2 dépôts
Systems for conducting non-blocking optimization search trials where workers independently sample and report results.
Distinct from Optimization Trials: None of the candidates cover the identity of an asynchronous framework specifically for optimization sampling.
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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
Provides an asynchronous optimization framework that decouples the generation of search points from their evaluation.
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
Provides an asynchronous engine that decouples parameter suggestions from result reporting to support non-blocking trials.