Downloading and Installing mlpack
Prebuilt mlpack packages are available for most operating systems;
these packages may not be the latest version,
so if you are encountering issues,
use the official source release.
Source download and build reference
C++ system-wide package installation
Debian/Ubuntu:
sudo apt-get install libmlpack-dev
Red Hat/Fedora:
sudo dnf install mlpack-devel
Arch Linux:
sudo pacman -S mlpack
macOS (Homebrew):
brew install mlpack
macOS (MacPorts):
sudo port install mlpack
Windows (vcpkg):
vcpkg install mlpack
Windows MSI installer:
mlpack-4.5.1.msi
Language-specific installation
Python (pip):
pip install mlpack
Python (conda):
conda install -c conda-forge mlpack
Julia:
import Pkg; Pkg.add("mlpack");
R:
install.packages("mlpack")
Go:
go get -u -d mlpack.org/v1/mlpack
Ready-to-use cloud images
Command-line program installation
Debian/Ubuntu:
sudo apt-get install mlpack-bin
Red Hat/Fedora:
sudo dnf install mlpack-bin
Arch Linux:
sudo pacman -S mlpack
macOS (Homebrew):
brew install mlpack
macOS (MacPorts):
sudo port install mlpack
Windows (vcpkg):
vcpkg install mlpack
Windows MSI installer:
mlpack-4.5.1.msi
Quickstart: Using mlpack
Once mlpack is installed, you can go through the quickstart tutorials:
You can also look at the
examples repository for fully
working mlpack data science pipelines and applications.
Further documentation
Once mlpack is installed and working, the reference documentation links
below can be helpful for building mlpack applications: