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https://github.com/mlpack/mlpack

mlpack: a fast, header-only C++ machine learning library
https://github.com/mlpack/mlpack

c-plus-plus deep-learning hacktoberfest machine-learning machine-learning-library nearest-neighbor-search regression scientific-computing

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mlpack: a fast, header-only C++ machine learning library

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README 

        


  mlpack: a fast, header-only machine learning library

  
a fast, header-only machine learning library







  Home |

  Download |

  Documentation |

  Help |






  Azure DevOps builds (job)

  License

  NumFOCUS






  

    Download:

    current stable version (4.5.1)

  




**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](https://www.mlpack.org/)).



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++](doc/quickstart/cpp.md),

   [CLI](doc/quickstart/cli.md), [Python](doc/quickstart/python.md),

   [R](doc/quickstart/r.md), [Julia](doc/quickstart/julia.md),

   [Go](doc/quickstart/go.md)

 - [mlpack homepage](https://www.mlpack.org/)

 - [mlpack documentation](https://www.mlpack.org/doc/index.html)

 - [Examples repository](https://github.com/mlpack/examples/)

 - [Tutorials](doc/user/tutorials.md)

 - [Development Site (Github)](https://github.com/mlpack/mlpack/)



[//]: # (numfocus-fiscal-sponsor-attribution)



mlpack uses an [open governance model](./GOVERNANCE.md) and is fiscally

sponsored by [NumFOCUS](https://numfocus.org/).  Consider making a

[tax-deductible donation](https://numfocus.org/donate-to-mlpack) to help the

project pay for developer time, professional services, travel, workshops, and a

variety of other needs.





  

    NumFOCUS logo

  







## 0. Contents



 1. [Citation details](#1-citation-details)

 2. [Dependencies](#2-dependencies)

 3. [Installation](#3-installation)

 4. [Usage from C++](#4-usage-from-c)

     1. [Reducing compile time](#41-reducing-compile-time)

 5. [Building mlpack's test suite](#5-building-mlpacks-test-suite)

 6. [Further resources](#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](https://arma.sourceforge.net)        >= 10.8

 - [ensmallen](https://ensmallen.org)       >= 2.10.0

 - [cereal](http://uscilab.github.io/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](doc/user/install.md) 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:



```c++

#include 

```



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:



```sh

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 ``.

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](doc/user/install.md#build-tests) for more details and simple

workarounds.



See also:



 * the [test program compilation section](doc/user/install.md#compiling-a-test-program)

   of the installation documentation,

 * the [C++ quickstart](doc/quickstart/cpp.md), and

 * the [examples repository](https://github.com/mlpack/examples) repository for

   some examples of mlpack applications in C++, with corresponding `Makefile`s.



### 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 ``, if you

   are only using one component, instead of ``.  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`](https://isocpp.org/wiki/faq/cpp11-language-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`](https://ccache.dev), and others.



## 5. Building mlpack's test suite



See the [installation instruction section](doc/user/install.md#build-tests).



## 6. Further Resources



More documentation is available for both users and developers.



 * [Documentation homepage](https://www.mlpack.org/doc/index.html)



To learn about the development goals of mlpack in the short- and medium-term

future, see the [vision document](https://www.mlpack.org/papers/vision.pdf).



If you have problems, find a bug, or need help, you can try visiting

the [mlpack help](https://www.mlpack.org/questions.html) page, or [mlpack on

Github](https://github.com/mlpack/mlpack/).  Alternately, mlpack help can be

found on Matrix at `#mlpack`; see also the

[community](https://www.mlpack.org/doc/developer/community.html) page.