首先呢,博主是想搞tensorflow的,但是呢,博主的机子是32位的,tensorflow支持64位的,所以呢,只能玩玩caffe了,然后呢,博主肯定首先是想搞GPU的,但是呢,驱动么去官网下,安装提示内核不对,可是没有很早以前的驱动了呀,其次呢,CUDA7.5现阶段下载不了,原因你去问英伟达。。。
所以只能搞无GPU加速的caffe了,这也好,省去了一大片的雷区。。。
首先,你需要下载anaconda,为啥要下这个,里面是关于矩阵计算的python。。。我们用这个代替原有的python2.7,这是链接https://www.continuum.io/downloads
然后,去下对应的系列,我下的是2.7版本的32位linux系列。。。
然后打开终端,输入
bash ~/Downloads/Anaconda2-5.1.0-Linux-x86.sh
这是我保存文件的地址,请对应。。。
然后就不停地按回车和选yes,直到提示安装成功
然后source一下环境变量
source ~ /.bashrc
这样就安装完成,这时,请你打开新的终端,这很重要,可以通过python版本查看
python --version
如果显示有带有anaconda的字样说明安装成功,没有的话么,仔细对照以上操作,博主也是装了几遍才好了的,并不存在其他会报错的可能
然后就是caffe的安装了,比较简单,先下载依赖项
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev
sudo apt_get install libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install --no-install-recommends libboost-all-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
sudo apt-get install libatlas-base-dev
然后打开终端,进入到你要的目录clone一个
cd /home/f/ Downloads
git clone https://github.com/BVLC/caffe.git
然后编译,先修改配置文件
cd caffe
sudo cp Makefile.config.example Makefile.config
sudo gedit Makefile.config
请你们对照我的配置文件修改
## 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
# 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.
# For CUDA >= 9.0, comment the *_20 and *_21 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 := $(HOME)/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
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 ?= @
ANACONDA_HOME := $(HOME)/anaconda2
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
$(ANACONDA_HOME)/include/python2.7 \
$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include
然后
make all
make test
make runtest
一般最后一部会报错,提示什么文件没有,请按如下操作
cp /home/f/anaconda2/lib/libhdf5.so.101 /usr/lib/i386-linux-gnu/libhdf5.so.101
注意,这里的文件请你和报错文件对应起来,我的只是我报错的文件名
然后配置pycaffe,关于python的接口
sudo apt-get install python-numpy python-scipy python-matplotlib python-sklearn python-skimage python-h5py python-protobuf python-leveldb python-networkx python-nose python-pandas python-gflags Cython ipython
sudo apt-get install protobuf-c-compiler protobuf-compiler
然后cd到caffe目录
make pycaffe
然后修改一下python的环境,因为我们用的是anaconda
gedit ~/.bashrc
然后请在最后添加地址
export PYTHONPATH="/home/f/Downloads/caffe/python:$PYTHONPATH"
一般这里就没问题了,输入
cd ~
python
import caffe
如果这里有报错请输入
import sys
>>> sys.path.append('/usr/lib/python2.7/dist-packages/')
>>> import caffe
这样就没问题了。
我第二遍安装caffe有bug 关于opencv的 国外友人给结果:
add "opencv_imgcodecs" in Makefile.(LIBRARIES += glog gflags protobuf leveldb snappy \
lmdb boost_system hdf5_hl hdf5 m \
opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs)
If you input "make all",the problem is the same again.But if you delete all the file in build(rm -rf ./build/*) before "make all"(I use make clean ),you will success.I just success