1、安装Anaconda
a)在官网下载文件
b)安装命令:sh Anaconda3-5.3.0-Linux-x86_64.sh
默认路径:/root/anaconda3
c)添加环境变量:export PATH=/root/anaconda3/bin:$PATH
2、安装cuda8.0
a)下载cuda:https://developer.nvidia.com/cuda-80-download-archive
b)安装命令:sh cuda_8.0.44_linux.run
c)安装选项
Do you accept the previously read EULA?
accept/decline/quit: accept
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64
361.62?
(y)es/(n)o/(q)uit: no
Install the CUDA 8.0 Toolkit?
(y)es/(n)o/(q)uit: yes
Enter Toolkit Location[ default is /usr/local/cuda-8.0 ]:
Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: yes
Install the CUDA 8.0 Samples?
(y)es/(n)o/(q)uit: no
3)下载cudann5.1,解压后lib文件上传到root,将include拷贝入
/usr/local/cuda/include
4)添加环境变量:
export PATH=/usr/local/cuda-8.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/root/cudnn5.1/lib64:$LD_LIBRARY_PATH
export CUDA_HOME=/usr/local/cuda-8.0
5)激活环境变量:source /root/.profile
3,tf12虚拟环境搭建
1)conda create -n tf12 python=3.6
删除:conda remove -n tf12 --all
克隆:conda create -n caf --clone caf_base
2)下载tf12,py36,x86_64的版本
3)安装:pip install tensorflow_gpu-1.2.0-cp36-cp36m-manylinux1_x86_64.whl
4)激活环境:source activate tf12
退出:deactivate
5)查看已经安装的包:conda list