Monthly Downloads: 27
Programming language: Haskell
License: BSD 3-clause "New" or "Revised" License
Tags: AI     Machine Learning     Hasktorch     Tensors    
Latest version: v0.0.1
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Hasktorch 0.2 Pre-Release

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.

This project is in early development and should only be used by contributing developers. Expect substantial changes to the library API as it evolves. Contributions and PRs are welcome.

Currently we are prepping development and migration for a major 2nd release (please excuse sparsity of docs in the meantime). If you're interested in details or contributing please get in touch (see contributing).

Project Structure

Basic functionality:

  • deps/ - submodules for build dependencies - libtorch, mklml, pytorch
  • examples/ - high level example models (xor mlp, typed cnn)
  • hasktorch/ - higher level user-facing library, calls into ffi/, used by examples/

Internals (for contributing developers):

  • codegen/ - code generation, parses Declarations.yaml spec from pytorch and produces ffi/ contents
  • inline-c/ - submodule to inline-cpp fork used for C++ FFI
  • libtorch-ffi/- low level FFI bindings to libtorch
  • spec/ - specification files used for codegen/

Getting Started

on OSX or Ubuntu-like OS's

The following steps run a toy linear regression example, assuming the hasktorch repository has just been cloned.

Starting at the top-level directory of the project, go to the deps/ (dependencies) directory and run the get-deps.sh shell script to retrieve project dependencies with the following commands:

pushd deps

If you are using CUDA-9, replace ./get-deps.sh with ./get-deps.sh -a cu92. Likewise for CUDA-10, replace ./get-deps.sh with ./get-deps.sh -a cu101.

These downloads include various pytorch shared libraries. Note get-deps.sh only has to be run once when the repo is initially cloned.

Next, set shell environment to reference the shared library locations:

source setenv

Note source setenv should be run from the top-level directory of the repo.

via nix-shell

nix-shell ./hasktorch/shell.nix

Will get you into a development environment for hasktorch using the CPU backend. On NixOS you may also pass in a cudaVersion argument of 9 or 10 to provision a CUDA environment:

nix-shell ./hasktorch/shell.nix --arg cudaVersion 9 # or 10

If you are running cabal or stack to develop hasktorch, there is a shell hook to tell you which extra-lib-dirs and extra-include-dirs fields to include in your stack.yaml or cabal.project.local. This hook will also explain how to create a cabal.project.freeze file.

Building examples

Finally, try building and running the linear regression example:

stack run regression

For additional examples, see the examples/ directory.


We welcome new contributors.

Contact Austin Huang or Sam Stites for access to the hasktorch slack channel. You can send an email to hasktorch@gmail.com or on twitter as @austinvhuang and @SamStites.

Developer Information

See the wiki for developer information.