Windows 10 version 2004 and on contains something called WSL 2.0, which is an actual lightweight Virtual Machine (VM). Before we talk about what the heck WSL 2.0 is, however, it would be nice to understand a bit about what WSL 1.0 is. WSL 1.0 is Window's answer to something called Cygwin. Cygwin was popular on older versions of Windows for developers who needed or wanted Linux-related tools and a bash shell (a GUI could be added after jumping through a few hoops, though, but unless you had nice hardware the GUI would've been pretty slow -- my experience a bit buggy, too). The suits and ties at Microsoft haven't given the go ahead for WSL 2.0 to support the Linux GUI …OR… the devs are running behind but the good folks at Kali solved it.

Adding GPU compute support to Windows Subsystem for Linux (WSL) has been the #1 most requested feature since the first WSL release.Learn how Windows and WSL 2 now support GPU Accelerated Machine Learning (GPU compute) using NVIDIA CUDA, including TensorFlow and PyTorch, as well as all the Docker and NVIDIA Container Toolkit support available in a native Linux environment. Clark Rahig will explain a bit about what it means to accelerate your GPU to help with training Machine Learning (ML) models, introducing concepts like parallelism, and then showing how to set up and run your full ML workflow (including GPU acceleration) with NVIDIA CUDA and TensorFlow in WSL 2.Additionally, Clarke will demonstrate how students and beginners can start building knowledge in the Machine Learning (ML) space on their existing hardware by using the TensorFlow with DirectML package.Learn more:

Adding GPU compute support to Windows Subsystem for Linux (WSL) has been the #1 most requested feature since the first WSL release. Learn how Windows and WSL 2 now support GPU Accelerated Machine Learning (GPU compute) using NVIDIA CUDA, including TensorFlow and PyTorch, as well as all the Docker and NVIDIA Container Toolkit support available in a native Linux environment. Clark Rahig will explain a bit about what it means to accelerate your GPU to help with training Machine Learning (ML) models, introducing concepts like parallelism, and then showing how to set up and run your full ML workflow (including GPU acceleration) with NVIDIA CUDA and TensorFlow in WSL 2. Additionally, Clarke will demonstrate how students and beginners can start building knowledge in the Machine Learning (ML) space on their existing hardware by using the TensorFlow with DirectML package.