引言： Tensorflow大名鼎鼎，这里不再赘述其为何物。这里讲描述在安装python包的时候碰到的“No matching distribution found for tensorflow”，其原因以及如何解决。
简单的安装tensorflow
这里安装的tensorflow的cpu版本，gpu版本可以自行搜索安装指南，或者参考如下指令：
代码语言：javascript复制pip3 install tensorflow #cpu这里使用的python 3.6.3版本。
代码语言：javascript复制pip3 install tensorflow-gpu这里是gpu的版本。
window的环境
window 7.
问题描述：
代码语言：javascript复制pip3 install tensorflow如此简单的指令，应该不会出错吧，结果得到如下错误信息：
Collecting tensorflow Could not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow
为什么没有找到tensorflow呢？那我们自行找找看吧？
代码语言：javascript复制pip3 search tensorflow具体的输出信息如下：
………………….. tensorbase (0.3) – Minimalistic TensorFlow Framework tensorbayes (0.3.0) – Deep Variational Inference in TensorFlow tensorflow-tensorboard (0.4.0rc3) – TensorBoard lets you watch Tensors Flow tensorboard_logger (0.0.4) – Log TensorBoard events without Tensorflow tensorboardX (0.8) – TensorBoardX lets you watch Tensors Flow without Tensorflow tensorbuilder (0.3.6) – A light wrapper over TensorFlow that enables you to easily create complex deep neural networks using the Builder Pattern through a functional fluent immutable API tensorflow-utils (0.1.0) – Classes and methods to make using TensorFlow easier tensorflow-transform (0.4.0) – A library for data preprocessing with TensorFlow tensorflow (1.5.0rc0) – TensorFlow helps the tensors flow tensorflow_forward_ad (0.3.3) – TensorFlow forward-mode automatic differentiation tensorflow_hmm (0.4.1) – Tensorflow and numpy implementations of the HMM viterbi and forward/backward algorithms tensorflow_nlp (0.0.1) – Deep Learning NLP Tasks implemented on Tensorflow tensorflowonspark (1.1.0) – Deep learning with TensorFlow on Apache Spark clusters tensorflowservingclient (0.5.1.post2) – Prebuilt tensorflow serving client tensorforce (0.3.4) – Reinforcement learning for TensorFlow tensorfunk (0.0.0) – tensorflow model converter to create tensorflow-independent prediction functions. tensorfuse (0.0.1) – Common interface for Theano, CGT, and TensorFlow tensorgraph (3.5.8) – A high level tensorflow library for building deep learning models tensorhive (0.1.1) – Lightweight computing resource management tool for executing distributed TensorFlow programs tensorlm (0.3) – TensorFlow wrapper for deep neural text generation on character or word level with RNNs / LSTMs TensorMol (0.1) – TensorFlow Molecules = TensorMol tensorpack (0.8.0) – Neural Network Toolbox on TensorFlow tensorpy (1.1.0) – Easy Image Classification with TensorFlow! tensorrec (0.1) – A TensorFlow recommendation algorithm and framework in Python. tensorspark (1.0.6) – Tensorflow on Spark, a scalable system for high-performance machine learning tensorvision (0.1.dev1) – A library to build and train neural networks in with TensorFlow for Computer Vision TFANN (1.0.1) – A neural network module containing implementations of MLP, and CNN networks in TensorFlow. TFBOYS (0.0.1) – TensorFlow BOYS tfcf (0.0.0) – A tensorflow-based recommender system. tfcoreml (0.1.0) – Tensorflow to Core ML converter tfdebugger (0.1.1) – TensorFlow Debugger tfdeploy (0.4.2) – Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy. tfgraph (0.2) – Python’s Tensorflow Graph Library tfgraphviz (0.0.6) – A visualization tool to show a graph like TensorFlow and TensorBoard …………………………………………
悲伤的我如此难以自抑，明明可以找到的，怎么却无法安装嗯？我需要自行好好找找明明是谁？ ：-）
问题分析
二话不说，直接上官网上查查看，虽然官网离我朝远隔万里，需要跋山涉水之后方可达到。翻过拿到看不见的墙之后，重要可以看到官方信息了。
官方路标如下： https://www.tensorflow.org/install/install_windows
其中所提安装步骤非常简洁，如此简洁的步骤，怎么可能出错？ 于是重新梳理了一下，难道是Python或者pip3本身的问题吗？
check pip3
代码语言：javascript复制pip –version发现其为最新版本：
代码语言：javascript复制pip 9.0.1 from d:program files (x86)pythonlibsite-packages (python 3.6)那Python呢？ 官方文档中提到如下：
If one of the following versions of Python is not installed on your machine, install it now: * Python 3.5.x 64-bit from python.org * Python 3.6.x 64-bit from python.org
难道我安装的python是假python不成？ 估计有可能吧，难道是64bit的问题？
检查python的版本
代码语言：javascript复制python -v得到了python的完整信息：
