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 -yapt 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:

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/ppasudo apt updatesudo apt upgradeubuntu-drivers devicessudo 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:

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：

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.tgzsudo cp cuda/include/cudnn.h /usr/local/cuda/includesudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64sudo 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 ~/.bashrcsudo ldconfigecho $CUDA_HOME

安装Bazel：

sudo apt install curlecho "deb [arch=amd64]  stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.listcurl  | sudo apt-key add -sudo apt update -ysudo apt upgrade -ysudo apt install bazelsudo apt upgrade bazelpip install keras

查看Nvidia版本：

cd ~git clone  ~/tensorflow# check current revision number from browsergit checkout r1.11cd ~/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/python3Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: YDo you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: NDo you wish to build TensorFlow with Hadoop File System support? [Y/n]: NDo you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: NDo you wish to build TensorFlow with Apache Kafka Platform support? [y/N]: NDo you wish to build TensorFlow with XLA JIT support? [y/N]: NDo you wish to build TensorFlow with GDR support? [y/N]: NDo you wish to build TensorFlow with VERBS support? [y/N]: NDo you wish to build TensorFlow with OpenCL SYCL support? [y/N]: NDo you wish to build TensorFlow with CUDA support? [y/N]: YPlease specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 9.0]: 9.0Please specify the location where CUDA 9.1 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cudaPlease specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: 7.1Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cudaDo you wish to build TensorFlow with TensorRT support? [y/N]: NPlease note that each additional compute capability significantly increases your build time and binary size. [Default is: 5.0] 3.0Do you want to use clang as CUDA compiler? [y/N]: NPlease specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: /usr/bin/gcc-4.8Do you wish to build TensorFlow with MPI support? [y/N]: NPlease specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]: -march=nativeWould 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_pkgcd 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 tfhello = 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：

您可以运行：

git clone  models/tutorials/image/imagenetpython classify_image.py

这是一些基本设置和测试。