🔻Speeding Up Deep Learning Inference Using TensorRT
📈 This version starts from a #PyTorch model instead of the #ONNX model, upgrades the sample application to use #TensorRT 7, and replaces the ResNet-50 #classification model with UNet, which is a segmentation model.
📈 NVIDIA TensorRT is an SDK for deep learning inference. TensorRT provides APIs and parsers to import trained models from all major deep learning frameworks. It then generates optimized runtime engines deployable in the datacenter as well as in automotive and embedded environments.
🔹Simple TensorRT example
🔻Convert the pretrained image segmentation PyTorch model into ONNX.
🔻Import the ONNX model into TensorRT.
🔻Apply optimizations and generate an engine.
🔻Perform inference on the GPU.
⭕️ DO NOT MISS OUT THIS ARTICLE
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://devblogs.nvidia.com/speeding-up-deep-learning-inference-using-tensorrt/
#NVIDIA #deeplearning #neuralnetworks #python
#machinelearning #AI
📈 This version starts from a #PyTorch model instead of the #ONNX model, upgrades the sample application to use #TensorRT 7, and replaces the ResNet-50 #classification model with UNet, which is a segmentation model.
📈 NVIDIA TensorRT is an SDK for deep learning inference. TensorRT provides APIs and parsers to import trained models from all major deep learning frameworks. It then generates optimized runtime engines deployable in the datacenter as well as in automotive and embedded environments.
🔹Simple TensorRT example
🔻Convert the pretrained image segmentation PyTorch model into ONNX.
🔻Import the ONNX model into TensorRT.
🔻Apply optimizations and generate an engine.
🔻Perform inference on the GPU.
⭕️ DO NOT MISS OUT THIS ARTICLE
—————————
📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://devblogs.nvidia.com/speeding-up-deep-learning-inference-using-tensorrt/
#NVIDIA #deeplearning #neuralnetworks #python
#machinelearning #AI