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A simple, extensible backend for developing auto-tuning systems


BTB (“Bayesian Tuning and Bandits”) is a simple, extensible backend for developing auto-tuning systems such as AutoML systems. It provides an easy-to-use interface for tuning and selection.

It is currently being used in several AutoML systems:


In its first iteration, in 2018, BTB was designed as an open source library that handles the problems of tuning the hyperparameters of a machine learning pipeline, selecting between multiple pipelines and recommending a pipeline. A good reference to see our design rationale at that time is Laura Gustafson’s thesis, written under the supervision of Kalyan Veeramachaneni:

Later in 2018, Carles Sala joined the project to make it grow as a reliable open-source library that would become part of a bigger software ecosystem designed to facilitate the development of robust end-to-end solutions based on Machine Learning tools. This second iteration of our work was presented in 2019 as part of the Machine Learning Bazaar:

Indices and tables