GPU in Windows Subsystem for Linux (WSL)
CUDA on Windows Subsystem for Linux - Public Preview
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.
Soon 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 Microsoft GPU in WSL and 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.
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.
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 Windows Subsystem for Linux (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.CUDA TOOLKIT ›
Simplifying Deep Learning
NVIDIA provides access to over a dozen deep learning frameworks and SDKs, including support for TensorFlow, PyTorch, MXNet, and more.
DL FRAMEWORKS ›
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.NVIDIA NGC CONTAINERS ›
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.RAPIDS ›
“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.”
The Microsoft GPU in WSL support is being introduced in June 2020 as a Public Preview via their Windows Insider Program Fast Ring.