Elam的caffe筆記之配置篇（六）：Centos6.5下編譯caffe及caffe的python3.6介面
阿新 • • 發佈：2018-11-22
Elam的caffe筆記之配置篇（六）：Centos6.5下編譯caffe及caffe的python3.6介面
配置要求：
系統：centos6.5
目標：基於CUDA8.0+Opencv3.1+Cudnnv5.1+python3.6介面的caffe框架
綜合來說，caffe的配置並沒有想象中的那麼難。還是那句話已官方文件為準，網上的教程很難找到完全對應的。
Centos系統下配置caffe 的官方文件，
http://caffe.berkeleyvision.org/install_yum.html
1.安裝前準備
一般依賴項：
sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel 剩下的依賴項：
sudo yum install gflags-devel glog-devel lmdb-devel以上就是caffe配置所需要的依賴包了，這裡我採用的方法是全部手動安裝，這樣成功率要比直接用yum高上非常多.
① Protobuf
由於我配置的是python3.6的介面，因此protobuf的版本必須大於3.0以上
https://github.com/google/protobuf/release
分別去下載cpp和python的包。
我選的是3.2版本，因此下載了protobuf-cpp-3.2.0.tar.gz和protobuf-python-3.2.0.tar.gz
tar -zxvf protobuf-cpp-3.2.0.tar.gz
cd protobuf-3.2.0
./configure
make
make check
make install
ldconfigtar -zxvf protobuf-python-3.2.0.tar.gz進入目錄之後
cd python
python setup.py build
python setup.py test
python setup.py install編譯完成後可以用一下命令確認是否安裝成功
conda list | grep protobuf② boost
http://www.boost.org/users/history/version_1_65_0.html
下載boost_1_65_0.tar.gz
tar -zxvf boost_1_65_0.tar.gz
cd boost_1_65_0
./bootstrap.sh
./b2
./b2 install完成後 若發現沒有libboost_python生成
重新
cd boost_1_65_0
./bootstrap.sh
./b2 –with-python include=”你pyconfig.h的路徑”←可用locate去尋找pyconfig.h的路徑在終端輸入
locate libboost_python3檢視/usr/local/lib/下有沒有
/usr/local/lib/libboost_python3.a
/usr/local/lib/libboost_python3.so
/usr/local/lib/libboost_python3.so.1.65.0直接建立軟連結
ln -s /usr/local/lib/libboost_python3.so.1.65.0 /usr/local/lib/libboost_python3.so這三個如果沒有，就從boost的stage資料夾下的lib資料夾當中把這三個檔案拷貝到/usr/local/lib/目錄下，然後建立軟連結
③ glog gflags lmdb
這三個依賴項直接根據caffe的官方文件的命令進行安裝編譯即可
1.glog
wget https://storage.googleapis.com/google-code-archive-downloads/v2/code.google.com/google-glog/glog-0.3.3.tar.gz
