TY  - JOUR
AU  - Bunino, Matteo
AU  - Sæther, Jarl Sondre
AU  - Eickhoff, Linus Maximilian
AU  - Lappe, Anna Elisa
AU  - Tsolaki, Kalliopi
AU  - Verder, Killian
AU  - Mutegeki, Henry
AU  - Machacek, Roman
AU  - Girone, Maria
AU  - Krochak, Oleksandr
AU  - Rüttgers, Mario
AU  - Sarma, Rakesh
AU  - Lintermann, Andreas
TI  - itwinai: A Python Toolkit for Scalable Scientific Machine Learning on HPC Systems
JO  - The journal of open source software
VL  - 11
IS  - 117
SN  - 2475-9066
CY  - [Erscheinungsort nicht ermittelbar]
PB  - [Verlag nicht ermittelbar]
M1  - FZJ-2026-00771
SP  - 9409
PY  - 2026
AB  - 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.
LB  - PUB:(DE-HGF)16
DO  - DOI:10.21105/joss.09409
UR  - https://juser.fz-juelich.de/record/1052123
ER  -