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    • Ray CoreScale general Python applications

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Managed Ray on Anyscale

Site Navigation

  • Getting Started

  • Use Cases

  • Example Gallery

  • Libraries

    • Ray CoreScale general Python applications

    • Ray DataScale data ingest and preprocessing

    • Ray TrainScale machine learning training

    • Ray TuneScale hyperparameter tuning

    • Ray ServeScale model serving

    • Ray RLlibScale reinforcement learning

  • Docs

  • Resources

    • Discussion ForumGet your Ray questions answered

    • TrainingHands-on learning

    • BlogUpdates, best practices, user-stories

    • EventsWebinars, meetups, office hours

    • Success StoriesReal-world workload examples

    • EcosystemLibraries integrated with Ray

    • CommunityConnect with us

Managed Ray on Anyscale
Ray 2.9.3
  • Overview
  • Getting Started
  • Installation
  • Use Cases
    • Ray for ML Infrastructure
  • Example Gallery
  • Ecosystem
  • Ray Core
    • Key Concepts
    • User Guides
      • Tasks
        • Nested Remote Functions
        • Dynamic generators
      • Actors
        • Named Actors
        • Terminating Actors
        • AsyncIO / Concurrency for Actors
        • Limiting Concurrency Per-Method with Concurrency Groups
        • Utility Classes
        • Out-of-band Communication
        • Actor Task Execution Order
      • Objects
        • Serialization
        • Object Spilling
      • Environment Dependencies
      • Scheduling
        • Resources
        • Accelerator Support
        • Placement Groups
        • Memory Management
        • Out-Of-Memory Prevention
      • Fault Tolerance
        • Task Fault Tolerance
        • Actor Fault Tolerance
        • Object Fault Tolerance
        • Node Fault Tolerance
        • GCS Fault Tolerance
      • Design Patterns & Anti-patterns
        • Pattern: Using nested tasks to achieve nested parallelism
        • Pattern: Using generators to reduce heap memory usage
        • Pattern: Using ray.wait to limit the number of pending tasks
        • Pattern: Using resources to limit the number of concurrently running tasks
        • Pattern: Using asyncio to run actor methods concurrently
        • Pattern: Using an actor to synchronize other tasks and actors
        • Pattern: Using a supervisor actor to manage a tree of actors
        • Pattern: Using pipelining to increase throughput
        • Anti-pattern: Returning ray.put() ObjectRefs from a task harms performance and fault tolerance
        • Anti-pattern: Calling ray.get in a loop harms parallelism
        • Anti-pattern: Calling ray.get unnecessarily harms performance
        • Anti-pattern: Processing results in submission order using ray.get increases runtime
        • Anti-pattern: Fetching too many objects at once with ray.get causes failure
        • Anti-pattern: Over-parallelizing with too fine-grained tasks harms speedup
        • Anti-pattern: Redefining the same remote function or class harms performance
        • Anti-pattern: Passing the same large argument by value repeatedly harms performance
        • Anti-pattern: Closure capturing large objects harms performance
        • Anti-pattern: Using global variables to share state between tasks and actors
      • Advanced Topics
        • Tips for first-time users
        • Starting Ray
        • Ray Generators
        • Using Namespaces
        • Cross-Language Programming
        • Working with Jupyter Notebooks & JupyterLab
        • Lazy Computation Graphs with the Ray DAG API
        • Miscellaneous Topics
        • Authenticating Remote URIs in runtime_env
    • Examples
      • Simple AutoML for time series with Ray Core
      • Batch Prediction with Ray Core
      • A Gentle Introduction to Ray Core by Example
      • Using Ray for Highly Parallelizable Tasks
      • A Simple MapReduce Example with Ray Core
      • Monte Carlo Estimation of π
      • Simple Parallel Model Selection
      • Parameter Server
      • Learning to Play Pong
      • Tips for testing Ray programs
      • Speed up your web crawler by parallelizing it with Ray
    • Ray Core API
      • Core API
        • ray.init
        • ray.shutdown
        • ray.is_initialized
        • ray.job_config.JobConfig
        • ray.remote
        • ray.remote_function.RemoteFunction.options
        • ray.cancel
        • ray.remote
        • ray.actor.ActorClass.options
        • ray.method
        • ray.get_actor
        • ray.kill
        • ray.get
        • ray.wait
        • ray.put
        • ray.runtime_context.get_runtime_context
        • ray.runtime_context.RuntimeContext
        • ray.get_gpu_ids
        • ray.cross_language.java_function
        • ray.cross_language.java_actor_class
      • Scheduling API
        • ray.util.scheduling_strategies.PlacementGroupSchedulingStrategy
        • ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy
        • ray.util.placement_group
        • ray.util.placement_group.PlacementGroup
        • ray.util.placement_group_table
        • ray.util.remove_placement_group
        • ray.util.get_current_placement_group
      • Runtime Env API
        • ray.runtime_env.RuntimeEnvConfig
        • ray.runtime_env.RuntimeEnv
      • Utility
        • ray.util.ActorPool
        • ray.util.queue.Queue
        • ray.nodes
        • ray.cluster_resources
        • ray.available_resources
        • ray.util.metrics.Counter
        • ray.util.metrics.Gauge
        • ray.util.metrics.Histogram
        • ray.util.rpdb.set_trace
        • ray.util.inspect_serializability
        • ray.timeline
      • Exceptions
        • ray.exceptions.RayError
        • ray.exceptions.RayTaskError
        • ray.exceptions.RayActorError
        • ray.exceptions.TaskCancelledError
        • ray.exceptions.TaskUnschedulableError
        • ray.exceptions.ActorUnschedulableError
        • ray.exceptions.AsyncioActorExit
        • ray.exceptions.LocalRayletDiedError
        • ray.exceptions.WorkerCrashedError
        • ray.exceptions.TaskPlacementGroupRemoved
        • ray.exceptions.ActorPlacementGroupRemoved
        • ray.exceptions.ObjectStoreFullError
        • ray.exceptions.OutOfDiskError
        • ray.exceptions.ObjectLostError
        • ray.exceptions.ObjectFetchTimedOutError
        • ray.exceptions.GetTimeoutError
        • ray.exceptions.OwnerDiedError
        • ray.exceptions.PlasmaObjectNotAvailable
        • ray.exceptions.ObjectReconstructionFailedError
        • ray.exceptions.ObjectReconstructionFailedMaxAttemptsExceededError
        • ray.exceptions.ObjectReconstructionFailedLineageEvictedError
        • ray.exceptions.RuntimeEnvSetupError
        • ray.exceptions.CrossLanguageError
        • ray.exceptions.RaySystemError
      • Ray Core CLI
      • State CLI
      • State API
        • ray.util.state.summarize_actors
        • ray.util.state.summarize_objects
        • ray.util.state.summarize_tasks
        • ray.util.state.list_actors
        • ray.util.state.list_placement_groups
        • ray.util.state.list_nodes
        • ray.util.state.list_jobs
        • ray.util.state.list_workers
        • ray.util.state.list_tasks
        • ray.util.state.list_objects
        • ray.util.state.list_runtime_envs
        • ray.util.state.get_actor
        • ray.util.state.get_placement_group
        • ray.util.state.get_node
        • ray.util.state.get_worker
        • ray.util.state.get_task
        • ray.util.state.get_objects
        • ray.util.state.list_logs
        • ray.util.state.get_log
        • ray.util.state.common.ActorState
        • ray.util.state.common.TaskState
        • ray.util.state.common.NodeState
        • ray.util.state.common.PlacementGroupState
        • ray.util.state.common.WorkerState
        • ray.util.state.common.ObjectState
        • ray.util.state.common.RuntimeEnvState
        • ray.util.state.common.JobState
        • ray.util.state.common.StateSummary
        • ray.util.state.common.TaskSummaries
        • ray.util.state.common.TaskSummaryPerFuncOrClassName
        • ray.util.state.common.ActorSummaries
        • ray.util.state.common.ActorSummaryPerClass
        • ray.util.state.common.ObjectSummaries
        • ray.util.state.common.ObjectSummaryPerKey
        • ray.util.state.exception.RayStateApiException
  • Ray Data
    • Overview
    • Key Concepts
    • User Guides
      • Loading Data
      • Transforming Data
      • Inspecting Data
      • Iterating over Data
      • Saving Data
      • Working with Images
      • Working with Text
      • Working with Tensors
      • Working with PyTorch
      • End-to-end: Offline Batch Inference
      • Advanced: Performance Tips and Tuning
      • Using Preprocessors
      • Monitoring Your Workload
      • Advanced: Read and Write Custom File Types
    • Ray Data Examples
      • Image Classification Batch Inference with Huggingface Vision Transformer
