{"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/horovod-fast-and-easy-distributed-deep","title":"Horovod: fast and easy distributed deep learning in TensorFlow","arxiv_id":"1802.05799","date":"2018-02-15","proceeding":null,"authors":["Alexander Sergeev","Mike Del Balso"],"abstract":"Training modern deep learning models requires large amounts of computation,\noften provided by GPUs. Scaling computation from one GPU to many can enable\nmuch faster training and research progress but entails two complications.\nFirst, the training library must support inter-GPU communication. Depending on\nthe particular methods employed, this communication may entail anywhere from\nnegligible to significant overhead. Second, the user must modify his or her\ntraining code to take advantage of inter-GPU communication. Depending on the\ntraining library's API, the modification required may be either significant or\nminimal.\n  Existing methods for enabling multi-GPU training under the TensorFlow library\nentail non-negligible communication overhead and require users to heavily\nmodify their model-building code, leading many researchers to avoid the whole\nmess and stick with slower single-GPU training. In this paper we introduce\nHorovod, an open source library that improves on both obstructions to scaling:\nit employs efficient inter-GPU communication via ring reduction and requires\nonly a few lines of modification to user code, enabling faster, easier\ndistributed training in TensorFlow. Horovod is available under the Apache 2.0\nlicense at https://github.com/uber/horovod","url_abs":"http://arxiv.org/abs/1802.05799v3","url_pdf":"http://arxiv.org/pdf/1802.05799v3.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":"horovod-fast-and-easy-distributed-deep","repo_url":"https://github.com/uber/horovod","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"horovod-fast-and-easy-distributed-deep","repo_url":"https://github.com/a0x8o/horovod","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"horovod-fast-and-easy-distributed-deep","repo_url":"https://github.com/axbaretto/horovod","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"horovod-fast-and-easy-distributed-deep","repo_url":"https://github.com/ctcyang/horovod","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"horovod-fast-and-easy-distributed-deep","repo_url":"https://github.com/gridgentoo/UberHorovod","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"horovod-fast-and-easy-distributed-deep","repo_url":"https://github.com/hcyang99/horovod","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"horovod-fast-and-easy-distributed-deep","repo_url":"https://github.com/horovod/horovod","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"horovod-fast-and-easy-distributed-deep","repo_url":"https://github.com/karakusc/horovod","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"horovod-fast-and-easy-distributed-deep","repo_url":"https://github.com/markWJJ/horovod","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"horovod-fast-and-easy-distributed-deep","repo_url":"https://github.com/teja5832/horovod-elastic","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"horovod-fast-and-easy-distributed-deep","repo_url":"https://github.com/wdlctc/horovod_SYS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"horovod-fast-and-easy-distributed-deep","repo_url":"https://github.com/zhonghual1206/bertcpu","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"gone","observed_at":"2026-09-18","how":"tree_404+repo_404"}},{"paper_slug":"horovod-fast-and-easy-distributed-deep","repo_url":"https://github.com/zhonghual1206/horvodval","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"gone","observed_at":"2026-09-18","how":"tree_404+repo_404"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":null,"task_name":"GPU"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.05799","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}