx', - 'sphinx>=7.4,<8', - 'sphinx_rtd_theme' - ], - 'google_cloud' : ['google-auth', 'google-api-python-client'], - 'gssapi' : ['python-gssapi'], - 'azure' : ['azure<=4', 'msrestazure'], - 'workqueue': ['work_queue'], - 'flux': ['pyyaml', 'cffi', 'jsonschema'], - 'proxystore': ['proxystore'], - 'radical-pilot': ['radical.pilot==1.90', 'radical.utils==1.90'], - 'globus_compute': ['globus_compute_sdk>=2.34.0'], - 'globus_transfer': ['globus-sdk'], - # Disabling psi-j since github direct links are not allowed by pypi - # 'psij': ['psi-j-parsl@git+https://github.com/ExaWorks/psi-j-parsl'] -} -extras_require['all'] = sum(extras_require.values(), []) - -setup( - name='parsl', - version=VERSION, - description='Simple data dependent workflows in Python', - long_description='Simple parallel workflows system for Python', - url='https://github.com/Parsl/parsl', - author='The Parsl Team', - author_email='parsl@googlegroups.com', - license='Apache 2.0', - download_url='https://github.com/Parsl/parsl/archive/{}.tar.gz'.format(VERSION), - include_package_data=True, - package_data={'parsl': ['py.typed']}, - packages=find_packages(), - python_requires=">=3.10.0", - install_requires=install_requires, - scripts = ['parsl/executors/high_throughput/process_worker_pool.py', - 'parsl/executors/high_throughput/interchange.py', - 'parsl/executors/workqueue/exec_parsl_function.py', - 'parsl/executors/workqueue/parsl_coprocess.py', - ], - - extras_require=extras_require, - classifiers=[ - # Maturity - 'Development Status :: 5 - Production/Stable', - # Intended audience - 'Intended Audience :: Developers', - # Licence, must match with licence above - 'License :: OSI Approved :: Apache Software License', - # Python versions supported - 'Programming Language :: Python :: 3.10', - 'Programming Language :: Python :: 3.11', - 'Programming Language :: Python :: 3.12', - ], - keywords=['Workflows', 'Scientific computing'], - entry_points={'console_scripts': - [ - 'parsl-globus-auth=parsl.data_provider.globus:cli_run', - 'parsl-visualize=parsl.monitoring.visualization.app:cli_run', - 'parsl-perf=parsl.benchmark.perf:cli_run', - ]} -) diff --git a/parsl/tag_and_release.sh b/parsl/tag_and_release.sh deleted file mode 100755 index 8fdfb34b..00000000 --- a/parsl/tag_and_release.sh +++ /dev/null @@ -1,67 +0,0 @@ -#!/bin/bash -set -euf -o pipefail - -create_tag () { - if [ -z "$1" ] - then - VERSION="unknown" - else - VERSION=$1 - fi - - PARSL_VERSION=$(python3 -c "import parsl; print(parsl.__version__)") - - if [[ $PARSL_VERSION == "$VERSION" ]] - then - echo "Version requested matches package version: $VERSION" - else - echo "[ERROR] Version mismatch. User request: '$VERSION' while package version is: '$PARSL_VERSION'" - exit 1 - fi - - echo "Creating tag" - git tag -a "$VERSION" -m "Parsl $VERSION" - - echo "Pushing tag" - git push origin --tags - -} - -package() { - - rm -f dist/* - - echo "=======================================================================" - echo "Starting clean builds" - echo "=======================================================================" - python3 setup.py sdist - python3 setup.py bdist_wheel - - echo "=======================================================================" - echo "Done with builds" - echo "=======================================================================" - - -} - -release () { - echo "=======================================================================" - echo "Push to PyPi. This will require your username and password" - echo "=======================================================================" - twine upload dist/* -} - -update_version () { - target_version=$VERSION - echo "Target version = $target_version" - cat << EOF > parsl/version.py -"""Set module version. - -Year.Month.Day[alpha/beta/..] -Alphas will be numbered like this -> 2024.12.10a0 -""" -VERSION = '$target_version' -EOF -} - -"$@" diff --git a/parsl/test-requirements.txt b/parsl/test-requirements.txt deleted