updated all multi-instance-helm with mlops

jessicaearayil · jessicaearayil · commit e9546caeeab4 · 2026-02-13T13:05:05.000+05:30
diff --git a/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/get-started/deploy-multiple-instances-with-helm.md b/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/get-started/deploy-multiple-instances-with-helm.md
@@ -740,3 +740,127 @@ Applications can take advantage of S3 publish feature from DL Streamer Pipeline
    ```sh
    ./run.sh helm_uninstall
    ```
+
+## MLOps using Model Download
+
+1. Run all the steps mentioned in above [section](#setup-the-application) to setup the application.
+
+2. Install the helm chart
+
+   ```sh
+   ./run.sh helm_install
+   ```
+
+3. Copy the resources such as video and model from local directory to the `dlstreamer-pipeline-server` pod to make them available for application while launching pipelines.
+
+   ```sh
+   # Below is an example for Pallet Defect Detection. Please adjust the source path of models and videos appropriately for other sample applications.
+
+   POD_NAME=$(kubectl get pods -n <INSTANCE_NAME> -o jsonpath='{.items[*].metadata.name}' | tr ' ' '\n' | grep deployment-dlstreamer-pipeline-server | head -n 1)
+
+   kubectl cp resources/pallet-defect-detection/videos/warehouse.avi $POD_NAME:/home/pipeline-server/resources/videos/ -c dlstreamer-pipeline-server -n <INSTANCE_NAME>
+
+   kubectl cp resources/pallet-defect-detection/models/* $POD_NAME:/home/pipeline-server/resources/models/ -c dlstreamer-pipeline-server -n <INSTANCE_NAME>
+   ```
+
+4. Modify the payload in `helm/temp_apps/<SAMPLE_APP>/<INSTANCE_NAME>/payload.json` to launch an instance for the mlops pipeline. 
+   
+   Below is an example for pallet-defect-detection. Please modify the payload for other sample applications.
+
+   ```json
+   [
+       {
+           "pipeline": "pallet_defect_detection_mlops",
+           "payload":{
+               "source": {
+                   "uri": "file:///home/pipeline-server/resources/videos/warehouse.avi",
+                   "type": "uri"
+               },
+               "destination": {
+               "frame": {
+                   "type": "webrtc",
+                   "peer-id": "pdd"
+               }
+               },
+               "parameters": {
+                   "detection-properties": {
+                       "model": "/home/pipeline-server/resources/models/pallet-defect-detection/deployment/Detection/model/model.xml",
+                       "device": "CPU"
+                   }
+               }
+           }
+       }
+   ]
+   ```
+
+5. Start the pipeline with the above payload. 
+
+   Below is an example for starting an instance for pallet-defect-detection:
+
+   ```sh
+   ./sample_start.sh helm -i <INSTANCE_NAME> -p pallet_defect_detection_mlops
+   ```
+   Note the instance-id.
+
+6. Download and prepare the model. Below is an example for downloading and preparing model for pallet-defect-detection. Please modify MODEL_URL for the other sample applications.
+   >NOTE- For sake of simplicity, we assume that the new model has already been downloaded by Model Download microservice. The following curl command is only a simulation that just downloads the model. In production, however, they will be downloaded by the Model Download service.
+
+   ```sh
+   export MODEL_URL='https://github.com/open-edge-platform/edge-ai-resources/raw/a7c9522f5f936c47de8922046db7d7add13f93a0/models/INT8/pallet_defect_detection.zip'
+
+   curl -L "$MODEL_URL" -o "$(basename $MODEL_URL)"
+
+   unzip "$(basename $MODEL_URL)" -d new-model # downloaded model is now extracted to `new-model` directory.
+   ```
+
+7. Copy the new model to the `dlstreamer-pipeline-server` pod to make it available for application while launching pipeline.
+
+   ```sh
+   
+   POD_NAME=$(kubectl get pods -n <INSTANCE_NAME> -o jsonpath='{.items[*].metadata.name}' | tr ' ' '\n' | grep deployment-dlstreamer-pipeline-server | head -n 1)
+
+   kubectl cp new-model $POD_NAME:/home/pipeline-server/resources/models/ -c dlstreamer-pipeline-server -n <INSTANCE_NAME>
+   ```
+   >NOTE- If there are multiple sample_apps in config.yml, repeat steps 6 and 7 for each sample app and instance.
+
+
+8. Stop the existing pipeline before restarting it with a new model. Use the instance-id generated from step 5.
