A Machine Learning Gateway for Scientific Workflow Design

A Machine Learning Gateway for Scientific Workflow Design

The paper introduces DeepForge, a gateway to deep learning for scientific computing. DeepForge provides an easy to use, yet powerful visual/textual interface to facilitate the rapid development of deep learning models by novices as well as experts. Utilizing a cloud-based infrastructure, built-in ve...

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Veröffentlicht in:Scientific programming 2020, Vol.2020 (2020), p.1-15
Hauptverfasser: Budavári, Tamás, Völgyesi, Péter, Timalsina, Umesh, Broll, Brian, Lédeczi, Ákos
Format: Artikel
Sprache:eng
Schlagworte:
Collaboration
Computer networks
Deep learning
Editing
Infrastructure
Machine learning
Network topologies
Neural networks
Science
Software services
User interface
Version control
Workflow
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Zusammenfassung:The paper introduces DeepForge, a gateway to deep learning for scientific computing. DeepForge provides an easy to use, yet powerful visual/textual interface to facilitate the rapid development of deep learning models by novices as well as experts. Utilizing a cloud-based infrastructure, built-in version control, and multiuser collaboration support, DeepForge promotes reproducibility and ease of access and enables remote execution of machine learning pipelines. The tool currently supports TensorFlow/Keras, but its extensible architecture enables easy integration of additional platforms.
ISSN:1058-9244
1875-919X
DOI:10.1155/2020/8867380