%0 Journal Article
%A Bunino, Matteo
%A Sæther, Jarl Sondre
%A Eickhoff, Linus Maximilian
%A Lappe, Anna Elisa
%A Tsolaki, Kalliopi
%A Verder, Killian
%A Mutegeki, Henry
%A Machacek, Roman
%A Girone, Maria
%A Krochak, Oleksandr
%A Rüttgers, Mario
%A Sarma, Rakesh
%A Lintermann, Andreas
%T itwinai: A Python Toolkit for Scalable Scientific Machine Learning on HPC Systems
%J The journal of open source software
%V 11
%N 117
%@ 2475-9066
%C [Erscheinungsort nicht ermittelbar]
%I [Verlag nicht ermittelbar]
%M FZJ-2026-00771
%P 9409
%D 2026
%X The integration of Artificial Intelligence (AI) into scientific research has expanded significantlyover the past decade, driven by the availability of large-scale datasets and Graphics ProcessingUnits (GPUs), in particular at High Performance Computing (HPC) sites. <br>However, many researchers face significant barriers when deploying AI workflows on HPCsystems, as their heterogeneous nature forces scientists to focus on low-level implementationdetails rather than on their core research. At the same time, the researchers often lackspecialized HPC/AI knowledge to implement their workflows efficiently. <br>To address this, we present itwinai, a Python library that simplifies scalable AI on HPC. Itsmodular architecture and standard interface allow users to scale workloads efficiently fromlaptops to supercomputers, reducing implementation overhead and improving resource usage.
%F PUB:(DE-HGF)16
%9 Journal Article
%R 10.21105/joss.09409
%U https://juser.fz-juelich.de/record/1052123