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Seattle, WA
December 10–13, 2018
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Tuesday, December 11 • 1:45pm - 2:20pm
Scaling AI Inference Workloads with GPUs and Kubernetes - Renaud Gaubert & Ryan Olson, NVIDIA

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Deep Learning (DL) is a computational intense form of machine learning that has revolutionize many fields including computer vision, automated speech recognition, natural language processing and artificial intelligence (AI).

DL impacts every vertical market from automotive to healthcare to cloud, as a result, the training and deployment of Deep Neural Networks (DNNs) has shifted datacenter workloads from traditional CPUs to AI-specific accelerators like NVIDIA GPUs.

Leveraging several popular CNCF projects such as Prometheus, Envoy, and gRPC, we will demonstrate an implementation of NVIDIA’s reference scale-out inference architecture, capable of delivering petaops per second of performance.

This is a new and challenging problem in the datacenter and we will discuss these challenges and ways to optimize for service delivery metrics (latency/throughput), cost, and redundancy.

Speakers
avatar for Renaud Gaubert

Renaud Gaubert

Lead Kubernetes Engineer, NVIDIA
Renaud Gaubert has been working since 2017 at NVIDIA on making GPU applications easier to deploy and manage in data centers. He focuses on supporting GPU-accelerated machine learning frameworks in container orchestration systems such as Kubernetes and Docker swarm. | | He is an... Read More →
avatar for Ryan Olson

Ryan Olson

Solutions Architect, NVIDIA
Ryan Olson is a Solutions Architect in the Worldwide Field Organization at NVIDIA. His primary responsibilities involve supporting deep learning and high performance computing applications. Ryan is particularly interested in scalable software design that leverages the unique capabilities... Read More →



Tuesday December 11, 2018 1:45pm - 2:20pm
611-614