Automatic hyperparameter optimization framework for machine learning


Optuna™, an open-source automatic hyperparameter optimization framework, automates the trial-and-error process of optimizing the hyperparameters. It automatically finds optimal hyperparameter values based on an optimization target. Optuna is framework agnostic and can be used with most Python frameworks, including Chainer, Scikit-learn, Pytorch, etc.

Optuna is used in PFN projects with good results. One example is the second place award in the Google AI Open Images 2018 – Object Detection Track competition.


Define-by-Run style API

Pruning of trials based on learning curves

Parallel distributed optimization

Optuna: A Define by Run Hyperparameter Optimization Framework | SciPy 2019

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