Deep Learning optimization and deployment with TensorRT in TensorFlow and Python
13:30-16:30 January 26

½ day
Advanced level

@ 2BC


In this hands-on workshop you will learn fundamentals of generating high-performance deep-learning models in TensorFlow platform using built-in TensorRT library (TF-TRT) and python. You will learn how to:

  • Pre-process classifications models and freeze graphs and weights in order to perform optimization
  • Get familiar with fundamentals of graph optimization and quantization using FP32, FP16 and INT8.
  • Use TF-TRT API to optimize subgraphs and select optimization parameters that best fit your model.
  • Design and embed custom operations in python to mitigate the non-supporting layers problem and optimize detection models.


Upon completion you will learn how to utilize TF-TRT to achieve deployment-ready optimized models.


Experience with TensorFlow and Python

Follow these steps prior to joining the training:

  • You must bring your own laptop in order to run the training.
  • A current browser is needed. For optimal performance, Chrome, Firefox or Safari for Macs are recommended. IE is operational but does not provide the best performance.
  • Create an account at
  • Ensure your laptop will run smoothly by going to
  • Make sure that WebSockets work for you by seeing under Environment, WebSockets is supported and Data Receive, Send and Echo Test all check Yes under WebSockets (Port 80).
  • If there are issues with WebSockets, try updating your browser.


Cristiana Dinea

Master Instructor, NVIDIA Deep Learning Institute

Nicola Rieke

Senior Deep Learning Solution Architect – Healthcare, NVIDIA


Deep Learning Institute