清除原有的nvidia driver
sudo apt-get purge nvidia*
加入顯卡 ppa
sudo add-apt-repository ppa:graphics-drivers
package 更新
sudo apt-get update
sudo apt upgrade
找出目前支援的GPU driver 版本
ubuntu-drivers list
上述懶人包
sudo apt-get purge nvidia* && \
sudo add-apt-repository ppa:graphics-drivers && \
sudo apt-get update && \
sudo apt upgrade && \
ubuntu-drivers list安裝460
sudo apt install nvidia-driver-460
重啟
sudo reboot
測試
nvidia-smi
安裝 CUDA 10.1 update2 Archive
https://developer.nvidia.com/cuda-toolkit-archive
下載並安裝
wget https://developer.download.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.243_418.87.00_linux.run
sudo sh cuda_10.1.243_418.87.00_linux.run安裝 cuDNN 7.6.5
登入nvidia cudnn
cuDNN Runtime Library for Ubuntu18.04 (Deb)
cuDNN Developer Library for Ubuntu18.04 (Deb)
cuDNN Code Samples and User Guide for Ubuntu18.04 (Deb)
複製檔名，解壓縮
tar -xzvf cudnn-10.1-linux-x64-v7.tgz
複製檔案，貼到安裝 cuda 的資料夾內
sudo cp cuda/include/cudnn.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
)三個Deb檔
使用 dpkg -i <file_name> 執行安裝
安裝 cuDNN runtime library
sudo dpkg -i libcudnn7_7.6.4.38-1+cuda10.1_amd64.deb
安裝 cuDNN developer library
sudo dpkg -i libcudnn7-devel_7.6.4.38–1+cuda10.1_amd64.deb
安裝cuDNN sample 和 user guide
sudo dpkg -i libcudnn7-doc_7.6.4.38–1+cuda10.1_amd64.deb
安裝 pip
sudo apt install python3-pip
安裝 torch 1.6.0 & torchvision 0.7.0
pip3 install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html