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Welcome to the PyTorch developer's wiki!
Hey! We're 9 developers and designers building something we believe in — free, open tools made to help real people.
We've been working on this project in our spare time, late at night, on weekends. It's grown into something bigger than we ever expected.
But now... we're hitting a wall.
Hosting, tools, testing — it all costs. And right now, we're running on passion alone. Some of us are burning out. Some are considering dropping out just to stay afloat.
We don't want that. We want to keep building. For you. For everyone.
If our project helped you — or if you just believe in open-source — please consider supporting us.
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Even a small donation helps us stay alive and keep improving. No pressure. Just thanks for being here.
The Dev Team — We work for you, thank you for your support!
Please read our best practices if you're interested in adding a page or making edits
- Release notes
- PyTorch Versions
- Public API definition and documentation
- Frontend Backward and Forward Compatibility Policy
- Docstring Guidelines
New to PyTorch? Don't know where to start?
- 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- PyTorch JIT IR format (slightly out of date now)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
- PyTorch-ONNX exporter
- 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
- 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
- Serialized operator test framework
- Observers
- Snapdragon NPE Support
- Using TensorBoard in ifbpy
- Introduction to Quantization
- Quantization Operation coverage
- Implementing native quantized ops
- Extend PyTorch Quantization to Custom Backends
- JIT Technical Overview
- Static Runtime
- TorchScript serialization
- PyTorch Fuser
- Implementation reference for the CUDA PyTorch JIT Fuser
- TorchScript
- TorchScript Language Reference
- TorchScript Unsupported Pytorch Constructs
- Distributed RPC Framework
- Distributed Autograd Design
- Remote Reference Protocol
- Distributed Data Parallel
- Distributed communication package
- Contributing to PyTorch Distributed
- 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