file mode 100644 index 8fff0425..00000000 --- a/parsl/test-requirements.txt +++ /dev/null @@ -1,24 +0,0 @@ -flake8==6.1.0 -pandas -pytest>=7.4.0,<8 -pytest-cov -pytest-random-order -nbsphinx -sphinx_rtd_theme -mypy==1.18.2 -types-mock -types-python-dateutil -types-requests -mpi4py - -# sqlalchemy is needed for typechecking, so it's here -# as well as at runtime for optional monitoring execution -# (where it's specified in setup.py) -sqlalchemy>=2,<2.1 -sortedcontainers-stubs - -Sphinx==4.5.0 -twine -wheel -isort - diff --git a/pyproject.toml b/pyproject.toml index 79af46d3..7d0908fd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,7 @@ classifiers = [ dynamic = ["version"] dependencies = [ "retrying", - "parsl" + "git+https://github.com/emlys/parsl.git@windows-support" ] [project.optional-dependencies] diff --git a/tox.ini b/tox.ini index d267c5fc..5885b580 100644 --- a/tox.ini +++ b/tox.ini @@ -39,4 +39,3 @@ deps = pytest-cov rstcheck psutil: psutil - parsl[monitoring] From 28fba383beca513d463b632cda3f84515755d093 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 28 Oct 2026 10:55:50 -0700 Subject: [PATCH 105/112] reinstate linting --- .github/workflows/pythonapp.yml | 13 +++++++------ taskgraph/Task.py | 3 +++ 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index 2bc996ea..a95a2629 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -34,12 +34,13 @@ jobs: # https://github.com/python/importlib_metadata/issues/406#issuecomment-1264666048 pip install flake8 "importlib_metadata<5" rstcheck setuptools_scm pytest pytest-cov psutil parsl[monitoring] - # - name: Lint with flake8 - # run: | - # # stop the build if there are Python syntax errors or undefined names - # flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # # exit-zero treats all errors as warnings - # flake8 . --count --exit-zero --max-line-length=80 --statistics + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings + flake8 . --count --exit-zero --max-line-length=80 --statistics + - name: Install modified parsl run: | cd parsl && pip install . diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 58ee888e..3f1b2488 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -26,9 +26,12 @@ from parsl.concurrent import ParslPoolExecutor from parsl.configs.local_threads import config from parsl.dataflow.taskrecord import TaskRecord +from parsl.errors import ConfigurationError +from parsl.utils import Timer from parsl.utils import get_all_checkpoints from parsl.dataflow.memoization import BasicMemoizer, id_for_memo from parsl.dataflow.memoization import CheckpointCommand +from parsl.dataflow.errors import BadCheckpoint from parsl.executors import HighThroughputExecutor from parsl.executors.high_throughput import interchange from parsl.executors import ThreadPoolExecutor From a90e1c759b1d771e8f43cc5d5510ef483bc41a16 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 28 Oct 2026 10:57:22 -0700 Subject: [PATCH 106/112] undo changes to gha workflow --- .github/workflows/pythonapp.yml | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index a95a2629..3f135bdf 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -32,7 +32,7 @@ jobs: python -m pip install --upgrade pip # See this comment about the importlib_metadata constraint: # https://github.com/python/importlib_metadata/issues/406#issuecomment-1264666048 - pip install flake8 "importlib_metadata<5" rstcheck setuptools_scm pytest pytest-cov psutil parsl[monitoring] + pip install tox tox-gh-actions flake8 "importlib_metadata<5" rstcheck - name: Lint with flake8 run: | @@ -41,11 +41,6 @@ jobs: # exit-zero treats all errors as warnings flake8 . --count --exit-zero --max-line-length=80 --statistics - - name: Install modified parsl - run: | - cd parsl && pip install . - - name: Run tests run: | - pip install . - pytest tests + tox From 5f4cdf81875b42b4c8563f1941a7466465eefedc Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 28 Oct 2026 10:58:37 -0700 Subject: [PATCH 107/112] fix