Broadcast Date: 21 April, 2020
Level: 200
In deep learning applications, inference accounts for up to 90% of compute cost. To reduce this high inference cost, you can use Amazon Elastic Inference, which allows you to attach just the right amount of GPU-powered inference acceleration to any EC2 or SageMaker instance type or ECS task. In this tech talk, you will learn about how to use Elastic Inference for deploying models built on PyTorch, a popular machine learning framework.
Learning Objectives
- Get an overview of Amazon Elastic Inference
- Learn about how to use Elastic Inference to reduce costs and improve latency for your PyTorch models on Amazon SageMaker
- Get a demo using TorchScript with Elastic Inference API
Who Should Attend?
Data Scientists, ML Developers. Researchers, and Data Engineers
Speakers
- David Thomas, Software Development Engineer, AWS
Learn More
To learn more about the services featured in this talk, please visit:
https://aws.amazon.com/machine-learning/elastic-inference/
Intro body copy here about 2018 re:Invent launches.
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Compute
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December 19th, 2018 | 1:00 PM PT
Developing Deep Learning Models for Computer Vision with
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