Done Some packages could not be installed. Done Building dependency tree Reading state information. ![]() Sudo apt install cuda-11-2 Reading package lists. Pip3 install pytorch=1.8.0 torchvision=0.9.0 # I choose version 1.8.0 because it is stable and compatible with CUDA 11.2 Toolkit and cuDNN 8.1 # install Pytorch (an open source machine learning framework) # Finally, to verify the installation, check Sudo chmod a+r /usr/local/cuda-11.2/lib64/libcudnn * Sudo cp -P cuda/lib64/libcudnn * /usr/local/cuda-11.2/lib64/ Sudo cp -P cuda/include/cudnn.h /usr/local/cuda-11.2/include # copy the following files into the cuda toolkit directory. # in order to download cuDNN you have to be regeistered here ĬUDNN_TAR_FILE= "cudnn-11.2-linux-圆4-v8.1.1.33.tgz " # install nvidia driver with dependenciesĮcho "deb / " | sudo tee /etc/apt//cuda.listĮcho 'export PATH=/usr/local/cuda-11.2/bin:$PATH ' > ~/.bashrcĮcho 'export LD_LIBRARY_PATH=/usr/local/cuda-11.2/lib64:$LD_LIBRARY_PATH ' > ~/.bashrc Sudo add-apt-repository ppa:graphics-drivers/ppa Sudo apt-get install g++ freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libglu1-mesa libglu1-mesa-dev to verify the version of gcc install enter # gcc compiler is required for development using the cuda toolkit. ![]() # to verify your gpu is cuda enable check Sudo apt-get autoremove & sudo apt-get autoclean # If you have previous installation remove it first. # download and install the nvidia cuda toolkit and cudnn # verify the system has a cuda-capable gpu ![]() # This gist contains instructions about cuda v11.2 and cudnn8.1 installation in Ubuntu 20.04 for Pytorch 1.8 & Tensorflow 2.7.0
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