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This paper reports the results and post-challenge analyses of ChaLearn'sAutoDL challenge series, which helped sorting out a profusion of AutoMLsolutions for Deep Learning (DL) that had been introduced in a variety ofsettings, but lacked fair comparisons. All input data modalities (time series,images, videos, text, tabular) were formatted as tensors and all tasks weremulti-label classification problems. Code submissions were executed on hiddentasks, with limited time and computational resources, pushing solutions thatget results quickly. In this setting, DL methods dominated, though popularNeural Architecture Search (NAS) was impractical. Solutions relied onfine-tuned pre-trained networks, with architectures matching data modality.Post-challenge tests did not reveal improvements beyond the imposed time limit.While no component is particularly original or novel, a high level modularorganization emerged featuring a "meta-learner", "data ingestor", "modelselector", "model/learner", and "evaluator". This modularity enabled ablationstudies, which revealed the importance of (off-platform) meta-learning,ensembling, and efficient data management. Experiments on heterogeneous modulecombinations further confirm the (local) optimality of the winning solutions.Our challenge legacy includes an ever-lasting benchmark(http://autodl.chalearn.org), the open-sourced code of the winners, and a free"AutoDL self-service".

Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019