Journal Article

FZJ-2026-00771

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png itwinai: A Python Toolkit for Scalable Scientific Machine Learning on HPC Systems

Bunino, M. (Corresponding author) ; Sæther, J. S. ; Eickhoff, L. M. ; Lappe, A. E. ; Tsolaki, K. ; Verder, K. ; Mutegeki, H. ; Machacek, R. ; Girone, M. ; Krochak, O.FZJ* ; Rüttgers, M. ; Sarma, R.FZJ* ; Lintermann, A.FZJ*

2026
[Verlag nicht ermittelbar] [Erscheinungsort nicht ermittelbar]

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Please use a persistent id in citations: doi:10.21105/joss.09409  doi:10.34734/FZJ-2026-00771

Abstract: 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.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. interTwin - An interdisciplinary Digital Twin Engine for science (101058386) (101058386)
3. SDLFSE - SDL Fluids & Solids Engineering (SDLFSE) (SDLFSE)

Appears in the scientific report 2026

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