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

Last update: Aug 19, 2022

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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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