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DirectML: Empowering Students and Beginners in Machine Learning

#artificialintelligence

These introductory courses play a key role in educating the future of machine learning professionals. DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported hardware and drivers, including all DirectX 12-capable GPUs from vendors such as AMD, Intel, NVIDIA, and Qualcomm. When used standalone, the DirectML API is a low-level DirectX 12 library and is suitable for high-performance, low-latency applications such as frameworks, games, and other real-time applications. The seamless interoperability of DirectML with Direct3D 12 as well as its low overhead and conformance across hardware makes DirectML ideal for accelerating machine learning when both high performance is desired, and the reliability and predictability of results across hardware is critical.


Windows 10 Linux subsystem: You get GPU acceleration – with Intel, AMD, Nvidia drivers

ZDNet

Nvidia, Intel and AMD have announced their support for Microsoft's new effort to bring graphics processor support to the Windows 10 Windows Subsystem for Linux to enhance machine-learning training. GPU support for WSL arrived on Wednesday in the Dev Channel preview of Windows 10 build 20150 under Microsoft's reorganized testing structure, which lets it test Windows 10 builds that aren't tied to a specific future feature release. Microsoft announced upcoming GPU support for WSL a few weeks ago at Build 2020, along with support for running Linux GUI apps. The move on GPU access for WSL is intended to bring the performance of applications running in WSL2 up to par with those running on Windows. GPU compute support is the feature most requested by WSL users, according to Microsoft. The 20150 update includes support for Nvidia's CUDA parallel computing platform and GPUs, as well as GPUs from AMD and Intel.


How machine learning can make prettier PC games that adapt to your play preferences

PCWorld

Microsoft wants your PC's hardware to get smart--and your gaming foes to become even more devious. Earlier this month, the company revealed Windows ML, an API that taps into your computer's CPU and GPU to bolster your software with machine learning capabilities. At GDC 2018 on Monday, Microsoft explained how Windows ML can benefit video games, and introduced new "DirectML" tools that provide GPU hardware acceleration for games that use WinML, built on the same no-hassle-for-gamers principles as the DirectX standard. What does it all mean? Machine learning can make games prettier, more adaptable to individual playstyles, and easier to create, Microsoft says.


Windows 10's Linux subsystem gets GPU compute and an easier install in new preview

PCWorld

Microsoft released improvements to its Windows Subsystem for Linux 2 (WSL) in a Windows 10 preview build on Wednesday, with features benefiting newcomers and developers alike. As part of the update, WSL2 can now perform GPU compute functions, including using Nvidia's CUDA technology. The new additions deliver on the promises Microsoft made at May's Build 2020 conference, where the company also teased graphical user interface support for the Windows Subsystem for Linux. WSL's improvements are part of Windows 10 Build 20150, part of the Dev Channel of Insider builds. Formerly known as the Fast Ring, the Dev Channel is devoted to testing new features which aren't necessarily tied to any upcoming Windows 10 feature release. As the name suggests, the Windows Subsystem for Linux 2 allows you to run a Linux kernel from within Windows.


GPU Accelerated Machine Learning with WSL 2

#artificialintelligence

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.