Workflow Provenance in the Lifecycle of Scientific Machine Learning [article]

Renan Souza, Leonardo G. Azevedo, Vítor Lourenço, Elton Soares, Raphael Thiago, Rafael Brandão, Daniel Civitarese, Emilio Vital Brazil, Marcio Moreno, Patrick Valduriez, Marta Mattoso, Renato Cerqueira (+1 others)
2021 arXiv   pre-print
Machine Learning (ML) has already fundamentally changed several businesses. More recently, it has also been profoundly impacting the computational science and engineering domains, like geoscience, climate science, and health science. In these domains, users need to perform comprehensive data analyses combining scientific data and ML models to provide for critical requirements, such as reproducibility, model explainability, and experiment data understanding. However, scientific ML is
more » ... inary, heterogeneous, and affected by the physical constraints of the domain, making such analyses even more challenging. In this work, we leverage workflow provenance techniques to build a holistic view to support the lifecycle of scientific ML. We contribute with (i) characterization of the lifecycle and taxonomy for data analyses; (ii) design principles to build this view, with a W3C PROV compliant data representation and a reference system architecture; and (iii) lessons learned after an evaluation in an Oil & Gas case using an HPC cluster with 393 nodes and 946 GPUs. The experiments show that the principles enable queries that integrate domain semantics with ML models while keeping low overhead (<1%), high scalability, and an order of magnitude of query acceleration under certain workloads against without our representation.
arXiv:2010.00330v2 fatcat:4xfpfcsocjajxbq4qumu57swya