I would like to share my pet project, Hypertunity, a Python library for black-box hyperparameter optimisation. It’s main features are: * Bayesian Optimisation using Gaussian process regression by wrapping GPyOpt; * Native support for random and grid search; * Visualisation of the results in Tensorboard using the HParams plugin; * Scheduled, parallel execution of experiments using joblib; * Also possible to schedule jobs on Slurm.

For the full set of features, check out the docs.

Your feedback is very much appreciated!