docker--cuda10.1-ubuntu18.04-opencv4.0-caffe
文章目录
- 1 cuda镜像
- 2 ubuntu18.04基础环境
- 3 安装python3.6
- 4 opencv4.0
- 5 caffe
1 cuda镜像
# 查找docker源中的cuda镜像
docker search nvida/cuda # 此镜像默认是cuda10.0，os是ubuntu16.04
# 查询docker hub网站链接到https://gitlab.com/nvidia/cuda/blob/ubuntu18.04/10.1/devel/cudnn7/Dockerfile到dockerfile内容，发现可以定制docker进行内容，因此下载如下docker镜像
docker pull nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04
2 ubuntu18.04基础环境
apt update
apt install vim -y
# ubuntu换国内源
vim /etc/apt/sources.list
# 文件头新增清华源：
deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic main restricted universe multiverse
deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic main restricted universe multiverse
deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-updates main restricted universe multiverse
deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-updates main restricted universe multiverse
deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-backports main restricted universe multiverse
deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-backports main restricted universe multiverse
deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-security main restricted universe multiverse
deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-security main restricted universe multiverse
deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-proposed main restricted universe multiverse
deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-proposed main restricted universe multiverse
apt update
apt upgrade
apt install wget git cmake -y
3 安装python3.6
apt install python3 -y
apt install python3-pip -y
pip3 install --upgrade pip
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
4 opencv4.0
# 安装依赖包
apt install build-essential libgtk2.0-dev libavcodec-dev libavformat-dev libjpeg-dev libswscale-dev libtiff5-dev -y
pip install numpy
# 下载opencv 4.0
https://opencv.org/releases.html
# 源码编译安装
cd opencv-4.0.1
mkdir build
cd build
cmake ..
make all -j8
make install
问题：
-
– Could not find OpenBLAS lib. Turning OpenBLAS_FOUND
apt install libopenblas-dev libopenblas-base liblapacke-dev -y # 软连接正确的库路径 ln -s /usr/include/lapacke.h /usr/include/x86_64-linux-gnu -
无法编译python3
解决方式如下：
cmake -DBUILD_LIBPROTOBUF_FROM_SOURCES=ON\ -D CMAKE_BUILD_TYPE=RELEASE \ -D CMAKE_INSTALL_PREFIX=/usr/local \ -D INSTALL_C_EXAMPLES=OFF \ -D INSTALL_PYTHON_EXAMPLES=ON \ -D BUILD_EXAMPLES=ON \ -D PYTHON3_EXECUTABLE=$(which python3) \ -D PYTHON_INCLUDE_DIR=$(python3 -c "from distutils.sysconfig import get_python_inc; print(get_python_inc())") \ -D PYTHON_INCLUDE_DIR2=$(python3 -c "from os.path import dirname; from distutils.sysconfig import get_config_h_filename; print(dirname(get_config_h_filename()))") \ -D PYTHON_LIBRARY=$(python3 -c "from distutils.sysconfig import get_config_var;from os.path import dirname,join ; print(join(dirname(get_config_var('LIBPC')),get_config_var('LDLIBRARY')))") \ -D PYTHON3_NUMPY_INCLUDE_DIRS=$(python3 -c "import numpy; print(numpy.get_include())") \ -D PYTHON3_PACKAGES_PATH=$(python3 -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())") \ -D WITH_QT=OFF \ -D WITH_TBB=ON \ -D WITH_V4L=ON \ -D WITH_EIGEN=ON \ -D CUDA_GENERATION=Kepler .. -
pyopencv_generated_include.h’ failed
./opencv 目录下运行
python3 ./modules/python/src2/gen2.py ./build/modules/python_bindings_generator ./build/modules/python_bindings_generator/headers.txt
5 caffe
-
源码安装
# 安装支持库 apt install doxygen libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler libatlas-base-dev libgflags-dev libgoogle-glog-dev liblmdb-dev -y apt install --no-install-recommends libboost-all-dev -y # 下载编译nccl git clone https://github.com/NVIDIA/nccl.git cd nccl make install -j8 # 升级默认的cmake 3.10.2到最新版3.14 wget https://cmake.org/files/v3.14/cmake-3.14.0-Linux-x86_64.tar.gz mv cmake-3.14.0-Linux-x86_64 /opt/cmake ln -sf /opt/cmake/bin/* /usr/bin/ # 下载caffe git clone https://github.com/BVLC/caffe.git cd caffe/ cp Makefile.config.example Makefile.config # 预编译 mkdir build cd build camke .. # 安装测试 make all -j8 make runtest -j8 # 安装pycaffe make pycaffe -j8 -
配置
-
使用opencv4
./include/caffe/common.hpp第70行后新增如下内容：
// Supporting OpenCV4 #if (CV_MAJOR_VERSION == 4) #define CV_LOAD_IMAGE_COLOR cv::IMREAD_COLOR #define CV_LOAD_IMAGE_GRAYSCALE cv::IMREAD_GRAYSCALE #endif -
配置./caffe/Makefile.config
