AI Vision Inspection & MLOps for Manufacturing

Neuro-R 5.0.2.7 | WEDA Deployment Guide | Neurocle

A customer deployment package for running Neurocle Neuro-R inference on Advantech WEDA Edge devices.

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About Neuro-R

Neuro-R is Neurocle's inference runtime for .net models exported from Neuro-T. This package runs Neuro-R as a headless container stack for manufacturing image inspection. WEDA is used to deploy the containers and model versions to an Edge device and to transfer selected inspection results.

Key Features

• Run Neuro-R .net models exported from Neuro-T.
• Support nine machine vision model types in one inference workflow.
• Run inference from images, folders, videos, USB cameras, and RTSP streams.
• Use CPU, NVIDIA CUDA GPU, DirectML GPU, or OpenVINO GPU/NPU execution.
• Store original and annotated images locally, in a shared volume, or in WEDA Cloud.
• Deploy model versions and retrieve selected results through WEDA.

Supported AI Model Types

Model Type	Description
Classification	Classifies an image into a defined category
Patch Classification	Divides a high-resolution image into patches and classifies each patch
Segmentation	Detects the precise shape and location of defects at the pixel level
Object Detection	Detects objects and identifies their locations
Oriented Object Detection	Detects objects and their locations at varying orientations
Anomaly Classification	Classifies an image as normal or anomalous using a model trained on normal images
Anomaly Segmentation	Detects anomalous regions at the pixel level using a model trained on normal images
OCR	Recognizes text and character regions in an image
Rotation	Detects image orientation and returns the corrected rotation

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Architecture

Container	Role
nrt	Model monitoring, inference, and version switching
nrt-subnode	WEDA Cloud result delivery
nrt-share-volume	Shared-volume result delivery
nrt-example-data	Example model and input data provisioning

The customer package contains the Neuro-R inference container and the WEDA delivery containers required for model and result transfer.

• WEDA Learning Hub — Deploy Container Stack
• WEDA Learning Hub — Container Stack Management

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Table of Contents

• System Requirements
• Before You Start
• Quick Start
• Runtime Options
• Result Storage
• Operations
• License and Support

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System Requirements

Host System

Install and configure these components before deploying the stack.

Component	Requirement
Tested host devices	Advantech AIR-310 and AIR-355. See the WEDA Learning Hub for additional supported Edge devices.
Device OS	Ubuntu 24.04 on Linux x86-64 (amd64)
Container runtime	Docker and Docker Compose
WEDA	WEDA device registration and WEDA Secured Volume
Licensing	CodeMeter network license server with a connected dongle and Edge access enabled
NVIDIA GPU	NVIDIA driver, NVIDIA Container Toolkit, and nvidia container runtime
Intel GPU/NPU	Device driver and container mapping for /dev/dri and /dev/accel
Operator PC	Windows or Linux, Python 3.12, and a WEDA account

Container Environment

Component	Version or Function
Container OS	Ubuntu 24.04.4 LTS
Neuro-R	5.0.2.7
Python	3.12.3
OpenCV	4.10.0.84
NumPy	2.2.6
Runtime services	Inference, model monitoring, version switching, and CodeMeter client

Model and Acceleration

Item	Support
Model format	.net file — Neurocle proprietary product format exported from Neuro-T
Input	Images, folders, videos, USB cameras, and RTSP streams
Acceleration	CPU; NVIDIA CUDA GPU; DirectML GPU; OpenVINO-optimized models on CPU, Intel GPU, and Intel NPU
Precision	Runtime/model default precision; FP16 execution on supported CUDA GPUs with --fp16

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Before You Start

Prepare the following information:

• Edge device IP address and SSH account
• WEDA endpoint, tenant, SSO settings, login, and password
• WEDA Node ID for each target device
• CodeMeter license server address
• WEDA Secured Volume address and export path
• Neuro-R .net model exported from Neuro-T
• Read-only registry token supplied for the Neuro-R images

The examples below use Windows CMD on the operator PC and an Ubuntu shell on the Edge device. Replace all uppercase placeholders with values from your environment.

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Quick Start

Step 1: Copy the Source to the Edge Device

Run from the downloaded repository directory on the operator PC.

ssh neurocle@EDGE_IP "mkdir -p /home/neurocle/neuror/src"
scp -r src/. neurocle@EDGE_IP:/home/neurocle/neuror/src/

Step 2: Register and Deploy the WEDA Stack

Choose the Compose file that matches the target hardware.

Target	Compose File
CPU or Intel GPU/NPU	docker/compose.yml
NVIDIA GPU	docker/compose.nvidia.yml

Register the selected file as a WEDA Stack. The images are provided through the neurocle/ai-vision-inspection-mlops-for-manufacturing project on harbor.edgesync.cloud. Add the supplied read-only token to the WEDA registry credentials.

