TOKYO – July 29, 2020 – Preferred Networks, Inc. (PFN) today released Optuna v2.0, the second major update of the open-source hyperparameter optimization framework for machine learning, first initiated by PFN in January 2020.

Hyperparameter importance graphic, showing which hyperparameters in a neural network matter most

Optuna v2.0 has the following new features:

• Hyperparameter importance evaluation

Optuna can provide feedback on how important each of the measured hyperparameters were on the overall performance of the algorithm. This valuable information can assist researchers and developers to focus on tuning the hyperparameters that matter most.

• Hyperband pruning

Pruning allows unpromising trials to be stopped early. One of the most recent and robust techniques for pruning is Hyperband, which is well-suited for deep learning and is now available in Optuna.

• Performance improvements

Optimization has been speeded up by improving the lower storage layer. Experiments show that searches can be up to ten times faster.

• Additional integrations

Additional integration modules are available for easy use with LightGBM to do efficient stepwise optimization as well as MLflow, AllenNLP, and TensorBoard.

Since open-sourced in December 2018, the interest from researchers and developers for Optuna has grown. In the last month, Optuna was downloaded over 100,000 times. Going forward, PFN plans to work on multi-objective optimization to allow multiple criteria to be optimized simultaneously, along with continuing to add integrations and improve the performance of Optuna.

About Optuna
Optuna was open-sourced by PFN in December 2018 as a hyperparameter optimization framework written in Python. Optuna automates the trial-and-error process of finding hyperparameters that deliver good performance. Optuna is used in many PFN projects and was an important factor in PFDet team’s award-winning performances in the first Kaggle Open Images object detection competition. https://optuna.org/