## 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 := 1 # USE_LEVELDB := 0 # USE_LMDB := 0 # This code is taken from https://github.com/sh1r0/caffe-android-lib # USE_HDF5 := 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 := 4 # 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-10.1 # 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 through *_61 lines for compatibility. # For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility. # For CUDA >= 9.0, comment the *_20 and *_21 lines for compatibility. CUDA_ARCH := -gencode arch=compute_30,code=sm_30 \ -gencode arch=compute_35,code=sm_35 \ -gencode arch=compute_50,code=sm_50 \ -gencode arch=compute_52,code=sm_52 \ -gencode arch=compute_60,code=sm_60 \ -gencode arch=compute_61,code=sm_61 \ -gencode arch=compute_75,code=sm_75 \ -gencode arch=compute_75,code=compute_75 # BLAS choice: # atlas for ATLAS (default) # mkl for MKL # open for OpenBlas BLAS := open # 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)/anaconda # PYTHON_INCLUDE := $(ANACONDA_HOME)/include \ # $(ANACONDA_HOME)/include/python2.7 \ # $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include # Uncomment to use Python 3 (default is Python 2) PYTHON_LIBRARIES := boost_python3 python3.6m PYTHON_INCLUDE := /usr/include/python3.6m \ /usr/lib/python3.6/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 # NCCL acceleration switch (uncomment to build with NCCL) # https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0) USE_NCCL := 1 # Uncomment to use `pkg-config` to specify OpenCV library paths. # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.) # USE_PKG_CONFIG := 1 # N.B. both build and distribute dirs are cleared on `make clean` BUILD_DIR := build DISTRIBUTE_DIR := distribute # Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171 # DEBUG := 1 # The ID of the GPU that 'make runtest' will use to run unit tests. TEST_GPUID := 0 # enable pretty build (comment to see full commands) Q ?= @ -
配置./caffe/Makefile
# 1 将 LIBRARIES += glog gflags protobuf boost_system boost_filesystem m # 修改为： LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial # 2 在第331行添加 # NCCL acceleration configuration ifeq ($(USE_NCCL), 1) LIBRARIES += nccl COMMON_FLAGS += -DUSE_NCCL endif -
配置./caffe/build/CMakeCache.txt
//Path to a program. PYTHON_EXECUTABLE:FILEPATH=/usr/bin/python3.6 //Path to a file. PYTHON_INCLUDE_DIR:PATH=/usr/include/python3.6 //Path to a library. PYTHON_LIBRARY:FILEPATH=/usr/lib/x86_64-linux-gnu/libpython3.6m.so //Boost python library (debug) Boost_PYTHON_LIBRARY_DEBUG:FILEPATH=/usr/lib/x86_64-linux-gnu/libboost_python3-py36.so //Boost python library (release) Boost_PYTHON_LIBRARY_RELEASE:FILEPATH=/usr/lib/x86_64-linux-gnu/libboost_python3-py36.so //Build Caffe with NCCL library support USE_NCCL:BOOL=ON
-
-
安装python依赖
pip install scipy scikit-image pandas matplotlib protobuf -
问题
-
CUDA_cublas_device_LIBRARY (ADVANCED)
升级默认的
cmake 3.10.2到最新版3.14解决CUDA_cublas_device_LIBRARY问题wget https://cmake.org/files/v3.14/cmake-3.14.0-Linux-x86_64.tar.gz
mv cmake-3.14.0-Linux-x86_64 /opt/cmake ln -sf /opt/cmake/bin/* /usr/bin/然后执行命令检查一下：
cmake --version -
出现nvcc fatal : Unsupported gpu architecture ‘compute_20
cd ./caffe/cmake vim Cuda.cmake # 移除第7行的20和21 set(Caffe_known_gpu_archs "30 35 50 52 60 61 75") -
/usr/lib/gcc/x86_64-linux-gnu/7/…/…/…/x86_64-linux-gnu/libboost_python.so: undefined reference to `PyClass_Type’
…….
tools/CMakeFiles/caffe.bin.dir/build.make:127: recipe for target ‘tools/caffe’ failed
# 修改CMakeCache.txt中boost库 //Boost python library (debug) Boost_PYTHON_LIBRARY_DEBUG:FILEPATH=/usr/lib/x86_64-linux-gnu/libboost_python3-py36.so //Boost python library (release) Boost_PYTHON_LIBRARY_RELEASE:FILEPATH=/usr/lib/x86_64-linux-gnu/libboost_python3-py36.so -
– Could NOT find GFlags (missing: GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
– Could NOT find Glog (missing: GLOG_INCLUDE_DIR GLOG_LIBRARY)
–Could NOT find LMDB (missing: LMDB_INCLUDE_DIR LMDB_LIBRARIES)
apt install libgflags-dev libgoogle-glog-dev liblmdb-dev -y -
src/caffe/layers/hdf5_output_layer.cu:4:10: fatal error: hdf5.h: No such file or directory
# ./caffe/Makefile中 LIBRARIES += glog gflags protobuf boost_system boost_filesystem m # 修改为： LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial -
F0325 11:10:19.416651 9160 caffe.cpp:245] Multi-GPU execution not available - rebuild with USE_NCCL
NCC is used for multi gpu communication. You have to enable USE_NCCL := 1 in makefile.config. Then rebuild. – lnman Apr 9 '17 at 13:05
# 1 下载编译nccl git clone https://github.com/NVIDIA/nccl.git cd nccl make CUDA_HOME=/usr/local/cuda-10.1/ make install -j8 ldconfig # 2 在Makefile.config文件中 USE_NCCL := 1 # 3 在Makefile中第331行添加 # NCCL acceleration configuration ifeq ($(USE_NCCL), 1) LIBRARIES += nccl COMMON_FLAGS += -DUSE_NCCL endif # 4 修改CMakeCache.txt //Build Caffe with NCCL library support USE_NCCL:BOOL=ON # 5 重新编译caffe sudo make clean sudo make all -j