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Ray 3.0.0.dev0
Ray
Getting Started Guide
Installation
Use Cases
Ecosystem
Ray Core
Key Concepts
User Guides
Tasks
Nested Remote Functions
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
Actor Design Patterns
Objects
Serialization
Object Spilling
Environment Dependencies
Scheduling
Resources
GPU Support
Placement Groups
Memory Management
Out-Of-Memory Prevention
Fault Tolerance
Task Fault Tolerance
Actor Fault Tolerance
Object 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 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
Using Namespaces
Cross-Language Programming
Working with Jupyter Notebooks & JupyterLab
Lazy Computation Graphs with the Ray DAG API
Miscellaneous Topics
Examples
Monte Carlo Estimation of π
Asynchronous Advantage Actor Critic (A3C)
Fault-Tolerant Fairseq Training
Simple Parallel Model Selection
Parameter Server
Learning to Play Pong
Using Ray for Highly Parallelizable Tasks
Batch Prediction
Batch Training with Ray Core
Simple AutoML for time series with Ray Core
API References
Ray Core API
Ray Core CLI
Ray State CLI
Ray State API
Ray Clusters
Key Concepts
Deploying on Kubernetes
Getting Started
User Guides
Managed Kubernetes services
RayCluster Configuration
KubeRay Autoscaling
Logging
Using GPUs
Experimental Features
Examples
Ray AIR XGBoostTrainer on Kubernetes
ML training with GPUs on Kubernetes
API Reference
Deploying on VMs
Getting Started
User Guides
Launching Ray Clusters on AWS, GCP, Azure, On-Prem
Best practices for deploying large clusters
Configuring Autoscaling
Community Supported Cluster Managers
Examples
Ray AIR XGBoostTrainer on VMs
API References
Cluster Launcher Commands
Cluster YAML Configuration Options
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: Interactive Development
Cluster Monitoring
Programmatic Cluster Scaling
FAQ
Ray Cluster Management API
Cluster Management CLI
Python SDK API Reference
Ray Jobs CLI API Reference
Programmatic Cluster Scaling
Ray AI Runtime (AIR)
Key Concepts
User Guides
Using Preprocessors
Using Trainers
Configuring Training Datasets
Configuring Hyperparameter Tuning
Using Predictors for Inference
Deploying Predictors with Serve
How to Deploy AIR
Examples
Training a Torch Image Classifier
Convert existing PyTorch code to Ray AIR
Convert existing Tensorflow/Keras code to Ray AIR
Tabular data training and serving with Keras and Ray AIR
Fine-tune a 🤗 Transformers model
Training a model with Sklearn
Training a model with distributed XGBoost
Hyperparameter tuning with XGBoostTrainer
Training a model with distributed LightGBM
Incremental Learning with Ray AIR
Serving reinforcement learning policy models
Online reinforcement learning with Ray AIR
Offline reinforcement learning with Ray AIR
Logging results and uploading models to Comet ML
Logging results and uploading models to Weights & Biases
Integrate Ray AIR with Feast feature store
Simple AutoML for time series with Ray AIR
Batch training & tuning on Ray Tune
Batch (parallel) Demand Forecasting using Prophet, ARIMA, and Ray Tune
Ray AIR API
Benchmarks
Ray Data
Getting Started
Key Concepts
User Guides
Creating Datasets
Transforming Datasets
Consuming Datasets
ML Preprocessing
ML Tensor Support
Custom Datasources
Pipelining Compute
Scheduling, Execution, and Memory Management
Performance Tips and Tuning
Examples
Processing the NYC taxi dataset
Batch Training with Ray Datasets
Large-scale ML Ingest
Scaling OCR with Ray Datasets
Advanced Pipeline Examples
Random Data Access (Experimental)
FAQ
Ray Datasets API
Input/Output
ray.data.range
ray.data.range_table
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.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.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_datasource
ray.data.Dataset.write_datasource
ray.data.Datasource
ray.data.ReadTask
ray.data.datasource.Reader
ray.data.datasource.BinaryDatasource
ray.data.datasource.CSVDatasource
ray.data.datasource.FileBasedDatasource
ray.data.datasource.ImageDatasource
ray.data.datasource.JSONDatasource
ray.data.datasource.NumpyDatasource
ray.data.datasource.ParquetDatasource
ray.data.datasource.RangeDatasource
ray.data.datasource.TFRecordDatasource
ray.data.datasource.MongoDatasource
