Brev.dev#

The Brev platform provides you with GPU instances on cloud providers including Amazon Web Services and Google Cloud with CUDA, Python, Jupyter Lab, all set up.

You can get up and running with RAPIDS in a few quicks thanks to the Brev RAPIDS quickstart.

Setting up your instance#

Navigate to the Brev console and click on “Create your first instance”.

Screenshot of the "Create your first instance" UI

Select “Container Mode”.

Screenshot showing "Container Mode" highlighted

Attach the “NVIDIA RAPIDS” Container.

Screenshot showing the "NVIDIA RAPIDS" container highlighted

Configure your own instance.

Screenshot of the "Create your Instance" UI with an NVIDIA T4 GPU highlighted

And hit “Deploy”.

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

Accessing your instance#

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

Visual Studio Code#

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

brev open

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

Terminal#

To access your Brev instance from the terminal run:

brev shell

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.

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.

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

Test RAPIDS#

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

Screenshot of a notebook cell importing and using cudf

Resources#