Setup VSS and Skills

Minimum path to a deployed VSS stack driven from the agent chat UI. Only VSS itself requires a GPU; NemoClaw and the VSS Orchestrator MCP server are CPU-only. NemoClaw can run on a separate machine from the VSS host, as remote deployment and VSS orchestration are supported by the VSS Orchestrator MCP server.

This guide uses the simplest layout and runs everything on a single VSS-capable host (such as the Brev instance) through two notebooks that run in order:

1. deploy/docker/scripts/deploy_nemoclaw.ipynb - pins Docker, installs NemoClaw, creates the sandbox from the VSS harness image, applies the VSS policy and deployment origin, optionally registers an HTTPS orchestrator path, configures optional webhooks, and opens the Agent UI.
2. deploy/docker/scripts/deploy_vss_orchestrator.ipynb - prepares the host for VSS, starts the VSS Orchestrator MCP server, and deploys and manages VSS from the Agent UI by chatting with the agent.

Follow the cells in each notebook in order. For the full variable reference and optional verification cells, see Additional Configuration.

Prerequisites

• Common to both notebooks

  • Access to the VSS repository.

  • Docker, curl, python3, and uv on the host that runs the notebook.

  • A Jupyter kernel with Python 3.11 or newer.

    • Auto-fulfilled when running from the Brev Launchable below.

• Part 1 - Set up NemoClaw (deploy_nemoclaw.ipynb, CPU only)

  • An agent chat model provider:

    • NVIDIA_API_KEY for the build.nvidia.com provider. Create one from NVIDIA API Keys, OR
    • COMPATIBLE_API_KEY for a cloud or local OpenAI-compatible endpoint, issued by that endpoint’s provider.

• Part 2 - Deploy VSS (deploy_vss_orchestrator.ipynb, GPU required)

  • A VSS-capable GPU host, such as a preconfigured Brev Launchable or your own GPU-enabled machine.

    • If you are using the Brev Launchable, you can edit the Brev hardware profile and cloud service provider (CSP). See Brev hardware and CSP selection before launching.
    • In the single-host layout used by this guide, this same machine also runs Part 1.

  • NGC_CLI_API_KEY (NGC Personal API Key) for nvcr.io login and the model weights the containers download from NGC.

  • NVIDIA_API_KEY for the VSS model endpoints.

  • A HARDWARE_PROFILE value matching the VSS deployment host.

Part 1 - Set up NemoClaw

1. Prepare the VSS repository at ~/video-search-and-summarization before opening either notebook.

   On Brev, the repository is cloned automatically. Click Open Notebook on the instance page, then open a terminal in JupyterLab and check out the repository reference selected for this documentation build:

   cd ~/video-search-and-summarization
   git fetch origin "develop"
   git checkout --detach FETCH_HEAD

   On your own host, clone the repository and check out the same reference:

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

   If your checkout is elsewhere or the host has multiple clones, set VSS_REPO_DIR before launching Jupyter.

2. Open deploy/docker/scripts/deploy_nemoclaw.ipynb on the VSS host. If either setup notebook is already open, close and reopen it after checking out the reference so it loads the selected version.

   • If you are re-onboarding a host that previously ran an older OpenShell gateway, review the NemoClaw sandbox lifecycle guidance before running the notebook.

3. Follow the notebook sections in order:

   Section	What it does
   1	Choose the agent chat model and review advanced settings.
   2	Check host and repository prerequisites, then pin Docker to the tested versions.
   3.1	Install NemoClaw and create the sandbox from the selected harness image, replacing an existing sandbox by default.
   3.2	Apply the VSS sandbox policy and deployment origin.
   3.3	Register the VSS Orchestrator MCP path only when ORCHESTRATOR_ENABLE_HTTPS=True; the default HTTP path requires no registration.
   3.4	Configure optional agent webhooks.
   3.5	Open the Agent UI.
   3.6	Optionally verify the sandbox, policy, workspace, and webhooks.

