ubuntu14.04 + cuda 7.5 +cudnn v3 +opencv3 配置
2017-05-12 09:54
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序
(1)感谢网上小伙伴分享的经验,无论是bug解决办法还是cudnn等资源,让我收益良多,有了写博客分享,互帮互助的想法。
(2)记录自己的安装历程,以备ubuntu再次崩溃。。。
资源链接:
链接: https://pan.baidu.com/s/1o8dmxcu 密码: 4ts4
配置过程
1. Cuda7.5安装
验证系统过程,请参考官方文档。
1)下载cuda7.5,链接在前面以给出。
2)执行以下代码
64位系统
2. Cudnnv4 安装
1)下载cudnnv4,链接前面以给出,
由于后面要配置fast-rcnn,安装的v4版本。
2)安装过程
更新软连接
3)环境变量配置
/etc/profile中添加cuda环境变量
3. Opencv3 安装
1))下载opencv3脚本,网上大神写好的,前面已经给出资源地址
2) 进入Install-OpenCV/Ubuntu/3.0
4 Caffe 安装
1)安装caffe以及所需依赖包
下载Caffe安装包,链接前面以及给出。
下载Anaconda, 前面以给出链接。
执行(注意自行修改版本号)
在/etc/ld.so.conf最后加入以下路径
5 Caffe 编译
进入caffe-master目录,执行:
cp Makefile.config.example Makefile.config 修改其中一些路径,配置文件参考
## Refer to http://caffe.berkeleyvision.org/installation.html # Contributions simplifying and improving our build system are welcome!
# cuDNN acceleration switch (uncomment to build with cuDNN).
USE_CUDNN := 1
# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1
# uncomment to disable IO dependencies and corresponding data layers
#USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0
# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
# You should not set this flag if you will be reading LMDBs with any
# possibility of simultaneous read and write
# ALLOW_LMDB_NOLOCK := 1
# Uncomment if you're using OpenCV 3
OPENCV_VERSION := 3
# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++
# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr
# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the *_50 lines for compatibility.
CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
-gencode arch=compute_20,code=sm_21 \
-gencode arch=compute_30,code=sm_30 \
-gencode arch=compute_35,code=sm_35 \
-gencode arch=compute_50,code=sm_50 \
-gencode arch=compute_50,code=compute_50
# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := atlas
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas
# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib
# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# MATLAB_DIR := /Applications/MATLAB_R2012b.app
# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
PYTHON_INCLUDE := /usr/include/python2.7 \
/usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
ANACONDA_HOME := $(HOME)/anaconda2
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
$(ANACONDA_HOME)/include/python2.7 \
$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
LIBRARIES += glog gflags protobuf leveldb snappy \
lmdb boost_system hdf5_hl hdf5 m \
opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs
# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.5m
# PYTHON_INCLUDE := /usr/include/python3.5m \
# /usr/lib/python3.5/dist-packages/numpy/core/include
# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/lib
# PYTHON_LIB := $(ANACONDA_HOME)/lib
# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib
# Uncomment to support layers written in Python (will link against Python libs)
# WITH_PYTHON_LAYER := 1
# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib
# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171 # DEBUG := 1
# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0
# enable pretty build (comment to see full commands)
Q ?= @
编译
make all -j8
make test
make runtest
注:以上是参考大神的博客以及结合自己配置的过程总结的,配置过程中一定要耐心,细心,用心,在配置中我遇到了各种问题,并不是那么顺利,但是有了前面的经验,
已经有信心去解决错误了。
参考博客:
http://blog.csdn.net/ubunfans/article/details/47724341#
(1)感谢网上小伙伴分享的经验,无论是bug解决办法还是cudnn等资源,让我收益良多,有了写博客分享,互帮互助的想法。
(2)记录自己的安装历程,以备ubuntu再次崩溃。。。
资源链接:
链接: https://pan.baidu.com/s/1o8dmxcu 密码: 4ts4
配置过程
1. Cuda7.5安装
验证系统过程,请参考官方文档。
1)下载cuda7.5,链接在前面以给出。
2)执行以下代码
sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb sudo apt-get update sudo apt-get install cuda sudo reboot3)环境配置
64位系统
$export PATH=/usr/local/cuda-7.5/bin:$PATH $ export LD_LIBRARY_PATH=/usr/local/cuda-7.5/lib64:$LD_LIBRARY_PATH32位系统
$export PATH=/usr/local/cuda-7.5/bin:$PATH $export LD_LIBRARY_PATH=/usr/local/cuda-7.5/lib:$LD_LIBRARY_PATH
2. Cudnnv4 安装
1)下载cudnnv4,链接前面以给出,
由于后面要配置fast-rcnn,安装的v4版本。
2)安装过程
