最近在学习google新开源的深度学习框架tensorflow。发现安装它的时候,需要依赖python2.7.X;我之前一直使用的linux是centos。而centos不更新了,里面的自带的python一般都是python2.6以下的。不仅如此,系统里面很多组件又依赖python2.6,所以导致你都不能替换掉它。无奈之下,选择ubuntu了。下面介绍一下使用ubuntu安装tensorflow遇到的一些问题。

1、ubuntu无法用Winscp连接

解决办法:

(1)、采用桥接的方式进行上网(由于是用虚拟机安装的操作系统)

(2)、利用ps -e  |grep ssh  查看是否有sshd进程开启。如果没有则需要安装openssh-server

    安装的方式:sudo apt-get install openssh-server

    启动相应的进程:/etc/init.d/ssh start

(3)、此时需要reboot系统

(4)、由于ubuntu最初root的用户是没有被激活的,所以需要通过修改root用户密码来激活root用户。

完成即可连接了。

2、安装tensorflow。

由于我的ubuntu是最新版的(ubuntu-16.04-desktop-amd64),里面自带的python是2.7.11。因此满足要求。由于tensorflow有三种安装方式,这里采用的是pip安装方式。下面开始安装tensorflow:

(1)首先安装pip

 sudo apt-get install python-pip python-dev

(2)利用pip安装tensorflow

sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl

安装好了后,如下图所示:

aaarticlea/png;base64,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" alt="" width="987" height="312" />

根据上面黄色的提示,叫我升级pip:于是我就按照他的要求升级了,执行:pip install --upgrade pip

3、检验tensorflow是否安装成功

通过下面一段代码来测试tensorflow是否安装成功:

$ 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
>>>
下面是我执行的结果如下图所示:
aaarticlea/png;base64,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" alt="" />

4、安装python-numpy ,python-scipy,python-matplotlib

sudo apt-get install python-numpy

sudo apt-get install python-scipy

sudo apt-get install python-matplotlib

验证是否安装成功:(如下图所示)

aaarticlea/png;base64,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" alt="" />

 

采用ubuntu系统来安装tensorflow的更多相关文章

  1. Linux:Ubuntu系统的安装

    好久没更了,今天就更完这一期的Linux系统吧,这次主要安装的是常用Linux系统的之一:Ubuntu(乌班图)系统,这个系统和CentOS 7的安装步骤也是类似的,(我不采取用虚拟机的方法来安装,当 ...

  2. 在64位Ubuntu系统上安装32位程序包

    在64位Ubuntu系统上安装32位的程序包 $sudo apt-get install package_name:i386 例如: $sudo apt-get install openjdk-7-j ...

  3. 在Debian/Ubuntu系统中安装*.sh与*.bin文件

    在Debian/Ubuntu系统中安装*.sh与*.bin文件的基本方法.一,安装*.sh文件运行命令行至文件目录下,执行:sudo sh *.sh直接运行在命令行中执行:sudo chmod +x ...

  4. ubuntu系统下安装pyspider:搭建pyspider服务器新手教程

    首先感谢“巧克力味腺嘌呤”的博客和Debian 8.1 安装配置 pyspider 爬虫,本人根据他们的教程在ubuntu系统中进行了实际操作,发现有一些不同,也出现了很多错误,因此做此教程,为新手服 ...

  5. ubuntu系统下安装pyspider:安装命令集合。

    本篇内容的前提是你已安装好python 3.5.在ubuntu系统中安装pyspider最大的困难是要依赖组件经常出错,特别是pycurl,但把对应的依赖组件安装好,简单了.下面直接上代码,所有的依赖 ...

  6. ubuntu 16.04 安装Tensorflow

    ubuntu 16.04 安装Tensorflow(CPU) 安装python ubuntu 16.04自带python2.7,因此可以略过这一步 安装pip sudo apt-get install ...

  7. linux/Ubuntu系统上安装mysql数据库(附图详解)

    在前面的文章中,我已经分享了如何在Ubuntu系统中安装以及搭建java开发环境,那么当我们需要跟数据打交道的时候,那么就需要在ubuntu系统中安装一个数据库了,那么废话就不多说了,我们这里主要是分 ...

  8. CentOS和Ubuntu系统下安装 HttpFS (助推Hue部署搭建)

    不多说,直接上干货! 我的集群机器情况是 bigdatamaster(192.168.80.10).bigdataslave1(192.168.80.11)和bigdataslave2(192.168 ...

  9. Ubuntu系统下安装并配置hive-2.1.0

    说在前面的话 默认情况下,Hive元数据保存在内嵌的Derby数据库中,只能允许一个会话连接,只适合简单的测试.实际生产环境中不使用,为了支持多用户会话, 则需要一个独立的元数据库,使用MySQL作为 ...

随机推荐

  1. SQL DDL

    Sql语言被分为四大类:数据查询语言(DQL),数据操纵语言(DML),数据定义语言(DDL),数据控制语言(DCL). 1. 数据查询语言(DQL) 数据查询语言基本结构由select子句,from ...

  2. select distinct

    select distinct select distinct 用于返回表中唯一不同的值. 语法 select distinct 列名称 from 表名称 使用 distinct 关键字 Studen ...

  3. RStudio相关

    1.设置默认目录,tool-Global Options,设定后要重启RStudio才能生效2.Ctrl+l清屏控制台3.↑健.回忆前一条命令,↓健相反4.Ctrl+↑,查找相应前缀的历史记录5.创建 ...

  4. 苹果MacBook Air安装win7

    同事的一台mba,说iOS不习惯,希望装一个win7系统.机器看上去很小巧精致,运行iOS速度飞快.试着点了下鼠标,没反应,翻过来看了下,有个电源开关.拨了一下,细小的指示灯闪了闪,应该加上电了.唉, ...

  5. 生产排产表DL-ZPPR002

    *&---------------------------------------------------------------------* *& Report ZPPR002 * ...

  6. Java8新特性——接口的默认方法和类方法

    Java8新增了接口的默认方法和类方法: 以前,接口里的方法要求全部是抽象方法,java8以后允许在接口里定义默认方法和类方法: 不同的是: 默认方法可以通过实现接口的类实例化的对象来调用,而类方法只 ...

  7. 如何实现textarea中获取动态剩余字数的实现

    工作中遇到一个案例,之前没有写过,今儿啃了半个下午硬是给写出来,灰常又成就感!当然对于js大牛来说这根本不算啥,但是对于我自己的js能力又向前迈出一小步. 案例介绍:我们常见到有的网站有textare ...

  8. JQUERY MOBILE 中文API站 和 官方论坛

    中文API站:http://www.jqmapi.com/api1.2/preview/quickstartquide.html 官方论坛:http://bbs.phonegapcn.com/foru ...

  9. 安装CAD2006装好了为什么不能用,显示系统错误无法启动此程序,因计算机丢失aclst.dll。尝试重新安装该程序以解

    我的电脑,右键 属性——>高级选项卡(win7的是高级系统设置)——>环境变量——>系统变量——>然后新建系统变量 变量名为:AutoCAD 变量值为:c:\program f ...

  10. callback 转换到 promise

    最近项目迭代,从express到koa,面对callback,想偷懒,就想到了Proxy对象 new Proxy(docker,{ get : function (obj,name) { return ...