Making large AI models cheaper, faster and more accessible
- deep-learning
- hpc
- large-scale
- data-parallelism
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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
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
20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.
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
A Flexible and Powerful Parameter Server for large-scale machine learning
- machine-learning
- parameter-server
- spark
- scala
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
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.
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
FlagAI (Fast LArge-scale General AI models) is a fast, easy-to-use and extensible toolkit for large-scale model.
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.
- tensorflow
- spark
- yahoo
- machine-learning
A high-performance distributed training framework for Reinforcement Learning
- reinforcement-learning
- large-scale
- parallelization
An Efficient and User-Friendly Scaling Library for Reinforcement Learning with Large Language Models
- agentic
- rlhf
- rlvr
PyTorch extensions for high performance and large scale training.
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