https://github.com/apple/axlearn

An Extensible Deep Learning Library
https://github.com/apple/axlearn

deep-learning jax

Last synced: 2 days ago
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An Extensible Deep Learning Library

• Host: GitHub
• URL: https://github.com/apple/axlearn
• Owner: apple
• License: apache-2.0
• Created: 2023-02-25T01:33:06.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-11-13T03:15:12.000Z (about 1 month ago)
• Last Synced: 2024-11-13T04:02:16.219Z (about 1 month ago)
• Topics: deep-learning, jax
• Language: Python
• Homepage:
• Size: 19.7 MB
• Stars: 1,871
• Watchers: 62
• Forks: 266
• Open Issues: 46
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Codeowners: CODEOWNERS

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README

        
# The AXLearn Library for Deep Learning

**This library is under active development and the API is subject to change.**

## Table of Contents

| Section | Description |

| - | - |

| [Introduction](#introduction) | What is AXLearn? |

| [Getting Started](docs/01-start.md) | Getting up and running with AXLearn. |

| [Concepts](docs/02-concepts.md) | Core concepts and design principles. |

| [CLI User Guide](docs/03-cli.md) | How to use the CLI. |

| [Infrastructure](docs/04-infrastructure.md) | Core infrastructure components. |

## Introduction

AXLearn is a library built on top of [JAX](https://jax.readthedocs.io/) and

[XLA](https://www.tensorflow.org/xla) to support the development of large-scale deep learning models.

AXLearn takes an object-oriented approach to the software engineering challenges that arise from

building, iterating, and maintaining models.

The configuration system of the library lets users compose models from reusable building blocks and

integrate with other libraries such as [Flax](https://flax.readthedocs.io/) and

[Hugging Face transformers](https://github.com/huggingface/transformers).

AXLearn is built to scale.

It supports the training of models with up to hundreds of billions of parameters across thousands of

accelerators at high utilization.

It is also designed to run on public clouds and provides tools to deploy and manage jobs and data.

Built on top of [GSPMD](https://arxiv.org/abs/2105.04663), AXLearn adopts a global computation

paradigm to allow users to describe computation on a virtual global computer rather than on a

per-accelerator basis.

AXLearn supports a wide range of applications, including natural language processing, computer

vision, and speech recognition and contains baseline configurations for training state-of-the-art

models.

Please see [Concepts](docs/02-concepts.md) for more details on the core components and design of AXLearn, or [Getting Started](docs/01-start.md) if you want to get your hands dirty.