# How to Deploy with Helm - **Time to Complete:** 30 minutes ## Get Started Complete this guide to confirm that your setup is working correctly and try out workflows in the sample application. ## Prerequisites - [System Requirements](system-requirements.md) - K8s installation on single or multi node must be done as pre-requisite 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 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). ## Download the helm chart 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/smart-parking --version 1.1.0` 2. unzip the package using the following command `tar xvf smart-parking-1.1.0.tgz` - Get into the helm directory `cd smart-parking` ## Configure and update the environment variables 1. Update the below fields in `values.yaml` file in the helm chart ``` sh HOST_IP: # replace localhost with system IP example: HOST_IP: 10.100.100.100 http_proxy: # example: http_proxy: http://proxy.example.com:891 https_proxy: # example: http_proxy: http://proxy.example.com:891 webrtcturnserver: username: # example: username: myuser password: # example: password: mypassword ``` ## Deploy the application and Run multiple AI pipelines Follow this procedure to run the sample application. In a typical deployment, multiple cameras deliver video streams that are connected to AI pipelines to improve the classification and recognition accuracy. The following demonstrates running multiple AI pipelines and visualization in the Grafana. 1. Deploy helm chart ```sh helm install smart-parking . -n sp --create-namespace ``` 2. Verify all the pods and services are running: ```sh kubectl get pods -n sp kubectl get svc -n sp ``` 3. Start the application with the Client URL (cURL) command by replacing the with the Node IP. (Total 8 places) ``` sh curl http://:30485/pipelines/user_defined_pipelines/yolov10_1_cpu -X POST -H 'Content-Type: application/json' -d ' { "source": { "uri": "file:///home/pipeline-server/videos/new_video_1.mp4", "type": "uri" }, "destination": { "metadata": { "type": "mqtt", "host": ":30483", "topic": "object_detection_1", "timeout": 1000 }, "frame": { "type": "webrtc", "peer-id": "object_detection_1" } }, "parameters": { "detection-device": "CPU" } }' curl http://:30485/pipelines/user_defined_pipelines/yolov10_1_cpu -X POST -H 'Content-Type: application/json' -d ' { "source": { "uri": "file:///home/pipeline-server/videos/new_video_2.mp4", "type": "uri" }, "destination": { "metadata": { "type": "mqtt", "host": ":30483", "topic": "object_detection_2", "timeout": 1000 }, "frame": { "type": "webrtc", "peer-id": "object_detection_2" } }, "parameters": { "detection-device": "CPU" } }' curl http://:30485/pipelines/user_defined_pipelines/yolov10_1_cpu -X POST -H 'Content-Type: application/json' -d ' { "source": { "uri": "file:///home/pipeline-server/videos/new_video_3.mp4", "type": "uri" }, "destination": { "metadata": { "type": "mqtt", "host": ":30483", "topic": "object_detection_3", "timeout": 1000 }, "frame": { "type": "webrtc", "peer-id": "object_detection_3" } }, "parameters": { "detection-device": "CPU" } }' curl http://:30485/pipelines/user_defined_pipelines/yolov10_1_cpu -X POST -H 'Content-Type: application/json' -d ' { "source": { "uri": "file:///home/pipeline-server/videos/new_video_4.mp4", "type": "uri" }, "destination": { "metadata": { "type": "mqtt", "host": ":30483", "topic": "object_detection_4", "timeout": 1000 }, "frame": { "type": "webrtc", "peer-id": "object_detection_4" } }, "parameters": { "detection-device": "CPU" } }' ``` 4. View the Grafana and WebRTC streaming on `http://:30480`. - Log in with the following credentials: - **Username:** `admin` - **Password:** `admin` - Check under the Dashboards section for the default dashboard named "Video Analytics Dashboard". ![Example of Grafana and WebRTC streaming](_images/grafana.png) Figure 1: Grafana and WebRTC streaming ## End the demonstration Follow this procedure to stop the sample application and end this demonstration. 1. Stop the sample application with the following command that uninstalls the release smart-parking. ```sh helm uninstall smart-parking -n sp ``` 2. Confirm the pods are no longer running. ```sh kubectl get pods -n sp ``` ## Summary In this guide, you installed and validated Smart Parking sample application. You also completed a demonstration where multiple pipelines run on a single system with near real-time classification. ## Troubleshooting The following are options to help you resolve issues with the sample application. ### Deploying with Intel GPU K8S Extension on ITEP If you're deploying a GPU based pipeline (example: with VA-API elements like `vapostproc`, `vah264dec` etc., and/or with `device=GPU` in `gvadetect` in `config.json`) with Intel GPU k8s Extension on ITEP, ensure to set the below details in the file `helm/values.yaml` appropriately in order to utilize the underlying GPU. ```sh gpu: enabled: true type: "gpu.intel.com/i915" count: 1 ``` ### Deploying without Intel GPU K8S Extension If you're deploying a GPU based pipeline (example: with VA-API elements like `vapostproc`, `vah264dec` etc., and/or with `device=GPU` in `gvadetect` in `config.json`) without Intel GPU k8s Extension, ensure to set the below details in the file `helm/values.yaml` appropriately in order to utilize the underlying GPU. ```sh privileged_access_required: true ``` ### Error Logs View the container logs using this command. kubectl logs -f -n sp