_toc.yml

format: jb-book
root: index
parts:
  - caption: Ray
    chapters:
      - file: ray-overview/index
        title: "Overview"
      - file: ray-overview/getting-started
        title: "Getting Started"
      - file: ray-overview/installation
        title: "Installation"
      - file: ray-overview/use-cases
        title: "Use Cases"
      - file: ray-overview/ray-libraries
        title: "Ecosystem"

      - file: ray-core/walkthrough
        title: "Ray Core"
        sections:
          - file: ray-core/key-concepts
            title: "Key Concepts"
          - file: ray-core/user-guide
            title: "User Guides"
          - file: ray-core/examples/overview
            title: "Examples"
            sections:
              - file: ray-core/examples/monte_carlo_pi
              - file: ray-core/examples/plot_example-a3c
              - file: ray-core/examples/plot_example-lm
              - file: ray-core/examples/plot_hyperparameter
              - file: ray-core/examples/plot_parameter_server
              - file: ray-core/examples/plot_pong_example
              - file: ray-core/examples/highly_parallel
              - file: ray-core/examples/batch_prediction
              - file: ray-core/examples/batch_training
              - file: ray-core/examples/automl_for_time_series
              - file: ray-core/examples/web-crawler
              - file: ray-core/examples/map_reduce
          - file: ray-core/api/index

      - file: ray-air/getting-started
        title: "Ray AI Runtime (AIR)"
        sections:
          - file: ray-air/key-concepts
          - file: ray-air/user-guides
            sections:
              - file: ray-air/preprocessors
              - file: ray-air/trainers
              - file: ray-air/check-ingest
              - file: ray-air/tuner
              - file: ray-air/predictors
              - file: ray-air/computer-vision
              - file: ray-air/examples/serving_guide
              - file: ray-air/deployment
          - file: ray-air/examples/index
            sections:
              - file: ray-air/examples/torch_image_example
              - file: ray-air/examples/torch_detection
              - file: ray-air/examples/convert_existing_pytorch_code_to_ray_air
              - file: ray-air/examples/convert_existing_tf_code_to_ray_air
              - file: ray-air/examples/tfx_tabular_train_to_serve
              - file: ray-air/examples/huggingface_text_classification
              - file: ray-air/examples/sklearn_example
              - file: ray-air/examples/xgboost_example
              - file: ray-air/examples/analyze_tuning_results
              - file: ray-air/examples/lightgbm_example
              - file: ray-air/examples/torch_incremental_learning
              - file: ray-air/examples/rl_serving_example
              - file: ray-air/examples/rl_online_example
              - file: ray-air/examples/rl_offline_example
              - file: ray-air/examples/upload_to_comet_ml
              - file: ray-air/examples/upload_to_wandb
              - file: ray-air/examples/feast_example
              - file: ray-air/examples/automl_with_ray_air
              - file: ray-air/examples/batch_tuning
              - file: ray-air/examples/batch_forecasting
              - file: ray-air/examples/pytorch_resnet_batch_prediction
              - file: ray-air/examples/stablediffusion_batch_prediction
              - file: ray-air/examples/gptj_deepspeed_fine_tuning
              - file: ray-air/examples/gptj_batch_prediction
              - file: ray-air/examples/gptj_serving
              - file: ray-air/examples/dreambooth_finetuning
          - file: ray-air/api/api
          - file: ray-air/benchmarks

      - file: data/dataset
        title: Ray Data
        sections:
          - file: data/getting-started
          - file: data/key-concepts
          - file: data/user-guide
          - file: data/examples/index
            sections:
              - file: data/examples/nyc_taxi_basic_processing
                title: Processing the NYC taxi dataset
              - file: data/examples/batch_training
                title: Batch Training with Ray Datasets
              - file: data/examples/ocr_example
                title: Scaling OCR with Ray Datasets
              - file: data/examples/advanced-pipelines
              - file: data/examples/random-access
          - file: data/faq
          - file: data/api/api
          - file: data/glossary
          - file: data/integrations

      - file: train/train
        title: Ray Train
        sections:
          - file: train/getting-started
            title: "Getting Started"
          - file: train/key-concepts
            title: "Key Concepts"
          - file: train/user-guides
            title: "User Guides"
            sections:
              - file: train/config_guide
                title: "Configuring Ray Train"
              - file: train/dl_guide
                title: "Deep Learning Guide"
              - file: train/gbdt
                title: "XGBoost/LightGBM guide"
              - file: train/architecture
                title: "Ray Train Architecture"
          - file: train/examples
            title: "Examples"
            sections:
              - file: train/examples/pytorch/torch_fashion_mnist_example
                title: "PyTorch Fashion MNIST Example"
              - file: train/examples/lightning/lightning_mnist_example
                title: "PyTorch Lightning MNIST Example"
              - file: train/examples/transformers/transformers_example
                title: "HF Transformers Example"
              - file: train/examples/tf/tensorflow_mnist_example
                title: "TensorFlow MNIST Example"
              - file: train/examples/horovod/horovod_example
                title: "Horovod Example"
              - file: train/examples/mlflow_fashion_mnist_example
                title: "MLflow Callback Example"
              - file: train/examples/tf/tune_tensorflow_mnist_example
                title: "Tune & TensorFlow Example"
              - file: train/examples/pytorch/tune_cifar_torch_pbt_example
                title: "Tune & PyTorch Example"
              - file: train/examples/pytorch/torch_data_prefetch_benchmark/benchmark_example
                title: "Torch Data Prefetching Benchmark"
              - file: train/examples/pytorch/pytorch_resnet_finetune
                title: "PyTorch Finetuning ResNet Example"
          - file: train/faq
          - file: train/api/api

