Awesome GitHub RepositoriesDistributed Training Platforms

LightGBM is a high-performance machine learning framework designed for constructing gradient-boosted decision tree ensembles. It provides a platform for training classification, regression, and ranking models, with a focus on memory efficiency and large-scale distributed computing. The framework distinguishes itself through specialized algorithmic strategies, including leaf-wise tree growth and histogram-based decision learning, which prioritize convergence speed. It optimizes memory usage by bundling mutually exclusive features and employs gradient-based sampling to reduce training complexit

h2o-3 is a distributed machine learning platform and automated machine learning framework designed for training and deploying predictive models using distributed in-memory computing. It functions as a deep learning framework and a distributed model scoring engine, capable of operating as a Kubernetes ML cluster to process large datasets in parallel. The platform distinguishes itself through automated machine learning capabilities that automatically select the best algorithms and hyperparameters to optimize model performance. It provides specialized deep learning toolkits for tasks including i

This project is a distributed machine learning platform and sparse deep learning framework designed for training and serving models with high-dimensional sparse data. It functions as an online model serving infrastructure and recommendation system engine, enabling real-time item retrieval and scoring using deep tree matching and neural networks. The system distinguishes itself through a multi-task learning framework that optimizes multiple objective functions within a shared representation space. It features a specialized online serving infrastructure that supports dynamic model hot-loading a

Azure Machine Learning Notebooks is a cloud-based environment for developing and executing interactive Jupyter notebooks within a managed machine learning workspace. It provides managed machine learning compute through cloud-based workstations and containerized environments pre-configured with GPU drivers and kernels for high-performance model training. The project functions as a distributed GPU training platform and an ML experiment tracking system to monitor training metrics and version data assets. It also serves as an MLOps pipeline orchestrator for automating modular workflows and a mode

FedML 是一个分布式机器学习训练库、联邦学习框架和 GPU 工作负载编排器。它提供了在多云、本地和去中心化 GPU 集群上执行大规模模型训练和微调所需的核心系统组件，同时为可扩展的模型服务提供专用引擎，并为端到端生命周期管理提供 MLOps 流水线管理器。 该平台的独特之处在于支持跨去中心化边缘设备和组织孤岛的隐私保护联邦学习，将原始数据保留在本地硬件上。它还具有资源池化计算市场，允许用户将未使用的 GPU 容量贡献给共享池以进行分布式任务执行。 该系统涵盖了广泛的功能，包括多云 GPU 编排、自动化机器学习流水线管理以及针对物联网设备和智能手机的边缘 AI 部署。它进一步集成了用于基础模型微调、低延迟推理部署以及带有硬件性能分析的训练实验追踪工具。 用户可以使用命令行界面和声明式配置文件来启动和调度工作负载。