Welcome to the PyTorch developer's wiki!

Please read our best practices if you're interested in adding a page or making edits

User docs

• Release notes
• PyTorch Versions

Onboarding

New to PyTorch? Don't know where to start?

• Core Onboarding

Developer docs

• Developer FAQ
• Where should I add documentation?
• PyTorch Data Flow and Interface Diagram
• Multiprocessing Technical Notes
• Software Architecture for c10
• PyTorch JIT IR format (slightly out of date now)
• TH to ATen porting guide
• Writing Python in C++ (a manifesto)
• Introducing Quantized Tensor
• Life of a Tensor
• How to use TensorIterator
• Running and writing tests
• Writing memory format aware operators
• Guide for adding type annotations to PyTorch
• The torch.fft module in PyTorch 1.7

Notes

• Automatic Mixed Precision package
• Automatic Mixed Precision examples
• Autograd mechanics
• Broadcasting semantics
• CPU threading and TorchScript inference
• CUDA semantics
• Frequently Asked Questions
• Extending PyTorch
• Features for large-scale deployments
• Multiprocessing best practices
• Reproducibility
• Serialization semantics
• Windows FAQ
• Python Language Reference Coverage
• Complex Numbers
• ONNX
• Android
• iOS
• How-to: Writing PyTorch & Caffe2 Operators
• CUDA IPC Refcounting implementation explained
• Autograd
• Code Coverage Tool for Pytorch
• How to write tests using FileCheck
• PyTorch Release Scripts
• Docker images for Jenkins
• Serialized operator test framework
• ONNX op coverage
• Observers
• Snapdragon NPE Support
• Using TensorBoard in ifbpy

Named Tensors

• Named Tensors
• Named Tensors operator coverage

Caffe2

• Caffe2
• Building Caffe2
• Doxygen Notes
• Docker & Caffe2
• Caffe2 implementation of Open Neural Network Exchange (ONNX)
• nomnigraph
• Caffe2 & TensorRT integration
• Playground for Caffe2 Models
• How to run FakeLowP vs Glow tests
• Using ONNX and ATen to export models from PyTorch to Caffe2
• An ATen operator for Caffe2

Quantization

• Introduction to Quantization
• Quantization Operation coverage
• Implementing native quantized ops
• Extend PyTorch Quantization to Custom Backends

JIT/TorchScript

• JIT Technical Overview
• Current workflow
• Static Runtime
• TorchScript serialization
• PyTorch Fuser
• Implementation reference for the CUDA PyTorch JIT Fuser
• TorchScript
• TorchScript Language Reference
• TorchScript Unsupported Pytorch Constructs

Distributed

• Distributed RPC Framework
• Distributed Autograd Design
• Remote Reference Protocol
• Distributed Data Parallel
• Distributed communication package
• Contributing to PyTorch Distributed

C++

• PyTorch with C++
• The C++ Frontend
• PyTorch C++ API
• Tensor basics
• Tensor Creation API
• Tensor Indexing API
• MaybeOwned<Tensor>
• Installing C++ Distributions of PyTorch
• Torch Library API
• libtorch
• C++ / Python API parity tracker
• TensorExpr C++ Tests
• JIT C++ Tests
• C++ Frontend Tests
• FAQ
• Best Practices to Edit and Compile Pytorch Source Code On Window

Benchmarks

• Distributed Data Parallel Benchmark
• Fast RNN benchmarks
• PyTorch/Caffe2 Operator Micro-benchmarks
• torch_function micro-benchmarks
• Benchmarking tool for the autograd AP
• Modular Benchmarking Components

DataLoader

• Implementing DataPipe
• DataPipe Typing System

Workflow docs

• Continuous Integration
• Bot commands
• Code review values
• Lint as you type
• Pull request review etiquette
• Docker image build on CircleCI
• Debugging with SSH on Github Actions
• Debugging with Remote Desktop on CircleCI
• Structure of CI on CircleCI

Community

• Code of Conduct
• Contributing
• PyTorch Contribution Guide
• PyTorch Governance

Archived

• Breaking Changes from Variable and Tensor merge (from 0.4 release)
• Tensor API changes for Caffe2 developers (from 1.0 release, plus some stuff on master)
• Autograd and Fork