ray.data.datasource.Partitioning
ray.data.datasource.PartitionStyle
ray.data.datasource.PathPartitionEncoder
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.train_test_split
ray.data.Dataset.union
ray.data.Dataset.zip
ray.data.Dataset.groupby
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.repeat
ray.data.Dataset.window
ray.data.Dataset.show
ray.data.Dataset.take
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_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.schema
ray.data.Dataset.default_batch_format
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.fully_executed
ray.data.Dataset.is_fully_executed
ray.data.Dataset.lazy
ray.data.Dataset.has_serializable_lineage
ray.data.Dataset.serialize_lineage
ray.data.Dataset.deserialize_lineage
DatasetIterator API
ray.data.DatasetIterator.iter_batches
ray.data.DatasetIterator.iter_torch_batches
ray.data.DatasetIterator.to_tf
ray.data.DatasetIterator.stats
DatasetPipeline API
ray.data.DatasetPipeline
ray.data.DatasetPipeline.map
ray.data.DatasetPipeline.map_batches
ray.data.DatasetPipeline.flat_map
ray.data.DatasetPipeline.foreach_window
ray.data.DatasetPipeline.filter
ray.data.DatasetPipeline.add_column
ray.data.DatasetPipeline.drop_columns
ray.data.DatasetPipeline.select_columns
ray.data.DatasetPipeline.sort_each_window
ray.data.DatasetPipeline.random_shuffle_each_window
ray.data.DatasetPipeline.randomize_block_order_each_window
ray.data.DatasetPipeline.repartition_each_window
ray.data.DatasetPipeline.split
ray.data.DatasetPipeline.split_at_indices
ray.data.DatasetPipeline.repeat
ray.data.DatasetPipeline.rewindow
ray.data.DatasetPipeline.from_iterable
ray.data.DatasetPipeline.show
ray.data.DatasetPipeline.show_windows
ray.data.DatasetPipeline.take
ray.data.DatasetPipeline.take_all
ray.data.DatasetPipeline.iterator
ray.data.DatasetPipeline.iter_rows
ray.data.DatasetPipeline.iter_batches
ray.data.DatasetPipeline.iter_torch_batches
ray.data.DatasetPipeline.iter_tf_batches
ray.data.DatasetPipeline.write_json
ray.data.DatasetPipeline.write_csv
ray.data.DatasetPipeline.write_parquet
ray.data.DatasetPipeline.write_datasource
ray.data.DatasetPipeline.to_tf
ray.data.DatasetPipeline.to_torch
ray.data.DatasetPipeline.schema
ray.data.DatasetPipeline.count
ray.data.DatasetPipeline.stats
ray.data.DatasetPipeline.sum
GroupedDataset API
ray.data.grouped_dataset.GroupedDataset
ray.data.grouped_dataset.GroupedDataset.count
ray.data.grouped_dataset.GroupedDataset.sum
ray.data.grouped_dataset.GroupedDataset.min
ray.data.grouped_dataset.GroupedDataset.max
ray.data.grouped_dataset.GroupedDataset.mean
ray.data.grouped_dataset.GroupedDataset.std
ray.data.grouped_dataset.GroupedDataset.aggregate
ray.data.grouped_dataset.GroupedDataset.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
DatasetContext API
ray.data.context.DatasetContext
ray.data.context.DatasetContext.get_current
Data Representations
ray.data.block.Block
ray.data.block.BlockExecStats
ray.data.block.BlockMetadata
ray.data.block.BlockAccessor
ray.data.block.DataBatch
ray.data.row.TableRow
ray.data.extensions.tensor_extension.TensorDtype
ray.data.extensions.tensor_extension.TensorArray
ray.data.extensions.tensor_extension.ArrowTensorType
ray.data.extensions.tensor_extension.ArrowTensorArray
ray.data.extensions.tensor_extension.ArrowVariableShapedTensorType
ray.data.extensions.tensor_extension.ArrowVariableShapedTensorArray
(Experimental) RandomAccessDataset API
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
API Guide for Users from Other Data Libraries
Integrations
Using Dask on Ray
Using Spark on Ray (RayDP)
Using Mars on Ray
Using Pandas on Ray (Modin)
Ray Train
Getting Started
Key Concepts
User Guides
Configuring Ray Train
Deep Learning Guide
XGBoost/LightGBM guide
Ray Train Architecture
Examples
PyTorch Fashion MNIST Example
HF Transformers Example
TensorFlow MNIST Example
Horovod Example
MLflow Callback Example
Tune & TensorFlow Example
Tune & PyTorch Example
Torch Data Prefetching Benchmark
Ray Train FAQ
Ray Train API
Ray Tune
Getting Started
Key Concepts
User Guides
Running Basic Experiments
Logging Tune Runs
Setting Trial Resources
Using Search Spaces
How to Stop and Resume
How to Configure Storage Options for a Distributed Tune Experiment?