      - file: tune/index
        title: Ray Tune
        sections:
          - file: tune/getting-started
            title: "Getting Started"
          - file: tune/key-concepts
            title: "Key Concepts"
          - file: tune/tutorials/overview
            sections:
              - file: tune/tutorials/tune-run
                title: "Running Basic Experiments"
              - file: tune/tutorials/tune-output
              - file: tune/tutorials/tune-resources
                title: "Setting Trial Resources"
              - file: tune/tutorials/tune-search-spaces
                title: "Using Search Spaces"
              - file: tune/tutorials/tune-stopping
              - file: tune/tutorials/tune-trial-checkpoints
              - file: tune/tutorials/tune-storage
              - file: tune/tutorials/tune-fault-tolerance
              - file: tune/tutorials/tune-metrics
                title: "Using Callbacks and Metrics"
              - file: tune/tutorials/tune_get_data_in_and_out
              - file: tune/examples/tune_analyze_results
              - file: tune/examples/pbt_guide
                sections:
                  - file: tune/examples/pbt_visualization/pbt_visualization
                    title: "Visualizing and Understanding PBT"
              - file: tune/tutorials/tune-distributed
                title: "Deploying Tune in the Cloud"
              - file: tune/tutorials/tune-lifecycle
                title: "Tune Architecture"
              - file: tune/tutorials/tune-scalability
                title: "Scalability Benchmarks"
          - file: tune/examples/index
            sections:
              - file: tune/examples/ml-frameworks
                sections:
                  - file: tune/examples/tune-sklearn
                    title: "Scikit-Learn Example"
                  - file: tune/examples/tune_mnist_keras
                    title: "Keras Example"
                  - file: tune/examples/tune-pytorch-cifar
                    title: "PyTorch Example"
                  - file: tune/examples/tune-pytorch-lightning
                    title: "PyTorch Lightning Example"
                  - file: tune/examples/mxnet_example
                    title: "MXNet Example"
                  - file: tune/examples/tune-serve-integration-mnist
                    title: "Ray Serve Example"
                  - file: tune/examples/pbt_ppo_example
                    title: "Ray RLlib Example"
                  - file: tune/examples/tune-xgboost
                    title: "XGBoost Example"
                  - file: tune/examples/lightgbm_example
                    title: "LightGBM Example"
                  - file: tune/examples/horovod_simple
                    title: "Horovod Example"
                  - file: tune/examples/pbt_transformers
                    title: "Hugging Face Transformers Example"
              - file: tune/examples/experiment-tracking
                sections:
                  - file: tune/examples/tune-wandb
                    title: "Weights & Biases Example"
                  - file: tune/examples/tune-mlflow
                    title: "MLflow Example"
                  - file: tune/examples/tune-aim
                    title: "Aim Example"
                  - file: tune/examples/tune-comet
                    title: "Comet Example"
              - file: tune/examples/hpo-frameworks
                sections:
                  - file: tune/examples/ax_example
                    title: "Ax Example"
                  - file: tune/examples/dragonfly_example
                    title: "Dragonfly Example"
                  - file: tune/examples/skopt_example
                    title: "Skopt Example"
                  - file: tune/examples/hyperopt_example
                    title: "HyperOpt Example"
                  - file: tune/examples/bayesopt_example
                    title: "Bayesopt Example"
                  - file: tune/examples/flaml_example
                    title: "FLAML Example"
                  - file: tune/examples/bohb_example
                    title: "BOHB Example"
                  - file: tune/examples/nevergrad_example
                    title: "Nevergrad Example"
                  - file: tune/examples/optuna_example
                    title: "Optuna Example"
                  - file: tune/examples/zoopt_example
                    title: "ZOOpt Example"
                  - file: tune/examples/sigopt_example
                    title: "SigOpt Example"
                  - file: tune/examples/hebo_example
                    title: "HEBO Example"
              - file: tune/examples/other-examples
                title: "Other Examples"
              - file: tune/examples/exercises
                title: "Exercises"
          - file: tune/faq
          - file: tune/api/api.rst

