Deep Learning on AWS at NVIDIA’s GPU Technology Conference, GTC 2017

This year at NVIDIA’s GPU Technology Conference, AWS is hosting several tech sessions ranging from how to get started with Apache MXNet to running deep learning on IoT devices on the edge. If you’re in Silicon Valley the week of May 8, we hope that you’ll join us for the following sessions.

An Introduction to Using Apache MXNet (S7853) | 4 hours

Get hands-on experience using Apache MXNet with the preconfigured AWS Deep Learning AMIs and AWS CloudFormation template to speed development and quickly spin up AWS GPU clusters to train at record speed. This course will include the following:

• Background on deep learning
• An overview of how to set up AMIs, AWS CloudFormation templates, and other deep learning frameworks on AWS
• A peek under the MXNet hood (MXNet internals) and a comparison with other deep learning frameworks
• Hands-on training with Apache MXNet: NDArrays, Symbols, and the mechanics of training deep neural networks
• More hands-on training with Apache MXNet: application examples, using Jupyter notebooks, and targeting computer vision and natural language processing

Getting Started with Apache MXNet (S7565) | 50 minutes

Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding, and recommendation engines. A key reason for this progress is the availability of highly flexible and developer-friendly deep learning frameworks. During this session, members of the AWS Deep Learning product team provide background on deep learning and how it’s applied at AWS, and the strategy for investing in the Apache MXNet project. You’ll also learn how to get started using NVIDIA GPUs in the AWS Cloud, which lets you easily scale to hundreds of GPUs in minutes.

High-Performance Deep Learning on Embedded Devices Using Apache MXNet (S7571) | 50 minutes

Learn how to compile and run an optimized version of the Apache MXNet deep learning framework for various embedded (IoT) devices. Also learn about the wide range of exciting applications running deep network inference in near real time on “edge” devices. To demonstrate the massive efficiency gains that Apache MXNet yields over comparable frameworks on embedded devices, we show performance numbers for a variety of deep learning models running on Raspberry Pis and TK1 processors. We then demo the power of real-time image processing with deep learning models by walking through an example application. Finally, we demonstrate how to use AWS IoT to significantly augment the flexibility and reliability of the models running in our example application.