DistML is a Ray extension library to support large-scale distributed ML training on heterogeneous multi-node multi-GPU clusters

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Machine Learningdistml
Overview

Introduction

DistML is a Ray extension library to support large-scale distributed ML training on heterogeneous multi-node multi-GPU clusters. This library is under active development and we are adding more advanced training strategies and auto-parallelization features.

DistML currently supports:

  • Distributed training strategies

    • Data parallelism
      • AllReduce strategy
      • Sharded parameter server strategy
      • BytePS strategy Pipeline parallleism
      • Micro-batch pipeline parallelism

  • DL Frameworks:

    • PyTorch
    • JAX

Installation

Install Dependencies

Depending on your CUDA version, install cupy following https://docs.cupy.dev/en/stable/install.html.

Install from source for dev

pip install -e .
Owner
GitHub Repository
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