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
- 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：
修改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 cudaPATH环境变量
修改export PATH=/usr/local/cuda-9.0/bin${PATH:+:${PATH}}LD_LIBRARY_PATH环境变量export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} - 安装显卡驱动，目前最新的驱动版本是 384
驱动安装成功后，可以使用下面的命令查看显卡状态：sudo apt install nvidia-384nvidia-smi - 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 - 安装 libcupti-dev库
修改sudo apt install libcupti-devLD_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。
- 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 - 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
- git 下载源码仓库，切到
r1.4分支git clone https://github.com/tensorflow/tensorflow git checkout r1.4 - 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 - 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 - 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
- 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 - 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
- 启动 Python
$ python - 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
恭喜你，安装成功了……Hello, TensorFlow!
更多的细节
请参考详细的官方文档