0.ubuntu18.04系统,分区方案:
/swap 4096M, 剩下的全部给 根目录 /,即boot,temp,home共享全盘资源。此问题可以良好的解决安装cuda、matlab等环境时tmp不够用的问题。
1.更换apt源:
sudo vim /etc/apt sources.list
此前可以先备份,用cp命令或者直接复制到外面。
将里面的内容换成:
deb http://mirrors.163.com/ubuntu/ bionic main restricted universe multiverse
deb http://mirrors.163.com/ubuntu/ bionic-security main restricted universe multiverse
deb http://mirrors.163.com/ubuntu/ bionic-updates main restricted universe multiverse
deb http://mirrors.163.com/ubuntu/ bionic-proposed main restricted universe multiverse
deb http://mirrors.163.com/ubuntu/ bionic-backports main restricted universe multiverse
deb-src http://mirrors.163.com/ubuntu/ bionic main restricted universe multiverse
deb-src http://mirrors.163.com/ubuntu/ bionic-security main restricted universe multiverse
deb-src http://mirrors.163.com/ubuntu/ bionic-updates main restricted universe multiverse
deb-src http://mirrors.163.com/ubuntu/ bionic-proposed main restricted universe multiverse
deb-src http://mirrors.163.com/ubuntu/ bionic-backports main restricted universe multiverse
2.cuda driver: sudo ubuntu-drivers autoinstall
3.gcc: 后面的数字代表优先级,系统会选择优先级高的作为默认版本。
sudo apt-get install gcc-4.8 g++-4.8
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.8 48
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.8 48
4.cuda9.0:
download 官网路径:cuda9.0 -> linux -> x86_64 -> Ubuntu -> 16.04 -> runfile[local]
$sudo sh cuda_9.0.176_384.81_linux.run
选项顺序:accept yes no yes yes
添加系统环境路径
$ sudo vim ~/.bashrc #打开配置文件,如果没安装vim,可执行
$ sudo apt-get install vim #安装vim
export PATH=/usr/local/cuda-9.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64:$LD_LIBRARY_PATH
$source ~/.bashrc
5.cudnn:
从官网下载cuda9.0对应的版本。
tar -zxvf cudnn-9.0-linux-x64-v7.tgz
sudo cp cuda/include/cudnn.h /usr/local/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/ -d
sudo chmod a+r /usr/local/cuda/include/cudnn.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*
6.tensorflow:
sudo apt-get install python-pip(这边要换个pip源,可参考之前配Windows环境的文章)
pip3 install tensorflow-gpu
7.测试:这里不知道为什么装在Python2下有些问题,最好用pip3安装到Python3下就好了。。。