如何在Ubuntu 18.04伺服器上安裝TensorFlow（Nvidia GPU）

本文介紹了如何使用Nvidia GPU在Ubuntu 18.04伺服器上安裝TensorFlow。安裝需要具有Nvidia顯卡的伺服器架構 ，這樣的專用伺服器可用於各種目的，包括遊戲。為了保障設備的使用壽命，建議不要在localhost上安裝繁重且耗時的程序。顯卡必須支持至少Nvidia compute 3.0才能獲得比TensorFlow更多的運用。

我們假設使用64位的操作系統，顯卡為GeForce 740m。SSH登錄到伺服器，更新和升級：

apt update -y
apt upgrade –y

運行這個命令來安裝Python庫：

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

繼續運行

sudo apt install libcurl4-openssl-dev

通過運行，我們可以看到安裝的顯卡硬體：

sudo lshw -C display | grep product

我們需要安裝Nvidia驅動程序。我們可以檢查SSH上的圖形驅動程序：

nvidia-smi

這是Ubuntu的PPA，瀏覽一下:

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

nvidia-graphics-drivers-396是最新的，但可能沒有太多測試。我們可以添加 nvidia-graphics-drivers-390 PPA 並安裝該應用程序。

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
sudo apt upgrade
ubuntu-drivers devices
sudo ubuntu-drivers autoinstall

如果有意外情況，autoinstall不起作用，則運行：

sudo apt install nvidia-390

現在，再次運行命令：

nvidia-smi

您將得到一個有用的輸出。我們應該保持住這個版本停止升級。

sudo apt-mark hold nvidia-driver-390

安裝 Linux—headers :

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

為了後續步驟正常進行，我們需要 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

現在我們必須安裝CUDA工具包：

apt install nvidia-cuda-toolkit libcupti-dev

重啟

sudo reboot

安裝CUDA工具包:

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

運行：

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

接下來，您需要安裝CUDNN，NCCL。您需要按照PyTorch老方法，使用Nvdia帳戶登錄，這很簡單。您將獲得鏈接：cuDNN v7.1.x Library for Linux。您需要下載deb文件，並將FTP上傳到伺服器。URL是：

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

https://developer.nvidia.com/nccl

找到已安裝CUDA的目錄。它正在將文件複製到/usr/local/cuda/。將上述內容移到安裝CUDA的目錄中並運行這些操作（注意版本編號的目錄，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*

以上將節省空間，並避免apt警告。打開配置文件，如.bashrc：

nano ~/.bashrc

添加這些：

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

重新載入：

source ~/.bashrc
sudo ldconfig
echo $CUDA_HOME

安裝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

通過運行創建配置文件：

./configure

您將得到這樣的輸出：

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

構建TensorFlow :

最後的步驟：

bazel-bin/tensorflow/tools/pip_package/build_pip_package tensorflow_pkg
cd tensorflow_pkg/
sudo pip3 install tensorflow-<name_of_generated_file>.whl

通過切換到另一個目錄並運行python來檢查您的構建是否正常工作：

import tensorflow as tf
hello = tf.constant("Hello World!")
sess = tf.Session()
print(sess.run(hello))

您將得到Hello World!輸出。TensorFlow有以下型號：

https://github.com/tensorflow/models

您可以運行：

git clone https://github.com/tensorflow/models.git
cd models/tutorials/image/imagenet
python classify_image.py

這是一些基本設置和測試。

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