Emulearner: Deep Learning Library for Utilizing Emulab

Recently, deep learning has been actively studied and applied in various fields even to novel writing and painting in ways we could not imagine before. A key feature is that high-performance computing device, especially CUDA-enabled GPU, supports this trend. Researchers who have difficulty accessing...

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Bibliographic Details
Published inJournal of information and communication convergence engineering Vol. 16; no. 4; pp. 235 - 241
Main Authors Song, Gi-Beom, Lee, Man-Hee
Format Journal Article
LanguageKorean
Published 2018
Subjects
Emulab
Distributed Learning
Deep Learning
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Summary:Recently, deep learning has been actively studied and applied in various fields even to novel writing and painting in ways we could not imagine before. A key feature is that high-performance computing device, especially CUDA-enabled GPU, supports this trend. Researchers who have difficulty accessing such systems fall behind in this fast-changing trend. In this study, we propose and implement a library called Emulearner that helps users to utilize Emulab with ease. Emulab is a research framework equipped with up to thousands of nodes developed by the University of Utah. To use Emulab nodes for deep learning requires a lot of human interactions, however. To solve this problem, Emulearner completely automates operations from authentication of Emulab log-in, node creation, configuration of deep learning to training. By installing Emulearner with a legitimate Emulab account, users can focus on their research on deep learning without hassle.
Bibliography:KISTI1.1003/JNL.JAKO201811459663569
ISSN:2234-8255
2234-8883