Tag - NVIDIA

TensorFlow 实战 01:安装 GPU 版本的开发环境 (Ubuntu)

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