ubuntu18.04+rtx2060安装cuda10.0+cudnn7+tensorflow-gpu

1.显卡驱动430

1）卸载现有驱动：sudo apt-get remove --purge nvidia*

2）安装ppa显卡驱动源：

sudo apt-get updatesudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update

3)检查显卡和推荐的驱动

sudo apt-cache search nvidia

ubuntu-drivers devices

结果：

vendor : NVIDIA Corporation

driver : nvidia-driver-418 - third-party free

driver : nvidia-driver-430 - third-party free recommended

driver : xserver-xorg-video-nouveau - distro free builtin

于是步骤：

4）sudo apt-get install nvidia-driver-430

期间可能会设置密码，进行重启后的修改确认“Enroll MOK”

5)重启并检查（输入密码，在“Enroll MOK”中确认修改！）

查看：nvidia-smi:

2.安装cuda10.0(下载链接https://developer.nvidia.com/cuda-toolkit-archive，对比关系图https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html)

1）sudo chmod a+x cuda_xxxxx_linux.run

2）sudo sh cuda_xxxxx_linux.run

中间再安装显卡驱动时，要NO，其他都是YES或者default

3）成功后，修改配置文件sudo vim ~/.bashrc

4）添加语句并保存：

export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

或者可以为：

export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

5）source ~/.bashrc

测试：

cd /usr/local/cuda-8.0/samples/1_Utilities/deviceQuery
sudo make
sudo ./deviceQuery

或者：nvcc--version

连接：sudo ln -s /usr/local/cuda-10.0 /usr/local/cuda

3.cudnn7.6.1(下载链接https://developer.nvidia.com/rdp/cudnn-download)

1）解压tar zxvf FileName.tgz

2）将文件倒入相关内容中

sudo cp cudnn.h /usr/local/cuda/include/ 

sudo cp lib* /usr/local/cuda/lib64/ 

3）动态文件进行链接

cd /usr/local/cuda/lib64/
sudo rm -rf libcudnn.so libcudnn.so.7  #删除原有动态文件
sudo ln -s libcudnn.so.7.6.1 libcudnn.so.7  #生成软衔接
sudo ln -s libcudnn.so.7 libcudnn.so     #生成软链接

4.安装pip，进行安装其他依赖库

sudo apt-get install python3-pip

之后pip install opencv-python numpy scipy sklearn

5.安装tensorflow-gpu

首先确认自己安装的版本，因为相互之间的对应关系https://tensorflow.google.cn/install/source#linux

pip install tensorflow-gpu或者pip install tensorflow-gpu==1.xx

pip install keras