September 26, 2019

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A Method for the Cost Optimization of Kubernetes-based Deep Learning Training and Inference

A Method for the Cost Optimization of Kubernetes-based Deep Learning Training and Inference

To improve the throughput capacity of the training or inference applications without adding extra GPU cores, we share one GPU core between multiple deep learning workloads in a kubernetes cluster by c …

Talk Title A Method for the Cost Optimization of Kubernetes-based Deep Learning Training and Inference
Speakers Lei Wang (Senior Engineer, Tencent Cloud), Pavee Han (Senior Product Manager, Tencent Cloud)
Conference KubeCon + CloudNativeCon
Conf Tag
Location Shanghai, China
Date Jun 23-26, 2019
URL Talk Page
Slides Talk Slides
Video

To improve the throughput capacity of the training or inference applications without adding extra GPU cores, we share one GPU core between multiple deep learning workloads in a kubernetes cluster by container-level virtual GPU technology. This technology has a better application prospect in the production environments because of its performance loss is lower than virtual-machine-level GPU virtualization.

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