前一段时间开始接触TensorFlow，因为看到周围的一些小伙伴在安装上总是出现问题，所以这里把它总结一下。这里给出的是参照Tensorflow英文官网的教程写的，方便一些上不了国外网站的小伙伴。另外，我没有仔细比对中文社区的教程，中文社区和英文官网在一些内容上还是有些差别的，建议有条件的小伙伴还是参考英文官网进行学习较好。当然，大家也可以直接去英文官网看他们的教程：
https://www.tensorflow.org/install/install_sources#ConfigureInstallation
一、选择TensorFlow的版本
TensorFlow为我们提供了两个版本，一个是CPU版本，一个是GPU版本。其中GPU版本需要电脑具有NVIDIA的GPU，一般是一些性能关键型应用程序使用的，TensorFlow程序通常在GPU上的运行速度也明显高于CPU。因为我不具备NVIDIA的GPU，所以这里选择CPU版本。（对于新手，CPU版本相对于GPU版本也容易安装）
二、克隆TensorFlow repository
使用下列命令，克隆最新的TensorFlow repository。
$ git clone https://github.com/tensorflow/tensorflow三、环境准备
我们需要在系统上安装bazel和TensorFlow Python依赖两种构建工具。
首先安装bazel：
bazel我们使用官方提供的定制APT存储库进行安装。
1.安装JDK 8
sudo add-apt-repository ppa:webupd8team/java
sudo apt-get update && sudo apt-get install oracle-java8-installer 2.添加bazel分发URI作为包源
echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add -3.安装和更新bazel
sudo apt-get update && sudo apt-get install bazelsudo apt-get upgrade bazel然后安装TensorFlow Python依赖：
为了安装TensorFlow，你需要安装以下依赖包：
- numpy，这是TensorFlow需要的数学处理包
- dev，它可以添加对Python的扩展
- pip，使用它能够安装和管理某些Python包
- wheel，使用它能够以wheel（.whl）格式管理Python压缩包
在Python 2.7上安装这些包，使用以下命令：
$ sudo apt-get install python-numpy python-dev python-pip python-wheel
$ sudo apt-get install python3-numpy python3-dev python3-pip python3-wheel四、配置安装
在tensorlfow目录下有一个名为configure的bash脚本。该脚本要求确定所有相关TensorFlow依赖关系的路径名，并指定其他构建配置选项，如编译器标志。我们必须在创建pip软件包并安装TensorFlow之前运行此脚本。
首先进入tensorflow目录
$ cd tensorflow$ ./configurePlease specify the location of python. [Default is /usr/bin/python]: /usr/bin/python2.7
Found possible Python library paths:
/usr/local/lib/python2.7/dist-packages
/usr/lib/python2.7/dist-packages
Please input the desired Python library path to use. Default is [/usr/lib/python2.7/dist-packages]
Using python library path: /usr/local/lib/python2.7/dist-packages
Do you wish to build TensorFlow with MKL support? [y/N]
No MKL support will be enabled for TensorFlow
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:
Do you wish to use jemalloc as the malloc implementation? [Y/n]
jemalloc enabled
Do you wish to build TensorFlow with Google Cloud Platform support? [y/N]
No Google Cloud Platform support will be enabled for TensorFlow
Do you wish to build TensorFlow with Hadoop File System support? [y/N]
No Hadoop File System support will be enabled for TensorFlow
Do you wish to build TensorFlow with the XLA just-in-time compiler (experimental)? [y/N]
No XLA support will be enabled for TensorFlow
Do you wish to build TensorFlow with VERBS support? [y/N]
No VERBS support will be enabled for TensorFlow
Do you wish to build TensorFlow with OpenCL support? [y/N]
No OpenCL support will be enabled for TensorFlow
Do you wish to build TensorFlow with CUDA support? [y/N] Y
CUDA support will be enabled for TensorFlow
Do you want to use clang as CUDA compiler? [y/N]
nvcc will be used as CUDA compiler
Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 8.0]: 8.0
Please specify the location where CUDA 8.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:
Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 6.0]: 6
Please specify the location where cuDNN 6 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
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"]: 3.0
Do you wish to build TensorFlow with MPI support? [y/N]
MPI support will not be enabled for TensorFlow
Configuration finished五、构建pip包
使用以下命令构建pip软件包：
$ bazel build --config=opt //tensorflow/tools/pip_package:build_pip_packagebazel build命令构建了一个名为build_pip_package的脚本。使用以下命令运行此脚本将在/ tmp / tensorflow_pkg目录中构建一个.whl文件：
$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg六、安装pip包
使用pip install来安装该pip包，.whl文件的文件名取决于你/tmp/tensorflow_pkg文件夹下具体的文件名，下面给出一个例子：
$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.3.0-py2-none-any.whl七、验证安装是否成功
首先启动terminal，转换到tensorflow目录之外的其他目录，例如/home。
调用python：
$ pythonimport tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
Hello, TensorFlow!