Deploy with Helm#

This guide provides step-by-step instructions for deploying the Industrial Edge Insights - Time Series sample application using Helm.

Prerequisites#

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#

Choose one of the following approaches to get the Helm charts:

Option A: Download the Helm charts

  1. Download Helm chart:

    helm pull oci://registry-1.docker.io/intel/wind-turbine-anomaly-detection-sample-app --version 2026.1.0-rc1

  2. Extract the Helm chart:

    tar -xvzf wind-turbine-anomaly-detection-sample-app-2026.1.0-rc1.tgz
cd wind-turbine-anomaly-detection-sample-app

Option B: Generate Helm charts

  1. Navigate to the source directory:

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

  2. Generate the charts:

    make gen_helm_charts app=wind-turbine-anomaly-detection
cd helm/

  3. Proceed to Step 2 to configure your values.yaml file present in the current directory.

Option A: Download the Helm charts

  1. Download Helm chart:

    helm pull oci://registry-1.docker.io/intel/weld-defect-detection-sample-app --version 2026.1.0-rc1

  2. Extract the Helm chart:

    tar -xvzf weld-defect-detection-sample-app-2026.1.0-rc1.tgz
cd weld-defect-detection-sample-app

  3. Proceed to Step 2 to configure your values.yaml file present in the current directory.

Option B: Generate Helm charts

  1. Navigate to the source directory:

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

  2. Generate the charts:

    make gen_helm_charts app=weld-defect-detection
cd helm/

  3. Proceed to Step 2 to configure your values.yaml file present in the current directory.

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

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.

  3. To deploy with GPU support for inferencing, use the following command:

    helm install <app_name> \
    --set privileged_access_required=true \
    --set env.TELEGRAF_INPUT_PLUGIN=<input_plugin> \
    . -n ts-sample-app --create-namespace

    The privileged_access_required=true setting enables Time Series Analytics Microservice access to GPU device through /dev/dri.

    E.g.:

     helm install ts-wind-turbine-anomaly \
 --set privileged_access_required=true \
 --set env.TELEGRAF_INPUT_PLUGIN=<input_plugin> \
 . -n ts-sample-app --create-namespace

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

  • OPC-UA ingestion flow:

    helm install ts-wind-turbine-anomaly --set env.TELEGRAF_INPUT_PLUGIN=opcua . -n ts-sample-app --create-namespace

  • MQTT ingestion flow:

    helm install ts-wind-turbine-anomaly --set env.TELEGRAF_INPUT_PLUGIN=mqtt_consumer . -n ts-sample-app --create-namespace

To install Helm charts, run the following command:

helm install ts-weld-anomaly . -n ts-sample-app --create-namespace

Step 4: Upload the UDF package for Helm deployment to Time Series Analytics Microservice#

To upload 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-time-series/apps/wind-turbine-anomaly-detection/time-series-analytics-config.

    - time-series-analytics-config/
    - models/
        - windturbine_anomaly_detector.pkl
    - tick_scripts/
        - windturbine_anomaly_detector.tick
    - udfs/
        - requirements.txt
        - windturbine_anomaly_detector.py

  2. Upload your new UDF package (using the wind turbine anomaly detection UDF package as an example) to the time-series-analytics-microservice pod:

    export SAMPLE_APP="wind-turbine-anomaly-detection"
cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-time-series/apps/wind-turbine-anomaly-detection/time-series-analytics-config # path relative to git clone folder
rm -f ${SAMPLE_APP}.tar
tar cf ${SAMPLE_APP}.tar models/ tick_scripts/ udfs/

curl -X POST https://localhost:30001/ts-api/udfs/package -F "file=@${SAMPLE_APP}.tar" -k

To upload 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-time-series/apps/weld-defect-detection/time-series-analytics-config.

    - time-series-analytics-config/
    - models/
        - weld_defect_detector.pkl
        - weld_defect_detector_labels.pkl
        - weld_defect_detector.json
    - tick_scripts/
        - weld_defect_detector.tick
    - udfs/
        - requirements.txt
        - weld_defect_detector.py

  2. Upload your new UDF package (using the Weld Defect Detection UDF package as an example) to the time-series-analytics-microservice pod:

    export SAMPLE_APP="weld-defect-detection"
cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-time-series/apps/weld-defect-detection/time-series-analytics-config # path relative to git clone folder
rm -f ${SAMPLE_APP}.tar
tar cf ${SAMPLE_APP}.tar models/ tick_scripts/ udfs/

curl -X POST https://localhost:30001/ts-api/udfs/package -F "file=@${SAMPLE_APP}.tar" -k

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

Step 5: Activate the New UDF Deployment Package#

NOTE: To activate the UDF inferencing on GPU, additionally run the following command as a prerequisite before activating the UDF deployment package:

cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-time-series/apps/wind-turbine-anomaly-detection/time-series-analytics-config
curl -k -X 'POST' \
'https://localhost:30001/ts-api/config' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d "$(sed 's/"device": "CPU"/"device": "GPU"/' config.json)"

GPU Inferencing is supported only for Wind Turbine Anomaly Detection sample app

Run the following command to activate the UDF deployment package:

cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-time-series/apps/${SAMPLE_APP}/time-series-analytics-config

curl -s -X POST https://localhost:30001/ts-api/config   -H 'accept: application/json'   -H 'Content-Type: application/json'   -d @config.json   -k

Step 6: Verify the Results#

Follow the verification steps in the Get Started guide.

Uninstall Helm Charts#

helm uninstall ts-wind-turbine-anomaly -n ts-sample-app
kubectl get all -n ts-sample-app # It may take a few minutes for all application resources to be cleaned up.

helm uninstall ts-weld-anomaly -n ts-sample-app
kubectl get all -n ts-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 in Time Series Analytics Helm Deployment.

Deploy the Application with a Custom UDF#

To deploy the application with a custom UDF, follow the steps in Custom UDF Helm Deployment.

Troubleshooting#

  • Check pod details or container logs to diagnose failures:

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