      • Image Classification Batch Inference with PyTorch
      • Object Detection Batch Inference with PyTorch
    • Ray Data API
      • Input/Output
        • ray.data.range
        • ray.data.range_tensor
        • ray.data.from_items
        • ray.data.read_parquet
        • ray.data.read_parquet_bulk
        • ray.data.Dataset.write_parquet
        • ray.data.read_csv
        • ray.data.Dataset.write_csv
        • ray.data.read_json
        • ray.data.Dataset.write_json
        • ray.data.read_text
        • ray.data.read_images
        • ray.data.Dataset.write_images
        • ray.data.read_binary_files
        • ray.data.read_tfrecords
        • ray.data.Dataset.write_tfrecords
        • ray.data.from_pandas
        • ray.data.from_pandas_refs
        • ray.data.Dataset.to_pandas
        • ray.data.Dataset.to_pandas_refs
        • ray.data.read_numpy
        • ray.data.from_numpy
        • ray.data.from_numpy_refs
        • ray.data.Dataset.write_numpy
        • ray.data.Dataset.to_numpy_refs
        • ray.data.from_arrow
        • ray.data.from_arrow_refs
        • ray.data.Dataset.to_arrow_refs
        • ray.data.read_mongo
        • ray.data.Dataset.write_mongo
        • ray.data.read_bigquery
        • ray.data.Dataset.write_bigquery
        • ray.data.read_sql
        • ray.data.Dataset.write_sql
        • ray.data.read_databricks_tables
        • ray.data.from_dask
        • ray.data.Dataset.to_dask
        • ray.data.from_spark
        • ray.data.Dataset.to_spark
        • ray.data.from_modin
        • ray.data.Dataset.to_modin
        • ray.data.from_mars
        • ray.data.Dataset.to_mars
        • ray.data.from_torch
        • ray.data.from_huggingface
        • ray.data.from_tf
        • ray.data.read_webdataset
        • ray.data.read_datasource
        • ray.data.Datasource
        • ray.data.ReadTask
        • ray.data.datasource.FilenameProvider
        • ray.data.Dataset.write_datasink
        • ray.data.Datasink
        • ray.data.datasource.RowBasedFileDatasink
        • ray.data.datasource.BlockBasedFileDatasink
        • ray.data.datasource.FileBasedDatasource
        • ray.data.datasource.Partitioning
        • ray.data.datasource.PartitionStyle
        • ray.data.datasource.PathPartitionParser
        • ray.data.datasource.PathPartitionFilter
        • ray.data.datasource.FileMetadataProvider
        • ray.data.datasource.BaseFileMetadataProvider
        • ray.data.datasource.ParquetMetadataProvider
        • ray.data.datasource.DefaultFileMetadataProvider
        • ray.data.datasource.DefaultParquetMetadataProvider
        • ray.data.datasource.FastFileMetadataProvider
      • Dataset API
        • ray.data.Dataset
        • ray.data.Dataset.map
        • ray.data.Dataset.map_batches
        • ray.data.Dataset.flat_map
        • ray.data.Dataset.filter
        • ray.data.Dataset.add_column
        • ray.data.Dataset.drop_columns
        • ray.data.Dataset.select_columns
        • ray.data.Dataset.random_sample
        • ray.data.Dataset.limit
        • ray.data.Dataset.sort
        • ray.data.Dataset.random_shuffle
        • ray.data.Dataset.randomize_block_order
        • ray.data.Dataset.repartition
        • ray.data.Dataset.split
        • ray.data.Dataset.split_at_indices
        • ray.data.Dataset.split_proportionately
        • ray.data.Dataset.streaming_split
        • ray.data.Dataset.train_test_split
        • ray.data.Dataset.union
        • ray.data.Dataset.zip
        • ray.data.Dataset.groupby
        • ray.data.Dataset.unique
        • ray.data.Dataset.aggregate
        • ray.data.Dataset.sum
        • ray.data.Dataset.min
        • ray.data.Dataset.max
        • ray.data.Dataset.mean
        • ray.data.Dataset.std
        • ray.data.Dataset.show
        • ray.data.Dataset.take
        • ray.data.Dataset.take_batch
        • ray.data.Dataset.take_all
        • ray.data.Dataset.iterator
        • ray.data.Dataset.iter_rows
        • ray.data.Dataset.iter_batches
        • ray.data.Dataset.iter_torch_batches
        • ray.data.Dataset.iter_tf_batches
        • ray.data.Dataset.write_parquet
        • ray.data.Dataset.write_json
        • ray.data.Dataset.write_csv
        • ray.data.Dataset.write_numpy
        • ray.data.Dataset.write_tfrecords
        • ray.data.Dataset.write_webdataset
        • ray.data.Dataset.write_mongo
        • ray.data.Dataset.write_datasource
        • ray.data.Dataset.to_torch
        • ray.data.Dataset.to_tf
        • ray.data.Dataset.to_dask
        • ray.data.Dataset.to_mars
        • ray.data.Dataset.to_modin
        • ray.data.Dataset.to_spark
        • ray.data.Dataset.to_pandas
        • ray.data.Dataset.to_pandas_refs
        • ray.data.Dataset.to_numpy_refs
        • ray.data.Dataset.to_arrow_refs
        • ray.data.Dataset.to_random_access_dataset
        • ray.data.Dataset.count
        • ray.data.Dataset.columns
        • ray.data.Dataset.schema
        • ray.data.Dataset.num_blocks
        • ray.data.Dataset.size_bytes
        • ray.data.Dataset.input_files
        • ray.data.Dataset.stats
        • ray.data.Dataset.get_internal_block_refs
        • ray.data.Dataset.materialize
        • ray.data.Dataset.has_serializable_lineage
        • ray.data.Dataset.serialize_lineage
        • ray.data.Dataset.deserialize_lineage
        • ray.data.block.Block
        • ray.data.block.BlockExecStats
        • ray.data.block.BlockMetadata
        • ray.data.block.BlockAccessor
      • DataIterator API
        • ray.data.DataIterator.iter_batches
        • ray.data.DataIterator.iter_torch_batches
        • ray.data.DataIterator.to_tf
        • ray.data.DataIterator.stats
      • ExecutionOptions API
        • ray.data.ExecutionOptions
        • ray.data.ExecutionResources
      • GroupedData API
        • ray.data.grouped_data.GroupedData
        • ray.data.grouped_data.GroupedData.count
        • ray.data.grouped_data.GroupedData.sum
        • ray.data.grouped_data.GroupedData.min
        • ray.data.grouped_data.GroupedData.max
        • ray.data.grouped_data.GroupedData.mean
        • ray.data.grouped_data.GroupedData.std
        • ray.data.grouped_data.GroupedData.aggregate
        • ray.data.grouped_data.GroupedData.map_groups
        • ray.data.aggregate.AggregateFn
        • ray.data.aggregate.Count
        • ray.data.aggregate.Sum
        • ray.data.aggregate.Max
        • ray.data.aggregate.Mean
        • ray.data.aggregate.Std
        • ray.data.aggregate.AbsMax
      • DataContext API
        • ray.data.DataContext
        • ray.data.DataContext.get_current
      • RandomAccessDataset (experimental)
        • ray.data.random_access_dataset.RandomAccessDataset
        • ray.data.random_access_dataset.RandomAccessDataset.get_async
        • ray.data.random_access_dataset.RandomAccessDataset.multiget
        • ray.data.random_access_dataset.RandomAccessDataset.stats
      • Utility
        • ray.data.set_progress_bars
      • Preprocessor
        • ray.data.preprocessor.Preprocessor
        • ray.data.preprocessor.Preprocessor.fit
        • ray.data.preprocessor.Preprocessor.fit_transform
        • ray.data.preprocessor.Preprocessor.transform
        • ray.data.preprocessor.Preprocessor.transform_batch
        • ray.data.preprocessor.Preprocessor.transform_stats
        • ray.data.preprocessors.Concatenator
        • ray.data.preprocessors.SimpleImputer
        • ray.data.preprocessors.Categorizer
        • ray.data.preprocessors.LabelEncoder
        • ray.data.preprocessors.MultiHotEncoder
        • ray.data.preprocessors.OneHotEncoder
        • ray.data.preprocessors.OrdinalEncoder
        • ray.data.preprocessors.MaxAbsScaler
        • ray.data.preprocessors.MinMaxScaler
        • ray.data.preprocessors.Normalizer
        • ray.data.preprocessors.PowerTransformer
        • ray.data.preprocessors.RobustScaler
        • ray.data.preprocessors.StandardScaler
        • ray.data.preprocessors.CustomKBinsDiscretizer
        • ray.data.preprocessors.UniformKBinsDiscretizer
      • API Guide for Users from Other Data Libraries
    • Ray Data Internals
  • Ray Train
    • Overview
    • PyTorch Guide
    • PyTorch Lightning Guide
    • Hugging Face Transformers Guide
    • More Frameworks
      • Hugging Face Accelerate Guide
      • DeepSpeed Guide
      • TensorFlow and Keras Guide
      • XGBoost and LightGBM Guide
      • Horovod Guide
    • User Guides
      • Data Loading and Preprocessing
      • Configuring Scale and GPUs
      • Configuring Persistent Storage
      • Monitoring and Logging Metrics
      • Saving and Loading Checkpoints
      • Experiment Tracking
      • Inspecting Training Results
      • Handling Failures and Node Preemption
      • Reproducibility
      • Hyperparameter Optimization
    • Examples
    • Benchmarks
    • Ray Train API
      • ray.train.torch.TorchTrainer
        • ray.train.torch.TorchTrainer.as_trainable
        • ray.train.torch.TorchTrainer.can_restore
