Use whpg-madlib to run data-parallel implementations of mathematical, statistical, graph, machine learning, and deep learning methods on structured and unstructured data without moving the data out of the database. It runs inside WarehousePG itself, so you don't need to install or maintain any additional services alongside your cluster.
whpg-madlib is a fork of Apache MADlib, an open source library for scalable in-database analytics, with changes specific to WarehousePG. The source is in the whpg-madlib repository.
To get started with whpg-madlib, see:
See Machine learning and deep learning using MADlib in the WarehousePG 7 analytics guide, or the WarehousePG 6 version, for an overview of the machine learning and deep learning capabilities. See the Apache MADlib documentation for the full function reference.
Release notes
Release notes for the whpg-madlib extension for WarehousePG.
Installing whpg-madlib
Install the whpg-madlib extension for WarehousePG, add the MADlib functions to a database, and upgrade or uninstall them.
Using
Call MADlib functions from SQL in WarehousePG, and run linear regression, association rules, and Naive Bayes classification examples.