open-edge-platform · sathyendranv · Jan 13, 2026 · Jan 6, 2026 · Jan 9, 2026 · Jan 9, 2026
@@ -172,6 +172,24 @@ push_images: build
 	@echo "Pushing the images to docker registry"
 	docker compose -f $(DOCKER_COMPOSE_FILE) push
 
+
+# Generate helm packages
+.PHONY: gen_helm_charts
+gen_helm_charts:
+	@echo "Generating Helm packages"
+	@cp -f configs/grafana/* helm/
+	@cp -f configs/influxdb/config/*.conf helm/
+	@cp -f configs/influxdb/init-influxdb.sh helm/
+	@cp -f configs/mqtt-broker/*.conf helm/
+	@cp -f configs/telegraf/config/*.conf helm/
+	@cp -f configs/telegraf/entrypoint.sh helm/telegraf_entrypoint.sh
+	@cp -f configs/time-series-analytics-microservice/config.json helm/
+	@cp -f configs/nginx/nginx-cert-gen.sh helm/nginx-cert-gen.sh
+	@cp -f configs/nginx/nginx.conf helm/nginx.conf
+	@cp -f configs/dlstreamer-pipeline-server/config.json helm/dlstreamer-pipeline-server.json
+	@sed -i 's/"auto_start": true,/"auto_start": false,/' helm/dlstreamer-pipeline-server.json
+	@echo "Helm packages generated"
+
 # Help
 .PHONY: help
 help:
@@ -183,4 +201,5 @@ help:
 	@echo "  make restart  - Restart Docker containers"
 	@echo "  make clean    - Remove all stopped containers and unused images"
 	@echo "  make push_images     - Push the images to docker registry"
+	@echo "  make gen_helm_charts              - Generate Helm packages for the selected sample app"
 	@echo "  make help     - Display this help message"
@@ -0,0 +1,204 @@
+# 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
+
+    ```bash
+    WORK_DIR: # Update with the absolute path to your industrial-edge-insights-multimodal directory
+    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: 
+    HOST_IP:  # IP address of server where DL Streamer Pipeline Server is running
+    ```
+
+## 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
+
+**DL Streamer Pipeline Server**
+
+To copy your own or existing model into DL Streamer 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.
+
+    ```bash
+    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 $POD_NAME:/home/pipeline-server/resources/ -c dlstreamer-pipeline-server -n multimodal-sample-app
+    ```
+
+**Time Series Analytics Microservice**
+
+To copy your own or existing model into Time Series Analytics Microservice in order to run this sample application in Kubernetes environment:
+
+1. The following udf package is placed in the repository under `edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-multimodal/configs/time-series-analytics-microservice`. 
+
+    ```
+    - time-series-analytics-microservice/
+        - models/
+            - weld_anomaly_detector.cb
+        - tick_scripts/
+            - weld_anomaly_detector.tick
+        - udfs/
+            - requirements.txt
+            - weld_anomaly_detector.py
+    ```
+
+2. Copy your new UDF package to the `time-series-analytics-microservice` pod:
+    ```bash
+    cd edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-multimodal/configs/time-series-analytics-microservice # path relative to git clone folder
+    mkdir -p weld_anomaly_detector
+    cp -r models tick_scripts udfs weld_anomaly_detector/.
+
+    POD_NAME=$(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
+
+
+**DL Streamer 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:
+
+```bash
+    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:
+
+```bash
+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).
+
+## Troubleshooting
+
+- Check pod details or container logs to diagnose failures:
+    ```bash
+    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
+    ```
+
+## Known Issues
+
+- The video stream is not rendering in the Grafana.
+- Time Series data and Fusion analytics results are initially displayed for the first 2-3 minutes, but then stop updating and no new results are loaded.
@@ -66,6 +66,47 @@ docker exec -ti ia-mqtt-broker mosquitto_sub -h localhost -v -t vision_weld_defe
 docker exec -ti ia-mqtt-broker mosquitto_sub -h localhost -v -t fusion/anomaly_detection_results -p 1883
 ```
 
