Awesome GitHub RepositoriesOptimization Trials

NNI is an AutoML toolkit designed to automate machine learning lifecycles. It functions as a hyperparameter optimization framework, a neural architecture search tool, and a model compression suite. The project provides a distributed training orchestrator to manage machine learning workloads across local machines, remote servers, and cloud platforms. It enables the discovery of efficient model structures through reinforcement learning and one-shot optimization methods, while utilizing Bayesian and evolutionary algorithms to automate hyperparameter tuning. Additional capabilities include tools

Optuna is a Python-based hyperparameter optimization framework designed to automate the search for optimal machine learning model configurations. It functions as a Bayesian optimization library that systematically tests parameter combinations to maximize or minimize objective functions, streamlining the model development process through iterative evaluation. The project distinguishes itself through a define-by-run dynamic construction model, which allows users to build complex, conditional search spaces using standard programming logic. Its architecture is highly modular, featuring a pluggabl

This is a scikit-learn automated machine learning framework designed to optimize model selection and hyperparameters. It functions as an automated model selector and hyperparameter optimization tool for classification and regression tasks, utilizing an automated ensemble builder to combine high-performing models for increased predictive accuracy. The system features a distributed search engine that uses Dask for parallel machine learning optimization across CPU cores or clusters. It implements a budget-based evaluation strategy through successive halving to prioritize promising model configur

Hyperopt is a Python library for hyperparameter optimization designed to minimize scalar-valued objective functions. It operates as a stochastic search space engine that finds optimal input parameters by searching through real-valued, discrete, and conditional spaces. The framework distinguishes itself through its support for complex search space configurations, allowing for conditional parameter hierarchies where specific hyperparameters are sampled only if their parent parameters meet certain criteria. It is built as an asynchronous optimization framework, decoupling the generation of searc

FLAML 是一个自动化机器学习框架、超参数优化工具和大型语言模型代理编排器。它提供了一个用于跨各种学习器和数据集进行模型选择和调优的系统，同时也提供了一个用于优化大型语言模型推理参数和微调设置的工具包。 该项目具有元学习调优系统，可分析历史任务数据以生成依赖于数据的默认配置，从而加速模型收敛。它进一步通过对话式工作流和事件驱动编排，支持协作式多代理系统的设计。 能力涵盖了针对机器学习模型和任意 Python 函数的资源高效超参数搜索，支持分层搜索空间和字典序目标优化。该框架还包括用于自动化模型选择、堆叠集成构建、零样本配置以及强制执行公平性约束的实用工具。 该系统支持分布式调优扩展和跨计算集群的并发试验执行，以缩短总搜索时长。