Building mlpack From Source


This document discusses how to build mlpack from source. These build directions will work for any Linux-like shell environment (for example Ubuntu, macOS, FreeBSD etc). However, mlpack is in the repositories of many Linux distributions and so it may be easier to use the package manager for your system. For example, on Ubuntu, you can install mlpack with the following command:

$ sudo apt-get install libmlpack-dev
Older Ubuntu versions may not have the most recent version of mlpack available—for instance, at the time of this writing, Ubuntu 16.04 only has mlpack 2.0.1 available. Options include upgrading Ubuntu to a newer release, finding a PPA or other non-official sources, or installing with a manual build (below).

If mlpack is not available in your system's package manager, then you can follow this document for how to compile and install mlpack from source.

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 on most Linux and UNIX-like systems (including OS X). If you want to build mlpack on Windows, see Building mlpack From Source on Windows (alternatively, you can read Keon's excellent tutorial which is based on older versions).

You can download the latest mlpack release from here: mlpack-3.4.2

Simple Linux build instructions

Assuming all dependencies are installed in the system, you can run the commands below directly to build and install mlpack.

$ wget
$ tar -xvzpf mlpack-3.4.2.tar.gz
$ mkdir mlpack-3.4.2/build && cd mlpack-3.4.2/build
$ cmake ../
$ make -j4 # The -j is the number of cores you want to use for a build.
$ sudo make install

If the cmake .. command fails, you are probably missing a dependency, so check the output and install any necessary libraries. (See Dependencies of mlpack.)

On many Linux systems, mlpack will install by default to /usr/local/lib and you may need to set the LD_LIBRARY_PATH environment variable:

export LD_LIBRARY_PATH=/usr/local/lib

The instructions above are the simplest way to get, build, and install mlpack. The sections below discuss each of those steps in further detail and show how to configure mlpack.

Creating Build Directory

First we should unpack the mlpack source and create a build directory.

$ tar -xvzpf mlpack-3.4.2.tar.gz
$ cd mlpack-3.4.2
$ mkdir build

The directory can have any name, not just 'build', but 'build' is sufficient.

Dependencies of mlpack

mlpack depends on the following libraries, which need to be installed on the system and have headers present:

  • Armadillo >= 8.400.0 (with LAPACK support)
  • Boost (math_c99, serialization, unit_test_framework, heap, spirit) >= 1.58
  • ensmallen >= 2.10.0 (will be downloaded if not found)

In addition, mlpack has the following optional dependencies:

  • STB: this will allow loading of images; the library is downloaded if not found and the CMake variable DOWNLOAD_STB_IMAGE is set to ON (the default)

For Python bindings, the following packages are required:

  • setuptools
  • cython >= 0.24
  • numpy
  • pandas >= 0.15.0
  • pytest-runner

In Ubuntu (>= 18.04) and Debian (>= 10) all of these dependencies can be installed through apt:

# apt-get install libboost-math-dev libboost-test-dev libboost-serialization-dev
libarmadillo-dev binutils-dev python3-pandas python3-numpy cython3

If you are using Ubuntu 19.10 or newer, you can also install libensmallen-dev and libstb-dev, so that CMake does not need to automatically download those packages:

# apt-get install libensmallen-dev libstb-dev
For older versions of Ubuntu and Debian, Armadillo needs to be built from source as apt installs an older version. So you need to omit libarmadillo-dev from the code snippet above and instead use this link to download the required file. Extract this file and follow the README in the uncompressed folder to build and install Armadillo.

On Fedora, Red Hat, or CentOS, these same dependencies can be obtained via dnf:

# dnf install boost-devel boost-test boost-math armadillo-devel binutils-devel
python3-Cython python3-setuptools python3-numpy python3-pandas ensmallen-devel

(It's also possible to use python3 packages from the package manager—mlpack will work with either. Also, the ensmallen-devel package is only available in Fedora 29 or RHEL7 or newer.)

Configuring CMake

Running CMake is the equivalent to running ./configure with autotools. If you run CMake with no options, it will configure the project to build without debugging or profiling information (for speed).

