Sensible extensions for exposing torch in Julia.
Author FluxML
128 Stars
Updated Last
1 Year Ago
Started In
February 2020


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++.


  • Needs a machine with a CUDA GPU (CUDA 10.1 or above)
    • will need lazy artifacts function without a GPU

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()

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.


We can take gradients using Zygote as well

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

# Or

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

Contributing and Issues

Please feel free to open issues you might encounter in the issue tracker. I would also appreciate contributions through PRs toward corrections, increased coverage, docs, etc. Testing currently runs on Linux, but that can be expanded as need arises.


Takes a lot of inspiration from existing such projects - ocaml-torch for generating the wrappers.

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