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
MXNet Example
Ray Serve Example
Ray RLlib Example
XGBoost Example
LightGBM Example
Horovod Example
Huggingface Example
Tune Experiment Tracking Examples
Comet Example
Weights & Biases Example
MLflow Example
Tune Hyperparameter Optimization Framework Examples
Ax Example
Dragonfly Example
Skopt Example
HyperOpt Example
Bayesopt Example
FLAML Example
BOHB Example
Nevergrad Example
Optuna Example
ZOOpt Example
SigOpt Example
HEBO Example
Other Examples
Exercises
Ray Tune FAQ
Ray Tune API
Tune Execution (Tuner, tune.Experiment)
Training in Tune (tune.Trainable, session.report)
Tune Search Space API
Tune Search Algorithms (tune.search)
Tune Trial Schedulers (tune.schedulers)
Tune Stopping mechanisms (tune.stopper)
ResultGrid (tune.ResultGrid)
Tune Console Output (Reporters)
Syncing in Tune (tune.SyncConfig, tune.Syncer)
Tune Loggers (tune.logger)
Environment variables used by Ray Tune
Tune Scikit-Learn API (tune.sklearn)
External library integrations for Ray Tune (tune.integration)
Tune Internals
Tune Client API
Tune CLI (Experimental)
Ray Serve
Getting Started
Key Concepts
User Guides
HTTP Handling
Scaling and Resource Allocation
Model Composition
Development Workflow
Production Guide
Serve Config Files (
serve
build
)
Deploying on VMs
Deploying on Kubernetes
Monitoring Ray Serve
Adding End-to-End Fault Tolerance
Performance Tuning
Handling Dependencies
Experimental Java API
1.x to 2.x API Migration Guide
Experimental Direct Ingress
Architecture
Examples
Serving ML Models (Tensorflow, PyTorch, Scikit-Learn, others)
Batching Tutorial
Serving RLlib Models
Scaling your Gradio app with Ray Serve
Visualizing a Deployment Graph with Gradio
Java Tutorial
Deployment Graph Patterns
Pattern: Linear Pipeline
Pattern: Branching Input
Pattern: Conditional
Ray Serve API
Serve CLI
Serve REST API
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
Connectors (Alpha)
Fault Tolerance And Elastic Training
How To Contribute to RLlib
Working with the RLlib CLI
Examples
Ray RLlib API
Algorithms
Environments
BaseEnv API
MultiAgentEnv API
VectorEnv API
ExternalEnv API
Policies
Base Policy class (ray.rllib.policy.policy.Policy)
TensorFlow-Specific Sub-Classes
Torch-Specific Policy: TorchPolicy
Building Custom Policy Classes
Model APIs
Evaluation and Environment Rollout
RolloutWorker
Sample Batches
WorkerSet
Environment Samplers
PolicyMap (ray.rllib.policy.policy_map.PolicyMap)
Offline RL
Parallel Requests Utilities
Training Operations Utilities
ReplayBuffer API
RLlib Utilities
Exploration API
Schedules API
RLlib Annotations/Decorators
Deep Learning Framework (tf vs torch) Utilities
TensorFlow Utility Functions
PyTorch Utility Functions
Numpy Utility Functions
Deprecation Tools/Utils
External Application API
More Libraries
Distributed Scikit-learn / Joblib
Distributed multiprocessing.Pool
Ray Collective Communication Lib
Using Ray with Pytorch Lightning
Ray Workflows (Alpha)
Key Concepts
Getting Started
Workflow Management
Workflow Metadata
Events
API Comparisons
Advanced Topics
Ray Workflows API
Monitoring and Debugging
Troubleshooting Failures
Troubleshooting Hangs
Troubleshooting Performance
Ray Gotchas
