# Optuna: A Next-generation Hyperparameter Optimization Framework

## TL;DR

This paper introduces Optuna, a flexible, efficient, and user-friendly hyperparameter optimization framework that employs a define-by-run API, enabling dynamic search space construction and versatile deployment options.

## Contribution

It presents the first optimization software built on a define-by-run principle, demonstrating innovative design techniques and superior performance through experiments and real-world applications.

## Key findings

- Optuna achieves efficient hyperparameter search with dynamic search space definition.
- The framework supports scalable distributed and lightweight experiments.
- Experimental results show improved optimization performance over existing tools.

## 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/).