        • ray.train.torch.TorchTrainer.fit
        • ray.train.torch.TorchTrainer.get_dataset_config
        • ray.train.torch.TorchTrainer.restore
        • ray.train.torch.TorchTrainer.setup
      • ray.train.torch.TorchConfig
        • ray.train.torch.TorchConfig.backend
        • ray.train.torch.TorchConfig.backend_cls
        • ray.train.torch.TorchConfig.init_method
        • ray.train.torch.TorchConfig.timeout_s
      • ray.train.torch.get_device
      • ray.train.torch.prepare_model
      • ray.train.torch.prepare_data_loader
      • ray.train.torch.enable_reproducibility
      • ray.train.lightning.prepare_trainer
      • ray.train.lightning.RayLightningEnvironment
      • ray.train.lightning.RayDDPStrategy
        • ray.train.lightning.RayDDPStrategy.distributed_sampler_kwargs
        • ray.train.lightning.RayDDPStrategy.root_device
      • ray.train.lightning.RayFSDPStrategy
        • ray.train.lightning.RayFSDPStrategy.lightning_module_state_dict
        • ray.train.lightning.RayFSDPStrategy.distributed_sampler_kwargs
        • ray.train.lightning.RayFSDPStrategy.root_device
      • ray.train.lightning.RayDeepSpeedStrategy
        • ray.train.lightning.RayDeepSpeedStrategy.distributed_sampler_kwargs
        • ray.train.lightning.RayDeepSpeedStrategy.root_device
      • ray.train.lightning.RayTrainReportCallback
        • ray.train.lightning.RayTrainReportCallback.CHECKPOINT_NAME
      • ray.train.huggingface.transformers.prepare_trainer
      • ray.train.huggingface.transformers.RayTrainReportCallback
        • ray.train.huggingface.transformers.RayTrainReportCallback.on_save
        • ray.train.huggingface.transformers.RayTrainReportCallback.CHECKPOINT_NAME
      • ray.train.tensorflow.TensorflowTrainer
        • ray.train.tensorflow.TensorflowTrainer.as_trainable
        • ray.train.tensorflow.TensorflowTrainer.can_restore
        • ray.train.tensorflow.TensorflowTrainer.fit
        • ray.train.tensorflow.TensorflowTrainer.get_dataset_config
        • ray.train.tensorflow.TensorflowTrainer.restore
        • ray.train.tensorflow.TensorflowTrainer.setup
      • ray.train.tensorflow.TensorflowConfig
        • ray.train.tensorflow.TensorflowConfig.backend_cls
      • ray.train.tensorflow.prepare_dataset_shard
      • ray.train.tensorflow.keras.ReportCheckpointCallback
      • ray.train.horovod.HorovodTrainer
        • ray.train.horovod.HorovodTrainer.as_trainable
        • ray.train.horovod.HorovodTrainer.can_restore
        • ray.train.horovod.HorovodTrainer.fit
        • ray.train.horovod.HorovodTrainer.get_dataset_config
        • ray.train.horovod.HorovodTrainer.restore
        • ray.train.horovod.HorovodTrainer.setup
      • ray.train.horovod.HorovodConfig
        • ray.train.horovod.HorovodConfig.backend_cls
        • ray.train.horovod.HorovodConfig.key
        • ray.train.horovod.HorovodConfig.nics
        • ray.train.horovod.HorovodConfig.placement_group_timeout_s
        • ray.train.horovod.HorovodConfig.ssh_identity_file
        • ray.train.horovod.HorovodConfig.ssh_port
        • ray.train.horovod.HorovodConfig.ssh_str
        • ray.train.horovod.HorovodConfig.start_timeout
        • ray.train.horovod.HorovodConfig.timeout_s
        • ray.train.horovod.HorovodConfig.verbose
      • ray.train.xgboost.XGBoostTrainer
        • ray.train.xgboost.XGBoostTrainer.as_trainable
        • ray.train.xgboost.XGBoostTrainer.can_restore
        • ray.train.xgboost.XGBoostTrainer.fit
        • ray.train.xgboost.XGBoostTrainer.get_model
        • ray.train.xgboost.XGBoostTrainer.restore
        • ray.train.xgboost.XGBoostTrainer.setup
      • ray.train.lightgbm.LightGBMTrainer
        • ray.train.lightgbm.LightGBMTrainer.as_trainable
        • ray.train.lightgbm.LightGBMTrainer.can_restore
        • ray.train.lightgbm.LightGBMTrainer.fit
        • ray.train.lightgbm.LightGBMTrainer.get_model
        • ray.train.lightgbm.LightGBMTrainer.restore
        • ray.train.lightgbm.LightGBMTrainer.setup
      • ray.train.CheckpointConfig
        • ray.train.CheckpointConfig.checkpoint_at_end
        • ray.train.CheckpointConfig.checkpoint_frequency
        • ray.train.CheckpointConfig.checkpoint_score_attribute
        • ray.train.CheckpointConfig.checkpoint_score_order
        • ray.train.CheckpointConfig.num_to_keep
      • ray.train.DataConfig
        • ray.train.DataConfig.__init__
        • ray.train.DataConfig.configure
        • ray.train.DataConfig.default_ingest_options
        • ray.train.DataConfig.set_train_total_resources
      • ray.train.FailureConfig
        • ray.train.FailureConfig.fail_fast
        • ray.train.FailureConfig.max_failures
      • ray.train.RunConfig
        • ray.train.RunConfig.callbacks
        • ray.train.RunConfig.checkpoint_config
        • ray.train.RunConfig.failure_config
        • ray.train.RunConfig.local_dir
        • ray.train.RunConfig.log_to_file
        • ray.train.RunConfig.name
        • ray.train.RunConfig.progress_reporter
        • ray.train.RunConfig.stop
        • ray.train.RunConfig.storage_filesystem
        • ray.train.RunConfig.storage_path
        • ray.train.RunConfig.sync_config
        • ray.train.RunConfig.verbose
      • ray.train.ScalingConfig
        • ray.train.ScalingConfig.as_placement_group_factory
        • ray.train.ScalingConfig.from_placement_group_factory
        • ray.train.ScalingConfig.additional_resources_per_worker
        • ray.train.ScalingConfig.num_cpus_per_worker
        • ray.train.ScalingConfig.num_gpus_per_worker
        • ray.train.ScalingConfig.num_workers
        • ray.train.ScalingConfig.placement_strategy
        • ray.train.ScalingConfig.resources_per_worker
        • ray.train.ScalingConfig.total_resources
        • ray.train.ScalingConfig.trainer_resources
        • ray.train.ScalingConfig.use_gpu
      • ray.train.SyncConfig
        • ray.train.SyncConfig.sync_artifacts
        • ray.train.SyncConfig.sync_artifacts_on_checkpoint
        • ray.train.SyncConfig.sync_on_checkpoint
        • ray.train.SyncConfig.sync_period
        • ray.train.SyncConfig.sync_timeout
        • ray.train.SyncConfig.syncer
        • ray.train.SyncConfig.upload_dir
      • ray.train.Checkpoint
        • ray.train.Checkpoint.__init__
        • ray.train.Checkpoint.as_directory
        • ray.train.Checkpoint.from_directory
        • ray.train.Checkpoint.get_metadata
        • ray.train.Checkpoint.set_metadata
        • ray.train.Checkpoint.to_directory
        • ray.train.Checkpoint.update_metadata
      • ray.train.context.TrainContext
        • ray.train.context.TrainContext.__init__
        • ray.train.context.TrainContext.get_experiment_name
        • ray.train.context.TrainContext.get_local_rank
        • ray.train.context.TrainContext.get_local_world_size
        • ray.train.context.TrainContext.get_metadata
        • ray.train.context.TrainContext.get_node_rank
        • ray.train.context.TrainContext.get_storage
        • ray.train.context.TrainContext.get_trial_dir
        • ray.train.context.TrainContext.get_trial_id
        • ray.train.context.TrainContext.get_trial_name
        • ray.train.context.TrainContext.get_trial_resources
        • ray.train.context.TrainContext.get_world_rank
        • ray.train.context.TrainContext.get_world_size
      • ray.train.get_checkpoint
      • ray.train.get_context
      • ray.train.get_dataset_shard
      • ray.train.report
      • ray.train.Result
      • ray.train.trainer.BaseTrainer
        • ray.train.trainer.BaseTrainer.as_trainable
        • ray.train.trainer.BaseTrainer.can_restore
        • ray.train.trainer.BaseTrainer.fit
        • ray.train.trainer.BaseTrainer.preprocess_datasets
        • ray.train.trainer.BaseTrainer.restore
        • ray.train.trainer.BaseTrainer.setup
        • ray.train.trainer.BaseTrainer.training_loop
      • ray.train.data_parallel_trainer.DataParallelTrainer
        • ray.train.data_parallel_trainer.DataParallelTrainer.as_trainable
        • ray.train.data_parallel_trainer.DataParallelTrainer.can_restore
        • ray.train.data_parallel_trainer.DataParallelTrainer.fit
        • ray.train.data_parallel_trainer.DataParallelTrainer.get_dataset_config
        • ray.train.data_parallel_trainer.DataParallelTrainer.restore
        • ray.train.data_parallel_trainer.DataParallelTrainer.setup
      • ray.train.gbdt_trainer.GBDTTrainer
        • ray.train.gbdt_trainer.GBDTTrainer.as_trainable
        • ray.train.gbdt_trainer.GBDTTrainer.can_restore
        • ray.train.gbdt_trainer.GBDTTrainer.fit
        • ray.train.gbdt_trainer.GBDTTrainer.preprocess_datasets
        • ray.train.gbdt_trainer.GBDTTrainer.restore
        • ray.train.gbdt_trainer.GBDTTrainer.setup
      • ray.train.backend.Backend
      • ray.train.backend.BackendConfig
  • Ray Tune
    • Getting Started
    • Key Concepts
    • User Guides
      • Running Basic Experiments
      • Logging and Outputs in Tune
      • Setting Trial Resources
      • Using Search Spaces
