Optimize TensorFlow Models For Deployment with TensorRT

4.6
stars
44 ratings
Offered By
Coursera Project Network
2,501 already enrolled
In this Free Guided Project, you will:

Optimize Tensorflow models using TensorRT (TF-TRT)

Use TF-TRT to optimize several deep learning models at FP32, FP16, and INT8 precision

Observe how tuning TF-TRT parameters affects performance and inference throughput

Showcase this hands-on experience in an interview

Clock1.5 hours
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

This is a hands-on, guided project on optimizing your TensorFlow models for inference with NVIDIA's TensorRT. By the end of this 1.5 hour long project, you will be able to optimize Tensorflow models using the TensorFlow integration of NVIDIA's TensorRT (TF-TRT), use TF-TRT to optimize several deep learning models at FP32, FP16, and INT8 precision, and observe how tuning TF-TRT parameters affects performance and inference throughput. Prerequisites: In order to successfully complete this project, you should be competent in Python programming, understand deep learning and what inference is, and have experience building deep learning models in TensorFlow and its Keras API. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Requirements

It is assumed that are competent in Python programming and have prior experience with building deep learning models with TensorFlow and its Keras API

Skills you will develop

Deep LearningNVIDIA TensorRT (TF-TRT)Python ProgrammingTensorflowkeras

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Introduction and Project Overview

  2. Setup your TensorFlow and TensorRT Runtime

  3. Load the Data and Pre-trained InceptionV3 Model

  4. Create batched Input

  5. Load the TensorFlow SavedModel

  6. Get Baseline for Prediction Throughput and Accuracy

  7. Convert a TensorFlow saved model into a TF-TRT Float32 Graph

  8. Benchmark TF-TRT Float32

  9. Convert to TF-TRT Float16 and Benchmark

  10. Converting to TF-TRT INT8

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Reviews

TOP REVIEWS FROM OPTIMIZE TENSORFLOW MODELS FOR DEPLOYMENT WITH TENSORRT

View all reviews

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.