大数据tensorflowonspark 进行安装和测试。

本文主要是介绍大数据tensorflowonspark 进行安装和测试。，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

1. 概述

大数据tensorflowonspark 进行安装和测试。

2 .环境

3 .安装

1.1 删除系统自带jdk：

# rpm -e --nodeps java-1.7.0-openjdk-1.7.0.99-2.6.5.1.el6.x86_64rpm -e --nodeps java-1.6.0-openjdk-1.6.0.38-1.13.10.4.el6.x86_64rpm -e --nodeps tzdata-java-2016c-1.el6.noarch1.2.3.

1.2 安装jdk

rpm -ivh jdk-8u144-linux-x64.rpm1.

1.3添加java路径

export JAVA_HOME=/usr/java/jdk1.8.0_1441.

1.4 验证java

[root@master opt]# java -versionjava version "1.8.0_144"Java(TM) SE Runtime Environment (build 1.8.0_144-b01)Java HotSpot(TM) 64-Bit Server VM (build 25.144-b01, mixed mode)1.2.3.4.

1.5 Ssh免登陆设置

cd /root/.ssh/
ssh-keygen -t rsacat id_rsa.pub >> authorized_keys 
scp id_rsa.pub authorized_keys root@192.168.2.32:/root/.ssh/scp id_rsa.pub authorized_keys root@192.168.2.31:/root/.ssh/1.2.3.4.5.

1.6安装python2.7和pip

yum install -y gcc 
wget https://www.python.org/ftp/python/2.7.13/Python-2.7.13.tgztar vxf Python-2.7.13.tgzcd Python-2.7.13.tgz
./configure --prefix=/usr/localmake && make install
 [root@master opt]# pythonPython 2.7.13 (default, Aug 24 2017, 16:10:35) [GCC 4.4.7 20120313 (Red Hat 4.4.7-18)] on linux2
Type "help", "copyright", "credits" or "license" for more information.1.2.3.4.5.6.7.8.9.10.11.

1.7 安装pip和setuptools

tar zxvf pip-1.5.4.tar.gztar zxvf setuptools-2.0.tar.gzcd setuptools-2.0
python setup.py installcd pip-1.5.4
python setup.py install1.2.3.4.5.6.

1.8 Hadoop安装和配置

1.8.1 三台机器都要安装Hadoop

tar zxvf hadoop-2.7.3.tar.gz -C /usr/local/cd /usr/local/hadoop-2.7.3/bin[root@master bin]# ./hadoop versionHadoop 2.7.3
Subversion https://git-wip-us.apache.org/repos/asf/hadoop.git -r baa91f7c6bc9cb92be5982de4719c1c8af91ccff
Compiled by root on 2016-08-18T01:41Z
Compiled with protoc 2.5.0
From source with checksum 2e4ce5f957ea4db193bce3734ff29ff4
This command was run using /usr/local/hadoop-2.7.3/share/hadoop/common/hadoop-common-2.7.3.jar1.2.3.4.5.6.7.8.9.

1.8.2 配置hadoop

配置mastervi /usr/local/hadoop-2.7.3/etc/hadoop/core-site.xml 
<configuration>
    <property>
        <name>hadoop.tmp.dir</name>
        <value>file:/usr/local/hadoop/tmp</value>
        <description>Abase for other temporary directories.</description>
    </property>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://master:9001</value>
    </property></configuration>1.2.3.4.5.6.7.8.9.10.11.12.13.

配置slave

[root@slave001 hadoop-2.7.3]# vi ./etc/hadoop/core-site.xml <configuration>
    <property>
        <name>hadoop.tmp.dir</name>
        <value>file:/usr/local/hadoop/tmp</value>
        <description>Abase for other temporary directories.</description>
    </property>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://slave001:9001</value>
    </property></configuration>1.2.3.4.5.6.7.8.9.10.11.12.

[root@slave002 hadoop-2.7.3]# vi ./etc/hadoop/core-site.xml <configuration>
    <property>
        <name>hadoop.tmp.dir</name>
        <value>file:/usr/local/hadoop/tmp</value>
        <description>Abase for other temporary directories.</description>
    </property>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://slave002:9001</value>
    </property></configuration>1.2.3.4.5.6.7.8.9.10.11.12.

1.8.3 配置hdfs

vi /usr/local/hadoop-2.7.3/etc/hadoop/hdfs-site.xml<configuration>
    <property>
        <name>dfs.replication</name>
        <value>1</value>
    </property>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>file:/usr/local/hadoop/tmp/dfs/name</value>
    </property>
    <property>
        <name>dfs.datanode.data.dir</name>
        <value>file:/usr/local/hadoop/tmp/dfs/data</value>
    </property>
    <property>
        <name>dfs.namenode.rpc-address</name>
        <value>master:9001</value>
    </property></configuration>1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.

