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SUMMARY:AI_INFN & CREST: towards a Geo-Distributed Platform for Machine Le
 arning across the INFN Federation
DTSTART;VALUE=DATE-TIME:20260908T094000Z
DTEND;VALUE=DATE-TIME:20260908T102000Z
DTSTAMP;VALUE=DATE-TIME:20260922T191357Z
UID:indico-contribution-2037@events.ncbj.gov.pl
DESCRIPTION:Speakers: Gioacchino Vino (INFN\, Unit of Bari)\nThe National 
 Institute for Nuclear Physics (INFN) is a distributed research institute w
 hose computing resources span its federation. Through the AI_INFN initiati
 ve and national projects such as TeRABIT and ICSC\, INFN has built a large
 -scale infrastructure for Machine Learning\, combining GPU clusters\, HPC 
 systems\, FPGA nodes and high-performance storage. It serves a growing com
 munity applying ML to physics research\, with a focus on scalability\, ope
 n science and emerging paradigms such as quantum computing.\nBuilding on t
 he architecture developed at the CNAF Tier-1 site\, which optimized hardwa
 re orchestration and the user experience for interactive and batch workloa
 ds\, the ReCaS-Bari site within the INFN Cloud federation has been selecte
 d for platform replication and functional extension. This deployment prove
 s the design replicable across data centres and serves as a testbed for fr
 ontier features\, including workload offloading to the Leonardo supercompu
 ter and other HPC centres\, already validated from CNAF with Argo Workflow
 s dispatching pods through interLink. Establishing ReCaS-Bari as an altern
 ative to the CNAF instance lays the foundation for cross-site federation a
 nd a geo-distributed AI computing infrastructure within INFN. Multi-tenant
  storage and POSIX object-storage access are in production\, while offload
 ing from ReCaS-Bari and Kubernetes accounting are under development\, on a
  backbone open to computing centres\, research groups and scientists acros
 s disciplines.\n\nhttps://events.ncbj.gov.pl/event/468/contributions/2037/
LOCATION:
URL:https://events.ncbj.gov.pl/event/468/contributions/2037/
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