![]() You can learn more about FX in the official documentation and GitHub examples of program transformations implemented using torch.fx. ![]() This toolkit aims to support a subset of Python language semantics-rather than the whole Python language-to facilitate ease of implementation of transforms. It is a toolkit for pass writers to facilitate Python-to-Python transformation of functions and nn.Module instances. Frontend APIs (Stable) Python code transformations with FXįX provides a Pythonic platform for transforming and lowering PyTorch programs. Android NNAPI support is now available in beta.Īlong with 1.10, we are also releasing major updates to the PyTorch libraries, which you can read about in this blog post.Support for automatic fusion in JIT Compiler expands to CPUs in addition to GPUs.Several frontend APIs such as FX, torch.special, and nn.Module Parametrization, have moved from beta to stable.CUDA Graphs APIs are integrated to reduce CPU overheads for CUDA workloads. ![]() The full release notes are available here. PyTorch 1.10 updates are focused on improving training and performance of PyTorch, and developer usability. ![]() We want to sincerely thank our community for continuously improving PyTorch. This release is composed of over 3,400 commits since 1.9, made by 426 contributors. We are excited to announce the release of PyTorch 1.10. ![]()
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