  Hyperparameter tuning is a fundamental aspect of machine learning research.
Setting up the infrastructure for systematic optimization of hyperparameters
can take a significant amount of time. Here, we present PyHopper, a black-box
optimization platform designed to streamline the hyperparameter tuning workflow
of machine learning researchers. PyHopper's goal is to integrate with existing
code with minimal effort and run the optimization process with minimal
necessary manual oversight. With simplicity as the primary theme, PyHopper is
powered by a single robust Markov-chain Monte-Carlo optimization algorithm that
scales to millions of dimensions. Compared to existing tuning packages,
focusing on a single algorithm frees the user from having to decide between
several algorithms and makes PyHopper easily customizable. PyHopper is publicly
available under the Apache-2.0 license at https://github.com/PyHopper/PyHopper.
