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Bayesian Optimisation (BO) refers to a suite of techniques for globaloptimisation of expensive black box functions, which use introspective Bayesianmodels of the function to efficiently search for the optimum. While BO has beenapplied successfully in many applications, modern optimisation tasks usher innew challenges where conventional methods fail spectacularly. In this work, wepresent Dragonfly, an open source Python library for scalable and robust BO.Dragonfly incorporates multiple recently developed methods that allow BO to beapplied in challenging real world settings; these include better methods forhandling higher dimensional domains, methods for handling multi-fidelityevaluations when cheap approximations of an expensive function are available,methods for optimising over structured combinatorial spaces, such as the spaceof neural network architectures, and methods for handling parallel evaluations.Additionally, we develop new methodological improvements in BO for selectingthe Bayesian model, selecting the acquisition function, and optimising overcomplex domains with different variable types and additional constraints. Wecompare Dragonfly to a suite of other packages and algorithms for globaloptimisation and demonstrate that when the above methods are integrated, theyenable significant improvements in the performance of BO. The Dragonfly libraryis available at dragonfly.github.io.

Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly