全网最详细的基于Ubuntu14.04/16.04 + Anaconda2 / Anaconda3 + Python2.7/3.4/3.5/3.6安装Tensorflow详细步骤（图文）（博主推荐）

　　不多说，直接上干货！

前言

　　建议参照最新的tensorflow安装步骤（Linux，官方网站经常访问不是很稳定，所以给了一个github的地址）：　　        https://github.com/tensorflow/tensorflow/blob/master/tensorflow/docs_src/install/install_linux.md

　　最近，tensorflow网站上给出了新的使用Anaconda配置和安装Tensorflow的步骤，经过测试，在国内可以无障碍的访问。Anaconda 是一个基于python的科学计算包集合，目前支持Python 2.7，3.4，3.5，3.6。

　　注意：在安装过程中如果出现很长的报错，观察错误信息的末尾，如果是网络链接相关，就重新运行一遍语句即可（如出现进度条不动的情况，也可重新运行语句），Anaconda自身约500M，tensorflow所需软件包约几十Ｍ。

　　操作系统：　Ubuntu 14.04   或  Ubuntu16.04

　　这是Github官网给出的安装步骤

https://github.com/tensorflow/tensorflow/blob/master/tensorflow/docs_src/install/install_linux.md

第一步、 安装Anaconda

　　从anaconda官网（https://www.continuum.io/downloads）上下载linux版本的安装文件，运行完成安装。

　　我这里是以Anaconda2-5.0.1-Linux-x86_64.sh为例，Anaconda3一样啦。这个很简单。

deeplearning@deeplearningsinglenode:~/SoftWare$ pwd
/home/deeplearning/SoftWare
deeplearning@deeplearningsinglenode:~/SoftWare$ ll
total
drwxrwxr-x   deeplearning deeplearning       12月   : ./
drwxr-xr-x  deeplearning deeplearning       12月   : ../
-rwxrw-r--   deeplearning deeplearning  12月   : Anaconda2-5.0.-Linux-x86_64.sh*
drwxr-xr-x   deeplearning deeplearning        8月     jdk1..0_60/
drwxrwxr-x  deeplearning deeplearning       12月   : pycharm-2017.3/
deeplearning@deeplearningsinglenode:~/SoftWare$ bash ./Anaconda2-5.0.-Linux-x86_64.sh 

Welcome to Anaconda2 5.0.

In order to continue the installation process, please review the license
agreement.
Please, press ENTER to continue
>>>
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    A collection of both secure hash functions (such as SHA256 and RIPEMD160), and various encryption algorithms (AES, DES, RSA, El
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    A thin Python wrapper around (a subset of) the OpenSSL library.

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Please answer 'yes' or 'no':'
>>> yes

Anaconda2 will now be installed into this location:
/home/deeplearning/anaconda2

  - Press ENTER to confirm the location
  - Press CTRL-C to abort the installation
  - Or specify a different location below

[/home/deeplearning/anaconda2] >>>
PREFIX=/home/deeplearning/anaconda2

installing: python-2.7.-hc2b0042_21 ...
Python 2.7. :: Anaconda, Inc.
installing: ca-certificates-2017.08.-h1d4fec5_0 ...
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installation finished.
Do you wish the installer to prepend the Anaconda2 install location
to PATH in your /home/deeplearning/.bashrc ? [yes|no]
[no] >>>
You may wish to edit your .bashrc to prepend the Anaconda2 install location to PATH:

export PATH=/home/deeplearning/anaconda2/bin:$PATH

Thank you for installing Anaconda2!

