Caffe 安装优化版 (CPU anaconda) 附Makefile.config

Caffe 安装优化版 (CPU anaconda)附Makefile.config

百度云链接链接：http://pan.baidu.com/s/1numfgTz 密码：4jbz

安装 anaconda

https://www.continuum.io/downloads
bash Anaconda2-4.3.1-Linux-x86_64.sh 

准备工作

sudo apt-get update
sudo apt-get install git vim cmake automake

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install --no-install-recommends libboost-all-dev

#安装BLAS
sudo apt-get install libatlas-base-dev
#安装opencv3.0
sudo apt-get install libgstreamer1.0-dev  libgstreamer-plugins-base1.0-dev
sudo sh dependencies.sh
cd 3.0
sudo sh opencv3_0_0.sh
pkg-config --modversion opencv

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 && rm glog-0.3.3.tar.gz
./configure  
sudo make  
sudo make install  


sudo apt-get install -y libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler protobuf-c-compiler

sudo apt-get install -y ipython-notebook pandoc 
sudo apt-get install -y python-numpy python-scipy python-matplotlib python-sklearn python-skimage python-h5py python-protobuf python-leveldb python-networkx python-nose python-pandas python-gflags Cython ipython

Caffe

下载Caffe

cd ~
git clone git://github.com/BVLC/caffe.git

cp Makefile.config.example Makefile.config
vim Makefile.config (见下)

sudo vim ~/.bashrc
＃ 加上以下
export PATH="/root/anaconda2/bin:$PATH"
export LD_LIBRARY_PATH="/root/anaconda2/lib":$LD_LIBRARY_PATH
export PYTHONPATH="/root/caffe/python":$PYTHONPATH
source ~/.bashrc


# sudo make clean (有问题可运行这个重新编译)
sudo make all
sudo make test
sudo make runtest
sudo make pycaffe

MNIST 测试

sh data/mnist/get_mnist.sh
sh data/mnist/get_mnist.sh
# 将prototxt文件修改成CPU模式
sh examples/mnist/train_lenet.sh

export CPLUS_INCLUDE_PATH=/usr/include/python2.7

libhdf5_hl.so.10与libhdf5.so.10问题

http://www.linuxdiyf.com/linux/22442.html

sudo cp -s /root/anaconda2/lib/libhdf5_hl.so.10.1.0 /usr/lib/libhdf5_hl.so.10
sudo cp -s /root/anaconda2/lib/libhdf5_hl.so.10.1.0 /usr/lib/x86_64-linux-gnu/libhdf5_hl.so.10
sudo ldconfig

libhdf5.so.10.2.0
libhdf5.so.10
sudo cp -s /root/anaconda2/lib/libhdf5.so.10.2.0 /usr/lib/libhdf5.so.10
sudo cp -s /root/anaconda2/lib/libhdf5.so.10.2.0 /usr/lib/x86_64-linux-gnu/libhdf5.so.10
sudo ldconfig

protobuf 问题

pip install protobuf

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 := 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
# 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_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_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/anaconda2
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 := $(PYTHON_INCLUDE) /usr/local/include /usr/lib/x86_64-linux-gnu/hdf5/serial/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial

# 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 ?= @

[1] http://blog.youkuaiyun.com/xuhang0910/article/details/50179759
[2] http://blog.youkuaiyun.com/u011762313/article/details/47262549