<div align="center", style="font-size: 50px"> <img src="https://github.com/lanl/pyDNMFk/actions/workflows/ci_test.yml/badge.svg?branch=main"></img> <img src="https://img.shields.io/badge/License-BSD%203--Clause-blue.svg"></img> <img src="https://img.shields.io/badge/python-v3.7.1-blue"></img> </div> <img src="https://img.shields.io/badge/-New-011B56?style=flat"></img> pyDNMFk/ Distributed pyNMFk is a software package for applying non-negative matrix factorization in a distributed memory to large datasets. It has the ability to minimize the difference between reconstructed data and the original data through various norms (Frobenious, KL-divergence). The current implementation utilizes optimization tools such as multiplicative updates, HALS, BCD and BPP. Additionally, the Custom Clustering algorithm allows for automated determination for the number of Latent features.