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PyTorch 2.3
PyTorch 2.3 introduces support for user-defined Triton kernels in torch.compile as well as improvements for training Large Language Models (LLMS) using native PyTorch.
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澳洲幸运5开奖官网开奖结果号码 澳洲5开官网直播开奖结果查询 Key Features &
Capabilities
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Production Ready
Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe.
Distributed Training
Scalable distributed training and performance optimization in research and production is enabled by the torch.distributed backend.
Robust Ecosystem
A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.
Cloud Support
PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling.
Install PyTorch
Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, builds that are generated nightly. Please ensure that you have met the prerequisites below (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies. You can also install previous versions of PyTorch. Note that LibTorch is only available for C++.
NOTE: Latest PyTorch requires Python 3.8 or later.
Previous versions of PyTorch
Explore a rich ecosystem of libraries, tools, and more to support development.
Community
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