centos6.5环境下tensoflow0.8.0的安装测试

安装环境

阿里云服务器
centos6.5 X64

参考教程

Pip安装

Pip 是一个 Python 的软件包安装与管理工具.

在安装 TensorFlow 过程中要涉及安装或升级的包详见 列表

首先安装 pip (或 Python3 的 pip3 ):
$ sudo yum install python-pip python-dev
注释：
apt是debian(ubuntu)的包管理器
Centos用yum来管理软件

注：tensorflow0.8只支持python2.7以上和python3，两者选一个安装即可

安装python3.4

系统预装的是python2.6.6

参考教程中只能用2.7和3.4

需要升级到3.4

安装相关包

# yum install zlib-devel bzip2-devel openssl-devel ncurses-devel sqlite-devel readline-devel tk-devel gcc make

下载 Python 3.4 源码包

# wget http://mirrors.sohu.com/python/3.4.1/Python-3.4.1.tar.xz

安装 Python 3.4

# tar xf Python-3.4.1.tar -C /usr/local/src/# cd /usr/local/src/Python-3.4.1/# ./configure --prefix=/usr/local/python34# make # make install

添加 python3.4 相关命令到环境变量

添加 python3.4 到环境变量，编辑 ~/.bash_profile，将：

PATH=PATH:HOME/bin
改为：
PATH=PATH:HOME/bin:/usr/local/python34/bin

使 python3.4 环境变量生效：

# . ~/.bash_profile

更新版本

此时已完成新版本的安装，但由于老版本还在系统中，所以需要将原来/usr/bin/python链接改为新的连接：

a.先修改老的连接，执行：mv /usr/bin/python /usr/bin/python_bak。

b.再建立新连接： ln -s /usr/local/python34/bin/python3.4 /usr/bin/python

查看版本

#python

安装tensorflow

# Ubuntu/Linux 64-bit, CPU only, Python 3.4:# pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp34-cp34m-linux_x86_64.whl# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4. Requires CUDA toolkit 7.5 and CuDNN v4.# For other versions, see "Install from sources" below.# pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.8.0-cp34-cp34m-linux_x86_64.whl

测试

运行 TensorFlow

打开一个 python 终端:

# python>>> import tensorflow as tf>>> hello = tf.constant('Hello, TensorFlow!')>>> sess = tf.Session()>>> print (sess.run(hello))Hello, TensorFlow!>>> a = tf.constant(10)>>> b = tf.constant(32)>>> print (sess.run(a+b))42>>>

需要说明的是，在centos系统下，无法直接换行，我们可以用touch命令新建.py文件，然后用python命令执行此文件

#python opt/hello.py

python2.7环境

安装python2.7

yum中最新的也是Python 2.6.6，只能下载Python 2.7.9的源代码自己编译安装。

操作步骤如下：

安装devtoolset

yum groupinstall "Development tools"

安装编译Python需要的包包

yum install zlib-develyum install bzip2-develyum install openssl-develyum install ncurses-develyum install sqlite-devel

下载并解压Python 2.7.9的源代码

cd /optwget --no-check-certificate https://www.python.org/ftp/python/2.7.9/Python-2.7.9.tar.xztar xf Python-2.7.9.tar.xzcd Python-2.7.9

编译与安装Python 2.7.9

./configure --prefix=/usr/localmake && make altinstall

将python命令指向Python 2.7.9

ln -s /usr/local/bin/python2.7 /usr/local/bin/python

检查Python版本

安装python2.7的pip

wget --no-check-certificate https://pypi.python.org/packages/source/s/setuptools/setuptools-1.4.2.tar.gztar -vxf setuptools-1.4.2.tar.gz cd setuptools-1.4.2python2.7 setup.py installeasy_install-2.7 pip

安装tensorflow

# Ubuntu/Linux 64-bit, CPU only, Python 2.7:$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7. Requires CUDA toolkit 7.5 and CuDNN v4.# For other versions, see "Install from sources" below.$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl

测试

# python>>> import tensorflow as tf>>> hello = tf.constant('Hello, TensorFlow!')>>> sess = tf.Session()>>> print sess.run(hello)Hello, TensorFlow!>>> a = tf.constant(10)>>> b = tf.constant(32)>>> print sess.run(a+b)42>>>

注释：python2.7和python3.X的语法结构不同

训练你的第一个 TensorFlow 神经网络模型

我的python3.4中tensorflow目录
/usr/local/python34/lib/python3.4/site-packages/tensorflow
python2.7.9目录
/usr/local/lib/python2.7/site-packages/tensorflow

从源代码树的根路径执行:

# cd tensorflow/models/image/mnist# python convolutional.pySuccesfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.Succesfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.Succesfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.Succesfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.Extracting data/train-images-idx3-ubyte.gzExtracting data/train-labels-idx1-ubyte.gzExtracting data/t10k-images-idx3-ubyte.gzExtracting data/t10k-labels-idx1-ubyte.gzInitialized!Epoch 0.00Minibatch loss: 12.054, learning rate: 0.010000Minibatch error: 90.6%Validation error: 84.6%Epoch 0.12Minibatch loss: 3.285, learning rate: 0.010000Minibatch error: 6.2%Validation error: 7.0%......

