原文地址：blogof33.com/post/2/
Tensorflow有Pip,Docker, Virtualenv, Anaconda 或 原始碼編譯的方法安裝 ,本文中採用Pip安裝。
因為國內中文教程中有關Pip安裝的很少，官方中文文件也有一些順序上的錯誤，語義的模糊，按照上面的來很容易出錯。所以我就在這裡寫一篇教程吧。
注：建議 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
因為筆者是裝的雙系統，用UEFI引導的沒有關Security Boot(安全啟動)，所以出了一點問題。
1.開機進入Bios,關閉Security Boot
2.系統設定(setting)->軟體和更新->附加驅動
-
先開啟terminal輸入：
sudo apt-get update -
然後將顯示卡驅動選擇為NVIDIA的顯示卡驅動(更改以後需要等待一段時間。)
3.安裝CUDA8
- 去cuda toolkit 8.0下載，如圖，選擇deb(local)安裝，網上有些說神馬一定要選runfile，其實deb也很簡單。
- 注：cuda 7.5只有ubuntu15.04和14.04版本，所以要用cuda 8.0
-
下載成功以後按照圖中的命令操作即可安裝完成。 安裝完成以後，cuda預設安裝在了/usr/local/cuda-8.0/目錄處，然後: vim ~/.profile
注：很多其他發行版是開啟.bash_profile，ubuntu沒有這個，而是.profile
設定環境變數(加在檔案末尾，每次登陸後自動生效)：
export PATH="$PATH:/usr/local/cuda-8.0"export LD_LIBRARY_PATH="/usr/local/cuda-8.0/lib64"儲存以後，繼續在terminal中輸入:： source ~/.profile #使更改的環境變數立即生效 nvidia-smi #測試是否配置成功 結果出現以下相識輸出說明配置成功：
4.降低gcc版本
因為ubuntu16.04的gcc編譯器是5.4.0，然而cuda8.0不支援5.0以上的編譯器，因此需要降級，把編譯器版本降到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
複製程式碼如果版本顯示出來是4.9就成功了
5.安裝深度學習庫cuDNN
一定要下載cuDNN5.1!!!!,至少現在下載6.0版本後面是有問題的，我之前下載的6.0版本，後面匯入tensorflow時一直報錯，在gaythub上看見有人說6.0“don not work”而5.1可以,不得已退回到5.1，然後便成功了。
-
下載之前要註冊一個NVIDA DEVELOPER帳號，然後填三個問題就可以啦。如圖選擇cuDNN v5.1 Library for Linux：
-
下載完成以後，解壓並拷貝 CUDNN 檔案到 Cuda Toolkit 8.0安裝路徑下. 假設 Cuda Toolkit 8.0 安裝在 /usr/local/cud-8.0(預設路徑), 執行以下命令(若/usr/local/cuda-8.0/include目錄不存在，先建立一個include目錄):
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*
複製程式碼6.Install and upgrade PIP
- 首先要確保安裝和更新pip：
sudo apt-get install python-pip python-dev
pip install --upgrade pip
複製程式碼7.安裝 Tensorflow Gpu enable python 2.7 版本
pip install --upgrade tensorflow-gpu
測試一下：
$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!
檢視一下GPU開啟情況：
- "/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.
複製程式碼 >>> 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.]]
複製程式碼