$ cd build
$ cmake ../

You can manually specify options to compile with debugging information and profiling information (useful if you are developing mlpack):

$ cd build
$ cmake -D DEBUG=ON -D PROFILE=ON ../

The full list of options mlpack allows:

  • DEBUG=(ON/OFF): compile with debugging symbols (default OFF)
  • PROFILE=(ON/OFF): compile with profiling symbols (default OFF)
  • ARMA_EXTRA_DEBUG=(ON/OFF): compile with extra Armadillo debugging symbols (default OFF)
  • BUILD_TESTS=(ON/OFF): compile the mlpack_test program (default ON)
  • BUILD_CLI_EXECUTABLES=(ON/OFF): compile the mlpack command-line executables (i.e. mlpack_knn, mlpack_kfn, mlpack_logistic_regression, etc.) (default ON)
  • BUILD_PYTHON_BINDINGS=(ON/OFF): compile the bindings for Python, if the necessary Python libraries are available (default ON except on Windows)
  • BUILD_JULIA_BINDINGS=(ON/OFF): compile Julia bindings, if Julia is found (default ON)
  • BUILD_SHARED_LIBS=(ON/OFF): compile shared libraries as opposed to static libraries (default ON)
  • TEST_VERBOSE=(ON/OFF): run test cases in mlpack_test with verbose output (default OFF)
  • DISABLE_DOWNLOADS=(ON/OFF): Disable downloads of dependencies during build (default OFF)
  • DOWNLOAD_ENSMALLEN=(ON/OFF): If ensmallen is not found, download it (default ON)
  • DOWNLOAD_STB_IMAGE=(ON/OFF): If STB is not found, download it (default ON)
  • BUILD_WITH_COVERAGE=(ON/OFF): Build with support for code coverage tools (gcc only) (default OFF)
  • PYTHON_EXECUTABLE=(/path/to/python_version): Path to specific Python executable
  • JULIA_EXECUTABLE=(/path/to/julia): Path to specific Julia executable
  • BUILD_MARKDOWN_BINDINGS=(ON/OFF): Build Markdown bindings for website documentation (default OFF)
  • MATHJAX=(ON/OFF): use MathJax for generated Doxygen documentation (default OFF)
  • FORCE_CXX11=(ON/OFF): assume that the compiler supports C++11 instead of checking; be sure to specify any necessary flag to enable C++11 as part of CXXFLAGS (default OFF)
  • USE_OPENMP=(ON/OFF): if ON, then use OpenMP if the compiler supports it; if OFF, OpenMP support is manually disabled (default ON)

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.

In addition, the following directories may be specified, to find include files and libraries. These also use the '-D' flag.

  • ARMADILLO_INCLUDE_DIR=(/path/to/armadillo/include/): path to Armadillo headers
  • ARMADILLO_LIBRARY=(/path/to/armadillo/ location of Armadillo library
  • BOOST_ROOT=(/path/to/boost/): path to root of boost installation
  • ENSMALLEN_INCLUDE_DIR=(/path/to/ensmallen/include): path to include directory for ensmallen
  • STB_IMAGE_INCLUDE_DIR=(/path/to/stb/include): path to include directory for STB image library
  • MATHJAX_ROOT=(/path/to/mathjax): path to root of MathJax installation

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'.

$ make
Scanning dependencies of target mlpack
[ 1%] Building CXX object

It's often useful to specify -jN to the make command, which will build on N processor cores. That can accelerate the build significantly.

You can specify individual components which you want to build, if you do not want to build everything in the library:

$ make mlpack_pca mlpack_knn mlpack_kfn

One particular component of interest is mlpack_test, which runs the mlpack test suite. You can build this component with

$ make mlpack_test

and then run all of the tests, or an individual test suite:

$ bin/mlpack_test
$ bin/mlpack_test -t KNNTest

If the build fails and you cannot figure out why, register an account on Github and submit an issue and the mlpack developers will quickly help you figure it out:

Alternately, mlpack help can be found in IRC at #mlpack on

Installing mlpack

If you wish to install mlpack to the system, make sure you have root privileges (or write permissions to those two directories), and simply type

# make install

You can now run the executables by name; you can link against mlpack with -lmlpack, and the mlpack headers are found in /usr/include or /usr/local/include (depending on the system and CMake configuration). If Python bindings were installed, they should be available when you start Python.

Using mlpack without installing

If you would prefer to use mlpack after building but without installing it to the system, this is possible. All of the command-line programs in the build/bin/ directory will run directly with no modification.

For running the Python bindings from the build directory, the situation is a little bit different. You will need to set the following environment variables:

export LD_LIBRARY_PATH=/path/to/mlpack/build/lib/:${LD_LIBRARY_PATH}
export PYTHONPATH=/path/to/mlpack/build/src/mlpack/bindings/python/:${PYTHONPATH}

(Be sure to substitute the correct path to your build directory for /path/to/mlpack/build/.)

Once those environment variables are set, you should be able to start a Python interpreter and import mlpack, then use the Python bindings.