Get Started#

  • Time to Complete: 30 minutes

  • Programming Language: Python 3

Prerequisites#

Set up 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 at docker.com.

  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.

Go to the target directory of your choice and clone the suite.

To learn more on partial cloning, check the Repository Cloning guide.

git clone --filter=blob:none --sparse --branch release-2026.1.0 https://github.com/open-edge-platform/edge-ai-suites.git
cd edge-ai-suites
git sparse-checkout set manufacturing-ai-suite
cd manufacturing-ai-suite/industrial-edge-insights-vision

  1. Set app-specific environment variable file

    cp .env_pallet-defect-detection .env

  2. Edit the below mentioned environment variables in the .env file as follows:

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

MINIO_ACCESS_KEY=   # MinIO service & client access key e.g. intel1234
MINIO_SECRET_KEY=   # MinIO service & client secret key e.g. intel1234

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

# application directory
SAMPLE_APP=pallet-defect-detection

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

    ./setup.sh

    This script sets up application pre-requisites, download 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:

    The Docker daemon service should start automatically at boot. If not, you can start it manually:

    sudo systemctl start docker

    Note: If you are running multiple instances of app, start the services using ./run.sh up instead.

    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 Pallet Defect Detection
Environment variables loaded from /home/intel/OEP/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/.env
Running sample app: pallet-defect-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": {
            "detection-properties": {
            "element": {
                "format": "element-properties",
                "name": "detection"
            }
            }
        },
        "type": "object"
        },
        "type": "GStreamer",
        "version": "pallet_defect_detection"
    }
    ...
]

  3. Start the sample application with a pipeline.

    ./sample_start.sh -p pallet_defect_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 DL Streamer Pipeline Server. Refer to the table, to learn about different available options.

    IMPORTANT: Before you run sample_start.sh script, make sure that jq is installed on your system. See the troubleshooting guide for more details.

    Output:

    # Example output for Pallet Defect Detection
Environment variables loaded from /home/intel/OEP/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/.env
Running sample app: pallet-defect-detection
Checking status of dlstreamer-pipeline-server...
Server reachable. HTTP Status Code: 200
Loading payload from /home/intel/OEP/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/apps/pallet-defect-detection/payload.json
Payload loaded successfully.
Starting pipeline: pallet_defect_detection
Launching pipeline: pallet_defect_detection
Extracting payload for pipeline: pallet_defect_detection
Found 1 payload(s) for pipeline: pallet_defect_detection
Payload for pipeline 'pallet_defect_detection' {"source":{"uri":"file:///home/pipeline-server/resources/videos/warehouse.avi","type":"uri"},"destination":{"frame":{"type":"webrtc","peer-id":"pdd"}},"parameters":{"detection-properties":{"model":"/home/pipeline-server/resources/models/pallet-defect-detection/model.xml","device":"CPU"}}}
Posting payload to REST server at https://<HOST_IP>/api/pipelines/user_defined_pipelines/pallet_defect_detection
Payload for pipeline 'pallet_defect_detection' posted successfully. Response: "4b36b3ce52ad11f0ad60863f511204e2"

    Note: This will start the pipeline. To view the inference stream on WebRTC, open a browser and navigate to https://<HOST_IP>/mediamtx/pdd/ for Pallet Defect Detection If you are running multiple instances of the app, ensure to provide NGINX_HTTPS_PORT number in the url for the app instance, i.e., replace <HOST_IP> with <HOST_IP>:<NGINX_HTTPS_PORT>. If you are running a single instance and using an NGINX_HTTPS_PORT other than the default 443, replace <HOST_IP> with <HOST_IP>:<NGINX_HTTPS_PORT>.

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

    ./sample_status.sh

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

    Output:

    # Example output for Pallet Defect Detection
Environment variables loaded from /home/intel/OEP/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/.env
Running sample app: pallet-defect-detection
[
{
    "avg_fps": 30.00446179356829,
    "elapsed_time": 36.927825689315796,
    "id": "4b36b3ce52ad11f0ad60863f511204e2",
    "message": "",
    "start_time": 1750956469.620569,
    "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 Pallet Defect Detection
No pipelines specified. Stopping all pipeline instances
Environment variables loaded from /home/intel/OEP/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/.env
Running sample app: pallet-defect-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: 4b36b3ce52ad11f0ad60863f511204e2
Pipeline instance with ID '4b36b3ce52ad11f0ad60863f511204e2' stopped successfully. Response: {
"avg_fps": 30.002200575353214,
"elapsed_time": 63.72864031791687,
"id": "4b36b3ce52ad11f0ad60863f511204e2",
"message": "",
"start_time": 1750956469.620569,
"state": "RUNNING"
}

    To stop a specific instance, identify it with the --id argument. For example, ./sample_stop.sh --id 4b36b3ce52ad11f0ad60863f511204e2

  6. Stop the Docker application.

    Note: If you are running multiple instances of app, stop the services using ./run.sh down instead.

    docker compose down -v

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

Further Reading#

Troubleshooting#