  Finding new ways to use artificial intelligence (AI) to accelerate the
analysis of gravitational wave data, and ensuring the developed models are
easily reusable promises to unlock new opportunities in multi-messenger
astrophysics (MMA), and to enable wider use, rigorous validation, and sharing
of developed models by the community. In this work, we demonstrate how
connecting recently deployed DOE and NSF-sponsored cyberinfrastructure allows
for new ways to publish models, and to subsequently deploy these models into
applications using computing platforms ranging from laptops to high performance
computing clusters. We develop a workflow that connects the Data and Learning
Hub for Science (DLHub), a repository for publishing machine learning models,
with the Hardware Accelerated Learning (HAL) deep learning computing cluster,
using funcX as a universal distributed computing service. We then use this
workflow to search for binary black hole gravitational wave signals in open
source advanced LIGO data. We find that using this workflow, an ensemble of
four openly available deep learning models can be run on HAL and process the
entire month of August 2017 of advanced LIGO data in just seven minutes,
identifying all four binary black hole mergers previously identified in this
dataset, and reporting no misclassifications. This approach, which combines
advances in AI, distributed computing, and scientific data infrastructure opens
new pathways to conduct reproducible, accelerated, data-driven gravitational
wave detection.
