Deploy with Helm* Chart#

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

  • Storage Requirement: Ensure enough storage is available in the cluster for PVC-backed services.

See also: System Requirements.

Helm Chart Installation#

To set up the end-to-end application, acquire the chart and install it with the required values and scenario overrides.

1. Acquire the Helm chart#

There are 2 options to get the charts in your workspace:

Option 1: Get the charts 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/live-video-search --version 2026.2.0-rc1-helm

Use chart version 2026.2.0-rc1-helm for this release workflow.

Step 2: Extract the .tgz file#

After pulling the chart, extract the .tgz file:

tar -xvf live-video-search-2026.2.0-rc1-helm.tgz

This creates a directory named live-video-search containing chart files. Navigate to the extracted directory:

cd live-video-search

Option 2: Install from Source#

Clone the repository and navigate to the chart directory:

# Clone the release branch
git clone https://github.com/open-edge-platform/edge-ai-suites.git edge-ai-suites -b release-2026.2.0
cd edge-ai-suites/metro-ai-suite/live-video-analysis/live-video-search/chart

2. Configure Required Values#

The application requires a few user-provided values. Use user_values_override.yaml as the single user-edit file:

nano user_values_override.yaml

Update these required values:

Key

Description

Example Value

global.credentials.minioRootUser

MinIO user

<your-minio-user>

global.credentials.minioRootPassword

MinIO password

<your-minio-password>

global.credentials.postgresUser

PostgreSQL user

<your-postgres-user>

global.credentials.postgresPassword

PostgreSQL password

<your-postgres-password>

global.credentials.mqttUser

MQTT user

<your-mqtt-user>

global.credentials.mqttPassword

MQTT password

<your-mqtt-password>

global.env.embeddingModelName

Embedding model used by search stack

CLIP/clip-vit-b-32

Common optional values:

Key

Description

Example Value

global.registry

Optional image registry override

intel

global.tag

Shared image tag

2026.2.0-rc1

global.vssStackTag

Override tag for VSS stack services

2026.2.0-rc1

global.smartNvrStackTag

Override tag for Smart NVR services

2026.2.0-rc1

global.pullPolicy

Pull-policy override for application images selected by the shared or stack-specific tags

Always, IfNotPresent, or Never

global.vectordbBackend

Vector database used by Multimodal DataPrep and Vector Retriever

vdms (default) or milvus

global.metricsManager.enabled

Deploy Metrics Manager and enable live system/DataPrep metrics

true (default) or false

metrics-manager.image.repository

Metrics Manager image repository

intel/metrics-manager

metrics-manager.image.tag

Metrics Manager image tag

2026.2.0-rc1

global.proxy.httpProxy

HTTP proxy

http://proxy-example.com:000

global.proxy.httpsProxy

HTTPS proxy

http://proxy-example.com:000

global.usePvc

Use PVC-backed storage paths for MME/DataPrep

true or false

global.keepPvc

Retain PVCs on uninstall

true or false

global.devices.multimodalEmbedding.device

MME embedding execution device

CPU, GPU, or NPU

global.devices.multimodalEmbedding.key

MME accelerator key (required when device=GPU/NPU)

gpu.intel.com/xe, gpu.intel.com/i915, or npu.intel.com/accel

global.devices.multimodalDataprep.embedding.device

DataPrep indexing-embedding device

CPU, GPU, or NPU

global.devices.multimodalDataprep.embedding.key

DataPrep embedding accelerator key (required when embedding.device=GPU/NPU)

gpu.intel.com/xe, gpu.intel.com/i915, or npu.intel.com/accel

global.devices.multimodalDataprep.detection.device

DataPrep detection execution device

CPU, GPU, or NPU

global.devices.multimodalDataprep.detection.key

DataPrep detection accelerator key (required when detection.device=GPU/NPU)

gpu.intel.com/xe, gpu.intel.com/i915, or npu.intel.com/accel

global.accelGroupIds

Host group ids owning the accelerator device nodes (/dev/dri, /dev/accel); added to the pod supplementalGroups when a service uses GPU/NPU

[992]

frigate.usbCameraDevice

USB device path (used with USB profile)

/dev/video0

Note: Scenario selection is profile-driven. Use override profiles for mode switching (default_override.yaml, rtsp_test_override.yaml, usb_camera_override.yaml) instead of setting mode switches in user_values_override.yaml.

