Package: torch-directml
PyTorch with DirectMLDirectML acceleration for PyTorch is currently available for Public Preview. PyTorch with DirectML enables training and inference of complex machine learning models on a wide range of DirectX 12-compatible hardware.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.More information about DirectML can be found inIntroduction to DirectML.PyTorch 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)SamplesRefer to thePytorch with DirectML Samples Repofor samples.Roadmaptorch-directmlis actively under development and we're always adding more operators. For a list of all the operators we support and their data type coverage, refer to thePyTorch DirectML Operator Roadmapin the DirectML repository wiki. If you require support for an operator that isn't in this list, see theFeedbacksection below on how to file an issue.FeedbackWe look forward to hearing from you!For TensorFlow with DirectML issues, bugs, and feedback; or for general DirectML issues and feedback, pleasefile an issueor contact us directly ataskdirectml@microsoft.com.For PyTorch with DirectML issues, bugs, and feedback; or for general DirectML issues and feedback, pleasefile an issueor contact us directly ataskdirectml@microsoft.com.For Windows ML issues, please file a GitHub issue atmicrosoft/Windows-Machine-Learningor contact us directly ataskwindowsml@microsoft.com.For ONNX Runtime issues, please file an issue atmicrosoft/onnxruntime.ContributingThis project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the 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 provide
a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
provided 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 FAQor
contactopencode@microsoft.comwith any additional questions or comments.Data Collection NoticeThe software may collect information about you and your use of the software and send it to Microsoft. Microsoft may use this information to provide services and improve our products and services. There are also some features in the software that may enable you and Microsoft to collect data from users of your applications. If you use these features, you must comply with applicable law, including providing appropriate notices to users of your applications together with a copy of Microsoft's privacy statement. Our privacy statement is located athttps://go.microsoft.com/fwlink/?LinkID=824704. You can learn more about data collection and use in the help documentation and our privacy statement. Your use of the software operates as your consent to these practices.Specifically, in torch-directml, we are collecting the GPU device info and operators that fall back to CPU for improving operator coverage.External LinksPyTorch homepagePyTorch DirectML Operator RoadmapPytorch with DirectML Samples Repo