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GTC ON-DEMAND

Science and Research
Presentation
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Deep Learning Network Deployment (End-to-end Series Part 3)
Abstract:

In this lab you will test three different approaches to deploying a trained DNN for inference. The first approach is to directly use inference functionality within a deep learning framework, in this case DIGITS and Caffe. The second approach is to integrate inference within a custom application by using a deep learning framework API, again using Caffe but this time through it's Python API. The final approach is to use the NVIDIA GPU Inference Engine (GIE) which will automatically create an optimized inference run-time from a trained Caffe model and network description file. You will learn about the role of batch size in inference performance as well as various optimizations that can be made in the inference process. You'll also explore inference for a variety of different DNN architecture

 
Topics:
Science and Research
Type:
Instructor-Led Lab
Event:
GTC Washington D.C.
Year:
2016
Session ID:
DCL16106
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