环境：Ubuntu 16.04 64bit
1. 安装Anaconda
Anaconda 是 Python 的一个科学计算发行版，内置了数百个Python经常会使用的库，也包括了许多机器学习和数据挖掘依赖的库，包括 Scikit-learn、NumPy、SciPy 和 Pandas等，其中可能有一些还是 Tensorflow 的依赖库。
Anaconda 提供了一个编译好的环境可以直接安装。同时 Anaconda 自动集成了最新版的MKL（Math Kernel Libaray）库，这是Intel推出的底层数值计算库，功能上包含了 BLAS（Basic Linear Algebra Software）等矩阵计算库的功能，可以作为 NumPy、SciPy、Scikit-learn、NumExpr 等库的底层依赖，加速这些库的矩阵运算。
简单来说，Anaconda 是目前最好的科学计算的 Python 环境，方便了安装，也提高了性能。
安装步骤：
1) 在官网上下载（www.continuum.io/downloads）相应版本
2) 在下载目录下执行命令，例如： bash Anaconda3-4.3.1-Linux-x86_64.sh
3) 回车确认，进入 Anaconda 的 License 文档，可以按 q 跳过，然后输入 yes 确认。下一步会让输入 Anaconda 的安装路径，可以按回车键使用默认路径
4) 安装完成，程序提示是否把 anaconda3 的 binary 路径加入到 .bashrc，建议添加。这样 python 命令就可以自动使用 Anaconda 的 Python 的环境了。
2. 安装Tensorflow
由于是在 Python 的环境下使用 Tensorflow，可以使用 Python 的默认包管理器安装。如果 pip 的版本>9，可以直接运行：
pip install tensorflow
[email protected]:~$ pip install tensorflow
Collecting tensorflow
Downloading tensorflow-1.1.0-cp36-cp36m-manylinux1_x86_64.whl (31.4MB)
100% |?..?..?..?..?..?..?..?..?..?..?..?..?..?..?..?..| 31.4MB 48kB/s
Requirement already satisfied: six>=1.10.0 in ./anaconda3/lib/python3.6/site-packages (from tensorflow)
Collecting protobuf>=3.2.0 (from tensorflow)
Downloading protobuf-3.3.0-cp36-cp36m-manylinux1_x86_64.whl (5.7MB)
100% |?..?..?..?..?..?..?..?..?..?..?..?..?..?..?..?..| 5.7MB 242kB/s
Requirement already satisfied: wheel>=0.26 in ./anaconda3/lib/python3.6/site-packages (from tensorflow)
Requirement already satisfied: werkzeug>=0.11.10 in ./anaconda3/lib/python3.6/site-packages (from tensorflow)
Requirement already satisfied: numpy>=1.11.0 in ./anaconda3/lib/python3.6/site-packages (from tensorflow)
Requirement already satisfied: setuptools in ./anaconda3/lib/python3.6/site-packages/setuptools-27.2.0-py3.6.egg (from protobuf>=3.2.0->tensorflow)
Installing collected packages: protobuf, tensorflow
Successfully installed protobuf-3.3.0 tensorflow-1.1.0
================================分割线========================================
但是直接安装时，由于一些编译参数的原因，在使用 Tensorflow 时，会出现如下的warnings：
[email protected]:~$ python >>> import tensorflow as tf >>> hello = tf.constant(‘Hello, TensorFlow!‘) >>> sess = tf.Session() >>> print(sess.run(hello))
在我的机器上当执行到 sess = tf.Session() 时，会提示:
2017-05-25 07:33:08.176641: 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. 2017-05-25 07:33:08.176687: 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. 2017-05-25 07:33:08.176701: 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.
意思是说：你的机器上有这些指令集可以用，并且用了他们会加快你的 CPU 运行速度，但是你的 TensorFlow 在编译的时候并没有用到这些指令集。
如果觉得不好的话，可以尝试用编译源码安装解决。
编译源码安装
1. 安装 Bazel
查看：https://bazel.build/versions/master/docs/install-ubuntu.html#install-on-ubuntu
Bazel 是 Google 自家的编译工具，使用它来编译 Tensorflow ：
1) Add Bazel distribution URI as a package source (one time setup)，执行命令
echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add -
If you want to use the JDK 7, please replace jdk1.8 with jdk1.7 and if you want to install the testing version of Bazel, replace stable with testing.