Profiling
Getting Help
Tools
Overview
Ray Dashboard
Monitoring Ray States
Ray Debugger
Logging
Metrics
Tracing
Debugging (internal)
Profiling (internal)
References
Ray AIR API
Ray Datasets API
Input/Output
ray.data.range
ray.data.range_table
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.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.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_datasource
ray.data.Dataset.write_datasource
ray.data.Datasource
ray.data.ReadTask
ray.data.datasource.Reader
ray.data.datasource.BinaryDatasource
ray.data.datasource.CSVDatasource
ray.data.datasource.FileBasedDatasource
ray.data.datasource.ImageDatasource
ray.data.datasource.JSONDatasource
ray.data.datasource.NumpyDatasource
ray.data.datasource.ParquetDatasource
ray.data.datasource.RangeDatasource
ray.data.datasource.TFRecordDatasource
ray.data.datasource.MongoDatasource
ray.data.datasource.Partitioning
ray.data.datasource.PartitionStyle
ray.data.datasource.PathPartitionEncoder
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.train_test_split
ray.data.Dataset.union
ray.data.Dataset.zip
ray.data.Dataset.groupby
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.repeat
ray.data.Dataset.window
ray.data.Dataset.show
ray.data.Dataset.take
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_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.schema
ray.data.Dataset.default_batch_format
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.fully_executed
ray.data.Dataset.is_fully_executed
ray.data.Dataset.lazy
ray.data.Dataset.has_serializable_lineage
ray.data.Dataset.serialize_lineage
ray.data.Dataset.deserialize_lineage
DatasetIterator API
ray.data.DatasetIterator.iter_batches
ray.data.DatasetIterator.iter_torch_batches
ray.data.DatasetIterator.to_tf
ray.data.DatasetIterator.stats
DatasetPipeline API
ray.data.DatasetPipeline
ray.data.DatasetPipeline.map
ray.data.DatasetPipeline.map_batches
ray.data.DatasetPipeline.flat_map
ray.data.DatasetPipeline.foreach_window
ray.data.DatasetPipeline.filter
ray.data.DatasetPipeline.add_column
ray.data.DatasetPipeline.drop_columns
ray.data.DatasetPipeline.select_columns
ray.data.DatasetPipeline.sort_each_window
ray.data.DatasetPipeline.random_shuffle_each_window
ray.data.DatasetPipeline.randomize_block_order_each_window
ray.data.DatasetPipeline.repartition_each_window
ray.data.DatasetPipeline.split
ray.data.DatasetPipeline.split_at_indices
ray.data.DatasetPipeline.repeat
ray.data.DatasetPipeline.rewindow
ray.data.DatasetPipeline.from_iterable
ray.data.DatasetPipeline.show
ray.data.DatasetPipeline.show_windows
ray.data.DatasetPipeline.take
ray.data.DatasetPipeline.take_all
ray.data.DatasetPipeline.iterator
ray.data.DatasetPipeline.iter_rows
ray.data.DatasetPipeline.iter_batches
ray.data.DatasetPipeline.iter_torch_batches
ray.data.DatasetPipeline.iter_tf_batches
ray.data.DatasetPipeline.write_json
ray.data.DatasetPipeline.write_csv
ray.data.DatasetPipeline.write_parquet
ray.data.DatasetPipeline.write_datasource
ray.data.DatasetPipeline.to_tf
ray.data.DatasetPipeline.to_torch
ray.data.DatasetPipeline.schema
ray.data.DatasetPipeline.count
ray.data.DatasetPipeline.stats
ray.data.DatasetPipeline.sum