1.9 安装scala

tar -zxvf scala-2.12.3.tgz -C /usr/local/ 
#修改变量添加scalavi /etc/profileexport SCALA_HOME=/usr/local/scala-2.12.3/export PATH=$PATH:/usr/local/scala-2.12.3/binsource /etc/profile1.2.3.4.5.6.7.

2.0三台机器都要安装spark

tar -zxvf spark-2.1.1-bin-hadoop2.7.tgz -C /usr/local/ 
vi /etc/profileexport JAVA_HOME=/usr/java/jdk1.8.0_144/export SCALA_HOME=/usr/local/scala-2.12.3/export PATH=$PATH:/usr/local/scala-2.12.3/binexport SPARK_HOME=/usr/local/spark-2.1.1-bin-hadoop2.7/export PATH=$PATH:$SPARK_HOME/bin:$SPARK_HOME/sbinsource /etc/profile1.2.3.4.5.6.7.8.9.

 修改spark配置

cd /usr/local/spark-2.1.1-bin-hadoop2.7/

vi ./conf/spark-env.sh.template

export JAVA_HOME=/usr/java/jdk1.8.0_144/

export SCALA_HOME=/usr/local/scala-2.12.3/

#export SPARK_HOME=/usr/local/spark-2.1.1-bin-hadoop2.7/

export SPARK_MASTER_IP=192.168.2.31

export SPARK_WORKER_MEMORY=1g

export HADOOP_CONF_DIR=/usr/local/hadoop-2.7.3/etc/hadoop

export HADOOP_HDFS_HOME=/usr/local/hadoop-2.7.3/

export SPARK_DRIVER_MEMORY=1g

保存退出

mv spark-env.sh.template spark-env.sh

#修改slaves

[root@master conf]# vi slaves.template

192.168.2.32

192.168.2.33

[root@master conf]# mv slaves.template slaves

2.1 三台主机上修改hosts

vi /etc/hosts

192.168.2.31 master

192.168.2.32 slave001

192.168.2.33 slave002

4. 启动服务

[root@master local]# cd hadoop-2.7.3/sbin/

修改配置文件vi /usr/local/hadoop-2.7.3/etc/hadoop/hadoop-env.sh

export JAVA_HOME=/usr/java/jdk1.8.0_144/

./start-all.sh

localhost: Warning: Permanently added 'localhost' (RSA) to the list of known hosts.

localhost: Error: JAVA_HOME is not set and could not be found.

修改配置文件

vi /usr/local/hadoop-2.7.3/etc/hadoop/hadoop-env.sh

export JAVA_HOME=/usr/java/jdk1.8.0_144/

重新启动服务

sbin/start-all.sh

#启动spark

cd /usr/local/spark-2.1.1-bin-hadoop2.7/sbin/

./start-all.sh

4. 安装tensorflow

前提下先安装cudavim /etc/yum.repos.d/linuxtech.testing.repo  
添加内容：[cpp] view plain copy[linuxtech-testing]  name=LinuxTECH Testing  
baseurl=http://pkgrepo.linuxtech.net/el6/testing/  
enabled=0  gpgcheck=1  gpgkey=http://pkgrepo.linuxtech.net/el6/release/RPM-GPG-KEY-LinuxTECH.NET  
 
sudo rpm -i cuda-repo-rhel6-8.0.61-1.x86_64.rpmsudo yum clean allsudo yum install cudarpm -ivh --nodeps dkms-2.1.1.2-1.el6.rf.noarch.rpm 
yum install cuda
yum install epel-release
yum install -y zlib* 
#软连接cudaln -s /usr/local/cuda-8.0 /usr/local/cudaldconfig /usr/local/cuda/lib64
Vi /etc/profileexport LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"export CUDA_HOME=/usr/local/cuda1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.

更新pip
pip install --upgrade pip
下载tensorflow
pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl
安装好后#python>>> import tensorflow
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/site-packages/tensorflow/__init__.py", line 23, in <module>
    from tensorflow.python import *
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/__init__.py", line 45, in <module>
    from tensorflow.python import pywrap_tensorflow
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 28, in <module>
    _pywrap_tensorflow = swig_import_helper()
  File "/usr/local/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: libcudart.so.7.5: cannot open shared object file: No such file or directory1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.

#这是因为lib库不完整yum install openssl -y
yum install openssl-devel -y
yum install gcc gcc-c++ gcc*#更新pip install --upgrade pippip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl1.2.3.4.5.6.

