SciPost Submission Page
The AI_INFN Platform: Artificial Intelligence Development in the Cloud
by Lucio Anderlini, Giulio Bianchini, Diego Ciangottini, Stefano Dal Pra, Diego Michelotto, Rosa Petrini, Daniele Spiga
Submission summary
| Authors (as registered SciPost users): | Rosa Petrini |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2509.22117v2 (pdf) |
| Date submitted: | Nov. 3, 2025, 2:27 p.m. |
| Submitted by: | Rosa Petrini |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | The 2nd European AI for Fundamental Physics Conference (EuCAIFCon2025) |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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Abstract
Machine Learning (ML) is profoundly reshaping the way researchers create, implement, and operate data-intensive software. Its adoption, however, introduces notable challenges for computing infrastructures, particularly when it comes to coordinating access to hardware accelerators across development, testing, and production environments. The INFN initiative AI_INFN (Artificial Intelligence at INFN) seeks to promote the use of ML methods across various INFN research scenarios by offering comprehensive technical support, including access to AI-focused computational resources. Leveraging the INFN Cloud ecosystem and cloud-native technologies, the project emphasizes efficient sharing of accelerator hardware while maintaining the breadth of the Institute's research activities. This contribution describes the deployment and commissioning of a Kubernetes-based platform designed to simplify GPU-powered data analysis workflows and enable their scalable execution on heterogeneous distributed resources. By integrating offloading mechanisms through Virtual Kubelet and the InterLink API, the platform allows workflows to span multiple resource providers, from Worldwide LHC Computing Grid sites to high-performance computing centers like CINECA Leonardo. We will present preliminary benchmarks, functional tests, and case studies, demonstrating both performance and integration outcomes.
Current status:
Reports on this Submission
Strengths
- clarity - the proceedings present a new computing platform architecture for INFN, adopting modern industry standards.
- Interest from the community of researchers using INFN infrastructure.
Weaknesses
- Application and benefits not demonstrated. Even if scalability tests have been conducted, there is no demonstration of the new architecture's use in production, nor of the claimed advantages (efficient GPU management, ease of use for users, etc.).
Report
Recommendation
Publish (meets expectations and criteria for this Journal)
Author: Rosa Petrini on 2025-11-19 [id 6048]
(in reply to Report 1 on 2025-11-07)Thank you very much for your careful reading of the proceedings and your positive feedback.
With regard to the limited discussion of the application and benefits of the proposed architecture, this was primarily a consequence of the 4-page limit. More detailed explanations and demonstrations can be found in the works cited in the manuscript that have been previously published.
We sincerely appreciate your constructive feedback and the time you dedicated to reviewing our submission.