继上一次倒腾caffe安装以后，因为博士毕业等原因，旧的服务器已经不能再使用，最近因论文等原因，不得不继续来安装一下我的caffe。这次运气比较好，经历了一晚上和一早上的痛苦之后，最终安装成功了，而且由于使用极链AI云服务器来安装的，所以应该这次的可复现性更强，下面总结一些必要的地方，以帮助大家复现安装我的caffe.
首先需要选择极链的云服务器。
- nvidia cuda和gpu的选择
这里我们使用的是极链-华北一区-GeForce RTX 2080 的gpu，cuda 10.1, python=2.7 ，禁用cudnn.
然后开始安装后面的包。
1. 首先仍然需要参考《深度学习：21天实战caffe》的第5天那一章来进行安装，最后的安装列表为：
这里可以不管caffe和opencv，只要确保其他的文件如图所示即可。
所有必备的caffe软件【更新版】我们已经上传到百度云盘，请大家需要的可以下载：
链接：https://pan.baidu.com/s/13P9PiTTnjOD6ZiiS4-dCBg
提取码：zlut
2. 在安装各个包的过程中，一些包的安装会有些困难，列举如下：
- Boost （这里一定要强调把filesystem也要加上！）
- glog
初始时刻，我用glog-0.3.3.tar来安装的，总是出错，后来换成了glog-0.3.5.tar安装，就对了。
- opencv
这里我们采用了opencv-3.4.14。 可以参考《复盘caffe安装》来安装opencv。
安装过程中，可能会卡在IPPICV: Download: ippicv_2020_lnx_intel64_20191018_general.tgz，这时果断停止编译。从刚才的百度云盘中下载ippicv_2020_lnx_intel64_20191018_general.tgz，然后上传到某一个位置，比如./opencv-3.4.14/3rdparty/ippicv/,
接着修改配置文件./opencv-3.4.14/3rdparty/ippicv/ippicv.cmake,
找到"https://raw.githubusercontent.com/opencv/opencv_3rdparty/${IPPICV_COMMIT}/ippicv/"
将其改为你保存ippicv_2020_lnx_intel64_20191018_general.tgz文件的路径，
我的是./opencv-3.4.14/3rdparty/ippicv/,如下：
最后再来编译。opencv安装需要花费较多时间。安装完成以后，为了能后面使用opencv.pc，需要在root或者home下面的bashrc中增加如下命令，然后使用 source ~/.bashrc更新。
export PATH="/user-data/caffe_local_install/bin/:$PATH" export PKG_CONFIG_PATH=/user-data/caffe_local_install/lib/pkgconfig/:$PKG_CONFIG_PATH export LD_LIBRARY_PATH=/user-data/caffe_local_install/lib:$LD_LIBRARY_PATH ##(静态库搜索路径) 程序编译期间查找动态链接库时指定查找共享库的路径 export LIBRARY_PATH=/user-data/caffe_local_install/lib:$LIBRARY_PATH ##c程序头文件搜索路径 export C_INCLUDE_PATH=/user-data/caffe_local_install/include:$C_INCLUDE_PATH ##c++程序头文件搜索路径 export CPLUS_INCLUDE_PATH=/user-data/caffe_local_install/include:$CPLUS_INCLUDE_PATH
这样以后，就可以查看是否安装成功
pkg-config --modversion opencv
显示opencv版本。
- python依赖包的安装
我们需要提前创造一个python2.7的虚拟环境caffe.
由于极链已经预装了anaconda，所以可以直接创造虚拟环境，否则应该先按照anaconda, 再创建虚拟环境。 可参考：安装依赖库常用命令 - 极链AI云支持中心
conda create -n your_env_name python=X.X（2.7、3.6等) 我的为： conda create -n caffe python=2.7
接着开始按照如下的python包
pip install opencv-python==4.2.0.32 pip install protobuf==2.6.1 pip install scikit-image==0.14.2
3. 配置Makefile.config文件，
cp Makefile.config.example 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 := 0 # 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 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 := 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 := /user-data/caffe_local_install/include BLAS_LIB := /user-data/caffe_local_install/lib # 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 := /data1/caiyong.wang/bin/matlab/ # 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 := /opt/conda/envs/caffe 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 python3.5m # 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 := /user-data/caffe_local_install/include $(PYTHON_INCLUDE) /usr/local/include /usr/include LIBRARY_DIRS := /user-data/caffe_local_install/lib $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib64/ # 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 $LIBRARY_DIRS.) USE_PKG_CONFIG := 1 # N.B. both build and distribute dirs are cleared on `make clean` BUILD_DIR := build DISTRIBUTE_DIR := distribute # Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171 # DEBUG := 1 # The ID of the GPU that 'make runtest' will use to run unit tests. TEST_GPUID := 0 # enable pretty build (comment to see full commands) Q ?= @
编译caffe
# Adjust Makefile.config (for example, if using Anaconda Python, or if cuDNN is desired) make all -j8 make pycaffe
编译到最后的结果：
4. 编辑root或者home下面的bashrc,
export PYTHONPATH=/user-data/software/caffe_tifs/python/:$PYTHONPATH
确保编译成功的caffe被添加到python的路径中，最终新增的命令汇总为：
export PATH="/user-data/caffe_local_install/bin/:$PATH" export PKG_CONFIG_PATH=/user-data/caffe_local_install/lib/pkgconfig/:$PKG_CONFIG_PATH export PYTHONPATH=/user-data/software/caffe_tifs/python/:$PYTHONPATH export LD_LIBRARY_PATH=/user-data/caffe_local_install/lib:$LD_LIBRARY_PATH ##(静态库搜索路径) 程序编译期间查找动态链接库时指定查找共享库的路径 export LIBRARY_PATH=/user-data/caffe_local_install/lib:$LIBRARY_PATH ##c程序头文件搜索路径 export C_INCLUDE_PATH=/user-data/caffe_local_install/include:$C_INCLUDE_PATH ##c++程序头文件搜索路径 export CPLUS_INCLUDE_PATH=/user-data/caffe_local_install/include:$CPLUS_INCLUDE_PATH
使用 source ~/.bashrc更新。
5. 验证caffe是否安装成功
至此安装成功。
