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.
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
20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.
Easy-to-use and powerful LLM and SLM library with awesome model zoo.
- nlp
- embedding
- bert
- ernie
An Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray (PPO & DAPO & REINFORCE++ & VLM & TIS & vLLM & Ray & Async RL)
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 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
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.
A unified inference and post-training framework for accelerated video generation.
- diffusers
- diffusion-models
- video-generation
- distillation
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
A high-performance distributed training framework for Reinforcement Learning
- reinforcement-learning
- large-scale
- parallelization
PyTorch extensions for high performance and large scale training.
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
- deep-learning
- machine-learning
- ml-platform
- ml-infrastructure
Proof Of Concept of DEcentralised Machine Learning on top of the Golem (https://golem.network/) architecture
- golem
- decentralised-machine-learning
- federated-learning