Training and Cross-Validating Machine Learning Pipelines with Limited Memory

Abstract

While automated machine learning (AutoML) can save human labor in finding well-performing pipelines, it often suffers from two problems: overfitting and using excessive resources. Unfortunately, the solutions are often at odds: cross-validation helps reduce overfitting at the expense of more resources; conversely, preprocessing on a separate compute cluster and then cross-validating only the final predictor saves resources at the expense of more overfitting. This paper shows how to train and cross-validate entire pipelines on a single moderate machine with limited memory by using monoids, which are associative, thus providing a flexible way for handling large data one batch at a time. To facilitate AutoML, our approach is designed to support the common sklearn APIs used by many AutoML systems for pipelines, training, cross-validation, and several operators. Abstracted behind those APIs, our approach uses task graphs to extend the benefits of monoids from operators to pipelines, and provides a multi-backend implementation. Overall, our approach lets users train and cross-validate pipelines on simple and inexpensive compute infrastructure.