Optimize TensorFlow Models For Deployment with TensorRT

4.6

59 個評分

提供方

3,860 人已註冊

在此免費指導項目中,您將:
1.5 hours
中級
無需下載
分屏視頻
英語(English)
僅限桌面

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.

必備條件

您要培養的技能

  • Deep Learning

  • NVIDIA TensorRT (TF-TRT)

  • Python Programming

  • Tensorflow

  • keras

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

審閱

來自OPTIMIZE TENSORFLOW MODELS FOR DEPLOYMENT WITH TENSORRT的熱門評論

查看所有評論

常見問題