>>> import tensorflow
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/site-packages/tensorflow/__init__.py", line 23, in <module>
    from tensorflow.python import *
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/__init__.py", line 45, in <module>
    from tensorflow.python import pywrap_tensorflow
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 28, in <module>
    _pywrap_tensorflow = swig_import_helper()
  File "/usr/local/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/lib/python2.7/site-packages/tensorflow/python/_pywrap_tensorflow.so)#这是因为tensorflow 使用的glibc版本库太高，系统自带太低了。可以使用。1.2.3.4.5.6.7.8.9.10.11.12.13.14.

# strings /usr/lib64/libstdc++.so.6 | grep GLIBCXX

GLIBCXX_3.4

GLIBCXX_3.4.1

GLIBCXX_3.4.2

GLIBCXX_3.4.3

GLIBCXX_3.4.4

GLIBCXX_3.4.5

GLIBCXX_3.4.6

GLIBCXX_3.4.7

GLIBCXX_3.4.8

GLIBCXX_3.4.9

GLIBCXX_3.4.10

GLIBCXX_3.4.11

GLIBCXX_3.4.12

GLIBCXX_3.4.13

GLIBCXX_FORCE_NEW

GLIBCXX_DEBUG_MESSAGE_LENGTH

放入最新的glibc库，解压出6.0.20

libstdc++.so.6.0.20 覆盖原来的libstdc++.so.6

[root@master 4.4.7]# ln -s /opt/libstdc++.so.6/libstdc++.so.6.0.20 /usr/lib64/libstdc++.so.6

ln: creating symbolic link `/usr/lib64/libstdc++.so.6': File exists

[root@master 4.4.7]# mv /usr/lib64/libstdc++.so.6 /root/

[root@master 4.4.7]# ln -s /opt/libstdc++.so.6/libstdc++.so.6.0.20 /usr/lib64/libstdc++.so.6

[root@master 4.4.7]# strings /usr/lib64/libstdc++.so.6 | grep GLIBCXX

[root@master ~]# strings /usr/lib64/libstdc++.so.6 | grep GLIBCXX

GLIBCXX_3.4

GLIBCXX_3.4.1

GLIBCXX_3.4.2

GLIBCXX_3.4.3

GLIBCXX_3.4.4

GLIBCXX_3.4.5

GLIBCXX_3.4.6

GLIBCXX_3.4.7

GLIBCXX_3.4.8

GLIBCXX_3.4.9

GLIBCXX_3.4.10

GLIBCXX_3.4.11

GLIBCXX_3.4.12

GLIBCXX_3.4.13

GLIBCXX_3.4.14

GLIBCXX_3.4.15

GLIBCXX_3.4.16

GLIBCXX_3.4.17

GLIBCXX_3.4.18

GLIBCXX_3.4.19

GLIBCXX_3.4.20

GLIBCXX_DEBUG_MESSAGE_LENGTH

这个地方特别要注意坑特别多，一定要覆盖原来的。

pip install tensorflowonspark

这样就可以使用了

报错信息：

报错：ImportError: /lib64/libc.so.6: version `GLIBC_2.17' not found (required by /usr/local/lib/python2.7/site-packages/tensorflow/python/_pywrap_tensorflow.so)

tar zxvf glibc-2.17.tar.gz  

mkdir build  

cd build  

../glibc-2.17/configure  --prefix=/usr --disable-profile --enable-add-ons --with-headers=/usr/include --with-binutils=/usr/bin  

make -j4  

make install  

测试验证tensorflow

import tensorflow as tfimport numpy as np
x_data = np.float32(np.random.rand(2, 100)) y_data = np.dot([0.100, 0.200], x_data) + 0.300
 b = tf.Variable(tf.zeros([1]))W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))y = tf.matmul(W, x_data) + b
 
loss = tf.reduce_mean(tf.square(y - y_data))optimizer = tf.train.GradientDescentOptimizer(0.5)train = optimizer.minimize(loss)
 init = tf.initialize_all_variables()
 sess = tf.Session()sess.run(init)
 
 for step in xrange(0, 201):
    sess.run(train)
    if step % 20 == 0:
        print step, sess.run(W), sess.run(b)
 # 得到最佳拟合结果 W: [[0.100  0.200]], b: [0.300]1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.

确保etc/profileexport JAVA_HOME=/usr/java/jdk1.8.0_144/export SCALA_HOME=/usr/local/scala-2.12.3/export PATH=$PATH:/usr/local/scala-2.12.3/binexport SPARK_HOME=/usr/local/spark-2.1.1-bin-hadoop2.7/export PATH=$PATH:$SPARK_HOME/bin:$SPARK_HOME/sbinexport LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"export CUDA_HOME=/usr/local/cudaexport PYTHONPATH=$SPARK_HOME/python:$SPARK_HOME/python/lib/py4j-0.10.4-src.zip:$PYTHONPATH1.2.3.4.5.6.7.8.9.

完成实验。

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