Deploy with Helm#

This section shows how to deploy the Live Video Alert Agent using the 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 installed on your system. See the Installation Guide.

  • Storage Requirement: The chart creates a 10 Gi PVC for the VLM model on first run.

Helm Chart Installation#

1. Acquire the Helm chart#

Option 1: Get the chart from Docker Hub#

helm pull oci://registry-1.docker.io/intel/live-video-alert-agent-chart --version <version-no>
tar -xvf live-video-alert-agent-chart<version-no>.tgz
cd live-video-alert-agent-chart

Refer to the Release Notes for the latest version.

Option 2: Install from Source#

git clone https://github.com/open-edge-platform/edge-ai-suites.git edge-ai-suites
cd edge-ai-suites/metro-ai-suite/live-video-analysis/live-video-alert-agent/chart

2. Configure Required Values#

Edit user_values_override.yaml with values for your environment:

Key

Description

Example

global.externalIP

(Required) IP of a cluster node reachable by your browser

<clusternodeip>

global.keepPvc

Retain model PVC on uninstall (avoids ~10 min re-download)

true

global.huggingfaceToken

Required only for gated models on first download

hf_xxx

global.proxy.httpProxy

HTTP proxy for outbound connections

http://proxy.example.com:<port>

global.proxy.httpsProxy

HTTPS proxy for outbound connections

http://proxy.example.com:<port>

global.proxy.noProxy

Addresses that bypass the proxy

localhost,127.0.0.1,...,.svc.cluster.local

global.gpu.enabled

Enable Intel GPU for VLM inference

true / false

global.gpu.key

GPU resource key from the device plugin

gpu.intel.com/i915

global.gpu.device

Target device for inference

GPU

global.gpu.supplementalGroups

Linux group IDs for GPU device access. Run getent group render video | cut -d: -f3 on the GPU node to find values

[109, 44]

app.rtspUrl

RTSP stream URL to load at startup (optional)

rtsp://host:port/stream

app.nodeSelector

Schedule app pod on a specific node

kubernetes.io/hostname: worker1

Note: user_values_override.yaml may contain credentials. Do not commit it to version control.

3. Build Helm Dependencies#

helm dependency build

4. Set and Create a Namespace#

my_release=lva
my_namespace=lva
kubectl create namespace $my_namespace || true

Note: All subsequent steps assume my_release and my_namespace are set in your shell session. The || true makes the namespace creation safe to re-run.

5. Deploy the Helm Chart#

helm install $my_release . -f user_values_override.yaml -n $my_namespace

6. Verify the Deployment#

kubectl get pods -n $my_namespace
kubectl get svc -n $my_namespace

Before proceeding, ensure all pods show Running status and 1/1 in the READY column.

Note: The OVMS pod may take up to 10 minutes on first start while the VLM model is downloaded. Set global.keepPvc: true to retain the model across reinstalls.

7. Access the Application#

node_ip=$(kubectl get pods -l app.kubernetes.io/component=app -n $my_namespace -o jsonpath='{.items[0].status.hostIP}')
app_port=$(kubectl get svc -l app.kubernetes.io/component=app -n $my_namespace -o jsonpath='{.items[0].spec.ports[0].nodePort}')
echo "http://${node_ip}:${app_port}"

Open the printed URL in your browser to access the Live Video Alert Agent dashboard.

8. Uninstall Helm Chart#

helm uninstall $my_release -n $my_namespace

PVC retention on uninstall is controlled by global.keepPvc. To delete the PVC manually:

kubectl delete pvc ${my_release}-ovms-models -n $my_namespace

Upgrading#

After modifying subchart sources or pulling a new chart version, rebuild dependencies before redeploying:

helm dependency build
helm upgrade $my_release . -f user_values_override.yaml -n $my_namespace

Troubleshooting#

  • Pods stuck in Pending: Check storage availability and node capacity.

    kubectl describe pod <pod-name> -n $my_namespace
kubectl get events -n $my_namespace --sort-by='.metadata.creationTimestamp'

  • OVMS pod slow to start: Expected on first deploy — model is downloading (~2 GB). Monitor with:

    kubectl logs -n $my_namespace deployment/${my_release}-ovms --follow

  • ImagePullBackOff: Check image name and tag overrides in user_values_override.yaml. Ensure registry is reachable.

  • GPU not working: Verify device plugin resource key with kubectl describe node <gpu-node> | grep gpu.intel.com.

  • Check logs:

    kubectl logs <pod-name> -n $my_namespace