GroupedDataset API
ray.data.grouped_dataset.GroupedDataset
ray.data.grouped_dataset.GroupedDataset.count
ray.data.grouped_dataset.GroupedDataset.sum
ray.data.grouped_dataset.GroupedDataset.min
ray.data.grouped_dataset.GroupedDataset.max
ray.data.grouped_dataset.GroupedDataset.mean
ray.data.grouped_dataset.GroupedDataset.std
ray.data.grouped_dataset.GroupedDataset.aggregate
ray.data.grouped_dataset.GroupedDataset.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
DatasetContext API
ray.data.context.DatasetContext
ray.data.context.DatasetContext.get_current
Data Representations
ray.data.block.Block
ray.data.block.BlockExecStats
ray.data.block.BlockMetadata
ray.data.block.BlockAccessor
ray.data.block.DataBatch
ray.data.row.TableRow
ray.data.extensions.tensor_extension.TensorDtype
ray.data.extensions.tensor_extension.TensorArray
ray.data.extensions.tensor_extension.ArrowTensorType
ray.data.extensions.tensor_extension.ArrowTensorArray
ray.data.extensions.tensor_extension.ArrowVariableShapedTensorType
ray.data.extensions.tensor_extension.ArrowVariableShapedTensorArray
(Experimental) RandomAccessDataset API
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
API Guide for Users from Other Data Libraries
Ray Train API
Ray Tune API
Tune Execution (Tuner, tune.Experiment)
Training in Tune (tune.Trainable, session.report)
Tune Search Space API
Tune Search Algorithms (tune.search)
Tune Trial Schedulers (tune.schedulers)
Tune Stopping mechanisms (tune.stopper)
ResultGrid (tune.ResultGrid)
Tune Console Output (Reporters)
Syncing in Tune (tune.SyncConfig, tune.Syncer)
Tune Loggers (tune.logger)
Environment variables used by Ray Tune
Tune Scikit-Learn API (tune.sklearn)
External library integrations for Ray Tune (tune.integration)
Tune Internals
Tune Client API
Tune CLI (Experimental)
Ray Serve API
Serve CLI
Serve REST API
Ray RLlib API
Algorithms
Environments
BaseEnv API
MultiAgentEnv API
VectorEnv API
ExternalEnv API
Policies
Base Policy class (ray.rllib.policy.policy.Policy)
TensorFlow-Specific Sub-Classes
Torch-Specific Policy: TorchPolicy
Building Custom Policy Classes
Model APIs
Evaluation and Environment Rollout
RolloutWorker
Sample Batches
WorkerSet
Environment Samplers
PolicyMap (ray.rllib.policy.policy_map.PolicyMap)
Offline RL
Parallel Requests Utilities
Training Operations Utilities
ReplayBuffer API
RLlib Utilities
Exploration API
Schedules API
RLlib Annotations/Decorators
Deep Learning Framework (tf vs torch) Utilities
TensorFlow Utility Functions
PyTorch Utility Functions
Numpy Utility Functions
Deprecation Tools/Utils
External Application API
Ray Workflows API
Ray Cluster Management API
Cluster Management CLI
Python SDK API Reference
Ray Jobs CLI API Reference
Programmatic Cluster Scaling
Ray Core API
Usage Stats Collection
Developer Guides
Getting Involved / Contributing
Building Ray from Source
Contributing to the Ray Documentation
Testing Autoscaling Locally
Tips for testing Ray programs
Configuring Ray
Architecture Whitepapers
repository
open issue
suggest edit
.rst
.pdf
Base Policy class (ray.rllib.policy.policy.Policy)
Base Policy class (ray.rllib.policy.policy.Policy)
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