In the past decade, we have seen Deep learning (DL) science adopted at a tremendous pace by AWS customers. The plentiful and jointly trained parameters of DL models have a large representational capacity that brought improvements in numerous customer use cases, including image and speech analysis, natural language processing (NLP), time series processing, and more. In this post, we highlight challenges commonly reported specifically in DL training, and how the open-source library MosaicML Composer helps solve them.
The challenge with DL training
DL models are trained iteratively, in a nested for loop. A loop iterates through the training dataset chunk by chunk and, if necessary, this loop is repeated several times over the whole dataset. ML practitioners working on DL training face several challenges:
Training duration grows with data size. With permanently-growing datasets, training times and costs grow too, and the rhythm of scientific discovery slows down.