Ubuntu 16.04 Anaconda3+opencv3.4.6+Caffe+多GPU+CUDA10.1

默認已經安裝NVIDIA顯卡驅動以及CUDA、cudnn
博主的安裝環境是是Ubuntu16.04 、NVIDIA 2080TI最新顯卡、CUDA10.1、CUDNN10.1
如果沒有安裝請自行解決相關安裝

Anaconda安裝

  wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-4.2.0-Linux-x86_64.sh #如果沒有，則需要安裝wget
  bash Anaconda3-4.2.0-Linux-x86_64.sh #安裝anaconda,一路yes

其他問題請查閱相關資料，建議添加中科大鏡像，速度要快點、

Caffe下載

sudo apt install git
git clone https://github.com/BVLC/caffe.git #下載linux版的caffe

這裏下載速度真的慢，可以直接去網站上下載，然後進行解壓

opencv安裝

這裏根據CUDA版本問題，樓主的版本是CUDA10.1。
opencv3.2一直無法安裝，所以建議安裝opencv-3.4.6.在這裏折騰了很久很久，親測，安裝3.4.6版本沒有問題

進入官網 : http://opencv.org/releases.html , 選擇 3.4.6 版本的 source , 下載 opencv-3.4.6.zip
解壓到你要安裝的位置，命令行進入已解壓的文件夾 opencv-3.4.6目錄下，執行：

mkdir build       #創建編譯的文件目錄
cd build
cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..
#  cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D FORCE_VTK=ON -D WITH_TBB=ON -D WITH_V4L=ON -D WITH_QT=ON -D WITH_OPENGL=ON -D WITH_CUBLAS=ON -D CUDA_NVCC_FLAGS="-D_FORCE_INLINES --expt-relaxed-constexpr" -D WITH_GDAL=ON -D WITH_XINE=ON -D BUILD_EXAMPLES=ON ..
make -j8  # CPU核數多就多設置一點

然後編譯，編譯成功後安裝：

sudo make install #安裝

安裝完成後通過查看 opencv 版本驗證是否安裝成功：
pkg-config --modversion opencv

caffe文件修改

在此之前，我們得加入以下環境變量

cd /home/user
gedit ~/.bashrc

在文件末尾添加以下兩行（注意 usr爲博主自己用戶名，筆者這裏爲amax,請根據情況自行修改）

export LD_LIBRARY_PATH=/home/amax/anaconda3/lib:$LD_LIBRARY_PATH
export PYTHONPATH=/home/amax/caffe/python:$PYTHONPATH

然後

source ~/.bashrc

然後進入caffe目錄下

cd /home/user/caffe  #進入caffe目錄
cp Makefile.config.example Makefile.config

官方推薦用kate修改make文件，不過 gedit也行
sudo apt-get install kate
博主vi也用不慣，就用low b gedit了
接着

Makefile.config文件修改

sudo gedit Makefile.config

1.查找
USE_CUDNN := 1
USE_OPENCV := 1
OPENCV_VERSION := 3
WITH_PYTHON_LAYER := 1 (這一句大概在94行自行查找 ctrl+F)
全部取消註釋

39-45行修改，刪除掉compute_20 21

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

2.然後修改python目錄，因爲採用的是anaconda，所以
註釋掉原先的python2.7引用目錄，修改爲anaconda
大概70-87行，這裏的usr不修改

# 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)/anaconda3
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
		$(ANACONDA_HOME)/include/python3.7m \
		$(ANACONDA_HOME)/lib/python3.7m/site-packages/numpy/core/include

# Uncomment to use Python 3 (default is Python 2)
PYTHON_LIBRARIES := boost_python-py35 python3.5m 
# 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

*注意PYTHON_LIBRARIES修改boost_python-py35,在/usr/lib/x86_64-linux-gnu目錄下查看是否有libboost_python-py35.so*
3. 大概97-100行
註釋原先的INCLUDE_DIRS和LIBRARY_DIRS
修改爲，並添加LINKFLAGS := -Wl,-rpath,$(ANACONDA_HOME)/lib

# Whatever else you find you need goes here.
# INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
# LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial
LINKFLAGS := -Wl,-rpath,$(ANACONDA_HOME)/lib #添加此行
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial/

ok! Makefile.config修改完成！保存關閉！
4. cuda10.1用戶請注意
終端輸入

find . -type f -exec sed -i -e 's^"hdf5.h"^"hdf5/serial/hdf5.h"^g' -e 's^"hdf5_hl.h"^"hdf5/serial/hdf5_hl.h"^g' '{}' \;

cd /usr/lib/x86_64-linux-gnu
sudo ln -s libhdf5_serial.so.10.1.0 libhdf5.so
sudo ln -s libhdf5_serial_hl.so.10.0.2 libhdf5_hl.so

