ubuntu14.04配置caffe，cuda8.0+cudnn v6

最新推荐文章于 2018-04-25 14:42:12 发布

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最新推荐文章于 2018-04-25 14:42:12 发布

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本文链接：https://blog.youkuaiyun.com/xddwz/article/details/73194844

本文详细介绍了如何安装配置OpenCV 3.2及Caffe，并提供了安装所需的依赖库列表，涵盖了从基本环境配置到CUDA支持的具体步骤，以及如何解决常见问题。

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1、安装opencv3.2

安装相关的依赖

sudo apt-add-repository universe
sudo apt update -y && sudo apt upgrade -y
sudo apt-get install -y build-essential make cmake cmake-curses-gui g++ libavformat-dev libavutil-dev libswscale-dev libv4l-dev libeigen3-dev libglew-dev libgtk2.0-dev pkg-config libjpeg8-dev libtiff5-dev libjasper-dev libpng12-dev libavcodec-dev libswscale-dev libv4l-dev libxvidcore-dev libx264-dev libgtk-3-dev libatlas-base-dev gfortran python2.7-dev python-pip python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libjasper-dev libdc1394-22-dev ffmpeg

下载opencv3.2和opencv_contrib
cd && wget -O opencv.zip https://github.com/opencv/opencv/archive/3.2.0.zip && unzip opencv.zip
cd && wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/3.2.0.zip && unzip opencv_contrib.zip
cd opencb-3-2-0
mkdir build && cd build

编译
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/usr/local -DOPENCV_EXTRA_MODULES_PATH=~/opencv_contrib-3.2.0/modules -DWITH_CUDA=ON -DCUDA_ARCH_BIN="5.3" -DCUDA_ARCH_PTX="" -DBUILD_EXAMPLES=ON -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=OFF -DCUDA_FAST_MATH=ON ..

如果make的时候卡在ippicv_linux_20151201.tgz ，则从下面链接中下载ippicv_linux_20151201.tgz ，然后替换opencv-3.2.0/3rdparty/ippicv/downloads/linux-808b791a6eac9ed78d32a7666804320e/下的同名文件，再次cmake，并且make && make install
https://raw.githubusercontent.com/Itseez/opencv_3rdparty/81a676001ca8075ada498583e4166079e5744668/ippicv/ippicv_linux_20151201.tgz

2 Install cudnn v6 for cuda 8
tar -zxvf cudnn-8.0-linux-x64-v6.0.tgz
cd cuda
sudo cp cuda/include/*.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
cd /usr/local/cuda/lib64
sudo ldconfig -v

3、安装caffe

安装相关依赖

sudo apt-get install libprotobuf-dev protobuf-compiler gfortran libboost-dev cmake libleveldb-dev libsnappy-dev libboost-thread-dev libboost-system-dev libatlas-base-dev libhdf5-serial-dev libgflags-dev libatlas-dev libboost-all-dev libprotobuf-dev libgflags-dev libhdf5-dev libleveldb-dev liblmdb-dev libsnappy-dev python-pip libgoogle-glog-dev liblmdb-dev gcc-4.7 g++-4.7 python-numpy -y
git clone https://github.com/BVLC/caffe.git && cd caffe
sudo cp Makefile.config.example Makefile.config
sudo gedit Makefile.config # Change below status
USE_CUDNN :=1  # Line 5
# save
sudo make -j10 all  # Some warning message is OK, don't worry!
sudo make -j10 py
# Test performance
build/tools/caffe time --model=models/bvlc_alexnet/deploy.prototxt --gpu=0 #GPU鐗?
build/tools/caffe time --model=models/bvlc_alexnet/deploy.prototxt      #CPU鐗?
sudo make -j4 test
sudo make -j4 runtest # Optional, take long time
sudo make py
sudo make distribute # Optional

最后用手写数字集测试

# mnist Test
sh data/mnist/get_mnist.sh
sh examples/mnist/create_mnist.sh
./examples/mnist/train_lenet.sh