OCaml bindings for PyTorch
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README.md

ocaml-torch

ocaml-torch provides some ocaml bindings for the PyTorch tensor library. This brings to OCaml NumPy-like tensor computations with GPU acceleration and tape-based automatic differentiation.

These bindings use the PyTorch C++ API and are mostly automatically generated. The current GitHub tip corresponds to PyTorch 1.0.0.

Opam Installation

The opam package can be installed using the following command. This automatically installs the CPU version of libtorch.

opam install torch

You can then compile some sample code, see some instructions below. ocaml-torch can also be used in interactive mode via utop or ocaml-jupyter.

Here is a sample utop session.

utop

Build a Simple Example

To build a first torch program, create a file example.ml with the following content.

open Torch

let () =
  let tensor = Tensor.randn [ 4; 2 ] in
  Tensor.print tensor

Then create a dune file with the following content:

(executables
  (names example)
  (libraries torch))

Run dune exec example.exe to compile the program and run it!

Alternatively you can first compile the code via dune build example.exe then run the executable _build/default/example.exe (note that building the bytecode target example.bc may not work on macos).

Tutorials

Examples

Below is an example of a linear model trained on the MNIST dataset (full code).

  (* Create two tensors to store model weights. *)
  let ws = Tensor.zeros [image_dim; label_count] ~requires_grad:true in
  let bs = Tensor.zeros [label_count] ~requires_grad:true in

  let model xs = Tensor.(mm xs ws + bs) in
  for index = 1 to 100 do
    (* Compute the cross-entropy loss. *)
    let loss =
      Tensor.cross_entropy_for_logits (model train_images) ~targets:train_labels
    in

    Tensor.backward loss;

    (* Apply gradient descent, disable gradient tracking for these. *)
    Tensor.(no_grad (fun () ->
        ws -= grad ws * f learning_rate;
        bs -= grad bs * f learning_rate));

    (* Compute the validation error. *)
    let test_accuracy =
      Tensor.(sum (argmax (model test_images) = test_labels) |> float_value)
      |> fun sum -> sum /. test_samples
    in
    printf "%d %f %.2f%%\n%!" index (Tensor.float_value loss) (100. *. test_accuracy);
  end
  • Some ResNet examples on CIFAR-10.
  • A simplified version of char-rnn illustrating character level language modeling using Recurrent Neural Networks.
  • Neural Style Transfer applies the style of an image to the content of another image. This uses some deep Convolutional Neural Network.

Models and Weights

Various pre-trained computer vision models are implemented in the vision library. The weight files can be downloaded at the following links:

Running the pre-trained models on some sample images can the easily be done via the following commands.

make all
_build/default/examples/pretrained/predict.exe path/to/resnet18.ot tiger.jpg

Alternative Installation Options

These alternative ways to install ocaml-torch could be useful to run with GPU acceleration enabled.

Option 1: Using PyTorch pre-built Binaries

The libtorch library can be downloaded from the PyTorch website (1.0.0 cpu version).

Download and extract the libtorch library then to build all the examples run:

export LIBTORCH=/path/to/libtorch
git clone https://github.com/LaurentMazare/ocaml-torch.git
cd ocaml-torch
make all

Option 2: Using PyTorch Conda package

Conda packages for PyTorch 1.0 can be used via the following command.

conda create -n torch
source activate torch
conda install pytorch-cpu=1.0.0 -c pytorch
# Or for the CUDA version
# conda install pytorch=1.0.0 -c pytorch

git clone https://github.com/LaurentMazare/ocaml-torch.git
cd ocaml-torch
make all