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アイテム

  1. 研究報告
  2. システム・アーキテクチャ(ARC)
  3. 2018
  4. 2018-ARC-232

Asynchronous Deep Learning Test-bed to Analyze Gradient Staleness Effect

https://ipsj.ixsq.nii.ac.jp/records/190710
https://ipsj.ixsq.nii.ac.jp/records/190710
08abe6a0-cf33-4557-9980-2a6763d44393
名前 / ファイル ライセンス アクション
IPSJ-ARC18232028.pdf IPSJ-ARC18232028.pdf (436.0 kB)
Copyright (c) 2018 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG.
ARC:会員:¥0, DLIB:会員:¥0
Item type SIG Technical Reports(1)
公開日 2018-07-23
タイトル
タイトル Asynchronous Deep Learning Test-bed to Analyze Gradient Staleness Effect
タイトル
言語 en
タイトル Asynchronous Deep Learning Test-bed to Analyze Gradient Staleness Effect
言語
言語 eng
キーワード
主題Scheme Other
主題 機械学習・ニューラルネットワーク
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_18gh
資源タイプ technical report
著者所属
University of Tsukuba/National Institute of Advanced Industrial Science and Technology
著者所属
National Institute of Advanced Industrial Science and Technology/University of Tsukuba
著者所属
National Institute of Advanced Industrial Science and Technology/University of Tsukuba
著者所属(英)
en
University of Tsukuba / National Institute of Advanced Industrial Science and Technology
著者所属(英)
en
National Institute of Advanced Industrial Science and Technology / University of Tsukuba
著者所属(英)
en
National Institute of Advanced Industrial Science and Technology / University of Tsukuba
著者名 Duo, Zhang

× Duo, Zhang

Duo, Zhang

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Yusuke, Tanimura

× Yusuke, Tanimura

Yusuke, Tanimura

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Hidemoto, Nakada

× Hidemoto, Nakada

Hidemoto, Nakada

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著者名(英) Duo, Zhang

× Duo, Zhang

en Duo, Zhang

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Yusuke, Tanimura

× Yusuke, Tanimura

en Yusuke, Tanimura

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Hidemoto, Nakada

× Hidemoto, Nakada

en Hidemoto, Nakada

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論文抄録
内容記述タイプ Other
内容記述 For modern machine learning systems, including deep learning systems, parallelization is inevitable since they are required to process massive amount of training data. One of the hot topic of this area is the data parallel learning where multiple nodes cooperate each other exchanging parameter / gradient periodically. In order to efficiently implement data-parallel machine learning in a collection of computers with a relatively sparse network, it is indispensable to asynchronously update model parameters through gradients, but the effect of the learning model through asynchronous analysis has not yet been fully understood. In this paper, we propose a software test-bed for analyzing gradient staleness effect on prediction performance, using deep learning framework TensorFlow and distributed computing framework Ray. We report the architecture of the test-bed and initial evaluation results.
論文抄録(英)
内容記述タイプ Other
内容記述 For modern machine learning systems, including deep learning systems, parallelization is inevitable since they are required to process massive amount of training data. One of the hot topic of this area is the data parallel learning where multiple nodes cooperate each other exchanging parameter / gradient periodically. In order to efficiently implement data-parallel machine learning in a collection of computers with a relatively sparse network, it is indispensable to asynchronously update model parameters through gradients, but the effect of the learning model through asynchronous analysis has not yet been fully understood. In this paper, we propose a software test-bed for analyzing gradient staleness effect on prediction performance, using deep learning framework TensorFlow and distributed computing framework Ray. We report the architecture of the test-bed and initial evaluation results.
書誌レコードID
収録物識別子タイプ NCID
収録物識別子 AN10096105
書誌情報 研究報告システム・アーキテクチャ(ARC)

巻 2018-ARC-232, 号 28, p. 1-6, 発行日 2018-07-23
ISSN
収録物識別子タイプ ISSN
収録物識別子 2188-8574
Notice
SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
出版者
言語 ja
出版者 情報処理学会
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