Deploy using Helm charts#

Prerequisites#

  • System Requirements

  • K8s installation on single or multi node must be done as pre-requisite to continue the following deployment. Note: The kubernetes cluster is set up with kubeadm, kubectl and kubelet packages on single and multi nodes with v1.30.2. Refer to tutorials online to setup kubernetes cluster on the web with host OS as ubuntu 22.04 and/or ubuntu 24.04.

  • For helm installation, refer to helm website

Setup the application#

Note: The following instructions assume Kubernetes is already running in the host system with helm package manager installed.

  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
cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/

  2. Set app specific values.yaml file.

    cp helm/values_pallet_defect_detection.yaml helm/values.yaml

  3. Optional: Pull the helm chart and replace the existing helm folder with it

    • Note: The helm chart should be downloaded when you are not using the helm chart provided in edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision/helm

    • Download helm chart with the following command

      helm pull oci://ghcr.io/open-edge-platform/edge-ai-suites/manufacturing-ai-suite-vision-helm-chart/pallet-defect-detection-reference-implementation --version 20250805-EAS1.2

    • unzip the package using the following command

      tar -xvf pallet-defect-detection-reference-implementation-20250805-EAS1.2.tgz

    • Replace the helm directory

      rm -rf helm && mv pallet-defect-detection-reference-implementation helm

  4. Edit the HOST_IP, proxy and other environment variables in helm/values.yaml as follows

    env:        
    HOST_IP: <HOST_IP>   # host IP address
    MINIO_ACCESS_KEY: <DATABASE USERNAME> #  example: minioadmin
    MINIO_SECRET_KEY: <DATABASE PASSWORD> #  example: minioadmin
    http_proxy: <http proxy> # proxy details if behind proxy
    https_proxy: <https proxy>
    POSTGRES_PASSWORD: <POSTGRES PASSWORD> #  example: intel1234
    MR_URL: <PROTOCOL>://<HOST_IP>:32002 # example: http://<ip-addr>:32002
    SAMPLE_APP: pallet-defect-detection # application directory
webrtcturnserver:
    username: <username>  # WebRTC credentials e.g. intel1234
    password: <password>

  5. Install pre-requisites. Run with sudo if needed.

    ./setup.sh helm

    This sets up application pre-requisites, download artifacts, sets executable permissions for scripts etc. Downloaded resource directories.

Deploy the application#

  1. Install the helm chart

    helm install app-deploy helm -n apps --create-namespace

    After installation, check the status of the running pods:

    kubectl get pods -n apps

    To view logs of a specific pod, replace <pod_name> with the actual pod name from the output above:

    kubectl logs -n apps -f <pod_name>

  2. Copy the resources such as video and model from local directory to the dlstreamer-pipeline-server pod to make them available for application while launching pipelines.

    # Below is an example for Pallet Defect Detection. Please adjust the source path of models and videos appropriately for other sample applications.

POD_NAME=$(kubectl get pods -n apps -o jsonpath='{.items[*].metadata.name}' | tr ' ' '\n' | grep deployment-dlstreamer-pipeline-server | head -n 1)

kubectl cp resources/pallet-defect-detection/videos/warehouse.avi $POD_NAME:/home/pipeline-server/resources/videos/ -c dlstreamer-pipeline-server -n apps

kubectl cp resources/pallet-defect-detection/models/* $POD_NAME:/home/pipeline-server/resources/models/ -c dlstreamer-pipeline-server -n apps

  3. Fetch the list of pipeline loaded available to launch

    ./sample_list.sh

    This lists the pipeline loaded in DLStreamer Pipeline Server.

    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"
    }
    ...
]

  4. Start the sample application with a pipeline.

    ./sample_start.sh -p pallet_defect_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:

    # 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/helm/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/models/pallet-defect-detection/model.xml","device":"CPU"}}}
Posting payload to REST server at http://<HOST_IP>:30107/pipelines/user_defined_pipelines/pallet_defect_detection
Payload for pipeline 'pallet_defect_detection' posted successfully. Response: "99ac50d852b511f09f7c2242868ff651"

    NOTE- This would start the pipeline. You can view the inference stream on WebRTC by opening a browser and navigating to http://<HOST_IP>:31111/pdd/ for Pallet Defect Detection.

