Installation

The only prerequisite for installing Nx is Elixir itself. If you don't have Elixir installed in your machine you can visit this installation page.

There are several ways to install Nx (Numerical Elixir), depending on your project type and needs.

Using Mix in a standardElixir Project

If you are working inside a Mix project, the recommended way to install Nx is by adding it to your mix.exs dependencies:

1. Open mix.exs and modify the deps function:

defp deps do
  [
    {:nx, "~> 0.9"}  # Install the latest stable version
  ]
end

2. Fetch the dependencies, run on the terminal:

mix deps.get

Installing Nx from GitHub (Latest Development Version)

If you need the latest, unreleased features, install Nx directly from the GitHub repository.

1. Modify mix.exs:

defp deps do
  [
    {:nx, github: "elixir-nx/nx", branch: "main", sparse: "nx"}
  ]
end

2. Fetch dependencies:

mix deps.get

Installing Nx in a Standalone Script (Without a Mix Project)

If you don’t have a Mix project and just want to run a standalone script, use Mix.install/1 to dynamically fetch and install Nx.

Mix.install([:nx])

require Nx

tensor = Nx.tensor([1, 2, 3])
IO.inspect(tensor)

Run the script with:

elixir my_script.exs

Best for: Quick experiments, small scripts, or one-off computations.

Installing the Latest Nx from GitHub in a Standalone Script

To use the latest development version in a script (without a Mix project):

Mix.install([
  {:nx, github: "elixir-nx/nx", branch: "main", sparse: "nx"}
])

require Nx

tensor = Nx.tensor([1, 2, 3])
IO.inspect(tensor)

Run:

elixir my_script.exs

Best for: Trying new features from Nx without creating a full project.

Installing Nx with EXLA for GPU Acceleration

To enable GPU/TPU acceleration with Google’s XLA backend, install Nx along with EXLA:

1. Modify mix.exs:

defp deps do
  [
    {:nx, "~> 0.9"},
    {:exla, "~> 0.9"}  # EXLA (Google XLA Backend)
  ]
end

2. Fetch dependencies:

mix deps.get

3. Run with EXLA enabled:

Nx.default_backend(EXLA.Backend)
Nx.Defn.default_options(compiler: EXLA)

Best for: Running Nx on GPUs or TPUs using Google’s XLA compiler.

Installing Nx with Torchx for PyTorch Acceleration

To run Nx operations on PyTorch’s backend (LibTorch):

1. Modify mix.exs:

defp deps do
  [
    {:nx, "~> 0.9"},
    {:torchx, "~> 0.9"}  # PyTorch Backend
  ]
end

2. Fetch dependencies:

mix deps.get

3. Run with Torchx enabled:

Nx.default_backend(Torchx.Backend)

Best for: Deep learning applications with PyTorch acceleration.