Shanghai, China
June 24–26, 2019
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Tuesday, June 25 • 16:00 - 16:35
Large Scale Distributed Deep Learning on Kubernetes Clusters - Yuan Tang, Ant Financial & Yong Tang, MobileIron

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The focus of this talk is the deployments of large scale distributed deep learning with Kubernetes. The usage of operators to manage and automate training processes for machine learning are discussed. We share our experiences and compare two open source Kubernetes operators, tf-operator and mpi-operator in this talk. Both operators manage training jobs for TensorFlow but they have different distribution strategies, which lead to different performance results with respect to the utilization ratio among CPU, GPU, and network.

Deep learning tasks are both network and GPU intensive such that a proper optimization for orchestration is very important. There could easily be an imbalance leads to idle compute capacity which is too expensive for GPU nodes (compared with CPUs). We will share our experiences with the hope to provide helpful insight for better economics with machine learning tasks.

avatar for Yong Tang

Yong Tang

Director of Engineering, Ivanti
Yong Tang is director of engineering at Ivanti. He is a core maintainer of CoreDNS and contributes to many container, cloud-native, and machine learning projects for the open source community. In addition to CoreDNS, he is a maintainer of Docker/Moby. He is also a maintainer and SIG... Read More →
avatar for Yuan Tang

Yuan Tang

Founding Engineer, Akuity
Yuan is a founding engineer at Akuity. Previously, he was a senior software engineer at Alibaba Group, building AI infrastructure and AutoML platform. He's a co-chair of Kubeflow, maintainer of TensorFlow and Argo, PMC member of XGBoost and Apache MXNet, as well as author of numerous... Read More →

Tuesday June 25, 2019 16:00 - 16:35 CST