  5. Get status of pipeline instance(s) running.

    ./sample_status.sh

    This command lists status 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": "99ac50d852b511f09f7c2242868ff651",
    "message": "",
    "start_time": 1750956469.620569,
    "state": "RUNNING"
}
]

  6. Stop pipeline instance.

    ./sample_stop.sh

    This command will stop all instances that are currently in RUNNING state and respond with the 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: 99ac50d852b511f09f7c2242868ff651
Pipeline instance with ID '99ac50d852b511f09f7c2242868ff651' stopped successfully. Response: {
"avg_fps": 30.01631239459745,
"elapsed_time": 49.30651903152466,
"id": "99ac50d852b511f09f7c2242868ff651",
"message": "",
"start_time": 1750960037.1471195,
"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 99ac50d852b511f09f7c2242868ff651

  7. Uninstall the helm chart.

    helm uninstall app-deploy -n apps

Storing frames to S3 storage#

Applications can take advantage of S3 publish feature from DLStreamer Pipeline Server and use it to save frames to an S3 compatible storage.

  1. Run all the steps mentioned in above section to setup the application.

  2. Install the helm chart

    helm install app-deploy helm -n apps --create-namespace

  3. Copy the resources such as video and model from local directory to the dlstreamer-pipeline-server pod to make them available for application while launching pipelines.

    # Below is an example for Pallet Defect Detection. Please adjust the source path of models and videos appropriately for other sample applications.

POD_NAME=$(kubectl get pods -n apps -o jsonpath='{.items[*].metadata.name}' | tr ' ' '\n' | grep deployment-dlstreamer-pipeline-server | head -n 1)

kubectl cp resources/pallet-defect-detection/videos/warehouse.avi $POD_NAME:/home/pipeline-server/resources/videos/ -c dlstreamer-pipeline-server -n apps

kubectl cp resources/pallet-defect-detection/models/* $POD_NAME:/home/pipeline-server/resources/models/ -c dlstreamer-pipeline-server -n apps

  4. Install the package boto3 in your python environment if not installed.

    It is recommended to create a virtual environment and install it there. You can run the following commands to add the necessary dependencies as well as create and activate the environment.

    sudo apt update && \
sudo apt install -y python3 python3-pip python3-venv

    python3 -m venv venv && \
source venv/bin/activate

    Once the environment is ready, install boto3 with the following command

    pip3 install --upgrade pip && \
pip3 install boto3==1.36.17

    Note DLStreamer Pipeline Server expects the bucket to be already present in the database. The next step will help you create one.

  5. Create a S3 bucket using the following script.

    Update the HOST_IP and credentials with that of the running MinIO server. Name the file as create_bucket.py.

    import boto3
url = "http://<HOST_IP>:30800"
user = "<value of MR_MINIO_ACCESS_KEY used in helm/values.yaml>"
password = "<value of MR_MINIO_SECRET_KEY used in helm/values.yaml>"
bucket_name = "ecgdemo"

client= boto3.client(
            "s3",
            endpoint_url=url,
            aws_access_key_id=user,
            aws_secret_access_key=password
)
client.create_bucket(Bucket=bucket_name)
buckets = client.list_buckets()
print("Buckets:", [b["Name"] for b in buckets.get("Buckets", [])])

    Run the above script to create the bucket.

    python3 create_bucket.py

  6. Start the pipeline with the following cURL command with <HOST_IP> set to system IP. Ensure to give the correct path to the model as seen below. This example starts an AI pipeline.

    curl http://<HOST_IP>:30107/pipelines/user_defined_pipelines/pallet_defect_detection_s3write -X POST -H 'Content-Type: application/json' -d '{
    "source": {
        "uri": "file:///home/pipeline-server/resources/videos/warehouse.avi",
        "type": "uri"
    },
    "destination": {
        "frame": {
            "type": "webrtc",
            "peer-id": "pdds3"
        }
    },
    "parameters": {
        "detection-properties": {
            "model": "/home/pipeline-server/resources/models/pallet-defect-detection/deployment/Detection/model/model.xml",
            "device": "CPU"
        }
    }
}'

  7. Go to MinIO console on http://<HOST_IP>:30800/ and login with MR_MINIO_ACCESS_KEY and MR_MINIO_SECRET_KEY provided in helm/values.yaml file. After logging into console, you can go to ecgdemo bucket and check the frames stored.

