a fast, header-only machine learning library

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mlpack is an intuitive, fast, and flexible header-only C++ machine learning library with bindings to other languages. It is meant to be a machine learning analog to LAPACK, and aims to implement a wide array of machine learning methods and functions as a "swiss army knife" for machine learning researchers.

mlpack's lightweight C++ implementation makes it ideal for deployment, and it can also be used for interactive prototyping via C++ notebooks (these can be seen in action on mlpack's homepage).

In addition to its powerful C++ interface, mlpack also provides command-line programs, Python bindings, Julia bindings, Go bindings and R bindings.

Quick links:

• Quickstart guides: C++, CLI, Python, R, Julia, Go
• mlpack homepage
• mlpack documentation
• Examples repository
• Tutorials
• Development Site (Github)

mlpack uses an open governance model and is fiscally sponsored by NumFOCUS. Consider making a tax-deductible donation to help the project pay for developer time, professional services, travel, workshops, and a variety of other needs.

0. Contents

1. Citation details
2. Dependencies
3. Installation
4. Usage from C++
   1. Reducing compile time
5. Building mlpack's test suite
6. Further resources

1. Citation details

If you use mlpack in your research or software, please cite mlpack using the citation below (given in BibTeX format):

@article{mlpack2023,
    title     = {mlpack 4: a fast, header-only C++ machine learning library},
    author    = {Ryan R. Curtin and Marcus Edel and Omar Shrit and 
                 Shubham Agrawal and Suryoday Basak and James J. Balamuta and 
                 Ryan Birmingham and Kartik Dutt and Dirk Eddelbuettel and 
                 Rishabh Garg and Shikhar Jaiswal and Aakash Kaushik and 
                 Sangyeon Kim and Anjishnu Mukherjee and Nanubala Gnana Sai and 
                 Nippun Sharma and Yashwant Singh Parihar and Roshan Swain and 
                 Conrad Sanderson},
    journal   = {Journal of Open Source Software},
    volume    = {8},
    number    = {82},
    pages     = {5026},
    year      = {2023},
    doi       = {10.21105/joss.05026},
    url       = {https://doi.org/10.21105/joss.05026}
}

Citations are beneficial for the growth and improvement of mlpack.

2. Dependencies

mlpack requires the following additional dependencies:

• C++17 compiler
• Armadillo   >= 10.8
• ensmallen  >= 2.10.0
• cereal     >= 1.1.2

If the STB library headers are available, image loading support will be available.

If you are compiling Armadillo by hand, ensure that LAPACK and BLAS are enabled.

3. Installation

Detailed installation instructions can be found on the Installing mlpack page.

4. Usage from C++

Once headers are installed with make install, using mlpack in an application consists only of including it. So, your program should include mlpack:

#include <mlpack.hpp>

and when you link, be sure to link against Armadillo. If your example program is my_program.cpp, your compiler is GCC, and you would like to compile with OpenMP support (recommended) and optimizations, compile like this:

g++ -O3 -std=c++17 -o my_program my_program.cpp -larmadillo -fopenmp

Note that if you want to serialize (save or load) neural networks, you should add #define MLPACK_ENABLE_ANN_SERIALIZATION before including <mlpack.hpp>. If you don't define MLPACK_ENABLE_ANN_SERIALIZATION and your code serializes a neural network, a compilation error will occur.

Warning: older versions of OpenBLAS (0.3.26 and older) compiled to use pthreads may use too many threads for computation, causing significant slowdown. OpenBLAS versions compiled with OpenMP do not suffer from this issue. See the test build guide for more details and simple workarounds.

See also:

• the test program compilation section of the installation documentation,
• the C++ quickstart, and
• the examples repository repository for some examples of mlpack applications in C++, with corresponding Makefiles.

4.1. Reducing compile time

mlpack is a template-heavy library, and if care is not used, compilation time of a project can be very high. Fortunately, there are a number of ways to reduce compilation time:

• Include individual headers, like <mlpack/methods/decision_tree.hpp>, if you are only using one component, instead of <mlpack.hpp>. This reduces the amount of work the compiler has to do.

• Only use the MLPACK_ENABLE_ANN_SERIALIZATION definition if you are serializing neural networks in your code. When this define is enabled, compilation time will increase significantly, as the compiler must generate code for every possible type of layer. (The large amount of extra compilation overhead is why this is not enabled by default.)

• If you are using mlpack in multiple .cpp files, consider using extern templates so that the compiler only instantiates each template once; add an explicit template instantiation for each mlpack template type you want to use in a .cpp file, and then use extern definitions elsewhere to let the compiler know it exists in a different file.

Other strategies exist too, such as precompiled headers, compiler options, ccache, and others.

5. Building mlpack's test suite

See the installation instruction section.

6. Further Resources

More documentation is available for both users and developers.

• Documentation homepage

To learn about the development goals of mlpack in the short- and medium-term future, see the vision document.

If you have problems, find a bug, or need help, you can try visiting the mlpack help page, or mlpack on Github. Alternately, mlpack help can be found on Matrix at #mlpack; see also the community page.