Anaconda+Tensorflow环境安装与配置

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Anaconda安装

清华大学 TUNA 镜像源选择对应的操作系统与所需的Python版本下载Anaconda安装包。Windows环境下的安装包直接执行.exe文件进行安装即可,Ubuntu环境下在终端执行

$ bash Anaconda2-4.3.1-Linux-x86_64.sh   #Python 2.7版本

或者

$ bash Anaconda3-4.3.1-Linux-x86_64.sh  #Python 3.5 版本

在安装的过程中,会询问安装路径,按回车即可。之后会询问是否将Anaconda安装路径加入到环境变量(.bashrc)中,输入yes,这样以后在终端中输入python即可直接进入Anaconda的Python版本(如果你的系统中之前安装过Python,自行选择yes or no)。安装成功后,会有当前用户根目录下生成一个anaconda2的文件夹,里面就是安装好的内容

查询安装信息

$ conda info

查询当前已经安装的库

$ conda list

安装库(***代表库名称)

$ conda install ***  

更新库

 $ conda update *** 

Anaconda仓库镜像

官方下载更新工具包的速度很慢,所以继续添加清华大学 TUNA提供的Anaconda仓库镜像,在终端或cmd中输入如下命令进行添加

$ conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
$ conda config --set show_channel_urls yes

$ conda install numpy   #测试是否添加成功

之后会自动在用户根目录生成“.condarc”文件,Ubuntu环境下路径为~/.condarc,Windows环境下路径为C:\用户\your_user_name\.condarc

channels:
 - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
 - defaults
show_channel_urls: yes

如果要删除镜像,直接删除“.condarc”文件即可

Tensorflow安装

在终端或cmd中输入以下命令搜索当前可用的tensorflow版本

$ anaconda search -t conda tensorflow

Using Anaconda API: https://api.anaconda.org
Run 'anaconda show <USER/PACKAGE>' to get more details:
Packages:
     Name                      |  Version | Package Types   | Platforms      
     ------------------------- |   ------ | --------------- | ---------------
     HCC/tensorflow            |    1.0.0 | conda           | linux-64       
     HCC/tensorflow-cpucompat  |    1.0.0 | conda           | linux-64       
     HCC/tensorflow-fma        |    1.0.0 | conda           | linux-64       
     SentientPrime/tensorflow  |    0.6.0 | conda           | osx-64         
                                          : TensorFlow helps the tensors flow
     acellera/tensorflow-cuda  |   0.12.1 | conda           | linux-64       
     anaconda/tensorflow       |    1.0.1 | conda           | linux-64       
     anaconda/tensorflow-gpu   |    1.0.1 | conda           | linux-64       
     conda-forge/tensorflow    |    1.0.0 | conda           | linux-64, win-64, osx-64
                                          : TensorFlow helps the tensors flow
     creditx/tensorflow        |    0.9.0 | conda           | linux-64       
                                          : TensorFlow helps the tensors flow
     derickl/tensorflow        |   0.12.1 | conda           | osx-64         
     dhirschfeld/tensorflow    | 0.12.0rc0 | conda           | win-64         
     dseuss/tensorflow         |          | conda           | osx-64         
     guyanhua/tensorflow       |    1.0.0 | conda           | linux-64       
     ijstokes/tensorflow       | 2017.03.03.1349 | conda, ipynb    | linux-64       
     jjh_cio_testing/tensorflow |    1.0.1 | conda           | linux-64       
     jjh_cio_testing/tensorflow-gpu |    1.0.1 | conda           | linux-64       
     jjh_ppc64le/tensorflow    |    1.0.1 | conda           | linux-ppc64le  
     jjh_ppc64le/tensorflow-gpu |    1.0.1 | conda           | linux-ppc64le  
     jjhelmus/tensorflow       | 0.12.0rc0 | conda, pypi     | linux-64, osx-64
                                          : TensorFlow helps the tensors flow
     jjhelmus/tensorflow-gpu   |    1.0.1 | conda           | linux-64       
     kevin-keraudren/tensorflow |    0.9.0 | conda           | linux-64       
     lcls-rhel7/tensorflow     |   0.12.1 | conda           | linux-64       
     marta-sd/tensorflow       |    1.0.1 | conda           | linux-64       
                                          : TensorFlow helps the tensors flow
     memex/tensorflow          |    0.5.0 | conda           | linux-64, osx-64
                                          : TensorFlow helps the tensors flow
     mhworth/tensorflow        |    0.7.1 | conda           | osx-64         
                                          : TensorFlow helps the tensors flow
     miovision/tensorflow      | 0.10.0.gpu | conda           | linux-64, osx-64
     msarahan/tensorflow       | 1.0.0rc2 | conda           | linux-64       
     mutirri/tensorflow        | 0.10.0rc0 | conda           | linux-64       
     mwojcikowski/tensorflow   |    1.0.1 | conda           | linux-64       
     rdonnelly/tensorflow      |    0.9.0 | conda           | linux-64       
     rdonnellyr/r-tensorflow   |    0.4.0 | conda           | osx-64         
     test_org_002/tensorflow   | 0.10.0rc0 | conda           |                
Found 32 packages

选择一个较新的CPU或GPU版本,如jjh_cio_testing/tensorflow-gpu的1.0.1版本,输入如下命令查询安装命令

$ anaconda show jjh_cio_testing/tensorflow-gpu

Using Anaconda API: https://api.anaconda.org
Name:    tensorflow-gpu
Summary: 
Access:  public
Package Types:  conda
Versions:
   + 1.0.1

To install this package with conda run:
     conda install --channel https://conda.anaconda.org/jjh_cio_testing tensorflow-gpu

使用最后一行的提示命令进行安装

$ conda install --channel https://conda.anaconda.org/jjh_cio_testing tensorflow-gpu

Fetching package metadata .............
Solving package specifications: .

Package plan for installation in environment /home/will/anaconda2:

The following packages will be SUPERSEDED by a higher-priority channel:

    tensorflow-gpu: 1.0.1-py27_4 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free --> 1.0.1-py27_4 jjh_cio_testing

Proceed ([y]/n)? 

conda会自动检测安装此版本的Tensorflow所依赖的库,如果你的Anaconda缺少这些依赖库,会提示你安装。因为我之前已经安装过了,所以这里只提示我安装Tensorflow。输入y并回车之后等待安装结束即可

  • 可以选择次高版本的Tensorflow安装,因为最新版本可能清华 TUNA的仓库镜像库没有及时更新,而官方更新连接总是失败,我最开始选择了jjhelmus/tensorflow-gpu的1.0.1版本,其他依赖库清华 TUNA的仓库镜像有资源,而到最后jjhelmus/tensorflow-gpu版本的Tensorflow安装包总是下载不下来,尝试20多次之后换了一个1.0.0的版本,终于顺利安装成功

进入python,输入

import tensorflow as tf

如果没有报错说明安装成功。

参考

  1. Anaconda 镜像使用帮助
  2. tensorflow学习笔记一:安装调试
    原文作者:tensorflow
    原文地址: https://www.cnblogs.com/willnote/p/6746499.html
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