Optuna: A Next-generation Hyperparameter Optimization Framework

Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, Masanori Koyama
7/25/2019
cs.LGstat.ML

Official code: optuna/optuna (14,582 stars, MIT, last commit 2026-05-01).

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/).

View on arXivView PDF

Code Implementations(1)

optuna/optunaOfficial100%
MIT

A hyperparameter optimization framework

14,5821,362Mako, PythonFeb 21, 20185 months agoMIT
distributedhyperparameter-optimizationmachine-learningparallelpython

Similar Papers

Cite this paper

@article{akiba2019optuna,
  title  = {Optuna: A Next-generation Hyperparameter Optimization Framework},
  author = {Takuya Akiba and Shotaro Sano and Toshihiko Yanase and Takeru Ohta and Masanori Koyama},
  year   = {2019},
  eprint = {1907.10902},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1907.10902}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/1907.10902.svg)](https://codeofpaper.com/paper/1907.10902)

Discussion