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In High Energy Physics facilities that provide High Performance Computingenvironments provide an opportunity to efficiently perform the statisticalinference required for analysis of data from the Large Hadron Collider, but canpose problems with orchestration and efficient scheduling. The computearchitectures at these facilities do not easily support the Python computemodel, and the configuration scheduling of batch jobs for physics oftenrequires expertise in multiple job scheduling services. The combination of thepure-Python libraries pyhf and funcX reduces the common problem in HEP analysesof performing statistical inference with binned models, that wouldtraditionally take multiple hours and bespoke scheduling, to an on-demand(fitting) "function as a service" that can scalably execute across workers injust a few minutes, offering reduced time to insight and inference. Wedemonstrate execution of a scalable workflow using funcX to simultaneously fit125 signal hypotheses from a published ATLAS search for new physics using pyhfwith a wall time of under 3 minutes. We additionally show performancecomparisons for other physics analyses with openly published probability modelsand argue for a blueprint of fitting as a service systems at HPC centers.

Distributed statistical inference with pyhf enabled through funcX