Katib: A Distributed General AutoML Platform on Kubernetes

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Authors Debo Dutta, Jinan Zhou, Kirill Prosvirov, Anubhav Garg, Andrey Velichkevich, Yuji Oshima
Journal/Conference Name USENIX Conference on Operational Machine Learning 2019 2019 1
Paper Category
Paper 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.
Date of publication 2019
Code Programming Language Jsonnet

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