Tag Resolution Note: global.tag is the fallback image tag. If global.vssStackTag is non-empty, VSS-side services use it instead of global.tag. If global.smartNvrStackTag is non-empty, Smart NVR-side services use it instead of global.tag. Leaving stack-specific tags empty makes those services inherit global.tag.

Pull Policy Note: Leaving global.pullPolicy empty preserves each subchart’s default. Values are case-insensitive and normalized to Kubernetes’ canonical spelling. Setting it overrides the pull policy for Pipeline Manager, Video Search, VSS UI, Multimodal DataPrep, Multimodal Embedding Serving, Vector Retriever, and NVR Event Router. Third-party infrastructure images retain their component-specific policies.

Device Note: All device selection is per-component via the global.devices.* block. Each component defaults to CPU and requires its matching key only when set to GPU or NPU.

Accelerator Note: Multimodal DataPrep creates indexing embeddings in-process and uses global.devices.multimodalDataprep.*. MME uses global.devices.multimodalEmbedding.* for query embeddings requested by Vector Retriever.

Device Permissions Note: When a component runs on GPU or NPU, its host accelerator nodes (/dev/dri for GPU, /dev/accel for NPU) are mounted and the gids in global.accelGroupIds are added to the pod supplementalGroups so the non-root container user can open the device. These gids are host-specific — check the target node with ls -ln /dev/accel and ls -ln /dev/dri and override global.accelGroupIds to match (default [992]).

OpenVINO Cache Note: On GPU/NPU, MME and DataPrep write the first-time OpenVINO model compilation to ovCacheDir (default /app/ov_models/ov_cache) on the persistent models mount, so the compile is reused across pod restarts instead of recompiling on every start.

Storage Note: MME and DataPrep now use independent PVCs (<release>-live-video-search-mmes-models-pvc and <release>-live-video-search-dataprep-models-pvc, with per-service *-data-pvc fallback), so they are no longer coupled through a shared PVC.

Metrics Manager Note: Metrics Manager runs with host PID access, privileged device access, and read-only /sys and /run mounts so it can collect host metrics. It does not use or share a PVC. Multimodal DataPrep publishes throughput metrics directly to metrics-manager:9090; NGINX exposes only the health and SSE stream endpoints to the UI. Publishing is non-blocking, so ingestion continues if Metrics Manager becomes unavailable. The bundled DataPrep image must support MM_DATAPREP_METRICS_MANAGER_URL.

3. Build Helm Dependencies#

Run from the chart directory:

helm dependency build

4. Set and Create a Namespace#

  1. Set a namespace variable:

    my_namespace=lvs

  2. Create namespace:

    kubectl create namespace $my_namespace

NOTE: All subsequent steps assume my_namespace is set in your shell.

5. Deploy the Helm Chart#

Deploy one of the following use cases.

Note: Before switching use cases, uninstall the existing release if it is already running: helm uninstall lvs -n $my_namespace

Use Case 2: RTSP Test Mode#

helm install lvs . -f user_values_override.yaml -f rtsp_test_override.yaml -n $my_namespace

Use Case 3: USB Camera Mode#

helm install lvs . -f user_values_override.yaml -f usb_camera_override.yaml -n $my_namespace

Use Case 4: Accelerator-enabled MME + DataPrep (GPU or NPU)#

First update user_values_override.yaml:

  • global.devices.multimodalEmbedding.device: GPU|NPU

  • global.devices.multimodalEmbedding.key: <accelerator-resource-key> (for example gpu.intel.com/xe or npu.intel.com/accel)

  • global.devices.multimodalDataprep.embedding.device: GPU|NPU

  • global.devices.multimodalDataprep.embedding.key: <accelerator-resource-key>

  • global.devices.multimodalDataprep.detection.device: GPU|NPU

  • global.devices.multimodalDataprep.detection.key: <accelerator-resource-key>

  • global.accelGroupIds: [<gid>] (host gids owning /dev/dri and /dev/accel; check the target node with ls -ln /dev/accel and ls -ln /dev/dri)

Then deploy with your selected scenario profile (example: default):

helm install lvs . -f user_values_override.yaml -f default_override.yaml -n $my_namespace

Use Case 5: Milvus Vector Database#

Milvus uses the same application and camera profiles. Add milvus_override.yaml after the selected scenario override:

helm install lvs . -f user_values_override.yaml -f default_override.yaml -f milvus_override.yaml -n $my_namespace

The override sets global.vectordbBackend: milvus, disables the VDMS workload, and enables the pinned Milvus standalone and etcd workloads. Vector Retriever automatically selects the vector-retriever-milvus image. Without this override, the chart deploys VDMS and vector-retriever-vdms.

