SMAC is a tool for algorithm configuration to optimize the parameters of arbitrary algorithms across a set of instances. This also includes hyperparameter optimization of ML algorithms. The main core consists of Bayesian Optimization in combination with an aggressive racing mechanism to efficiently decide which of two configurations performs better.