前言：
对于深度学习来说，各种框架torch,caffe,keras,mxnet,tensorflow,pandapanda环境要求各一，如果我们在一台服务器上部署了较多的这样的框架，那么各种莫名的冲突
会一直伴随着你，吃过很多次亏之后，慢慢的接触了Anaconda，真的是很爽的一个功能，来管理环境配置。我们进行tensorflow安装的时候，还是使用Anaconda，鉴于国内墙太高
，我们使用了Tsinghua的镜像文件，清华大学的Anaconda介绍地址见：https://mirror.tuna.tsinghua.edu.cn/help/anaconda/
这里记录下linux的安装方式：
所使用的系统: ubuntu16.10 安装步骤 1： 先登录到这个页面：https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/ 2. 下载： wget -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda2-2.4.1-Linux-x86_64.sh 3. 运行： bash Anaconda2-2.i.1-Linux-x86_64.sh [中间会有几个询问，全部设置yes或者y] 4. 设置镜像仓库： TUNA 还提供了 Anaconda 仓库的镜像，运行以下命令: conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ conda config --set show_channel_urls yes 即可添加 Anaconda Python 免费仓库。 运行 conda install numpy 测试一下吧。 5. 安装tensorflow: 5.1 查询conda下的tensorflow可以利用的镜像： anaconda search -t conda tensorflow
大概会出现这些信息：
gxjun@gxjun:~$ 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 | 1.1.0 | conda | linux-ppc64le, linux-64, osx-64, win-64 anaconda/tensorflow-gpu | 1.1.0 | conda | linux-ppc64le, linux-64, win-64 conda-forge/r-tensorflow | 0.7 | conda | linux-64, osx-64, win-64 conda-forge/tensorflow | 1.2.0 | 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 | 1.1.0 | conda | osx-64 dhirschfeld/tensorflow | 1.2.0 | conda | win-64 : Computation using data flow graphs for scalable machine learning 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 | 1.2.1 | conda | linux-64 : TensorFlow is a machine learning library jjh_cio_testing/tensorflow-gpu | 1.2.1 | conda | linux-64 : TensorFlow is a machine learning library jjh_ppc64le/tensorflow | 1.2.1 | conda | linux-ppc64le : TensorFlow is a machine learning library jjh_ppc64le/tensorflow-gpu | 1.2.1 | conda | linux-ppc64le : TensorFlow is a machine learning library 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 | 1.1.0 | conda | linux-64 marta-sd/tensorflow | 1.2.0 | conda | linux-64 marta-sd/tensorflow-gpu | 1.2.0 | conda | linux-64 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 nehaljwani/tensorflow | 1.1.0 | conda | win-64, osx-64 : TensorFlow is a machine learning library nehaljwani/tensorflow-gpu | 1.1.0 | conda | win-64 : TensorFlow is a machine learning library 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 36 packages
我们选择其中的一个进行安装之前，先查询这个分支的URL路径：
gxjun@gxjun:~$ anaconda show nehaljwani/tensorflow-gpu Using Anaconda API: https://api.anaconda.org Name: tensorflow-gpu Summary: TensorFlow is a machine learning library Access: public Package Types: conda Versions: + 1.1.0 To install this package with conda run: conda install --channel https://conda.anaconda.org/nehaljwani tensorflow-gpu
5.2 安装
conda install --channel https://conda.anaconda.org/nehaljwani tensorflow-gpu
5.3 检测是否安装成功：
在控制端输入： python -> 进入python编辑环境 import tensorflow as tf
如果没有报错，则说明幸运的安装成功了~
对于失败的情况，我这里给出最容易出现的：
>>> import tensorflow as tf Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/gxjun/anaconda2/lib/python2.7/site-packages/tensorflow/__init__.py", line 24, in <module> from tensorflow.python import * File "/home/gxjun/anaconda2/lib/python2.7/site-packages/tensorflow/python/__init__.py", line 49, in <module> from tensorflow.python import pywrap_tensorflow File "/home/gxjun/anaconda2/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 52, in <module> raise ImportError(msg) ImportError: Traceback (most recent call last): File "/home/gxjun/anaconda2/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 41, in <module> from tensorflow.python.pywrap_tensorflow_internal import * File "/home/gxjun/anaconda2/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in <module> _pywrap_tensorflow_internal = swig_import_helper() File "/home/gxjun/anaconda2/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description) ImportError: libcusolver.so.7.5: cannot open shared object file: No such file or directory
这种问题，是说我们没有找到这个动态库，或者干脆就没有这个动态库.
解决方法：
先问是不是： 输入这条命令查查看有没有： locate libcusolver.so
gxjun@gxjun:~$ locate libcusolver.so /usr/lib/x86_64-linux-gnu/libcusolver.so /usr/lib/x86_64-linux-gnu/libcusolver.so.8.0 /usr/lib/x86_64-linux-gnu/libcusolver.so.8.0.44 /usr/lib/x86_64-linux-gnu/stubs/libcusolver.so /usr/local/cuda-8.0/doc/man/man7/libcusolver.so.7 /usr/local/cuda-8.0/targets/x86_64-linux/lib/libcusolver.so /usr/local/cuda-8.0/targets/x86_64-linux/lib/libcusolver.so.8.0 /usr/local/cuda-8.0/targets/x86_64-linux/lib/libcusolver.so.8.0.61 /usr/local/cuda-8.0/targets/x86_64-linux/lib/stubs/libcusolver.so /usr/share/man/man7/libcusolver.so.7.gz
我们发现我们只有libcusolver.so.8.0，并没有我们要找的libcusolver.so.7.5,看了一下官方的文档：
给出的建议是： 可以使用.8.0来替代.7.5，我们命名一个.8.0的软连接为.7.5
我们先到/usr/lib/cuda/lib64 下：
ln -s libcusolver.so.8.0 libcusolver.so.7.5
然后在.bashrc系统环境下配置一下这个路径：
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/lib/cuda/lib64" export CUDA_HOME=/usr/local/cuda
然后在测试：
gxjun@gxjun:~$ python Python 2.7.12 |Anaconda 4.2.0 (64-bit)| (default, Jul 2 2016, 17:42:40) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2 Type "help", "copyright", "credits" or "license" for more information. Anaconda is brought to you by Continuum Analytics. Please check out: http://continuum.io/thanks and https://anaconda.org >>> import tensorflow as tf >>>
正常了，说明已经完全安装好了~
参考：
https://mirror.tuna.tsinghua.edu.cn/help/anaconda/
http://www.jianshu.com/p/7be2498785b1
https://stackoverflow.com/questions/42013316/after-building-tensorflow-from-source-seeing-libcudart-so-and-libcudnn-errors
https://github.com/tensorflow/tensorflow/issues/1501