+   ```sh
+   curl -k --location -X DELETE https://<HOST_IP>:<NGINX_HTTPS_PORT>/api/pipelines/{instance_id}
+   ```
+
+9. Modify the payload in `helm/temp_apps/<SAMPLE_APP>/<INSTANCE_NAME>/payload.json` to launch an instance for the mlops pipeline with this new model.
+   
+   Below is an example for pallet-defect-detection. Please modify the payload for other sample applications.
+
+   ```json
+   [
+       {
+           "pipeline": "pallet_defect_detection_mlops",
+           "payload":{
+               "source": {
+                   "uri": "file:///home/pipeline-server/resources/videos/warehouse.avi",
+                   "type": "uri"
+               },
+               "destination": {
+               "frame": {
+                   "type": "webrtc",
+                   "peer-id": "pdd"
+               }
+               },
+               "parameters": {
+                   "detection-properties": {
+                       "model": "/home/pipeline-server/resources/models/new-model/deployment/Detection/model/model.xml",
+                       "device": "CPU"
+                   }
+               }
+           }
+       }
+   ]
+
+10. View the WebRTC streaming on `https://<HOST_IP>:<NGINX_HTTPS_PORT>/mediamtx/<peer-str-id>/` by replacing `<peer-str-id>` with the value used in the original cURL command to start the pipeline.
+
+
+## Troubleshooting
+
+- [Troubleshooting Guide](../troubleshooting.md)
diff --git a/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/get-started/deploy-with-helm.md b/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/get-started/deploy-with-helm.md
@@ -456,7 +456,8 @@ Applications can take advantage of S3 publish feature from DL Streamer Pipeline
 
 10. View the WebRTC streaming on `https://<HOST_IP>:30443/mediamtx/<peer-str-id>/` by replacing `<peer-str-id>` with the value used in the original cURL command to start the pipeline.
 
-
+   ![WebRTC streaming](../_assets/webrtc-streaming.png)
+   
 ## Troubleshooting
 
 - [Troubleshooting Guide](../troubleshooting.md)
diff --git a/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pcb-anomaly-detection/get-started/deploy-multiple-instances-with-helm.md b/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pcb-anomaly-detection/get-started/deploy-multiple-instances-with-helm.md
@@ -740,3 +740,127 @@ Applications can take advantage of S3 publish feature from DL Streamer Pipeline
    ```sh
    ./run.sh helm_uninstall
    ```
+
+## MLOps using Model Download
+
+1. Run all the steps mentioned in above [section](#setup-the-application) to setup the application.
+
+2. Install the helm chart
+
+   ```sh
+   ./run.sh helm_install
+   ```
+
+3. Copy the resources such as video and model from local directory to the `dlstreamer-pipeline-server` pod to make them available for application while launching pipelines.
+
+   ```sh
+   # Below is an example for Pallet Defect Detection. Please adjust the source path of models and videos appropriately for other sample applications.
+
+   POD_NAME=$(kubectl get pods -n <INSTANCE_NAME> -o jsonpath='{.items[*].metadata.name}' | tr ' ' '\n' | grep deployment-dlstreamer-pipeline-server | head -n 1)
+
+   kubectl cp resources/pallet-defect-detection/videos/warehouse.avi $POD_NAME:/home/pipeline-server/resources/videos/ -c dlstreamer-pipeline-server -n <INSTANCE_NAME>
+
+   kubectl cp resources/pallet-defect-detection/models/* $POD_NAME:/home/pipeline-server/resources/models/ -c dlstreamer-pipeline-server -n <INSTANCE_NAME>
+   ```
+
+4. Modify the payload in `helm/temp_apps/<SAMPLE_APP>/<INSTANCE_NAME>/payload.json` to launch an instance for the mlops pipeline. 
+   
+   Below is an example for pallet-defect-detection. Please modify the payload for other sample applications.
+
+   ```json
+   [
+       {
+           "pipeline": "pallet_defect_detection_mlops",
+           "payload":{
+               "source": {
+                   "uri": "file:///home/pipeline-server/resources/videos/warehouse.avi",
+                   "type": "uri"
+               },
+               "destination": {
+               "frame": {
+                   "type": "webrtc",
+                   "peer-id": "pdd"
+               }
+               },
+               "parameters": {
+                   "detection-properties": {
+                       "model": "/home/pipeline-server/resources/models/pallet-defect-detection/deployment/Detection/model/model.xml",
+                       "device": "CPU"
+                   }
+               }
+           }
+       }
+   ]
+   ```
+
+5. Start the pipeline with the above payload. 
