caffe+ubuntu18.04+cuda10.2编译配置

安装相应依赖

apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
apt-get install --no-install-recommends libboost-all-dev
apt-get install python-dev
apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
apt-get install libatlas-base-dev
apt-get install the python-matplotlib python-scipy python-numpy
pip3 install pytest numpy boost

安装CMAKE

注意：使用apt-get安装的cmake版本太低，在使用CUDA10.2的环境下进行caffe编译时会出现CUDA_cublas_device_LIBRARY/CUDA_cublas_LIBRA报错。这是由于低版本cmake找不到cuda的库所导致。为解决这个问题，我们需要手动安装cmake，版本要求至少为3.12.0。我们选择3.14.3。

如果已经存在cmake请确认版本 cmake --version

1. 从cmake官网下载cmake-3.14.3-Linux-x86_64.tar.gz
2. 解压 tar -zxvf cmake-3.14.3-Linux-x86_64.tar.gz
3. 在~/.bashrc文件中添加环境变量，在文件末尾添加：
   export CMAKE_ROOT=/home/shiyh/download/cmake-3.14.3-Linux-x86_64
   export PATH=$PATH:$CMAKE_ROOT/bin:
   注意/home/shiyh/download/请替换为具体路径
4. 保存，并执行source ~/.bashrc

cmake编译

1. 修改Makefile.config
   源码文件夹中已提供Makefile.config.example
   执行cp Makefile.config.example Makefile.config
2. 修改CMakeList.txt中第35行
   第35行的set(python_version “2” CACHE STRING “Specify which Python version to use”)中的2改为3.6（根据自己的python版本确定），保存退出
3. vim Makefile.config  对配置文件中相关参数修改

   ## 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  #如果使用cudnn就取消注释
   
   # 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
   # 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 := 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.
   # 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_61,code=compute_61 \
                   -gencode arch=compute_70,code=[sm_70,compute_70]
   
   # 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)/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 ?= @
   

   需要注意的是在CUDA_ARCH 的配置中，如果使用cuda版本>=9.0需要注释或删除如下两行
   -gencode arch=compute_20,code=sm_20 \
   -gencode arch=compute_20,code=sm_21 \
   此外，请根据所使用的GPU型号酌情添加。（大部分显卡的参数配置文件中已包含，如使用V100，请加入 ARCH= -gencode arch=compute_70,code=[sm_70,compute_70]   
   否则在使用时会出现
   Check failed: error == cudaSuccess (48 vs. 0) no kernel image is available for execution on the device
    

   Tesla V100
   # ARCH= -gencode arch=compute_70,code=[sm_70,compute_70]
   
   GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4
   # ARCH= -gencode arch=compute_61,code=sm_61 -gencode arch=compute_61,code=compute_61
   
   GP100/Tesla P100  DGX-1
   # ARCH= -gencode arch=compute_60,code=sm_60
   
   For Jetson Tx1 uncomment:
   # ARCH= -gencode arch=compute_51,code=[sm_51,compute_51]
   
   For Jetson Tx2 or Drive-PX2 uncomment:
   # ARCH= -gencode arch=compute_62,code=[sm_62,compute_62]

4. 回到caffe源码根目录

5. mkdir build

6. cd build/

7. cmake ..        (注意：如果出现少包，缺哪个包装哪个包。然后回到源码根目录，执行make clean以后从第4步重新开始)

8. make all

9. make pycaffe

10. make install

11. make runtest