NVIDIA Brev#

The NVIDIA Brev platform provides you a one stop menu of available GPU instances across many cloud providers, including Amazon Web Services and Google Cloud, with CUDA, Python, Jupyter Lab, all set up.

Brev Instance Setup#

There are two options to get you up and running with RAPIDS in a few steps, thanks to the Brev RAPIDS quickstart:

  1. Brev GPU Instances - quickly get the GPU, across most clouds, to get your work done.

  2. Brev Launchables - quickly create one-click starting, reusable instances that you customized to your MLOps needs.

Option 1. Setting up your Brev GPU Instance#

  1. Navigate to the Brev bash and click on “Create new instance”.

Screenshot of the "Create your first instance" UI

  1. Choose a compute type.

Hint

New users commonly choose L4 GPUs for trying things out.

Screenshot of the "Choose a compute type" UI

  1. Select the button to change the container or runtime (the default is “VM Mode w/ Jupyter”)

Screenshot of the "Changing container or runtime" UI

  1. Select “Featured Containers”.

Screenshot showing "Featured Containers" highlighted

  1. Attach the “NVIDIA RAPIDS” Container and choose “Apply”.

Screenshot showing the "NVIDIA RAPIDS" container highlighted

  1. Give your instance a name and hit “Deploy”.

Screenshot of the instance creation summary screen with the deploy button highlighted

Option 2. Setting up your Brev Launchable#

Brev Launchables are shareable environment configurations that combine code, containers, and compute into a single portable recipe. This option is most applicable if you want to set up a custom environment for a blueprint, like our Single-cell Analysis Blueprint. However, you can use this to create quick-start templates for many different kinds of projects when you want users to drop into an environment that is ready to go (e.g. tutorials, workshops, demos, etc.).

You can read more about Brev Launchables in the Getting Started Guide.

  1. Go to Brev’s Launchable Creator (requires account)

  2. When asked How would you like to provide your code files?.

    • Select “I have code files in a git repository”, and provide the link to a GitHub repository, if you have one that you’d like to be mounted in the instance once is up.

    • Otherwise, select “I don’t have any code files”.

  3. When asked What type of runtime environment do you need? select “With container(s)”, and proceed.

Screenshot showing the Brev launchable setup with container

  1. When prompted to Choose a Container Configuration, you have two options:

    1. “Featured Container” and select the “NVIDIA RAPIDS” container: For a ready to go environment with the entire RAPIDS stack and Jupyter configured.

      • Select your desired compute environment. Make sure you select sufficient disk size to download the datasets you want to work with. Note, you will not be able to resize the instance once created.

      • Create a name for your launchable, and deploy.

    2. Docker Compose: For a custom container that you can tailor to your needs.

      • You can provide a docker-compose.yaml via url o from a local file. In the following template, make sure to replace <name_of_your_github_repo> in the volumes path, with the name of your repository if you have one. Otherwise, remove the volumesentry.

      services:
backend:
   image: "rapidsai/notebooks:25.12a-cuda12-py3.13"
   pull_policy: always
   ulimits:
      memlock: -1
      stack: 67108864
   shm_size: 1g
   deploy:
      resources:
      reservations:
         devices:
            - driver: nvidia
            count: all
            capabilities: [gpu]
   environment:
      EXTRA_CONDA_PACKAGES: "hdbscan>=0.8.39 umap-learn>=0.5.7" # example of packages
   ports:
      - "8888:8888"      # Expose JupyterLab
   volumes:
      - /home/ubuntu/<name_of_your_github_repo>:/notebooks/ # e.g tutorial if repo at https://github.com/rapidsai-community/tutorial
   user: root
   working_dir: /notebooks
   entrypoint: ["/home/rapids/entrypoint.sh"]
   command: python -m jupyter lab --allow-root --ip=0.0.0.0 --no-browser --NotebookApp.token='' --NotebookApp.password='' --notebook-dir=/notebooks
   restart: unless-stopped

      • Click “Validate”.

      • Select your desired compute environment. Make sure you select sufficient disk size to download the datasets you want to work with. Note, you will not be able to resize the instance once created.

      • On the next page, when asked Do you want a Jupyter Notebook experience? select No, I don’t want Jupyter. This is because the RAPIDS notebook container already have Jupyter setup. For convenience name the Secure Link to jupyter.

      Screenshot showing the Brev launchable Jupyter experience setup

      • Create a name for your launchable, and deploy.

Accessing your instance#

There are a few ways to access your instance:

  1. Directly access Jupyter Lab from the Brev GUI

  2. Using the Brev CLI to connect to your instance….

  3. Using Visual Studio Code

  4. Using SSH via your terminal

  5. Access using the Brev tunnel

  6. Sharing a service with others

1. Jupyter Notebook#

To create and use a Jupyter Notebook, click “Open Notebook” at the top right after the page has deployed.

Screenshot of the instance UI with the "Open Notebook" button highlighted

2. Brev CLI Install#

If you want to access your launched Brev instance(s) via Visual Studio Code or SSH using terminal, you need to install the Brev CLI according to these instructions or this code below:

$ sudo bash -c "$(curl -fsSL https://raw.githubusercontent.com/brevdev/brev-cli/main/bin/install-latest.sh)" && brev login

2.1 Brev CLI using Visual Studio Code#

To connect to your Brev instance from VS Code open a new VS Code window and run:

$ brev open <instance-id>

It will automatically open a new VS Code window for you to use with RAPIDS.

2.2 Brev CLI using SSH via your Terminal#

To access your Brev instance from the terminal run:

$ brev shell <instance-id>
Forwarding a Port Locally#

Assuming your Jupyter Notebook is running on port 8888 in your Brev environment, you can forward this port to your local machine using the following SSH command:

$ ssh -L 8888:localhost:8888 <username>@<ip> -p 22

This command forwards port 8888 on your local machine to port 8888 on the remote Brev environment.

Or for port 2222 (default port).

$ ssh <username>@<ip> -p 2222

Replace username with your username and ip with the ip listed if it’s different.

Accessing the Service#

After running the command, open your web browser and navigate to your local host. You will be able to access the Jupyter Notebook running in your Brev environment as if it were running locally.

3. Access the Jupyter Notebook via the Tunnel#

The “Deployments” section will show that your Jupyter Notebook is running on port 8888, and it is accessible via a shareable URL Ex: jupyter0-i55ymhsr8.brevlab.com.

Click on the link or copy and paste the URL into your web browser’s address bar to access the Jupyter Notebook interface directly.

4. Share the Service#

If you want to share access to this service with others, you can click on the “Share a Service” button.

You can also manage access by clicking “Edit Access” to control who has the ability to use this service.

Check that your notebook has GPU Capabilities#

You can verify that you have your requested GPU by running the nvidia-smi command.

Screenshot of a notebook terminal running the command nvidia-smi and showing the NVIDIA T4 GPU in the output

Testing your RAPIDS Instance#

You can verify your RAPIDS installation is working by importing cudf and creating a GPU dataframe.

import cudf

gdf = cudf.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
print(gdf)

Resources and tips#

  • Brev Docs

  • Please note: Git is not preinstalled in the RAPIDS container, but can be installed into the container when it is running using

$ apt update

$ apt install git -y