cuda、cudnn、驱动版本查看及nvidia驱动、cuda安装及卸载

• 查看cuda 版本
  cat /usr/local/cuda/version.txt

• 查看cudnn 版本
  cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2

• 查看显卡驱动版本
  cat /proc/driver/nvidia/version
  或输入nvidia-smi

• cuda安装
  1.驱动安装完成后，下载cuda安装包（https://developer.nvidia.com/cuda-toolkit-archive）
  2.sudo ./cuda_8.0.61_375.26_linux.run
  3.一直按ctrl+F，直到100%
  4.其它的按部就班，但选择不安装驱动
  5.出现***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 361.00 is required for CUDA 8.0 functionality to work时，不用管它，此时只要安装的驱动版本更高比它更高即可
  6.将cuda添加到环境变量，在~/.bashrc文件中末尾加上
  export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
  7.在NVIDIA_CUDA-8.0_Samples中编译相关samples，能编译通过则说明安装成功

• nvidia驱动安装
  sudo apt-get purge nvidia*
  sudo add-apt-repository ppa:graphics-drivers/ppa
  sudo apt-get update
  sudo apt-get install nvidia-384 nvidia-settings
  reboot
  注：如果要是不行，可能需要在bios里将显卡先设置成CPU集卡
  验证
  1.输入nvidia-smi查看
  2.prime-select query查看当前选用的显卡

• cuda卸载
  cd /usr/local/cuda/bin
  sudo ./uninstall_cuda_8.0.pl
  cd /usr/local
  sudo rm -rf cuda-8.0

• cudnn安装
  参考：https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html

1. Installing from a Tar File

   Navigate to your directory containing the cuDNN Tar file.
   Unzip the cuDNN package.

   $ tar -xzvf cudnn-9.0-linux-x64-v7.tgz

   Copy the following files into the CUDA Toolkit directory, and change the file permissions.

   $ 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*

2. Installing from a Debian File

   Navigate to your directory containing cuDNN Debian file.
   Install the runtime library, for example:

   sudo dpkg -i libcudnn7_7.0.3.11-1+cuda9.0_amd64.deb

   Install the developer library, for example:

   sudo dpkg -i libcudnn7-devel_7.0.3.11-1+cuda9.0_amd64.deb

   Install the code samples and the cuDNN Library User Guide, for example:

   sudo dpkg -i libcudnn7-doc_7.0.3.11-1+cuda9.0_amd64.deb