Tuning hyperparameters for machine learning algorithms is a tedious task, one that is typically done manually. To enable automated hyperparameter tuning, recent works have started to use techniques based on Bayesian optimization. However, to practically enable automated tuning for large scale machine learning training pipelines, significant gaps remain in existing libraries, including lack of abstractions, fault tolerance, and flexibility to support scheduling on any distributed computing framework. To address these challenges, we present Mango, a Python library for parallel hyperparameter tuning. Mango enables the use of any distributed scheduling framework, implements intelligent parallel search strategies, and provides rich abstractions for defining complex hyperparameter search spaces that are compatible with scikit-learn. Mango is comparable in performance to Hyperopt, another widely used library. Mango is available open-source and is currently used in production at Arm Research to provide state-of-art hyperparameter tuning capabilities.
Competence Center for High Performance Computing Fraunhofer ITWM Kaiserslautern（弗劳恩霍夫研究所ITWM高性能计算能力中心（凯泽斯劳滕））；Institute for Machine Learning and Analytics (IMLA) Offenburg University（奥芬堡大学机器学习与分析研究所（IMLA））
milearn: A Python Package for Multi-Instance Machine Learning
Aix Marseille University（艾克斯-马赛大学）；CNRS（法国国家科学研究中心）；Institute of Molecular and Translational Medicine, Palacky University（帕拉茨基大学分子与转化医学研究所）；Institut Universitaire de France（法国大学研究院）
PHOTONAI -- A Python API for Rapid Machine Learning Model Development
Institute for Translational Psychiatry, University of Münster（明斯特大学转化精神病学研究所）；Faculty of Mathematics and Computer Science, University of Münster（明斯特大学数学与计算机科学学院）
Foolbox: A Python toolbox to benchmark the robustness of machine learning models
University of Tübingen（图宾根大学）；Bernstein Center for Computational Neuroscience, Tübingen（图宾根伯恩斯坦计算神经科学中心）；International Max Planck Research School for Intelligent Systems, Tübingen（图宾根国际马克斯·普朗克智能系统研究所）；Max Planck Institute for Biological Cybernetics（马克斯·普朗克生物控制研究所）；Institute for Theoretical Physics, University of Tübingen（图宾根大学理论物理研究所）
BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization
School of Software, BNRist, \ University Beijing China；School of Software, EIRI, \ University Beijing China；School of Software, \ University Beijing China；School of Software, BNRist, \ University；School of Software, EIRI, \ University；School of Software, \ University
TelescopeML -- I. An End-to-End Python Package for Interpreting Telescope Datasets through Training Machine Learning Models, Generating Statistical Reports, and Visualizing Results
NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；Bay Area Environmental Research Institute（湾区环境研究所）；Universities Space Research Association（大学空间研究协会）；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；University of California, Riverside（加州大学河滨分校）
RiverText: A Python Library for Training and Evaluating Incremental Word Embeddings from Text Data Streams
Department of Computer Science, University of Chile（智利大学计算机科学系）；National Center for Artificial Intelligence (CENIA)（人工智能国家中心）；Millennium Institute for Foundational Research on Data (IMFD)（数据基础研究千年研究所）
PrismSSL: One Interface, Many Modalities; A Single-Interface Library for Multimodal Self-Supervised Learning
dept. Computer Engineering University of Isfahan（计算机工程系 哈尔拉克大学）；dept. Computer Engineering University of Texas at Dallas（计算机工程系 德克萨斯大学达拉斯分校）；dept. Computer Science University of Oxford（计算机科学系 奥克兰大学）；dept. Artificial Intelligence University of Isfahan（人工智能系 哈尔拉克大学）
