2019 年 6 月 24–26 日

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Simultaneous translation will be provided for all keynote and breakout sessions.

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Tuesday, June 25 • 11:00 - 11:35
使用 Kubeflow 进行超参数调优 - Richard Liu,Google;Johnu George, Cisco

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在机器学习中,超参数调优指的是为训练模型寻找最优配置的过程。选择最优超参数可以大幅提高算法的性能,但随着新超参数的添加, 搜索空间呈指数级增长。

自动化机器学习的一个与超参数调优密切相关的子领域是神经网络结构搜索 (NAS)。在最近的研究中, 由 NAS 算法生成的神经网络甚至在性能上可以超越手工生成的神经网络。但是如同超参数调优, 此过程可能既耗时又昂贵。

有鉴于此,我们推出了 Katib - 一个基于Kubernetes云原生的自动化机器学习平台。作为Kubeflow平台的一部分,Katib 以自定义资源的形式提供了一套丰富的管理 API。我们将演示如何配置超参数调优研究,以及如何在用户界面中比较实验结果。

avatar for Johnu George

Johnu George

Staff Engineer, Nutanix
Johnu George is a staff engineer at Nutanix.  His research interests are in the areas of distributed systems and scalable infrastructure for big data applications. He is an active open source contributor and currently a PMC member of Apache Mnemonic.  He is actively involved in... Read More →
avatar for Richard Liu

Richard Liu

Senior Software Engineer, Google
Richard Liu is a Senior Software Engineer at Google Cloud. He is currently an owner and maintainer of the TensorFlow operator and Katib projects in Kubeflow. Previously he had worked as a software developer at Microsoft Azure.

Tuesday June 25, 2019 11:00 - 11:35 CST