一个可扩展的云原生超参数调优系统
A Scalable and Cloud-Native Hyperparameter Tuning System
摘要：
在本文中，我们介绍Katib：一个可扩展的，云原生的，生产就绪的超参数调优系统，是不可知的底层机器学习框架。虽然有多个超参数调优系统可用，但这是第一个同时满足系统用户和管理员需求的系统。我们提出了系统的动机和设计，并将其与现有的超参数调优系统进行对比，特别是在多租户，可扩展性，容错性和可扩展性方面。它可以部署在本地机器上，或作为服务托管在内部数据中心或私有/公共云中。我们使用实验结果以及现实世界的生产用例来展示我们的系统的优势。Katib拥有来自多家公司的积极贡献者，并在Apache 2.0许可下在\emph{https://github.com/kubeflow/katib}开源。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
