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/).

Keywords

Computer scienceResource-oriented architectureSoftwareSoftware developmentPruningScalabilitySoftware constructionSearch-based software engineeringSoftware engineeringDistributed computingProgramming languageOperating system

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Publication Info

Year
2019
Type
article
Pages
2623-2631
Citations
5681
Access
Closed

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Cite This

Takuya Akiba, Shotaro Sano, Toshihiko Yanase et al. (2019). Optuna. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2623-2631. https://doi.org/10.1145/3292500.3330701

Identifiers

DOI
10.1145/3292500.3330701
arXiv
1907.10902

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Data completeness: 79%