Ubuntu14.04-x64+Caffe

我电脑没有独立显卡，没有装cuda

大体安装过程参照http://blog.csdn.net/ubunfans/article/details/47724341。2/3/4/5步骤都跳过了。

相关记录

cmake
gcc


sudo uname --m 查看自己运行ubuntu是32位还是64位
sudo uname --s 显示内核名字s
sudo uname --r 显示内核版本
sudo uname --n 显示网络主机名
sudo uname --p 显示cpu 




unzip -o  filename.zip -d targetpath


sudo apt-get install libopencv-dev  则不用从源码编译额....


解决tiff函数找不到的问题 Undefined reference to TIFFReadDirectory@LIBTIFF_4.0
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D BUILD_TIFF=ON ..


undefined reference to `lzma_index_buffer_decode@XZ_5.0
在~/.bashrc最后添加路径：LD_LIBRARY_PATH="/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH"
然后在caffe路径sudo make，而make pycaffe不用sudo，否则也会找不到Python.h头文件的


libopencv_core.so.3.1: cannot open shared object file
进入目录：/etc/ld.so.conf.d
创建：opencv.conf
添加：/opt/opencv-3.0.0/build/lib   libopencv_core.so.3.1所在路径
执行：ldconfig


fatal error: Python.h: No such file or
./bashrc最后添加：
export CPLUS_INCLUDE_PATH="/home/blade/anaconda3/include:/home/blade/anaconda3/include/python3.5m:$CPLUS_INCLUDE_PATH"


build/examples/mnist/convert_mnist_data.bin: not found
./examples/mnist/create_mnist.sh   需要从根目录执行


I0317 23:38:14.744825 14885 caffe.cpp:185] Using GPUs 0
F0317 23:38:14.745519 14885 common.cpp:66] Cannot use GPU in CPU-only Caffe: check mode.
caffe-master的CMakeLists.txt里CPU-ONLY要设置为ON


makefile打印变量 $(warning var is $(VAR)) 

我的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 3OPENCV_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 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# BLAS choice:# atlas for ATLAS (default)# mkl for MKL# open for OpenBlasBLAS := 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)/blade/anaconda3PYTHON_INCLUDE := $(ANACONDA_HOME)/include \  $(ANACONDA_HOME)/include/python3.5m \  $(ANACONDA_HOME)/lib/python3.5/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/libPYTHON_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/includeLIBRARY_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 := 1BUILD_DIR := buildDISTRIBUTE_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 ?= @

用的3.5版本的python

最终步骤 make pycaffe 打印信息如下

参照http://www.csdn.net/article/2015-01-22/2823663?_client_version=4.0.1调用跑mnist的脚本时，都需要在caffe-master目录调用，而不是cd到里面再调用。另外，train_lenet.sh时，报错 Cannot use GPU in CPU-only Caffe，原因是我没有NVIDIA独立显卡，没有装gpu版本配置，所以要设置examples/mnist/lenet_solver.prototxt里的solver_mode从GPU改为CPU

最终可以训练mnist了！