Training machine learning models faster with Dask

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