The power of RAPIDS, now available for Windows

Install Now

Use RAPIDS
on Windows

Windows users can now tap into GPU accelerated data science on their local machines using RAPIDS on Windows Subsystem for Linux 2 (WSL 2)! WSL 2 is a Windows feature that enables users to run native Linux command-line tools directly on Windows. Using this feature does not require a dual boot environment, removing complexity and saving you time. An NVIDIA GPU with Compute Capability 7.0 or higher is required.

Before Installing

Prerequisites

OS: Windows 11.

WSL Version: WSL 2. WSL 1 is not supported.

GPU: Only GPUs with Compute Capability 7.0 or higher are supported on RAPIDS in WSL 2. 16GB or more of GPU RAM is recommended.

WSL 2 Instance: Ubuntu 20.04 instance for WSL 2.

Connect

Join our community conversations about RAPIDS on WSL 2 using Twitter, Slack, or ask a question on StackOverflow.

Limitations

Only single GPU is supported.

GPU Direct Storage is not supported.

At least 8 GB of RAM and a relatively fast CPU are strongly recommended.

Troubleshooting

When installing with conda, if an http 000 connection error occurs when accessing the repository data, run wsl --shutdown and then restart the WSL instance. More information

Installation Instructions

Conda
(Preferred Method)

  1. Install WSL 2 and the Ubuntu 20.04 package using Microsoft’s instructions.
  2. Install the latest NVIDIA Drivers on the Windows host.
  3. Log in to the WSL 2 Linux instance.
  4. Install Conda in the WSL 2 Linux Instance using our Conda instructions.
  5. Install RAPIDS via Conda, using the RAPIDS Release Selector tool.
  6. Run this code to check that the RAPIDS installation is working:
     import cudf
     print(cudf.Series([1, 2, 3]))
    
  7. Install additional GitHub repositories and enablements from the Learn More section.

pip

  1. Install WSL 2 and the Ubuntu 20.04 package using Microsoft’s instructions.
  2. Install the latest NVIDIA Drivers on the Windows host.
  3. Log in to the WSL 2 Linux instance.
  4. Follow this guide to install the CUDA Toolkit without drivers into the WSL 2 instance.
  5. Install RAPIDS pip packages on the WSL 2 Linux Instance using the pip instructions.
  6. Run this code to check that the RAPIDS installation is working:
     import cudf
     print(cudf.Series([1, 2, 3]))
    
  7. Install additional GitHub repos, enablements, and 3rd party tools from the Learn More section.

Docker Desktop

  1. Install WSL 2 and the Ubuntu 20.04 package using Microsoft’s instructions.
  2. Install the latest NVIDIA Drivers on the Windows host.
  3. Install latest Docker Desktop for Windows according to your applicable licensing terms.
  4. Log in to the WSL 2 Linux instance.
  5. Generate and run the RAPIDS docker pull and docker run commands based on your desired configuration using the RAPIDS Release Selector.
  6. Inside the Docker instance, run this code to check that the RAPIDS installation is working:
     import cudf
     print(cudf.Series([1, 2, 3]))
    
  7. Install additional GitHub repos, enablements, and 3rd party tools from the Learn More section.