+
+   Below is an example for starting an instance for pallet-defect-detection:
+
+   ```sh
+   ./sample_start.sh helm -i <INSTANCE_NAME> -p pallet_defect_detection_mlops
+   ```
+   Note the instance-id.
+
+6. Download and prepare the model. Below is an example for downloading and preparing model for pallet-defect-detection. Please modify MODEL_URL for the other sample applications.
+   >NOTE- For sake of simplicity, we assume that the new model has already been downloaded by Model Download microservice. The following curl command is only a simulation that just downloads the model. In production, however, they will be downloaded by the Model Download service.
+
+   ```sh
+   export MODEL_URL='https://github.com/open-edge-platform/edge-ai-resources/raw/a7c9522f5f936c47de8922046db7d7add13f93a0/models/INT8/pallet_defect_detection.zip'
+
+   curl -L "$MODEL_URL" -o "$(basename $MODEL_URL)"
+
+   unzip "$(basename $MODEL_URL)" -d new-model # downloaded model is now extracted to `new-model` directory.
+   ```
+
+7. Copy the new model to the `dlstreamer-pipeline-server` pod to make it available for application while launching pipeline.
+
+   ```sh
+   
+   POD_NAME=$(kubectl get pods -n <INSTANCE_NAME> -o jsonpath='{.items[*].metadata.name}' | tr ' ' '\n' | grep deployment-dlstreamer-pipeline-server | head -n 1)
+
+   kubectl cp new-model $POD_NAME:/home/pipeline-server/resources/models/ -c dlstreamer-pipeline-server -n <INSTANCE_NAME>
+   ```
+   >NOTE- If there are multiple sample_apps in config.yml, repeat steps 6 and 7 for each sample app and instance.
+
+
+8. Stop the existing pipeline before restarting it with a new model. Use the instance-id generated from step 5.
+   ```sh
+   curl -k --location -X DELETE https://<HOST_IP>:<NGINX_HTTPS_PORT>/api/pipelines/{instance_id}
+   ```
+
+9. Modify the payload in `helm/temp_apps/<SAMPLE_APP>/<INSTANCE_NAME>/payload.json` to launch an instance for the mlops pipeline with this new model.
+   
+   Below is an example for pallet-defect-detection. Please modify the payload for other sample applications.
+
+   ```json
+   [
+       {
+           "pipeline": "pallet_defect_detection_mlops",
+           "payload":{
+               "source": {
+                   "uri": "file:///home/pipeline-server/resources/videos/warehouse.avi",
+                   "type": "uri"
+               },
+               "destination": {
+               "frame": {
+                   "type": "webrtc",
+                   "peer-id": "pdd"
+               }
+               },
+               "parameters": {
+                   "detection-properties": {
+                       "model": "/home/pipeline-server/resources/models/new-model/deployment/Detection/model/model.xml",
+                       "device": "CPU"
+                   }
+               }
+           }
+       }
+   ]
+
+10. View the WebRTC streaming on `https://<HOST_IP>:<NGINX_HTTPS_PORT>/mediamtx/<peer-str-id>/` by replacing `<peer-str-id>` with the value used in the original cURL command to start the pipeline.
+
+
+## Troubleshooting
+
+- [Troubleshooting Guide](../troubleshooting.md)
diff --git a/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/weld-porosity/get-started/deploy-multiple-instances-with-helm.md b/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/weld-porosity/get-started/deploy-multiple-instances-with-helm.md
@@ -739,4 +739,128 @@ Applications can take advantage of S3 publish feature from DL Streamer Pipeline
 
    ```sh
    ./run.sh helm_uninstall
-   ```
+   ```
+
+## MLOps using Model Download
+
+1. Run all the steps mentioned in above [section](#setup-the-application) to setup the application.
+
+2. Install the helm chart
+
+   ```sh
+   ./run.sh helm_install
+   ```
+
+3. Copy the resources such as video and model from local directory to the `dlstreamer-pipeline-server` pod to make them available for application while launching pipelines.
+
+   ```sh
+   # Below is an example for Pallet Defect Detection. Please adjust the source path of models and videos appropriately for other sample applications.
+
+   POD_NAME=$(kubectl get pods -n <INSTANCE_NAME> -o jsonpath='{.items[*].metadata.name}' | tr ' ' '\n' | grep deployment-dlstreamer-pipeline-server | head -n 1)
+
+   kubectl cp resources/pallet-defect-detection/videos/warehouse.avi $POD_NAME:/home/pipeline-server/resources/videos/ -c dlstreamer-pipeline-server -n <INSTANCE_NAME>
+
+   kubectl cp resources/pallet-defect-detection/models/* $POD_NAME:/home/pipeline-server/resources/models/ -c dlstreamer-pipeline-server -n <INSTANCE_NAME>
+   ```
+
+4. Modify the payload in `helm/temp_apps/<SAMPLE_APP>/<INSTANCE_NAME>/payload.json` to launch an instance for the mlops pipeline. 
