[docs] landing page (fixes #21750)#21859
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@richardliaw @ericl @edoakes wording matters here, please give me all your nitpicks. |
doc/source/index.md
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| To help you, Ray Core provides a simple, universal API for building distributed applications. | ||
| And Ray's native libraries and tools enable you to run complex ML applications with Ray. | ||
| You can deploy these applications on any of the major cloud providers, including AWS, GCP, and Azure, or run them on your own servers. | ||
| Ray also has a growing [ecosystem of community integrations](ray-overview/ray-libraries), including [Dask](https://docs.ray.io/en/latest/data/dask-on-ray.html), [MARS](https://docs.ray.io/en/latest/data/mars-on-ray.html), [Modin](https://github.com/modin-project/modin), [Horovod](https://horovod.readthedocs.io/en/stable/ray_include.html), [Hugging Face](https://huggingface.co/transformers/main_classes/trainer.html#transformers.Trainer.hyperparameter_search), [Scikit-learn](ray-more-libs/joblib), [and others](ray-more-libs/index). | ||
| The following figure gives you an overview of the Ray ecosystem. |
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Right now this reads as a big paragraph; can we insert some linespaces?
doc/source/index.md
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| --- | ||
| **Run machine learning workflows with <img src="ray-overview/images/ray_svg_logo.svg" alt="ray" width="50px">ML** | ||
| ^^^ | ||
| With Ray ML you can run tasks like [distributed data loading and compute](data/dataset.rst), |
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Ray ML is a toolkit for distributed machine learning. It provides libraries for distributed data processing, model training, tuning, reinforcement learning, model serving, and more.
Perhaps we can add links to each, but I think we should condense the wording
doc/source/index.md
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| --- | ||
| **Deploy large-scale workloads with <img src="ray-overview/images/ray_svg_logo.svg" alt="ray" width="50px">Cluster** | ||
| ^^^ | ||
| With Ray Cluster you can deploy your workloads on [AWS, GCP, Azure](cluster/quickstart) or |
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| With Ray Cluster you can deploy your workloads on [AWS, GCP, Azure](cluster/quickstart) or | |
| With a Ray cluster, you can deploy your workloads on [AWS, GCP, Azure](cluster/quickstart) or |
doc/source/index.md
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| ```{link-button} cluster/quickstart | ||
| :type: ref | ||
| :text: Get Started with Ray Cluster |
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| :text: Get Started with Ray Cluster | |
| :text: Get Started with Ray Clusters |
doc/source/index.md
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| ``` | ||
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| --- | ||
| **Deploy large-scale workloads with <img src="ray-overview/images/ray_svg_logo.svg" alt="ray" width="50px">Cluster** |
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| **Deploy large-scale workloads with <img src="ray-overview/images/ray_svg_logo.svg" alt="ray" width="50px">Cluster** | |
| **Deploy large-scale workloads with <img src="ray-overview/images/ray_svg_logo.svg" alt="ray" width="50px">Clusters** |
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Also consider removing the svg? Not sure if it scales well across devices.
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It's the size of the font, which does not change. can remove it later if people hate it.
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
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@richardliaw I addressed your comments and added a getting started guide today. This should be good to go now. |
| title: "Tutorials and Examples" | ||
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| - caption: Deploying Ray Clusters | ||
| - caption: Ray Clusters | Deployments |
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FYI, the caption says "Clusters" here but elsewhere we have "Ray Cluster", should we make that consistent?
| Ray Core provides a [simple and flexible API](ray-core/walkthrough.rst) for building and running your distributed applications. | ||
| You can often [parallelize](ray-core/walkthrough.rst) single machine code with little to zero code changes. |
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nit to make these parts just a bit shorter (and reduce vertical height even more)
* [docs] landing page (fixes #21750) (#21859) Co-authored-by: Richard Liaw <rliaw@berkeley.edu> * [Doc] Fix bad doc and recover doc of c++ api (#22213) * [Docs] Ray Data docs target state (#21931) Preview: [docs](https://ray--21931.org.readthedocs.build/en/21931/data/dataset.html) The Ray Data project's docs now have a clearer structure and have partly been rewritten/modified. In particular we have - [x] A Getting Started Guide - [x] An explicit User / How-To Guide - [x] A dedicated Key Concepts page - [x] A consistent naming convention in `Ray Data` whenever is is referred to the project. This surfaces quite clearly that, apart from the "Getting Started" sections, we really only have one real example. Once we have more, we can create an "Example" section like many other sub-projects have. This will be addressed in #21838. * [Datasets] [Docs] Datasets library branding + positioning tweaks (#22067) * [train] Minor fixes on Ray Train user guide doc (#22379) Fixes some typos and format issues. * [Doc] Fix bad links of dask and mars in ray-libraries.rst (#22210) * merge Signed-off-by: Max Pumperla <max.pumperla@googlemail.com> * merge Signed-off-by: Max Pumperla <max.pumperla@googlemail.com> * merge Signed-off-by: Max Pumperla <max.pumperla@googlemail.com> * [docs] Clean up long titles in TOC (#22016) * LINT Signed-off-by: Max Pumperla <max.pumperla@googlemail.com> Co-authored-by: Richard Liaw <rliaw@berkeley.edu> Co-authored-by: Guyang Song <guyang.sgy@antfin.com> Co-authored-by: Clark Zinzow <clarkzinzow@gmail.com> Co-authored-by: Hao Chen <chenh1024@gmail.com> Co-authored-by: Eric Liang <ekhliang@gmail.com>




Preview: docs
More informative and attractive landing page with a focus on