      • How to Define Stopping Criteria for a Ray Tune Experiment
      • How to Save and Load Trial Checkpoints
      • How to Configure Persistent Storage in Ray Tune
      • How to Enable Fault Tolerance in Ray Tune
      • Using Callbacks and Metrics
      • Getting Data in and out of Tune
      • Analyzing Tune Experiment Results
      • A Guide to Population Based Training with Tune
        • Visualizing and Understanding PBT
      • Deploying Tune in the Cloud
      • Tune Architecture
      • Scalability Benchmarks
    • Ray Tune Examples
      • Examples using Ray Tune with ML Frameworks
        • Scikit-Learn Example
        • Keras Example
        • PyTorch Example
        • PyTorch Lightning Example
        • Ray RLlib Example
        • XGBoost Example
        • LightGBM Example
        • Horovod Example
        • Hugging Face Transformers Example
      • Tune Experiment Tracking Examples
        • Weights & Biases Example
        • MLflow Example
        • Aim Example
        • Comet Example
      • Tune Hyperparameter Optimization Framework Examples
        • Ax Example
        • HyperOpt Example
        • Bayesopt Example
        • BOHB Example
        • Optuna Example
      • Other Examples
      • Exercises
    • Ray Tune FAQ
    • Ray Tune API
      • Tune Execution (tune.Tuner)
        • ray.tune.Tuner
        • ray.tune.Tuner.fit
        • ray.tune.Tuner.get_results
        • ray.tune.TuneConfig
        • ray.tune.Tuner.restore
        • ray.tune.Tuner.can_restore
        • ray.tune.run_experiments
        • ray.tune.Experiment
      • Tune Experiment Results (tune.ResultGrid)
        • ray.tune.ResultGrid
        • ray.tune.ResultGrid.get_best_result
        • ray.tune.ResultGrid.get_dataframe
        • ray.tune.ExperimentAnalysis
      • Training in Tune (tune.Trainable, train.report)
        • ray.tune.Trainable
        • ray.tune.Trainable.setup
        • ray.tune.Trainable.save_checkpoint
        • ray.tune.Trainable.load_checkpoint
        • ray.tune.Trainable.step
        • ray.tune.Trainable.reset_config
        • ray.tune.Trainable.cleanup
        • ray.tune.Trainable.default_resource_request
        • ray.tune.with_parameters
        • ray.tune.with_resources
        • ray.tune.execution.placement_groups.PlacementGroupFactory
        • ray.tune.utils.wait_for_gpu
        • ray.tune.utils.diagnose_serialization
        • ray.tune.utils.validate_save_restore
      • Tune Search Space API
        • ray.tune.uniform
        • ray.tune.quniform
        • ray.tune.loguniform
        • ray.tune.qloguniform
        • ray.tune.randn
        • ray.tune.qrandn
        • ray.tune.randint
        • ray.tune.qrandint
        • ray.tune.lograndint
        • ray.tune.qlograndint
        • ray.tune.choice
        • ray.tune.grid_search
        • ray.tune.sample_from
      • Tune Search Algorithms (tune.search)
        • ray.tune.search.basic_variant.BasicVariantGenerator
        • ray.tune.search.ax.AxSearch
        • ray.tune.search.bayesopt.BayesOptSearch
        • ray.tune.search.bohb.TuneBOHB
        • ray.tune.search.hebo.HEBOSearch
        • ray.tune.search.hyperopt.HyperOptSearch
        • ray.tune.search.optuna.OptunaSearch
        • ray.tune.search.skopt.SkOptSearch
        • ray.tune.search.zoopt.ZOOptSearch
        • ray.tune.search.Repeater
        • ray.tune.search.ConcurrencyLimiter
        • ray.tune.search.Searcher
        • ray.tune.search.Searcher.suggest
        • ray.tune.search.Searcher.save
        • ray.tune.search.Searcher.restore
        • ray.tune.search.Searcher.on_trial_result
        • ray.tune.search.Searcher.on_trial_complete
        • ray.tune.search.create_searcher
      • Tune Trial Schedulers (tune.schedulers)
        • ray.tune.schedulers.AsyncHyperBandScheduler
        • ray.tune.schedulers.ASHAScheduler
        • ray.tune.schedulers.HyperBandScheduler
        • ray.tune.schedulers.MedianStoppingRule
        • ray.tune.schedulers.PopulationBasedTraining
        • ray.tune.schedulers.PopulationBasedTrainingReplay
        • ray.tune.schedulers.pb2.PB2
        • ray.tune.schedulers.HyperBandForBOHB
        • ray.tune.schedulers.ResourceChangingScheduler
        • ray.tune.schedulers.resource_changing_scheduler.DistributeResources
        • ray.tune.schedulers.resource_changing_scheduler.DistributeResourcesToTopJob
        • ray.tune.schedulers.FIFOScheduler
        • ray.tune.schedulers.TrialScheduler
        • ray.tune.schedulers.TrialScheduler.choose_trial_to_run
        • ray.tune.schedulers.TrialScheduler.on_trial_result
        • ray.tune.schedulers.TrialScheduler.on_trial_complete
        • ray.tune.schedulers.create_scheduler
      • Tune Stopping Mechanisms (tune.stopper)
        • ray.tune.stopper.Stopper
        • ray.tune.stopper.Stopper.__call__
        • ray.tune.stopper.Stopper.stop_all
        • ray.tune.stopper.MaximumIterationStopper
        • ray.tune.stopper.ExperimentPlateauStopper
        • ray.tune.stopper.TrialPlateauStopper
        • ray.tune.stopper.TimeoutStopper
        • ray.tune.stopper.CombinedStopper
      • Tune Console Output (Reporters)
        • ray.tune.ProgressReporter
        • ray.tune.ProgressReporter.report
        • ray.tune.ProgressReporter.should_report
        • ray.tune.CLIReporter
        • ray.tune.JupyterNotebookReporter
      • Syncing in Tune (train.SyncConfig)
      • Tune Loggers (tune.logger)
        • ray.tune.logger.LoggerCallback
        • ray.tune.logger.LoggerCallback.log_trial_start
        • ray.tune.logger.LoggerCallback.log_trial_restore
        • ray.tune.logger.LoggerCallback.log_trial_save
        • ray.tune.logger.LoggerCallback.log_trial_result
        • ray.tune.logger.LoggerCallback.log_trial_end
        • ray.tune.logger.JsonLoggerCallback
        • ray.tune.logger.CSVLoggerCallback
        • ray.tune.logger.TBXLoggerCallback
        • ray.tune.logger.aim.AimLoggerCallback
      • Tune Callbacks (tune.Callback)
        • ray.tune.Callback
        • ray.tune.Callback.setup
        • ray.tune.Callback.on_checkpoint
        • ray.tune.Callback.on_experiment_end
        • ray.tune.Callback.on_step_begin
        • ray.tune.Callback.on_step_end
        • ray.tune.Callback.on_trial_complete
        • ray.tune.Callback.on_trial_error
        • ray.tune.Callback.on_trial_restore
        • ray.tune.Callback.on_trial_result
        • ray.tune.Callback.on_trial_save
        • ray.tune.Callback.on_trial_start
        • ray.tune.Callback.get_state
        • ray.tune.Callback.set_state
      • Environment variables used by Ray Tune
      • External library integrations for Ray Tune
        • ray.tune.integration.pytorch_lightning.TuneReportCallback
        • ray.tune.integration.pytorch_lightning.TuneReportCheckpointCallback
        • ray.tune.integration.xgboost.TuneReportCallback
        • ray.tune.integration.xgboost.TuneReportCheckpointCallback
        • ray.tune.integration.lightgbm.TuneReportCallback
        • ray.tune.integration.lightgbm.TuneReportCheckpointCallback
      • Tune Internals
      • Tune CLI (Experimental)
  • Ray Serve
    • Getting Started
    • Key Concepts
    • Develop and Deploy an ML Application
    • Deploy Compositions of Models
    • Deploy Multiple Applications
    • Model Multiplexing
    • Configure Ray Serve deployments
    • Set Up FastAPI and HTTP
    • Production Guide
      • Serve Config Files
      • Deploy on Kubernetes
      • Custom Docker Images
      • Add End-to-End Fault Tolerance
      • Handle Dependencies
      • Best practices in production
    • Monitor Your Application
    • Resource Allocation
    • Ray Serve Autoscaling
    • Advanced Guides
      • Pass Arguments to Applications
      • Advanced Ray Serve Autoscaling
      • Performance Tuning
      • Dynamic Request Batching
      • Updating Applications In-Place
      • Development Workflow
      • Set Up a gRPC Service
      • Experimental Java API
      • Deploy on VM
      • Run Multiple Applications in Different Containers
    • Architecture
    • Examples
      • Serving ML Models (Tensorflow, PyTorch, Scikit-Learn, others)
      • Serving a Stable Diffusion Model
      • Serving a Distilbert Model
      • Serving an Object Detection Model
      • Serving an inference model on AWS NeuronCores using FastAPI (Experimental)
      • Serving RLlib Models
      • Scaling your Gradio app with Ray Serve
      • Batching Tutorial
      • Streaming Tutorial
      • Java Tutorial
      • Serving models with Triton Server in Ray Serve
    • Ray Serve API
      • ray.serve.Deployment
      • ray.serve.Application
      • ray.serve.deployment
      • ray.serve.ingress
      • ray.serve.batch
      • ray.serve.multiplexed
      • ray.serve.handle.DeploymentHandle
      • ray.serve.handle.DeploymentResponse
      • ray.serve.handle.DeploymentResponseGenerator
      • ray.serve.handle.RayServeHandle
      • ray.serve.handle.RayServeSyncHandle
      • ray.serve.start
      • ray.serve.run
