Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning

05/31/2023

∙

by   Yuxin Tang, et al.

∙

0

∙

share

The relational data model was designed to facilitate large-scale data management and analytics. We consider the problem of how to differentiate computations expressed relationally. We show experimentally that a relational engine running an auto-differentiated relational algorithm can easily scale to very large datasets, and is competitive with state-of-the-art, special-purpose systems for large-scale distributed machine learning.