A Software Ecosystem for Deploying Deep Learning in Gravitational Wave Physics

Abstract

The recent application of neural network algorithms to problems in gravitational-wave physics invites the study of how best to build production-ready applications on top of them. By viewing neural networks not as standalone models, but as components or functions in larger data processing pipelines, we can apply lessons learned from both traditional software development practices as well as successful deep learning applications from the private sector. This paper highlights challenges presented by straightforward but naïve deployment strategies for deep learning models, and identifies solutions to them gleaned from these sources. It then presents HERMES, a library of tools for implementing these solutions, and describes how HERMES is being used to develop a particular deep learning application which will be deployed during the next data collection run of the International Gravitational-Wave Observatories.