+   
+   Below is an example for pallet-defect-detection. Please modify the payload for other sample applications.
+
+   ```json
+   [
+       {
+           "pipeline": "pallet_defect_detection_mlops",
+           "payload":{
+               "source": {
+                   "uri": "file:///home/pipeline-server/resources/videos/warehouse.avi",
+                   "type": "uri"
+               },
+               "destination": {
+               "frame": {
+                   "type": "webrtc",
+                   "peer-id": "pdd"
+               }
+               },
+               "parameters": {
+                   "detection-properties": {
+                       "model": "/home/pipeline-server/resources/models/pallet-defect-detection/deployment/Detection/model/model.xml",
+                       "device": "CPU"
+                   }
+               }
+           }
+       }
+   ]
+   ```
+
+5. Start the pipeline with the above payload. 
+
+   Below is an example for starting an instance for pallet-defect-detection:
+
+   ```sh
+   ./sample_start.sh helm -i <INSTANCE_NAME> -p pallet_defect_detection_mlops
+   ```
+   Note the instance-id.
+
+6. Download and prepare the model. Below is an example for downloading and preparing model for pallet-defect-detection. Please modify MODEL_URL for the other sample applications.
+   >NOTE- For sake of simplicity, we assume that the new model has already been downloaded by Model Download microservice. The following curl command is only a simulation that just downloads the model. In production, however, they will be downloaded by the Model Download service.
+
+   ```sh
+   export MODEL_URL='https://github.com/open-edge-platform/edge-ai-resources/raw/a7c9522f5f936c47de8922046db7d7add13f93a0/models/INT8/pallet_defect_detection.zip'
+
+   curl -L "$MODEL_URL" -o "$(basename $MODEL_URL)"
+
+   unzip "$(basename $MODEL_URL)" -d new-model # downloaded model is now extracted to `new-model` directory.
+   ```
+
+7. Copy the new model to the `dlstreamer-pipeline-server` pod to make it available for application while launching pipeline.
+
+   ```sh
+   
+   POD_NAME=$(kubectl get pods -n <INSTANCE_NAME> -o jsonpath='{.items[*].metadata.name}' | tr ' ' '\n' | grep deployment-dlstreamer-pipeline-server | head -n 1)
+
+   kubectl cp new-model $POD_NAME:/home/pipeline-server/resources/models/ -c dlstreamer-pipeline-server -n <INSTANCE_NAME>
+   ```
+   >NOTE- If there are multiple sample_apps in config.yml, repeat steps 6 and 7 for each sample app and instance.
+
+
+8. Stop the existing pipeline before restarting it with a new model. Use the instance-id generated from step 5.
+   ```sh
+   curl -k --location -X DELETE https://<HOST_IP>:<NGINX_HTTPS_PORT>/api/pipelines/{instance_id}
+   ```
+
+9. Modify the payload in `helm/temp_apps/<SAMPLE_APP>/<INSTANCE_NAME>/payload.json` to launch an instance for the mlops pipeline with this new model.
+   
+   Below is an example for pallet-defect-detection. Please modify the payload for other sample applications.
+
+   ```json
+   [
+       {
+           "pipeline": "pallet_defect_detection_mlops",
+           "payload":{
+               "source": {
+                   "uri": "file:///home/pipeline-server/resources/videos/warehouse.avi",
+                   "type": "uri"
+               },
+               "destination": {
+               "frame": {
+                   "type": "webrtc",
+                   "peer-id": "pdd"
+               }
+               },
+               "parameters": {
+                   "detection-properties": {
+                       "model": "/home/pipeline-server/resources/models/new-model/deployment/Detection/model/model.xml",
+                       "device": "CPU"
+                   }
+               }
+           }
+       }
+   ]
+
+10. View the WebRTC streaming on `https://<HOST_IP>:<NGINX_HTTPS_PORT>/mediamtx/<peer-str-id>/` by replacing `<peer-str-id>` with the value used in the original cURL command to start the pipeline.
+
+
+## Troubleshooting
+
+- [Troubleshooting Guide](../troubleshooting.md)
diff --git a/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/worker-safety-gear-detection/get-started/deploy-multiple-instances-with-helm.md b/manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/worker-safety-gear-detection/get-started/deploy-multiple-instances-with-helm.md
