| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01287 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701287 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701287
Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform
1 Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Firenze, Italy
2 Istituto Nazionale di Fisica Nucleare (INFN), CNAF, Italy
3 Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Perugia, Italy
4 Department of Physics, University of Perugia, Italy
* e-mail: matteo.barbetti@cnaf.infn.it
** e-mail: rosa.petrini@fi.infn.it
Published online: 7 October 2025
Machine Learning (ML) is driving a revolution in the way scientists design, develop, and deploy data-intensive software. However, the adoption of ML presents new challenges for the computing infrastructure, particularly in terms of provisioning and orchestrating access to hardware accelerators for development, testing, and production. The INFN-funded project AI_INFN (“Artificial Intelligence at INFN”) aims at fostering the adoption of ML techniques within INFN use cases by providing support on multiple aspects, including the provision of AI-tailored computing resources. It leverages cloud-native solutions in the context of INFN Cloud, to share hardware accelerators as effectively as possible, ensuring the diversity of the Institute’s research activities is not compromised. In this contribution, we provide an update on the commissioning of a Kubernetes platform designed to ease the development of GPU-powered data analysis workflows and their scalability on heterogeneous, distributed computing resources, possibly federated as Virtual Kubelets with the interLink provider.
© The Authors, published by EDP Sciences, 2025
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.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.