我的运行界面

常见问题

测试出错

>>> import tensorflow as tfTraceback (most recent call last):  File "<stdin>", line 1, in <module>  File "/usr/local/python27/lib/python2.7/site-packages/tensorflow/__init__.py", line 23, in <module>from tensorflow.python import *  File "/usr/local/python27/lib/python2.7/site-packages/tensorflow/python/__init__.py", line 45, in <module>from tensorflow.python import pywrap_tensorflow  File "/usr/local/python27/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 28, in <module>_pywrap_tensorflow = swig_import_helper()  File "/usr/local/python27/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 24, in swig_import_helper_mod = imp.load_module('_pywrap_tensorflow', fp, pathname, description)ImportError: /lib64/libc.so.6: version `GLIBC_2.15' not found (required by /usr/local/python27/lib/python2.7/site-packages/tensorflow/python/_pywrap_tensorflow.so)

解决方案参考http://blog.csdn.net/levy_cui/article/details/51251095

直接升级到2.17

wget http://ftp.gnu.org/pub/gnu/glibc/glibc-2.17.tar.xzxz -d glibc-2.17.tar.xztar -xvf glibc-2.17.tarcd glibc-2.17mkdir buildcd build../configure --prefix=/usr --disable-profile --enable-add-ons --with-headers=/usr/include --with-binutils=/usr/bin  make && make install

需要等大概10分钟。

输入strings /lib64/libc.so.6|grep GLIBC发现已经更新 GLIBC_2.2.5GLIBC_2.2.6GLIBC_2.3GLIBC_2.3.2GLIBC_2.3.3GLIBC_2.3.4GLIBC_2.4GLIBC_2.5GLIBC_2.6GLIBC_2.7GLIBC_2.8GLIBC_2.9GLIBC_2.10GLIBC_2.11GLIBC_2.12GLIBC_2.13GLIBC_2.14GLIBC_2.15GLIBC_2.16GLIBC_2.17GLIBC_PRIVATE

再次运行，还是报错

>>> import tensorflow as tfTraceback (most recent call last):  File "<stdin>", line 1, in <module>  File "/usr/local/python27/lib/python2.7/site-packages/tensorflow/__init__.py", line 23, in <module>from tensorflow.python import *  File "/usr/local/python27/lib/python2.7/site-packages/tensorflow/python/__init__.py", line 45, in <module>from tensorflow.python import pywrap_tensorflow  File "/usr/local/python27/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 28, in <module>_pywrap_tensorflow = swig_import_helper()  File "/usr/local/python27/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 24, in swig_import_helper_mod = imp.load_module('_pywrap_tensorflow', fp, pathname, description)ImportError: /usr/lib64/libstdc++.so.6: version `GLIBCXX_3.4.14' not found (required by /usr/local/python27/lib/python2.7/site-packages/tensorflow/python/_pywrap_tensorflow.so)

再次直接升级到3.4.20

从网上下载libstdc++.so.6.0.20

http://ftp.de.debian.org/debian/pool/main/g/gcc-4.8/或者http://download.csdn.net/detail/pomelover/7524227

放到/usr/lib64/下
#chmod +x libstdc++.so.6.0.20
#rm libstdc++.so.6
#ln -s libstdc++.so.6.0.20 libstdc++.so.6
#strings /usr/lib64/libstdc++.so.6 | grep GLIBCXX

# strings /usr/lib64/libstdc++.so.6 | grep GLIBCXXGLIBCXX_3.4GLIBCXX_3.4.1GLIBCXX_3.4.2GLIBCXX_3.4.3GLIBCXX_3.4.4GLIBCXX_3.4.5GLIBCXX_3.4.6GLIBCXX_3.4.7GLIBCXX_3.4.8GLIBCXX_3.4.9GLIBCXX_3.4.10GLIBCXX_3.4.11GLIBCXX_3.4.12GLIBCXX_3.4.13GLIBCXX_3.4.14GLIBCXX_3.4.15GLIBCXX_3.4.16GLIBCXX_3.4.17GLIBCXX_3.4.18GLIBCXX_3.4.19GLIBCXX_3.4.20GLIBCXX_FORCE_NEWGLIBCXX_DEBUG_MESSAGE_LENGTH

终于成功了

GPU 相关问题

如果在尝试运行一个 TensorFlow 程序时出现以下错误:

ImportError: libcudart.so.7.0: cannot open shared object file: No such file or directory

请确认你正确安装了 GPU 支持, 参见 相关章节.

在 Linux 上如果出现错误:

... "__add__", "__radd__", ^SyntaxError: invalid syntax

解决方案: 确认正在使用的 Python 版本为 Python 2.7.

参考教程：
安装教程：
https://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/get_started/os_setup.md
错误解决方案：
http://blog.csdn.net/levy_cui/article/details/51251095