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Tensorflow 安装与使用 (ubuntu16.04 GPU)
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由于计算机里已安装 caffe， cuda， cudnn， 为了安装tensorflow后不影响其使用，所以在anaconda中新建了一个环境,名称：tensorflow， 建完之后选择 numpy 安装
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查看cuda版本 cat /usr/local/cuda/version.txt
查看cudnn版本 cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
本计算机 cuda: 8.0 cudnn: 5.1.5
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激活环境
source activate tensorflow
下载安装Bazel，Bazel是Google开源构建工具，类似于Make的工具，用来编译构建tensorflow
官网提供的地址一直连接不上，放在百度网盘里(bazel-0.5.4-installer-linux-x86_64.sh)：https://pan.baidu.com/s/1o93507o
放在主目录文件夹 /tensorflow_install 下,执行
cd /home/majinlei/tensorflow_install
chmod +x bazel-0.5.4-installer-linux-x86_64.sh
./bazel-0.5.4-installer-linux-x86_64.sh --user
export PATH="$PATH:$HOME/bin"执行上述 4 个命令之后开始配置（不要使用sudo ./configure）
./configure具体配置选择如下：
(tensorflow) majinlei@majinlei:~/tensorflow$ ./configure
WARNING: Running Bazel server needs to be killed, because the startup options are different.
You have bazel 0.5.4 installed.
Please specify the location of python. [Default is /home/majinlei/anaconda2/envs/tensorflow/bin/python]: /home/majinlei/anaconda2/envs/tensorflow/bin/python
Found possible Python library paths:
/usr/local/lib/python2.7/site-packages
/home/majinlei/anaconda2/envs/tensorflow/lib/python2.7/site-packages
Please input the desired Python library path to use. Default is [/usr/local/lib/python2.7/site-packages]
/home/majinlei/anaconda2/envs/tensorflow/lib/python2.7/site-packages
Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: y
jemalloc as malloc support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: y
Google Cloud Platform support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: y
Hadoop File System support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: y
Amazon S3 File System support will be enabled for TensorFlow.
Do you wish to build TensorFlow with XLA JIT support? [y/N]: y
XLA JIT support will be enabled for TensorFlow.
Do you wish to build TensorFlow with GDR support? [y/N]: y
GDR support will be enabled for TensorFlow.
Do you wish to build TensorFlow with VERBS support? [y/N]: y
VERBS support will be enabled for TensorFlow.
Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.
Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 9.0]: 8.0
Please specify the location where CUDA 8.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda
Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: 5.1.5
Please specify the location where cuDNN 5.1.5 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:/usr/local/cuda
Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 6.1]6.1
Do you want to use clang as CUDA compiler? [y/N]: n
nvcc will be used as CUDA compiler.
Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: /usr/bin/gcc
Do you wish to build TensorFlow with MPI support? [y/N]: n
No MPI support will be enabled for TensorFlow.
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]: -match=native
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.
Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See tools/bazel.rc for more details.
--config=mkl # Build with MKL support.
--config=monolithic # Config for mostly static monolithic build.
Configuration finished%#################################################################%
接下来下载tensorflow源码（下载至主目录tensorflow文件夹下）
git clone--recurse-submodules https://github.com/tensorflow/tensorflowcd ~/tensorflow使用Bazel编译构建
bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package中途出现错误：
ERROR: /home/majinlei/tensorflow/tensorflow/contrib/gdr/BUILD:52:1: C++ compilation of rule '//tensorflow/contrib/gdr:gdr_memory_manager' failed (Exit 1).
tensorflow/contrib/gdr/gdr_memory_manager.cc:28:27: fatal error: rdma/rdma_cma.h: No such file or directory解决办法：
sudo apt-get install librdmacm-dev重新输入命令：
bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package进行编译，编译过程时间很长， 等待完成之后
生成 pip 安装包
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkgcd 到 /tmp/tensorflow_pkg目录下，找到编译好的whl文件，使用PIP工具安装
cd /tmp/tensorflow_pkg
pip install --config=cuda /tmp/tensorflow_pkg/tensorflow-1.5.0rc1-cp27-cp27mu-linux_x86_64.whl安装过程如下
(tensorflow) majinlei@majinlei:/tmp/tensorflow_pkg$ pip install /tmp/tensorflow_pkg/tensorflow-1.5.0rc1-cp27-cp27mu-linux_x86_64.whl
