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RAPIDS + WSL 2
Use RAPIDS on Windows with WSL 2
The power of RAPIDS, now available for Windows
Install Now
#install
default

![RAPIDS WSL 2]({{ site.baseurl }}{% link /assets/images/csp+hpo.png %}){: .projects-logo}

Use RAPIDS
on Windows

{: .section-title-full}

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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{: target="_blank"} 7.0 or higher is required. {: .subtitle}

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{% capture start_single %} # Before Installing {: .section-title-full .text-white} {% endcapture %}

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Prerequisites

OS: Windows 11.

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

GPU: Only GPUs with Compute Capability{: target="_blank"} 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{: target="_blank"}, [Slack]({{ site.slack_invite }}){: target="_blank"}, or ask a question on StackOverflow{: target="_blank"}.

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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

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{% capture yd_header %} # Installation Instructions {: .section-title-full}

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Conda
(Preferred Method)

  1. Install WSL 2 and the Ubuntu 20.04 package using Microsoft's instructions{: target="_blank"}.
  2. Install the latest NVIDIA Drivers{: target="_blank"} 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{: target="_blank"}.
  5. Install RAPIDS via Conda, using the RAPIDS Release Selector tool{: target="_blank"}.
  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{: target="_blank"}.

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pip

  1. Install WSL 2 and the Ubuntu 20.04 package using Microsoft's instructions{: target="_blank"}.
  2. Install the latest NVIDIA Drivers{: target="_blank"} on the Windows host.
  3. Log in to the WSL 2 Linux instance.
  4. Follow this guide to install the CUDA Toolkit without drivers{: target="_blank"} into the WSL 2 instance.
  5. Install RAPIDS pip packages on the WSL 2 Linux Instance using the pip instructions{: target="_blank"}.
  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{: target="_blank"}.

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Docker Desktop

  1. Install WSL 2 and the Ubuntu 20.04 package using Microsoft's instructions{: target="_blank"}.
  2. Install the latest NVIDIA Drivers{: target="_blank"} on the Windows host.
  3. Install latest Docker Desktop for Windows according to your applicable licensing terms{: target="_blank"}.
  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{: target="_blank"}.
  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{: target="_blank"}.

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