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Wednesday, May 22 • 15:55 - 16:30
Large Scale Distributed Deep Learning with Kubernetes Operators - Yuan Tang, Ant Financial & Yong Tang, MobileIron

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The focus of this talk is the usage of Kubernetes operators to manage and automate training process for machine learning tasks. Two open source Kubernetes operators, tf-operator and mpi-operator, will be discussed. Both operators manage training jobs for TensorFlow but they have different distribution strategies. The tf-operator fits the parameter server distribution strategy which has a centralized parameter server for coordination. The mpi-operator, on the other hand, utilize MPI allreduce primitive implementation. While the parameter server strategy requires a right ratio of CPU (for parameter servers) and GPU (for workers) to reach network-optimal, the all reduce distribution might be easier to optimize network cost. We will share our performance numbers in out talk for comparison of those two operators.

Speakers
avatar for Yong Tang

Yong Tang

Director of Engineering, MobileIron
Yong Tang is the Director of Engineering at MobileIron working on cloud infrastructure. He contributes to different container and machine learning projects for the open source community. He is a maintainer of CoreDNS and Docker/Moby projects, and had multiple talks in KubeCon before... Read More →
avatar for Yuan Tang

Yuan Tang

Senior Software Engineer, Ant Financial
Yuan is currently a senior software engineer at Ant Financial, building AI infrastructure and AutoML platform. He's a committer of TensorFlow, XGBoost, Apache MXNet, maintainer of several Kubeflow projects, and author of numerous open source softwares. He's also the author of best-selling... Read More →



Wednesday May 22, 2019 15:55 - 16:30
Hall 8.0 D2