DiffSharp: Differentiable Functional Programming
This is the development branch of DiffSharp 1.0.
NOTE: This branch is undergoing development. It has incomplete code, functionality, and design that are likely to change without notice; when using TorchSharp backend, only x64 platform is currently supported out of the box, see [DEVGUIDE.md] for more details.
DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains.
Key features
- Nested and mixed-mode differentiation
- Common optimizers, model elements, differentiable probability distributions
- F# for robust functional programming
- PyTorch familiar naming and idioms, efficient LibTorch CUDA/C++ tensors with GPU support
- Linux, macOS, Windows supported
- Use interactive notebooks in Jupyter and Visual Studio Code
- 100% open source
Documentation
You can find the documentation here, including information on installation and getting started.
Release notes can be found here.
Communication
Please use GitHub issues to share bug reports, feature requests, installation issues, suggestions etc.
Contributing
We welcome all contributions.
- Bug fixes: if you encounter a bug, please open an issue describing the bug. If you are planning to contribute a bug fix, please feel free to do so in a pull request.
- New features: if you plan to contribute new features, please first open an issue to discuss the feature before creating a pull request.
The Team
DiffSharp is developed by Atılım Güneş Baydin, Don Syme and other contributors, having started as a project supervised by the automatic differentiation wizards Barak Pearlmutter and Jeffrey Siskind.
License
DiffSharp is licensed under the BSD 2-Clause “Simplified” License, which you can find in the LICENSE file in this repository.