<h1><img src="" alt="Torchx" width="400"></h1>

Elixir client for PyTorch (through the LibTorch C++ frontend).
It includes a backend for `Nx` for native execution of tensor
operations (inside and outside of `defn`).

## Installation

In order to use `Torchx`, you will need Elixir installed. Then create an Elixir project
via the `mix` build tool:

$ mix new my_app

Then you can add `Torchx` as dependency in your `mix.exs`. At the moment you will have to
use a Git dependency while we work on our first release:

def deps do
    {:torchx, "~> 0.1.0-dev", github: "elixir-nx/nx", sparse: "torchx"}

If you are using Livebook or IEx, you can instead run:

  {:torchx, "~> 0.1.0-dev", github: "elixir-nx/nx", sparse: "torchx"}

We will automatically download a precompiled version of `LibTorch` that
runs on the CPU. If you want to use another version, you can set `LIBTORCH_VERSION`
to one of the supported values:

- 1.9.0
- 1.9.1
- 1.10.0
- 1.10.1
- 1.10.2

If you want torch with CUDA support, please use `LIBTORCH_TARGET` to choose
CUDA versions. The current supported targets are:

- `cpu` default CPU only version
- `cu102` CUDA 10.2 and CPU version (no OSX support)
- `cu111` CUDA 11.1 and CPU version (no OSX support)

Once downloaded, we will compile `Torchx` bindings. You will need `make`/`nmake`,
`cmake` (3.12+) and a `C++` compiler. If building on Windows, you will need:

- [Microsoft Build Tools 2019](
- [Microsoft Visual C++ 2019 Redistributable](
- [CMake](

For Apple M1-series, you can download precompiled LibTorch binaries with

brew install libtorch
export LIBTORCH_DIR="$(brew --cellar libtorch)/$(brew list --versions libtorch | tr ' ' '\n' | tail -1)"
# for convenience, the export above can be added to your .bashrc, .zshrc or equivalent
# adding to .bashrc for example
echo -e "\nexport LIBTORCH_DIR=\"${LIBTORCH_DIR}\"" >> .bashrc

Other platforms may require compiling `libtorch` from scratch.

## Usage

The main mechanism to use `Torchx` is by setting it as a backend to your tensors:

Nx.tensor([1, 2, 3], backend: Torchx.Backend)
Nx.iota({100, 100}, backend: Torchx.Backend)

Then you can proceed to use `Nx` functions as usual!

You can also set `Torchx` as a default backend, which will apply to all tensors created
by the current Elixir process:

Nx.tensor([1, 2, 3])
Nx.iota({100, 100})

See `Nx.default_backend/1` for more information.

## License

Copyright (c) 2021 Stas Versilov, Dashbit

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at [](

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
See the License for the specific language governing permissions and
limitations under the License.