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”.

Select “Container Mode”.

Attach the “NVIDIA RAPIDS” Container.

Configure your own instance.

And hit “Deploy”.

Accessing your instance

Jupyter Notebook

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

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.

Test RAPIDS

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

Resources

• Brev Docs