Hasktorch is a powerful Haskell library for tensor computation and neural network modeling, built on top of libtorch (the backend of PyTorch). It brings differentiable programming, automatic differentiation, and efficient tensor operations into Haskell’s strongly typed functional paradigm. This project is in active development, so expect changes to the library API as it evolves. We would like to invite new users to join our Hasktorch discord space for questions and discussions. Contributions/PR are encouraged. Hasktorch is a library for tensors and neural networks in Haskell. It is an independent open source community project which leverages the core C++ libraries shared by PyTorch.
Features
- High-performance tensor operations utilizing libtorch for CPU and GPU computation
- Support for both typed and untyped tensors to ensure type safety and flexibility
- Automatic differentiation for gradient-based optimization and learning
- Abundant mathematical operations including linear algebra, neural network layers, and probability distributions
- Lightweight, idiomatic Haskell design leveraging algebraic data types and functional composition
- Active open-source community with tutorials, examples, and multi-platform support (Linux, macOS, Nix, Docker)
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