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Automated Machine Learning with ensembling (or AutoML with ensembling) seeksto automatically build ensembles of Deep Neural Networks (DNNs) to achievequalitative predictions. Ensemble of DNNs are well known to avoid over-fittingbut they are memory and time consuming approaches. Therefore, an ideal AutoMLwould produce in one single run time different ensembles regarding accuracy andinference speed. While previous works on AutoML focus to search for the bestmodel to maximize its generalization ability, we rather propose a new AutoML tobuild a larger library of accurate and diverse individual models to thenconstruct ensembles. First, our extensive benchmarks show asynchronousHyperband is an efficient and robust way to build a large number of diversemodels to combine them. Then, a new ensemble selection method based on amulti-objective greedy algorithm is proposed to generate accurate ensembles bycontrolling their computing cost. Finally, we propose a novel algorithm tooptimize the inference of the DNNs ensemble in a GPU cluster based onallocation optimization. The produced AutoML with ensemble method shows robustresults on two datasets using efficiently GPU clusters during both the trainingphase and the inference phase.

A Deep Neural Networks ensemble workflow from hyperparameter search to inference leveraging GPU clusters