学习Tensorflow，使用源码安装

PC上装好Ubuntu系统，我们一步一步来讲解如何使用源码安装tensorflow?（我的Ubuntu系统是15.10）

• 安装cuda

根据你的系统型号选择相应的cuda版本下载

https://developer.nvidia.com/cuda-downloads

我选的是cuda-repo-ubuntu1504-7-5-local_7.5-18_amd64.deb

右击下载到的文件，Open inTerminal，输入一下命令：

sudo dpkg -icuda-repo-ubuntu1504-7-5-local_7.5-18_amd64.deb

sudo apt-get update

sudo apt-get install cuda

安装到:/usr/local/cuda

下载cudnn，https://developer.nvidia.com/cudnn

我下的版本是cudnn-7.0-linux-x64-v4.0-prod.tgz

同上，输入以下命令：

tar xvzf cudnn-7.0-linux-x64-v4.0-prod.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/lib64/libcudnn*

• 安装tensorflow

下载tensorflow源文件

git clone--recurse-submodules https://github.com/tensorflow/tensorflow

安装Bazel，Bazel是Google开源构建工具，类似于Make的工具，用来编译构建tensorflow

http://bazel.io/docs/install.html

Bazel下载地址：

https://github.com/bazelbuild/bazel/releases（bazel-0.2.2b-installer-linux-x86_64.sh）

sudo apt-get install pkg-config zip g++zlib1g-dev unzip

chmod +x bazel-version-installer-os.sh

./bazel-version-installer-os.sh --user

export PATH="$PATH:$HOME/bin"

安装tensorflow其他的依赖

sudo apt-get install python-numpy swig python-dev python-wheel

配置tensorflow，需要你指定相应文件的安装目录。cd进tensorflow源文件。

sudo ./configure

配置好，会出现以下内容：

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

使用Bazel编译构建

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]

OK!

Test:

cd /home/lei/tensorflow/tensorflow/models/image/mnist

python convolutional.py

Tips:

• configure, gcc version <=4.9
• cudnn version = v4

参考资料：

• Installing from sources
• one problem