It then generates optimized runtime engines deployable in the datacenter as well as in automotive and embedded environments. TensorRT provides APIs and parsers to import trained models from all major deep learning frameworks. You will learn how to deploy a deep learning application onto a GPU, increasing throughput and reducing latency during inference. Welcome to this introduction to TensorRT, our platform for deep learning inference. The new version of this post, Speeding Up Deep Learning Inference Using TensorRT, has been updated to start from a PyTorch model instead of the ONNX model, upgrade the sample application to use TensorRT 7, and replaces the ResNet-50 classification model with UNet, which is a segmentation model.
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