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如何在Ubuntu 18.04服务器上安装TensorFlow

2018-09-29 15:20    it168网站 原创  作者: 刘美利 编辑: 刘美利

  【IT168 技术】本文介绍了如何使用Nvidia GPU在Ubuntu 18.04服务器上安装TensorFlow。安装需要具有Nvidia显卡的服务器架构 ,这样的专用服务器可用于各种目的,包括游戏。为了保障设备的使用寿命,建议不要在localhost上安装繁重且耗时的程序。显卡必须支持至少Nvidia compute 3.0才能获得比TensorFlow更多的运用。

  

  我们假设使用64位的操作系统,显卡为GeForce 740m。SSH登录到服务器,更新和升级:

  apt update -y

  apt upgrade –y

  运行这个命令来安装Python库:

  sudo apt install openjdk-8-jdk git python-dev python3-dev python-numpy python3-numpy python-six python3-six build-essential python-pip python3-pip python-virtualenv swig python-wheel python3-wheel libcurl3-dev libcupti-dev

  继续运行

  sudo apt install libcurl4-openssl-dev

  通过运行,我们可以看到安装的显卡硬件:

  sudo lshw -C display | grep product

  我们需要安装Nvidia驱动程序。我们可以检查SSH上的图形驱动程序:

  nvidia-smi

  这是Ubuntu的PPA,浏览一下:

  https://launchpad.net/~graphics-drivers/+archive/ubuntu/ppa

  nvidia-graphics-drivers-396是最新的,但可能没有太多测试。我们可以添加 nvidia-graphics-drivers-390 PPA 并安装该应用程序。

  sudo add-apt-repository ppa:graphics-drivers/ppa

  sudo apt update

  sudo apt upgrade

  ubuntu-drivers devices

  sudo ubuntu-drivers autoinstall

  如果有意外情况,autoinstall不起作用,则运行:

  sudo apt install nvidia-390

  现在,再次运行命令:

  nvidia-smi

  您将得到一个有用的输出。我们应该保持住这个版本停止升级。

  sudo apt-mark hold nvidia-driver-390

  安装 Linux—headers :

  sudo apt install linux-headers-$(uname -r)

  为了后续步骤正常进行,我们需要 gcc, g++ 等等:

  apt install gcc g++ gcc-6 g++-6 gcc-4.8 g++-4.8

  # if gcc-4.8 package not found run

  # sudo add-apt-repository ppa:ubuntu-toolchain-r/test

  # sudo apt update

  # sudo apt install gcc-4.8 g++-4.8

  现在我们必须安装CUDA工具包:

  apt install nvidia-cuda-toolkit libcupti-dev

  重启

  sudo reboot

  安装CUDA工具包:

  https://developer.nvidia.com/cuda-toolkit

  运行:

  cd Downloads/

  sudo sh cuda_9.0.176_384.81_linux.run --override --silent –toolkit

  接下来,您需要安装CUDNN,NCCL。您需要按照PyTorch老方法,使用Nvdia帐户登录,这很简单。您将获得链接:cuDNN v7.1.x Library for Linux。您需要下载deb文件,并将FTP上传到服务器。URL是:

  https://developer.nvidia.com/rdp/cudnn-download

  https://developer.nvidia.com/nccl

  找到已安装CUDA的目录。它正在将文件复制到/usr/local/cuda/。将上述内容移到安装CUDA的目录中并运行这些操作(注意版本编号的目录,以下是格式示例):

  tar -xzvf cudnn-9.0-linux-x64-v7.1.tgz

  sudo cp cuda/include/cudnn.h /usr/local/cuda/include

  sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64

  sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

  以上将节省空间,并避免apt警告。打开配置文件,如.bashrc:

  nano ~/.bashrc

  添加这些:

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

  export CUDA_HOME=/usr/local/cuda

  重新加载:

  source ~/.bashrc

  sudo ldconfig

  echo $CUDA_HOME

  安装Bazel:

  sudo apt install curl

  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 update -y

  sudo apt upgrade -y

  sudo apt install bazel

  sudo apt upgrade bazel

  pip install keras

  查看Nvidia版本:

  cd ~

  git clone https://github.com/tensorflow/tensorflow

  cd ~/tensorflow

  # check current revision number from browser

  git checkout r1.11

  cd ~/tensorflow

  通过运行创建配置文件:

  ./configure

  您将得到这样的输出:

  Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3

  Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: Y

  Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: N

  Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: N

  Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: N

  Do you wish to build TensorFlow with Apache Kafka Platform support? [y/N]: N

  Do you wish to build TensorFlow with XLA JIT support? [y/N]: N

  Do you wish to build TensorFlow with GDR support? [y/N]: N

  Do you wish to build TensorFlow with VERBS support? [y/N]: N

  Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: N

  Do you wish to build TensorFlow with CUDA support? [y/N]: Y

  Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 9.0]: 9.0

  Please specify the location where CUDA 9.1 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda

  Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: 7.1

  Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda

  Do you wish to build TensorFlow with TensorRT support? [y/N]: N

  Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 5.0] 3.0

  Do you want to use clang as CUDA compiler? [y/N]: N

  Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: /usr/bin/gcc-4.8

  Do you wish to build TensorFlow with MPI support? [y/N]: N

  Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]: -march=native

  Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]:N

  构建TensorFlow :

  最后的步骤:

  bazel-bin/tensorflow/tools/pip_package/build_pip_package tensorflow_pkg

  cd tensorflow_pkg/

  sudo pip3 install tensorflow-<name_of_generated_file>.whl

  通过切换到另一个目录并运行python来检查您的构建是否正常工作:

  import tensorflow as tf

  hello = tf.constant('Hello World!')

  sess = tf.Session()

  print(sess.run(hello))

  您将得到Hello World!输出。TensorFlow有以下型号:

  https://github.com/tensorflow/models

  您可以运行:

  git clone https://github.com/tensorflow/models.git

  cd models/tutorials/image/imagenet

  python classify_image.py

  这是一些基本设置和测试。

关键字: TensorFlow
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