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Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API. Thanks to our define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters. Optuna Dashboard is a real-time web dashboard for Optuna. You can check the optimization history, hyperparameter importances, etc. in graphs and tables. You don't need to create a Python script to call Optuna's visualization functions. Automated search for optimal hyperparameters using Python conditionals, loops, and syntax. Efficiently search large spaces and prune unpromising trials for faster results. Parallelize hyperparameter searches over multiple threads or processes without modifying code.
Features
- Handle a wide variety of tasks with a simple installation that has few requirements
- Define search spaces using familiar Python syntax including conditionals and loops
- Adopt state-of-the-art algorithms for sampling hyperparameters and efficiently pruning unpromising trials
- Scale studies to tens or hundreds or workers with little or no changes to the code
- Inspect optimization histories from a variety of plotting functions
- Lightweight, versatile, and platform agnostic architecture
Project Samples
