Package: experimental-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.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.WorkflowDevelopers will work on release branches daily and RI to main branch as needed.main branchbuilds with thenightly-builtpytorchconda install pytorch torchvision cpuonly -c pytorch-nightlyrelease branches build with thecorresponding publishedpytorchbothrelease/1.13andrelease/1.13+build withpytorch 1.13.comingrelease/1.13.1should build withpytorch 1.13.1Motivations why in this pattern.Avoid pushing identical changes twice into release and main.Avoid to be blocked by broken changes from pytorch master.Examples of cutting branches:A new branch for pytorch-1.13 (e.g. [release/1.13+2]) will be cut from the last released branchrelease/1.13+for new releases, and developers will stay on the new branch daily until that release is published.For new pytorch version1.13.1, release/1.13.1 should be cut from mainafterall release  workloads are RIed.External LinksPyTorch homepage