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.
verl/HybridFlow: A Flexible and Efficient RL Post-Training Framework
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
Easy-to-use and powerful LLM and SLM library with awesome model zoo.
- nlp
- embedding
- bert
- ernie
DeepEP: an efficient expert-parallel communication library
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
An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries
- deepspeed-library
- gpt-3
- transformers
- language-model
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.
Search-R1: An Efficient, Scalable RL Training Framework for Reasoning & Search Engine Calling interleaved LLM based on veRL
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
Translatedcube studio is an open-source, cloud-native, one-stop machine learning / deep learning / large model AI platform. It covers the full mlops algorithm pipeline, a computing power rental platform, online notebook development, drag-and-drop task flow pipeline orchestration, multi-machine multi-GPU distributed training, hyperparameter search, inference services with VGPU virtualization, edge computing, a labeling platform with automated labeling, SFT fine-tuning / reward model / reinforcement learning training for large models such as deepseek, multi-machine large model inference with vllm/ollama/mindie, a private knowledge base, and an AI model marketplace. It supports domestic Chinese CPUs/GPUs/NPUs and the Ascend ecosystem, supports RDMA, and supports distributed frameworks such as pytorch/tf/mxnet/deepspeed/paddle/colossalai/horovod/ray/volcano.
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.
Meta Lingua: a lean, efficient, and easy-to-hack codebase to research LLMs.
A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)
Massively Parallel Deep Reinforcement Learning. 🔥
- pytorch
- reinforcement-learning
- ppo
- sac
Open-source framework for the research and development of foundation models.
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
Scenic: A Jax Library for Computer Vision Research and Beyond