MLBench: Distributed Machine Learning Benchmark

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

Features

  • 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.

Repositories

MLBench consists of 5 Github repositories: