A config-search data model and a linear representative-point transfer method can reuse measurements across similar cloud workloads, cutting sampled configurations by up to 92% when a linear relationship holds.
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters
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Hyperparameter optimization and neural architecture search can become prohibitively expensive for regular black-box Bayesian optimization because the training and evaluation of a single model can easily take several hours. To overcome this, we introduce a comprehensive tool suite for effective multi-fidelity Bayesian optimization and the analysis of its runs. The suite, written in Python, provides a simple way to specify complex design spaces, a robust and efficient combination of Bayesian optimization and HyperBand, and a comprehensive analysis of the optimization process and its outcomes.
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Efficient and Reuseable Cloud Configuration Search Using Discovery Spaces
A config-search data model and a linear representative-point transfer method can reuse measurements across similar cloud workloads, cutting sampled configurations by up to 92% when a linear relationship holds.