BLAS - ATLAS openBLAS and MKL installation on Ubuntu

BLAS (Basic Linear Algebra Subprograms) 

It is a specification that prescribes a set of low-level routines for performing common linear algebra operations.

There are a lot of implements of BLAS:

• Accelerate : Apple's framework for Mac OS X and iOS, which includes tuned versions of BLAS

• ACML : The AMD Core Math Library, supporting the AMD Athlon and Opteron CPUs under Linux and Windows.

• ATLAS : Automatically Tuned Linear Algebra Software, an open source implementation of BLAS APIs for C and Fortran.

• BLIS : BLAS-like Library Instantiation Software framework for rapid instantiation.

• cuBLAS : Optimized BLAS for NVIDIA based GPU cards.

• clBLAS : An OpenCL implemenation of BLAS.

• Intel MKL : The Intel Math Kernel Library, supporting x86 32-bits and 64-bits. Includes optimizations for Intel Pentium, Core and Intel   Xeon CPUs and Intel Xeon Phi; support for Linux, Windows and Mac OS X.

• etc..

MKL installation 

Step1: Go to the bellow links and register for Parallel Studio XE Cluster Edition. You will receive an email containing install and download instructions.
https://software.intel.com/en-us/intel-education-offerings

Step2: 
  $ tar zxvf parallel_studio_xe_2015_update3.tgz
  $ chmod a+x parallel_studio_xe_2015_update3 -R
  $ cd parallel_studio_xe_2015_update3
  $ sudo ./install_GUI.sh
And then enter the serial keys as follow figure

Step3:
Extend default lib search path for MKL in ubuntu. Do not need to export LD_LIBRARY_PATH
Create intel_mkl.conf, and edit it.
  $ cd /etc/ld.so.conf.d
  $ sudo vi intel_mkl.conf
Paste the bellows:
  /opt/intel/lib/intel64
  /opt/intel/mkl/lib/intel64
  $ sudo ldconfig -v

The faster way to setup MKL:
Download MKL libraray from 
https://drive.google.com/file/d/0Bz2RXKLpvFgITU16YUs1TmNGcDA/view?usp=sharing
$ tar -xf intel_mkl.tar
$ sudo mv intel_mkl /opt/intel 
$ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/opt/intel/lib/intel64:$LD_LIBRARY_PATH

ATLAS installation

  $ sudo apt-get install libatlas-base-dev

OpenBLAS installation

  $ sudo apt-get install libopenblas-dev

C/C++ gflags and glog

Install gflags and glog

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

gflag Source code on GitHub: https://github.com/gflags/gflags
glog Source code on GitHub: https://github.com/google/glog

gflag sample code

===============================================
// Test.cpp
#include <iostream>
#include <gflags/gflags.h> // #include <google/gflags.h>
using namespace std;

DEFINE_bool(big_menu, true, "Include 'advanced' options in the menu listing");
DEFINE_string(languages, "english,french,german", "comma-separated list of languages to offer in the 'lang' menu");


int main(int argc, char** agrv)
{
    ::google::ParseCommandLineFlags(&argc, &agrv, true);
    std::cout<< "FLAGS_big_menu : " << FLAGS_big_menu << "\n";
    return 0;
}

$ g++ -o test Test.cpp -I /usr/include/gflags -L /usr/lib/x86_64-linux-gnu -lgflags
$ ./Test  --big_menu=false
Output:
FLAGS_big_menu : 0

gflag Defining Flags In Program

Defining a flag is easy: just use the appropriate macro for the type you want the flag to be
DEFINE_bool(big_menu, true, "Include 'advanced' options in the menu listing");
DEFINE_string(languages, "english,french,german",
                 "comma-separated list of languages to offer in the 'lang' menu");
DEFINE_bool defines a boolean flag. Here are the types supported:
DEFINE_bool: boolean
DEFINE_int32: 32-bit integer
DEFINE_int64: 64-bit integer
DEFINE_uint64: unsigned 64-bit integer
DEFINE_double: double
DEFINE_string: C++ string

gflag Setting Flags on the Command Line

app_containing_foo --languages="chinese,japanese,korean"
app_containing_foo -languages="chinese,japanese,korean"
app_containing_foo --languages "chinese,japanese,korean"
app_containing_foo -languages "chinese,japanese,korean"
For boolean flags, the possibilities are slightly different:
app_containing_foo --big_menu
app_containing_foo --nobig_menu
app_containing_foo --big_menu=true
app_containing_foo --big_menu=false

glog sample code

=============================================

#include <glog/logging.h>

int main(int argc, char** argv) {

    FLAGS_alsologtostderr = 1; // It will dump to console

    google::InitGoogleLogging("test");

    LOG(INFO) << "Dump log test";

    return 0;

}

Output:

I0825 14:45:06.432374 22528 objectdection.cpp:15] Dump log test

Miniglog

If you would like to use glog for cross-platform like Android, you can use miniglog as bellows because glog didn't support Android NDK.

https://github.com/tzutalin/miniglog

Ref:

https://google-gflags.googlecode.com/svn/trunk/doc/gflags.html#intro

Integrate Caffe into ROS

Purpose:

Integrate Caffe into ROS to do image classification.

Please go to my Github, there are more details:

https://github.com/tzutalin/ros_caffe

Setup Caffe from scratch

Introduction

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.

Installation on Ubuntu 12.04 / 14.04

Use the script to install Caffe's requirements 

$ git clone https://gist.github.com/tzutalin/b24937905a2480da1723 
$ sh b24937905a2480da1723/installCaffeDep.sh

If you would like to install Caffe's requirement manually or install CUDA depedencies, please refer to http://caffe.berkeleyvision.org/install_apt.html

Get Caffe source and compile it

Clone the source

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

After cloning, confing Makefile.config for Makefile

$ cp Makefile.config.example Makefile.config
$ vi Makefile.config

For CPU-only Caffe, uncomment CPU_ONLY := 1 in Makefile.config.

Start compiling

$ make -j 8 all ; make -j 8 test ; make -j 8 runtest ; make pycaffe ; make distribute 

My Makefile.config's screenshot is as bellow. I uncomment CPU_ONLY := 1

Run and test Caffe

Test whether caffe can run or not, use benchmarking cmd as bellos 

$ cd caffe 
$ build/tools/caffe time --model=models/bvlc_alexnet/deploy.prototxt

Run Caffe python
The search path can be manipulated from within a Python program as the variable sys.path.
$ echo PYTHONPATH=[/to/your/path]/caffe/python/:$PYTHONPATH > ~/.bashrc

Try to run classifiy_test.py
$ cd [/to/your/path]/caffe/example 
$ git clone https://gist.github.com/912d1774d96266c4e76b.git 
$ python classify_test.py




If using default python(Python 2.7.6), might need the following dependencies 
$ sudo apt-get install python-scipy python-skimage libqt4-core libqt4-gui libqt4-dev libzmq-dev ; sudo pip install -U scikit-image; sudo pip install pyzmq; sudo pip install protobuf; sudo pip install pygments 

References

Install Intel MKL and other BLAS for Caffe

http://tzutalin.blogspot.tw/2015/06/blas-atlas-openblas-and-mkl.html

Using Caffe with Eclipse
http://tzutalin.blogspot.tw/2015/05/caffe-on-ubuntu-eclipse-cc.html
Manual Install Caffe
http://caffe.berkeleyvision.org/install_apt.html
Instant way to use Caffe
http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb