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Edoardo Holzl
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Update _debug.yml and index
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‎_debug.yml‎

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@@ -9,7 +9,7 @@ title: 'MLBench '
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tagline: 'Distributed Machine Learning Benchmark'
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description: 'A public and reproducible collection of reference implementations and benchmark suite for <u>distributed machine learning</u> algorithms, frameworks and systems.'
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url: http://127.0.0.1:4000
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base_url: /
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baseurl: /
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debug: true
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future: true

‎index.md‎

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layout: default
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title: Home
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---
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<h1>mlbench: Distributed Machine Learning Benchmark</h1>
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<h1>MLBench: Distributed Machine Learning Benchmark</h1>
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<a href="https://travis-ci.com/mlbench/mlbench-dashboard"><img src="https://travis-ci.com/mlbench/mlbench-dashboard.svg?branch=develop"></a>
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<a href="https://travis-ci.com/mlbench/mlbench-core"><img src="https://travis-ci.com/mlbench/mlbench-core.svg?branch=develop"></a>
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<a href="https://travis-ci.com/mlbench/mlbench-benchmarks"><img src="https://travis-ci.com/mlbench/mlbench-benchmarks.svg?branch=develop"></a>
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<a href="https://mlbench.readthedocs.io/en/latest/?badge=latest"><img src="https://readthedocs.org/projects/mlbench/badge/?version=latest" alt="Documentation Status"></a>
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<hr>
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<a style="display: inline-block" href="https://travis-ci.com/mlbench/mlbench-dashboard"><img src="https://travis-ci.com/mlbench/mlbench-dashboard.svg?branch=develop"></a>
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<a style="display: inline-block" href="https://travis-ci.com/mlbench/mlbench-core"><img src="https://travis-ci.com/mlbench/mlbench-core.svg?branch=develop"></a>
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<a style="display: inline-block" href="https://travis-ci.com/mlbench/mlbench-benchmarks"><img src="https://travis-ci.com/mlbench/mlbench-benchmarks.svg?branch=develop"></a>
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<a style="display: inline-block" href="https://mlbench.readthedocs.io/en/latest/?badge=latest"><img src="https://readthedocs.org/projects/mlbench/badge/?version=latest" alt="Documentation Status"></a>
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Check out our <a href="https://mlbench.github.io/blog/">blog</a>!
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</p>
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<p>
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<hr>
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<h2>Resources and Community:</h2>
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<ul>
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<li>Github: <a href="https://github.com/mlbench/">github.com/mlbench</a></li>
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<li>Documentation: <a href="https://mlbench.readthedocs.io">mlbench.readthedocs.io</a></li>
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<li>Mailing list: <a href="https://groups.google.com/d/forum/mlbench">groups.google.com/d/forum/mlbench</a></li>
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<li>
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Slack channel:
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<a href="https://join.slack.com/t/mlbench/shared_invite/zt-6sznc8fa-_diIdB7~XtLYmCLaQuOA9Q">join.slack.com/t/mlbench/shared_invite/zt-6sznc8fa-_diIdB7~XtLYmCLaQuOA9Q
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</a>
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</li>
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</ul>
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<h2>Features</h2>
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<ul>
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<li>Github: <a href="https://github.com/mlbench/">github.com/mlbench</a></li>
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<li>Documentation: <a href="https://mlbench.readthedocs.io">mlbench.readthedocs.io</a></li>
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<li>Mailing list: <a href="https://groups.google.com/d/forum/mlbench">groups.google.com/d/forum/mlbench</a></li>
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<li>
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Slack channel:
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<a href="https://join.slack.com/t/mlbench/shared_invite/zt-6sznc8fa-_diIdB7~XtLYmCLaQuOA9Q">join.slack.com/t/mlbench/shared_invite/zt-6sznc8fa-_diIdB7~XtLYmCLaQuOA9Q
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</a>
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</li>
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</ul>
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<hr>
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<h2>Features</h2>
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<ul>
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<li> For reproducibility and simplicity, we currently focus on standard <strong>supervised ML</strong>, including standard deep learning tasks as well as classic linear ML models.</li>
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<li> We provide <strong>reference implementations</strong> for each algorithm and task, to make it easy to port to a new framework.</li>
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<li> Our goal is to benchmark all/most currently relevant <strong>distributed execution frameworks</strong>. We welcome contributions of new frameworks in the benchmark suite.</li>
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<li> We provide <strong>precisely defined tasks</strong> and datasets to have a fair and precise comparison of all algorithms, frameworks and hardware.</li>
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<li> Independently of all solver implementations, we provide universal <strong>evaluation code</strong> allowing to compare the result metrics of different solvers and frameworks.</li>
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<li> Our benchmark code is easy to run on <strong>public clouds</strong>.</li>
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</ul>
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<hr>
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<h2>Sponsors</h2>
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<ul style="list-style-type:none;">
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<li><img src="{{ site.baseurl }}public/images/Logo_EPFL.png" alt="EPFL" style="max-width:200px;border-radius:0px;"/></li>
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<li><img src="{{ site.baseurl }}public/images/pwc_logo.png" alt="PwC" style="max-width:200px;border-radius:0px;"/></li>
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<li><img src="{{ site.baseurl }}public/images/google.png" alt="Google" style="max-width:200px;border-radius:0px;"/></li>
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<li><img src="{{ site.baseurl }}public/images/Facebook-Wordmark-Gray.png" alt="Facebook" style="max-width:200px;border-radius:0px;"/></li>
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</ul>
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<ul>
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<li> For reproducibility and simplicity, we currently focus on standard <strong>supervised ML</strong>, including standard deep learning tasks as well as classic linear ML models.</li>
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<li> We provide <strong>reference implementations</strong> for each algorithm and task, to make it easy to port to a new framework.</li>
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<li> Our goal is to benchmark all/most currently relevant <strong>distributed execution frameworks</strong>. We welcome contributions of new frameworks in the benchmark suite.</li>
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<li> We provide <strong>precisely defined tasks</strong> and datasets to have a fair and precise comparison of all algorithms, frameworks and hardware.</li>
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<li> Independently of all solver implementations, we provide universal <strong>evaluation code</strong> allowing to compare the result metrics of different solvers and frameworks.</li>
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<li> Our benchmark code is easy to run on <strong>public clouds</strong>.</li>
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</ul>
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<h2>Sponsors</h2>
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<ul style="list-style-type:none;">
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<li><img src="{{ site.baseurl }}public/images/Logo_EPFL.png" alt="EPFL" style="max-width:200px;border-radius:0px;"/></li>
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<li><img src="{{ site.baseurl }}public/images/pwc_logo.png" alt="PwC" style="max-width:200px;border-radius:0px;"/></li>
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<li><img src="{{ site.baseurl }}public/images/google.png" alt="Google" style="max-width:200px;border-radius:0px;"/></li>
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<li><img src="{{ site.baseurl }}public/images/Facebook-Wordmark-Gray.png" alt="Facebook" style="max-width:200px;border-radius:0px;"/></li>
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</ul>
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</p>
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