这里将介绍如何在 Ubuntu 16.04 LTS 系统上搭建 支持 GPU 的 TensorFlow 1.4.0 开发环境。

是否需要 GPU 支持？

这取决于你有没有一块儿支持 CUDA 的 NVIDIA 显卡。如果没有，只能选择 CPU 版本。如果有，继续往下看。

安装 NVIDIA 依赖

1. 安装 CUDA Toolkit 9.0：在 CUDA Downloads 页面选择操作系统及版本，安装类型选择deb (network)，最后会给出一个下载链接和一系列的命令，类似：

   sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
   sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
   sudo apt-get update
   sudo apt-get install cuda
   

   修改 PATH 环境变量

   export PATH=/usr/local/cuda-9.0/bin${PATH:+:${PATH}}
   

   修改 LD_LIBRARY_PATH 环境变量

   export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
   

2. 安装显卡驱动，目前最新的驱动版本是 384

   sudo apt install nvidia-384
   

   驱动安装成功后，可以使用下面的命令查看显卡状态：

   nvidia-smi
   

3. 安装 cuDNN 7，在 cuDNN下载页面 点击 Download 并填写调查问卷后，根据自己的系统环境下载对应的安装包并安装，以下是 64 位系统的示例：

   sudo dpkg -i libcudnn7_7.0.3.11-1+cuda9.0_amd64.deb
   sudo dpkg -i libcudnn7-dev_7.0.3.11-1+cuda9.0_amd64.deb
   sudo dpkg -i libcudnn7-doc_7.0.3.11-1+cuda9.0_amd64.deb
   

4. 安装 libcupti-dev库

   sudo apt install libcupti-dev
   

   修改 LD_LIBRARY_PATH 环境变量

   export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
   

安装 TensorFlow

安装预编译的包或者从源码编译都是可行的。

原生 pip 安装

python 2.7/3.n 都可以。

1. 先安装并升级 pip

   # for Python 2.7
   sudo apt-get install python-pip python-dev
   sudo pip install -U pip setuptools
   # for Python 3.n
   sudo apt-get install python3-pip python3-dev
   sudo pip3 install -U pip setuptools
   

2. 安装 tensorflow，根据需求只执行一条命令即可

   pip install tensorflow      # Python 2.7; CPU support (no GPU support)
   pip3 install tensorflow     # Python 3.n; CPU support (no GPU support)
   pip install tensorflow-gpu  # Python 2.7; GPU support
   pip3 install tensorflow-gpu # Python 3.n; GPU support
   

源码编译安装

1. git 下载源码仓库，切到 r1.4 分支

   git clone https://github.com/tensorflow/tensorflow
   git checkout r1.4
   

2. 安装 bazel

   sudo apt-get install openjdk-8-jdk
   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 -
   sudo apt-get update && sudo apt-get install bazel
   

3. 安装 TensorFlow 的 Python 依赖

   sudo apt-get install python-numpy python-dev python-pip python-wheel     # for Python 2.7
   sudo apt-get install python3-numpy python3-dev python3-pip python3-wheel # for Python 3.n
   

4. 执行安装配置，务必注意每一步的选择

   cd tensorflow # 进入第 1 步克隆的仓库根目录
   ./configure
   Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python2.7
   Found possible Python library paths:
   /usr/local/lib/python2.7/dist-packages
   /usr/lib/python2.7/dist-packages
   Please input the desired Python library path to use.  Default is [/usr/lib/python2.7/dist-packages]
   Using python library path: /usr/local/lib/python2.7/dist-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] Y
   CUDA support will be enabled for TensorFlow
   Do you want to use clang as CUDA compiler? [y/N]
   nvcc will be used as CUDA compiler
   Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 8.0]: 9.0
   Please specify the location where CUDA 8.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
   Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:
   Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 6.0]: 7
   Please specify the location where cuDNN 6 library is installed. Refer to README.md for more details. [Default is /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: "3.5,5.2"]: 6.1
   Do you wish to build TensorFlow with MPI support? [y/N] 
   MPI support will not be enabled for TensorFlow
   Configuration finished
   

5. 编译生成 pip 包

   bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
   bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
   

6. 安装生成好的 pip 包，具体的 whl 包在 /tmp/tensorflow_pkg 目录下，文件名可能略有不同

   sudo pip install /tmp/tensorflow_pkg/tensorflow-1.4.0-cp27-cp27mu-linux_x86_64.whl
   

验证一下是否装成功了

1. 启动 Python

   $ python
   

2. 逐行敲入下面的代码

   # Python
   import tensorflow as tf
   hello = tf.constant('Hello, TensorFlow!')
   sess = tf.Session()
   print(sess.run(hello))
   

   如果能看到下面的输出

   Hello, TensorFlow!
   

   恭喜你，安装成功了……

更多的细节

请参考详细的官方文档

1. Installing TensorFlow on Ubuntu
2. Installing TensorFlow from Sources
3. NVIDIA CUDA Installation Guide for Linux
4. NVIDIA cuDNN
5. CUDA GPUs