    S3 minio image storage

  8. Uninstall the helm chart.

    helm uninstall app-deploy -n apps

MLOps using Model Registry#

  1. Run all the steps mentioned in above section to setup the application.

  2. Install the helm chart

    helm install app-deploy helm -n apps --create-namespace

  3. Copy the resources such as video and model from local directory to the dlstreamer-pipeline-server pod to make them available for application while launching pipelines.

    # Below is an example for Pallet Defect Detection. Please adjust the source path of models and videos appropriately for other sample applications.

POD_NAME=$(kubectl get pods -n apps -o jsonpath='{.items[*].metadata.name}' | tr ' ' '\n' | grep deployment-dlstreamer-pipeline-server | head -n 1)

kubectl cp resources/pallet-defect-detection/videos/warehouse.avi $POD_NAME:/home/pipeline-server/resources/videos/ -c dlstreamer-pipeline-server -n apps

kubectl cp resources/pallet-defect-detection/models/* $POD_NAME:/home/pipeline-server/resources/models/ -c dlstreamer-pipeline-server -n apps

  4. Modify the payload in helm/apps/pallet-defect-detection/payload.json to launch an instance for the mlops pipeline

    [
    {
        "pipeline": "pallet_defect_detection_mlops",
        "payload":{
            "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/deployment/Detection/model/model.xml",
                    "device": "CPU"
                }
            }
        }
    }
]

  5. Start the pipeline with the above payload.

    ./sample_start.sh -p pallet_defect_detection_mlops

  6. Download and prepare the model.

    export MODEL_URL='https://github.com/open-edge-platform/edge-ai-resources/raw/c13b8dbf23d514c2667d39b66615bd1400cb889d/models/pallet_defect_detection.zip'

curl -L "$MODEL_URL" -o "$(basename $MODEL_URL)"

  7. Run the following curl command to upload the local model.

    curl -L -X POST "http://<HOST_IP>:32002/models" \
-H 'Content-Type: multipart/form-data' \
-F 'name="YOLO_Test_Model"' \
-F 'precision="fp32"' \
-F 'version="v1"' \
-F 'origin="Geti"' \
-F 'file=@<model_file_path.zip>;type=application/zip' \
-F 'project_name="pallet-defect-detection"' \
-F 'architecture="YOLO"' \
-F 'category="Detection"'

    NOTE: Replace model_file_path.zip in the cURL request with the actual file path of your model’s .zip file, and HOST_IP with the IP address of the host machine.

  8. Check if the model is uploaded successfully.

    curl 'http://<HOST_IP>:32002/models'

  9. Check the instance ID of the currently running pipeline to use it for the next step.

    curl --location -X GET http://<HOST_IP>:30107/pipelines/status

  10. Restart the model with a new model from Model Registry. The following curl command downloads the model from Model Registry using the specs provided in the payload. Upon download, the running pipeline is restarted with replacing the older model with this new model. Replace the <instance_id_of_currently_running_pipeline> in the URL below with the id of the pipeline instance currently running.

    curl 'http://<HOST_IP>:30107/pipelines/user_defined_pipelines/pallet_defect_detection_mlops/{instance_id_of_currently_running_pipeline}/models' \
--header 'Content-Type: application/json' \
--data '{
"project_name": "pallet-defect-detection",
"version": "v1",
"category": "Detection",
"architecture": "YOLO",
"precision": "fp32",
"deploy": true,
"pipeline_element_name": "detection",
"origin": "Geti",
"name": "YOLO_Test_Model"
}'

    NOTE- The data above assumes there is a model in the registry that contains these properties. Also, the pipeline name that follows user_defined_pipelines/, will affect the deployment folder name.

  11. View the WebRTC streaming on http://<HOST_IP>:<mediamtx-port>/<peer-str-id> by replacing <peer-str-id> with the value used in the original cURL command to start the pipeline.

    WebRTC streaming

Troubleshooting#