　　因为这是一个坑，是安装时最后一步添加环境变量的时候没有选择yes导致运行 conda info 时出错，很好解决，根据错误提示：

　　然后，紧接着去配置Anaconda2的环境变量。怎么做呢？很简单。

　　在命令行输入就可以了。

$ export PATH=/home/deeplearning/anaconda2/bin:$PATH

第二步、建立一个tensorflow的运行环境

# Python 2.7 （选好自己的）
$ conda create -n tensorflow python=2.7  

# Python 3.4  （选好自己的）
$ conda create -n tensorflow python=3.4  

# Python 3.5  （选好自己的）
$ conda create -n tensorflow python=3.5  

　　注意：在这一步，你也许会遇到conda: command not found

　　遇到这个问题的时候，

　　解决方法是：

export PATH="/home/[your_name]/anaconda/bin:$PATH"

　　比如我这里是

export PATH=/home/deeplearning/anaconda2/bin:$PATH

　　但是下一次重启之后，还是会出现这个问题，所以我们要激活下 ~/.bash_profile

. ~/.bash_profile
#或者
source ~/.bash_profile

或者source /etc/profile

那是因为我的环境变量是如下：

#Anaconda2
ANACONDA2_HOME=/home/deeplearning/anaconda2
ANACONDA2_BIN=/home/deeplearning/anaconda2/bin
PATH=$PATH:$ANACONDA2_BIN
export ANACONDA2_HOME ANACONDA2_BIN PATH

　　所以，

deeplearning@deeplearningsinglenode:~$ conda create -n tensorflow python=2.7
Fetching package metadata ...........
Solving package specifications: .

Package plan for installation in environment /home/deeplearning/anaconda2/envs/tensorflow:

The following NEW packages will be INSTALLED:

    ca-certificates: 2017.08.-h1d4fec5_0
    certifi:         2017.11.-py27h71e7faf_0
    libedit:         3.1-heed3624_0
    libffi:          3.2.-hd88cf55_4
    libgcc-ng:       7.2.-h7cc24e2_2
    libstdcxx-ng:    7.2.-h7a57d05_2
    ncurses:         6.0-h9df7e31_2
    openssl:         1.0.2m-h26d622b_1
    pip:             9.0.-py27ha730c48_4
    python:          2.7.-hdd48546_24
    readline:        7.0-ha6073c6_4
    setuptools:      36.5.-py27h68b189e_0
    sqlite:          3.20.-hb898158_2
    tk:              8.6.-hc745277_3
    wheel:           0.30.-py27h2bc6bb2_1
    zlib:            1.2.-ha838bed_2       

Proceed ([y]/n)? y

第三步、在conda环境中安装tensorflow

　　在conda环境中安装tensorflow的好处是可以便捷的管理tensorflow的依赖包。

　　分为两个步骤：激活上一步建立的名为tensorflow的conda环境；用conda或者pip工具安装Tensorflow，我选择的是pip方式。

3.1 pip方式（可以这种方式来安装）

　　pip方式需要首先激活conda环境

deeplearning@deeplearningsinglenode:~$ source activate tensorflow
(tensorflow) deeplearning@deeplearningsinglenode:~$ 

　　然后根据要安装的不同tensorflow版本选择对应的一条环境变量设置export语句（操作系统，Python版本，CPU版本还是CPU+GPU版本）

# Ubuntu/Linux -bit, CPU only, Python 2.7
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0-cp27-none-linux_x86_64.whl  

# Ubuntu/Linux -bit, GPU enabled, Python 2.7
# Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.10.0-cp27-none-linux_x86_64.whl  

# Mac OS X, CPU only, Python 2.7:
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.10.0-py2-none-any.whl  

# Mac OS X, GPU enabled, Python 2.7:
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow-0.10.0-py2-none-any.whl  

# Ubuntu/Linux -bit, CPU only, Python 3.4
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0-cp34-cp34m-linux_x86_64.whl  

# Ubuntu/Linux -bit, GPU enabled, Python 3.4
# Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.10.0-cp34-cp34m-linux_x86_64.whl  

# Ubuntu/Linux -bit, CPU only, Python 3.5
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0-cp35-cp35m-linux_x86_64.whl  

# Ubuntu/Linux -bit, GPU enabled, Python 3.5
# Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.10.0-cp35-cp35m-linux_x86_64.whl  

# Mac OS X, CPU only, Python 3.4 or 3.5:
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.10.0-py3-none-any.whl  

# Mac OS X, GPU enabled, Python 3.4 or 3.5:
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow-0.10.0-py3-none-any.whl  

