搭建caffe環境及訓練模型
Mac OS搭建caffe環境
一、相關軟件安裝
1、 安裝CUDA8
（1） 安裝xcode及命令行
（2） 安裝CUDA的dmg包
（3） 配置環境變量並source ~/.bash_profile
export CUDA_ROOT=/Developer/NVIDIA/CUDA-8.0
export PATH=$CUDA_ROOT/bin${PATH:+:${PATH}}
export DYLD_LIBRARY_PATH=$CUDA_ROOT/lib${DYLD_LIBRARY_PATH:+:${DYLD_LIBRARY_PATH}}
export LD_LIBRARY_PATH=$CUDA_ROOT/lib:$LD_LIBRARY_PATH
（4） 執行nvcc –V驗證安裝
2、 安裝必要的包
brew install –vd snappy leveldb gflags glog szip lmdb
brew tap homebrew/science
brew install hdf5 opencv
3、 brew edit opencv並修改兩行
-DPYTHON_LIBRARY=#{py_prefix}/lib/libpython2.7.dylib
-DPYTHON_INCLUDE_DIR=#{py_prefix}/include/python2.7
4、 安裝protobuf和boost-python
brew install --build-from-source --with-python –vd protobuf
brew install --build-from-source -vd boost boost-python
5、下載NVIDIA Mac drivers
二、caffe安裝
1、下載caffe
git clone https://github.com/BVLC/caffe.git
2、打開Makefile.config, 配置BLAS
BLAS_INCLUDE := /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.12.sdk/System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/Headers
BLAS_LIB := /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A
去掉CPU_ONLY的註釋
去掉USE_LEVELDB := 0的註釋
修改PYTHON_INCLUDE的目錄為python2.7的目錄
三、編譯caffe
make all
make test
make runtest
make pycaffe
make pytest
make distribute
會有一些warning, 需要安裝什麽包就安裝什麽包
1)“dot” not found in path問題需要安裝graphviz，不用pip用brew安裝
2) make pytest出現了如下問題:
註釋掉/python/caffe/proto/caffe_pb2.py 所有 “syntax = proto2 “
3）需要進入python文件夾執行python才能import caffe
4）安裝scikit-image庫
5）pip不好使，就pip install –U pip
Centos 7安裝Caffe
一、安裝CUDA
wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda-repo-rhel7-8-0-local-ga2-8.0.61-1.x86_64-rpm
rpm -i cuda-repo-rhel7-8-0-local-ga2-8.0.61-1.x86_64-rpm
yum clean all
yum install cuda
設置CUDA環境變量：
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:/usr/local/cuda-8.0/extras/CUPTI/lib64:$LD_LIBRARY_PATH（指定共享庫，編譯opencv、cudasample等）
export CUDA_HOME=/usr/local/cuda-8.0/（指定CUDA安裝路徑）
export PATH=/usr/local/cuda-8.0/bin:$PATH（在任意環境下調用cuda可執行文件）
更新運行庫緩存：
ldconfig
檢驗CUDA是否安裝成功：
nvcc --version
GPU支持：
安裝Nvidia驅動：
根據GPU型號從相應網站下載驅動，例如使用NVIDIA Tesla M60，從NVIDIA網站選擇對應的型號和操作系統，CUDA Toolkit版本，下載驅動文件，如NVIDIA-Linux-x86_64-375.66.run，運行驅動文件，根據提示安裝：
sh NVIDIA-Linux-x86_64-375.66.run
通過NVIDIA命令工具nvidia-smi檢驗GPU情況:
nvidia-smi
安裝cuDNN：
從NVIDIA網站下載cuDNN 安裝包，根據GPU及CUDA版本選擇對應cuDNN版本，下載cuDNN v5.1 for CUDA8.0,解壓拷貝到CUDA安裝目錄
cp include/* /usr/local/cuda/include
cp lib64/* /usr/local/cuda/lib64
二、yum安裝依賴包
yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel gflags-devel glog-devel lmdb-devel atlas-devel
n cd /usr/lib64/atlas
n sudo ln -sv libsatlas.so.3.10 libcblas.so
n sudo ln -sv libsatlas.so.3.10 libatlas.so
（如果有需要，可安裝Openblas或者MKL作為atlas的備用）
二、非yum手動安裝依賴包
安裝 gflags：
git clone https://github.com/gflags/gflags
cd gflags
mkdir build && cd build
export CXXFLAGS="-fPIC" && cmake .. -DCMAKE_INSTALL_PREFIX=/data2/shixi_wuhao3/local -DBUILD_SHARED_LIBS=ON
make VERBOSE=1
sudo make install
安裝 glog：
git clone https://github.com/google/glog
cd glog
./configure
make
automake --add-missing
sudo make install
安裝 lmdb：
git clone https://github.com/LMDB/lmdb
cd lmdb/libraries/liblmdb
make
sudo make install
安裝 hdf5：
wget https://support.hdfgroup.org/ftp/HDF5/current18/src/hdf5-1.8.18.tar.gz
tar -xvf hdf5-1.8.18.tar.gz
cd hdf5-1.8.18
./configure --prefix=/usr/local
