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 console and click on “Create your first instance”.

2. Select “Container Mode”.

3. Attach the “NVIDIA RAPIDS” Container.

4. Configure your own instance.

5. And hit “Deploy”.

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 where they can quickly prototype.

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 the option that corresponds to your use case. If you are using a blueprint, you will select “I have code files in a git repository” paste a link to the blueprint GitHub repository here. Otherwise, select “I don’t have any code files”.

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

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

   1. If you need a custom container: Select “Docker Compose” and click on the toggle to select “I have an existing docker-compose.yaml file”. Under Upload Docker Compose select “Provide GitHub/Gitlab URL” and provide a link to a Docker Compose YAML file (e.g. docker-compose-nb-2412.yaml). Click “Validate”. Note, you needto pass a link to the file in GitHub, not to the raw.github.com file.

   2. If you need a standard container: Select “Featured Container” and then select the “NVIDIA RAPIDS” container.

5. On the next page, when asked Do you want a Jupyter Notebook experience?, if you are using a standard container, you will be told there is a preconfigured Jupyter experience. Click “Next”. If you are using a custom container with Docker Compose, follow the instructions associated with the Blueprint you are using. Some will include Jupyter pre-installed.

6. In the section titled Do you need to expose services?, if you are using a standard container, you will not need to edit anything. If you are using a custom configuration, make sure to follow the instructions associated with your blueprint.

7. 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.

8. 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.

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.

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