titipata/caffe_install.md

## caffe_install.md

      
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              caffe_install.md
            
          
    Caffe Installation

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 nvidia
uname -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.deb
sudo dpkg -i cuda-repo-ubuntu1404_6.5-14_amd64.deb
sudo apt-get update
sudo 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 unzip

install opencv, follow this site where I use this bash script to install opencv

wget https://raw.githubusercontent.com/jayrambhia/Install-OpenCV/master/Ubuntu/2.4/opencv2_4_9.sh
chmod +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.sh
bash 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-dev

install others by following Caffe documentation

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev
sudo 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/caffe
cp Makefile.config.example Makefile.config
# Adjust Makefile.config (for example, if using Anaconda Python)
make all
make test
make 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.8
sudo cp libhdf5.so.7 libhdf5.so.8
(according to this issue on Caffe)

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

###Customization Caffe

This is what I added to .bashrc

# CUDA                                                                                                     
export PATH=/usr/local/cuda-6.5/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-6.5/lib64:$LD_LIBRARY_PATH
export 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