ASYNCHRONOUS BAYESIAN OPTIMIZATION-BASED MACHINE LEARNING SUPER-PARAMETER OPTIMIZATION SYSTEM AND METHOD

摘要

The present invention relates to an asynchronous Bayesian optimization-based machine learning super-parameter optimization system and method. The system comprises: a Bayesian optimization module, a model parameter pool model, a Kmeans clustering module, a task scheduling module, and an adaptive determining model parallelism module. The present invention efficiently performs automatic parameter adjustment on machine learning in a big data environment, effectively uses multi-host parallel computing capability, and efficiently performs automatic parameter adjustment for big data machine learning, so that people can better use big data machine learning in production practice.