Katib: A Distributed General AutoML Platform on Kubernetes

Authors: 

Jinan Zhou, Andrey Velichkevich, Kirill Prosvirov, and Anubhav Garg, Cisco Systems; Yuji Oshima, NTT Software Innovation Center; Debo Dutta, Cisco Systems

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.

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BibTeX
@inproceedings {232981,
author = {Jinan Zhou and Andrey Velichkevich and Kirill Prosvirov and Anubhav Garg and Yuji Oshima and Debo Dutta},
title = {Katib: A Distributed General AutoML Platform on Kubernetes},
booktitle = {2019 {USENIX} Conference on Operational Machine Learning (OpML 19)},
year = {2019},
isbn = {978-1-939133-00-7},
address = {Santa Clara, CA},
pages = {55--57},
url = {https://www.usenix.org/conference/opml19/presentation/zhou},
publisher = {{USENIX} Association},
month = may,
}