tar -zxvf cudnn-7.5-linux-x64-v5.0-ga.tgz cd cuda sudo cp lib/lib* /usr/local/cuda/lib64/ sudo cp include/cudnn.h /usr/local/cuda/include/
更新软连接
cd /usr/local/cuda/lib64/ sudo chmod +r libcudnn.so.4.0.4 sudo ln -sf libcudnn.so.4.0.4 libcudnn.so.4 sudo ln -sf libcudnn.so.4 libcudnn.sohttp://write.blog.csdn.net/postedit?ref=toolbar&ticket=ST-221158-qpbGKJ1CbDUnyRDKnhhT-passport.csdn.net sudo ldconfig
3)环境变量配置
/etc/profile中添加cuda环境变量
PATH=/usr/local/cuda/bin:$PATH export PATH source /etc/profile/etc/ld.so.conf.d/加入文件 cuda.conf
/usr/local/cuda/lib64 sudo ldconfig
3. Opencv3 安装
1))下载opencv3脚本,网上大神写好的,前面已经给出资源地址
2) 进入Install-OpenCV/Ubuntu/3.0
sh sudo ./opencv3_0_0.sh
4 Caffe 安装
for req in $(cat requirements.txt); do pip install $req; done
1)安装caffe以及所需依赖包
下载Caffe安装包,链接前面以及给出。
sudo apt-get install build-essential # basic requirement sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler #required by caffe2)安装Atlas
sudo apt-get install libatlas-base-dev3)安装Python环境
下载Anaconda, 前面以给出链接。
执行(注意自行修改版本号)
bash Anaconda-4.3.1-Linux-x86_64.s<em>h</em>添加Anaconda Library Path
在/etc/ld.so.conf最后加入以下路径
/home/username/anaconda/lib在~/.bashrc最后添加下边路径
export LD_LIBRARY_PATH="/home/username/anaconda/lib:$LD_LIBRARY_PATH"安装python 依赖库,进入caffe-master/python,执行:
for req in $(cat requirements.txt); do pip install $req; done
5 Caffe 编译
进入caffe-master目录,执行:
cp Makefile.config.example Makefile.config 修改其中一些路径,配置文件参考
## Refer to http://caffe.berkeleyvision.org/installation.html # Contributions simplifying and improving our build system are welcome!
# cuDNN acceleration switch (uncomment to build with cuDNN).
USE_CUDNN := 1
# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1
# uncomment to disable IO dependencies and corresponding data layers
#USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0
# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
# You should not set this flag if you will be reading LMDBs with any
# possibility of simultaneous read and write
# ALLOW_LMDB_NOLOCK := 1
# Uncomment if you're using OpenCV 3
OPENCV_VERSION := 3
# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++
# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr
# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the *_50 lines for compatibility.
CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
-gencode arch=compute_20,code=sm_21 \
-gencode arch=compute_30,code=sm_30 \
-gencode arch=compute_35,code=sm_35 \
-gencode arch=compute_50,code=sm_50 \
-gencode arch=compute_50,code=compute_50
# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := atlas
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas
# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib
# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# MATLAB_DIR := /Applications/MATLAB_R2012b.app
# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
PYTHON_INCLUDE := /usr/include/python2.7 \
/usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
ANACONDA_HOME := $(HOME)/anaconda2
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
$(ANACONDA_HOME)/include/python2.7 \
$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
LIBRARIES += glog gflags protobuf leveldb snappy \
lmdb boost_system hdf5_hl hdf5 m \
opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs
# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.5m
# PYTHON_INCLUDE := /usr/include/python3.5m \
# /usr/lib/python3.5/dist-packages/numpy/core/include
# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/lib
# PYTHON_LIB := $(ANACONDA_HOME)/lib
# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib
# Uncomment to support layers written in Python (will link against Python libs)
# WITH_PYTHON_LAYER := 1
# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib
# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171 # DEBUG := 1
# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0
# enable pretty build (comment to see full commands)
Q ?= @
编译
make all -j8
make test
make runtest
make pycaffe
注:以上是参考大神的博客以及结合自己配置的过程总结的,配置过程中一定要耐心,细心,用心,在配置中我遇到了各种问题,并不是那么顺利,但是有了前面的经验,
已经有信心去解决错误了。
参考博客:
http://blog.csdn.net/ubunfans/article/details/47724341#
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