Introducing MLBench

MLBench is a framework for distributed machine learning. Its purpose is to improve transparency, reproducibility, robustness, and to provide fair performance measures as well as reference implementations, helping adoption of distributed machine learning methods both in industry and in the academic community.

The MLBench Dashboard

MLBench is public, open source and vendor independent, and has two main goals:

  1. to be an easy-to-use and fair benchmarking suite for algorithms as well as for systems (software frameworks and hardware).
  2. to provide re-usable and reliable reference implementations of distributed ML algorithms.

Main Features

MLBench is based on Kubernetes to ease deployment in a distributed setting, both on public clouds and on dedicated hardware. It supports several standard machine-learning frameworks and algorithms


Our project is open, vendor independent and backed by academic standards, and we highly value contributions from the community

Getting Started

Please refer to our getting-started tutorial on how to set up and start using MLBench.