convert pytorch model to tensorrt

Converting a PyTorch model to TensorRT can significantly accelerate inference on NVIDIA GPUs. TensorRT is a high-performance deep learning inference library developed by NVIDIA. In this tutorial, we will guide you through the process of converting a PyTorch model to TensorRT using the torch2trt library. We'll assume you have PyTorch and TensorRT installed on your system.
Make sure you have the necessary dependencies installed:
Install the torch2trt library, which facilitates the conversion from PyTorch to TensorRT:
Let's start by creating a simple PyTorch model. For this tutorial, we'll use a basic example with a pre-trained ResNet18 model from torchvision:
Now, let's use torch2trt to convert the PyTorch model to TensorRT:
Now that we have the TensorRT model saved, we can load it and use it for inference:
That's it! You've successfully converted a PyTorch model to TensorRT and performed inference using the optimized model.
Note: Ensure that your GPU supports TensorRT and you have the appropriate version of the CUDA toolkit installed. Additionally, keep in mind that the performance gain may vary depending on the complexity of your model and the hardware specifications.
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