5. 進入caffe/python環境目錄下

cd python
for req in $(cat requirements.txt); do pip install $req; done

如果報錯

for req in $(cat requirements.txt); do sudo -H pip install $req --upgrade; done

Makefile文件修改

返回caffe目錄
cd ..

sudo  gedit Makefile

修改429行
1. 註釋NVCCFLAGS += -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
修改爲
NVCCFLAGS += -D_FORCE_INLINES -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)

2. LIBRARIES修改大約181行左右
註釋掉
# LIBRARIES += glog gflags protobuf boost_system boost_filesystem m

修改爲
LIBRARIES += glog gflags protobuf leveldb snappy \ lmdb boost_system boost_filesystem hdf5_hl hdf5 m \ opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs opencv_videoio

ok！保存！

CMakeLists.txt文件修改

69行# ---[ Includes修改，添加如下內容

set(${CMAKE_CXX_FLAGS} "-D_FORCE_INLINES ${CMAKE_CXX_FLAGS}")

至此文件修改基本完成！！！

make

make all -j48

樓主兩顆CPU，hahahahahaha 一般 -j4

make test -j48

make runtest -j48

這一步，很可能會報錯
1.

[----------] Global test environment tear-down
[==========] 1096 tests from 150 test cases ran. (106016 ms total)
[  PASSED  ] 1095 tests.
[  FAILED  ] 1 test, listed below:
[  FAILED  ] BenchmarkTest/1.TestTimerMilliSeconds, where TypeParam = caffe::CPUDevice<double>
 1 FAILED TEST
Makefile:523: recipe for target 'runtest' failed
make: *** [runtest] Error 1

因爲是多GPU所以
export MKL_CBWR=AUTO
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 1.8.16, library is 1.8.18
	    SUMMARY OF THE HDF5 CONFIGURATION
	    =================================

General Information:
-------------------
		   HDF5 Version: 1.8.18
		  Configured on: Thu Nov 16 20:07:06 UTC 2017
		  Configured by: root@0dbf0ee2-5a1e-455f-760a-510cd936b9c5
		 Configure mode: production
		    Host system: x86_64-conda_cos6-linux-gnu
	      Uname information: Linux 0dbf0ee2-5a1e-455f-760a-510cd936b9c5 4.4.0-62-generic #83-Ubuntu SMP Wed Jan 18 14:10:15 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux
		       Byte sex: little-endian
		      Libraries: static, shared
	     Installation point: /home/amax/anaconda3

Compiling Options:
------------------
               Compilation Mode: production
                     C Compiler: /home/amax/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc
                         CFLAGS: -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -pipe -I/home/amax/anaconda3/include
                      H5_CFLAGS:   -std=c99 -pedantic -Wall -W -Wundef -Wshadow -Wpointer-arith -Wbad-function-cast -Wcast-qual -Wcast-align -Wwrite-strings -Wconversion -Waggregate-return -Wstrict-prototypes -Wmissing-prototypes -Wmissing-declarations -Wredundant-decls -Wnested-externs -Winline -O -finline-functions
                      AM_CFLAGS: 
                       CPPFLAGS: -D_FORTIFY_SOURCE=2 -O2
                    H5_CPPFLAGS: -D_GNU_SOURCE -D_POSIX_C_SOURCE=200112L   -DNDEBUG -UH5_DEBUG_API
                    AM_CPPFLAGS:  -I/home/amax/anaconda3/include
               Shared C Library: yes
               Static C Library: yes
  Statically Linked Executables: no
                        LDFLAGS: -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/amax/anaconda3/lib -L/home/amax/anaconda3/lib
                     H5_LDFLAGS: 
                     AM_LDFLAGS:  -L/home/amax/anaconda3/lib
 	 	Extra libraries: -lrt -lpthread -lz -ldl -lm 
 		       Archiver: /home/amax/anaconda3/bin/x86_64-conda_cos6-linux-gnu-ar
 		 	 Ranlib: /home/amax/anaconda3/bin/x86_64-conda_cos6-linux-gnu-ranlib
 	      Debugged Packages: 
		    API Tracing: no

