Last week, I started work on a new open source software project whose goal is to streamline hyperparameter optimization for machine learning algorithms. The tool is called osprey, and it's available on github, pypi, and readthedocs. It integrates closely with scikit-learn.
Osprey is designed to make hyperparameter optimization as easy as possible to run, by optimizing the cross-validation performance of your model with respect to its hyperparameters using random search, or Bayesian methods via Gaussian processes or tree-structured Parzen estimators. Multiple osprey processes can run in parallel, to easily leverage cluster compute resources without needing to boot up any external server.
Take it for a spin, and let me know what you think!