{"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/katib-a-distributed-general-automl-platform","title":"Katib: A Distributed General AutoML Platform on Kubernetes","arxiv_id":null,"date":"2019-01-01","proceeding":"USENIX Conference on Operational Machine Learning 2019 2019 1","authors":["Jinan Zhou","Andrey Velichkevich","Kirill Prosvirov","Anubhav Garg","Yuji Oshima","Debo Dutta"],"abstract":"Automatic Machine Learning (AutoML) is a powerful mechanism to design and tune models. We present Katib, a scalable Kubernetes-native general AutoML platform that can support a range of AutoML algorithms including both hyper-parameter tuning and neural architecture search. The system is divided into separate components, encapsulated as micro-services. Each micro-service operates within a Kubernetes pod and communicates with others via well-defined APIs, thus allowing flexible management and scalable deployment at a minimal cost. Together with a powerful user interface, Katib provides a universal platform for researchers as well as enterprises to try, compare and deploy their AutoML algorithms, on any Kubernetes platform.","url_abs":"https://www.usenix.org/conference/opml19/presentation/zhou","url_pdf":"https://www.usenix.org/system/files/opml19papers-zhou.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":"katib-a-distributed-general-automl-platform","repo_url":"https://github.com/kubeflow/katib","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"automl","task_name":"AutoML"},{"task_slug":"hyperparameter-optimization","task_name":"Hyperparameter Optimization"},{"task_slug":"management","task_name":"Management"},{"task_slug":"architecture-search","task_name":"Neural Architecture Search"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}