GPU in Windows Subsystem for Linux (WSL)

Microsoft Windows is a ubiquitous platform for enterprise, business, and personal computing systems. However, industry AI tools, models, frameworks, and libraries are predominantly available on Linux OS. Now all users of AI — whether they are experienced professionals, or students and beginners just getting started — can benefit from innovative GPU-accelerated infrastructure, software, and container support on Windows.

The NVIDIA CUDA on WSL Public Preview brings NVIDIA CUDA and advanced AI together with the ubiquitous Microsoft Windows platform to deliver advanced machine learning capabilities across numerous industry segments and application domains.

Interested parties will need to join the appropriate user programs, and will download specific components from both NVIDIA and Microsoft to set-up the complete WSL environment.

NVIDIA drivers for WSL with CUDA and DirectML support are available as preview for Microsoft Windows Insider Program members who have registered for the NVIDIA Developer Program.

Enable Developers

GPU support is the number one requested feature from worldwide WSL users — including data scientists, ML engineers, and even novice developers.

Access Advanced AI

The most advanced and innovative AI frameworks and libraries are already integrated with NVIDIA CUDA support, including industry leading frameworks like PyTorch and TensorFlow.

Reduce Obstacles

The overhead and duplication of investments in multiple OS compute platforms can be prohibitive — AI users, developers, and data scientists need quick access to run Linux software on their productive Windows platforms.

WHY USE NVIDIA GPUS ON WINDOWS for AI?

If you are a Microsoft Windows user who wants access to state of the art AI technology, NVIDIA enables GPU-accelerated AI development, running advanced Linux-based ML applications on Microsoft Windows by leveraging the WSL application layer.

GPUs have a robust history of accelerating AI applications for both training and inference. NVIDIA provides a wide variety of proven machine learning solutions, and are validated to work with numerous industry frameworks. We leverage our extensive AI experience and domain knowledge to deliver solutions that accelerate your learning, adoption, and results.

Join the NVIDIA Developer Program and come take advantage of our developer tools, training, platforms, and integrations.

Get Started Developing GPUs Quickly

The CUDA Toolkit provides everything developers need to get started building GPU accelerated applications — including compiler toolchains, Optimized libraries, and a suite of developer tools. Use CUDA within WSL and CUDA containers to get started quickly. Features and capabilities will be added to the Preview version of the CUDA Toolkit over the life of the preview program.

Simplifying Deep Learning

NVIDIA provides access to over a dozen deep learning frameworks and SDKs, including support for TensorFlow, PyTorch, MXNet, and more.

Additionally, you can even run pre-built framework containers with Docker and the NVIDIA Container Toolkit in WSL. Frameworks, pre-trained models and workflows are available from NGC.

Accelerate Analytics and Data Science

RAPIDS is an open source NVIDIA suite of software libraries to accelerate data science and analytics pipelines on GPUs.

Reduce training time and increase model accuracy by iterating faster with proven, pre-built libraries.

CUDA Everywhere

Numerous NVIDIA platforms in different form factors and at different price points exist for hosting your work environment, including GPU-enabled graphics cards, laptops, and more.

“The Microsoft — NVIDIA collaboration around WSL enables masses of expert and new users to learn, experiment with, and adopt premier GPU-accelerated AI platforms without leaving the familiarity of their everyday MS Windows environment.”

Kam VedBrat, Partner Group Program Manager for Windows AI Platform, Microsoft Corp.

Cuda linux or windows

Minimal first-steps instructions to get CUDA running on a standard system.

1. Introduction

This guide covers the basic instructions needed to install CUDA and verify that a CUDA application can run on each supported platform.

These instructions are intended to be used on a clean installation of a supported platform. For questions which are not answered in this document, please refer to the Windows Installation Guide and Linux Installation Guide.

The CUDA installation packages can be found on the CUDA Downloads Page.

2. Windows

When installing CUDA on Windows, you can choose between the Network Installer and the Local Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. For more details, refer to the Windows Installation Guide.

2.1. Network Installer

Perform the following steps to install CUDA and verify the installation.

1. Launch the downloaded installer package.
2. Read and accept the EULA.
3. Select «next» to download and install all components.
4. Once the download completes, the installation will begin automatically.
5. Once the installation completes, click «next» to acknowledge the Nsight Visual Studio Edition installation summary.
6. Click «close» to close the installer.
7. Navigate to the CUDA Samples’ nbody directory.
8. Open the nbody Visual Studio solution file for the version of Visual Studio you have installed.

2.2. Local Installer

Perform the following steps to install CUDA and verify the installation.

1. Launch the downloaded installer package.
2. Read and accept the EULA.
3. Select «next» to install all components.
4. Once the installation completes, click «next» to acknowledge the Nsight Visual Studio Edition installation summary.
5. Click «close» to close the installer.
6. Navigate to the CUDA Samples’ nbody directory.
7. Open the nbody Visual Studio solution file for the version of Visual Studio you have installed.

3. Linux

CUDA on Linux can be installed using an RPM, Debian, or Runfile package, depending on the platform being installed on.

3.1. Linux x86_64

For development on the x86_64 architecture. In some cases, x86_64 systems may act as host platforms targeting other architectures. See the Linux Installation Guide for more details.

