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mlpack/doc/guide/build.hpp
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/*! @page build Building mlpack From Source
@section buildintro Introduction
mlpack uses CMake as a build system and allows several flexible build
configuration options. One can consult any of numerous CMake tutorials for
further documentation, but this tutorial should be enough to get mlpack built
and installed.
@section Download latest mlpack build
Download latest mlpack build from here:
<a href="http://www.mlpack.org/files/mlpack-2.0.1.tar.gz">mlpack-2.0.1</a>
@section builddir Creating Build Directory
Once the mlpack source is unpacked, you should create a build directory.
@code
$ cd mlpack-2.0.1
$ mkdir build
@endcode
The directory can have any name, not just 'build', but 'build' is sufficient
enough.
@section dep Dependencies of mlpack
mlpack depends on the following libraries, which need to be installed on the
system and have headers present:
- Armadillo >= 4.100.0 (with LAPACK support)
- Boost (math_c99, program_options, serialization, unit_test_framework, heap)
>= 1.49
In Ubuntu and Debian, you can get all of these dependencies through apt:
@code
# apt-get install libboost-math-dev libboost-program-options-dev
libboost-test-dev libboost-serialization-dev libarmadillo-dev binutils-dev
@endcode
If you are using an Ubuntu version older than 13.10 ("Saucy Salamander") or
Debian older than Jessie, you will have to compile Armadillo from source. See
the README.txt distributed with Armadillo for more information.
On Fedora, Red Hat, or CentOS, these same dependencies can be obtained via yum:
@code
# yum install boost-devel boost-test boost-program-options boost-math
armadillo-devel binutils-devel
@endcode
On Red Hat Enterprise Linux 5 and older (as well as CentOS 5), the Armadillo
version available is too old and must be compiled by hand. The same applies for
Fedora 16 and older.
@section config Configuring CMake
Running CMake is the equivalent to running `./configure` with autotools. If you
are working with the svn trunk version of mlpack and run CMake with no options,
it will configure the project to build with debugging symbols and profiling
information: If you are working with a release of mlpack, running CMake with no
options will configure the project to build without debugging or profiling
information (for speed).
@code
$ cd build
$ cmake ../
@endcode
You can manually specify options to compile with or without debugging
information and profiling information (i.e. as fast as possible):
@code
$ cd build
$ cmake -D DEBUG=OFF -D PROFILE=OFF ../
@endcode
The full list of options mlpack allows:
- DEBUG=(ON/OFF): compile with debugging symbols (default ON in svn trunk, OFF
in releases)
- PROFILE=(ON/OFF): compile with profiling symbols (default ON in svn trunk,
OFF in releases)
- ARMA_EXTRA_DEBUG=(ON/OFF): compile with extra Armadillo debugging symbols
(default OFF)
Each option can be specified to CMake with the '-D' flag. Other tools can also
be used to configure CMake, but those are not documented here.
@section build Building mlpack
Once CMake is configured, building the library is as simple as typing 'make'.
This will build all library components as well as 'mlpack_test'.
@code
$ make
Scanning dependencies of target mlpack
[ 1%] Building CXX object
src/mlpack/CMakeFiles/mlpack.dir/core/optimizers/aug_lagrangian/aug_lagrangian_test_functions.cpp.o
<...>
@endcode
You can specify individual components which you want to build, if you do not
want to build everything in the library:
@code
$ make pca allknn allkfn
@endcode
If the build fails and you cannot figure out why, register an account on Trac
and submit a ticket and the mlpack developers will quickly help you figure it
out:
http://mlpack.org/
Alternately, mlpack help can be found in IRC at \#mlpack on irc.freenode.net.
@section install Installing mlpack
If you wish to install mlpack to /usr/include/mlpack/ and /usr/lib/ and
/usr/bin/, once it has built, make sure you have root privileges (or write
permissions to those two directories), and simply type
@code
# make install
@endcode
You can now run the executables by name; you can link against mlpack with
-lmlpack, and the mlpack headers are found in /usr/include/mlpack/.
*/