Caffe Installation安装笔记

转载自https://gist.github.com/titipata/f0ef48ad2f0ebc07bcb9

Note on how to install caffe on Ubuntu. Sucessfully install using CPU, more information for GPU see this link

Installation

verify all the preinstallation according to CUDA guide e.g.

lspci | grep -i nvidiauname -m && cat /etc/*release gcc --version

install CUDA on Ubuntu, following this site to install CUDA. We get .deb file and dpkg from CUDA download page (add CUDA path to .bashrc, see below)

wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_6.5-14_amd64.debsudo dpkg -i cuda-repo-ubuntu1404_6.5-14_amd64.debsudo apt-get updatesudo apt-get install cuda

More to do see post installion at this link where we change directory to ~/NVIDIA_CUDA-6.5_Samples then type make. Afterward, run deviceQuery under ~/NVIDIA_CUDA-6.5_Samples

install BLAS (from libopenblas) and git (and unzip for opencv)

sudo apt-get install libopenblas-dev git unzipinstall opencv, follow this site where I use this bash script to install opencvwget https://raw.githubusercontent.com/jayrambhia/Install-OpenCV/master/Ubuntu/2.4/opencv2_4_9.shchmod +x opencv2_4_9.sh ./opencv2_4_9.sh 

install Anaconda from this link then run

wget http://09c8d0b2229f813c1b93-c95ac804525aac4b6dba79b00b39d1d3.r79.cf1.rackcdn.com/Anaconda-2.1.0-Linux-x86_64.shbash Anaconda-2.1.0-Linux-x86.sh

(add Anaconda path to .bashrc, see below)

install Boost using this command:

sudo apt-get install libboost-all-devinstall others by following Caffe documentationsudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-devsudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler

Get latest version of protobuf using pip

pip install protobuf

Then clone caffe and follow the instruction

git clone https://github.com/BVLC/caffecp Makefile.config.example Makefile.config

Adjust Makefile.config (for example, if using Anaconda Python)

make allmake testmake runtest

Note that we apply this to anaconda according to Caffe issue

rm ~/anaconda/lib/libm.*

And I also do something like in /usr/lib/x86_64-linux-gnu/:

sudo cp libhdf5_hl.so.7 libhdf5_hl.so.8sudo cp libhdf5.so.7 libhdf5.so.8

(according to this issue on Caffe)

另外我还做了如下步骤

INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/ andLIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial/

After that we can make python interface for caffe - make pycaffe (in caffe/python)

Customization Caffe

This is what I added to .bashrc 添加./.bashrc文件内容

CUDA

export PATH=/usr/local/cuda-6.5/bin:$PATHexport LD_LIBRARY_PATH=/usr/local/cuda-6.5/lib64:$LD_LIBRARY_PATHexport PATH

Anaconda

export PATH=/home/ubuntu/anaconda/bin:$PATH

Caffe Root

export CAFFE_ROOT=/home/ubuntu/caffe

Error Found

According to tutorial When running ./examples/mnist/train_lenet.sh, I got following error:
libdc1394 error: Failed to initialize libdc1394
I0109 02:31:21.168457 30295 caffe.cpp:99] Use GPU with device ID 0
F0109 02:31:21.168894 30295 common.cpp:53] CPU-only Mode: cannot make GPU call.
Above problem solved by changing solver_mode: GPU to CPU in /caffe/examples/mnist/lenet_solver.prototxt
More installation:

pip install protobuf

To do list

Set python path for caffe so we are able to import caffe see more on http://caffe.berkeleyvision.org/tutorial/interfaces.html
See more in IPython notebook example from Caffe