Ubuntu 16.04 配置tensorflow with GPU

一、安装CUDA

1.1 安装Nvidia驱动

NVIDIA 驱动程序下载链接

1.2 安装CUDA 8.0

CUDA 8.0下载链接

# 1.Install kernel headers and development packages for the currently running kernel
$ sudo apt-get install linux-headers-$(uname -r)
# 2.Install repository meta-data
$ sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb
# 3.Update the Apt repository cache
$ sudo apt-get update
# 4.Install CUDA
$ sudo apt-get install cuda

1.3 gcc降级

Ubuntu 16.04的gcc编译器是5.4.0，然而CUDA 8.0不支持5.0以上的编译器，因此需要降级，把gcc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++

*可通过以下命令查看版本

# Kernel: 
$ uname -sr #或
$ uname -r
# GCC
$ gcc -v #或
$ gcc --version
# GLIBC
$ ldd --version

1.4 安装cuDNN v5.1

下载cuDNN v5.1（需要注册并填写一个简单的问卷调查）：
https://developer.nvidia.com/cudnn

# 1.Navigate to your <installpath> directory containing cuDNN.
# 2.Unzip the cuDNN package.
$ tar -xzvf cudnn-9.0-linux-x64-v7.tgz
# 3.Copy the following files into the CUDA Toolkit directory.
$ 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*

1.5 配置环境变量

在终端输入：
$ sudo gedit ~/.bashrc #打开.bashrc文件
在此文件末尾加入如下环境变量设置语句：

export CUDA_HOME=/usr/local/cuda-8.0
export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda-8.0/lib64:/usr/local/cuda-8.0/extras/CUPTI/lib64:~/cuDNN_installpath"

使设置立即生效，在终端执行：
$ source ~/.bashrc

二、安装Bazel

sudo add-apt-repository ppa:webupd8team/java
sudo apt-get update
sudo apt-get install oracle-java8-installer
echo “deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8” | sudo tee /etc/apt/sources.list.d/bazel.list
curl https://storage.googleapis.com/bazel-apt/doc/apt-key.pub.gpg | sudo apt-key add -
sudo apt-get update
sudo apt-get install bazel
sudo apt-get upgrade bazel

三、安装TensorFlow(GPU support)

3.1 clone tensorFlow Repository

$ git clone https://github.com/tensorflow/tensorflow

3.2 Configure

$ cd tensorflow  # cd to the top-level directory created
$ ./configure
$ bazel clean

按如下命令进行配置

Please specify the location of python. [Default is /usr/bin/python]:
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 JIT support will be enabled for TensorFlow
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/local/lib/python2.7/dist-packages]
Using python library path: /usr/local/lib/python2.7/dist-packages
Do you wish to build TensorFlow with OpenCL support? [y/N] 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
Please specify which gcc should be used by nvcc 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]: 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 the cuDNN version you want to use. [Leave empty to use system default]: 5
Please specify the location where cuDNN 5 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"]: 6.1
Setting up Cuda include
Setting up Cuda lib
Setting up Cuda bin
Setting up Cuda nvvm
Setting up CUPTI include
Setting up CUPTI lib64
Configuration finished

3.3 Build GPU support Package

$ sudo bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

3.4 Install Package

# for TensorFlow 1.x.x on Linux:
$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.x.x-xxx....whl

3.5 测试

import tensorflow as tf
# Creates a graph.
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)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print(sess.run(c))

4.更简单的方法

sudo pip install tensorflow-gpu

祝好！！！