Languages:
----------
                        Fortran: yes
               Fortran Compiler: /home/amax/anaconda3/bin/x86_64-conda_cos6-linux-gnu-gfortran
          Fortran 2003 Compiler: yes
                  Fortran Flags: 
               H5 Fortran Flags:  
               AM Fortran Flags: 
         Shared Fortran Library: yes
         Static Fortran Library: yes

                            C++: yes
                   C++ Compiler: /home/amax/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++
                      C++ Flags: -fvisibility-inlines-hidden -std=c++17 -fmessage-length=0 -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -pipe -I/home/amax/anaconda3/include
                   H5 C++ Flags:  
                   AM C++ Flags: 
             Shared C++ Library: yes
             Static C++ Library: yes

Features:
---------
                  Parallel HDF5: no
             High Level library: yes
                   Threadsafety: yes
            Default API Mapping: v18
 With Deprecated Public Symbols: yes
         I/O filters (external): deflate(zlib)
                            MPE: no
                     Direct VFD: no
                        dmalloc: no
Clear file buffers before write: yes
           Using memory checker: no
         Function Stack Tracing: no
      Strict File Format Checks: no
   Optimization Instrumentation: no
Bye...
*** Aborted at 1563351176 (unix time) try "date -d @1563351176" if you are using GNU date ***
PC: @     0x7f9e5c2c2428 gsignal
*** SIGABRT (@0x3e8000037bd) received by PID 14269 (TID 0x7f9e65dd3440) from PID 14269; stack trace: ***
    @     0x7f9e5c668390 (unknown)
    @     0x7f9e5c2c2428 gsignal
    @     0x7f9e5c2c402a abort
    @     0x7f9e60caaf7c H5check_version
    @           0x4a9dc9 caffe::HDF5OutputLayerTest_TestForward_Test<>::TestBody()
    @           0x95b523 testing::internal::HandleExceptionsInMethodIfSupported<>()
    @           0x954b3a testing::Test::Run()
    @           0x954c88 testing::TestInfo::Run()
    @           0x954d65 testing::TestCase::Run()
    @           0x95603f testing::internal::UnitTestImpl::RunAllTests()
    @           0x956363 testing::UnitTest::Run()
    @           0x47129d main
    @     0x7f9e5c2ad830 __libc_start_main
    @           0x479249 _start
    @                0x0 (unknown)
Makefile:548: recipe for target 'runtest' failed
make: *** [runtest] 已放棄 (core dumped)

這個問題很麻煩
Headers are 1.8.16, library is 1.8.18
HDF5 library and header mismatch error 頭文件與庫文件匹配的錯誤
conda list

有兩個方法

conda remove hdf5
再重新sudo make runtest -j
但是博主測試了發現報錯

error while loading shared libraries: libhdf5_hl.so.100: cannot open shared object file:

參考https://blog.csdn.net/qq_33144323/article/details/81275540

將Anaconda lib的路徑添加到環境變量LD_LIBRARY_PATH中
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:{anaconda_dir}/lib
source ~/.bashrc
可是已經添加過，所以重新創建軟鏈接發現最終還是HDF5 library and header 匹配問題。
2. 所以第二種，安裝一樣的版本。
還是先卸載hdf5conda remove hdf5
conda search hdf5

很可惜，沒有1.8.16版本.怎麼辦？？？
所以執行conda install hdf5=1.8.16失敗，
查閱官網https://anaconda.org/biobuilds/hdf5

conda install -c biobuilds hdf5

安裝1.8.16成功！！！

make pycaffe
make distribute

python #python環境測試

python
>>> import caffe as cf
>>> print(cf.__version__)
1.0.0

完成！！
—————————————————————————————————————————————————————
[1]:https://github.com/BVLC/caffe/wiki/Ubuntu-16.04-or-15.10-Installation-Guide#the-gpu-support-prerequisites
[2]: https://blog.csdn.net/lilin020401/article/details/94674866#第7步安裝 opencv3.4.6
[3]: https://blog.csdn.net/baidu_28342107/article/details/82022342
[4]: https://www.cnblogs.com/zjutzz/p/5716453.html?utm_source=itdadao&utm_medium=referral
[5]:https://www.cnblogs.com/bitterain/p/10529030.html

Ubuntu 16.04 Anaconda3+opencv3.4.6+Caffe+多GPU+CUDA10.1

Ubuntu 16.04 Anaconda3+opencv3.4.6+Caffe+多GPU+CUDA10.1

Anaconda安裝

Caffe下載

相關依賴庫安裝

opencv安裝

caffe文件修改

Makefile.config文件修改

Makefile文件修改

CMakeLists.txt文件修改

make

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