GPyOpt is a Python open-source library for Bayesian Optimization developed by
the Machine Learning group of the University of Sheffield. It is based on GPy,
a Python framework for Gaussian process modelling.

With GPyOpt you can:
* Automatically configure your models and Machine Learning algorithms.
* Design your wet-lab experiments saving time and money.

Among other functionalities, with GPyOpt you can design experiments in parallel,
use cost models and mix different types of variables in your designs. Many users
already use GpyOpt for research purposes.