      - file: serve/index
        title: Ray Serve
        sections:
          - file: serve/getting_started
          - file: serve/key-concepts
          - file: serve/user-guide
            sections:
              - file: serve/http-guide
              - file: serve/scaling-and-resource-allocation
              - file: serve/model_composition
              - file: serve/dev-workflow
              - file: serve/multi-app
              - file: serve/production-guide/index
                sections:
                  - file: serve/production-guide/config
                  - file: serve/production-guide/deploy-vm
                  - file: serve/production-guide/kubernetes
                  - file: serve/production-guide/monitoring
                  - file: serve/production-guide/fault-tolerance
              - file: serve/performance
              - file: serve/handling-dependencies
              - file: serve/managing-java-deployments
              - file: serve/migration
              - file: serve/direct-ingress
          - file: serve/architecture
          - file: serve/tutorials/index
            sections:
              - file: serve/tutorials/deployment-graph-patterns
                sections:
                  - file: serve/tutorials/deployment-graph-patterns/linear_pipeline
                  - file: serve/tutorials/deployment-graph-patterns/branching_input
                  - file: serve/tutorials/deployment-graph-patterns/conditional
          - file: serve/api/index

      - file: rllib/index
        title: Ray RLlib
        sections:
          - file: rllib/rllib-training
          - file: rllib/key-concepts
          - file: rllib/rllib-env
          - file: rllib/rllib-algorithms
          - file: rllib/user-guides
            sections:
              - file: rllib/rllib-advanced-api
              - file: rllib/rllib-models
              - file: rllib/rllib-saving-and-loading-algos-and-policies
              - file: rllib/rllib-concepts
              - file: rllib/rllib-sample-collection
              - file: rllib/rllib-replay-buffers
              - file: rllib/rllib-offline
              - file: rllib/rllib-catalogs
              - file: rllib/rllib-connector
              - file: rllib/rllib-rlmodule
              - file: rllib/rllib-fault-tolerance
              - file: rllib/rllib-dev
              - file: rllib/rllib-cli
          - file: rllib/rllib-examples
          - file: rllib/package_ref/index

      - file: ray-more-libs/index
        title: More Libraries
        sections:
          - file: ray-more-libs/joblib
          - file: ray-more-libs/multiprocessing
          - file: ray-more-libs/ray-collective
          - file: ray-more-libs/using-ray-with-pytorch-lightning
          - file: workflows/index
            title: Ray Workflows (Alpha)
            sections:
              - file: workflows/key-concepts
              - file: workflows/basics
              - file: workflows/management
              - file: workflows/metadata
              - file: workflows/events
              - file: workflows/comparison
              - file: workflows/advanced
              - file: workflows/api/api

      - file: cluster/getting-started
        title: "Ray Clusters"
        sections:
          - file: cluster/key-concepts
            title: Key Concepts
          - file: cluster/kubernetes/index
            title: Deploying on Kubernetes
            sections:
            - file: cluster/kubernetes/getting-started
            - file: cluster/kubernetes/user-guides
              sections:
                - file: cluster/kubernetes/user-guides/k8s-cluster-setup.md
                - file: cluster/kubernetes/user-guides/config.md
                - file: cluster/kubernetes/user-guides/configuring-autoscaling.md
                - file: cluster/kubernetes/user-guides/logging.md
                - file: cluster/kubernetes/user-guides/gpu.md
                - file: cluster/kubernetes/user-guides/experimental.md
                - file: cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.md
            - file: cluster/kubernetes/examples
              sections:
                - file: cluster/kubernetes/examples/ml-example.md
                - file: cluster/kubernetes/examples/gpu-training-example.md
            - file: cluster/kubernetes/references
          - file: cluster/vms/index
            title: Deploying on VMs
            sections:
            - file: cluster/vms/getting-started
            - file: cluster/vms/user-guides/index
              title: User Guides
              sections:
              - file: cluster/vms/user-guides/launching-clusters/index
              - file: cluster/vms/user-guides/large-cluster-best-practices
              - file: cluster/vms/user-guides/configuring-autoscaling
              - file: cluster/vms/user-guides/community/index
                title: Community-supported Cluster Managers
                sections:
                - file: cluster/vms/user-guides/community/yarn
                - file: cluster/vms/user-guides/community/slurm
                - file: cluster/vms/user-guides/community/lsf
            - file: cluster/vms/examples/index
              title: Examples
              sections:
              - file: cluster/vms/examples/ml-example
            - file: cluster/vms/references/index
          - file: cluster/running-applications/index
            title: Applications Guide
          - file: cluster/faq
          - file: cluster/package-overview

      - file: ray-observability/monitoring-debugging/monitoring-debugging
        title: "Monitoring and Debugging"

      - file: ray-references/api
        title: References
        sections:
          - file: cluster/usage-stats
          - file: ray-references/glossary

      - file: ray-contribute/stability
        title: Developer Guides
        sections:
          - file: ray-contribute/getting-involved
            sections:
              - file: ray-contribute/development
              - file: ray-contribute/docs
              - file: ray-contribute/fake-autoscaler
              - file: ray-core/examples/testing-tips
          - file: ray-core/configure
          - file: ray-contribute/whitepaper