![]() TensorRT's power allows for larger models if desired! Note: You can pick a different YOLOv5 model size. yolov5s.pt -include engine -half -imgsz 320 320 -device 0 Patience is key it might look frozen, but it's just concentrating hard! Can take up to 20 mintues. Time to execute export.py with the following command. We recommend yolov5s.py or yolov5m.py HERE □. But if it doesn't work, then you will need to re-export it. C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin.C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\libnvvp.C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib.□ Just locate the correct file and replace the path with your new one. We're not looking for the 'lean' or 'dispatch' versions. □ If the following steps didn't work, don't stress out! □ The labeling of the files corresponds with the Python version you have installed on your machine. Pip install "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\python\tensorrt-8.6.1-cp311-none-win_amd64.whl" If you do, good, then run the following command to install TensorRT in python. Once you have all the files copied over, you should have a folder at C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\python. zip TensorRT file and move all the folders/files to where the CUDA Toolkit is on your machine, usually at C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8. zip CuDNN file and move all the folders/files to where the CUDA Toolkit is on your machine, usually at C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8. Run the following pip install cupy-cuda11xĬlick to install CUDNN □. ![]() If you ever feel lost, you can always your questions in our Discord □. We forgot to mention adding environmental variable paths in the video. Watch the TensorRT section of the setup video □ before you begin.
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