Ray Train API

PyTorch Ecosystem

TorchTrainer	A Trainer for data parallel PyTorch training.
TorchConfig	Configuration for torch process group setup.
TorchXLAConfig	Configuration for torch XLA setup.

PyTorch

get_device	Gets the correct torch device configured for this process.
get_devices	Gets the correct torch device list configured for this process.
prepare_model	Prepares the model for distributed execution.
prepare_data_loader	Prepares DataLoader for distributed execution.
enable_reproducibility	Limits sources of nondeterministic behavior.

PyTorch Lightning

prepare_trainer	Prepare the PyTorch Lightning Trainer for distributed execution.
RayLightningEnvironment	Setup Lightning DDP training environment for Ray cluster.
RayDDPStrategy	Subclass of DDPStrategy to ensure compatibility with Ray orchestration.
RayFSDPStrategy	Subclass of FSDPStrategy to ensure compatibility with Ray orchestration.
RayDeepSpeedStrategy	Subclass of DeepSpeedStrategy to ensure compatibility with Ray orchestration.
RayTrainReportCallback	A simple callback that reports checkpoints to Ray on train epoch end.

Hugging Face Transformers

prepare_trainer	Prepare your HuggingFace Transformer Trainer for Ray Train.
RayTrainReportCallback	A simple callback to report checkpoints and metrics to Ray Train.

More Frameworks

Tensorflow/Keras

TensorflowTrainer	A Trainer for data parallel Tensorflow training.
TensorflowConfig	PublicAPI (beta): This API is in beta and may change before becoming stable.
prepare_dataset_shard	A utility function that overrides default config for Tensorflow Dataset.
ReportCheckpointCallback	Keras callback for Ray Train reporting and checkpointing.

Horovod

HorovodTrainer	A Trainer for data parallel Horovod training.
HorovodConfig	Configurations for Horovod setup.

XGBoost

XGBoostTrainer	A Trainer for data parallel XGBoost training.
RayTrainReportCallback	XGBoost callback to save checkpoints and report metrics.

LightGBM

LightGBMTrainer	A Trainer for data parallel LightGBM training.
RayTrainReportCallback	Creates a callback that reports metrics and checkpoints model.

Ray Train Configuration

CheckpointConfig	Configurable parameters for defining the checkpointing strategy.
DataConfig	Class responsible for configuring Train dataset preprocessing.
FailureConfig	Configuration related to failure handling of each training/tuning run.
RunConfig	Runtime configuration for training and tuning runs.
ScalingConfig	Configuration for scaling training.
SyncConfig	Configuration object for Train/Tune file syncing to RunConfig(storage_path).

Ray Train Utilities

Classes

Checkpoint	A reference to data persisted as a directory in local or remote storage.
TrainContext	Context containing metadata that can be accessed within Ray Train workers.

Functions

get_checkpoint	Access the latest reported checkpoint to resume from if one exists.
get_context	Get or create a singleton training context.
get_dataset_shard	Returns the ray.data.DataIterator shard for this worker.
report	Report metrics and optionally save a checkpoint.

Ray Train Output

Result

The final result of a ML training run or a Tune trial.

Ray Train Errors

SessionMisuseError	Indicates a method or function was used outside of a session.
TrainingFailedError	An error indicating that training has failed.

Ray Train Developer APIs

Trainer Base Classes

BaseTrainer	Defines interface for distributed training on Ray.
DataParallelTrainer	A Trainer for data parallel training.

Train Backend Base Classes

Backend	Singleton for distributed communication backend.
BackendConfig	Parent class for configurations of training backend.