# Deploy with Helm

This guide provides step-by-step instructions for deploying the MultiModal - Weld Defect Detection 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.


- 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:

    ```bash
    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

    ``` sh
    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:
    ```sh
    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:

```sh
    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](get-started.md#verify-the-weld-defect-detection-results)

## Uninstall Helm Charts

To uninstall Helm charts:

```sh
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](./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 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
    ```