dependency syntax --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 7d0908fd..8deba3e3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,7 @@ classifiers = [ dynamic = ["version"] dependencies = [ "retrying", - "git+https://github.com/emlys/parsl.git@windows-support" + "parsl @ git+https://github.com/emlys/parsl.git@windows-support" ] [project.optional-dependencies] From 5df9f16a38a4ad8b16bd7a9c5237ea8db72358ce Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 28 Oct 2026 11:08:13 -0700 Subject: [PATCH 108/112] clean up; invoke script path relative to __file__' --- taskgraph/Task.py | 9 --------- taskgraph/__init__.py | 4 ++-- tests/test_task.py | 1 - 3 files changed, 2 insertions(+), 12 deletions(-) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index 3f1b2488..bab1da56 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -1,5 +1,4 @@ """Task graph framework.""" -import collections import concurrent.futures from concurrent.futures import Future from functools import lru_cache @@ -7,11 +6,9 @@ import inspect import logging import logging.handlers -import multiprocessing import os import pathlib import pickle -import pprint import sys import threading import time @@ -35,7 +32,6 @@ from parsl.executors import HighThroughputExecutor from parsl.executors.high_throughput import interchange from parsl.executors import ThreadPoolExecutor -from parsl.log_utils import set_file_logger from parsl.log_utils import DEFAULT_FORMAT from parsl.logconfigs.base import LogConfig @@ -241,7 +237,6 @@ def get_task_hash_info(func, args, kwargs, target_path_list): def get_task_hash(func, args, kwargs, target_path_list): hash_info = get_task_hash_info(func, args, kwargs, target_path_list) - print('hash info:', hash_info) argument_hash_string = ':'.join([ repr(hash_info[key]) for key in sorted(hash_info.keys())]) @@ -828,7 +823,6 @@ def __init__( label=self.executor_label, max_threads=2) else: - print('single threaded') self.executor_label = "threaded" executor = ThreadPoolExecutor( label=self.executor_label, @@ -1099,13 +1093,10 @@ def join(self, timeout=None): True if successful join, False if timed out. """ - print('join taskgraph') - print(self._task_hash_map) LOGGER.debug("joining taskgraph") try: LOGGER.debug("attempting to join threads") for task in self._task_hash_map.values(): - print('joining task', task.task_name) try: task.join(timeout=timeout) except concurrent.futures._base.TimeoutError: diff --git a/taskgraph/__init__.py b/taskgraph/__init__.py index 20cd0748..cc346160 100644 --- a/taskgraph/__init__.py +++ b/taskgraph/__init__.py @@ -1,8 +1,8 @@ """TaskGraph init module.""" -import sys from .Task import TaskGraph +from .Task import Task from .Task import _TASKGRAPH_DATABASE_FILENAME from .Task import __version__ -__all__ = ['__version__', 'TaskGraph', '_TASKGRAPH_DATABASE_FILENAME'] +__all__ = ['__version__', 'TaskGraph', 'Task', '_TASKGRAPH_DATABASE_FILENAME'] diff --git a/tests/test_task.py b/tests/test_task.py index 10be7866..e7a74472 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -32,7 +32,6 @@ def setUp(self): """Create temp workspace directory.""" # this lets us delete the workspace after its done no matter the # the rest result - print('user name:', os.environ.get('USERNAME')) self.workspace_dir = tempfile.mkdtemp() @retrying.retry( From 56ea12b11344f4ac7a7ecf092783b373eff5becb Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Wed, 28 Oct 2026 11:24:09 -0700 Subject: [PATCH 109/112] debugging --- taskgraph/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/taskgraph/__init__.py b/taskgraph/__init__.py index cc346160..20cd0748 100644 --- a/taskgraph/__init__.py +++ b/taskgraph/__init__.py @@ -1,8 +1,8 @@ """TaskGraph init module.""" +import sys from .Task import TaskGraph -from .Task import Task from .Task import _TASKGRAPH_DATABASE_FILENAME from .Task import __version__ -__all__ = ['__version__', 'TaskGraph', 'Task', '_TASKGRAPH_DATABASE_FILENAME'] +__all__ = ['__version__', 