| github_url: | https://github.com/pytorch/pytorch |
|---|
PyTorch is an optimized tensor library for deep learning using GPUs and CPUs.
Features described in this documentation are classified by release status:
Stable: These features will be maintained long-term and there should generally be no major performance limitations or gaps in documentation. We also expect to maintain backwards compatibility (although breaking changes can happen and notice will be given one release ahead of time).
Beta: These features are tagged as Beta because the API may change based on user feedback, because the performance needs to improve, or because coverage across operators is not yet complete. For Beta features, we are committing to seeing the feature through to the Stable classification. We are not, however, committing to backwards compatibility.
Prototype: These features are typically not available as part of binary distributions like PyPI or Conda, except sometimes behind run-time flags, and are at an early stage for feedback and testing.
.. toctree:: :glob: :maxdepth: 1 :caption: Community community/*
.. toctree:: :glob: :maxdepth: 1 :caption: Developer Notes notes/*
.. toctree:: :glob: :maxdepth: 1 :caption: torch.compile compile/index compile/get-started compile/troubleshooting compile/faq compile/technical-overview compile/guards-overview compile/custom-backends compile/fine_grained_apis compile/inductor_profiling compile/deep-dive compile/cudagraph_trees compile/performance-dashboard compile/torchfunc-and-torchcompile ir compile/dynamic-shapes compile/fake-tensor logging compile/transformations
.. toctree:: :maxdepth: 1 :caption: Language Bindings cpp_index Javadoc <https://pytorch.org/javadoc/> torch::deploy <deploy>
.. toctree:: :glob: :maxdepth: 2 :caption: Python API torch nn nn.functional tensors tensor_attributes tensor_view torch.amp <amp> torch.autograd <autograd> torch.library <library> cuda mps torch.backends <backends> torch.distributed <distributed> torch.distributed.algorithms.join <distributed.algorithms.join> torch.distributed.elastic <distributed.elastic> torch.distributed.fsdp <fsdp> torch.distributed.optim <distributed.optim> torch.distributed.tensor.parallel <distributed.tensor.parallel> torch.distributed.checkpoint <distributed.checkpoint> torch.distributions <distributions> torch._dynamo <_dynamo> torch.fft <fft> torch.func <func> futures fx torch.hub <hub> torch.jit <jit> torch.linalg <linalg> torch.monitor <monitor> torch.signal <signal> torch.special <special> torch.overrides torch.package <package> profiler nn.init onnx onnx_diagnostics optim complex_numbers ddp_comm_hooks pipeline quantization rpc torch.random <random> masked torch.nested <nested> sparse storage torch.testing <testing> torch.utils.benchmark <benchmark_utils> torch.utils.bottleneck <bottleneck> torch.utils.checkpoint <checkpoint> torch.utils.cpp_extension <cpp_extension> torch.utils.data <data> torch.utils.jit <jit_utils> torch.utils.dlpack <dlpack> torch.utils.mobile_optimizer <mobile_optimizer> torch.utils.model_zoo <model_zoo> torch.utils.tensorboard <tensorboard> type_info named_tensor name_inference torch.__config__ <config_mod> logging
.. toctree:: :maxdepth: 1 :caption: Libraries torchaudio <https://pytorch.org/audio/stable> TorchData <https://pytorch.org/data> TorchRec <https://pytorch.org/torchrec> TorchServe <https://pytorch.org/serve> torchtext <https://pytorch.org/text/stable> torchvision <https://pytorch.org/vision/stable> PyTorch on XLA Devices <https://pytorch.org/xla/>