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.
MLBench consists of 5 Github repositories:
- Documentation: http://github.com/mlbench/mlbench-docs
- Helm Charts for Kubernetes: http://github.com/mlbench/mlbench-helm
- Python Core Library: http://github.com/mlbench/mlbench-core
- Closed-Division Benchmark Implementations: http://github.com/mlbench/mlbench-benchmarks
- Dashboard: http://github.com/mlbench/mlbench-dashboard
About us: See :doc:`Authors </authors>`
Mailing list: https://groups.google.com/d/forum/mlbench
Contact Email: [email protected]
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Facebook
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Alibaba
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