Ubuntu 16.04 安裝 Tensorflow Gpu版本

原文地址：blogof33.com/post/2/

Tensorflow有Pip,Docker, Virtualenv, Anaconda 或 源碼編譯的方法安裝 ,本文中採用Pip安裝。

由於國內中文教程中有關Pip安裝的不多，官方中文文檔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。因此我就在這裏寫一篇教程吧。

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

由於筆者是裝的雙系統，用UEFI引導的沒有關Security Boot(安全啓動)，因此出了一點問題。

1.開機進入Bios,關閉Security Boot

2.系統設置(setting)->軟件和更新->附加驅動

• 先打開terminal輸入：

  sudo apt-get update

• 而後將顯卡驅動選擇爲NVIDIA的顯卡驅動(更改之後須要等待一段時間。)

3.安裝CUDA8

• 去cuda toolkit 8.0下載，如圖，選擇deb(local)安裝，網上有些說神馬必定要選runfile，其實deb也很簡單。
• 注：cuda 7.5只有ubuntu15.04和14.04版本，因此要用cuda 8.0

• 下載成功之後按照圖中的命令操做便可安裝完成。 安裝完成之後，cuda默認安裝在了/usr/local/cuda-8.0/目錄處，而後: vim ~/.profile

  注：不少其餘發行版是打開.bash_profile，ubuntu沒有這個，而是.profile

  設置環境變量(加在文件末尾，每次登錄後自動生效)： export PATH="$PATH:/usr/local/cuda-8.0" export LD_LIBRARY_PATH="/usr/local/cuda-8.0/lib64" 保存之後，繼續在terminal中輸入:： source ~/.profile #使更改的環境變量當即生效 nvidia-smi #測試是否配置成功 結果出現如下相識輸出說明配置成功：

  

4.下降gcc版本

由於ubuntu16.04的gcc編譯器是5.4.0，然而cuda8.0不支持5.0以上的編譯器，所以須要降級，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

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若是版本顯示出來是4.9就成功了

5.安裝深度學習庫cuDNN

必定要下載cuDNN5.1!!!!,至少如今下載6.0版本後面是有問題的，我以前下載的6.0版本，後面導入tensorflow時一直報錯，在gaythub上看見有人說6.0「don not work」而5.1能夠,不得已退回到5.1，而後便成功了。

• 下載以前要註冊一個NVIDA DEVELOPER賬號，而後填三個問題就能夠啦。如圖選擇cuDNN v5.1 Library for Linux：

  

  ​

• 下載完成之後，解壓並拷貝 CUDNN 文件到 Cuda Toolkit 8.0安裝路徑下. 假設 Cuda Toolkit 8.0 安裝在 /usr/local/cud-8.0(默認路徑), 執行如下命令(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*
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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
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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! 查看一下GPU開啓狀況： - "/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. 複製代碼

>>> 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.]] 
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至此，Tensorflow安裝成功。