      • ray.serve.delete
      • ray.serve.status
      • ray.serve.shutdown
      • ray.serve.config.ProxyLocation
        • ray.serve.config.ProxyLocation.capitalize
        • ray.serve.config.ProxyLocation.casefold
        • ray.serve.config.ProxyLocation.center
        • ray.serve.config.ProxyLocation.count
        • ray.serve.config.ProxyLocation.encode
        • ray.serve.config.ProxyLocation.endswith
        • ray.serve.config.ProxyLocation.expandtabs
        • ray.serve.config.ProxyLocation.find
        • ray.serve.config.ProxyLocation.format
        • ray.serve.config.ProxyLocation.format_map
        • ray.serve.config.ProxyLocation.index
        • ray.serve.config.ProxyLocation.isalnum
        • ray.serve.config.ProxyLocation.isalpha
        • ray.serve.config.ProxyLocation.isascii
        • ray.serve.config.ProxyLocation.isdecimal
        • ray.serve.config.ProxyLocation.isdigit
        • ray.serve.config.ProxyLocation.isidentifier
        • ray.serve.config.ProxyLocation.islower
        • ray.serve.config.ProxyLocation.isnumeric
        • ray.serve.config.ProxyLocation.isprintable
        • ray.serve.config.ProxyLocation.isspace
        • ray.serve.config.ProxyLocation.istitle
        • ray.serve.config.ProxyLocation.isupper
        • ray.serve.config.ProxyLocation.join
        • ray.serve.config.ProxyLocation.ljust
        • ray.serve.config.ProxyLocation.lower
        • ray.serve.config.ProxyLocation.lstrip
        • ray.serve.config.ProxyLocation.maketrans
        • ray.serve.config.ProxyLocation.partition
        • ray.serve.config.ProxyLocation.removeprefix
        • ray.serve.config.ProxyLocation.removesuffix
        • ray.serve.config.ProxyLocation.replace
        • ray.serve.config.ProxyLocation.rfind
        • ray.serve.config.ProxyLocation.rindex
        • ray.serve.config.ProxyLocation.rjust
        • ray.serve.config.ProxyLocation.rpartition
        • ray.serve.config.ProxyLocation.rsplit
        • ray.serve.config.ProxyLocation.rstrip
        • ray.serve.config.ProxyLocation.split
        • ray.serve.config.ProxyLocation.splitlines
        • ray.serve.config.ProxyLocation.startswith
        • ray.serve.config.ProxyLocation.strip
        • ray.serve.config.ProxyLocation.swapcase
        • ray.serve.config.ProxyLocation.title
        • ray.serve.config.ProxyLocation.translate
        • ray.serve.config.ProxyLocation.upper
        • ray.serve.config.ProxyLocation.zfill
        • ray.serve.config.ProxyLocation.Disabled
        • ray.serve.config.ProxyLocation.HeadOnly
        • ray.serve.config.ProxyLocation.EveryNode
      • ray.serve.config.gRPCOptions
      • ray.serve.config.HTTPOptions
      • ray.serve.config.AutoscalingConfig
      • ray.serve.get_replica_context
      • ray.serve.context.ReplicaContext
        • ray.serve.context.ReplicaContext.app_name
        • ray.serve.context.ReplicaContext.deployment
        • ray.serve.context.ReplicaContext.replica_tag
        • ray.serve.context.ReplicaContext.servable_object
      • ray.serve.get_multiplexed_model_id
      • ray.serve.get_app_handle
      • ray.serve.get_deployment_handle
      • ray.serve.grpc_util.RayServegRPCContext
        • ray.serve.grpc_util.RayServegRPCContext.auth_context
        • ray.serve.grpc_util.RayServegRPCContext.code
        • ray.serve.grpc_util.RayServegRPCContext.details
        • ray.serve.grpc_util.RayServegRPCContext.invocation_metadata
        • ray.serve.grpc_util.RayServegRPCContext.peer
        • ray.serve.grpc_util.RayServegRPCContext.peer_identities
        • ray.serve.grpc_util.RayServegRPCContext.peer_identity_key
        • ray.serve.grpc_util.RayServegRPCContext.set_code
        • ray.serve.grpc_util.RayServegRPCContext.set_compression
        • ray.serve.grpc_util.RayServegRPCContext.set_details
        • ray.serve.grpc_util.RayServegRPCContext.set_on_grpc_context
        • ray.serve.grpc_util.RayServegRPCContext.set_trailing_metadata
      • ray.serve.schema.ServeDeploySchema
      • ray.serve.schema.gRPCOptionsSchema
      • ray.serve.schema.HTTPOptionsSchema
      • ray.serve.schema.ServeApplicationSchema
      • ray.serve.schema.DeploymentSchema
      • ray.serve.schema.RayActorOptionsSchema
      • ray.serve.schema.ServeInstanceDetails
      • ray.serve.schema.ApplicationDetails
      • ray.serve.schema.DeploymentDetails
      • ray.serve.schema.ReplicaDetails
      • ray.serve.metrics.Counter
        • ray.serve.metrics.Counter.inc
        • ray.serve.metrics.Counter.record
        • ray.serve.metrics.Counter.info
      • ray.serve.metrics.Histogram
        • ray.serve.metrics.Histogram.observe
        • ray.serve.metrics.Histogram.record
        • ray.serve.metrics.Histogram.info
      • ray.serve.metrics.Gauge
        • ray.serve.metrics.Gauge.record
        • ray.serve.metrics.Gauge.set
        • ray.serve.metrics.Gauge.info
      • ray.serve.schema.LoggingConfig
  • Ray RLlib
    • Getting Started with RLlib
    • Key Concepts
    • Environments
    • Algorithms
    • User Guides
      • Advanced Python APIs
      • Models, Preprocessors, and Action Distributions
      • Saving and Loading your RL Algorithms and Policies
      • How To Customize Policies
      • Sample Collections and Trajectory Views
      • Replay Buffers
      • Working With Offline Data
      • Catalog (Alpha)
      • Connectors (Beta)
      • RL Modules (Alpha)
      • Learner (Alpha)
      • Using RLlib with torch 2.x compile
      • Fault Tolerance And Elastic Training
      • How To Contribute to RLlib
      • Working with the RLlib CLI
    • Examples
    • Ray RLlib API
      • Algorithms
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.build
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.freeze
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.copy
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.validate
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.callbacks
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.debugging
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.environment
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.evaluation
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.experimental
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.fault_tolerance
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.framework
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.multi_agent
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.offline_data
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.python_environment
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.reporting
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.resources
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.rl_module
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.rollouts
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.training
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.get_default_learner_class
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.get_default_rl_module_spec
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.get_evaluation_config_object
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.get_marl_module_spec
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.get_multi_agent_setup
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.get_rollout_fragment_length
        • ray.rllib.algorithms.algorithm_config.AlgorithmConfig.validate_train_batch_size_vs_rollout_fragment_length
        • ray.rllib.algorithms.algorithm.Algorithm
        • ray.rllib.algorithms.algorithm.Algorithm.compute_actions
        • ray.rllib.algorithms.algorithm.Algorithm.compute_single_action
        • ray.rllib.algorithms.algorithm.Algorithm.evaluate
        • ray.rllib.algorithms.algorithm.Algorithm.from_checkpoint
        • ray.rllib.algorithms.algorithm.Algorithm.from_state
        • ray.rllib.algorithms.algorithm.Algorithm.get_weights
        • ray.rllib.algorithms.algorithm.Algorithm.set_weights
        • ray.rllib.algorithms.algorithm.Algorithm.export_model
        • ray.rllib.algorithms.algorithm.Algorithm.export_policy_checkpoint
        • ray.rllib.algorithms.algorithm.Algorithm.export_policy_model
        • ray.rllib.algorithms.algorithm.Algorithm.import_policy_model_from_h5
        • ray.rllib.algorithms.algorithm.Algorithm.restore
        • ray.rllib.algorithms.algorithm.Algorithm.restore_workers
        • ray.rllib.algorithms.algorithm.Algorithm.save
        • ray.rllib.algorithms.algorithm.Algorithm.save_checkpoint
        • ray.rllib.algorithms.algorithm.Algorithm.train
        • ray.rllib.algorithms.algorithm.Algorithm.training_step
        • ray.rllib.algorithms.algorithm.Algorithm.add_policy
        • ray.rllib.algorithms.algorithm.Algorithm.remove_policy
      • Environments
        • BaseEnv API
        • MultiAgentEnv API
        • VectorEnv API
        • ExternalEnv API
      • Policy API
        • ray.rllib.policy.policy.Policy
        • ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2
