Optuna: A Next-generation Hyperparameter Optimization Framework
About
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/).
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Code Generation | HumanEval | Pass@127.44 | 1036 | |
| Mathematical Reasoning | MATH | Accuracy4.74 | 535 | |
| CO2 saturation prediction | CO2 storage simulation | R2 Score0.8915 | 13 | |
| Pressure Prediction | CO2 storage simulation | R20.9804 | 13 | |
| Federated Learning | NASA Bearings Label Skew (downstream) | Weighted Accuracy69.48 | 10 | |
| Classification | Breastcancer | Accuracy96.14 | 8 | |
| Global Optimization | Branin | Best Objective Value-0.041 | 6 | |
| Drag Minimization | CFD Airfoil | Best CD0.0675 | 6 | |
| Hyperparameter Optimization | MuJoCo HalfCheetah | Average Return8.79e+3 | 6 | |
| Hyperparameter Optimization | CIFAR-10 (val) | Validation Accuracy92.3 | 6 |