mirror of
https://github.com/vale981/ray
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38 lines
1.4 KiB
HTML
38 lines
1.4 KiB
HTML
---
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layout: default
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---
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<link rel="stylesheet" href="{{ "/css/main.css" | prepend: site.baseurl }}">
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<embed>
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<a href="https://github.com/ray-project/ray"><img style="position: absolute; top: 0; right: 0; border: 0;" src="https://camo.githubusercontent.com/365986a132ccd6a44c23a9169022c0b5c890c387/68747470733a2f2f73332e616d617a6f6e6177732e636f6d2f6769746875622f726962626f6e732f666f726b6d655f72696768745f7265645f6161303030302e706e67" alt="Fork me on GitHub" data-canonical-src="https://s3.amazonaws.com/github/ribbons/forkme_right_red_aa0000.png"></a>
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</embed>
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<div class="home">
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<div>
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| Home | <a class href="blog.html">Blog</a> | <a href="get_ray.html">Get Ray!</a> |
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</div>
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<p>
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<img src="https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png"/>
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</p>
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<p>
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<b>Ray provides a simple, universal API for building distributed applications.</b>
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</p>
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<p>
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Ray is packaged with the following libraries for accelerating machine learning workloads:
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</p>
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<ul>
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<li><em>Tune</em>: Scalable Hyperparameter Tuning</li>
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<li><em>RLlib</em>: Scalable Reinforcement Learning</li>
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<li><em>Distributed Training</em></li>
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</ul>
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<p>
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To get started, visit the Ray Project <a href="https://ray.io">web site</a>, <a href="https://docs.ray.io/en/latest/">documentation</a>, <a href="https://github.com/ray-project/">GitHub project</a>, or <a href="https://github.com/ray-project/tutorial">Tutorials</a>.
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</p>
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</div>
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