Step 6: Verify the Deployment#

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

Before proceeding, ensure:

  1. Pods are in Running state.

  2. Containers are in ready state.

Note: init-resources runs as a Kubernetes Job. Its pod can show 0/1 Completed (for example, lvs-live-video-search-init-resources-xxxxx 0/1 Completed), which is expected. Use kubectl get jobs -n $my_namespace and confirm lvs-live-video-search-init-resources shows COMPLETIONS 1/1 and STATUS Complete.

If needed, inspect specific workloads:

kubectl describe pod <pod-name> -n $my_namespace
kubectl logs <pod-name> -n $my_namespace

When metrics are enabled, verify the same-origin endpoints through NGINX:

kubectl get pods -n $my_namespace -l app.kubernetes.io/name=metrics-manager
kubectl port-forward svc/nginx 12345:80 -n $my_namespace
curl http://localhost:12345/metrics-manager/health
curl -N -H "Accept: text/event-stream" \
  http://localhost:12345/metrics-manager/metrics/stream

Step 7: Accessing the application#

Nginx service runs as a reverse proxy in one of the pods and is exposed via NodePort by default. Get the host IP and NodePort using:

lvs_hostip=$(kubectl get pods -l app=nginx -n $my_namespace -o jsonpath='{.items[0].status.hostIP}')
lvs_port=$(kubectl get service nginx -n $my_namespace -o jsonpath='{.spec.ports[0].nodePort}')
echo "http://${lvs_hostip}:${lvs_port}"

Copy the printed URL and open it in your browser to access the Live Video Search Application.

If you prefer local access without NodePort:

kubectl port-forward svc/nginx 12345:80 -n $my_namespace

Open http://localhost:12345.

Step 8: Update Helm Dependencies#

If subchart dependencies change:

helm dependency build

Step 9: Uninstall Helm chart#

helm uninstall lvs -n $my_namespace

PVC retention on uninstall is controlled by global.keepPvc.

When global.keepPvc: true, PVC-backed data is retained across uninstall/reinstall and pod restarts. This includes persisted application state (for example, stored query-related data in backing services) and converted OpenVINO model assets stored on persistent volumes.

If you want a clean reset, delete all PVCs for the lvs release:

kubectl delete pvc -n "$my_namespace" -l app.kubernetes.io/instance=lvs

Troubleshooting#

  • Pods stay Pending or not Ready: Check storage provisioning, node capacity, and device plugin availability (for GPU/NPU accelerator mode).

  • Node allocation/scheduling issues caused by PVC affinity conflicts (often from old PVCs): Delete old release PVCs and redeploy:

    kubectl delete pvc -n "$my_namespace" -l app.kubernetes.io/instance=lvs

  • VolumeBinding “object has been modified” warning during scheduling: A one-off FailedScheduling ... running PreBind plugin "VolumeBinding": ... the object has been modified warning that is immediately followed by a successful Scheduled event is a transient optimistic-lock retry and is safe to ignore — it self-heals on the scheduler’s next attempt. The MME and DataPrep pods now reference each model PVC through a single volume, so this should no longer recur. If a pod stays Pending and the warning repeats indefinitely, treat it as a real failure: check that the model PVC is Bound (kubectl get pvc -n "$my_namespace") and that the WaitForFirstConsumer storage class can provision on the target node.

  • Search not returning expected results: Verify global.env.embeddingModelName, confirm clips are ingested, and check that Vector Retriever is ready with the same backend selected by global.vectordbBackend.

  • Live metrics are not displayed: Confirm global.metricsManager.enabled is true, the Metrics Manager pod is ready, and kubectl logs deployment/metrics-manager -n "$my_namespace" does not report missing host access.

  • USB mode does not detect camera: Confirm device path and override frigate.usbCameraDevice in user_values_override.yaml when not using /dev/video0.

  • Accelerator deployment fails validation: Verify the required key is set for each accelerator path (global.devices.multimodalEmbedding.key for MME, global.devices.multimodalDataprep.embedding.key / global.devices.multimodalDataprep.detection.key for DataPrep) whenever the matching device is set to GPU or NPU.

  • Accelerator pod cannot access the device (NPU/GPU init fails): Confirm global.accelGroupIds matches the host gids owning /dev/accel (NPU) and /dev/dri (GPU) on the scheduled node (ls -ln /dev/accel, ls -ln /dev/dri). The non-root container needs these in its supplementalGroups to open the device.