Get Started#

  • Time to Complete: 30 minutes

  • Programming Language: Python 3

Prerequisites#

Setup the application#

The following instructions assume Docker engine is correctly set up in the host system. If not, follow the installation guide for docker engine.

  1. Clone the edge-ai-suites repository and change into industrial-edge-insights-vision directory. The directory contains the utility scripts required in the instructions that follows.

    git clone https://github.com/open-edge-platform/edge-ai-suites.git -b release-1.2.0
cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/

  2. Set app specific environment variable file:

    cp .env_pcb_anomaly_detection .env

  3. Edit the HOST_IP, MTX_WEBRTCICESERVERS2_0_USERNAME and MTX_WEBRTCICESERVERS2_0_PASSWORD environment variables in the .env file as follows:

    HOST_IP=<HOST_IP>   # IP address of server where DLStreamer Pipeline Server is running.

MTX_WEBRTCICESERVERS2_0_USERNAME=<username>  # WebRTC credentials e.g. intel1234
MTX_WEBRTCICESERVERS2_0_PASSWORD=<password>

# application directory
SAMPLE_APP=pcb-anomaly-detection

  4. Install the pre-requisites. Run with sudo if needed.

    ./setup.sh

    This script sets up application pre-requisites, downloads artifacts, sets executable permissions for scripts etc. Downloaded resource directories are made available to the application via volume mounting in docker compose file automatically.

Deploy the Application#

  1. Start the Docker application:

    docker compose up -d

  2. Fetch the list of pipeline loaded available to launch:

    ./sample_list.sh

    This lists the pipeline loaded in DL Streamer Pipeline Server.

    Example Output:

    # Example output for PCB Anomaly Detection
Environment variables loaded from [WORKDIR]/manufacturing-ai-suite/industrial-edge-insights-vision/.env
Running sample app: pcb-anomaly-detection
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Loaded pipelines:
[
    ...
    {
        "description": "DL Streamer Pipeline Server pipeline",
        "name": "user_defined_pipelines",
        "parameters": {
        "properties": {
            "classification-properties": {
                "element": {
                    "format": "element-properties",
                    "name": "classification"
                }
            }
        },
        "type": "object"
        },
        "type": "GStreamer",
        "version": "pcb_anomaly_detection"
    }
    ...
]

  3. Start the sample application with a pipeline.

    ./sample_start.sh -p pcb_anomaly_detection

    This command will look for the payload for the pipeline specified in the -p argument above, inside the payload.json file and launch a pipeline instance in DLStreamer Pipeline Server. Refer to the table, to learn about different available options.

    Output:

    # Example output for PCB Anomaly Detection
Environment variables loaded from [WORKDIR]/manufacturing-ai-suite/industrial-edge-insights-vision/.env
Running sample app: pcb-anomaly-detection
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Loading payload from [WORKDIR]/manufacturing-ai-suite/industrial-edge-insights-vision/apps/pcb-anomaly-detection/payload.json
Payload loaded successfully.
Starting pipeline: pcb_anomaly_detection
Launching pipeline: pcb_anomaly_detection
Extracting payload for pipeline: pcb_anomaly_detection
Found 1 payload(s) for pipeline: pcb_anomaly_detection
Payload for pipeline 'pcb_anomaly_detection' {"source":{"uri":"file:///home/pipeline-server/resources/videos/anomalib_pcb_test.avi","type":"uri"},"destination":{"frame":{"type":"webrtc","peer-id":"anomaly"}},"parameters":{"classification-properties":{"model":"/home/pipeline-server/resources/models/pcb-anomaly-detection/deployment/Anomaly classification/model/model.xml","device":"CPU"}}}
Posting payload to REST server at http://10.223.23.156:8080/pipelines/user_defined_pipelines/pcb_anomaly_detection
Payload for pipeline 'pcb_anomaly_detection' posted successfully. Response: "f0c0b5aa5d4911f0bca7023bb629a486"

    NOTE: This will start the pipeline. The inference stream can be viewed on WebRTC, in a browser at the following url:

    http://<HOST_IP>:8889/anomaly/

  4. Get the status of running pipeline instance(s).

    ./sample_status.sh

    This command lists status of pipeline instances launched during the lifetime of sample application.

    Output:

    # Example output for PCB Anomaly Detection
Environment variables loaded from [WORKDIR]/manufacturing-ai-suite/industrial-edge-insights-vision/.env
Running sample app: pcb-anomaly-detection
[
{
    "avg_fps": 24.123323428597942,
    "elapsed_time": 9.865960359573364,
    "id": "f0c0b5aa5d4911f0bca7023bb629a486",
    "message": "",
    "start_time": 1752123260.5558383,
    "state": "RUNNING"
}
]

  5. Stop pipeline instances.

    ./sample_stop.sh

    This command will stop all instances that are currently in the RUNNING state and return their last status.

    Output:

    # Example output for PCB Anomaly Detection
No pipelines specified. Stopping all pipeline instances
Environment variables loaded from [WORKDIR]/manufacturing-ai-suite/industrial-edge-insights-vision/.env
Running sample app: pcb-anomaly-detection
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Instance list fetched successfully. HTTP Status Code: 200
Found 1 running pipeline instances.
Stopping pipeline instance with ID: f0c0b5aa5d4911f0bca7023bb629a486
Pipeline instance with ID 'f0c0b5aa5d4911f0bca7023bb629a486' stopped successfully. Response: {
    "avg_fps": 26.487679514091333,
    "elapsed_time": 25.634552478790283,
    "id": "f0c0b5aa5d4911f0bca7023bb629a486",
    "message": "",
    "start_time": 1752123260.5558383,
    "state": "RUNNING"
}

To stop a specific instance, identify it with the --id argument. For example, ./sample_stop.sh --id f0c0b5aa5d4911f0bca7023bb629a486

  1. Stop the Docker application:

    docker compose down -v

    This will bring down the services in the application and remove any volumes.

Further Reading#

Troubleshooting#