PyTorch 1.7 released w/ CUDA 11, New APIs for FFTs, Windows support for Distributed training and more

PyTorch 1.7 released w/ CUDA 11, New APIs for FFTs, Windows support for Distributed training and more pytorch / pytorch Public Notifications You must be signed in to change notification settings Fork 30.5k Star 103k PyTorch 1.7 released w/ CUDA 11, New APIs for FFTs, Windows support for Distributed training and more albanD released this 27 Oct 16:35 · 29 commits to release/1.7 since this release v1.7.0 e85d494 This commit was created on GitHub.com and signed with GitHub’s verified signature . GPG key ID: 4AEE18F83AFDEB23 Expired Verified Learn about vigilant mode .

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• pytorch / pytorch Public Notifications You must be signed in to change notification settings Fork 30.5k Star 103k PyTorch 1.7 released w/ CUDA 11, New APIs for FFTs, Windows support for Distributed training and more albanD released this 27 Oct 16:35 · 29 commits to release/1.7 since this release v1.7.0 e85d494 This commit was created on GitHub.com and signed with GitHub’s verified signature .
• GPG key ID: 4AEE18F83AFDEB23 Expired Verified Learn about vigilant mode .
• PyTorch 1.7.0 Release Notes Highlights Backwards Incompatible Change New Features Improvements Performance Documentation Highlights The PyTorch 1.7 release includes a number of new APIs including support for NumPy-Compatible FFT operations, profiling tools and major updates to both distributed data parallel (DDP) and remote procedure call (RPC) based distributed training.
• In addition, several features moved to stable including custom C++ Classes, the memory profiler, the creation of custom tensor-like objects, user async functions in RPC and a number of other features in torch.distributed such as Per-RPC timeout, DDP dynamic bucketing and RRef helper.
• A few of the highlights include: CUDA 11 is now officially supported with binaries available at PyTorch.org Updates and additions to profiling and performance for RPC, TorchScript and Stack traces in the autograd profiler (Beta) Support for NumPy compatible Fast Fourier transforms (FFT) via torch.fft (Prototype) Support for Nvidia A100 generation GPUs and native TF32 format (Prototype) Distributed training on Windows now supported To reiterate, starting PyTorch 1.6 , features are now classified as stable, beta and prototype.
• You can see the detailed announcement here .