> This page is for version Dev.
> For other versions, use one of these documentation indexes:
> - 3.3.0 (default): https://docs.nvidia.com/vss/3.3.0/llms.txt
> - Dev: https://docs.nvidia.com/vss/dev/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/vss/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/vss/_mcp/server.

# Brev Launchable

Launchables are a part of [NVIDIA Brev](https://developer.nvidia.com/brev#section-browse-launchables) that deliver preconfigured, fully optimized compute and software environments.

This deployment uses the Docker Compose deployment method to set up this blueprint.

[Link to GitHub Repository](https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop) used in this launchable.

[Launch Blueprint](https://brev.nvidia.com/launchable/deploy?launchableID=env-2tYIjRXL4eMCbH9Az8mJC5WPAI4)

## Prerequisites

### Host System Prerequisites

Brev Launchable takes care of all these requirements. The provided notebook adjusts the system requirements.

* Ubuntu 24.04
* NVIDIA driver 595.91.07 (Brev RTX PRO 6000 instances)
* Docker Compose v2.39.1+ (Recommended minimum version)

### Obtain NGC API Key

To obtain the required NGC API Key, follow the steps in:

* [NGC User Guide](https://docs.nvidia.com/ngc/gpu-cloud/ngc-user-guide/index.html#generating-api-key)
* [9.3. Generating NGC API Keys](https://docs.nvidia.com/ngc/gpu-cloud/ngc-user-guide/index.html#generating-api-key)

## Steps to Launch the Blueprint

1. Go to the [Launchable page](https://brev.nvidia.com/launchable/deploy?launchableID=env-2tYIjRXL4eMCbH9Az8mJC5WPAI4).

   ![Brev Launchable configuration page for the VSS Blueprint](/vss/_fern-img/3075d29b634d1f17b40130eb0646f657d93fea6162b08e44fdf6cb342fcc3c7c.webp)

2. Click on 'Deploy Launchable' on top right.

3. Click on 'Go to Instance Page'

4. Click on 'Open Notebook' button after  it is enabled (This could take a couple of minutes).

   ![VSS Brev Launchable Notebook](/vss/_fern-img/4bce4e9044c61b9f6101aa41b0ee1b5afdad56dcb74c1f764f6bfb0f8e554b18.webp)

5. Open a terminal in JupyterLab and check out the [repository reference selected for this documentation build](https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop) before opening the launchable notebook:

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

6. Navigate to and open the `video-search-and-summarization/deploy/docker/scripts/deploy_vss_launchable.ipynb` notebook. If it is already open, close and reopen it after checking out the reference so it loads the selected version.
   This notebook is designed to run as a launchable on 2xRTX PRO 6000 SE GPUs on AWS Cloud Provider.

7. Add your NGC_API_KEY in the first code cell.

8. Run each cell and follow the instructions in the notebook to access video search and summarization blueprint UI.

> **Note**
>
> The complete setup takes around 10 minutes as it downloads the containers and sets up the services.