《PyTorch documentation》（PyTorch 文档）

PyTorch documentation（PyTorch 文档）

PyTorch is an optimized tensor library for deep learning using GPUs and CPUs.

（PyTorch是一个优化的张量库，用于使用GPU和CPU进行深度学习。）

Features described in this documentation are classified by release status:

（此留档中描述的功能按发布状态分类：）

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).

稳定：这些特性将长期保持，通常不应该有主要的性能限制或留档差距。我们还希望保持向后兼容性（尽管可能会发生重大更改，并且会提前通知一个版本）。

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.

测试版：这些功能被标记为测试版，因为应用编程接口可能会根据用户反馈而改变，因为性能需要提高，或者因为跨运营商的覆盖尚未完成。对于测试版功能，我们承诺将该功能纳入稳定分类。但是，我们不承诺向后兼容。

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.

原型：这些功能通常不能作为PyPI或Conda等二进制发行版的一部分提供，除非有时在运行时标志之后，并且处于反馈和测试的早期阶段。

Community（社区）

PyTorch Governance | Build + CI
PyTorch Contribution Guide
PyTorch Design Philosophy
PyTorch Governance | Mechanics
PyTorch Governance | Maintainers

Developer Notes（开发者笔记）

Automatic Mixed Precision examples
Autograd mechanics
Broadcasting semantics
CPU threading and TorchScript inference
CUDA semantics
PyTorch Custom Operators Landing Page
Distributed Data Parallel
Extending PyTorch
Extending torch.func with autograd.Function
Frequently Asked Questions
FSDP Notes
Getting Started on Intel GPU
Gradcheck mechanics
HIP (ROCm) semantics
Features for large-scale deployments
LibTorch Stable ABI
Modules
MPS backend
Multiprocessing best practices
Numerical accuracy
Reproducibility
Serialization semantics
Windows FAQ

Language Bindings（语言绑定）

C++
Javadoc
torch::deploy

Python API（Python 应用程序编程接口）

torch
torch.nn
torch.nn.functional
torch.Tensor
Tensor Attributes
Tensor Views
torch.amp
torch.autograd
torch.library
torch.accelerator
torch.cpu
torch.cuda
Understanding CUDA Memory Usage
Generating a Snapshot
Using the visualizer
Snapshot API Reference
torch.mps
torch.xpu
torch.mtia
torch.mtia.memory
Meta device
torch.backends
torch.export
torch.distributed
torch.distributed.tensor
torch.distributed.algorithms.join
torch.distributed.elastic
torch.distributed.fsdp
torch.distributed.fsdp.fully_shard
torch.distributed.tensor.parallel
torch.distributed.optim
torch.distributed.pipelining
torch.distributed.checkpoint
torch.distributions
torch.compiler
torch.fft
torch.func
torch.futures
torch.fx
torch.fx.experimental
torch.hub
torch.jit
torch.linalg
torch.monitor
torch.signal
torch.special
torch.overrides
torch.package
torch.profiler
torch.nn.init
torch.nn.attention
torch.onnx
torch.optim
Complex Numbers
DDP Communication Hooks
Quantization
Distributed RPC Framework
torch.random
torch.masked
torch.nested
torch.Size
torch.sparse
torch.Storage
torch.testing
torch.utils
torch.utils.benchmark
torch.utils.bottleneck
torch.utils.checkpoint
torch.utils.cpp_extension
torch.utils.data
torch.utils.deterministic
torch.utils.jit
torch.utils.dlpack
torch.utils.mobile_optimizer
torch.utils.model_zoo
torch.utils.tensorboard
torch.utils.module_tracker
Type Info
Named Tensors
Named Tensors operator coverage
torch.__config__
torch.__future__
torch._logging
Torch Environment Variables

Libraries（库）

torchaudio
TorchData
TorchRec
TorchServe
torchtext
torchvision
PyTorch on XLA Devices
torchao

Indices and tables（索引和表格）

Index

Module Index