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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Ongoing research training transformer models at scale
- large-language-models
- model-para
- transformers
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3.8, DeepSeek-V4, GLM-5.1, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM5.3, Gemma4, Llava, Phi4, ...) (AAAI 2025).
An Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray (PPO & DAPO & REINFORCE++ & VLM & TIS & vLLM & Ray & Async RL)
🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch
Simple and Distributed Machine Learning Python Library porting ML algorithms for Spark
A unified inference and post-training framework for accelerated video generation.
- diffusers
- diffusion-models
- video-generation
- distillation
A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)
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
Official codebase used to develop Vision Transformer, SigLIP, MLP-Mixer, LiT and more.