data-parallelism

Making large AI models cheaper, faster and more accessible

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Distributed Deep Learning, with a focus on distributed training, using Keras and Apache Spark.

A state-of-the-art multithreading runtime: message-passing based, fast, scalable, ultra-low overhead

飞桨大模型开发套件，提供大语言模型、跨模态大模型、生物计算大模型等领域的全流程开发工具链。

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

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

Distributed Keras Engine, Make Keras faster with only one line of code.

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

Orkhon: ML Inference Framework and Server Runtime

Large scale 4D parallelism pre-training for 🤗 transformers in Mixture of Experts *(still work in progress)*

Distributed training (multi-node) of a Transformer model

Multi-GPU training for Keras

SC23 Deep Learning at Scale Tutorial Material

WIP. Veloce is a low-code Ray-based parallelization library that makes machine learning computation novel, efficient, and heterogeneous.

Deep Learning at Scale Training Event at NERSC

♨ Optimized Gaussian blur filter on CPU.

☕Implement of Parallel Matrix Multiplication Methods Using FOX Algorithm on Peking University's High-performance Computing System

This repository provides hands-on labs on PyTorch-based Distributed Training and SageMaker Distributed Training. It is written to make it easy for beginners to get started, and guides you through step-by-step modifications to the code based on the most basic BERT use cases.

Fast and easy distributed model training examples.