![]() Sudo cp cuda/include/cudnn.h /usr/local/cuda-9.0/include/ Sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda-9.0/lib64/ Move the unpacked contents to your CUDA directory Install CUDNN (ensure you are registered for the NVIDIA Developer Program) Sudo apt install nvidia-cuda-toolkit gcc-6 g++-6 Sudo add-apt-repository ppa:graphics-drivers/ppa Install tensorflow-gpu (GPU version) on Ubuntu 18.04 (Optional)Install tqdm for model evaluation D OPENCV_EXTRA_MODULES_PATH=/root/OpenCV-tmp/opencv_contrib/modules \ I/usr/include/python3.6m -I/usr/include/python3.6mĬp /usr/include/x86_64-linux-gnu/python3.5m/pyconfig.h /usr/include/python3.5m/ Sudo apt-get install build-essential cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev By Jason Tzu-Cheng Chuang 8-18-2018 Purpose: Easily setting up OpenCV CUDA ready environment for Deep Neural Network accelerator This demonstration has been tested on Linux Kernel Ubuntu 18.04 on Windows 10 圆4 and pure Ubuntu 18.04 OpenCV Nvidia CUDA GPU driver installationĬlean installation of Ubuntu 18.04 Requirement
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