format: jb-book
root: index
parts:
  - caption: Overview
    chapters:
      - file: ray-overview/index
      - file: ray-overview/installation
      - file: ray-overview/ray-libraries

  - caption: Ray ML
    chapters:
      - file: data/dataset
        title: Ray Data
        sections:
          - file: data/dataset-pipeline
          - file: data/dataset-ml-preprocessing
          - file: data/dataset-execution-model
          - file: data/dataset-tensor-support
          - file: data/examples/big_data_ingestion
          - file: data/dask-on-ray
          - file: data/mars-on-ray
          - file: data/modin/index
          - file: data/raydp
      - file: train/train
        title: Ray Train
        sections:
          - file: train/user_guide
          - file: train/examples
          - file: train/architecture
          - file: train/migration-guide
          - file: raysgd/raysgd
            title: "RaySGD v1: Distributed Training Wrappers"
      - file: tune/index
        title: Ray Tune
        sections:
          - file: tune/key-concepts
          - file: tune/user-guide
          - file: tune/tutorials/overview
            sections:
              - file: tune/tutorials/tune-tutorial.rst
              - file: tune/tutorials/tune-advanced-tutorial.rst
              - file: tune/tutorials/tune-distributed.rst
              - file: tune/tutorials/tune-lifecycle.rst
              - file: tune/tutorials/tune-mlflow.rst
              - file: tune/tutorials/tune-pytorch-cifar.rst
              - file: tune/tutorials/tune-pytorch-lightning.rst
              - file: tune/tutorials/tune-serve-integration-mnist.rst
              - file: tune/tutorials/tune-sklearn.rst
              - file: tune/tutorials/tune-xgboost.rst
              - file: tune/tutorials/tune-wandb.rst
          - file: tune/examples/index
          - file: tune/contrib
      - file: serve/index
        title: Ray Serve
        sections:
          - file: serve/tutorial
          - file: serve/core-apis
          - file: serve/http-servehandle
          - file: serve/deployment
          - file: serve/ml-models
          - file: serve/pipeline
          - file: serve/performance
          - file: serve/architecture
          - file: serve/tutorials/index
          - file: serve/faq
      - file: rllib/index
        title: Ray RLlib
        sections:
          - file: rllib/rllib-toc
          - file: rllib/core-concepts
          - file: rllib/rllib-training
          - file: rllib/rllib-env
          - file: rllib/rllib-models
          - file: rllib/rllib-algorithms
          - file: rllib/rllib-sample-collection
          - file: rllib/rllib-offline
          - file: rllib/rllib-concepts
          - file: rllib/rllib-examples
          - file: rllib/rllib-dev
      - file: workflows/concepts
        title: Ray Workflows
        sections:
          - file: workflows/basics
          - file: workflows/management
          - file: workflows/actors
          - file: workflows/metadata
          - file: workflows/events
          - file: workflows/comparison
          - file: workflows/advanced
      - file: ray-more-libs/index
        title: More Ray ML Libraries

  - caption: Ray Core
    chapters:
      - file: ray-core/walkthrough
        title: Getting Started
      - file: ray-core/using-ray
        title: "User Guide"
        sections:
          - file: ray-core/starting-ray
          - file: ray-core/actors
          - file: ray-core/namespaces
          - file: ray-core/handling-dependencies
          - file: ray-core/async_api
          - file: ray-core/concurrency_group_api
          - file: ray-core/using-ray-with-gpus
          - file: ray-core/serialization
          - file: ray-core/memory-management
          - file: ray-core/placement-group
          - file: ray-core/troubleshooting
          - file: ray-core/fault-tolerance
          - file: ray-core/advanced
          - file: ray-core/cross-language
          - file: ray-core/using-ray-with-tensorflow
          - file: ray-core/using-ray-with-pytorch
          - file: ray-core/using-ray-with-jupyter
      - file: ray-core/examples/overview
        title: "Tutorials and Examples"
        sections:
          - file: ray-core/examples/tips-for-first-time
          - file: ray-core/examples/testing-tips
          - file: ray-core/examples/progress_bar
          - file: ray-core/examples/plot_streaming
          - file: ray-core/examples/placement-group
          - file: ray-core/examples/plot_parameter_server
          - file: ray-core/examples/plot_hyperparameter
          - file: ray-core/examples/plot_lbfgs
          - file: ray-core/examples/plot_example-lm
          - file: ray-core/examples/plot_newsreader
          - file: ray-core/examples/dask_xgboost/dask_xgboost
          - file: ray-core/examples/modin_xgboost/modin_xgboost
          - file: ray-core/examples/plot_pong_example
          - file: ray-core/examples/plot_example-a3c
          - file: ray-core/examples/using-ray-with-pytorch-lightning

  - caption: Deploying Ray Clusters
    chapters:
      - file: cluster/quickstart
      - file: cluster/user-guide
        sections:
          - file: cluster/index
          - file: cluster/guide
          - file: cluster/job-submission
            title: "Submitting Ray Jobs"
          - file: cluster/ray-client
      - file: cluster/cloud
        sections:
          - file: cluster/aws-tips
      - file: cluster/deploy
        sections:
          - file: cluster/kubernetes
          - file: cluster/yarn
          - file: cluster/slurm
          - file: cluster/lsf

  - caption: References
    chapters:
      - file: ray-references/api
      - file: ray-references/faq

  - caption: Developer Guide
    chapters:
      - file: ray-contribute/getting-involved
        sections:
          - file: ray-contribute/development
          - file: ray-contribute/fake-autoscaler
      - file: ray-core/configure
      - file: ray-observability/index
      - file: ray-design-patterns/index
      - file: ray-contribute/whitepaper

# TODO: Add examples section