{"updated":"2025-01-20T01:06:06.115062+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00190710","sets":["1164:1579:9341:9527"]},"path":["9527"],"owner":"11","recid":"190710","title":["Asynchronous Deep Learning Test-bed to Analyze Gradient Staleness Effect"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-07-23"},"_buckets":{"deposit":"8cb1387a-a203-4c18-8b2e-0da0ac343e75"},"_deposit":{"id":"190710","pid":{"type":"depid","value":"190710","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"Asynchronous Deep Learning Test-bed to Analyze Gradient Staleness Effect","author_link":["437254","437253","437251","437252","437256","437255"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Asynchronous Deep Learning Test-bed to Analyze Gradient Staleness Effect"},{"subitem_title":"Asynchronous Deep Learning Test-bed to Analyze Gradient Staleness Effect","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"機械学習・ニューラルネットワーク","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2018-07-23","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"University of Tsukuba／National Institute of Advanced Industrial Science and Technology"},{"subitem_text_value":"National Institute of Advanced Industrial Science and Technology／University of Tsukuba"},{"subitem_text_value":"National Institute of Advanced Industrial Science and Technology／University of Tsukuba"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"University of Tsukuba / National Institute of Advanced Industrial Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Advanced Industrial Science and Technology / University of Tsukuba","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Advanced Industrial Science and Technology / University of Tsukuba","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/190710/files/IPSJ-ARC18232028.pdf","label":"IPSJ-ARC18232028.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-ARC18232028.pdf","filesize":[{"value":"436.0 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"16"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"14cf5350-2011-4612-8f82-ee5c9cabd3e1","displaytype":"detail","licensetype":"license_note","license_note":"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."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Duo, Zhang"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yusuke, Tanimura"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hidemoto, Nakada"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Duo, Zhang","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yusuke, Tanimura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hidemoto, Nakada","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10096105","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8574","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"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.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"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.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告システム・アーキテクチャ（ARC）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2018-07-23","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"28","bibliographicVolumeNumber":"2018-ARC-232"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-19T00:56:39.753974+00:00","id":190710,"links":{}}