Speaker
Description
The ML_INFN initiative (“Machine Learning at INFN”) is an effort to foster Machine Learning activities at the Italian National Institute for Nuclear Physics (INFN).
In recent years, AI inspired activities have flourished bottom-up in many efforts in Physics, both at the experimental and theoretical level.
Many researchers have procured desktop-level devices, with consumer oriented GPUs, and have trained themselves in a variety of ways, from webinars, books, tutorials.
ML_INFN aims to help and systematize such effort, in multiple ways: by offering state-of-the art hardware for Machine Learning, leveraging on the INFN-Cloud provisioning solutions and thus sharing more efficiently GPU-like resources and leveling the access to such resources to all INFN researchers, and by organizing and curating Knowledge Bases with production grade examples from successful activities already in production.
Moreover, training events have been organized for beginners, based on existing INFN ML research and focussed on flattening the learning curve.
In this contribution we will update the status of the project reporting in particular on the development of tools to take advantage of High-Performance computing resources provisioned by CINECA for interactive and batch support to machine learning activities and on the organization of the first in-person advanced-level training event, with a GPU-equipped cloud-based envioronment provided to each participant.
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