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).
Basic functionality:
deps/
- submodules for build dependencies - libtorch, mklml, pytorchexamples/
- high level example models (xor mlp, typed cnn)hasktorch/
- higher level user-facing library, calls intoffi/
, used byexamples/
Internals (for contributing developers):
codegen/
- code generation, parsesDeclarations.yaml
spec from pytorch and producesffi/
contentsinline-c/
- submodule to inline-cpp fork used for C++ FFIlibtorch-ffi/
- low level FFI bindings to libtorchspec/
- specification files used forcodegen/
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
./get-deps.sh
popd
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.
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.
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 [email protected] or on twitter as @austinvhuang and @SamStites.
See the wiki for developer information.