Shanghai, China
June 24–26, 2019
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Tuesday, June 25 • 11:00 - 11:35
Hyperparameter Tuning Using Kubeflow - Richard Liu, Google & Johnu George, Cisco Systems

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In machine learning, hyperparameter tuning refers to the process of finding the optimal constraints for training models. Choosing optimal hyperparameters can drastically improve the performance of a model, but the search space grows exponentially with the addition of new hyperparameters.

A closely related subfield of automated machine learning is neural architecture search (NAS). In recent research, networks generated by NAS algorithms can even outperform handcrafted neural networks. However, like hyperparameter tuning, the process can be time-consuming and expensive.

We present Katib - a Kubernetes-native automated machine learning platform for hyperparameter tuning and NAS. Part of the Kubeflow platform, Katib offers a rich set of management APIs in the form of custom resources. We will demonstrate how to configure and run an experiment and compare performance in Katib’s UI dashboard.

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