Configuring hyperparameters in a machine learning problem can be a dauntingly boring and suboptimal task as each evaluation may be fairly long to conduct and thus performing an exhaustive grid search may be infeasible. Bayesian optimization provides a seemingly nice and intuitive framework for this instead of manual search and there are some packages out there in other languages. For example HyperOpt is an open source package developed in Python.
My question is this: How do people perform bayesian optimization for hyperparameter search in Julia? Is there currently a package out there that I was not able to find or do they use PyCall for example and use HyperOpt of Python instead?