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
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).
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
PaddleFormers is an easy-to-use library of pre-trained large language model zoo based on PaddlePaddle.
- model
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 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
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch
An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries
- deepspeed-library
- gpt-3
- transformers
- language-model
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
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.
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.
- tensorflow
- spark
- yahoo
- machine-learning