elixir-nx/nx

Multi-dimensional arrays (tensors) and numerical definitions for Elixir

What it solves

It provides the fundamental building blocks for numerical computing and machine learning in the Elixir language, allowing developers to work with multi-dimensional arrays (tensors) and execute them on high-performance backends.

How it works

The project consists of three primary components: Nx provides the definitions and API for tensors and numerical operations; EXLA provides a integration with Google's XLA (Accelerated Linear Algebra) compiler to speed up computations; and Torchx provides a backend based on LibTorch.

Who it’s for

Elixir developers who want to perform numerical computing or build machine learning models within the Elixir ecosystem.

Highlights

  • Multi-dimensional array (tensor) support for Elixir.
  • Integration with Google's XLA compiler for high-performance execution.
  • Support for LibTorch as a backend via Torchx.

Related

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    A library of traditional machine learning algorithms for Elixir, providing tools for classification, regression, clustering, and preprocessing built on top of Nx.

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    A neural network framework for Elixir that provides high-level APIs for model creation and training, powered by Nx for numerical acceleration.

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    openxla/xla

    XLA is an open-source machine learning compiler that optimizes models from PyTorch, TensorFlow, and JAX for high-performance execution on GPUs, CPUs, and ML accelerators.

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