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.
Optimize TensorFlow Models For Deployment with TensorRT
Instructor: Snehan Kekre
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(74 reviews)
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What you'll learn
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
Skills you'll practice
Details to know
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About this Guided Project
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:
Introduction and Project Overview
Setup your TensorFlow and TensorRT Runtime
Load the Data and Pre-trained InceptionV3 Model
Create batched Input
Load the TensorFlow SavedModel
Get Baseline for Prediction Throughput and Accuracy
Convert a TensorFlow saved model into a TF-TRT Float32 Graph
Benchmark TF-TRT Float32
Convert to TF-TRT Float16 and Benchmark
Converting to TF-TRT INT8
Recommended experience
It is assumed that are competent in Python programming and have prior experience with building deep learning models with TensorFlow and its Keras API
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How you'll learn
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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Reviewed on Jun 14, 2023
good content, but some code is out of date, especially the package installation part.
Reviewed on Jun 3, 2021
Great workshop, all the concepts were very well explained.
Reviewed on Mar 14, 2022
The first to introduce such a rare and important topic.
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