Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Making large AI models cheaper, faster and more accessible
- deep-learning
- hpc
- large-scale
- data-parallelism
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
TranslatedPArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (Core framework of 'PaddlePaddle' (飞桨, Feijiang), for deep learning & machine learning: high-performance single-machine and distributed training, and cross-platform deployment)
- paddlepaddle
- deep-learning
- scalability
- machine-learning
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
- gbdt
- gbm
- machine-learning
- data-mining
Python package built to ease deep learning on graph, on top of existing DL frameworks.
- deep-learning
- graph-neural-networks
20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.
An Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray (PPO & DAPO & REINFORCE++ & VLM & TIS & vLLM & Ray & Async RL)
OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.
A unified ecosystem and workspace for large-scale federated learning of Large Models & LLMS. Store compute as Wifi
- compute-orchestration
- distributed-systems
- edge-computing
- edge-intelligence
Flower: A Friendly Federated AI Framework
- flower
- federated-learning
- federated-learning-framework
- federated-analytics
A Flexible and Powerful Parameter Server for large-scale machine learning
- machine-learning
- parameter-server
- spark
- scala
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility
A unified ecosystem and workspace for large-scale federated learning, bridging the gap between centralized model evolution and decentralized edge intelligence.
Simple and Distributed Machine Learning Python Library porting ML algorithms for Spark
A Next-Generation Training Engine Built for Ultra-Large MoE Models
EasyR1: An Efficient, Scalable, Multi-Modality RL Training Framework based on veRL
The central nervous system and backend orchestrator for Fractal. It handles multi-tenant task scheduling, real-time TFLOPs budgeting, and federated weight aggregation using FedAvg.
A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)
MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
Massively Parallel Deep Reinforcement Learning. 🔥
- pytorch
- reinforcement-learning
- ppo
- sac
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.
- federated-learning
- deep-learning
- distributed-training
- edge-ai