Speaker
Description
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 MLaaS4HEP solution we have already proposed aims to provide such kind of service and to be HEP experiment agnostic. Recently new features have been introduced, such as the possibility to provide pre-processing operations, defining new branches, and applying cuts. 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.
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