Processing ./tensorflow-1.5.0rc1-cp27-cp27mu-linux_x86_64.whl
Collecting enum34>=1.1.6 (from tensorflow==1.5.0rc1)
Downloading enum34-1.1.6-py2-none-any.whl
Collecting absl-py>=0.1.6 (from tensorflow==1.5.0rc1)
Downloading absl-py-0.1.9.tar.gz (79kB)
100% |████████████████████████████████| 81kB 176kB/s
Collecting six>=1.10.0 (from tensorflow==1.5.0rc1)
Using cached six-1.11.0-py2.py3-none-any.whl
Collecting tensorflow-tensorboard>=0.4.0 (from tensorflow==1.5.0rc1)
Downloading tensorflow_tensorboard-1.5.0-py2-none-any.whl (3.0MB)
100% |████████████████████████████████| 3.0MB 71kB/s
Collecting termcolor>=1.1.0 (from tensorflow==1.5.0rc1)
Downloading termcolor-1.1.0.tar.gz
Collecting gast>=0.2.0 (from tensorflow==1.5.0rc1)
Downloading gast-0.2.0.tar.gz
Requirement already satisfied: numpy>=1.12.1 in /home/majinlei/anaconda2/envs/tensorflow/lib/python2.7/site-packages (from tensorflow==1.5.0rc1)
Requirement already satisfied: wheel in /home/majinlei/anaconda2/envs/tensorflow/lib/python2.7/site-packages (from tensorflow==1.5.0rc1)
Collecting backports.weakref>=1.0rc1 (from tensorflow==1.5.0rc1)
Downloading backports.weakref-1.0.post1-py2.py3-none-any.whl
Collecting astor>=0.6.0 (from tensorflow==1.5.0rc1)
Downloading astor-0.6.2-py2.py3-none-any.whl
Collecting mock>=2.0.0 (from tensorflow==1.5.0rc1)
Downloading mock-2.0.0-py2.py3-none-any.whl (56kB)
100% |████████████████████████████████| 61kB 258kB/s
Collecting protobuf>=3.4.0 (from tensorflow==1.5.0rc1)
Downloading protobuf-3.5.1-cp27-cp27mu-manylinux1_x86_64.whl (6.4MB)
100% |████████████████████████████████| 6.4MB 194kB/s
Collecting werkzeug>=0.11.10 (from tensorflow-tensorboard>=0.4.0->tensorflow==1.5.0rc1)
Downloading Werkzeug-0.14.1-py2.py3-none-any.whl (322kB)
100% |████████████████████████████████| 327kB 668kB/s
Collecting html5lib==0.9999999 (from tensorflow-tensorboard>=0.4.0->tensorflow==1.5.0rc1)
Downloading html5lib-0.9999999.tar.gz (889kB)
100% |████████████████████████████████| 890kB 1.4MB/s
Collecting markdown>=2.6.8 (from tensorflow-tensorboard>=0.4.0->tensorflow==1.5.0rc1)
Downloading Markdown-2.6.11-py2.py3-none-any.whl (78kB)
100% |████████████████████████████████| 81kB 798kB/s
Collecting futures>=3.1.1 (from tensorflow-tensorboard>=0.4.0->tensorflow==1.5.0rc1)
Downloading futures-3.2.0-py2-none-any.whl
Collecting bleach==1.5.0 (from tensorflow-tensorboard>=0.4.0->tensorflow==1.5.0rc1)
Downloading bleach-1.5.0-py2.py3-none-any.whl
Collecting funcsigs>=1; python_version < "3.3" (from mock>=2.0.0->tensorflow==1.5.0rc1)
Downloading funcsigs-1.0.2-py2.py3-none-any.whl
Collecting pbr>=0.11 (from mock>=2.0.0->tensorflow==1.5.0rc1)
Downloading pbr-3.1.1-py2.py3-none-any.whl (99kB)
100% |████████████████████████████████| 102kB 733kB/s
Requirement already satisfied: setuptools in /home/majinlei/anaconda2/envs/tensorflow/lib/python2.7/site-packages (from protobuf>=3.4.0->tensorflow==1.5.0rc1)
Building wheels for collected packages: absl-py, termcolor, gast, html5lib
Running setup.py bdist_wheel for absl-py ... done
Stored in directory: /home/majinlei/.cache/pip/wheels/04/f5/7c/5d4eab10ddf87dec875016e74ba289d87270a90fb2662a76fc
Running setup.py bdist_wheel for termcolor ... done
Stored in directory: /home/majinlei/.cache/pip/wheels/de/f7/bf/1bcac7bf30549e6a4957382e2ecab04c88e513117207067b03
Running setup.py bdist_wheel for gast ... done
Stored in directory: /home/majinlei/.cache/pip/wheels/8e/fa/d6/77dd17d18ea23fd7b860e02623d27c1be451521af40dd4a13e
Running setup.py bdist_wheel for html5lib ... done
Stored in directory: /home/majinlei/.cache/pip/wheels/6f/85/6c/56b8e1292c6214c4eb73b9dda50f53e8e977bf65989373c962
Successfully built absl-py termcolor gast html5lib
Installing collected packages: enum34, six, absl-py, protobuf, werkzeug, html5lib, markdown, futures, bleach, tensorflow-tensorboard, termcolor, gast, backports.weakref, astor, funcsigs, pbr, mock, tensorflow
Successfully installed absl-py-0.1.9 astor-0.6.2 backports.weakref-1.0.post1 bleach-1.5.0 enum34-1.1.6 funcsigs-1.0.2 futures-3.2.0 gast-0.2.0 html5lib-0.9999999 markdown-2.6.11 mock-2.0.0 pbr-3.1.1 protobuf-3.5.1 six-1.11.0 tensorflow-1.5.0rc1 tensorflow-tensorboard-1.5.0 termcolor-1.1.0 werkzeug-0.14.1测试（命令行输入）
python
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello)) 输出
Hello, TensorFlow!