Deploy with Helm#

This guide provides step-by-step instructions for deploying the MultiModal - Weld Defect Detection sample application using Helm.

Prerequisites#

  • System Requirements

  • K8s installation on single or multi node must be done as prerequisite 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/installing-kubernetes-on-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

Note If Ubuntu Desktop is not installed on the target system, follow the instructions from Ubuntu to install Ubuntu desktop. The target system refers to the system where you are installing the application.

Step 1: Generate or download the Helm charts#

You can either generate or download the Helm charts.

  • To download the Helm charts:

    Follow this procedure on the target system to install the package.

    1. Download Helm chart with the following command:

      helm pull oci://registry-1.docker.io/intel/multimodal-weld-defect-detection-sample-app --version 1.0.0-weekly

    2. Unzip the package using the following command:

      tar -xvzf multimodal-weld-defect-detection-sample-app-1.0.0-weekly.tgz

    • Get into the Helm directory:

      cd multimodal-weld-defect-detection-sample-app

  • To generate the Helm charts:

    cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-multimodal # path relative to git clone folder

make gen_helm_charts 

cd helm/

Step 2: Configure and update the environment variables#

  1. Update the following fields in values.yaml file of the helm chart

    INFLUXDB_USERNAME:
INFLUXDB_PASSWORD:
VISUALIZER_GRAFANA_USER:
VISUALIZER_GRAFANA_PASSWORD:
HTTP_PROXY: # example: http_proxy: http://proxy.example.com:891
HTTPS_PROXY: # example: http_proxy: http://proxy.example.com:891
MTX_WEBRTCICESERVERS2_0_USERNAME:
MTX_WEBRTCICESERVERS2_0_PASSWORD

Step 3: Install Helm charts#

Note:

  1. Uninstall the Helm charts if already installed.

  2. Note the helm install command fails if the above required fields are not populated as per the rules called out in values.yaml file.

To install Helm charts, use one of the following options:

```bash
helm install multimodal-weld-defect-detection . -n multimodal-sample-app --create-namespace
```

Verify Installation:

Note:

  1. deployment-coturn, deployment-fusion-analytics, deployment-ia-weld-data-simulator and deployment-telegraf pods might restart since its depended on deployment-mqtt-broker and deployment-mediamtx

Use the following command to verify if all the application resources got installed w/ their status:

```bash
kubectl get all -n multimodal-sample-app
```

Step 4: Copy the udf package for helm deployment#

DLStreamer Pipeline Server

To copy your own or existing model into DLStreamer Pipeline Server in order to run this sample application in Kubernetes environment:

The model package is available in the repository at edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-multimodal/configs/dlstreamer-pipeline-server/.

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

```sh
cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-multimodal/configs/dlstreamer-pipeline-server/

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

kubectl cp models $POD_NAME:/home/pipeline-server/resources/ -c dlstreamer-pipeline-server -n multimodal-sample-app
```

Time Series Analytics Microservice

To copy your own or existing model into Time Series Analytics Microservice in order to run this sample application in Kubernetes environment:

  1. The following udf package is placed in the repository under edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-multimodal/configs/time-series-analytics-microservice.

    - time-series-analytics-microservice/
    - models/
        - weld_anomaly_detector.cb
    - tick_scripts/
        - weld_anomaly_detector.tick
    - udfs/
        - requirements.txt
        - weld_anomaly_detector.py

  2. Copy your new UDF package to the time-series-analytics-microservice pod:

    cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-multimodal/configs/time-series-analytics-microservice # path relative to git clone folder
mkdir -p weld_anomaly_detector
cp -r models tick_scripts udfs weld_anomaly_detector/.

POD_NAME=$(kubectl get pods -n multimodal-sample-app -o jsonpath='{.items[*].metadata.name}' | tr ' ' '\n' | grep deployment-time-series-analytics-microservice | head -n 1)

kubectl cp weld_anomaly_detector $POD_NAME:/tmp/ -n multimodal-sample-app

Note: Run the commands only after performing the Helm install.

Step 5: Activate the Pipeline/UDF Deployment Package#

DLStreamer Pipeline Server

You use a Client URL (cURL) command to start the pipeline. Start this pipeline with the following cURL command.

    curl -k https://localhost:30001/dsps-api/pipelines/user_defined_pipelines/weld_defect_classification -X POST -H 'Content-Type: application/json' -d '{
        "destination": {
            "metadata": {
                "type": "mqtt",
                "topic": "vision_weld_defect_classification"
            },
            "frame": {
                "type": "webrtc",
                "peer-id": "samplestream"
            }
        },
        "parameters": {
            "classification-properties": {
                "model": "/home/pipeline-server/resources/models/weld-defect-classification-f16-DeiT/deployment/Classification/model/model.xml",
                "device": "CPU"
            }
        }
    }'

Time Series Analytics Microservice

NOTE: UDF inferencing on GPU is not supported.

Run the following command to activate the UDF deployment package:

    curl -k -X 'GET' \
    'https://localhost:30001/ts-api/config?restart=true' \
    -H 'accept: application/json'

Step 6: Verify the Results#

Follow the verification steps in the Get Started guide

Uninstall Helm Charts#

To uninstall Helm charts:

helm uninstall multimodal-weld-defect-detection -n multimodal-sample-app
kubectl get all -n multimodal-sample-app # It may take a few minutes for all application resources to be cleaned up.

Configure Alerts in Time Series Analytics Microservice#

To configure alerts in Time Series Analytics Microservice, follow the steps here.

Deploy the Application with a Custom UDF#

To deploy the application with a custom UDF, follow the steps here.

Troubleshooting#

  • Check pod details or container logs to diagnose failures:

    kubectl get pods -n multimodal-sample-app
kubectl describe pod <pod_name> -n multimodal-sample-app # Shows details of the pod
kubectl logs -f <pod_name> -n multimodal-sample-app # Shows logs of the container in the pod