本來安裝tensorflow是一件無比簡單的事，但在我的電腦上卻裝了一個星期。期間遇到各種麻煩事、各種坑，在此記錄一下，方便大家。報錯包括：

• undefined symbol: zgelsd_
• ImportError: cannot import name ‘multiarray’
• whl is not a supported wheel

1，安裝Anaconda

下載地址：https://www.continuum.io/downloads/（我安裝的是linux-64-python3.6)
我一開始是直接在python上裝，但是python3.4(和python3.5）的numpy版本（1.12.0）似乎有問題，tensorflow可以安裝成功，但是執行時呼叫numpy便報錯了。報錯如下：

import numpy Traceback (most recent call last):

File "", line 1, in

File "/usr/local/lib/python3.4/dist-packages/numpy/init.py", line 142, in from . import add_newdocs

File "/usr/local/lib/python3.4/dist-packages/numpy/add_newdocs.py", line 13, in from numpy.lib import add_newdoc

File "/usr/local/lib/python3.4/dist-packages/numpy/lib/init.py"
 
, line 18, in from .polynomial import *

File "/usr/local/lib/python3.4/dist-packages/numpy/lib/polynomial.py", line 20, in from numpy.linalg import eigvals, lstsq, inv

File "/usr/local/lib/python3.4/dist-packages/numpy/linalg/init.py", line 51, in from .linalg import *

File "/usr/local/lib/python3.4/dist-packages/numpy/linalg/linalg.py"
 
, line 29, in from numpy.linalg import lapack_lite, _umath_linalg

ImportError: /usr/local/lib/python3.4/dist-packages/numpy/linalg/lapack_lite.cpython-34m.so: undefined symbol: zgelsd_

在github https://github.com/numpy/numpy/issues/8697上也提問了，但是也沒有解決我的問題 ：undefined symbol: zgelsd_。（期間還出現過ImportError: cannot import name ‘multiarray’ 這種問題，對於linux菜鳥完全不知道怎麼辦）
這是numpy的問題，與tensorflow無關，但是我也遲遲無法解決。無果，轉向直接安裝anaconda，裝好之後，numpy可以正常執行，tensorflow的安裝卻無比曲折。

2，安裝tensorflow(cpu版）

對anaconda命令的熟悉，可以參考http://www.jianshu.com/p/d2e15200ee9b
官方的建議是即時你有gpu，但也可以先裝一個cpu版，建立環境的命令為：conda create -n tensorflow python=3.6
（一定要指定python版本，我一開始沒有寫python=3.6，後面各種失敗）

tensorflow-1.0.0-cp36-cp36m-linux_x86_64.whl is not a supported wheel on this platform.

source activate tensorflow #啟用tensorflow環境
cd /Downloads #切換到whl檔案所在資料夾
pip install --ignore-installed --upgrade tensorflow-1.0.0-py3-none-linux_x86_64.whl #切記，不要用sudo pip，也不要用pip3，然後--ignore-installed --upgrade等引數也不能省略，否則會出錯。

3，安裝tensorflow(gpu版）

source activate tensorflow-gpu #啟用tensorflow環境
cd /Downloads #切換到whl檔案所在資料夾
pip install --ignore-installed --upgrade tensorflow_gpu-1.0.0-cp36-cp36m-linux_x86_64.whl #切記，不要用sudo pip，也不要用pip3，然後--ignore-installed --upgrade等引數也不能省略，否則會出錯。

4，驗證安裝

成功。

(tensorflow)$ python
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
sess.run(hello)

存在的問題，執行時，兩個版本均有warning, NOT error，但是不影響結果，只是執行速度比較慢，據說是因為為了不同框架上的可遷移性，還沒有對cpu進行編譯，他建議你為了更快的速度，可以從編碼編譯，執行速度會更快。參考https://github.com/tensorflow/tensorflow/issues/8037

W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.