{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/chainermn-scalable-distributed-deep-learning","title":"ChainerMN: Scalable Distributed Deep Learning Framework","arxiv_id":"1710.11351","date":"2017-10-31","proceeding":null,"authors":["Takuya Akiba","Keisuke Fukuda","Shuji Suzuki"],"abstract":"One of the keys for deep learning to have made a breakthrough in various\nfields was to utilize high computing powers centering around GPUs. Enabling the\nuse of further computing abilities by distributed processing is essential not\nonly to make the deep learning bigger and faster but also to tackle unsolved\nchallenges. We present the design, implementation, and evaluation of ChainerMN,\nthe distributed deep learning framework we have developed. We demonstrate that\nChainerMN can scale the learning process of the ResNet-50 model to the ImageNet\ndataset up to 128 GPUs with the parallel efficiency of 90%.","url_abs":"http://arxiv.org/abs/1710.11351v1","url_pdf":"http://arxiv.org/pdf/1710.11351v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"chainermn-scalable-distributed-deep-learning","repo_url":"https://github.com/chainer/chainermn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}