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.. PyTorch documentation master file, created by
sphinx-quickstart on Fri Dec 23 13:31:47 2016.
You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive.
:github_url: https://github.com/pytorch/pytorch
PyTorch documentation
===================================
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::
:maxdepth: 1
:caption: Language Bindings
cpp_index
Javadoc <https://pytorch.org/javadoc/>
torch::deploy <deploy>
.. toctree::
:glob:
:maxdepth: 1
:caption: Python API
torch
nn
nn.functional
tensors
tensor_attributes
tensor_view
torch.amp <amp>
torch.autograd <autograd>
torch.library <library>
accelerator
cpu
cuda
torch.cuda.memory <torch_cuda_memory>
mps
xpu
mtia
mtia.memory
meta
torch.backends <backends>
torch.export <export>
torch.distributed <distributed>
torch.distributed.tensor <distributed.tensor>
torch.distributed.algorithms.join <distributed.algorithms.join>
torch.distributed.elastic <distributed.elastic>
torch.distributed.fsdp <fsdp>
torch.distributed.fsdp.fully_shard <distributed.fsdp.fully_shard>
torch.distributed.tensor.parallel <distributed.tensor.parallel>
torch.distributed.optim <distributed.optim>
torch.distributed.pipelining <distributed.pipelining>
torch.distributed.checkpoint <distributed.checkpoint>
torch.distributions <distributions>
torch.compiler <torch.compiler>
torch.fft <fft>
torch.func <func>
futures
fx
fx.experimental
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
nn.attention
onnx
optim
complex_numbers
ddp_comm_hooks
quantization
rpc
torch.random <random>
masked
torch.nested <nested>
size
sparse
storage
torch.testing <testing>
torch.utils <utils>
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.deterministic <deterministic>
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>
torch.utils.module_tracker <module_tracker>
type_info
named_tensor
name_inference
torch.__config__ <config_mod>
torch.__future__ <future_mod>
logging
torch_environment_variables
.. 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/>
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
|
