本文主要是介绍搭建深度学习平台+CUDA10.0+cuDNN7.3.1+Tensorflow-GPU1.13，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
安装Ubuntu16.04系统（自己网上查找）
升级系统软件
sudo apt-get update
sudo apt-get upgradesudo apt-add-repository ppa:fixnix/netspeed
sudo apt-get update
sudo apt-get install indicator-netspeed-unity
indicator-netspeed-unity &安装图像、视频和人机界面等工具包
sudo apt-get install build-essential cmake git unzip pkg-config
sudo apt-get install libjpeg-dev libtiff5-dev libjasper-dev libpng12-dev
sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
sudo apt-get install libxvidcore-dev libx264-dev
sudo apt-get install libgtk-3-dev
sudo apt-get install libhdf5-serial-dev graphviz
sudo apt-get install libopenblas-dev libatlas-base-dev gfortran
sudo apt-get install python-tk python3-tk python-imaging-tk
sudo apt-get install vim
sudo apt-get install build-essential
sudo apt-get install cmake git unzip zip
sudo apt-get install python2.7-dev python3-dev pylint
sudo apt-get install python-dev python3-dev python-pip python3-pip
开始创建virtualenv独立环境，用来处理不同项目需要不同版本软件的问题
wget https://bootstrap.pypa.io/get-pip.py
sudo python get-pip.py
sudo python3 get-pip.py
sudo pip install virtualenv virtualenvwrapper
sudo rm -rf ~/.cache/pip get-pip.py安装virtualenv和virtualenvwrapper之后，更新~/.bashrc文件，在文档最后加上以下几行
# virtualenv and virtualenvwrapper
export WORKON_HOME=$HOME/.virtualenvs
export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3
source /usr/local/bin/virtualenvwrapper.sh建立‘cv_p3’虚拟工作环境，并在‘cv_p3’中安装组件
source ~/.bashrc
mkvirtualenv cv_p3 -p python3
workon cv_p3
pip install numpy编译和安装OpenCV（下载OpenCV4.0安装包两个，注意要在'cv_p3'环境中安装）
cd ~
wget -O opencv.zip https://github.com/opencv/opencv/archive/4.0.0.zip
wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/4.0.0.zip
unzip opencv.zip
unzip opencv_contrib.zip
cd ~/opencv-4.0.0/
mkdir build
cd build输入以下指令：
cmake -D CMAKE_BUILD_TYPE=RELEASE \-D CMAKE_INSTALL_PREFIX=/usr/local \-D INSTALL_PYTHON_EXAMPLES=ON \-D INSTALL_C_EXAMPLES=OFF \-D OPENCV_ENABLE_NONFREE=ON \-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib-4.0.0/modules \-D PYTHON_EXECUTABLE=~/.virtualenvs/cv_p3/bin/python \-D BUILD_EXAMPLES=ON ..编译OpenCV
make -j4
sudo make install
sudo ldconfig
cd ~链接OpenCV和虚拟环境‘cv_p3’
cd /usr/local/python/cv2/python-3.5
sudo mv cv2.cpython-35m-x86_64-linux-gnu.so cv2.so
cd ~/.virtualenvs/cv_p3/lib/python3.5/site-packages/
ln -s /usr/local/python/cv2/python-3.5/cv2.so cv2.so安装TensorFlow
首先测试当地是否有英伟达GPU
lspci | grep -i nvidia测试linux版本（ x86_64说明该系统是 64-bit系统，被cuda 9.1支持）
uname -m && cat /etc/*release安装linux 核的抬头
uname -r
sudo apt-get install linux-headers-$(uname -r)下载安装英伟达CUDA 10.0
sudo apt-get purge nvidia*
sudo apt-get autoremove
sudo apt-get autoclean
sudo rm -rf /usr/local/cuda*
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64 /" | sudo tee /etc/apt/sources.list.d/cuda.list
sudo apt-get update
sudo apt-get -o Dpkg::Options::="--force-overwrite" install cuda-10-0 cuda-driversDownload '7fa2af80.pub' from 'http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/'
sudo apt-key add Downloads/7fa2af80.pub
sudo apt-get update
sudo apt-get -o Dpkg::Options::="--force-overwrite" install cuda-10-0 cuda-driversReboot
echo 'export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}' >> ~/.bashrc
source ~/.bashrc
sudo ldconfig
nvidia-smiCheck CUDA
cuda-install-samples-10.0.sh ~
cd ~/NVIDIA_CUDA-10.0_Samples/5_Simulations/nbody
make
./nbody登录注册网站https://developer.nvidia.com/cudnn下载以下文件
cuDNN v7.3.1 Library for Linux [ cuda 10.0]
tar -xf cudnn-10.0-linux-x64-v7.3.1.20.tgz
sudo cp -R cuda/include/* /usr/local/cuda-10.0/include
sudo cp -R cuda/lib64/* /usr/local/cuda-10.0/lib64安装NCCL 2.3.5:
Download NCCL v2.3.5, for CUDA 10.0 -> NCCL 2.3.5 O/S agnostic and CUDA 10.0tar -xf nccl_2.3.5-2+cuda10.0_x86_64.txz
cd nccl_2.3.5-2+cuda10.0_x86_64
sudo cp -R * /usr/local/cuda-10.0/targets/x86_64-linux/
sudo ldconfig
workon cv_p3
sudo pip install -U pip six numpy wheel mock
sudo pip install -U keras_applications
sudo pip install -U keras_preprocessing安装Bazel
cd ~/
wget https://github.com/bazelbuild/bazel/releases/download/0.19.2/bazel-0.19.2-installer-linux-x86_64.sh
chmod +x bazel-0.19.2-installer-linux-x86_64.sh
./bazel-0.19.2-installer-linux-x86_64.sh --user
echo 'export PATH="$PATH:$HOME/bin"' >> ~/.bashrc
source ~/.bashrc
sudo ldconfig安装TensorFlow
cd ~/
workon cv_p3
git clone https://github.com/tensorflow/tensorflow.git
cd tensorflow
git checkout r1.13
./configure完成以下环境设置
Please specify the location of python. [Default is /usr/bin/python]: /usr/local/lib/python3.5/dist-packages
Do you wish to build TensorFlow with Apache Ignite support? [Y/n]: Y
Do you wish to build TensorFlow with XLA JIT support? [Y/n]: Y
Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: N
Do you wish to build TensorFlow with ROCm 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. [Leave empty to default to CUDA 9.0]: 10.0
Please specify the location where CUDA 10.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda-10.0
Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7]: 7.3.1
Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda-10.0]: /usr/local/cuda-10.0
Do you wish to build TensorFlow with TensorRT support? [y/N]: N
Please specify the NCCL version you want to use. If NCCL 2.2 is not installed, then you can use version 1.3 that can be fetched automatically but it may have worse performance with multiple GPUs. [Default is 2.2]: 2.3.5
Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 7.5,7.5]:
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
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用Bazel来安装TensorFlow
bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package tensorflow_pkg
cd tensorflow_pkg
pip install tensorflow*.whl
pip3 install tensorflow*.whl测试TensporFlow
python
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
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