make
sudo make install
安裝protobuf
tar -xvf protobuf
cd protobuf
./configure --prefix=/usr/local/protobuf
make
make check
make install
安裝boost
wget http://sourceforge.net/projects/boost/files/boost/1.59.0/boost_1_59_0.tar.gz
tar -zxvf boost_1_59_0.tar.gz
cd boost_1_59_0
./bootstrap.sh --prefix=path/to/install
./b2 install
這會把boost安裝到path/to/install，若不指定，默認將安裝到/usr/local目錄，但需要權限。
安裝opencv
leveldb要和snappy一起安裝
三、下載caffe
git clone https://github.com/BVLC/caffe
四、安裝Python依賴包（註意python2和python3的）
for req in $(cat caffe/python/requirements.txt); do pip install $req; done
五、編輯Makefile.config 文件，根據情況修改配置：
BLAS := atlas
BLAS_INCLUDE := /usr/include/atlas
BLAS_LIB := /usr/lib64/atlas
PYTHON_INCLUDE := /usr/local/include/python2.7 \
/usr/local/lib/python2.7/site-packages/numpy/core/include
PYTHON_LIB := /usr/lib64
USE_CUDNN := 1（使用cuDNN）
（去掉CPU_ONLY的註釋——只用CPU）
（去掉USE_LEVELDB := 0的註釋——使用LevelDB）
根據情況確定是否修改Makefile.cofig中的相關位置路徑
## 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
"CuDNN是NVIDIA專門針對Deep Learning框架設計的一套GPU計算加速庫，用於實現高性能的並行計算,在有GPU並且安裝CuDNN的情況下可以打開即將註釋去掉。"
# CPU-only switch (uncomment to build without GPU support).
#CPU_ONLY := 1
"表示是否用GPU,如果只有CPU這裏要打開"
# uncomment to disable IO dependencies and corresponding data layers
USE_OPENCV := 1
"因為要用到OpenCV庫所以要打開，下面這兩個選項表示是選擇Caffe的數據管理第三方庫，兩者都不打開 Caffe默認用的是LMDB，這兩者均是嵌入式數據庫管理系統編程庫。"
# 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
"當需要讀取LMDB文件時可以取消註釋，默認不打開。"
# Uncomment if you‘re using OpenCV 3
OPENCV_VERSION := 2.4.10 （我的2.4.5）
"用pkg-config --modversion opencv命令查看opencv版本"
# 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++
"linux系統默認使用g++編譯器，OSX則是clang++。"
# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
"CUDA的安裝目錄"
# 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 lines for compatibility.
CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
-gencode arch=compute_20,code=sm_21 \
-gencode arch=compute_30,code=sm_30 \
-gencode arch=compute_35,code=sm_35 \
-gencode arch=compute_50,code=sm_50 \
-gencode arch=compute_50,code=compute_50
"這些參數需要根據GPU的計算能力
(http://blog.csdn.net/jiajunlee/article/details/52067962)來進行設置，6.0以下的版本不支持×_50的計算能力。"
# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := open
"如果用的是ATLAS計算庫則賦值atlas，MKL計算庫則用mkl賦值，OpenBlas則賦值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 := /usr/local/OpenBlas/include
BLAS_LIB := /usr/local/OpenBlas/lib
"blas庫安裝目錄"
# 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
"如果不是安裝在標準路徑則要指明"
# 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
"matlab安裝庫的目錄"
# 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
"python安裝目錄"
# 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 python3.5m
# 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庫位置"
# 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
# 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
"所用的GPU的ID編號"
# enable pretty build (comment to see full commands)
Q ?= @
六、編譯及測試：
make all
make test
make runtest
測試成功，結果如下：
編譯caffe的python環境及測試：
make pycaffe
make pytest
這樣進入python交互行import caffe成功即可
http://caffe.berkeleyvision.org/installation.html
pip安裝future庫
安裝最新版的cudnn（美團有鏈接，可能要註冊）
cmake要設置CPU參數OFF, cmake -DUSE_CUDA=OFF ..
git要加參數recursive
make install時要加sudo
在site-package目錄下編寫.pth來使得caffe在任何目錄下都可以import
搭建caffe環境及訓練模型