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
>>>
===================================
Anaconda End User License Agreement
=================================== Copyright , Anaconda, Inc. All rights reserved under the -clause BSD License: Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditio
ns are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer
in the documentation and/or other materials provided with the distribution.
* Neither the name of Continuum Analytics, Inc. (dba Anaconda, Inc.) ("Continuum") nor the names of its contributors may be used
to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT N
OT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CON
TINUUM BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY TH
EORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Notice of Third Party Software Licenses
======================================= Anaconda contains open source software packages from third parties. These are available on an "as is" basis and subject to their in
dividual license agreements. These licenses are available in Anaconda or at https://docs.anaconda.com/anaconda/packages/pkg-docs .
Any binary packages of these third party tools you obtain via Anaconda are subject to their individual licenses as well as the Anac
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ries"). You are specifically authorized to use the MKL binaries with your installation of Anaconda. You are also authorized to redi
stribute the MKL binaries with Anaconda or in the conda package that contains them. Use and redistribution of the MKL binaries are
subject to the licensing terms located at https://software.intel.com/en-us/license/intel-simplified-software-license. If needed, in
structions for removing the MKL binaries after installation of Anaconda are available at http://www.anaconda.com. Anaconda also contains cuDNN software binaries from NVIDIA Corporation ("cuDNN binaries"). You are specifically authorized to use t
he cuDNN binaries with your installation of Anaconda. You are also authorized to redistribute the cuDNN binaries with an Anaconda p
ackage that contains them. If needed, instructions for removing the cuDNN binaries after installation of Anaconda are available at
http://www.anaconda.com. Cryptography Notice
=================== This distribution includes cryptographic software. The country in which you currently reside may have restrictions on the import, p
ossession, use, and/or re-export to another country, of encryption software. BEFORE using any encryption software, please check you
r country's laws, regulations and policies concerning the import, possession, or use, and re-export of encryption software, to see
if this is permitted. See the Wassenaar Arrangement <http://www.wassenaar.org/> for more information. Continuum has self-classified this software as Export Commodity Control Number (ECCN) 5D002.C., which includes information securit
y software using or performing cryptographic functions with asymmetric algorithms. The form and manner of this distribution makes i
t eligible for export under the License Exception ENC Technology Software Unrestricted (TSU) exception (see the BIS Export Administ
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The OpenSSL Project is a collaborative effort to develop a robust, commercial-grade, full-featured, and Open Source toolkit imp
lementing the Transport Layer Security (TLS) and Secure Sockets Layer (SSL) protocols as well as a full-strength general purpose cr
yptography library. pycrypto
A collection of both secure hash functions (such as SHA256 and RIPEMD160), and various encryption algorithms (AES, DES, RSA, El
Gamal, etc.). pyopenssl
A thin Python wrapper around (a subset of) the OpenSSL library. kerberos (krb5, non-Windows platforms)
A network authentication protocol designed to provide strong authentication for client/server applications by using secret-key
cryptography. cryptography
A Python library which exposes cryptographic recipes and primitives.
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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