How to deploy with Helm* Chart#

This section shows how to deploy the Video Search and Summary Sample Application using Helm chart.

Prerequisites#

Before you begin, ensure that you have the following:

  • Kubernetes* cluster set up and running.

  • The cluster must support dynamic provisioning of Persistent Volumes (PV). Refer to the Kubernetes Dynamic Provisioning Guide for more details.

  • Install kubectl on your system. See the Installation Guide. Ensure access to the Kubernetes cluster.

  • Helm chart installed on your system. See the Installation Guide.

  • Video Search and Summary requires PVC storage class to support RWMany mode. In case the default storage class used does not support it, consider using storage solution like LongHorn that provides this support. Video Search and Summary intends to remove this prerequisite in future release and use only RWOnce mode.

Steps to deploy with Helm#

Do the following to deploy VSS using Helm chart.

Steps 1 to 3 varies depending on if the user prefers to build or pull the Helm details.

Option 1: Install from Docker Hub#

Step 1: Pull the Specific Chart#

Use the following command to pull the Helm chart from Docker Hub:

helm pull oci://registry-1.docker.io/intel/video-search-and-summarization --version 1.2.0

Refer to the release notes for details on the latest version number to use for the sample application. Use the above specified version for release-1.2.0.

Step 2: Extract the .tgz File#

After pulling the chart, extract the .tgz file:

tar -xvf video-search-and-summarization-<version-no>.tgz

This will create a directory named video-search-and-summarization containing the chart files. Navigate to the extracted directory.

cd video-search-and-summarization

Step 3: Configure the values.yaml File#

Edit the values.yaml file to set the necessary environment variables. At minimum, ensure you set the services credentials, provide model details (LLM and VLM), and proxy settings as required.

Key

Description

Example Value

global.huggingface.apiToken

Your Hugging Face API token

<your-huggingface-token>

global.proxy.http_proxy

HTTP proxy if required

http://proxy-example.com:000

global.proxy.https_proxy

HTTPS proxy if required

http://proxy-example.com:000

global.vlmName

VLM model to be used by VLM Inference Microservice

Qwen/Qwen2.5-VL-7B-Instruct

global.llmName

LLM model to be used by OVMS (used only when OVMS is enabled)

Intel/neural-chat-7b-v3-3

global.env.UI_NODEPORT

UI service NodePort. Change the default value in case of port conflicts.

31998

global.env.POSTGRES_USER

PostgreSQL user

<your-postgres-user>

global.env.POSTGRES_PASSWORD

PostgreSQL password

<your-postgres-password>

global.env.POSTGRES_DB

PostgreSQL database name

video_summary_db

global.env.MINIO_ROOT_USER

MinIO server user name

<your-minio-user> (at least 3 characters)

global.env.MINIO_ROOT_PASSWORD

MinIO server password

<your-minio-password> (at least 8 characters)

global.env.MINIO_PROTOCOL

MinIO protocol

http:

global.env.MINIO_BUCKET

MinIO bucket name

video-search-summary

global.env.RABBITMQ_DEFAULT_USER

RabbitMQ username

<your-rabbitmq-username>

global.env.RABBITMQ_DEFAULT_PASS

RabbitMQ password

<your-rabbitmq-password>

global.env.OTLP_ENDPOINT

OTLP endpoint

Leave empty if not using telemetry

global.env.OTLP_ENDPOINT_TRACE

OTLP trace endpoint

Leave empty if not using telemetry

global.env.keeppvc

Set true to persists the storage. Default is false

false

Option 2: Install from Source#

Step 1: Clone the Repository#

Clone the repository containing the Helm chart:

git clone https://github.com/open-edge-platform/edge-ai-libraries.git edge-ai-libraries -b release-1.2.0

Step 2: Change to the Chart Directory#

Navigate to the chart directory:

cd edge-ai-libraries/sample-applications/video-search-and-summarization/chart

Step 3: Configure the values.yaml File#

Edit the values.yaml file located in the chart directory to set the necessary environment variables. Refer to the table in Option 1, Step 3 for the list of keys and example values. Choose the appropriate *.yaml file based on the model server/usecase you want to use:

Step 4: Build Helm Dependencies#

Navigate to the chart directory and build the Helm dependencies using the following command:

helm dependency build

Step 5: Deploy the Helm Chart#

Depending on your use case, deploy the Helm chart using the appropriate command.

NOTE: Before switching to a different mode always stop the current application stack by running:

helm uninstall vss -n <your-namespace>

Use Case 1: Video Summary Only#

Deploy the Video Summary application:

helm install vss . -f summary_override.yaml -n <your-namespace>

Note delete the chart for installing the chart in other modes helm uninstall vss -n <your-namespace>

Replace <your-namespace> with your desired Kubernetes namespace.

Note: If your namespace doesn’t exist yet, create it with kubectl create namespace <your-namespace> before running the helm install command.

Sub Use Case 1: Video Summary with OVMS#

If you want to use OVMS for LLM Summarization, deploy with the OVMS override values:

helm install vss . -f summary_override.yaml -f ovms_override.yaml -n <your-namespace>

Note: When deploying OVMS, the OVMS service may take more time to start due to model conversion.

Use Case 2: Video Search Only#

To deploy only the Video Search functionality, use the search override values:

helm install vss . -f search_override.yaml -n <your-namespace>

Step 6: Verify the Deployment#

Check the status of the deployed resources to ensure everything is running correctly:

kubectl get pods -n <your-namespace>
kubectl get services -n <your-namespace>

Ensure all pods are in the “Running” state before proceeding.

Step 7: Retrieving the Service Endpoint (NodePort and NodeIP)#

To access the vss-nginx service running in your Kubernetes cluster using NodePort, you need to retrieve:

  • NodeIP – The internal IP of a worker node.

  • NodePort – The port exposed by the service (default is 31998 as specified in values.yaml).

Run the following command to get the service URL:

echo "http://$(kubectl get pods -l app=vss-nginx -n <your-namespace> -o jsonpath='{.items[0].status.hostIP}')":<ui-nodeport>

Simply copy and paste the output into your browser to access the Video Summary application UI.

Step 8: Update Helm Dependencies#

If any changes are made to the subcharts, update the Helm dependencies using the following command:

helm dependency update

Step 9: Uninstall Helm chart#

To uninstall the Video Summary Helm chart, use the following command:

helm uninstall vss -n <your-namespace>

Verification#

  • Ensure that all pods are running and the services are accessible.

  • Access the Video Summary application dashboard and verify that it is functioning as expected.

  • Upload a test video to verify that the ingestion, processing, and summary pipeline works correctly.

  • Check that all components (MinIO, PostgreSQL, RabbitMQ, video ingestion, VLM inference, audio analyzer) are functioning properly.

Troubleshooting#

  • If helm is not able to install because nginx is facing port conflicts, please try to increment the port number in global.env.UI_NODEPORT and re-try the helm installation.

  • If you encounter any issues during the deployment process, check the Kubernetes logs for errors:

    kubectl logs <pod-name> -n <your-namespace>

  • For component-specific issues:

    • Video ingestion problems: Check the logs of the videoingestion pod

    • VLM inference issues: Check the logs of the vlm-inference-microservice pod

    • Database connection problems: Verify the PostgreSQL pod is running correctly

    • Storage issues: Check the MinIO server status and connectivity

  • If you’re experiencing issues with the Hugging Face API, ensure your API token is valid and properly set in the values.yaml file.