        • ray.rllib.policy.torch_policy_v2.TorchPolicyV2
        • ray.rllib.policy.Policy.make_rl_module
        • ray.rllib.policy.torch_policy_v2.TorchPolicyV2.make_model
        • ray.rllib.policy.torch_policy_v2.TorchPolicyV2.make_model_and_action_dist
        • ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2.make_model
        • ray.rllib.policy.policy.Policy.compute_actions
        • ray.rllib.policy.policy.Policy.compute_actions_from_input_dict
        • ray.rllib.policy.policy.Policy.compute_single_action
        • ray.rllib.policy.torch_policy_v2.TorchPolicyV2.action_sampler_fn
        • ray.rllib.policy.torch_policy_v2.TorchPolicyV2.action_distribution_fn
        • ray.rllib.policy.torch_policy_v2.TorchPolicyV2.extra_action_out
        • ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2.action_sampler_fn
        • ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2.action_distribution_fn
        • ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2.extra_action_out_fn
        • ray.rllib.policy.Policy.compute_gradients
        • ray.rllib.policy.Policy.apply_gradients
        • ray.rllib.policy.torch_policy_v2.TorchPolicyV2.extra_compute_grad_fetches
        • ray.rllib.policy.torch_policy_v2.TorchPolicyV2.extra_grad_process
        • ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2.grad_stats_fn
        • ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2.compute_gradients_fn
        • ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2.apply_gradients_fn
        • ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2.extra_learn_fetches_fn
        • ray.rllib.policy.Policy.learn_on_batch
        • ray.rllib.policy.Policy.load_batch_into_buffer
        • ray.rllib.policy.Policy.learn_on_loaded_batch
        • ray.rllib.policy.Policy.learn_on_batch_from_replay_buffer
        • ray.rllib.policy.Policy.get_num_samples_loaded_into_buffer
        • ray.rllib.policy.Policy.loss
        • ray.rllib.policy.Policy.compute_log_likelihoods
        • ray.rllib.policy.Policy.on_global_var_update
        • ray.rllib.policy.Policy.postprocess_trajectory
        • ray.rllib.policy.torch_policy_v2.TorchPolicyV2.optimizer
        • ray.rllib.policy.torch_policy_v2.TorchPolicyV2.get_tower_stats
        • ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2.optimizer
        • ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2.stats_fn
        • ray.rllib.policy.Policy.from_checkpoint
        • ray.rllib.policy.Policy.export_checkpoint
        • ray.rllib.policy.Policy.export_model
        • ray.rllib.policy.Policy.from_state
        • ray.rllib.policy.Policy.get_weights
        • ray.rllib.policy.Policy.set_weights
        • ray.rllib.policy.Policy.get_state
        • ray.rllib.policy.Policy.set_state
        • ray.rllib.policy.Policy.import_model_from_h5
        • ray.rllib.policy.Policy.reset_connectors
        • ray.rllib.policy.Policy.restore_connectors
        • ray.rllib.policy.Policy.get_connector_metrics
        • ray.rllib.Policy.get_initial_state
        • ray.rllib.Policy.num_state_tensors
        • ray.rllib.Policy.is_recurrent
        • ray.rllib.policy.Policy.apply
        • ray.rllib.policy.Policy.get_session
        • ray.rllib.policy.Policy.init_view_requirements
        • ray.rllib.policy.Policy.get_host
        • ray.rllib.policy.Policy.get_exploration_state
        • ray.rllib.policy.torch_policy_v2.TorchPolicyV2.get_batch_divisibility_req
        • ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2.variables
        • ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2.get_batch_divisibility_req
      • Model APIs
        • ray.rllib.models.modelv2.ModelV2
        • ray.rllib.models.torch.torch_modelv2.TorchModelV2
        • ray.rllib.models.tf.tf_modelv2.TFModelV2
        • ray.rllib.models.modelv2.ModelV2.forward
        • ray.rllib.models.modelv2.ModelV2.value_function
        • ray.rllib.models.modelv2.ModelV2.last_output
        • ray.rllib.models.modelv2.ModelV2.get_initial_state
        • ray.rllib.models.modelv2.ModelV2.is_time_major
        • ray.rllib.models.modelv2.ModelV2.variables
        • ray.rllib.models.modelv2.ModelV2.trainable_variables
        • ray.rllib.models.modelv2.ModelV2.custom_loss
        • ray.rllib.models.modelv2.ModelV2.metrics
      • Catalog API
        • ray.rllib.core.models.catalog.Catalog
        • ray.rllib.core.models.catalog.Catalog.build_encoder
        • ray.rllib.core.models.catalog.Catalog.get_action_dist_cls
        • ray.rllib.core.models.catalog.Catalog.get_tokenizer_config
        • ray.rllib.core.models.catalog.Catalog.latent_dims
        • ray.rllib.core.models.catalog.Catalog._determine_components_hook
        • ray.rllib.core.models.catalog.Catalog._get_encoder_config
        • ray.rllib.core.models.catalog.Catalog._get_dist_cls_from_action_space
      • RLModule API
        • ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec
        • ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec.build
        • ray.rllib.core.rl_module.rl_module.SingleAgentRLModuleSpec.get_rl_module_config
        • ray.rllib.core.rl_module.rl_module.RLModuleConfig
        • ray.rllib.core.rl_module.rl_module.RLModuleConfig.to_dict
        • ray.rllib.core.rl_module.rl_module.RLModuleConfig.from_dict
        • ray.rllib.core.rl_module.rl_module.RLModuleConfig.get_catalog
        • ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec
        • ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec.build
        • ray.rllib.core.rl_module.marl_module.MultiAgentRLModuleSpec.get_marl_config
        • ray.rllib.core.rl_module.rl_module.RLModule
        • ray.rllib.core.rl_module.rl_module.RLModule.as_multi_agent
        • ray.rllib.core.rl_module.rl_module.RLModule.forward_train
        • ray.rllib.core.rl_module.rl_module.RLModule.forward_exploration
        • ray.rllib.core.rl_module.rl_module.RLModule.forward_inference
        • ray.rllib.core.rl_module.rl_module.RLModule.input_specs_inference
        • ray.rllib.core.rl_module.rl_module.RLModule.input_specs_exploration
        • ray.rllib.core.rl_module.rl_module.RLModule.input_specs_train
        • ray.rllib.core.rl_module.rl_module.RLModule.output_specs_inference
        • ray.rllib.core.rl_module.rl_module.RLModule.output_specs_exploration
        • ray.rllib.core.rl_module.rl_module.RLModule.output_specs_train
        • ray.rllib.core.rl_module.rl_module.RLModule.get_state
        • ray.rllib.core.rl_module.rl_module.RLModule.set_state
        • ray.rllib.core.rl_module.rl_module.RLModule.save_state
        • ray.rllib.core.rl_module.rl_module.RLModule.load_state
        • ray.rllib.core.rl_module.rl_module.RLModule.save_to_checkpoint
        • ray.rllib.core.rl_module.rl_module.RLModule.from_checkpoint
        • ray.rllib.core.rl_module.marl_module.MultiAgentRLModule
        • ray.rllib.core.rl_module.marl_module.MultiAgentRLModule.setup
        • ray.rllib.core.rl_module.marl_module.MultiAgentRLModule.as_multi_agent
        • ray.rllib.core.rl_module.marl_module.MultiAgentRLModule.add_module
        • ray.rllib.core.rl_module.marl_module.MultiAgentRLModule.remove_module
        • ray.rllib.core.rl_module.marl_module.MultiAgentRLModule.save_state
        • ray.rllib.core.rl_module.marl_module.MultiAgentRLModule.load_state
      • Learner API
        • ray.rllib.core.learner.learner.FrameworkHyperparameters
        • ray.rllib.core.learner.learner.LearnerHyperparameters
        • ray.rllib.core.learner.learner.TorchCompileWhatToCompile
        • ray.rllib.core.learner.learner.Learner
        • ray.rllib.core.learner.learner.Learner.build
        • ray.rllib.core.learner.learner.Learner._check_is_built
        • ray.rllib.core.learner.learner.Learner._make_module
        • ray.rllib.core.learner.learner.Learner.update
        • ray.rllib.core.learner.learner.Learner._update
        • ray.rllib.core.learner.learner.Learner.additional_update
        • ray.rllib.core.learner.learner.Learner.additional_update_for_module
        • ray.rllib.core.learner.learner.Learner._convert_batch_type
        • ray.rllib.core.learner.learner.Learner.compute_loss
        • ray.rllib.core.learner.learner.Learner.compute_loss_for_module
        • ray.rllib.core.learner.learner.Learner._is_module_compatible_with_learner
        • ray.rllib.core.learner.learner.Learner._get_tensor_variable
        • ray.rllib.core.learner.learner.Learner.configure_optimizers_for_module
        • ray.rllib.core.learner.learner.Learner.configure_optimizers
        • ray.rllib.core.learner.learner.Learner.register_optimizer
        • ray.rllib.core.learner.learner.Learner.get_optimizers_for_module
        • ray.rllib.core.learner.learner.Learner.get_optimizer
        • ray.rllib.core.learner.learner.Learner.get_parameters
        • ray.rllib.core.learner.learner.Learner.get_param_ref
        • ray.rllib.core.learner.learner.Learner.filter_param_dict_for_optimizer
