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Lightning Talk: Scaling Distributed Deep Learning with Service Discovery: How CoreDNS Helps Distributed TensorFlow Tasks - Yong Tang, Infoblox Inc. (Intermediate Skill Level)
Training models with modern deep learning architecture is often computationally intensive and requires an efficient distributed system at scale. Such systems in distributed machine learning community often have special requirements and may involve additional efforts. This talk discusses the usage of CoreDNS for service discovery on distributed TensorFlow clusters for resolving deep learning problems. While CoreDNS has been widely used for service discovery in Kubernetes, its unique plugin based design allows CoreDNS to be easily extended and deployed in non-traditional distributed systems as well. Deployed on cloud (AWS), our distributed TensorFlow clusters have been greatly helped by CoreDNS for robustness against partial node failures. The deployment has also been simplified for non-DevOps (e.g., machine learning researchers) to launch and execute deep learning tasks at great ease.
About Yong
Yong Tang is a Principal Software Engineer at CTO Office in Infoblox Inc. He works on CoreDNS at Infoblox for the open source community, with a focus on service discovery and Kubernetes integration. He also works on different machine learning projects in Infoblox. Yong Tang received his PhD in Computer Science for network security at the University of Florida. He is currently a committer of CoreDNS. He is also a committer of Moby/Docker, SwarmKit, and TensorFlow, and actively contributing to various open source projects in container space and machine learning.
Join us for KubeCon + CloudNativeCon in Barcelona May 20 - 23, Shanghai June 24 - 26, and San Diego November 18 - 21! Learn more at https://kubecon.io. The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy and all of the other CNCF-hosted projects.
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| Science & Technology | Upload TimePublished on 4 May 2018 |
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