1、关于ubuntu系统的安装在此就不再详述，可见ubuntu安装。
2、由于实验过程中使用到GPU，所以在安装完ubuntu14.04之后，安装相对应于GPU的显卡驱动，安装方法可见nvidia驱动安装。
3、首先我们得从github上克隆Tensorflow库：
$ git clone --recurse-submodules https://github.com/tensorflow/tensorflow
4、接下来安装Bazel
5、安装一些依赖
$sudo apt-get install python-numpy swig python-dev python-wheel
6、安装cuda
检测显卡的计算能力，Tensorflow要求显卡的计算能力大于3.5，查询显卡计算能力。
下载和安装cuda toolkit7.5，cuda下载。
安装依赖库：
$sudo apt-get install freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev
安装cuda
$ sudo dpkg -i cuda-repo-ubuntu1504-7-5-local_7.5-18_amd64
$ sudo apt-get update
$ sudo apt-get install -y cuda
装好之后将cuda的路径bin和lib64写进环境变量。
7、安装cudnn v5
下载cudnn v5。cudnn下载
tar xvzf cudnn-7.5-linux-x64-v5.tgz
sudo cp cudnn-7.5-linux-x64-v5/cudnn.h /usr/local/cuda/include
sudo cp cudnn-7.5-linux-x64-v5/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
8、配置Tensorflow
进入Tensorflow目录
$ ./configure
Please specify the location of python. [Default is /usr/bin/python]:
Do you wish to build TensorFlow with GPU support? [y/N] y
GPU support will be enabled for TensorFlow
Please specify which gcc nvcc should use as the host compiler. [Default is
/usr/bin/gcc]: /usr/bin/gcc-4.9
Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave
empty to use system default]: 7.5
Please specify the location where CUDA 7.5 toolkit is installed. Refer to
README.md for more details. [default is: /usr/local/cuda]: /usr/local/cuda
Please specify the Cudnn version you want to use. [Leave empty to use system
default]: 4.0.4
Please specify the location where the cuDNN 4.0.4 library is installed. Refer to
README.md for more details. [default is: /usr/local/cuda]: /usr/local/cudnn-r4-rc/
Please specify a list of comma-separated Cuda compute capabilities you want to
build with. You can find the compute capability of your device at:
https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your
build time and binary size. [Default is: \"3.5,5.2\"]: 3.5
Setting up Cuda include
Setting up Cuda lib64
Setting up Cuda bin
Setting up Cuda nvvm
Setting up CUPTI include
Setting up CUPTI lib64
Configuration finished
9、构建GPU支持
$ bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer
$ bazel-bin/tensorflow/cc/tutorials_example_trainer --use_gpu
# Lots of output. This tutorial iteratively calculates the major eigenvalue of
# a 2x2 matrix, on GPU. The last few lines look like this.
000009/000005 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
000006/000001 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
000009/000009 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
10、问题
在配置Tensorflow时，会安装boringssl,由于网络的限制，无法从google上下载软件，需要从github中下载。
解决方案：打开/tensorflow/tensorflow/wokspca.bzl,修改commit和remote
commit = "053931e",remote = "https://github.com/google/boringssl",
以上是安装Tensorflow过程，本过程是由Tensorflow官网所提供，其他解决方案都是由github上的大神所提供。
已经将Tensorflow装好了，但是还是没有办法将Tensorflow导入Python使用，这篇博客就是解决这个问题。
1、接着上面的工作继续安装，建立一个pip package
$ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package
# To build with GPU support:
$ bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
# The name of the .whl file will depend on your platform.
$ sudo pip install /tmp/tensorflow_pkg/tensorflow-0.8.0-py2-none-any.whl
上述代码最后一行的.whl的文件名会不一样，可以在路径下查看
2、Tensorflow的扩展
bazel build -c opt //tensorflow/tools/pip_package:build_pip_package
# To build with GPU support:
bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
mkdir _python_build
cd _python_build
ln -s ../bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow* .
ln -s ../tensorflow/tools/pip_package/* .
python setup.py develop
3、训练第一个Tensorflow网络模型
$ cd tensorflow/models/image/mnist
$ python convolutional.py
在进行上述操作时，出现GPU连接问题
这主要是lib没有写进环境变量里，没有找到这样的连接
sudo gedit ~/.bashrc
将安装cuda的路径写进去，然后重新构建就成。
以上安装Tensorflow的步骤都是根据Tensorflow官网的教程。