'TaskGraph', '_TASKGRAPH_DATABASE_FILENAME'] From 434ec9b08c6e0729a08660e72cf7b2dd27134974 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Thu, 29 Oct 2026 10:46:58 -0700 Subject: [PATCH 110/112] move parsl app definition to its own module --- taskgraph/Task.py | 25 ++----------------------- taskgraph/__init__.py | 7 ++----- 2 files changed, 4 insertions(+), 28 deletions(-) diff --git a/taskgraph/Task.py b/taskgraph/Task.py index bab1da56..f1a25d51 100644 --- a/taskgraph/Task.py +++ b/taskgraph/Task.py @@ -19,7 +19,6 @@ from importlib.metadata import version import parsl -from parsl.app.app import python_app from parsl.concurrent import ParslPoolExecutor from parsl.configs.local_threads import config from parsl.dataflow.taskrecord import TaskRecord @@ -34,6 +33,7 @@ from parsl.executors import ThreadPoolExecutor from parsl.log_utils import DEFAULT_FORMAT from parsl.logconfigs.base import LogConfig +from .parsl_app import create_parsl_app LOGGER = logging.getLogger(__name__) @@ -48,7 +48,6 @@ pass _VALID_PATH_TYPES = (str, pathlib.PurePath) -_TASKGRAPH_DATABASE_FILENAME = 'taskgraph_data.db' try: import psutil @@ -104,23 +103,6 @@ def listener_process(queue, configurer): traceback.print_exc(file=sys.stderr) -def create_parsl_app( - cache, executor_label, - ignore_for_cache=[], - parsl_resource_specification={}): - - @python_app( - cache=cache, - ignore_for_cache=ignore_for_cache, - executors=[executor_label]) - def wrap_func(func, *args, inputs=[], - parsl_resource_specification=parsl_resource_specification, - **kwargs): - return func(*args, **kwargs) - - return wrap_func - - def _null_func(): """Use when func=None on add_task.""" return None @@ -1245,11 +1227,8 @@ def __init__( ignore_for_cache=ignore_path_list, parsl_resource_specification=parsl_resource_specification) - def inner_wrap_func(func, *args, _taskgraph_task_info=None, **kwargs): - return func(*args, **kwargs) - self.future = wrap_func( - inner_wrap_func, func, *args, **kwargs, + func, *args, **kwargs, _taskgraph_task_info=self._taskgraph_task_info, inputs=[task.future for task in dependent_task_list]) diff --git a/taskgraph/__init__.py b/taskgraph/__init__.py index 20cd0748..c31c2aea 100644 --- a/taskgraph/__init__.py +++ b/taskgraph/__init__.py @@ -1,8 +1,5 @@ -"""TaskGraph init module.""" -import sys - +from .Task import Task from .Task import TaskGraph -from .Task import _TASKGRAPH_DATABASE_FILENAME from .Task import __version__ -__all__ = ['__version__', 'TaskGraph', '_TASKGRAPH_DATABASE_FILENAME'] +__all__ = ['__version__', 'Task', 'TaskGraph'] From d3da14392ac93edf9e6f043e296f94e2b54a1e2b Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Thu, 29 Oct 2026 11:19:31 -0700 Subject: [PATCH 111/112] revert unneeded changes to pyproject.toml --- pyproject.toml | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 8deba3e3..a3158b3e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,7 +26,7 @@ classifiers = [ # the version is provided dynamically by setuptools_scm dynamic = ["version"] dependencies = [ - "retrying", + "retrying>=1.3.0", "parsl @ git+https://github.com/emlys/parsl.git@windows-support" ] @@ -40,9 +40,6 @@ requires = [ ] build-backend = "setuptools.build_meta" -[tool.setuptools] -packages = ["taskgraph"] - [tool.setuptools_scm] version_scheme = "post-release" local_scheme = "node-and-date" From 4bd5ac35935db5986698ac5230235a084594aa77 Mon Sep 17 00:00:00 2001 From: Emily Soth Date: Thu, 29 Oct 2026 11:30:07 -0700 Subject: [PATCH 112/112] clean up tests --- tests/test_task.py | 70 +++++++++++++++------------------------------- 1 file changed, 22 insertions(+), 48 deletions(-) diff --git a/tests/test_task.py b/tests/test_task.py index e7a74472..8e75ee77 100644 --- a/tests/test_task.py +++ b/tests/test_task.py @@ -317,18 +317,6 @@ def test_task_chain_single_thread(self): task_graph = None self.assertEqual(result3, expected_result) - # # we should have 4 completed values in the database, 5 total but one - # # was