原文地址：https://blogof33.com/post/2/
Tensorflow有Pip,Docker, Virtualenv, Anaconda 或 源码编译的方法安装 ,本文中采用Pip安装。
因为国内中文教程中有关Pip安装的很少，官方中文文档也有一些顺序上的错误，语义的模糊，按照上面的来很容易出错。所以我就在这里写一篇教程吧。
注：建议 Ubuntu 16.04版本
配置：
Linux Distribution: Ubuntu 16.04 64位
Cpu: Intel Core i5 6300HQ
Gpu:GTX 960M
Python:2.7
因为笔者是装的双系统，用UEFI引导的没有关Security Boot(安全启动)，所以出了一点问题。
1.开机进入Bios,关闭Security Boot
2.系统设置(setting)->软件和更新->附加驱动
-
先打开terminal输入：
sudo apt-get update 然后将显卡驱动选择为NVIDIA的显卡驱动(更改以后需要等待一段时间。)
3.安装CUDA8
- 去cuda toolkit 8.0下载，如图，选择deb(local)安装，网上有些说神马一定要选runfile，其实deb也很简单。
- 注：cuda 7.5只有ubuntu15.04和14.04版本，所以要用cuda 8.0
[图片上传失败...(image-5d8b27-1529399633042)]
- 下载成功以后按照图中的命令操作即可安装完成。
安装完成以后，cuda默认安装在了/usr/local/cuda-8.0/目录处，然后:
vim ~/.profile
注：很多其他发行版是打开.bash_profile，ubuntu没有这个，而是.profile
设置环境变量(加在文件末尾，每次登陆后自动生效)：
export PATH="$PATH:/usr/local/cuda-8.0"
export LD_LIBRARY_PATH="/usr/local/cuda-8.0/lib64"
保存以后，继续在terminal中输入:：
source ~/.profile #使更改的环境变量立即生效
nvidia-smi #测试是否配置成功
结果出现以下相识输出说明配置成功：
[图片上传失败...(image-b3923c-1529399633042)]
4.降低gcc版本
因为ubuntu的gcc编译器是5.4.0，然而cuda8.0不支持5.0以上的编译器，因此需要降级，把编译器版本降到4.9：
sudo apt-get install g++-4.9
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.9 20
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 10
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.9 20
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 10
sudo update-alternatives --install /usr/bin/cc cc /usr/bin/gcc 30
sudo update-alternatives --set cc /usr/bin/gcc
sudo update-alternatives --install /usr/bin/c++ c++ /usr/bin/g++ 30
sudo update-alternatives --set c++ /usr/bin/g++
gcc --version
如果版本显示出来是4.9就成功了
5.安装深度学习库cuDNN
一定要下载<font color=#0099ff face="微软雅黑">cuDNN5.1</font>!!!!**,至少现在下载6.0版本后面是有问题的，我之前下载的6.0版本，后面导入tensorflow时一直报错，在gaythub上看见有人说6.0“don not work”而5.1可以,不得已退回到5.1，然后便成功了。
下载之前要注册一个NVIDA DEVELOPER帐号，然后填三个问题就可以啦。如图选择cuDNN v5.1 Library for Linux：
[图片上传失败...(image-8c75d-1529399633042)]下载完成以后，解压并拷贝 CUDNN 文件到 Cuda Toolkit 8.0安装路径下. 假设 Cuda Toolkit 8.0 安装在 /usr/local/cud-8.0(默认路径), 执行以下命令(若/usr/local/cuda-8.0/include目录不存在，先创建一个include目录):
tar xvzf cudnn-8.0-linux-x64-v5.1.tgz
sudo cp cuda/include/cudnn.h /usr/local/cuda-8.0/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda-8.0/lib64
sudo chmod a+r /usr/local/cuda-8.0/include/cudnn.h /usr/local/cuda-8.0/lib64/libcudnn*
6.Install and upgrade PIP
- 首先要确保安装和更新pip：
sudo apt-get install python-pip python-dev
pip install --upgrade pip
7.安装 Tensorflow Gpu enable python 2.7 版本
pip install --upgrade tensorflow-gpu
测试一下：
$python
Python 2.7.12 (default, Nov 19 2016, 06:48:10)
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
2017-08-25 14:34:54.825013: W tensorflow/core/platform/cpu_feature_guard.cc:45] The
TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your
machine and could speed up CPU computations.
2017-08-25 14:34:54.825065: W tensorflow/core/platform/cpu_feature_guard.cc:45] The
TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your
machine and could speed up CPU computations.
2017-08-25 14:34:54.825081: W tensorflow/core/platform/cpu_feature_guard.cc:45] The
TensorFlow library wasn't compiled to use AVX instructions, but these are available on your
machine and could speed up CPU computations.
2017-08-25 14:34:54.825093: W tensorflow/core/platform/cpu_feature_guard.cc:45] The
TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your
machine and could speed up CPU computations.
2017-08-25 14:34:54.825105: W tensorflow/core/platform/cpu_feature_guard.cc:45] The
TensorFlow library wasn't compiled to use FMA instructions, but these are available on your
machine and could speed up CPU computations.
2017-08-25 14:34:55.071951: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893]
successful NUMA node read from SysFS had negative value (-1), but there must be at least one
NUMA node, so returning NUMA node zero
2017-08-25 14:34:55.072542: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found
device 0 with properties:
name: GeForce GTX 960M
major: 5 minor: 0 memoryClockRate (GHz) 1.176
pciBusID 0000:01:00.0
Total memory: 1.95GiB
Free memory: 1.31GiB
2017-08-25 14:34:55.072632: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA:
0
2017-08-25 14:34:55.072695: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: Y
2017-08-25 14:34:55.072730: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030]
Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 960M, pci bus id:
0000:01:00.0)
>>> print(sess.run(hello))
Hello, TensorFlow!
查看一下GPU开启情况：
- "/cpu:0": The CPU of your machine.
- "/gpu:0": The GPU of your machine, if you have one.
- "/gpu:1": The second GPU of your machine, etc.
>>> import tensorflow as tf
>>> a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
>>> b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
>>> c = tf.matmul(a, b)
>>> sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
>>> print(sess.run(c))
MatMul: (MatMul): /job:localhost/replica:0/task:0/gpu:0
2017-08-09 09:47:39.461702: I tensorflow/core/common_runtime/simple_placer.cc:847] MatMul: (MatMul)/job:localhost/replica:0/task:0/gpu:0
b: (Const): /job:localhost/replica:0/task:0/gpu:0
2017-08-09 09:47:39.461942: I tensorflow/core/common_runtime/simple_placer.cc:847] b: (Const)/job:localhost/replica:0/task:0/gpu:0
a: (Const): /job:localhost/replica:0/task:0/gpu:0
2017-08-09 09:47:39.461976: I tensorflow/core/common_runtime/simple_placer.cc:847] a: (Const)/job:localhost/replica:0/task:0/gpu:0
[[ 22. 28.]
[ 49. 64.]]
