Please use a VPN when doing the following
Install the necessary support software
sudo apt-get update sudo apt-get upgrade
Install toolkits such as images, videos and HMI
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 build-essential sudo apt-get install cmake git unzip zip sudo apt-get install python2.7-dev python3.5-dev python3.6-dev pylint
Start to create a virtualenv independent environment to deal with the problem that different projects require different versions of software
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
After installing virtualenv and virtualenvwrapper , update the ~ / .bashrc file and add the following lines at the end of the document
# virtualenv and virtualenvwrapper export WORKON_HOME=$HOME/.virtualenvs export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3 source /usr/local/bin/virtualenvwrapper.sh
Create 'ruzw' virtual working environment and install numpy in 'ruzw'
source ~/.bashrc mkvirtualenv ruzw -p python3 workon ruzw pip install numpy
Compile and install OpenCV
cd ~ wget -O opencv.zip https://github.com/Itseez/opencv/archive/3.3.0.zip wget -O opencv_contrib.zip https://github.com/Itseez/opencv_contrib/archive/3.3.0.zip unzip opencv.zip unzip opencv_contrib.zip
Create a build path for CMake operations
cd ~/opencv-3.3.0/ mkdir build cd build
Enter the following commands:
cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D WITH_CUDA=OFF \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib-3.3.0/modules \
-D BUILD_EXAMPLES=ON ..
Now compile OpenCV
make -j4
The next step is to install OpenCV3.3
sudo make install sudo ldconfig cd ~
Link OpenCV and virtual environment 'ruzw'
cd ~/.virtualenvs/ruzw/lib/python3.5/site-packages/ ln -s /usr/local/lib/python3.5/dist-packages/cv2.cpython-35m-x86_64-linux-gnu.so cv2.so cd ~
Test OpenCV installation and linking
workon ruzw python import cv2 cv2.__version__ '3.3.0'
安装TensorFlow（tensorflow 1.7.0 GPU、CUDA Toolkit 9.1 、cuDNN 7.1.2）
First test if there is an NVIDIA GPU locally
lspci | grep -i nvidia
Test the linux version (x86_64 indicates that the system is a 64-bit system, supported by cuda 9.1)
uname -m && cat /etc/*release
Heads up to install the linux kernel
uname -r sudo apt-get install linux-headers-$(uname -r)
Download and install NVIDIA KUDA Toolkit
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.1.85-1_amd64.deb sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub sudo dpkg -i cuda-repo-ubuntu1604_9.1.85-1_amd64.deb sudo apt-get update sudo apt-get install cuda-9.1
Reboot the system to load the Nvidia driver
reboot
Edit ~/.bashrc file
vim ~/.bashrc
add on the last line
export PATH=/usr/local/cuda-9.1/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-9.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
Test drive version
source ~/.bashrc sudo ldconfig nvidia-smi
Log in to the registration website https://developer.nvidia.com/cudnn to download the following files
cuDNN v7.1.2 Runtime Library for Ubuntu16.04 (Deb)
cuDNN v7.1.2 Developer Library for Ubuntu16.04 (Deb)
cuDNN v7.1.2 Code Samples and User Guide for Ubuntu16.04 (Deb)
Go to the download file folder in the terminal and do the following
sudo dpkg -i libcudnn7_7.1.2.21-1+cuda9.1_amd64.deb sudo dpkg -i libcudnn7-dev_7.1.2.21-1+cuda9.1_amd64.deb sudo dpkg -i libcudnn7-doc_7.1.2.21-1+cuda9.1_amd64.deb
Install confirmation cuDNN
cp -r /usr/src/cudnn_samples_v7/ $HOME cd $HOME/cudnn_samples_v7/mnistCUDNN make clean && make ./mnistCUDNN
Install libcupti (required)
sudo apt-get install libcupti-dev echo 'export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
Install Bazel (required)
sudo apt-get install openjdk-8-jdk wget https://github.com/bazelbuild/bazel/releases/download/0.11.1/bazel_0.11.1-linux-x86_64.deb sudo dpkg -i bazel_0.11.1-linux-x86_64.deb
Python3.0 version
sudo apt-get install python3-numpy python3-dev python3-pip python3-wheel
Install TensorFlow
source ~/.bashrc sudo ldconfig wget https://github.com/tensorflow/tensorflow/archive/v1.7.0.zip unzip v1.7.0.zip cd tensorflow-1.7.0 ./configure
Set Python default address
Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3
Complete the following environment settings
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]: Y Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: Y Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: Y 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]: 9.1 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]: 7.1.2 Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/lib/x86_64-linux-gnu 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] 5.0 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 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
Install TensorFlow with Bazel (under the tensorflow-1.7.0 path, it may last 1~2 hours)
sudo ln -s /usr/local/cuda/include/crt/math_functions.hpp /usr/local/cuda/include/math_functions.hpp bazel build --config=opt --config=cuda --incompatible_load_argument_is_label=false //tensorflow/tools/pip_package:build_pip_package bazel-bin/tensorflow/tools/pip_package/build_pip_package tensorflow_pkg
Activate virtual environment (python3)
cd tensorflow_pkg pip3 install tensorflow*.whl
Test TensorFlow installation results
python
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sex = tf.Session ()
print (sess.run (hello))
If the following results appear, TensorFlow is installed successfully
b'Hello, TensorFlow'
Install Keras
First make sure you are in a virtual environment (ruzw)
Install the necessary basic software
pip install scipy matplotlib pillow pip install imutils h5py requests progressbar2 pip install scikit-learn scikit-image
Install Keras
pip install hardGet familiar with the ~ / .keras / keras.json file, make sure image_data_format is set to channels_last and backend is set to tensorflow
{
"image_data_format": "channels_last",
"backend": "tensorflow",
"epsilon": 1e-07,
"floatx": "float32"
}
install mxnet
Copy the 0.11.0 branch in mxnet
cd ~ git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet --branch 0.11.0 cd mxnet make -j4 USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1
Link to our ruzw virtual environment
cd ~/.virtualenvs/ruzw/lib/python3.5/site-packages/ ln -s ~/mxnet/python/mxnet mxnet cd ~
test mxnet
python import mxnetNote that mxnet documents cannot be deleted!