  In this paper, we introduce Katib: a scalable, cloud-native, and
production-ready hyperparameter tuning system that is agnostic of the
underlying machine learning framework. Though there are multiple hyperparameter
tuning systems available, this is the first one that caters to the needs of
both users and administrators of the system. We present the motivation and
design of the system and contrast it with existing hyperparameter tuning
systems, especially in terms of multi-tenancy, scalability, fault-tolerance,
and extensibility. It can be deployed on local machines, or hosted as a service
in on-premise data centers, or in private/public clouds. We demonstrate the
advantage of our system using experimental results as well as real-world,
production use cases. Katib has active contributors from multiple companies and
is open-sourced at \emph{https://github.com/kubeflow/katib} under the Apache
2.0 license.

Distributed, Parallel, and Cluster Computing;Machine Learning