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AppendixAdditional Deployments

Helm Deployment

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This guide covers deploying VSS developer profiles on Kubernetes using Helm. It is an alternative to the Docker Compose method used in Quickstart and the agent-workflows guides.

Install steps, values files, GPU sizing, ingress setup, and troubleshooting for each profile are documented in the chart README.md under deploy/helm/developer-profiles/ in the video-search-and-summarization GitHub repository.

Available developer profiles

  • base
  • alerts
  • search
  • lvs

Prerequisites

  • A Kubernetes cluster (version 1.34 or greater) you can reach with kubectl
  • Helm 3.7+
  • NVIDIA GPU Operator on the cluster
  • NVIDIA NIM Operator when deploying in-cluster LLM and VLM models
  • StorageClass - for PVC provisioning
  • NGC API key — for image pull and NIM model access

GPU requirements depend on the profile and values you choose (for example, in-cluster NIMs vs remote LLM/VLM endpoints). See the GPU requirements section in the chart README for the profile you are deploying.

Steps to deploy a profile

  1. Clone the blueprint repository:

    git clone https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization.git
    cd video-search-and-summarization
    git checkout "develop"
    git lfs install
    git lfs pull

The Helm charts in this checkout use documentation reference develop. Keep that aligned with the image tags selected for the documentation build.

  1. Change into the Helm developer-profiles directory:

    cd ./deploy/helm/developer-profiles

  2. Open the chart README.md for the profile you are deploying and follow the installation and uninstall instructions there:

WorkflowDeveloper ProfileChart documentation
Q&A and Report Generationbasedev-profile-base
Alertsalertsdev-profile-alerts
Searchsearchdev-profile-search
Video Summarizationlvsdev-profile-lvs

After helm upgrade --install, wait until all pods in the release namespace are in Running state and show Ready (for example, kubectl get pods -n <NAMESPACE>) before opening the UI or sending requests. Image pulls, persistent volume binding, and in-cluster NIM model download and warm-up add startup time. For some profiles, bringing every pod to Ready can take 20–25 minutes depending on cluster resources and network speed.