A public and reproducible collection of reference implementations and benchmark suite for distributed machine learning algorithms, frameworks and systems.

• Project website: https://mlbench.github.io/
• Free software: Apache Software License 2.0
• Documentation: https://mlbench.readthedocs.io.

• For reproducibility and simplicity, we currently focus on standard supervised ML, including standard deep learning tasks as well as classic linear ML models.
• We provide reference implementations for each algorithm, to make it easy to port to a new framework.
• Our goal is to benchmark all/most currently relevant distributed execution frameworks. We welcome contributions of new frameworks in the benchmark suite.
• We provide precisely defined tasks and datasets to have a fair and precise comparison of all algorithms, frameworks and hardware.
• Independently of all solver implementations, we provide universal evaluation code allowing to compare the result metrics of different solvers and frameworks.
• Our benchmark code is easy to run on public clouds.

About us: See Authors </authors>

Mailing list: https://groups.google.com/d/forum/mlbench

Contact Email: mlbench-contact@googlegroups.com