README.md

Torch.jl

Sensible extensions for exposing torch in Julia.

This package is aimed at providing the Tensor type, which offloads all computations over to ATen, the foundational tensor library for PyTorch, written in C++.

Supported platforms

Operating System	Architecture	Acceleration Runtime
macOS	aarch64	-
macOS	x86_64	-
Linux (glibc)	aarch64	-
Linux (glibc)	x86_64	CUDA 10.2
Linux (glibc)	x86_64	CUDA 11.3

Windows support is pending, cf. issue #26.

The binary dependencies are available for Linux (glibc) on i686 (32-bit), but not all tests run succesfully.

Quick Start

To add the package, from the Julia REPL, enter the Pkg prompt by typing ] and execute the following:

pkg> add Torch

Or via Julia's package manager Pkg.

julia> using Pkg; Pkg.add("Torch");

Usage Example

using Metalhead, Metalhead.Flux, Torch
using Torch: torch

resnet = ResNet(18)

We can move our object over to Torch via a simple call to torch

tresnet = resnet.layers |> torch

Or if we need more control over the device to be used like so:

ip = rand(Float32, 224, 224, 3, 1) # An RGB Image
tip = tensor(ip, dev = 0) # 0 => GPU:0 in Torch
cpu_tensor = tensor(ip, dev = -1) # -1 => CPU:0

Calling into the model is done via the usual Flux mechanism.

tresnet(tip);

We can take gradients using Zygote as well

gs = gradient(x -> sum(tresnet(x)), tip);

# Or

ps = Flux.params(tresnet);
gs = gradient(ps) do
  sum(tresnet(tip))
end

Contributing and Issues

Please feel free to open issues you might encounter in the issue tracker. Contributions through PRs toward corrections, increased coverage, docs, etc. are most welcome.

Acknowledgements

Takes a lot of inspiration from existing such projects, in particular ocaml-torch, and tch-rs.