terrydang/install_tensorflow_form_source_Ubuntu_16_04.md

## install_tensorflow_form_source_Ubuntu_16_04.md

      
    Raw
  

              install_tensorflow_form_source_Ubuntu_16_04.md
            
          
    Ubuntu 16.04 安装 tensorflow with GPU support（源码编译安装）

显卡型号

NVIDIA Corporation GM204 [GeForce GTX 970]
1. 安装依赖


安装CUDA 7.5
安装完 CUDA 后需要配置环境变量
  $ sudo vim /etc/profile.d/cuda.sh

  export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64"
  export CUDA_HOME=/usr/local/cuda

  $ source  /etc/profile.d/cuda.sh


下载安装 cuDNN v5 下载地址
  tar xvzf cudnn-7.5-linux-x64-v5.0-ga.tgz
  sudo cp cuda/include/cudnn.h /usr/local/cuda/include
  sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
  sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*


安装 tensorflow 的编译工具 bazel 安装方法


安装其它依赖
  # For Python 2.7:
  $ sudo apt-get install python-numpy swig python-dev python-wheel
  # For Python 3.x:
  $ sudo apt-get install python3-numpy swig python3-dev python3-wheel


2 .下载 tensorflow 源码，并配置编译选项


下载源码
  $ git clone --recurse-submodules https://github.com/tensorflow/tensorflow


配置编译选项
  $ cd tensorflow/

  $ ./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]: 
  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]: 5.0.5

  Please specify the location where the cuDNN 5.0.5 library is installed. Refer to
  README.md for more details. [default is: /usr/local/cuda]:/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\"]: 5.2

  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


3. 编译 tensorflow 的 example_trainer 并运行


GFW 问题修复。如果你使用国内的网络，编译过程中可能会遇到 https://boringssl.googlesource.com/boringssl 无法下载的问题。可以通过修改 workspace.bzl 将该源替换掉解决。
  $ sudo vim tensorflow/workspace.bzl

并将以下内容
  native.new_git_repository(

  name = "boringssl_git",

  commit = "e72df93461c6d9d2b5698f10e16d3ab82f5adde3",

  remote = "https://boringssl.googlesource.com/boringssl",

  build_file = path_prefix + "boringssl.BUILD",

  )

替换为
  native.new_git_repository(

  name = "boringssl_git",

  commit = “1eca1d3",

  remote = "https://github.com/google/boringssl.git",

  build_file = path_prefix + "boringssl.BUILD",

  )


GCC 版本问题修复。目前Ubuntu 16.04 默认编译器版本为 GCC 5.3.1 ，使用此版本编译 tensorflow 可能会出现 memcpy 未定义的错误，可以通过添加编译选项 -D_FORCE_INLINES 解决。再下一步的编译 tensorflow 安装文件时也可能会遇到 builtin_ia32_mwaitx 未定义的错误，可以通过添加 编译选项 -D_MWAITXINTRIN_H_INCLUDED 和 -D__STRICT_ANSI__ 解决。具体修复方法如下
  $ vim third_party/gpus/crosstool/CROSSTOOL

在该文件如下位置处
  toolchain {
  # Use "-std=c++11" for nvcc. For consistency, force both the host compiler
  # and the device compiler to use "-std=c++11".
  cxx_flag: "-std=c++11"
  linker_flag: "-lstdc++"
  linker_flag: "-B/usr/bin/"

增加编译选项
  toolchain {
  # Use "-std=c++11" for nvcc. For consistency, force both the host compiler
  # and the device compiler to use "-std=c++11".
  cxx_flag: "-std=c++11"
  cxx_flag: "-D_FORCE_INLINES"
  cxx_flag: "-D_MWAITXINTRIN_H_INCLUDED"
  cxx_flag: "-D__STRICT_ANSI__"
  linker_flag: "-lstdc++"
  linker_flag: "-B/usr/bin/"


编译运行 example_trainer
  $ 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]

选项 --config=cuda 表示其支持 GPU


4. 将 tensorflow 打包成 python wheel 文件并用 pip 安装


编译 build_pip_package
  # To build with GPU support:
  $ bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package


打包到 pythone wheel
  $ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

打包的过程中可能会遇到找不到文件的错误提示：
  cp: cannot stat ‘bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/tensorflow’: No such file or directory
  cp: cannot stat ‘bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/external’: No such file or directory

执行如下命令可以解决:(实际上是因为缺少的文件在 build_pip_package.runfiles/main/ 目录下)
  cp -r bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/__main__/* bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/


安装打包好的 whl 文件
  # 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


5. 运行一个 sample model 测试安装结果

$ cd ~/

$ python -m tensorflow.models.image.mnist.convolutional
Extracting data/train-images-idx3-ubyte.gz
Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz
...etc...

参考

[1]https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md
[2]tensorflow/tensorflow#1066 (comment)
[3]tensorflow/tensorflow#1346