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LINUX上安装Caffe

简介: LINUX上安装Caffe
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 法不孤起,仗境方生;道不虚行,遇缘即应。吾以为安装caffe很容易。结果网上搜索,都没有提供pip方法。


自己使用pip caffe2,又出错了。怎么办?只好下载源码编译。


注意,目前caffe只支持Python2,Python3是不支持的。编译能够成功,无法运行。所以如果遇到这样的情况,老老实实的安装Python2,不要有侥幸心理。


  • 安装依赖库
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
sudo apt-get install libopenblas-dev liblapack-dev libatlas-base-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
 
# 有可能需要的库
sudo pip install graphviz

怎么这么多?管他,都装了吧。

运气好,顺利安装。运气不好……提示什么卸载什么,再重新安装。

  • 下载

https://github.com/BVLC/caffe

  • 解压

  略。

  • 安装python需要的东西

cd python

for req in $(cat requirements.txt); do sudo pip3 install $req; done
  • 配置Makefile.config
sudo cp Makefile.config.example Makefile.config

修改参数:

## 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 := 1
# 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
 
# 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 python3.5m
# PYTHON_INCLUDE := /usr/include/python3.5m \
#                   /usr/local/lib/python3.5/site-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/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  
 
# 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 ?= @
  • 配置Makefile
NVCCFLAGS +=-ccbin=$(CXX) -Xcompiler-fPIC $(COMMON_FLAGS)
替换为:
NVCCFLAGS += -D_FORCE_INLINES -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)

OpenCV3.0

# 找到这一句,添加 opencv_imgcodecs
LIBRARIES += opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs
  • 编译
sudo make all -j8
sudo make test -j8
 
sudo make pycaffe -j8
sudo make distribute
  • 配置.bashrc

PYTHONPATH=${HOME}/gh-install/caffe/python:$PYTHONPATH

  • 错误:make: protoc:命令未找到

解决:sudo apt-get install protobuf-c-compiler protobuf-compiler

  • 错误:fatal error: boost/shared_ptr.hpp:

解决:sudo apt-get install --no-install-recommends libboost-all-dev

  •  无法修正错误,因为您要求某些软件包保持现状,就是它们破坏了软件包间的依赖关系

https://blog.csdn.net/quantum7/article/details/83410518

  • usr/include/stdlib.h:139:8: error: ‘size_t’ does not name a type

include出了问题。这个yiban


make clean之后,再重新编译。

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