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| Hauptseite > Publikationsdatenbank > itwinai: A Python Toolkit for Scalable Scientific Machine Learning on HPC Systems > print |
| Hauptseite > Publikationsdatenbank > itwinai: A Python Toolkit for Scalable Scientific Machine Learning on HPC Systems > print |
| 001 | 1052123 | ||
| 005 | 20260127203441.0 | ||
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| 100 | 1 | _ | |a Bunino, Matteo |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a itwinai: A Python Toolkit for Scalable Scientific Machine Learning on HPC Systems |
| 260 | _ | _ | |a [Erscheinungsort nicht ermittelbar] |c 2026 |b [Verlag nicht ermittelbar] |
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| 520 | _ | _ | |a 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. 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. 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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| 700 | 1 | _ | |a Lappe, Anna Elisa |0 P:(DE-HGF)0 |b 3 |
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| 773 | _ | _ | |a 10.21105/joss.09409 |g Vol. 11, no. 117, p. 9409 - |0 PERI:(DE-600)2891760-1 |n 117 |p 9409 |t The journal of open source software |v 11 |y 2026 |x 2475-9066 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1052123/files/10.21105.joss.09409.pdf |y OpenAccess |
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