If you are not running on Brev, forward the gateway port from your local machine before opening the UI:

ssh -L 18789:127.0.0.1:18789 <user>@<nemoclaw-host>

Part 2 - Deploy VSS from chat

With the Agent UI open, open deploy/docker/scripts/deploy_vss_orchestrator.ipynb on the VSS host.

1. Set NEMOCLAW_SANDBOX_NAME to the sandbox you onboarded in Part 1 and set HARDWARE_PROFILE to match the VSS deployment host. For edge hardware profiles (DGX-SPARK, IGX-THOR, AGX-THOR), see Run on DGX Spark before you deploy.

2. Follow the notebook sections in order. The notebook covers:

   • Set NVIDIA credentials and hardware profile
   • Check host and repo prerequisites
   • Prepare the host for local NIM-backed profiles
   • Start the VSS Orchestrator MCP server
   • Deploy and manage VSS from chat
   • Optionally verify host reachability from inside the sandbox

For the complete orchestrator tool reference, see VSS Orchestrator MCP.

Run on DGX Spark

DGX Spark is a single-GPU edge system, so it needs a few adjustments before you deploy VSS from chat. Apply the following in deploy_vss_orchestrator.ipynb. The same guidance applies to the other edge profiles, IGX-THOR and AGX-THOR.

1. Select the DGX Spark hardware profile (Section 1.1):

   HARDWARE_PROFILE = "DGX-SPARK"

2. Deploy only the base or alerts profiles. DGX-SPARK is an edge profile, and the orchestrator supports only base and alerts on edge hardware. Requests to deploy search or lvs on an edge profile are rejected.

3. Point the VSS LLM at a remote endpoint (Section 1.2). A single GPU can run both a local LLM and the local VLM, but sharing the device often exhausts GPU memory. A remote LLM is recommended:

   LLM_NAME = "nvidia/nemotron-3.5-lightning-30b-a3b"
   LLM_ENDPOINT_URL = "https://integrate.api.nvidia.com"
   LLM_MODEL_TYPE = "openai"
   OPENAI_API_KEY = ""

   Leave OPENAI_API_KEY blank to fall back to NVIDIA_API_KEY, which is the key the integrate.api.nvidia.com endpoint expects. Set OPENAI_API_KEY only when the endpoint authenticates with its own key rather than an NVIDIA key, such as https://api.openai.com.

4. Share the single GPU (Section 1.2). With a remote LLM from the previous step, set VLM_DEVICE_ID to GPU 0 so the local VLM uses the only available GPU:

   LLM_DEVICE_ID = "0"  # ignored when LLM_ENDPOINT_URL is set (no local LLM NIM)
   VLM_DEVICE_ID = "0"  # meaningful setting: place the local VLM on the single GPU

5. Expect the Docker pin to skip the downgrade (deploy_nemoclaw.ipynb Section 2.1). On DGX Spark (DGX-OS, arm64), Pin Docker skips the exact-version downgrade when the installed Docker Engine is already within the tested range (28.3.3 through 29.5.0), because the DGX-OS apt repository may not carry the exact pinned versions. The packages are still held with apt-mark hold so the stack cannot drift past the tested range.

6. Leave the orchestrator MCP config defaults unless you need to tune them. deploy/docker/scripts/vss_orchestrator_mcp_config.yml already sets DGX Spark defaults such as VLM_NIM_KVCACHE_PERCENT, RTVI_VLLM_GPU_MEMORY_UTILIZATION, and the SBSA image tags. Change those values only if you need different GPU memory or image settings, then restart the MCP server. See VSS Orchestrator MCP config YAML.

For the full variable reference, see Additional Configuration.

Add Video Sources

Once the VSS stack is deployed, open the VSS UI and use the Video Management tab to upload video files (MP4, MKV) or add live RTSP camera streams for the agent to ingest. See Video Management Tab for the full walkthrough of upload, RTSP setup, and stream management.