An amateur tried Deep Learning using Caffe (Introduction)

Introduction

This article

1. What is Deep Learning? Overview
2. Installation of Caffe, one of the Deep Learning libraries Introduction (this post)
3. Let's do simple learning using Caffe Practice It is composed of three

Although it is also in the title, it is a post about a record left by an amateur who is not a researcher of Deep Learning, so please forgive me for any mistakes and read it. (If there is something wrong, I would appreciate it if you could point it out in the comments)

Installation environment

OS:Ubuntu 14.04 LTS CPU: Core i7 2.93GHz GPU: GeForce GTX 960 Memory: 4GB

Caffe installation procedure

Basically, do as described on the Caffe official page. Here, the configuration is as follows, but please change it appropriately according to the environment. CUDA 7.0 use cudnn ATLAS Caffe (latest in github master) Make PyCaffe available

Installation of General dependencies

Obediently follow the formula

python

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

Install Remaining dependencies (for 14.04)

This also obediently follows the formula

python

sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

ATLAS installation

What is ATLAS? It feels like, but a library for numerical calculation (probably matrix differentiation) Easy with Ubuntu

python

sudo apt-get install libatlas-base-dev

Install CUDA (GPU users only)

It says that you can use apt-get on Ubuntu, but from various points of view it seems better to take it from the nVidia official and put it in by yourself, so I decided to put it in by myself. (The latest seems to be 7.5, but I think it is the same as 7.0, so I will write it as it is)

Installation of CUDA main unit

From nvidia developer site, go to CUDA ZONE → CUDA DOWNLOADS to drop the CUDA installer. Then run the installer (replace xxx with the version you dropped)

python

chmod +x cuda_xxx_linux.run
sudo ./cuda_xxx_linux.run

All you have to do is follow the instructions on the screen

installation of cudnn

It doesn't seem to be required, but according to the official, it's faster to put it in, so put it in.

cuDNN Caffe: for fastest operation Caffe is accelerated by drop-in integration of NVIDIA cuDNN. To speed up your Caffe models, install cuDNN then uncomment the USE_CUDNN := 1 flag in Makefile.config when installing Caffe. Acceleration is automatic. The current version is cuDNN v3; older versions are supported in older Caffe.

user registration

Register as a developer from the above nvidia developer site

Installation

After registration, cudnn will be available for download, so download it from here. Then

python

sudo cp cudnn.h /usr/local/cuda/include/
sudo cp *.so /usr/local/cuda/lib64/
sudo cp *.a /usr/local/cuda/lib64/

Copy the library as In addition, the same symbolic link in the extracted file, Create it in / usr / local / cuda / lib64 (the following is 6.5 at the time of download)

python

cd /usr/local/cuda/lib64/
sudo ln -s libcudnn.so.6.5.48 libcudnn.so.6.5
sudo ln -s libcudnn.so.6.5 libcudnn.so

Install Caffe

Now that we are ready, we will put it in immediately (assuming that the necessary items around python are prepared separately)

Download Caffe

For some reason, it's not officially written here, but it seems that it is official to drop it from github.

git clone https://github.com/BVLC/caffe.git

Install what you need for PyCaffe

As officially

python

for req in $(cat requirements.txt); do pip install $req; done

That's it. requirements.txt is in caffe / python I didn't have gfortran in my environment, so I installed scipy once.

python

error: Setup script exited with error: library dfftpack has Fortran sources but no Fortran compiler found

In that case, please insert gfortran.

python

sudo apt-get install gfortran

Modify Makefile.config

This is important. If you make a mistake here, you may try to build with something that doesn't exist, or you may get stuck in an error point. First, copy Makefile.config.example to create the prototype of Makefile.config.

python

cp Makefile.config.example Makefile.config

Make sure that Makefile.config looks like this: (Please change according to the environment) USE_CUDNN: = 1 (when using cudnn) CPU_ONLY: = 1 (comment out when using GPU) CUDA_DIR: = / usr / local / cuda (probably not needed for CPU_ONLY) None of the lines in CUDA_ARCH: = -gencode arch = compute ... are commented out (probably commented out if CPU_ONLY) BLAS := atlas PYTHON_INCLUDE := /usr/include/python2.7 \ /usr/lib/python2.7/dist-packages/numpy/core/include PYTHON_LIB := /usr/lib The rest remains as an example

make

It will be soon after the above is over. Make at the root of your project (It is OK without the j8 part. Please change the number appropriately according to the number of CPU cores)

python

make all -j8

Run the test to see if it was installed successfully

I will do a final test to make sure it's done right here

python

make runtest

A log like the following lasts for a few minutes

python

[----------] 3 tests from DeconvolutionLayerTest/0, where TypeParam = caffe::CPUDevice<float>
[ RUN      ] DeconvolutionLayerTest/0.TestSetup
[       OK ] DeconvolutionLayerTest/0.TestSetup (0 ms)
[ RUN      ] DeconvolutionLayerTest/0.TestSimpleDeconvolution
[       OK ] DeconvolutionLayerTest/0.TestSimpleDeconvolution (1 ms)
[ RUN      ] DeconvolutionLayerTest/0.TestGradient
[       OK ] DeconvolutionLayerTest/0.TestGradient (623 ms)
[----------] 3 tests from DeconvolutionLayerTest/0 (624 ms total)

Finally, if you get PASSED as follows, you are successful.

python

[==========] 1404 tests from 222 test cases ran. (221949 ms total)
[  PASSED  ] 1404 tests.

If it fails, please check if the Makefile.config settings are correct and try again.

at the end

Thank you for your support. In the next article, I would like to actually run deep learning learning using Caffe.