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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.

More information about DirectML can be found inIntroduction to DirectML.

• Getting Started with DirectML
  • Hardware requirements
  • For application developers
  • For users, data scientists, and researchers
• DirectML Samples
• DxDispatch Tool
• Windows ML on DirectML
• ONNX Runtime on DirectML
• PyTorch with DirectML
• TensorFlow with DirectML
• Feedback
• External Links
  • Documentation
  • More information
• Contributing

Visit theDirectX Landing Page for more resources for DirectX developers.

DirectML is distributed as a system component of Windows 10, and is available as part of the Windows 10 operating system (OS) in Windows 10, version 1903 (10.0; Build 18362), and newer.

Starting with DirectMLversion 1.4.0, DirectML is also available as a standalone redistributable package (seeMicrosoft.AI.DirectML), which is useful for applications that wish to use a fixed version of DirectML, or when running on older versions of Windows 10.

DirectML requires a DirectX 12 capable device. Almost all commercially-available graphics cards released in the last several years support DirectX 12. Examples of compatible hardware include:

• AMD GCN 1st Gen (Radeon HD 7000 series) and above
• Intel Haswell (4th-gen core) HD Integrated Graphics and above
• NVIDIA Kepler (GTX 600 series) and above
• Qualcomm Adreno 600 and above

DirectML exposes a native C++ DirectX 12 API. The header and library (DirectML.h/DirectML.lib) are available as part of theredistributable NuGet package, and are also included in the Windows 10 SDK version 10.0.18362 or newer.

• The Windows 10 SDK can be downloaded from theWindows Dev Center
• Microsoft.AI.DirectML on the NuGet Gallery
• DirectML programming guide
• DirectML API reference

DirectML is built-in as a backend to several frameworks such as Windows ML, ONNX Runtime, and TensorFlow.

See the following sections for more information:

• Windows ML on DirectML
• ONNX Runtime on DirectML
• TensorFlow with DirectML
• PyTorch with DirectML

DirectML C++ sample code is available underSamples.

• HelloDirectML: A minimal "hello world" application that executes a single DirectML operator.
• DirectMLNpuInference: A sample that showcases how to utilize NPU hardware with DirectML.
• DirectMLSuperResolution: A sample that uses DirectML to execute a basic super-resolution model to upscale video from 540p to 1080p in real time.
• yolov4: YOLOv4 is an object detection model capable of recognizing up to 80 different classes of objects in an image. This sample contains a complete end-to-end implementation of the model using DirectML, and is able to run in real time on a user-provided video stream.

DirectML Python sample code is available underPython/samples. The samples require PyDirectML, an open source Python projection library for DirectML, which can be built and installed to a Python executing environment fromPython/src. Refer to thePython/README.md file for more details.

• MobileNet: Adapted from theONNX MobileNet model. MobileNet classifies an image into 1000 different classes. It is highly efficient in speed and size, ideal for mobile applications.
• MNIST: Adapted from theONNX MNIST model. MNIST predicts handwritten digits using a convolution neural network.
• SqueezeNet: Based on theONNX SqueezeNet model. SqueezeNet performs image classification trained on the ImageNet dataset. It is highly efficient and provides results with good accuracy.
• FNS-Candy: Adapted from theWindows ML Style Transfer model sample, FNS-Candy re-applies specific artistic styles on regular images.
• Super Resolution: Adapted from theONNX Super Resolution model, Super-Res upscales and sharpens the input images to refine the details and improve image quality.

DxDispatch is simple command-line executable for launching DirectX 12 compute programs (including DirectML operators) without writing all the C++ boilerplate.

Windows ML (WinML) is a high-performance, reliable API for deploying hardware-accelerated ML inferences on Windows devices. DirectML provides the GPU backend for Windows ML.

DirectML acceleration can be enabled in Windows ML using theLearningModelDevice with any one of theDirectX DeviceKinds.

For more information, seeGet Started with Windows ML.

• Windows Machine Learning Overview (docs.microsoft.com)
• Windows Machine Learning GitHub
• WinMLRunner, a tool for executing ONNX models using WinML with DirectML

ONNX Runtime is a cross-platform inferencing and training accelerator compatible with many popular ML/DNN frameworks, including PyTorch, TensorFlow/Keras, scikit-learn, and more.

DirectML is available as an optionalexecution provider for ONNX Runtime that provides hardware acceleration when running on Windows 10.

For more information about getting started, seeUsing the DirectML execution provider.

• ONNX Runtime homepage
• ONNX Runtime GitHub
• DirectML Execution Provider readme

PyTorch with DirectML enables training and inference of complex machine learning models on a wide range of DirectX 12-compatible hardware. This is done throughtorch-directml, a plugin for PyTorch.

PyTorch with DirectML is supported on both the latest versions of Windows and theWindows Subsystem for Linux, and is available for download as a PyPI package. For more information about getting started withtorch-directml, see ourWindows orWSL 2 guidance on Microsoft Learn.

• torch-directml PyPI project
• PyTorch with DirectML samples
• PyTorch homepage

TensorFlow is a popular open source platform for machine learning and is a leading framework for training of machine learning models.

DirectML acceleration for TensorFlow 1.15 is currently available for Public Preview. TensorFlow on DirectML enables training and inference of complex machine learning models on a wide range of DirectX 12-compatible hardware.

TensorFlow on DirectML is supported on both the latest versions of Windows 10 and theWindows Subsystem for Linux, and is available for download as a PyPI package. For more information about getting started, seeGPU accelerated ML training (docs.microsoft.com)

• TensorFlow on DirectML GitHub repo
• TensorFlow on DirectML samples
• tensorflow-directml PyPI project
• TensorFlow GitHub | RFC: TensorFlow on DirectML
• TensorFlow homepage

We look forward to hearing from you!

• For TensorFlow with DirectML issues, bugs, and feedback; or for general DirectML issues and feedback, pleasefile an issue or contact us directly ataskdirectml@microsoft.com.

• For PyTorch with DirectML issues, bugs, and feedback; or for general DirectML issues and feedback, pleasefile an issue or contact us directly ataskdirectml@microsoft.com.

• For Windows ML issues, please file a GitHub issue atmicrosoft/Windows-Machine-Learning or contact us directly ataskwindowsml@microsoft.com.

• For ONNX Runtime issues, please file an issue atmicrosoft/onnxruntime.

DirectML programming guide
DirectML API reference

Introducing DirectML (Game Developers Conference '19)
Accelerating GPU Inferencing with DirectML and DirectX 12 (SIGGRAPH '18)
Windows AI: hardware-accelerated ML on Windows devices (Microsoft Build '20)
Gaming with Windows ML (DirectX Developer Blog)
DirectML at GDC 2019 (DirectX Developer Blog)
DirectX ❤ Linux (DirectX Developer Blog)

This project welcomes contributions and suggestions. Most contributions require you to agree to aContributor License Agreement (CLA) declaring that you have the right to, and actually do, grant usthe rights to use your contribution. For details, visithttps://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to providea CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructionsprovided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted theMicrosoft Open Source Code of Conduct.For more information see theCode of Conduct FAQ orcontactopencode@microsoft.com with any additional questions or comments.