Linear algebra is essential to deep learning and scientific computing, and the torch.linalg module extends PyTorch’s support for it with implementations of every function from NumPy’s linear algebra module (now with support for accelerators and autograd) and more, like _norm and _product. In 1.9, the torch.linalg module is moving to a stable release. You can learn more about the definitions in this blog post. We’d especially like to thank Quansight and Microsoft for their contributions.įeatures in PyTorch releases are classified as Stable, Beta, and Prototype. We’d like to thank the community for their support and work on this latest release. Support for Distributed training, GPU utilization and SM efficiency in the PyTorch ProfilerĪlong with 1.9, we are also releasing major updates to the PyTorch libraries, which you can read about in this blog post.New APIs to optimize performance and packaging for model inference deployment.Major updates to the PyTorch RPC framework to support large scale distributed training with GPU support.Native support for elastic-fault tolerance training through the upstreaming of TorchElastic into PyTorch Core.Major improvements in on-device binary size with Mobile Interpreter.Major improvements to support scientific computing, including torch.linalg, torch.special, and Complex Autograd.The release is composed of more than 3,400 commits since 1.8, made by 398 contributors. We are excited to announce the release of PyTorch 1.9.
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