Flare & Lantern: Efficiently Swapping Horses Midstream.

Running machine learning (ML) workloads at scale is as much a data management problem as a model engineering problem. Big performance challenges exist when data management systems invoke ML classifiers as user-defined functions (UDFs) or when stand-alone ML frameworks interact with data stores for data loading and pre-processing (ETL). In particular, UDFs can be precompiled or simply a black box for the data management system and the data layout may be completely different from the native layout, thus adding overheads at the boundaries. In this demo, we will show how bottlenecks between existing systems can be eliminated when their engines are designed around runtime compilation and native code generation, which is the case for many state-of-the-art relational engines as well as ML frameworks. We demonstrate an integration of Flare (an accelerator for Spark SQL), and Lantern (an accelerator for TensorFlow and PyTorch) that results in a highly optimized end-to-end compiled data path, switching between SQL and ML processing with negligible overhead.