mlpack

Quick Start

There are multiple ways to get mlpack up and running. Python bindings can be installed using pip or conda, or built from source (see Build from source tutorials). Julia bindings can be installed via Julia's 'Pkg' package manager. For C++, if mlpack is not available via your preferred OS package manager, or if you need to build your own version (e.g. to apply optimizations, use a different set of BLAS/LAPACK, or build a different configuration), please refer to the Build from source tutorials. For Windows, prebuilt binaries will help you get started without the need of building mlpack. These packages include both the C++ mlpack library as well as the CLI tools.

Once you get mlpack running, check out the documentation or the examples repository, which contains simple example usages of mlpack.


Get up and running with mlpack quickly through popular cloud platforms and ready to use images.


Jupyter cloud
Docker Linux
Docker Jupyter

Local installation, select your preferences and run the install command. Stable represents the most currently tested and supported version of mlpack. Please ensure that you have met the prerequisites below.



Build
Stable
Development
OS
macOS
Linux (Debian)
Windows
Package
Language
C++
Python
Julia
All
Run this command

Get your assets

Here is a summary of the currently available assets you can use depending on your needs:


Asset
OS
Arch

mlpack-3.4.1.msi

Windows

x86|x64


mlpack-3.4.1.tar.gz

All

x86|x64


mlpack-3.4.1.zip

All

x86|x64


mlpack-3.4.1-windows-no-libs.zip

Windows

x86|x64

Compiling manually

In case none of the binary packages listed on our website work for your system, or you want to modify mlpack, you will need to build it from source. See the "source" section for your operating system above, or the extended directions below.






© 2007 - 2020 mlpack developers (BSD License).

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