计算机科学
修剪
软件
软件开发
超参数
可扩展性
基于搜索的软件工程
面向资源的体系结构
点(几何)
软件工程
软件建设
分布式计算
机器学习
程序设计语言
操作系统
数学
生物
农学
几何学
作者
Takuya Akiba,Shotaro Sano,Toshihiko Yanase,Takeru Ohta,Masanori Koyama
出处
期刊:Cornell University - arXiv
日期:2019-01-01
被引量:778
标识
DOI:10.48550/arxiv.1907.10902
摘要
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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