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September 19, 2024
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Instructor: Snehan Kekre
5,511 already enrolled
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(75 reviews)
Recommended experience
Intermediate level
It is assumed that are competent in Python programming and have prior experience with building deep learning models with TensorFlow and its Keras API
(75 reviews)
Recommended experience
Intermediate level
It is assumed that are competent in Python programming and have prior experience with building deep learning models with TensorFlow and its Keras API
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
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Only available on desktop
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.
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
It is assumed that are competent in Python programming and have prior experience with building deep learning models with TensorFlow and its Keras API
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
The Coursera Project Network is a select group of instructors who have demonstrated expertise in specific tools or skills through their industry experience or academic backgrounds in the topics of their projects. If you're interested in becoming a project instructor and creating Guided Projects to help millions of learners around the world, please apply today at teach.coursera.org.
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.
75 reviews
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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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DeepLearning.AI
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Imperial College London
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Imperial College London
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Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, Guided Projects are not available on your mobile device.
Guided Project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.
You can download and keep any of your created files from the Guided Project. To do so, you can use the “File Browser” feature while you are accessing your cloud desktop.
At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.
Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.