Stack environment variables

NRT_IMAGE=harbor.edgesync.cloud/neurocle/ai-vision-inspection-mlops-for-manufacturing:nrt-5.0.2.7-deploy-r1
SUBNODE_IMAGE=harbor.edgesync.cloud/neurocle/ai-vision-inspection-mlops-for-manufacturing:subnode-1.2.0-r1
SHARE_VOLUME_IMAGE=harbor.edgesync.cloud/neurocle/ai-vision-inspection-mlops-for-manufacturing:share-volume-1.0.0-r1
EXAMPLE_DATA_IMAGE=harbor.edgesync.cloud/neurocle/ai-vision-inspection-mlops-for-manufacturing:example-data-5.0.2.7-r1
SOURCE_DIR=/home/neurocle/neuror/src
EDGE_ROOT=/opt/neuro-r
CM_REMOTE_SERVER=YOUR_LICENSE_SERVER_ADDRESS
USB_CAMERA_DEVICE=/dev/video0
SHARED_RESULTS_DIR=/mnt/neuro-r-results
WEDA_DATA_DIR=/opt/Advantech/system-agent/weda-data
WEDA_SECURED_VOLUME_ADDR=10.30.0.2
WEDA_SECURED_VOLUME_EXPORT=:/decrypted/ai-model

For Intel GPU/NPU deployments, identify the host devices with ls -l /dev/dri /dev/accel. Configure the applicable paths and set unused paths to /dev/null.

Intel GPU/NPU device variables

OPENVINO_GPU_CARD_DEVICE=/dev/null
OPENVINO_GPU_RENDER_DEVICE=/dev/null
OPENVINO_NPU_DEVICE=/dev/null

Deploy the Stack to the WEDA Node and confirm the container status on the Edge device.

sudo docker ps -a

Step 3: Configure WEDA Authentication

Run on the operator PC.

copy config\weda.env.example config\weda.local.env

Open config\weda.local.env and enter the WEDA endpoint, tenant and SSO settings, WEDA_LOGIN, and WEDA_PASSWORD. WEDA_ORG_ID can be discovered automatically or entered directly.

Step 4: Deploy a Model

python tools\deploy.py deploy --model-file "C:\Models\obd_ex_model.net" --model-name obd --edition v1 --device-id YOUR_WEDA_NODE_ID
python tools\deploy.py status

Repeat --device-id to deploy the same model to multiple devices.

Step 5: Run Inference

USB camera example:

sudo docker exec nrt python3 /workspace/src/inference.py start --model-name obd --edition v1 --source /dev/neuro-r-camera --device cuda_gpu --save-targets local,core --save-formats original,predicted --max-frames 300 --output-fps 5

Image-folder example:

sudo docker exec nrt python3 /workspace/src/inference.py start --model-file /workspace/models/example.net --source /workspace/input/images --device cpu --save-targets local --save-formats original,predicted

Place input images under /opt/neuro-r/input/images and local models under /opt/neuro-r/models on the Edge host.

Check the current inference status:

sudo docker exec nrt python3 /workspace/src/inference.py status

Step 6: Download WEDA Cloud Results

Run on the operator PC after Cloud delivery completes.

python tools\deploy.py pull-core-results --device-id YOUR_WEDA_NODE_ID --model-name obd --edition v1 --output-dir "C:\Results"

The downloaded directory contains original images and labels, annotated images, an execution summary, and WEDA device metadata.

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Runtime Options

Run the control interface inside the nrt container:

python3 /workspace/src/inference.py [common options] COMMAND [command options]

Goal	Main options
Select input and model	--source, --model-file or --model-name with --edition
Select compute	--device, --device-index, --batch-size, --fp16
Select saved results	--save-targets, --save-formats, --output-dir
Control processing	--threshold-flag, --output-fps, --max-frames, --trigger-class
Control lifecycle	--auto-resume, --detach, start, stop, status, change

See the complete runtime CLI reference for every command, option, alias, default, and constraint. The installed version is always authoritative:

sudo docker exec nrt python3 /workspace/src/inference.py --help
sudo docker exec nrt python3 /workspace/src/inference.py start --help

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Result Storage

Destination	Description
local	Store results on the Edge device
shared-volume	Deliver results to the connected shared volume
core	Deliver results to WEDA Cloud
none	Do not save results

Use comma-separated values such as --save-targets local,shared-volume,core and --save-formats original,predicted.

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Operations

Task	Command
Check inference status	sudo docker exec nrt python3 /workspace/src/inference.py status
Switch to a deployed version	sudo docker exec nrt python3 /workspace/src/inference.py change
Stop inference	sudo docker exec nrt python3 /workspace/src/inference.py stop
View inference logs	sudo docker logs --tail 100 -f nrt
View Cloud delivery logs	sudo docker logs --tail 100 -f nrt-subnode

For license errors, check CM_REMOTE_SERVER and the available CodeMeter license. For accelerator errors, check the driver and container device mapping.

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License and Support

The AI Vision Inspection & MLOps for Manufacturing End User License Agreement permits internal industrial use and modification of the supplied source code. Third-party components remain subject to their respective licenses.

• Website: https://www.neuro-cle.com/
• Contact: info@neuro-cle.com