Optimize TensorFlow Models For Deployment with TensorRT
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.
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- Level Intermediate
- Duration 3 hours
- Course by Coursera Project Network
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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.Modules
Optimize TensorFlow Models For Deployment with TensorRT
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- Graded Quiz: Test your Project Understanding
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- Optimize TensorFlow Models For Deployment with TensorRT
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- Project-based Course Overview
Auto Summary
Discover how to enhance the performance of your TensorFlow models for deployment using NVIDIA's TensorRT in this hands-on, guided project. This intermediate-level course, offered by Coursera, delves into the integration of TensorFlow with TensorRT (TF-TRT) to optimize deep learning models. Over the span of 1.5 hours, you'll learn to use TF-TRT for optimizing models at various precision levels (FP32, FP16, and INT8) and understand the impact of tuning TF-TRT parameters on performance and inference throughput. Ideal for IT and Computer Science enthusiasts with a solid foundation in Python programming, deep learning concepts, and TensorFlow’s Keras API, this course ensures you gain practical skills in model optimization. Although designed primarily for learners in North America, efforts are underway to extend the same experience globally. Best of all, you can access this valuable training for free, making it an excellent opportunity to advance your technical prowess in model deployment.
Snehan Kekre