Popular GitHub Repositories for Distributed Training

Learn how to design, develop, deploy and iterate on production-grade ML applications.

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）

Fengshenbang-LM(封神榜大模型)是IDEA研究院认知计算与自然语言研究中心主导的大模型开源体系，成为中文AIGC和认知智能的基础设施。

FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, TensorOpera AI (https://TensorOpera.ai) is your generative AI platform at scale.

A high performance and generic framework for distributed DNN training

Fast and flexible AutoML with learning guarantees.

Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

Training and serving large-scale neural networks with auto parallelization.

Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.

DLRover: An Automatic Distributed Deep Learning System

Library for Fast and Flexible Human Pose Estimation

Efficient Deep Learning Systems course materials (HSE, YSDA)

Official code for ReLoRA from the paper Stack More Layers Differently: High-Rank Training Through Low-Rank Updates

LiBai(李白): A Toolbox for Large-Scale Distributed Parallel Training

YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud (ECCV 2018)

HandyRL is a handy and simple framework based on Python and PyTorch for distributed reinforcement learning that is applicable to your own environments.

A Jax-based library for designing and training transformer models from scratch.

Collection of best practices, reference architectures, model training examples and utilities to train large models on AWS.

Easy Parallel Library (EPL) is a general and efficient deep learning framework for distributed model training.

Ternary Gradients to Reduce Communication in Distributed Deep Learning (TensorFlow)