　　最后根据是python 2还是３版本选择一句进行安装。

# Python
(tensorflow)$ pip install --ignore-installed --upgrade $TF_BINARY_URL  

# Python
(tensorflow)$ pip3 install --ignore-installed --upgrade $TF_BINARY_URL 

 

(tensorflow) deeplearning@deeplearningsinglenode:~$ pip install --ignore-installed --upgrade $TF_BINARY_URL
Collecting tensorflow==0.10. from https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0-cp27-none-linux_x86_64.whl
  Downloading https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0-cp27-none-linux_x86_64.whl (36.6MB)
    % |████                            | .5MB .0MB/s eta ::^[^A^[^AException:
Traceback (most recent call last):
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/site-packages/pip/basecommand.py", line , in main
    status = self.run(options, args)
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/site-packages/pip/commands/install.py", line , in run
    wb.build(autobuilding=True)
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/site-packages/pip/wheel.py", line , in build
    self.requirement_set.prepare_files(self.finder)
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/site-packages/pip/req/req_set.py", line , in prepare_files
    ignore_dependencies=self.ignore_dependencies))
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/site-packages/pip/req/req_set.py", line , in _prepare_file
    session=self.session, hashes=hashes)
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/site-packages/pip/download.py", line , in unpack_url
    hashes=hashes
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/site-packages/pip/download.py", line , in unpack_http_url
    hashes)
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/site-packages/pip/download.py", line , in _download_http_url
    _download_url(resp, link, content_file, hashes)
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/site-packages/pip/download.py", line , in _download_url
    consume(downloaded_chunks)
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/site-packages/pip/utils/__init__.py", line , in consume
    deque(iterator, maxlen=)
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/site-packages/pip/download.py", line , in written_chunks
    for chunk in chunks:
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/site-packages/pip/utils/ui.py", line , in iter
    for x in it:
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/site-packages/pip/download.py", line , in resp_read
    decode_content=False):
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/site-packages/pip/_vendor/requests/packages/urllib3/response.py", line , in stream
    data = self.read(amt=amt, decode_content=decode_content)
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/site-packages/pip/_vendor/requests/packages/urllib3/response.py", line , in read
    flush_decoder = True
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/contextlib.py", line , in __exit__
    self.gen.throw(type, value, traceback)
  File "/home/deeplearning/anaconda2/envs/tensorflow/lib/python2.7/site-packages/pip/_vendor/requests/packages/urllib3/response.py", line , in _error_catcher
    raise ReadTimeoutError(self._pool, None, 'Read timed out.')
ReadTimeoutError: HTTPSConnectionPool(host='storage.googleapis.com', port=): Read timed out.
(tensorflow) deeplearning@deeplearningsinglenode:~$ 

　　

　　注意：这是在安装tensorflow的时候创建tensorflow环境失败，这是个坑，因为有些版本地址失效了。

换其他版本试试。比如如下我现在是2017年12月份，采用conda方式安装tensorflow，版本已经是1.4.0-py27_0

 

 

 

 

 

 

 

 

3.2 conda方式（或者也可以这种方式来安装）

　　conda上面目前有人已经做好了tensorflow的pkg，但是版本不一定最新，且只有CPU版本，不支持GPU。

　　步骤也是首先激活conda环境，然后调用conda install 语句安装.

$ source activate tensorflow
(tensorflow)$  # Your prompt should change  

# Linux/Mac OS X, Python 2.7/3.4/3.5, CPU only:
(tensorflow)$ conda install -c conda-forge tensorflow  

(tensorflow) deeplearning@deeplearningsinglenode:~$ conda install -c conda-forge tensorflow
Fetching package metadata .............
Solving package specifications: .