+## Helm Deployment
+
+### Helm - Publish MQTT Alerts
+
+For detailed instructions on configuring and publishing MQTT alerts, refer to the [Publish MQTT Alerts](#docker---publish-mqtt-alerts) section.
+
+### Helm - Subscribe to MQTT Alerts
+
+Follow the steps to subscribe to the published MQTT alerts.
+
+To subscribe to MQTT topics in a Helm deployment, execute the following command:
+
+- Identify the MQTT broker pod name by running:
+
+```bash
+kubectl get pods -n multimodal-sample-app | grep mqtt-broker
+```
+
+- Use the pod name from the output of the above command to subscribe to all topics:
+```bash
+kubectl exec -it -n multimodal-sample-app <mqtt_broker_pod_name> -- mosquitto_sub -h localhost -v -t '#' -p 1883
+```
+
+#### Helm - Subscribing to Time Series Analytics Microservice Alerts
+
+```bash
+kubectl exec -it -n multimodal-sample-app <mqtt_broker_pod_name> -- mosquitto_sub -h localhost -v -t alerts/weld_defect_detection -p 1883
+```
+
+#### Helm - Subscribing to DL Streamer Pipeline Server Results
+
+```bash
+kubectl exec -it -n multimodal-sample-app <mqtt_broker_pod_name> -- mosquitto_sub -h localhost -v -t vision_weld_defect_classification -p 1883
+```
+
+#### Helm - Subscribing to Fusion Analytics Results
+
+```bash
+kubectl exec -it -n multimodal-sample-app <mqtt_broker_pod_name> -- mosquitto_sub -h localhost -v -t fusion/anomaly_detection_results -p 1883
+```
+
 ## Supporting Resources
 
 - [Kapacitor MQTT Alert Documentation](https://docs.influxdata.com/kapacitor/v1/reference/event_handlers/mqtt/).
@@ -0,0 +1,30 @@
+#
+# Apache v2 license
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+apiVersion: v2
+name: multimodal-weld-defect-detection-sample-app
+description: Helm charts for multimodal-weld-defect-detection sample app
+
+# A chart can be either an 'application' or a 'library' chart.
+#
+# Application charts are a collection of templates that can be packaged into versioned archives
+# to be deployed.
+#
+# Library charts provide useful utilities or functions for the chart developer. They're included as
+# a dependency of application charts to inject those utilities and functions into the rendering
+# pipeline. Library charts do not define any templates and therefore cannot be deployed.
+type: application
+
+# This is the chart version. This version number should be incremented each time you make changes
+# to the chart and its templates, including the app version.
+# Versions are expected to follow Semantic Versioning (https://semver.org/)
+version: 1.0.0-weekly
+
+# This is the version number of the application being deployed. This version number should be
+# incremented each time you make changes to the application. Versions are not expected to
+# follow Semantic Versioning. They should reflect the version the application is using.
+# It is recommended to use it with quotes.
+appVersion: "1.0.0-weekly"
@@ -0,0 +1 @@
+Please refer [link](../docs/user-guide/how-to-deploy-with-helm.md) for the helm deployment
@@ -0,0 +1,25 @@
+CHART NAME: {{ .Chart.Name }}
+CHART VERSION: {{ .Chart.Version }}
+APP VERSION: {{ .Chart.AppVersion }}
+
+** Please be patient while the chart is being deployed **
+
+Get the list of pods by executing:
+
+`kubectl get pods --namespace {{ $.Values.namespace }}` 
+
+By default, all our helm charts are deployed with `{{ $.Values.namespace }}` namespace, below commands will help us to deploy helm chart and kube pods with specific namespace
+
+
+`helm install --set namespace=<namespace> <helm_app_name> <helm_charts_directory>/ --namespace <namespace> --create-namespace`
+
+Access the pod you want to debug by executing
+
+`kubectl exec --namespace {{ $.Values.namespace }} -ti <NAME OF THE POD> -- bash`
+
+Verify the pod logs by executing
+
+`kubectl logs -f <NAME OF THE POD>`
+
+Access Grafana at `https://nodeip:{{ $.Values.config.nginx.ext.https_port }}`
+
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1 @@
		Please refer [link](../docs/user-guide/how-to-deploy-with-helm.md) for the helm deployment