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A Computational Workflow for Interdisciplinary Deep Learning Projects utilizing bwHPC Infrastructure

Schilling, Marcel P.; Neumann, Oliver; Scherr, Tim; Cui, Haijun; Popova, Anna A.; Levkin, Pavel A.; Götz, Markus; Reischl, Markus

Deep neural networks have the capability to solve complex tasks through accurate function approximation.
The process from submitting domain data and defining process requirements to analyzed data consists of multiple steps, disallowing a simplistic straightforward procedure.
It follows that one of the core questions is: how does an application development process facilitating interaction between data scientists and domain experts look like?
Practically, two connected challenges need to be addressed.
Firstly, it requires a solution for handling large amounts of domain-specific data.
Secondly, when dealing with complex deep neural networks, it is essential to find a concept of how model training can be designed in an computationally efficient manner.
While tailored solutions for addressing these challenges in interdisciplinary deep learning projects exist, a comprehensive and structured approach is missing.
Hence, we present a computational workflow to enhance these kinds of projects concerning data handling, integration of cluster computing resources such as bwHPC infrastructure, and development processes. We exemplify our proposal by means of a biomedical image analysis project.

Volltext §
DOI: 10.5445/IR/1000139674
Veröffentlicht am 09.11.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Institut für Biologische und Chemische Systeme (IBCS)
Steinbuch Centre for Computing (SCC)
Publikationstyp Poster
Publikationsdatum 08.11.2021
Sprache Englisch
Identifikator KITopen-ID: 1000139674
HGF-Programm 47.14.02 (POF IV, LK 01) Information Storage and Processing in the Cell Nucleus
Veranstaltung 7th bwHPC Symposium (2021), Online, 08.11.2021
Schlagwörter Deep learning, Cluster computing, bwHPC
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