Package plan for installation in environment /home/deeplearning/anaconda2/envs/tensorflow:

The following NEW packages will be INSTALLED:

    bleach:       1.5.-py27_0          conda-forge
    enum34:       1.1.-py27_1          conda-forge
    funcsigs:     1.0.-py_2            conda-forge
    futures:      3.2.-py27_0          conda-forge
    html5lib:     0.9999999-py27_0      conda-forge
    intel-openmp: 2018.0.-hc7b2577_8
    markdown:     2.6.-py27_0          conda-forge
    mkl:          2018.0.-h19d6760_4
    mock:         2.0.-py27_0          conda-forge
    numpy:        1.13.-py27hbcc08e0_0
    pbr:          3.1.-py27_0          conda-forge
    protobuf:     3.5.-py27_0          conda-forge
    six:          1.11.-py27_1         conda-forge
    tensorboard:  0.4.0rc3-py27_0       conda-forge
    tensorflow:   1.4.-py27_0          conda-forge
    webencodings: 0.5-py27_0            conda-forge
    werkzeug:     0.12.-py_1           conda-forge

Proceed ([y]/n)? y

intel-openmp- % |#################################| Time: :: 478.61 kB/s
mkl-2018.0.-h % |#################################| Time: ::   2.84 MB/s
enum34-1.1.-p % |#################################| Time: ::  32.00 kB/s
funcsigs-1.0. % |#################################| Time: ::  38.56 kB/s
futures-3.2.- % |#################################| Time: ::  74.10 kB/s
markdown-2.6. % |#################################| Time: ::  73.17 kB/s
six-1.11.-py2 % |#################################| Time: ::  62.19 kB/s
webencodings- % |#################################| Time: ::  25.65 kB/s
werkzeug-0.12. % |#################################| Time: ::  17.24 kB/s
html5lib-0.999 % |#################################| Time: ::  39.10 kB/s
bleach-1.5.-p % |#################################| Time: ::  66.33 kB/s
protobuf-3.5. % |#################################| Time: :: 128.41 kB/s
tensorboard-. % |#################################| Time: ::  77.40 kB/s
pbr-3.1.-py27 % |#################################| Time: ::  41.01 kB/s
mock-2.0.-py2 % |#################################| Time: ::  30.23 kB/s
tensorflow-1.4 % |#################################| Time: :: 153.09 kB/s
(tensorflow) deeplearning@deeplearningsinglenode:~$
(tensorflow) deeplearning@deeplearningsinglenode:~$ 

　　上面的步骤完成后，从conda环境中退出:

(tensorflow)$ source deactivate  

 

 

 

 

 

第四步、测试安装是否成功

　　首先激活 tensorflow 环境，然后进入 python，最后导入 tensorflow 库。如果导入成功则表明安装成功。

(tensorflow) deeplearning@deeplearningsinglenode:~$ source deactivate
deeplearning@deeplearningsinglenode:~$
deeplearning@deeplearningsinglenode:~$
deeplearning@deeplearningsinglenode:~$ source activate tensorflow
(tensorflow) deeplearning@deeplearningsinglenode:~$
(tensorflow) deeplearning@deeplearningsinglenode:~$ python
Python 2.7. |Anaconda, Inc.| (default, Nov  , ::)
[GCC 7.2.] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant('Hi,TensorFlow!')
>>> sess = tf.Session()
-- ::08.790862: I tensorflow/core/platform/cpu_feature_guard.cc:] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4. SSE4. AVX AVX2 FMA
>>> print sess.run(hello)
Hi,TensorFlow!
>>> 

第五步、需要使用 TensorFlow 的时候必须重新激活

　　当使用完毕后，关闭 tensorflow 环境。

Use exit() or Ctrl-D (i.e. EOF) to exit
>>> exit()
(tensorflow) deeplearning@deeplearningsinglenode:~$
(tensorflow) deeplearning@deeplearningsinglenode:~$
(tensorflow) deeplearning@deeplearningsinglenode:~$
(tensorflow) deeplearning@deeplearningsinglenode:~$ source deactivate
deeplearning@deeplearningsinglenode:~$ 

　　然后你的终端提示符就会变会原的样子。

　　当你需要再次使用的时候就必须再次激活 tensorflow 环境。

source activate tensorflow

　　..........

　　......

　　关闭 tensorflow 环境，并重新激活

第五步、 Finally

　　至此，你已经拥有了一个可以玩耍机器学习的 tensorflow 环境，好好玩耍吧：）

　　你可以参照官方文档快速的运行一个手写数字识别的示例。友情提示：仅 CPU 版本你需要有足够的耐心。。。。。。

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