Ubuntu14.04+CUDA7.5+CUDNN.V5+Anaconda+tensorflow

1安装teamviwer（非必要，目的为远程控制，可跳过）

官网下载deb包https://www.teamviewer.com/en/download/linux/

$ sudo dpkg -i teamviewer_12.0.69753_i386.deb

安装可能会出现依赖错误，执行下条命令后再执行安装

$ sudo apt-get install -f

2安装CUDA7.5

2.1卸载原有NVIDIA驱动

$ sudo apt-get –purge remove nvidia-*

2.2禁用通用显卡驱动

终端中运行：$ lsmod | grep nouveau，如果有输出则代表nouveau正在加载。

在/etc/modprobe.d中创建文件blacklist-nouveau.conf，在文件中输入

blacklist nouveau  options nouveau modeset=0

运行

$ sudo update-initramfs –u

重启电脑

2.3执行安装

1）关闭图形化界面

$ sudo service lightdm stop

2）进入CUDA7.5安装文件目录执行安装，推荐runfile，下载地址https://developer.nvidia.com/cuda-75-downloads-archive

$ sudo sh cuda_7.5.18_linux.run

开始后会有一段用户协议，空格调到最后输入accept,接下来就是各种yes和确认路径，OpenGL可以不装
3）安装完成后重启图形界面服务

$ sudo service lightdm start

4）重启电脑，检查/dev目录,是否存在4个NVIDIA文件
$ ls /dev/nvidia*

2.4设置环境变量

1）打开profile文件，写入环境变量

$ sudo vim /etc/profile

Ubuntu中的vi编辑器可能有问题，建议安装vim

$ sudo apt-get install vim

2）写入环境变量

export PATH=/usr/local/cuda-7.5/bin:$PATH export
LD_LIBRARY_PATH=/usr/local/cuda7.5/lib64

3）设置生效

$ sudo source /etc/profile

2.5检查是否安装成功

1）检查驱动安装是否成功，成功则会驱动输出版本号

$ cat /proc/driver/nvidia/version

2）检查CUDA Toolkit安装是否成功，成功输出CUDA版本号

$ nvcc -V

3)编译CUDA的例程

$ cd /home/xxxx/NVIDIA_CUDA-7.5_Samples #xxxx为用户名

$ make

若失败，可能是缺少g++，执行安装

$ sudo apt-get install g++

编译可能需要比较长的时间
完成后，进入生成的bin目录/home/xxxx/NVIDIA_CUDA-7.5_Samples/bin运行例程

$ ./deviceQuery

若输出显卡信息即表示CUDA成功安装配置

3安装cuDNN

3.1下载解压

下载地址https://developer.nvidia.com/rdp/cudnn-download（需要注册下NVIDIA账号）
根据我们的版本选择cuDNN v5，文件名为cudnn-7.5-linux-x64-v5.0-ga.tgz
进入目录，解压后得到cuda目录

$ sudo tar -xvf cudnn-7.5-linux-x64-v5.0-ga.tgz

3.2复制文件

cd cuda sudo cp lib64/* /usr/local/cuda/lib64/ sudo cp include/cudnn.h
/usr/local/cuda/include/

3.3更新系统

sudo apt-get update && sudo apt-get upgrade sudo apt-get install
linux-source sudo apt-get install linux-headers-$(uname -r)

4Anaconda环境下安装tensorflow

4.1安装Anaconda

官网下载对应版本进行安装，我的如下

$ bash Anaconda2-4.2.0-Linux-x86_64.sh

安装过程一路yes和回车即可

4.2创建tensorflow环境执行安装

1）创建环境

$ conda create -n tensorflow python=2.7

2）pip方式进行安装

$ source activate tensorflow

3）根据不同版本，设置环境变量。

# Ubuntu/Linux 64-bit, CPU only, Python 2.7  (tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0-cp27-none-linux_x86_64.whl  # Ubuntu/Linux 64-bit, GPU enabled, Python 2.7  # Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below.  (tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.10.0-cp27-none-linux_x86_64.whl  # Mac OS X, CPU only, Python 2.7:  (tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.10.0-py2-none-any.whl  # Mac OS X, GPU enabled, Python 2.7:  (tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow-0.10.0-py2-none-any.whl  # Ubuntu/Linux 64-bit, CPU only, Python 3.4  (tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0-cp34-cp34m-linux_x86_64.whl  # Ubuntu/Linux 64-bit, GPU enabled, Python 3.4  # Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below.  (tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.10.0-cp34-cp34m-linux_x86_64.whl  # Ubuntu/Linux 64-bit, CPU only, Python 3.5  (tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0-cp35-cp35m-linux_x86_64.whl  # Ubuntu/Linux 64-bit, GPU enabled, Python 3.5  # Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below.  (tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.10.0-cp35-cp35m-linux_x86_64.whl  # Mac OS X, CPU only, Python 3.4 or 3.5:  (tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.10.0-py3-none-any.whl  # Mac OS X, GPU enabled, Python 3.4 or 3.5:  (tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow-0.10.0-py3-none-any.whl

4）根据使用的python2或者python3执行安装

# Python 2  (tensorflow)$ pip install --ignore-installed --upgrade $TF_BINARY_URL  # Python 3  (tensorflow)$ pip3 install --ignore-installed --upgrade $TF_BINARY_URL

4）退出环境
$ source deactivate

4.3测试安装，执行下列代码

>>> import tensorflow as tfI tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locallyI tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locallyI tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locallyI tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locallyI tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally>>> tf.__version__'0.10.0'>>> tf.__path__['/home/dskj/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow']（tensorflow的安装目录）