tar zxvf glog-0.3.3.tar.gz
cd glog-0.3.3
./configure
make && make install2. gflags
wget https://github.com/schuhschuh/gflags/archive/master.zip
unzip master.zip
cd gflags-master
mkdir build && cd build
export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
make && make install3.lmdb
git clone https://github.com/LMDB/lmdb
cd lmdb/libraries/liblmdb
make && make install④ hdf5
建議安裝1.8.17版本，因為anaconda自帶的hdf5也是這個版本
http://download.csdn.net/download/cumt08113684/9574931
tar -zxvf hdf5-1.8.17.tar.gz
cd hdf5-1.8.17
./configure --prefix=/usr/local/hdf5-1.8.17/
make
make check
make install
make check-install ⑤ snappy
yum install snappy⑥ leveldb
http://download.csdn.net/download/cl_gamer/4847996
tar –xvf leveldb-1.7.0.tar.gz
cd leveldb-1.7.0
make
cp libleveldb* /usr/lib/
cp –r include/leveldb /usr/local/include⑦atlas-devel
直接使用yum install atlas-devel 安裝
caffe編譯
1.caffe下載
git clone https://github.com/bvlc/caffe.git 2.caffe編譯
cd caffe
vi Makefile找到Configure build其下的
COMMON_FLAGS +=後面加上-I/usr/local/hdf5/include
LDFLAGS +=後面加上-L/usr/local/hdf5/lib
當然如果你之前路徑配的都沒問題的話，沒可以不加
修改Makefile.config
如果沒有Makefile.config,
cp Makefile.config.example Makefile.config
vi Makefile.config以下是我修改後的完整的Makefile.config,左箭頭（←）所指部分是需要修改的地方
## 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-8.0←
# 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.
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_61,code=compute_61
# 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 := /root/anaconda3←
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \←
$(ANACONDA_HOME)/include/python3.6m \←
$(ANACONDA_HOME)/lib/python3.6/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 \←
$(ANACONDA_HOME)/pkgs/python-3.6.1-2/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 /usr/local/hdf5/include←
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib64/atlas /usr/local/hdf5/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 ?= @修改完成後儲存退出。
主要需要指出的是
PYTHON_LIBRARIES: 同意確保boost_python3這個動態連結庫在ld.so.conf檔案中（記住ldconfig）或LD_LIBRARY_PATH中能找到。
INCLUDE_DIRS和LIBRARY_DIRS的話需要加上的路徑是沒有放在local或者usr資料夾下的include或者lib資料夾中的依賴項。
然後
make all -jn
make test -jn
make runtest -jn
make pycaffe -jn在安裝完成之後，如果想要匯入caffePython模組，則新增模組路徑到你的環境變數 $PYTHONPATH 中。比如在你的~/.bashrc中新增如下一行：
export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH開啟終端
python
import caffe如果沒有錯誤，表示caffe的python介面配置完成
碰到的問題
①
[[email protected] caffe]# make runtest
.build_release/tools/caffe
.build_release/tools/caffe: /usr/lib64/libstdc++.so.6: version `GLIBCXX_3.4.15' not found (required by /home/HY/caffe/caffe/.build_release/tools/../lib/libcaffe.so.1.0.0)
.build_release/tools/caffe: /usr/lib64/libstdc++.so.6: version `GLIBCXX_3.4.15' not found (required by /usr/local/lib/libopencv_core.so.3.1)
.build_release/tools/caffe: /usr/lib64/libstdc++.so.6: version `GLIBCXX_3.4.15' not found (required by /usr/local/lib/libopencv_imgcodecs.so.3.1)
make: *** [runtest] Error 1問題產生的原因:新安裝的高版本的gcc生成的動態庫沒有替換老版本的gcc的動態庫導致的
解決方案參考這個博主寫的部落格：http://blog.chinaunix.net/uid-14735472-id-5546498.html
②
Warning! ***HDF5 library version mismatched error***
The HDF5 header files used to compile this application do not match
the version used by the HDF5 library to which this application is linked.
Data corruption or segmentation faults may occur if the application continues.
This can happen when an application was compiled by one version of HDF5 but
linked with a different version of static or shared HDF5 library.
You should recompile the application or check your shared library related
settings such as 'LD_LIBRARY_PATH'.
You can, at your own risk, disable this warning by setting the environment
variable 'HDF5_DISABLE_VERSION_CHECK' to a value of '1'.
Setting it to 2 or higher will suppress the warning messages totally.
Headers are 1.8.3, library is 1.8.17這個問題產生的原因是hdf5版本不一致所造成的系統本身已經安裝的是1.8.3版本，但是anaconda3所帶的hdf5的版本的是1.8.17，所以在編譯的時候會發生版本衝突。
為了解決這個相容性問題，只能是重新編譯hdf5-1.8.17版本，具體方法參考上文安裝依賴項中的hdf5編譯安裝方法
③
[[email protected] caffe]# make pycaffe
CXX/LD -o python/caffe/_caffe.so python/caffe/_caffe.cpp
python/caffe/_caffe.cpp:1:52: fatal error: Python.h: No such file or directory
#include <Python.h> // NOLINT(build/include_alpha)
^
compilation terminated.
make: *** [python/caffe/_caffe.so] Error 1顧名思義找不到Python.h這個標頭檔案，於是我利用
find / -name "Python.h"去找檔案的路徑發現
/root/anaconda2/include/python2.7/Python.h
/root/anaconda2/pkgs/python-2.7.13-0/include/python2.7/Python.h
/root/anaconda3/include/python3.6m/Python.h
/root/anaconda3/pkgs/python-3.6.1-2/include/python3.6m/Python.h可以看到第三個路徑是上面修改Makefile.config時修改PYTHON_INCLUDE時應該要改的路徑，開啟Makefile.config到指定位置，果然發現自己的路徑配置錯誤，當時指向了python3.6而不是python3.6m。改成python3.6m之後重新make不再出現這個問題，因此這個問題出現的原因就是python的路徑配置錯誤。
④
The following directory should be added to compiler include paths:
/home/HY/boost_1_59_0
The following directory should be added to linker library paths:
/home/HY/boost_1_59_0/stage/lib這個問題是最早幾次編譯caffe的時候出現的問題，查了好久是因為在編譯boost的時候沒有把python模組編譯出來，如果安裝上文正確編譯libboost_python3的話 並不會出現這個問題，出現這個問題的朋友，可以進入boost資料夾編譯一下python模組，因為boost是可以重複編譯的，命令參考上文
⑤ 編譯boost庫時
...failed gcc.compile.c++ bin.v2/libs/python/build/gcc-4.8.2/release/link-static/threading-multi/numpy/scalars.o...
gcc.compile.c++ bin.v2/libs/python/build/gcc-4.8.2/release/link-static/threading-multi/numpy/ufunc.o
In file included from ./boost/python/detail/prefix.hpp:13:0,
from ./boost/python/args.hpp:8,
from ./boost/python.hpp:11,
from ./boost/python/numpy/internal.hpp:17,
from libs/python/src/numpy/ufunc.cpp:8:
./boost/python/detail/wrap_python.hpp:50:23: fatal error: pyconfig.h: No such file or directory
# include <pyconfig.h>
^
compilation terminated.這個問題和第四個問題出現的地方差不多，都是在編譯boost的python模組的時候發生的，這個問題是因為編譯的時候找不到pyconfig.h。
解決方法:編譯boost_python的時候use
./b2 --with-python include="path/to/pyconfig.h"引號裡面的路徑可以利用
locate pyconfig.h去確定
⑥ 在make runtest的時候
error while loading shared libraries: libpython3.6m.so.1.0: cannot open shared object file: No such file or directory 顧名思義找不到libpython3.6m.so.1.0
解決方法利用locate去找到libpython3.6m.so.1.0所在位置
然後複製libpython3.6m.so.1.0到/usr/local/lib目錄下
⑦ NVCC的警告
產生原因好像在CUDA8.0以後把compute 20，21都棄用了，因此解決方法也很簡單。
vi Makefile.conf把CUDA_ARCH中
-gencode arch=compute_20,code=sm_20和-gencode arch=compute_20,code=sm_21直接去掉