SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data. The library provides a family of regular and sparse online learning algorithms for large-scale binary and multi-class classification tasks with high efficiency, scalability, portability, and extensibility. SOL was implemented in C++, and provided with a collection of easy-to-use command-line tools, python wrappers and library calls for users and developers, as well as comprehensive documents for both beginners and advanced users. SOL is not only a practical machine learning toolbox, but also a comprehensive experimental platform for online learning research. Experiments demonstrate that SOL is highly efficient and scalable for large-scale machine learning with high-dimensional data.
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library
ReLER, AAII, University of Technology Sydney（悉尼科技大学）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；NetEase Fuxi AI Lab（网易伏羲人工智能实验室）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能工程学院）；Shandong Artificial Intelligence Institute, Qilu University of Technology（齐鲁工业大学山东省人工智能研究院）
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning
Columbia University（哥伦比亚大学）；University of California, Berkeley（加州大学伯克利分校）；Shenzhen Inst. of Advanced Tech.（深圳先进技术研究院）；Northwestern University（西北大学）；Bell labs（贝尔实验室）；Amazon（亚马逊）；IDEA Research（IDEA研究院）
CLT-Forge: A Scalable Library for Cross-Layer Transcoders and Attribution Graphs
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Jinesis AI Lab（Jinesis人工智能实验室）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；CMU（卡内基梅隆大学）；EuroSafeAI；ELLIS Institute Tübingen（图宾根ELLIS研究所）
gridfm-datakit-v1: A Python Library for Scalable and Realistic Power Flow and Optimal Power Flow Data Generation
IBM Research（IBM研究院）；ETH Zurich（苏黎世联邦理工学院）；RSE S.p.A.（RSE公司）；Hydro-Québec Research Institute（魁北克水电研究所）；Argonne National Laboratory（阿贡国家实验室）；Harvard University（哈佛大学）；Stony Brook University（石溪大学）；Brookhaven National Laboratory（布鲁克海文国家实验室）
ProSper -- A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions
Sorbonne Université（索邦大学）；INSERM（法国国家健康与医学研究院）；CNRS（法国国家科学研究中心）；Institut de la Vision（视觉研究所）；FIAS（法兰克福高等研究院）；Goethe-Universität Frankfurt am Main（法兰克福大学）；Zalando Research（Zalando研究院）；University of Sheffield（谢菲尔德大学）；d-fine GmbH（d-fine有限公司）；C. v. Ossietzky Universität Oldenburg（奥尔登堡大学）
