Getting Start

🐟Tunny🐟 is Grasshopper's optimization component using Optuna, an open source hyperparameter auto-optimization framework.

This page is the documentation for version 1 of Tunny.

For more information on how to use the examples, please go to the Examples page from the sidebar on the left.

Tunny is inspired by components such as Galapagos, Opossum, and Wallacei, and can be used in a similar way to them.

Tunny has been wrapped around Optuna; it might be called Optuna's UI for Grasshopper. For this reason, Optuna is also introduced. The following is taken from the Optuna official website

Optuna™, an open-source automatic hyperparameter optimization framework, automates the trial-and-error process of optimizing the hyperparameters. It automatically finds optimal hyperparameter values based on an optimization target. Optuna is framework agnostic and can be used with most Python frameworks, including Chainer, Scikit-learn, Pytorch, etc. Optuna is used in PFN projects with good results. One example is the second place award in the Google AI Open Images 2018 – Object Detection Track competition.

More detail, Please see Optuna official site : https://optuna.org/