Inference with Torch-TensorRT Deep Learning Prediction for Beginners - CPU vs CUDA vs TensorRT
Hi everyone! 😀 In the last video we’ve seen how to accelerate the speed of our programs with Pytorch and CUDA - today we will take it another step further with Torch-TensorRT!
We will focus on a Machine Learning process called Inference (which is when the model is trained, perfected and ready to make a prediction).
For this we will load a state-of-the-art artificial neural network and we will use it to classify a picture of my cat! 🙀🙀🙀
Specifically - we will borrow ResNet50 for our little Pytorch experiment! 😉
We will also run a speed test comparing Pytorch models running on CPU, on CUDA and on Torch-TensorRT - which of these do you think is faster??
⏲️ TIMESTAMPS ⏲️
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00:00 - intro
01:05 - clone Torch-TensorRT
01:40 - install and setup Docker
03:52 - install Nvidia Container Toolkit & Nvidia Docker 2
05:02 - Torch-TensorRT container (option #1)
07:22 - Torch-TensorRT Nvidia NGC container (option #2)
09:00 - import Pytorch
09:16 - load ResNet50
10:25 - load sample image
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