…………………………………… D:Program Files (x86)pythonlib__pycache__sysconfig.cpython-36.pyc matches D:Program Files (x86)pythonlibsysconfig.py code object from ‘D:\Program Files (x86)\python\lib\__pycache__\sysconfig.cpython-36.pyc’ import ‘sysconfig’ # <_frozen_importlib_external.SourceFileLoader object at 0x006A1230 D:Program Files (x86)pythonlib__pycache___bootlocale.cpython-36.pyc matches D:Program Files (x86)pythonlib_bootlocale.py code object from ‘D:\Program Files (x86)\python\lib\__pycache__\_bootlocale.cpython-36.pyc’ import ‘_locale’ # <class ‘_frozen_importlib.BuiltinImporter’ import ‘_bootlocale’ # <_frozen_importlib_external.SourceFileLoader object at 0x007911D0 D:Program Files (x86)pythonlibencodings__pycache__gbk.cpython-36.pyc matches D:Program Files (x86)pythonlibencodingsgbk.py code object from ‘D:\Program Files (x86)\python\lib\encodings\__pycache__\gbk.cpython-36.pyc’ import ‘_codecs_cn’ # <class ‘_frozen_importlib.BuiltinImporter’ import ‘_multibytecodec’ # <class ‘_frozen_importlib.BuiltinImporter’ import ‘encodings.gbk’ # <_frozen_importlib_external.SourceFileLoader object at 0x00791490 import ‘site’ # <_frozen_importlib_external.SourceFileLoader object at 0x004F73D0 Python 3.6.3 (v3.6.3:2c5fed8, Oct 3 2017, 17:26:49) [MSC v.1900 32 bit (Intel)] on win32 Type “help”, “copyright”, “credits” or “license” for more information. import ‘atexit’ # <class ‘_frozen_importlib.BuiltinImporter’
其中关于python的关键信息：
代码语言：javascript复制Python 3.6.3 (v3.6.3:2c5fed8, Oct 3 2017, 17:26:49) [MSC v.1900 32 bit (Intel)] on win32“32bit” !!! 一口老血喷出，众里寻他千百度，蓦然回首bug正在这灯火阑珊处。原来是python版本的问题导致的。
修复问题
重新下载一个64bit的python版本，之后重新操作就可以了。
代码语言：javascript复制python -v查看其中的关键信息：
代码语言：javascript复制Python 3.6.4 (v3.6.4:d48eceb, Dec 19 2017, 06:54:40) [MSC v.1900 64 bit (AMD64)] on win32确认是64位，没有问题。
然后直接安装tensorflow:
代码语言：javascript复制pip3 install tensorflow安装过程如下：
C:windowssystem32 pip3 install tensorflow Collecting tensorflow Downloading tensorflow-1.4.0-cp36-cp36m-win_amd64.whl (28.3MB) 100% |████████████████████████████████| 28.3MB 39kB/s Collecting enum34 =1.1.6 (from tensorflow) Downloading enum34-1.1.6-py3-none-any.whl Requirement already satisfied: wheel =0.26 in d:program files (x86)pythonlibsite-packages (from tensorflow) Collecting protobuf =3.3.0 (from tensorflow) Downloading protobuf-3.5.1-py2.py3-none-any.whl (388kB) 100% |████████████████████████████████| 389kB 593kB/s Collecting tensorflow-tensorboard<0.5.0, =0.4.0rc1 (from tensorflow) Downloading tensorflow_tensorboard-0.4.0rc3-py3-none-any.whl (1.7MB) 100% |████████████████████████████████| 1.7MB 182kB/s Requirement already satisfied: six =1.10.0 in d:program files (x86)pythonlibsite-packages (from tensorflow) Collecting numpy =1.12.1 (from tensorflow) Downloading numpy-1.13.3-cp36-none-win_amd64.whl (13.1MB) 100% |████████████████████████████████| 13.1MB 81kB/s Requirement already satisfied: setuptools in d:program files (x86)pythonlibsite-packages (from protobuf =3.3.0- tensorflow) Collecting html5lib==0.9999999 (from tensorflow-tensorboard<0.5.0, =0.4.0rc1- tensorflow) Downloading html5lib-0.9999999.tar.gz (889kB) 100% |████████████████████████████████| 890kB 504kB/s Collecting bleach==1.5.0 (from tensorflow-tensorboard<0.5.0, =0.4.0rc1- tensorflow) Downloading bleach-1.5.0-py2.py3-none-any.whl Requirement already satisfied: werkzeug =0.11.10 in d:program files (x86)pythonlibsite-packages (from tensorflow-tensorboard<0.5.0, =0.4.0rc1- tensorflow) Collecting markdown =2.6.8 (from tensorflow-tensorboard<0.5.0, =0.4.0rc1- tensorflow) Downloading Markdown-2.6.11-py2.py3-none-any.whl (78kB) 100% |████████████████████████████████| 81kB 583kB/s Building wheels for collected packages: html5lib Running setup.py bdist_wheel for html5lib … done Stored in directory: C:Userschenjunfeng1AppDataLocalpipCachewheels6f856c56b8e1292c6214c4eb73b9dda50f53e8e977bf65989373c962 Successfully built html5lib Installing collected packages: enum34, protobuf, html5lib, numpy, bleach, markdown, tensorflow-tensorboard, tensorflow Successfully installed bleach-1.5.0 enum34-1.1.6 html5lib-0.9999999 markdown-2.6.11 numpy-1.13.3 protobuf-3.5.1 tensorflow-1.4.0 tensorflow-tensorboard-0.4.0rc3
然后大家就可以愉快地写代码了.
总结
问题总在认为不可能的地方发生。如果存在问题，则一定会有原因存在。见或者不见，它都在那里。
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