https://doi.org/10.1051/epjconf/202429507040
Progress on cloud native solution of Machine Learning as a Service for HEP
1 INFN-CNAF, Viale Carlo Berti Pichat, 6/2, 40127 Bologna ( ITALY )
2 INFN Sezione di Perugia, Via Alessandro Pascoli 23c, 06123 Perugia ( ITALY )
3 Cornell University, 616 Thurston Ave., Ithaca, NY 14853 ( USA )
4 University of Bologna, Via Zamboni 33, 40126 Bologna ( ITALY )
5 INFN Sezione di Bologna, Viale Carlo Berti Pichat, 6/2, 40127 Bologna ( ITALY )
* e-mail: luca.giommi@cnaf.infn.it
Published online: 6 May 2024
Nowadays Machine Learning (ML) techniques are successfully used in many areas of High-Energy Physics (HEP) and will play a significant role also in the upcoming High-Luminosity LHC upgrade foreseen at CERN, when a huge amount of data will be produced by LHC and collected by the experiments, facing challenges at the exascale. To favor the usage of ML in HEP analyses, it would be useful to have a service allowing to perform the entire ML pipeline (in terms of reading the data, processing data, training a ML model, and serving predictions) directly using ROOT files of arbitrary size from local or remote distributed data sources. The Machine Learning as a Service for HEP (MLaaS4HEP) solution we have already proposed aims to provide such kind of service and to be HEP experiment agnostic. To provide users with a real service and to integrate it into the INFN Cloud, we started working on MLaaS4HEP cloudification. This would allow to use cloud resources and to work in a distributed environment. In this work, we provide updates on this topic and discuss a working prototype of the service running on INFN Cloud. It includes an OAuth2 proxy server as authentication/authorization layer, a MLaaS4HEP server, an XRootD proxy server for enabling access to remote ROOT data, and the TensorFlow as a Service (TFaaS) service in charge of the inference phase. With this architecture a HEP user can submit ML pipelines, after being authenticated and authorized, using local or remote ROOT files simply using HTTP calls.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
