# Deploy with Helm

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

## Prerequisites

- [System Requirements](system-requirements.md)
-  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](https://helm.sh/docs/intro/install/)

> **Note**
> If Ubuntu Desktop is not installed on the target system, follow the instructions from Ubuntu to [install Ubuntu desktop](https://ubuntu.com/tutorials/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.

- Wind Turbine Anomaly Detection Sample App

    - 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/wind-turbine-anomaly-detection-sample-app --version 1.1.0-weekly`

        2. Unzip the package using the following command:

            `tar -xvzf wind-turbine-anomaly-detection-sample-app-1.1.0-weekly.tgz`

        - Get into the Helm directory:

            `cd wind-turbine-anomaly-detection-sample-app`

    - To generate the Helm charts:

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

        make gen_helm_charts app=wind-turbine-anomaly-detection

        cd helm/
        ```

- Weld Anomaly Detection Sample App

    - 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/weld-anomaly-detection-sample-app --version 1.0.0-weekly`

        2. Unzip the package using the following command:

            `tar -xvzf weld-anomaly-detection-sample-app-1.0.0-weekly.tgz`

        - Get into the Helm directory:

            `cd weld-anomaly-detection-sample-app`

    - To generate the Helm charts:
      ```bash
        cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-time-series # path relative to git clone folder

        make gen_helm_charts app=weld-anomaly-detection version=1.0.0-weekly

        cd helm/
      ```

## Step 2: Configure and update the environment variables

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

    ``` sh
    INFLUXDB_USERNAME:
    INFLUXDB_PASSWORD:
    VISUALIZER_GRAFANA_USER:
    VISUALIZER_GRAFANA_PASSWORD:
    POSTGRES_PASSWORD:
    MINIO_ACCESS_KEY:
    MINIO_SECRET_KEY:
    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.

**Wind Turbine Anomaly Detection**

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

- OPC-UA ingestion flow:

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

- MQTT ingestion flow:

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

> **Note:**
> To deploy with GPU support for inferencing, use the following command:
> ```bash
> helm install ts-wind-turbine-anomaly \
>   --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`.

**Weld Anomaly Detection**

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

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

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

## Step 4: Copy the udf package for helm deployment to Time Series Analytics Microservice

**Wind Turbine Anomaly Detection**

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-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. Copy your new UDF package (using the windturbine anomaly detection UDF package as an example) to the `time-series-analytics-microservice` pod:
    ```sh
    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
    mkdir -p $SAMPLE_APP
    cp -r models tick_scripts udfs $SAMPLE_APP/.

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

    kubectl cp $SAMPLE_APP $POD_NAME:/tmp/ -n ts-sample-app
    ```

**Weld Anomaly Detection**

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-time-series/apps/weld-anomaly-detection/time-series-analytics-config`.

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

2. Copy your new UDF package (using the windturbine anomaly detection UDF package as an example) to the `time-series-analytics-microservice` pod:
    ```sh
    export SAMPLE_APP="weld-anomaly-detection"
    cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-time-series/apps/weld-anomaly-detection/time-series-analytics-config # path relative to git clone folder
    mkdir -p $SAMPLE_APP
    cp -r models tick_scripts udfs $SAMPLE_APP/.

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

    kubectl cp $SAMPLE_APP $POD_NAME:/tmp/ -n ts-sample-app
    ```

> **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:
> ```sh
> curl -k -X 'POST' \
> 'https://<HOST_IP>:30001/ts-api/config' \
> -H 'accept: application/json' \
> -H 'Content-Type: application/json' \
> -d '<Add contents of edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-time-series/apps/wind-turbine-anomaly-detection/time-series-analytics-config/config.json with device
>     value updated to gpu from cpu>'
> ```
> GPU Inferencing is supported only for `Wind Turbine Anomaly Detection` sample app

Run the following command to activate the UDF deployment package:
```sh
curl -k -X 'GET' \
  'https://<HOST_IP>:30001/ts-api/config?restart=true' \
  -H 'accept: application/json'
```

## Step 6: Verify the Results

Follow the verification steps in the [Get Started guide](get-started.md):
- [Wind Turbine Anomaly Detection Results](get-started.md#verify-the-wind-turbine-anomaly-detection-results)
- [Weld Anomaly Detection Results](get-started.md#verify-the-weld-anomaly-detection-results)

## Uninstall Helm Charts

To uninstall Helm charts:

```sh
# Wind Turbine Anomaly Detection
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.

# Weld Anomaly Detection
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 [here](./how-to-configure-alerts.md#helm-deployment).

## Deploy the Application with a Custom UDF

To deploy the application with a custom UDF, follow the steps [here](./how-to-configure-custom-udf.md#helm-deployment).

## Troubleshooting

- Check pod details or container logs to diagnose failures:
    ```sh
    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
    ```