With NVIDIA CUDA support for WSL 2, developers can leverage NVIDIA GPU accelerated computing technology for data science, machine learning and inference on Windows through WSL. WSL 2 support for GPU allows for these applications to benefit from GPU accelerated computing and expands the domain of applications that can be developed on WSL 2. More importantly WSL 2 enables applications that were hitherto only available on Linux to be available on Windows. WSL enables users to have a seamless transition across the two environments without the need for a resource intensive traditional virtual machine and to improve productivity and develop using tools and integrate their workflow. Also this has historically restricted the development of seamless, well integrated tools and software systems across two dominant ecosystems. In both cases, developers have to stop all the work and then switch the system or reboot. install Linux and Windows in separate partitions on the same or different hard disks on the system and boot to the OS of choice. Use different systems for Linux and Windows, orĭual Boot i.e. Typically, developers working across both Linux and Windows environments have a very disruptive workflow. WSL 2 is tightly integrated with the Microsoft Windows operating system, which allows it to run Linux applications alongside and even interop with other Windows desktop and modern store apps.įor the rest of this user guide, WSL and WSL 2 may be used interchangeably. WSL 2 is characteristically a VM with a Linux WSL Kernel in it that provides full compatibility with mainstream Linux kernel allowing support for native Linux applications including popular Linux distros.įaster file system support and that’s more performant. Linux applications can run as is in WSL 2. CUDA support in this user guide is specifically for WSL 2, which is the second generation of WSL that offers the following benefits WSL or Windows Subsystem for Linux is a Windows feature that enables users to run native Linux applications, containers and command-line tools directly on Windows 11 and later OS builds. NVIDIA GPU Accelerated Computing on WSL 2 Tesla Deployment Kit - Linux Edition (CUDA 4.2)įor more product details and specifications please visit The GPU Deployment Online Documentation.įor additional information visit NVIDIA Management Library (NVML) product page.The guide for using NVIDIA CUDA on Windows Subsystem for Linux.Tesla Deployment Kit - Windows Edition (CUDA 4.2).Tesla Deployment Kit - Linux Edition (Jan 25th, 2013).Tesla Deployment Kit - Windows Edition (Oct 15th, 2012).Tesla Deployment Kit - Linux Edition (Jan 29th, 2014).Tesla Deployment Kit - Windows Edition (Aug 1st, 2013).GPU Deployment Kit - Linux Edition (March 19th, 2014).GPU Deployment Kit - Windows Edition (March 14th, 2014).GPU Deployment Kit - Linux 64bit (August 20th, 2014).GPU Deployment Kit - Linux 32bit (August 20th, 2014).GPU Deployment Kit - Power8 Linux 64bit (April, 2015). GPU Deployment Kit - Linux 64bit (March 17th, 2015).
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