Optuna: A Next-generation Hyperparameter Optimization Framework

Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, Masanori Koyama
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引用次数: 2524

Abstract

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/).
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Optuna:下一代超参数优化框架
本研究的目的是为下一代超参数优化软件引入新的设计准则。我们提出的标准包括:(1)允许用户动态构建参数搜索空间的运行定义API,(2)搜索和修剪策略的有效实现,以及(3)易于设置的通用架构,可以部署用于各种目的,从可扩展的分布式计算到通过交互界面进行的轻量级实验。为了证明我们的观点,我们将介绍Optuna,这是一款优化软件,它是我们在开发下一代优化软件方面努力的成果。Optuna作为一款采用逐运行定义原则设计的优化软件,在同类软件中独领有。我们将介绍在开发满足上述标准的软件时所必需的设计技术,并通过实验结果和实际应用来展示我们的新设计的力量。我们的软件在MIT许可下可用(https://github.com/pfnet/optuna/)。
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