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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 https://github.com/ray-project/ray/issues/21838.
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3.9 KiB
YAML
122 lines
No EOL
3.9 KiB
YAML
format: jb-book
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root: index
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parts:
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- caption: Overview
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chapters:
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- file: ray-overview/index
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- file: ray-overview/installation
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- file: ray-overview/learn-more
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- file: ray-overview/ray-libraries
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- caption: Ray ML | Machine Learning
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chapters:
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- file: data/dataset
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title: Ray Data
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sections:
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- file: data/getting-started
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- file: data/user-guide
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- file: data/key-concepts
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- file: data/examples/big_data_ingestion
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- file: train/train
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title: Ray Train
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sections:
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- file: train/user_guide
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- file: train/examples
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- file: train/architecture
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- file: train/migration-guide
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- file: raysgd/raysgd
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title: "RaySGD v1: Distributed Training Wrappers"
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- file: tune/index
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title: Ray Tune
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sections:
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- file: tune/key-concepts
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- file: tune/user-guide
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- file: tune/tutorials/overview
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sections:
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- file: tune/tutorials/tune-tutorial.rst
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- file: tune/tutorials/tune-advanced-tutorial.rst
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- file: tune/tutorials/tune-distributed.rst
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- file: tune/tutorials/tune-lifecycle.rst
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- file: tune/tutorials/tune-mlflow.rst
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- file: tune/tutorials/tune-pytorch-cifar.rst
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- file: tune/tutorials/tune-pytorch-lightning.rst
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- file: tune/tutorials/tune-serve-integration-mnist.rst
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- file: tune/tutorials/tune-sklearn.rst
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- file: tune/tutorials/tune-xgboost.rst
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- file: tune/tutorials/tune-wandb.rst
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- file: tune/examples/index
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- file: tune/contrib
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- file: serve/index
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title: Ray Serve
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sections:
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- file: serve/end_to_end_tutorial.rst
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- file: serve/core-apis
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- file: serve/http-servehandle
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- file: serve/deployment
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- file: serve/ml-models
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- file: serve/pipeline
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- file: serve/performance
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- file: serve/architecture
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- file: serve/tutorials/index
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- file: serve/faq
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- file: rllib/index
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title: Ray RLlib
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sections:
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- file: rllib/rllib-toc
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- file: rllib/core-concepts
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- file: rllib/rllib-training
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- file: rllib/rllib-env
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- file: rllib/rllib-models
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- file: rllib/rllib-algorithms
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- file: rllib/rllib-sample-collection
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- file: rllib/rllib-offline
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- file: rllib/rllib-concepts
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- file: rllib/rllib-examples
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- file: rllib/rllib-dev
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- file: workflows/concepts
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title: Ray Workflows
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sections:
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- file: workflows/basics
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- file: workflows/management
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- file: workflows/actors
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- file: workflows/metadata
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- file: workflows/events
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- file: workflows/comparison
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- file: workflows/advanced
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- file: ray-more-libs/index
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title: More Ray ML Libraries
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- caption: Ray Core | Distributed Applications
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chapters:
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- file: ray-core/walkthrough
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title: Getting Started
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- file: ray-core/using-ray
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title: "User Guide"
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- file: ray-core/examples/overview
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title: "Tutorials and Examples"
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- caption: Ray Clusters | Deployments
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chapters:
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- file: cluster/quickstart
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- file: cluster/user-guide
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- file: cluster/cloud
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- file: cluster/deploy
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- caption: References
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chapters:
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- file: ray-references/api
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- file: ray-references/faq
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- caption: Developer Guides
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chapters:
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- file: ray-contribute/getting-involved
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sections:
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- file: ray-contribute/development
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- file: ray-contribute/fake-autoscaler
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- file: ray-core/configure
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- file: ray-observability/index
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- file: ray-design-patterns/index
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- file: ray-contribute/whitepaper
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# TODO: Add examples section |