2017-05-20 18:33:36 -07:00
---
layout: default
---
2017-08-30 23:40:46 -07:00
< link rel = "stylesheet" href = "{{ " / css / main . css " | prepend: site . baseurl } } " >
2019-12-06 17:18:47 -06:00
< embed >
< 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 >
< / embed >
2017-05-20 18:33:36 -07:00
< div class = "home" >
2019-12-06 17:18:47 -06:00
< div >
2019-12-07 16:31:50 -06:00
| Home | < a class href = "blog.html" > Blog< / a > | < a href = "get_ray.html" > Get Ray!< / a > |
2019-12-06 17:18:47 -06:00
< / div >
< p >
< img src = "https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png" / >
< / p >
< p >
< b > Ray is a fast and simple framework for building and running distributed applications.< / b >
< / p >
< p >
Ray is packaged with the following libraries for accelerating machine learning workloads:
< / p >
< ul >
< li > < em > Tune< / em > : Scalable Hyperparameter Tuning< / li >
< li > < em > RLlib< / em > : Scalable Reinforcement Learning< / li >
< li > < em > Distributed Training< / em > < / li >
2017-08-30 23:40:46 -07:00
< / ul >
2017-05-20 18:33:36 -07:00
2019-12-06 17:18:47 -06:00
< p >
2020-04-13 16:17:05 -07:00
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 > .
2019-12-06 17:18:47 -06:00
< / p >
2017-05-20 18:33:36 -07:00
< / div >