a duplicate - # database_path = os.path.join( - # self.workspace_dir, taskgraph._TASKGRAPH_DATABASE_FILENAME) - # conn = sqlite3.connect(database_path) - # with conn: - # cursor = conn.cursor() - # cursor.execute("SELECT * FROM taskgraph_data") - # result = cursor.fetchall() - # conn.close() - # self.assertEqual(len(result), 4) - def test_task_broken_chain(self): """TaskGraph: Test a multiprocess chain with exception raised.""" task_graph = taskgraph.TaskGraph(self.workspace_dir, 4) @@ -377,32 +365,30 @@ def test_broken_task(self): def test_broken_task_chain(self): """TaskGraph: test dependent tasks fail on ancestor fail.""" task_graph = taskgraph.TaskGraph(self.workspace_dir, 4) - value = 5 list_len = 1000 - task_id = 0 target_path = os.path.join( - self.workspace_dir, '1000_%d.dat' % task_id) + self.workspace_dir, '1000.dat') normal_task = task_graph.add_task( func=_test_fixtures._create_list_on_disk, args=(value, list_len), kwargs={'target_path': target_path}, target_path_list=[target_path], - task_name='create list on disk %d' % task_id) + task_name='create list on disk') zero_div_task = task_graph.add_task( func=_test_fixtures._div_by_zero, dependent_task_list=[normal_task], - task_name='test_broken_task_chain_%d' % task_id) + task_name='test_broken_task_chain') target_path = os.path.join( - self.workspace_dir, 'after_zerodiv_1000_%d.dat' % task_id) + self.workspace_dir, 'after_zerodiv_1000.dat') _ = task_graph.add_task( func=_test_fixtures._create_list_on_disk, args=(value, list_len), kwargs={'target_path': target_path}, dependent_task_list=[zero_div_task], target_path_list=[target_path], - task_name='create list on disk after zero div%d' % task_id) - + task_name='create list on disk after zero div') + task_graph.close() with self.assertRaises(ZeroDivisionError): task_graph.join() @@ -412,17 +398,6 @@ def test_empty_task(self): _ = task_graph.add_task() task_graph.close() task_graph.join() - # # we shouldn't have anything in the database - # database_path = os.path.join( - # self.workspace_dir, taskgraph._TASKGRAPH_DATABASE_FILENAME) - - # conn = sqlite3.connect(database_path) - # with conn: - # cursor = conn.cursor() - # cursor.executescript("SELECT * FROM taskgraph_data") - # result = cursor.fetchall() - # conn.close() - # self.assertEqual(len(result), 0) def test_closed_graph(self): """TaskGraph: Test adding to an closed task graph fails.""" @@ -441,9 +416,8 @@ def test_closed_graph(self): def test_single_task_multiprocessing(self): """TaskGraph: Test a single task with multiprocessing.""" - workspace_dir = 'workdir' - task_graph = taskgraph.TaskGraph(workspace_dir, 1) - target_path = os.path.join(workspace_dir, '1000.dat') + task_graph = taskgraph.TaskGraph(self.workspace_dir, 1) + target_path = os.path.join(self.workspace_dir, '1000.dat') value = 5 list_len = 1000 _ = task_graph.add_task( @@ -1109,20 +1083,20 @@ def test_return_value(self): task_graph = None - # def test_dictionary_arguments(self): - # """TaskGraph: test that large dictionary arguments behave well.""" - # task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) - # dict_arg = {} - # x = {None: None} - # for _ in range(10000): - # dict_arg[_] = x - - # def my_op(dict_arg): - # pass - # task_graph.add_task( - # func=my_op, args=(), kwargs={'dict_arg': dict_arg}) - # task_graph.join() - # self.assertTrue(True, 'no memory error so everything is fine') + def test_dictionary_arguments(self): + """TaskGraph: test that large dictionary arguments behave well.""" + task_graph = taskgraph.TaskGraph(self.workspace_dir, -1) + dict_arg = {} + x = {None: None} + for _ in range(10000): + dict_arg[_] = x + + def my_op(dict_arg): + pass + task_graph.add_task( + func=my_op, args=(), kwargs={'dict_arg': dict_arg}) + task_graph.join() + self.assertTrue(True, 'no memory error so everything is fine') def test_filter_non_files(self): """TaskGraph: test internal filter non-files function."""

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