2) Install and update Bazel，执行命令
sudo apt-get update && sudo apt-get install bazel
3) Once installed, you can upgrade to a newer version of Bazel with:
sudo apt-get upgrade bazel
2. 卸载已经安装的 Tensorflow：
pip uninstall tensorflow
3. 克隆 Tensorflow 仓库：
git clone --recurse-submodules https://github.com/tensorflow/tensorflow
4. 配置Tensorflow
运行configure脚本
[email protected]:~/share/tensorflow$ cd tensorflow/ [email protected]:~/share/tensorflow/tensorflow$ ./configure Please specify the location of python. [Default is /home/jingyg/anaconda3/bin/python]: Found possible Python library paths: /home/jingyg/anaconda3/lib/python3.6/site-packages Please input the desired Python library path to use. Default is [/home/jingyg/anaconda3/lib/python3.6/site-packages] Using python library path: /home/jingyg/anaconda3/lib/python3.6/site-packages Do you wish to build TensorFlow with MKL support? [y/N] No MKL 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]: Do you wish to use jemalloc as the malloc implementation? [Y/n] jemalloc enabled Do you wish to build TensorFlow with Google Cloud Platform support? [y/N] No Google Cloud Platform support will be enabled for TensorFlow Do you wish to build TensorFlow with Hadoop File System support? [y/N] No Hadoop File System support will be enabled for TensorFlow Do you wish to build TensorFlow with the XLA just-in-time compiler (experimental)? [y/N] No XLA support will be enabled for TensorFlow Do you wish to build TensorFlow with VERBS support? [y/N] No VERBS support will be enabled for TensorFlow Do you wish to build TensorFlow with OpenCL support? [y/N] No OpenCL support will be enabled for TensorFlow Do you wish to build TensorFlow with CUDA support? [y/N] No CUDA support will be enabled for TensorFlow Extracting Bazel installation... ................. INFO: Starting clean (this may take a while). Consider using --async if the clean takes more than several minutes. Configuration finished
可以根据自己的情况进行选择。
5. 编译(比较耗时)：
bazel build -c opt --copt=-msse4.1 --copt=-msse4.2 --copt=-mavx //tensorflow/tools/pip_package:build_pip_package
Tips：最好先增加下 swap 的空间：
# 生成swap镜像文件 sudo dd if=/dev/zero of=/mnt/1024Mb.swap bs=1M count=1024 # 对该镜像文件格式化 sudo mkswap /mnt/1024Mb.swap # 挂载该镜像文件 sudo swapon /mnt/1024Mb.swap 使用free -m 即可查看到swap空间已经增加成功 否则，在编译过程中，可能出现内存不足的问题： ERROR: /home/jingyg/share/tensorflow/tensorflow/tensorflow/core/kernels/BUILD:2339:1: C++ compilation of rule ‘//tensorflow/core/kernels:cwise_op‘ failed: gcc failed: error executing command /usr/bin/gcc -U_FORTIFY_SOURCE -fstack-protector -Wall -B/usr/bin -B/usr/bin -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer -g0 -O2 ‘-D_FORTIFY_SOURCE=1‘ -DNDEBUG ... (remaining 134 argument(s) skipped): com.google.devtools.build.lib.shell.BadExitStatusException: Process exited with status 4. virtual memory exhausted: Cannot allocate memory
6. 上述命令会生成一个叫做 build_pip_package 的脚本，按照如下命令运行这个脚本，在 /tmp/tensorflow_pkg 文件夹中创建pip的安装包：
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
7. 用pip命令安装：
pip install /tmp/tensorflow_pkg/tensorflow-1.2.0rc0-cp36-cp36m-linux_x86_64.whl
Tensorflow测试：
退出 Tensorflow 目录，如果不退出执行下面的命令，会出现 Failed to load the native TensorFlow runtime 的错误
[email protected]:~/share/tensorflow/tensorflow$ cd .. [email protected]:~/share/tensorflow$ python Python 3.6.0 |Anaconda 4.3.1 (64-bit)| (default, Dec 23 2016, 12:22:00) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow as tf
Tips: ImportError: /home/jingyg/anaconda3/bin/../lib/libstdc++.so.6: version `GLIBCXX_3.4.21‘ not found
原因是 Anaconda 里的 libgcc 版本低了
解决办法，安装最新的：
[email protected]:~/share/tensorflow$ conda install libgcc