Optuna: A Next-generation Hyperparameter Optimization Framework
This addresses the need for more flexible and efficient hyperparameter optimization tools for machine learning practitioners, though it is incremental in advancing software design rather than a paradigm shift.
The study tackled the design of next-generation hyperparameter optimization software by proposing criteria like a define-by-run API and efficient search strategies, resulting in the development of Optuna, which is presented as the first of its kind with experimental demonstrations.
The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that allows users to construct the parameter search space dynamically, (2) efficient implementation of both searching and pruning strategies, and (3) easy-to-setup, versatile architecture that can be deployed for various purposes, ranging from scalable distributed computing to light-weight experiment conducted via interactive interface. In order to prove our point, we will introduce Optuna, an optimization software which is a culmination of our effort in the development of a next generation optimization software. As an optimization software designed with define-by-run principle, Optuna is particularly the first of its kind. We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications. Our software is available under the MIT license (https://github.com/pfnet/optuna/).