# Get Started - **Time to Complete:** 30 minutes - **Programming Language:** Python 3 ## Prerequisites - [System Requirements](system-requirements.md) ## Setup the application > Note that the following instructions assume Docker engine is setup in the host system. 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. ```sh git clone https://github.com/open-edge-platform/edge-ai-suites.git cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/ ``` 2. Set app specific environment variable file ```sh cp .env_pcb_anomaly_detection .env ``` 3. Edit the HOST_IP and other environment variables in `.env` file as follows ```sh HOST_IP= # IP address of server where DLStreamer Pipeline Server is running. MR_PSQL_PASSWORD= #PostgreSQL service & client adapter e.g. intel1234 MR_MINIO_ACCESS_KEY= # MinIO service & client access key e.g. intel1234 MR_MINIO_SECRET_KEY= # MinIO service & client secret key e.g. intel1234 MR_URL= # Model registry url. Example http://:32002 MTX_WEBRTCICESERVERS2_0_USERNAME= # WebRTC credentials e.g. intel1234 MTX_WEBRTCICESERVERS2_0_PASSWORD= # application directory SAMPLE_APP=pcb-anomaly-detection ``` 4. Install pre-requisites. Run with sudo if needed. ```sh ./setup.sh ``` This 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 5. Bring up the application ```sh docker compose up -d ``` 6. Fetch the list of pipeline loaded available to launch ```sh ./sample_list.sh ``` This lists the pipeline loaded in DL Streamer Pipeline Server. Example Output: ```sh # 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" } ... ] ``` 7. Start the sample application with a pipeline. ```sh ./sample_start.sh -p pcb_anomaly_detection ``` This command would look for the payload for the pipeline specified in `-p` argument above, inside the `payload.json` file and launch the a pipeline instance in DLStreamer Pipeline Server. Refer to the table, to learn about different options available. Output: ```sh # 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 would start the pipeline. We can view the inference stream on WebRTC by opening a browser and navigating to below url ``` http://:8889/anomaly/ ``` 8. Get status of pipeline instance(s) running. ```sh ./sample_status.sh ``` This command lists status of pipeline instances launched during the lifetime of sample application. Output: ```sh # 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" } ] ``` 9. Stop pipeline instance. ```sh ./sample_stop.sh ``` This command will stop all instances that are currently in `RUNNING` state and respond with the last status. Output: ```sh # 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" } ``` If you wish to stop a specific instance, you can provide it with an `--id` argument to the command. For example, `./sample_stop.sh --id f0c0b5aa5d4911f0bca7023bb629a486` 10. Bring down the application ```sh docker compose down -v ``` This will bring down the services in the application and remove any volumes. ## Further Reading - [Helm based deployment](how-to-deploy-using-helm-charts.md) - [MLOps using Model Registry](how-to-enable-mlops.md) - [Run multiple AI pipelines](how-to-run-multiple-ai-pipelines.md) - [Publish frames to S3 storage pipelines](how-to-run-store-frames-in-s3.md) - [View telemetry data in Open Telemetry](how-to-view-telemetry-data.md) - [Publish metadata to OPCUA](how-to-use-opcua-publisher.md) ## Troubleshooting - [Troubleshooting Guide](troubleshooting-guide.md)