3.1.1. Redhat / CentOS

When installing CUDA on Redhat or CentOS, you can choose between the Runfile Installer and the RPM Installer. The Runfile Installer is only available as a Local Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the RPM installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.1.1.1. RPM Installer

Perform the following steps to install CUDA and verify the installation.

1. Install EPEL to satisfy the DKMS dependency by following the instructions at EPEL’s website.
2. Enable optional repos:

On RHEL 7 Linux only, execute the following steps to enable optional repositories.

• On x86_64 workstation:
• On POWER9 system:
• On x86_64 server:

Install the repository meta-data, clean the yum cache, and install CUDA:

Reboot the system to load the NVIDIA drivers.

Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:

Install a writable copy of the samples then build and run the nbody sample:

3.1.1.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

3.1.2. Fedora

When installing CUDA on Fedora, you can choose between the Runfile Installer and the RPM Installer. The Runfile Installer is only available as a Local Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the RPM installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.1.2.1. RPM Installer

Perform the following steps to install CUDA and verify the installation.

1. Install the RPMFusion free repository to satisfy the Akmods dependency:
2. Install the repository meta-data, clean the dnf cache, and install CUDA:
3. Reboot the system to load the NVIDIA drivers.
4. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
5. Install a writable copy of the samples then build and run the nbody sample:

3.1.2.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

3.1.3. SUSE Linux Enterprise Server

When installing CUDA on SUSE Linux Enterprise Server, you can choose between the Runfile Installer and the RPM Installer. The Runfile Installer is only available as a Local Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the RPM installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.1.3.1. RPM Installer

Perform the following steps to install CUDA and verify the installation.

1. Install the repository meta-data, refresh the Zypper cache, and install CUDA:
2. Add the user to the video group:
3. Reboot the system to load the NVIDIA drivers.
4. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
5. Install a writable copy of the samples then build and run the vectorAdd sample:

3.1.3.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

1. Reboot into runlevel 3 by temporarily adding the number «3» and the word «nomodeset» to the end of the system’s kernel boot parameters.
2. Run the installer silently to install with the default selections (implies acceptance of the EULA):
3. Create an xorg.conf file to use the NVIDIA GPU for display:
4. Reboot the system to load the graphical interface.
5. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
6. Install a writable copy of the samples then build and run the vectorAdd sample:

3.1.4. OpenSUSE

When installing CUDA on OpenSUSE, you can choose between the Runfile Installer and the RPM Installer. The Runfile Installer is only available as a Local Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the RPM installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.1.4.1. RPM Installer

Perform the following steps to install CUDA and verify the installation.

1. Install the repository meta-data, refresh the Zypper cache, and install CUDA:
2. Add the user to the video group:
3. Reboot the system to load the NVIDIA drivers.
4. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
5. Install a writable copy of the samples then build and run the nbody sample:

3.1.4.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

3.1.5. WSL

These instructions must be used if you are installing in a WSL environment. Do not use the Ubuntu instructions in this case.

Install repository meta-data

When installing using the local repo:

When installing using the network repo:

Pin file to prioritize CUDA repository:

Update the Apt repository cache and install CUDA

3.1.6. Ubuntu

When installing CUDA on Ubuntu, you can choose between the Runfile Installer and the Debian Installer. The Runfile Installer is only available as a Local Installer. The Debian Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the Debian installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.1.6.1. Debian Installer

Perform the following steps to install CUDA and verify the installation.

1. Install the repository meta-data, install GPG key, update the apt-get cache, and install CUDA:
2. Reboot the system to load the NVIDIA drivers.
3. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
4. Install a writable copy of the samples then build and run the nbody sample:

3.1.6.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

3.1.7. Debian

When installing CUDA on Debian 10, you can choose between the Runfile Installer and the Debian Installer. The Runfile Installer is only available as a Local Installer. The Debian Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. For more details, refer to the Linux Installation Guide.

3.1.7.1. Debian Installer

Perform the following steps to install CUDA and verify the installation.

1. Install the repository meta-data, install GPG key, update the apt-get cache, and install CUDA:
2. Reboot the system to load the NVIDIA drivers.
3. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
4. Install a writable copy of the samples then build and run the nbody sample:

3.1.7.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

3.2. Linux POWER8

For development on the POWER8 architecture.

3.2.1. Ubuntu

When installing CUDA on Ubuntu on POWER8, you must use the Debian Installer. The Debian Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. The instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.2.1.1. Debian Installer

Perform the following steps to install CUDA and verify the installation.

1. Install the repository meta-data, update the apt-get cache, and install CUDA:
2. Reboot the system to load the NVIDIA drivers.
3. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
4. Install a writable copy of the samples then build and run the vectorAdd sample:

3.2.2. Redhat / CentOS

When installing CUDA on Redhat on POWER8, you must use the RPM Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. The instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.2.2.1. RPM Installer

Perform the following steps to install CUDA and verify the installation.

1. Install EPEL to satisfy the DKMS dependency by following the instructions at EPEL’s website.
2. Install the repository meta-data, clean the yum cache, and install CUDA:
3. Reboot the system to load the NVIDIA drivers.
4. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
5. Install a writable copy of the samples then build and run the vectorAdd sample:

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