198 lines
6.5 KiB
C++
198 lines
6.5 KiB
C++
/*! @page build Building mlpack From Source
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@section build_buildintro Introduction
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This document discusses how to build mlpack from source. However, mlpack is in
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the repositories of many Linux distributions and so it may be easier to use the
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package manager for your system. For example, on Ubuntu, you can install mlpack
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with the following command:
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@code
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$ sudo apt-get install libmlpack-dev
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@endcode
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If mlpack is not available in your system's package manager, then you can follow
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this document for how to compile and install mlpack from source.
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mlpack uses CMake as a build system and allows several flexible build
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configuration options. One can consult any of numerous CMake tutorials for
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further documentation, but this tutorial should be enough to get mlpack built
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and installed on most Linux and UNIX-like systems (including OS X). If you want
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to build mlpack on Windows, see <a
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href="https://keon.io/mlpack-on-windows/">Keon's excellent tutorial</a>.
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You can download the latest mlpack release from here:
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<a href="http://www.mlpack.org/files/mlpack-2.2.5.tar.gz">mlpack-2.2.5</a>
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@section build_simple Simple Linux build instructions
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Assuming all dependencies are installed in the system, you can run the commands
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below directly to build and install mlpack.
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@code
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$ wget http://www.mlpack.org/files/mlpack-2.2.5.tar.gz
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$ tar -xvzpf mlpack-2.2.5.tar.gz
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$ mkdir mlpack-2.2.5/build && cd mlpack-2.2.5/build
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$ cmake ../
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$ make -j4 # The -j is the number of cores you want to use for a build.
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$ sudo make install
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@endcode
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If the \c cmake \c .. command fails, you are probably missing a dependency, so
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check the output and install any necessary libraries. (See \ref build_dep.)
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The instructions above are the simplest way to get, build, and install mlpack.
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The sections below discuss each of those steps in further detail and show how to
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configure mlpack.
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@section build_builddir Creating Build Directory
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First we should unpack the mlpack source and create a build directory.
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@code
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$ tar -xvzpf mlpack-2.2.5.tar.gz
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$ cd mlpack-2.2.5
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$ mkdir build
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@endcode
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The directory can have any name, not just 'build', but 'build' is sufficient.
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@section build_dep Dependencies of mlpack
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mlpack depends on the following libraries, which need to be installed on the
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system and have headers present:
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- Armadillo >= 6.500.0 (with LAPACK support)
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- Boost (math_c99, program_options, serialization, unit_test_framework, heap,
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spirit) >= 1.49
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For Python bindings, the following packages are required:
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- setuptools
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- cython >= 0.24
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- numpy
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- pandas >= 0.15.0
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- pytest-runner
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In Ubuntu and Debian, you can get all of these dependencies through apt:
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@code
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# apt-get install libboost-math-dev libboost-program-options-dev
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libboost-test-dev libboost-serialization-dev libarmadillo-dev binutils-dev
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python-pandas python-numpy python-cython python-setuptools
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@endcode
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On Fedora, Red Hat, or CentOS, these same dependencies can be obtained via dnf:
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@code
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# dnf install boost-devel boost-test boost-program-options boost-math
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armadillo-devel binutils-devel python3-Cython python3-setuptools
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python3-numpy python3-pandas
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@endcode
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@section build_config Configuring CMake
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Running CMake is the equivalent to running `./configure` with autotools. If you
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run CMake with no options, it will configure the project to build without
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debugging or profiling information (for speed).
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@code
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$ cd build
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$ cmake ../
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@endcode
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You can manually specify options to compile with debugging information and
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profiling information (useful if you are developing mlpack):
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@code
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$ cd build
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$ cmake -D DEBUG=ON -D PROFILE=ON ../
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@endcode
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The full list of options mlpack allows:
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- DEBUG=(ON/OFF): compile with debugging symbols (default OFF)
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- PROFILE=(ON/OFF): compile with profiling symbols (default OFF)
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- ARMA_EXTRA_DEBUG=(ON/OFF): compile with extra Armadillo debugging symbols
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(default OFF)
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- BUILD_TESTS=(ON/OFF): compile the \c mlpack_test program (default ON)
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- BUILD_CLI_EXECUTABLES=(ON/OFF): compile the mlpack command-line executables
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(i.e. \c mlpack_knn, \c mlpack_kfn, \c mlpack_logistic_regression, etc.)
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(default ON)
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- BUILD_PYTHON_BINDINGS=(ON/OFF): compile the bindings for Python, if the
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necessary Python libraries are available (default ON except on Windows)
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- BUILD_SHARED_LIBRARIES=(ON/OFF): compile shared libraries as opposed to
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static libraries (default ON)
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- TEST_VERBOSE=(ON/OFF): run test cases in \c mlpack_test with verbose output
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(default OFF)
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- MATHJAX=(ON/OFF): use MathJax for generated Doxygen documentation (default
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OFF)
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- FORCE_CXX11=(ON/OFF): assume that the compiler supports C++11 instead of
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checking; be sure to specify any necessary flag to enable C++11 as part
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of CXXFLAGS (default OFF)
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Each option can be specified to CMake with the '-D' flag. Other tools can also
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be used to configure CMake, but those are not documented here.
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@section build_build Building mlpack
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Once CMake is configured, building the library is as simple as typing 'make'.
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This will build all library components as well as 'mlpack_test'.
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@code
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$ make
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Scanning dependencies of target mlpack
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[ 1%] Building CXX object
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src/mlpack/CMakeFiles/mlpack.dir/core/optimizers/aug_lagrangian/aug_lagrangian_test_functions.cpp.o
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<...>
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@endcode
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It's often useful to specify \c -jN to the \c make command, which will build on
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\c N processor cores. That can accelerate the build significantly.
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You can specify individual components which you want to build, if you do not
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want to build everything in the library:
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@code
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$ make mlpack_pca mlpack_knn mlpack_kfn
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@endcode
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One particular component of interest is mlpack_test, which runs the mlpack test
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suite. You can build this component with
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@code
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$ make mlpack_test
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@endcode
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and then run all of the tests, or an individual test suite:
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@code
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$ bin/mlpack_test
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$ bin/mlpack_test -t KNNTest
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@endcode
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If the build fails and you cannot figure out why, register an account on Github
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and submit an issue and the mlpack developers will quickly help you figure it
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out:
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http://mlpack.org/
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http://github.com/mlpack/mlpack
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Alternately, mlpack help can be found in IRC at \#mlpack on irc.freenode.net.
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@section install Installing mlpack
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If you wish to install mlpack to the system, make sure you have root privileges
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(or write permissions to those two directories), and simply type
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@code
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# make install
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@endcode
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You can now run the executables by name; you can link against mlpack with
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\c -lmlpack, and the mlpack headers are found in \c /usr/include or
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\c /usr/local/include (depending on the system and CMake configuration).
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*/
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