Goto

Collaborating Authors

directml


Use DirectML to add machine learning to C code

#artificialintelligence

The modern GPU is more than a graphics device. Technologies such as the open-standard OpenCL and Nvidia's CUDA turn the many small processors in a GPU into a parallel computing fabric, allowing desktop PCs to complete tasks that used to be the sole purview of supercomputers. Those same GPUs are also capable of supporting many modern machine learning tasks, using GPU compute to build neural networks and to support model-building, data-parallel analytical and processing tasks. Microsoft has been investing in simplifying GPU programming for a long time now, starting with its DirectX GPU tools, initially via the Direct3D graphics tools, and extending it to GPU compute with DirectCompute. Recent developments have included tools to map OpenGL calls to Direct3D, related to work building a graphical layer onto the WSL 2 Linux virtual machine system bundled with Windows 10. Although they make it easier to work with hardware, these remain low-level programming tools, using C to access hardware features.


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.