Getting to Know Dragonfly Better
Discover how to use Dragonfly, a Python library designed for scalable Bayesian optimization, including its modes for multifidelity and multitask optimization. Learn to control the optimization loop with the ask-tell mode and implement efficient optimization workflows in Python, enhancing capabilities for high-dimensional, expensive black-box function optimization.
Dragonfly is a library that is designed for scaling Bayesian optimization and experimental design. The library offers a wide range of features that allow for high dimensionality, multifidelity evaluations, multitask settings, parallel evaluations, and derivative evaluations.
The library is developed to be both modular and flexible, allowing users to plug and play different components, optimizers, acquisition functions, and surrogate models. It also provides an interface for customizing various parameters and settings to suit specific use cases.
Modes in Dragonfly
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