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6110effa86
Chatted with @stas00 on slack and here are some great improvements he suggested to the compile docs - [x] Rename `dynamo` folder to `compile` - [x] Link `compile` docstring on `torch.html` to main index page for compile - [x] Create a new index page that describes why people should care - [x] easy perf, memory reduction, 1 line - [x] Short benchmark table - [x] How to guide - [x] TOC that links to the more technical pages folks have written, make the existing docs we have a Technical overview - [x] Highlight the new APIs for `torch._inductor.list_options()` and `torch._inductor.list_mode_options()` - clarify these are inductor specific and add more prose around which ones are most interesting He also highlighted an interesting way to think about who is reading this doc we have - [x] End users, that just want things to run fast - [x] Library maintainers wrapping torch.compile which would care for example about understanding when in their code they should compile a model, which backends are supported - [x] Debuggers who needs are somewhat addressed by the troubleshooting guide and faq but those could be dramatically reworked to say what we expect to break And in a seperate PR I'll work on the below with @SherlockNoMad - [ ] Authors of new backends that care about how to plug into dynamo or inductor layer so need to explain some more internals like - [ ] IR - [ ] Where to plugin, dynamo? inductor? triton? Pull Request resolved: https://github.com/pytorch/pytorch/pull/96706 Approved by: https://github.com/svekars
154 lines
3.9 KiB
ReStructuredText
154 lines
3.9 KiB
ReStructuredText
.. PyTorch documentation master file, created by
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sphinx-quickstart on Fri Dec 23 13:31:47 2016.
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You can adapt this file completely to your liking, but it should at least
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contain the root `toctree` directive.
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:github_url: https://github.com/pytorch/pytorch
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PyTorch documentation
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===================================
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PyTorch is an optimized tensor library for deep learning using GPUs and CPUs.
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Features described in this documentation are classified by release status:
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*Stable:* These features will be maintained long-term and there should generally
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be no major performance limitations or gaps in documentation.
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We also expect to maintain backwards compatibility (although
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breaking changes can happen and notice will be given one release ahead
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of time).
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*Beta:* These features are tagged as Beta because the API may change based on
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user feedback, because the performance needs to improve, or because
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coverage across operators is not yet complete. For Beta features, we are
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committing to seeing the feature through to the Stable classification.
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We are not, however, committing to backwards compatibility.
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*Prototype:* These features are typically not available as part of
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binary distributions like PyPI or Conda, except sometimes behind run-time
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flags, and are at an early stage for feedback and testing.
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.. toctree::
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:glob:
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:maxdepth: 1
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:caption: Community
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community/*
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.. toctree::
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:glob:
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:maxdepth: 1
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:caption: Developer Notes
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notes/*
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.. toctree::
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:glob:
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:maxdepth: 1
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:caption: torch.compile
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compile/index
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compile/get-started
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compile/troubleshooting
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compile/faq
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compile/technical-overview
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compile/guards-overview
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compile/custom-backends
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compile/deep-dive
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ir
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.. toctree::
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:maxdepth: 1
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:caption: Language Bindings
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cpp_index
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Javadoc <https://pytorch.org/javadoc/>
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torch::deploy <deploy>
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.. toctree::
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:glob:
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:maxdepth: 2
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:caption: Python API
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torch
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nn
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nn.functional
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tensors
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tensor_attributes
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tensor_view
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torch.amp <amp>
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torch.autograd <autograd>
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torch.library <library>
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cuda
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mps
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torch.backends <backends>
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torch.distributed <distributed>
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torch.distributed.algorithms.join <distributed.algorithms.join>
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torch.distributed.elastic <distributed.elastic>
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torch.distributed.fsdp <fsdp>
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torch.distributed.optim <distributed.optim>
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torch.distributed.tensor.parallel <distributed.tensor.parallel>
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torch.distributed.checkpoint <distributed.checkpoint>
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torch.distributions <distributions>
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torch._dynamo <_dynamo>
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torch.fft <fft>
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torch.func <func>
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futures
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fx
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torch.hub <hub>
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torch.jit <jit>
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torch.linalg <linalg>
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torch.monitor <monitor>
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torch.signal <signal>
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torch.special <special>
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torch.overrides
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torch.package <package>
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profiler
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nn.init
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onnx
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onnx_diagnostics
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optim
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complex_numbers
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ddp_comm_hooks
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pipeline
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quantization
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rpc
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torch.random <random>
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masked
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torch.nested <nested>
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sparse
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storage
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torch.testing <testing>
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torch.utils.benchmark <benchmark_utils>
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torch.utils.bottleneck <bottleneck>
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torch.utils.checkpoint <checkpoint>
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torch.utils.cpp_extension <cpp_extension>
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torch.utils.data <data>
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torch.utils.jit <jit_utils>
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torch.utils.dlpack <dlpack>
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torch.utils.mobile_optimizer <mobile_optimizer>
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torch.utils.model_zoo <model_zoo>
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torch.utils.tensorboard <tensorboard>
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type_info
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named_tensor
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name_inference
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torch.__config__ <config_mod>
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.. toctree::
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:maxdepth: 1
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:caption: Libraries
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torchaudio <https://pytorch.org/audio/stable>
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TorchData <https://pytorch.org/data>
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TorchRec <https://pytorch.org/torchrec>
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TorchServe <https://pytorch.org/serve>
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torchtext <https://pytorch.org/text/stable>
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torchvision <https://pytorch.org/vision/stable>
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PyTorch on XLA Devices <https://pytorch.org/xla/>
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Indices and tables
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==================
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* :ref:`genindex`
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* :ref:`modindex`
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