mirror of
https://github.com/vale981/ray
synced 2025-03-11 05:46:37 -04:00
197 lines
6.6 KiB
Markdown
197 lines
6.6 KiB
Markdown
```{include} /_includes/overview/announcement.md
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```
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# Welcome to the Ray documentation
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```{image} https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png
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```
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```{image} https://readthedocs.org/projects/ray/badge/?version=master
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:target: http://docs.ray.io/en/master/?badge=master
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```
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```{image} https://img.shields.io/badge/Ray-Join%20Slack-blue
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:target: https://forms.gle/9TSdDYUgxYs8SA9e8
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```
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```{image} https://img.shields.io/badge/Discuss-Ask%20Questions-blue
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:target: https://discuss.ray.io/
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```
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```{image} https://img.shields.io/twitter/follow/raydistributed.svg?style=social&logo=twitter
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:target: https://twitter.com/raydistributed
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```
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## What can you do with Ray?
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````{panels}
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:container: text-center
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:column: col-lg-4 px-2 py-2
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:card:
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**Scale machine learning workloads with**\
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**<img src="ray-overview/images/ray_svg_logo.svg" alt="ray" width="50px">AIR**
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^^^
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Ray AI Runtime (AIR) is an open-source toolkit for building ML applications.
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It provides libraries for distributed
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[data processing](data/dataset.rst),
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[model training](train/train.rst),
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[tuning](tune/index.rst),
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[reinforcement learning](rllib/index.rst),
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[model serving](serve/index.rst),
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and [more](ray-more-libs/index.rst).
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+++
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```{link-button} ray-air/getting-started
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:type: ref
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:text: Get Started
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:classes: btn-outline-info btn-block
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```
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---
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**Build distributed applications with**\
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**<img src="ray-overview/images/ray_svg_logo.svg" alt="ray" width="50px">Core**
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^^^
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Ray Core provides a [simple and flexible API](ray-core/walkthrough.rst) for building and running your distributed applications.
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You can often [parallelize](ray-core/walkthrough.rst) single machine code with little to zero code changes.
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+++
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```{link-button} ray-core/walkthrough
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:type: ref
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:text: Get Started
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:classes: btn-outline-info btn-block
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```
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---
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**Deploy large-scale workloads with**\
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**<img src="ray-overview/images/ray_svg_logo.svg" alt="ray" width="50px">Clusters**
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^^^
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With a Ray cluster you can deploy your workloads on [AWS, GCP, Azure](cluster/quickstart) or
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[on premise](cluster/cloud.html#cluster-private-setup).
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You can also use [Ray Cluster Managers](cluster/deploy) to run Ray on your existing
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[Kubernetes](cluster/kubernetes),
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[YARN](cluster/yarn),
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or [Slurm](cluster/slurm) clusters.
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+++
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```{link-button} cluster/quickstart
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:type: ref
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:text: Get Started
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:classes: btn-outline-info btn-block
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```
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````
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## What is Ray?
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Ray is a unified framework for scaling AI and Python applications.
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Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for
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accelerating ML workloads:
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<img src="images/what-is-ray-padded.svg" alt="what-is-ray">
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Learn more about [Ray AIR](ray-air/getting-started) and its libraries:
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- [Datasets](data/dataset): Distributed Data Preprocessing
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- [Train](train/train): Distributed Training
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- [Tune](tune/index): Scalable Hyperparameter Tuning
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- [Serve](serve/index): Scalable and Programmable Serving
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- [RLlib](rllib/index): Scalable Reinforcement Learning
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Or more about [Ray Core](ray-core/walkthrough) and its key abstractions:
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- [Tasks](ray-core/tasks): Stateless functions executed in the cluster.
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- [Actors](ray-core/actors): Stateful worker processes created in the cluster.
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- [Objects](ray-core/objects): Immutable values accessible across the cluster.
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Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing
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[ecosystem of community integrations](ray-overview/ray-libraries).
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## Why Ray?
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Today's ML workloads are increasingly compute-intensive. As convenient as they are, single-node development environments such as your laptop cannot scale to meet these demands.
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Ray is a unified way to scale Python and AI applications from a laptop to a cluster.
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With Ray, you can seamlessly scale the same code from a laptop to a cluster. Ray is designed to be general-purpose, meaning that it can performantly run any kind of workload. If your application is written in Python, you can scale it with Ray, no other infrastructure required.
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## How to get involved?
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Ray is more than a framework for distributed applications but also an active community of developers, researchers, and folks that love machine learning.
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Here's a list of tips for getting involved with the Ray community:
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```{include} _includes/_contribute.md
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```
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If you're interested in contributing to Ray, check out our
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[contributing guide for this release](ray-contribute/getting-involved)
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or see the
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[latest version of our contributing guide](https://docs.ray.io/en/latest/ray-contribute/getting-involved.html)
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to read about the contribution process and see what you can work on.
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## What documentation resource is right for you?
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````{panels}
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:container: text-center
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:column: col-lg-6 px-2 py-2
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:card:
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---
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**Getting Started**
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<img src="images/getting_started.svg" alt="getting_started" height="40px">
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^^^^^^^^^^^^^^^
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If you're new to Ray, check out the getting started guide.
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You will learn how to install Ray, how to compute an example with the Ray Core API, and how to use each of Ray's ML libraries.
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You will also understand where to go from there.
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+++
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{link-badge}`ray-overview/index.html,"Getting Started",cls=badge-light`
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---
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**User Guides**
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<img src="images/user_guide.svg" alt="user_guide" height="40px">
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^^^^^^^^^^^
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Our user guides provide you with in-depth information about how to use Ray's libraries and tooling.
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You will learn about the key concepts and features of Ray and how to use them in practice.
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+++
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{link-badge}`ray-core/user-guide.html,"Core",cls=badge-light`
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{link-badge}`data/user-guide.html,"Data",cls=badge-light`
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{link-badge}`train/user_guide.html,"Train",cls=badge-light`
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{link-badge}`tune/user-guide.html,"Tune",cls=badge-light`
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{link-badge}`serve/tutorial.html,"Serve",cls=badge-light`
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{link-badge}`cluster/user-guide.html,"Clusters",cls=badge-light`
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---
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**API reference**
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<img src="images/api.svg" alt="api" height="40px">
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^^^^^^^^^^^^^
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Our API reference guide provides you with a detailed description of the different Ray APIs.
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It assumes familiarity with the key concepts and gives you information about functions, classes, and methods.
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+++
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{link-badge}`ray-references/api.html,"API References",cls=badge-light`
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---
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**Developer guides**
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<img src="images/contribute.svg" alt="contribute" height="40px">
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^^^^^^^^^^^^^^^
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You need more information on how to debug or profile Ray?
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You want more information about Ray's internals?
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Maybe you saw a typo in the documentation, want to fix a bug or contribute a new feature?
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Our developer guides will help you get started.
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+++
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{link-badge}`https://docs.ray.io/en/master/ray-contribute/getting-involved.html,"Developer Guides",cls=badge-light`
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````
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