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/).

本研究旨在介绍下一代超参数优化软件的新设计标准，其中包括可动态构建参数搜索空间的define-by-run API、高效的搜索和修剪策略的实施以及易于设置且多用途的架构，并通过Optuna来证明我们的观点，我们将介绍在满足上述标准的软件开发中所必需的设计技术，并通过实验结果和真实世界应用程序展示我们新设计的强大之处。

Optuna: 一款下一代超参数优化框架