DeCUVE: Deep Learning Cloud Unified Virtual Environment

Bo Seon Kang; Chang Sung Jeong

doi:10.1109/CSCI.2017.145

DeCUVE: Deep Learning Cloud Unified Virtual Environment

Bo Seon Kang, Chang Sung Jeong

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Recently, with advancement in deep learning technology in CCTV, drone and other various fields, large scale image processing environment becomes crucial and essential for fast real-time processing. In this paper, we shall present a distributed processing environment DeCUVE(Deep Learning Cloud Unified Virtual Environment) which provides scalability and provisioning for the deep learning inference. We shall show that deep learning training inference can be executed fast on our environment.

Original language	English
Title of host publication	Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017
Editors	Fernando G. Tinetti, Quoc-Nam Tran, Leonidas Deligiannidis, Mary Qu Yang, Mary Qu Yang, Hamid R. Arabnia
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	836-839
Number of pages	4
ISBN (Electronic)	9781538626528
DOIs	https://doi.org/10.1109/CSCI.2017.145
Publication status	Published - 2018 Dec 4
Event	2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 - Las Vegas, United States Duration: 2017 Dec 14 → 2017 Dec 16

Publication series

Name	Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

Other

Other	2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017
Country/Territory	United States
City	Las Vegas
Period	17/12/14 → 17/12/16

Keywords

Deep learning
Distributed Environment
Image Processing

ASJC Scopus subject areas

Computer Science (miscellaneous)
Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Safety, Risk, Reliability and Quality

Access to Document

10.1109/CSCI.2017.145

Cite this

Kang, B. S., & Jeong, C. S. (2018). DeCUVE: Deep Learning Cloud Unified Virtual Environment. In F. G. Tinetti, Q-N. Tran, L. Deligiannidis, M. Q. Yang, M. Q. Yang, & H. R. Arabnia (Eds.), Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 (pp. 836-839). [8560904] (Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCI.2017.145

DeCUVE: Deep Learning Cloud Unified Virtual Environment. / Kang, Bo Seon; Jeong, Chang Sung.
Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017. ed. / Fernando G. Tinetti; Quoc-Nam Tran; Leonidas Deligiannidis; Mary Qu Yang; Mary Qu Yang; Hamid R. Arabnia. Institute of Electrical and Electronics Engineers Inc., 2018. p. 836-839 8560904 (Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kang, BS & Jeong, CS 2018, DeCUVE: Deep Learning Cloud Unified Virtual Environment. in FG Tinetti, Q-N Tran, L Deligiannidis, MQ Yang, MQ Yang & HR Arabnia (eds), Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017., 8560904, Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017, Institute of Electrical and Electronics Engineers Inc., pp. 836-839, 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017, Las Vegas, United States, 17/12/14. https://doi.org/10.1109/CSCI.2017.145

Kang BS, Jeong CS. DeCUVE: Deep Learning Cloud Unified Virtual Environment. In Tinetti FG, Tran Q-N, Deligiannidis L, Yang MQ, Yang MQ, Arabnia HR, editors, Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 836-839. 8560904. (Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017). doi: 10.1109/CSCI.2017.145

Kang, Bo Seon ; Jeong, Chang Sung. / DeCUVE : Deep Learning Cloud Unified Virtual Environment. Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017. editor / Fernando G. Tinetti ; Quoc-Nam Tran ; Leonidas Deligiannidis ; Mary Qu Yang ; Mary Qu Yang ; Hamid R. Arabnia. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 836-839 (Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017).

@inproceedings{0985996c855e41ec95c0f2466d9ad440,

title = "DeCUVE: Deep Learning Cloud Unified Virtual Environment",

abstract = "Recently, with advancement in deep learning technology in CCTV, drone and other various fields, large scale image processing environment becomes crucial and essential for fast real-time processing. In this paper, we shall present a distributed processing environment DeCUVE(Deep Learning Cloud Unified Virtual Environment) which provides scalability and provisioning for the deep learning inference. We shall show that deep learning training inference can be executed fast on our environment.",

keywords = "Deep learning, Distributed Environment, Image Processing",

author = "Kang, {Bo Seon} and Jeong, {Chang Sung}",

note = "Funding Information: ACKNOWLEDGMENT This work was supported by the Brain Korea 21 Plus Project in 2017 and this research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1D1A1B03035461). Funding Information: This work was supported by the Brain Korea 21 Plus Project in 2017 and this research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1D1A1B03035461). Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 ; Conference date: 14-12-2017 Through 16-12-2017",

year = "2018",

month = dec,

day = "4",

doi = "10.1109/CSCI.2017.145",

language = "English",

series = "Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "836--839",

editor = "Tinetti, {Fernando G.} and Quoc-Nam Tran and Leonidas Deligiannidis and Yang, {Mary Qu} and Yang, {Mary Qu} and Arabnia, {Hamid R.}",

booktitle = "Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017",

}

TY - GEN

T1 - DeCUVE

T2 - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

AU - Kang, Bo Seon

AU - Jeong, Chang Sung

N1 - Funding Information: ACKNOWLEDGMENT This work was supported by the Brain Korea 21 Plus Project in 2017 and this research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1D1A1B03035461). Funding Information: This work was supported by the Brain Korea 21 Plus Project in 2017 and this research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1D1A1B03035461). Publisher Copyright: © 2017 IEEE.

PY - 2018/12/4

Y1 - 2018/12/4

N2 - Recently, with advancement in deep learning technology in CCTV, drone and other various fields, large scale image processing environment becomes crucial and essential for fast real-time processing. In this paper, we shall present a distributed processing environment DeCUVE(Deep Learning Cloud Unified Virtual Environment) which provides scalability and provisioning for the deep learning inference. We shall show that deep learning training inference can be executed fast on our environment.

AB - Recently, with advancement in deep learning technology in CCTV, drone and other various fields, large scale image processing environment becomes crucial and essential for fast real-time processing. In this paper, we shall present a distributed processing environment DeCUVE(Deep Learning Cloud Unified Virtual Environment) which provides scalability and provisioning for the deep learning inference. We shall show that deep learning training inference can be executed fast on our environment.

KW - Deep learning

KW - Distributed Environment

KW - Image Processing

UR - http://www.scopus.com/inward/record.url?scp=85060606462&partnerID=8YFLogxK

U2 - 10.1109/CSCI.2017.145

DO - 10.1109/CSCI.2017.145

M3 - Conference contribution

AN - SCOPUS:85060606462

T3 - Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

SP - 836

EP - 839

BT - Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

A2 - Tinetti, Fernando G.

A2 - Tran, Quoc-Nam

A2 - Deligiannidis, Leonidas

A2 - Yang, Mary Qu

A2 - Arabnia, Hamid R.

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 14 December 2017 through 16 December 2017

ER -

DeCUVE: Deep Learning Cloud Unified Virtual Environment

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this