Get Started Guide Helm#

  • Time to Complete: 5 - 15 minutes

  • Programming Language: Python 3

Prerequisites#

  • 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 such as https://adamtheautomator.com/install-kubernetes-ubuntu and many other online tutorials to setup kubernetes cluster on the web with host OS as ubuntu 22.04.

  • For helm installation, refer to helm website

Quick try out#

Follow the steps in this section to quickly pull the latest pre-built Edge Video Analytics Microservice helm charts followed by running a sample usecase.

Pull the helm chart and start#

  • Download helm chart with the following command

    helm pull oci://registry-1.docker.io/intel/edge-video-analytics-microservice --version 2.3.0-helm

  • unzip the package using the following command

    tar xvf edge-video-analytics-microservice-2.3.0-helm.tgz

  • Get into the helm directory

    cd edge-video-analytics-microservice

  • Install the helm chart

    helm install evam . -n apps --create-namespace

  • Check if EVAM is running fine

    kubectl get pods --namespace appsand monitor its logs using kubectl logs -f <pod_name> -n apps

Run default sample#

Once the pods are up, we will send a pipeline request to EVAM to run a detection model on a warehouse video. Both the model and video are provided as default sample in the docker image.

We will send the below curl request to run the inference. It comprises of a source file path which is warehouse.avi, a destination, with metadata directed to a json fine in /tmp/resuts.jsonl and frames streamed over RTSP with id pallet-defect-detection. Additionally, we will also provide the GETi model path that would be used for detecting defective boxes on the video file.

Open another terminal and send the following curl request

  curl http://localhost:30007/pipelines/user_defined_pipelines/pallet_defect_detection -X POST -H 'Content-Type: application/json' -d '{
    "source": {
      "uri": "file:///home/pipeline-server/resources/videos/warehouse.avi",
      "type": "uri"
    },
    "destination": {
      "metadata": {
        "type": "file",
        "path": "/tmp/results.jsonl",
        "format": "json-lines"
      },
      "frame": {
        "type": "rtsp",
        "path": "pallet-defect-detection"
      }
    },
    "parameters": {
      "detection-properties": {
        "model": "/home/pipeline-server/resources/models/geti/pallet_defect_detection/deployment/Detection/model/model.xml",
        "device": "CPU"
      }
    }
  }'

The REST request will return a pipeline instance ID, which can be used as an identifier to query later the pipeline status or stop the pipeline instance. For example, a6d67224eacc11ec9f360242c0a86003.

  • To view the metadata, open another terminal and run the following command,

      tail -f /tmp/results.jsonl

  • RTSP Stream will be accessible at rtsp://<SYSTEM_IP_ADDRESS>:30025/pallet-defect-detection. Users can view this on any media player e.g. vlc (as a network stream), ffplay etc

    sample frame RTSP stream

To check the pipeline status and stop the pipeline send the following requests,

  • view the pipeline status that you triggered in the above step.

     curl --location -X GET http://localhost:30007/pipelines/status

  • stop a running pipeline instance,

     curl --location -X DELETE http://localhost:30007/pipelines/{instance_id}

  • Uninstall the helm chart,

     helm uninstall evam -n apps

Now you have successfully run the Edge Video Analytics Microservice container, sent a curl request to start a pipeline within the microservice which runs the Geti based pallet defect detection model on a sample warehouse video. Then, you have also looked into the status of the pipeline to see if everything worked as expected and eventually stopped the pipeline as well.

Summary#

In this get started guide, you learned how to start the EVAM service and start default pipeline.

Learn More#

  • For getting started with the deployment using docker, please go here

  • Understand the components, services, architecture, and data flow, in the Overview.

  • For more details on advanced configuration, usage of features refer to Detailed Usage

  • For more tutorials, refer to Tutorials How-to guide.

  • For more details on Intel Deep Learning Streamer visit this page.