本文介绍tensorflow源代码方式安装。安装的系统为 Ubuntu 15.04。
获取TensorFlow源代码
git clone --recurse-submodules https://github.com/tensorflow/tensorflow
使用 --recurse-submodules 选项来获取 TensorFlow 需要依赖的 protobuf 库文件。
安装 Bazel
遵从以下 指令 来安装 bazel 依赖。bazel 安装文件：下载地址
bazel 缺省需要使用JDK1.8，如你使用JDK1.7，请下载相应的安装包。
安装 Bazel 其他所需依赖：
sudo apt-get install pkg-config zip g++ zlib1g-dev unzip
执行如下命令来安装Bazel：
chmod +x PATH_TO_INSTALL.SH ./PATH_TO_INSTALL.SH --user
记住把 PATH_TO_INSTALL.SH 替换为你下载的Bazel安装文件名，如：
./bazel-0.1.4-installer-linux-x86_64.sh --user
安装其他依赖
sudo apt-get install python-numpy swig python-dev
配置安装
运行 tensorflow 根目录下的 configure 脚本。这个脚本会要求你输入 python 解释器的安装路径，并允许你可选择安装CUDA库。
如果不安装CUDA，则这一步主要是定位python和numpy头文件所在位置：
./configure Please specify the location of python. [Default is /usr/bin/python]:
如果要安装CUDA，则除了指定 python 外，还需指定 CUDA 安装位置：
./configure Please specify the location of python. [Default is /usr/bin/python]: Do you wish to build TensorFlow with GPU support? [y/N] y GPU support will be enabled for TensorFlow Please specify the location where CUDA 7.0 toolkit is installed. Refer to README.md for more details. [default is: /usr/local/cuda]: /usr/local/cuda Please specify the location where the cuDNN v2 library is installed. Refer to README.md for more details. [default is: /usr/local/cuda]: /usr/local/cuda Setting up Cuda include Setting up Cuda lib64 Setting up Cuda bin Setting up Cuda nvvm Configuration finished
构建支持GPU的Tensorflow
在tensorflow 根目录下执行如下命令：
$ bazel build -c opt --config=cuda --spawn_strategy=standalone //tensorflow/cc:tutorials_example_trainer
$ bazel-bin/tensorflow/cc/tutorials_example_trainer --use_gpu
# Lots of output. This tutorial iteratively calculates the major eigenvalue of
# a 2x2 matrix, on GPU. The last few lines look like this.
000009/000005 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
000006/000001 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
000009/000009 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
Note that "--config=cuda" is needed to enable the GPU support.