RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure. (arXiv:2211.05239v1 [cs.LG])

We present RecD (Recommendation Deduplication), a suite of end-to-end
infrastructure optimizations across the Deep Learning Recommendation Model
(DLRM) training pipeline. RecD addresses immense storage, preprocessing, and
training overheads caused by feature duplication inherent in industry-scale
DLRM training datasets. Feature duplication arises because DLRM datasets are
generated from interactions. While each user session can generate multiple
training samples, many features' values do not change across these samples. We
demonstrate how RecD exploits this property, end-to-end, across a deployed
training pipeline. RecD …

arxiv deep learning infrastructure recommendation recommendation model training