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This is a Python scientific computing library for finding the global maximum of expensive black-box functions. It operates as a global optimization framework that identifies optimal input parameters within defined bounds to maximize a target output. The library utilizes Gaussian process regression to predict function values and uncertainty, guiding the search for optimal parameters. It employs a surrogate-model optimization approach to approximate high-cost objective functions, reducing the total number of required evaluations. The system manages the trade-off between exploration and exploit
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
Nevergrad is a gradient-free optimization library and hyperparameter optimization framework designed to find the minimum of objective functions without using derivatives. It serves as an asynchronous optimization engine that decouples parameter suggestions from result reporting to support parallel function evaluations. The project specializes in multi-objective optimization to identify Pareto fronts for competing goals and provides a suite for benchmarking the performance and convergence of different optimization routines. It supports black-box system optimization, enabling the tuning of exte
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
This is a Bayesian optimization library for Python designed to find the maximum value of expensive black box functions. It operates as a global optimizer that uses probabilistic models to identify the peak value of unknown functions through iterative sampling.
The main features of bayesian-optimization/bayesianoptimization are: Black-Box Optimization, Bayesian Optimization Libraries, Black-Box Maximizers, Gaussian Processes, Hyperparameter Tuning, Hyperparameter Optimization, Bayesian Optimization Loops, Black-Box Optimizers.
Open-source alternatives to bayesian-optimization/bayesianoptimization include: fmfn/bayesianoptimization — This is a Python scientific computing library for finding the global maximum of expensive black-box functions. It… optuna/optuna — Optuna is a Python-based hyperparameter optimization framework designed to automate the search for optimal machine… facebookresearch/nevergrad — Nevergrad is a gradient-free optimization library and hyperparameter optimization framework designed to find the… microsoft/nni — NNI is an AutoML toolkit designed to automate machine learning lifecycles. It functions as a hyperparameter… hyperopt/hyperopt — Hyperopt is a Python library for hyperparameter optimization designed to minimize scalar-valued objective functions.… uber/ludwig — Ludwig is a declarative machine learning framework designed for training neural networks and large language models…