Machine learning (ML) relies on stochastic algorithms, all of which rely on gradient approximations with \textquotedbl{}batch size\textquotedbl{} examples. Growing the batch size as the optimization proceeds is a simple and usable method to reduce the training time, provided that the number of workers grows with the batch size. In this work, we provide a package that trains PyTorch models on Dask clusters, and can grow the batch size if desired. Our simulations indicate that for a particular model that uses GPUs for a popular image classification task, the training time can be reduced from about 120 minutes with standard SGD to 45 minutes with a variable batch size method.

Keywords:machine learningmodel trainingdistributed computation