        • ray.rllib.core.learner.learner.Learner._check_registered_optimizer
        • ray.rllib.core.learner.learner.Learner._set_optimizer_lr
        • ray.rllib.core.learner.learner.Learner._get_clip_function
        • ray.rllib.core.learner.learner.Learner.compute_gradients
        • ray.rllib.core.learner.learner.Learner.postprocess_gradients
        • ray.rllib.core.learner.learner.Learner.postprocess_gradients_for_module
        • ray.rllib.core.learner.learner.Learner.apply_gradients
        • ray.rllib.core.learner.learner.Learner.save_state
        • ray.rllib.core.learner.learner.Learner.load_state
        • ray.rllib.core.learner.learner.Learner._save_optimizers
        • ray.rllib.core.learner.learner.Learner._load_optimizers
        • ray.rllib.core.learner.learner.Learner.get_state
        • ray.rllib.core.learner.learner.Learner.set_state
        • ray.rllib.core.learner.learner.Learner.get_optimizer_state
        • ray.rllib.core.learner.learner.Learner.set_optimizer_state
        • ray.rllib.core.learner.learner.Learner._get_metadata
        • ray.rllib.core.learner.learner.Learner.add_module
        • ray.rllib.core.learner.learner.Learner.remove_module
        • ray.rllib.core.learner.learner.Learner.compile_results
        • ray.rllib.core.learner.learner.Learner.register_metric
        • ray.rllib.core.learner.learner.Learner.register_metrics
        • ray.rllib.core.learner.learner.Learner._check_result
      • Sampling the Environment or offline data
        • ray.rllib.evaluation.rollout_worker.RolloutWorker
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.add_policy
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.remove_policy
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.get_policy
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.set_is_policy_to_train
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.set_policy_mapping_fn
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.for_policy
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.foreach_policy
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.foreach_policy_to_train
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.get_filters
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.get_global_vars
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.set_global_vars
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.get_host
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.get_metrics
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.get_node_ip
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.get_weights
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.set_weights
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.get_state
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.set_state
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.lock
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.unlock
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.sample
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.sample_with_count
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.sample_and_learn
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.learn_on_batch
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.setup_torch_data_parallel
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.compute_gradients
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.apply_gradients
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.foreach_env
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.foreach_env_with_context
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.stop
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.apply
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.sync_filters
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.find_free_port
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.creation_args
        • ray.rllib.evaluation.rollout_worker.RolloutWorker.assert_healthy
        • ray.rllib.evaluation.worker_set.WorkerSet
        • ray.rllib.evaluation.worker_set.WorkerSet.stop
        • ray.rllib.evaluation.worker_set.WorkerSet.reset
        • ray.rllib.evaluation.worker_set.WorkerSet.add_workers
        • ray.rllib.evaluation.worker_set.WorkerSet.foreach_worker
        • ray.rllib.evaluation.worker_set.WorkerSet.foreach_worker_with_id
        • ray.rllib.evaluation.worker_set.WorkerSet.foreach_worker_async
        • ray.rllib.evaluation.worker_set.WorkerSet.fetch_ready_async_reqs
        • ray.rllib.evaluation.worker_set.WorkerSet.num_in_flight_async_reqs
        • ray.rllib.evaluation.worker_set.WorkerSet.local_worker
        • ray.rllib.evaluation.worker_set.WorkerSet.remote_workers
        • ray.rllib.evaluation.worker_set.WorkerSet.num_healthy_remote_workers
        • ray.rllib.evaluation.worker_set.WorkerSet.num_healthy_workers
        • ray.rllib.evaluation.worker_set.WorkerSet.num_remote_worker_restarts
        • ray.rllib.evaluation.worker_set.WorkerSet.probe_unhealthy_workers
        • ray.rllib.evaluation.worker_set.WorkerSet.add_policy
        • ray.rllib.evaluation.worker_set.WorkerSet.foreach_env
        • ray.rllib.evaluation.worker_set.WorkerSet.foreach_env_with_context
        • ray.rllib.evaluation.worker_set.WorkerSet.foreach_policy
        • ray.rllib.evaluation.worker_set.WorkerSet.foreach_policy_to_train
        • ray.rllib.evaluation.worker_set.WorkerSet.sync_weights
        • ray.rllib.offline.input_reader.InputReader
        • ray.rllib.offline.input_reader.InputReader.next
        • ray.rllib.evaluation.sampler.SamplerInput
        • ray.rllib.evaluation.sampler.SamplerInput.get_data
        • ray.rllib.evaluation.sampler.SamplerInput.get_extra_batches
        • ray.rllib.evaluation.sampler.SamplerInput.get_metrics
        • ray.rllib.evaluation.sampler.SyncSampler
        • ray.rllib.offline.json_reader.JsonReader
        • ray.rllib.offline.json_reader.JsonReader.read_all_files
        • ray.rllib.offline.mixed_input.MixedInput
        • ray.rllib.offline.d4rl_reader.D4RLReader
        • ray.rllib.offline.io_context.IOContext
        • ray.rllib.offline.io_context.IOContext.default_sampler_input
        • ray.rllib.policy.policy_map.PolicyMap
        • ray.rllib.policy.policy_map.PolicyMap.items
        • ray.rllib.policy.policy_map.PolicyMap.keys
        • ray.rllib.policy.policy_map.PolicyMap.values
        • ray.rllib.policy.sample_batch.SampleBatch
        • ray.rllib.policy.sample_batch.SampleBatch.set_get_interceptor
        • ray.rllib.policy.sample_batch.SampleBatch.is_training
        • ray.rllib.policy.sample_batch.SampleBatch.set_training
        • ray.rllib.policy.sample_batch.SampleBatch.as_multi_agent
        • ray.rllib.policy.sample_batch.SampleBatch.get
        • ray.rllib.policy.sample_batch.SampleBatch.to_device
        • ray.rllib.policy.sample_batch.SampleBatch.right_zero_pad
        • ray.rllib.policy.sample_batch.SampleBatch.slice
        • ray.rllib.policy.sample_batch.SampleBatch.split_by_episode
        • ray.rllib.policy.sample_batch.SampleBatch.shuffle
        • ray.rllib.policy.sample_batch.SampleBatch.columns
        • ray.rllib.policy.sample_batch.SampleBatch.rows
        • ray.rllib.policy.sample_batch.SampleBatch.copy
        • ray.rllib.policy.sample_batch.SampleBatch.is_single_trajectory
        • ray.rllib.policy.sample_batch.SampleBatch.is_terminated_or_truncated
        • ray.rllib.policy.sample_batch.SampleBatch.env_steps
        • ray.rllib.policy.sample_batch.SampleBatch.agent_steps
        • ray.rllib.policy.sample_batch.MultiAgentBatch
        • ray.rllib.policy.sample_batch.MultiAgentBatch.env_steps
        • ray.rllib.policy.sample_batch.MultiAgentBatch.agent_steps
      • Replay Buffer API
        • ray.rllib.utils.replay_buffers.replay_buffer.StorageUnit
        • ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer
        • ray.rllib.utils.replay_buffers.prioritized_replay_buffer.PrioritizedReplayBuffer
        • ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer
        • ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer.sample
        • ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer.add
        • ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer.get_state
        • ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer.set_state
        • ray.rllib.utils.replay_buffers.multi_agent_replay_buffer.MultiAgentReplayBuffer
        • ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer.MultiAgentPrioritizedReplayBuffer
        • ray.rllib.utils.replay_buffers.utils.update_priorities_in_replay_buffer
        • ray.rllib.utils.replay_buffers.utils.sample_min_n_steps_from_buffer
      • RLlib Utilities
        • ray.rllib.utils.exploration.exploration.Exploration
        • ray.rllib.utils.exploration.random.Random
        • ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling
        • ray.rllib.utils.exploration.epsilon_greedy.EpsilonGreedy
