{"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/mltuner-system-support-for-automatic-machine","title":"MLtuner: System Support for Automatic Machine Learning Tuning","arxiv_id":"1803.07445","date":"2018-03-20","proceeding":null,"authors":["Henggang Cui","Gregory R. Ganger","Phillip B. Gibbons"],"abstract":"MLtuner automatically tunes settings for training tunables (such as the\nlearning rate, the momentum, the mini-batch size, and the data staleness bound)\nthat have a significant impact on large-scale machine learning (ML)\nperformance. Traditionally, these tunables are set manually, which is\nunsurprisingly error-prone and difficult to do without extensive domain\nknowledge. MLtuner uses efficient snapshotting, branching, and\noptimization-guided online trial-and-error to find good initial settings as\nwell as to re-tune settings during execution. Experiments show that MLtuner can\nrobustly find and re-tune tunable settings for a variety of ML applications,\nincluding image classification (for 3 models and 2 datasets), video\nclassification, and matrix factorization. Compared to state-of-the-art ML\nauto-tuning approaches, MLtuner is more robust for large problems and over an\norder of magnitude faster.","url_abs":"http://arxiv.org/abs/1803.07445v1","url_pdf":"http://arxiv.org/pdf/1803.07445v1.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":"mltuner-system-support-for-automatic-machine","repo_url":"https://github.com/cuihenggang/geeps","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"video-classification","task_name":"Video Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}