        • ray.rllib.utils.exploration.gaussian_noise.GaussianNoise
        • ray.rllib.utils.exploration.ornstein_uhlenbeck_noise.OrnsteinUhlenbeckNoise
        • ray.rllib.utils.exploration.random_encoder.RE3
        • ray.rllib.utils.exploration.curiosity.Curiosity
        • ray.rllib.utils.exploration.parameter_noise.ParameterNoise
        • ray.rllib.utils.exploration.exploration.Exploration.get_exploration_action
        • ray.rllib.utils.exploration.exploration.Exploration.before_compute_actions
        • ray.rllib.utils.exploration.exploration.Exploration.on_episode_start
        • ray.rllib.utils.exploration.exploration.Exploration.on_episode_end
        • ray.rllib.utils.exploration.exploration.Exploration.postprocess_trajectory
        • ray.rllib.utils.exploration.exploration.Exploration.get_state
        • ray.rllib.utils.exploration.exploration.Exploration.set_state
        • ray.rllib.utils.schedules.schedule.Schedule
        • ray.rllib.utils.schedules.constant_schedule.ConstantSchedule
        • ray.rllib.utils.schedules.linear_schedule.LinearSchedule
        • ray.rllib.utils.schedules.piecewise_schedule.PiecewiseSchedule
        • ray.rllib.utils.schedules.exponential_schedule.ExponentialSchedule
        • ray.rllib.utils.schedules.polynomial_schedule.PolynomialSchedule
        • ray.rllib.utils.schedules.schedule.Schedule.value
        • ray.rllib.utils.schedules.schedule.Schedule.__call__
        • ray.rllib.execution.train_ops.multi_gpu_train_one_step
        • ray.rllib.execution.train_ops.train_one_step
        • ray.rllib.utils.framework.try_import_torch
        • ray.rllib.utils.framework.try_import_tf
        • ray.rllib.utils.framework.try_import_tfp
        • ray.rllib.utils.tf_utils.explained_variance
        • ray.rllib.utils.tf_utils.flatten_inputs_to_1d_tensor
        • ray.rllib.utils.tf_utils.get_gpu_devices
        • ray.rllib.utils.tf_utils.get_placeholder
        • ray.rllib.utils.tf_utils.huber_loss
        • ray.rllib.utils.tf_utils.l2_loss
        • ray.rllib.utils.tf_utils.make_tf_callable
        • ray.rllib.utils.tf_utils.minimize_and_clip
        • ray.rllib.utils.tf_utils.one_hot
        • ray.rllib.utils.tf_utils.reduce_mean_ignore_inf
        • ray.rllib.utils.tf_utils.scope_vars
        • ray.rllib.utils.tf_utils.warn_if_infinite_kl_divergence
        • ray.rllib.utils.tf_utils.zero_logps_from_actions
        • ray.rllib.utils.torch_utils.apply_grad_clipping
        • ray.rllib.utils.torch_utils.concat_multi_gpu_td_errors
        • ray.rllib.utils.torch_utils.convert_to_torch_tensor
        • ray.rllib.utils.torch_utils.explained_variance
        • ray.rllib.utils.torch_utils.flatten_inputs_to_1d_tensor
        • ray.rllib.utils.torch_utils.get_device
        • ray.rllib.utils.torch_utils.global_norm
        • ray.rllib.utils.torch_utils.huber_loss
        • ray.rllib.utils.torch_utils.l2_loss
        • ray.rllib.utils.torch_utils.minimize_and_clip
        • ray.rllib.utils.torch_utils.one_hot
        • ray.rllib.utils.torch_utils.reduce_mean_ignore_inf
        • ray.rllib.utils.torch_utils.sequence_mask
        • ray.rllib.utils.torch_utils.warn_if_infinite_kl_divergence
        • ray.rllib.utils.torch_utils.set_torch_seed
        • ray.rllib.utils.torch_utils.softmax_cross_entropy_with_logits
        • ray.rllib.utils.numpy.aligned_array
        • ray.rllib.utils.numpy.concat_aligned
        • ray.rllib.utils.numpy.convert_to_numpy
        • ray.rllib.utils.numpy.fc
        • ray.rllib.utils.numpy.flatten_inputs_to_1d_tensor
        • ray.rllib.utils.numpy.make_action_immutable
        • ray.rllib.utils.numpy.huber_loss
        • ray.rllib.utils.numpy.l2_loss
        • ray.rllib.utils.numpy.lstm
        • ray.rllib.utils.numpy.one_hot
        • ray.rllib.utils.numpy.relu
        • ray.rllib.utils.numpy.sigmoid
        • ray.rllib.utils.numpy.softmax
      • External Application API
  • More Libraries
    • Distributed Scikit-learn / Joblib
    • Distributed multiprocessing.Pool
    • Ray Collective Communication Lib
    • Using Dask on Ray
    • Using Spark on Ray (RayDP)
    • Using Mars on Ray
    • Using Pandas on Ray (Modin)
    • Ray Workflows (Alpha)
      • Key Concepts
      • Getting Started
      • Workflow Management
      • Workflow Metadata
      • Events
      • API Comparisons
      • Advanced Topics
      • Ray Workflows API
        • Workflow Execution API
        • Workflow Management API
  • Ray Clusters
    • Key Concepts
    • Deploying on Kubernetes
      • Getting Started with KubeRay
        • RayCluster Quickstart
        • RayJob Quickstart
        • RayService Quickstart
      • User Guides
        • Deploy Ray Serve Apps
        • RayService high availability
        • KubeRay Observability
        • KubeRay upgrade guide
        • Managed Kubernetes services
        • Best Practices for Storage and Dependencies
        • RayCluster Configuration
        • KubeRay Autoscaling
        • GCS fault tolerance in KubeRay
        • Configuring KubeRay to use Google Cloud Storage Buckets in GKE
        • Log Persistence
        • Using GPUs
        • Developing Ray Serve Python scripts on a RayCluster
        • Specify container commands for Ray head/worker Pods
        • Pod Security
        • Helm Chart RBAC
        • TLS Authentication
        • (Advanced) Understanding the Ray Autoscaler in the Context of Kubernetes
        • (Advanced) Deploying a static Ray cluster without KubeRay
      • Examples
        • Ray Train XGBoostTrainer on Kubernetes
        • Train PyTorch ResNet model with GPUs on Kubernetes
        • Serve a StableDiffusion text-to-image model on Kubernetes
        • Serve a MobileNet image classifier on Kubernetes
        • Serve a text summarizer on Kubernetes
        • RayJob Batch Inference Example
      • KubeRay Ecosystem
        • Ingress
        • Using Prometheus and Grafana
        • Profiling with py-spy
        • KubeRay integration with Volcano
        • Kubeflow: an interactive development solution
      • KubeRay Benchmarks
        • KubeRay memory and scalability benchmark
      • KubeRay Troubleshooting
        • Troubleshooting guide
        • RayService troubleshooting
      • API Reference
    • Deploying on VMs
      • Getting Started
      • User Guides
        • Launching Ray Clusters on AWS, GCP, Azure, vSphere, On-Prem
        • Best practices for deploying large clusters
        • Configuring Autoscaling
        • Log Persistence
        • Community Supported Cluster Managers
      • Examples
        • Ray Train XGBoostTrainer on VMs
      • API References
        • Cluster Launcher Commands
        • Cluster YAML Configuration Options
    • Collecting and monitoring metrics
    • Configuring and Managing Ray Dashboard
    • Applications Guide
      • Ray Jobs Overview
        • Quickstart using the Ray Jobs CLI
        • Python SDK Overview
        • Python SDK API Reference
        • Ray Jobs CLI API Reference
        • Ray Jobs REST API
        • Ray Client
      • Programmatic Cluster Scaling
    • FAQ
    • Ray Cluster Management API
      • Cluster Management CLI
      • Python SDK API Reference
        • ray.job_submission.JobSubmissionClient
        • ray.job_submission.JobSubmissionClient.submit_job
        • ray.job_submission.JobSubmissionClient.stop_job
        • ray.job_submission.JobSubmissionClient.get_job_status
        • ray.job_submission.JobSubmissionClient.get_job_info
        • ray.job_submission.JobSubmissionClient.list_jobs
        • ray.job_submission.JobSubmissionClient.get_job_logs
        • ray.job_submission.JobSubmissionClient.tail_job_logs
        • ray.job_submission.JobStatus
        • ray.job_submission.JobInfo
        • ray.job_submission.JobDetails
        • ray.job_submission.JobType
        • ray.job_submission.DriverInfo
      • Ray Jobs CLI API Reference
      • Programmatic Cluster Scaling
    • Usage Stats Collection
  • Monitoring and Debugging
    • Ray Dashboard
    • Key Concepts
    • User Guides
      • Debugging Applications
        • General Debugging
        • Debugging Memory Issues
        • Debugging Hangs
        • Debugging Failures
        • Optimizing Performance
        • Using the Ray Debugger
      • Monitoring with the CLI or SDK
      • Configuring Logging
      • Profiling
      • Adding Application-Level Metrics
      • Tracing
    • Reference
      • State API
        • ray.util.state.summarize_actors
        • ray.util.state.summarize_objects
        • ray.util.state.summarize_tasks
        • ray.util.state.list_actors
        • ray.util.state.list_placement_groups
        • ray.util.state.list_nodes
        • ray.util.state.list_jobs
        • ray.util.state.list_workers
        • ray.util.state.list_tasks
        • ray.util.state.list_objects
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ray.train.RunConfig.callbacks#

RunConfig.callbacks: List[Callback] | None = None#

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