Table of Contents

Shortcuts

`ride.runner`¶

Module Contents¶

Classes¶

Functions¶

is_runnable(cls)

Attributes¶

`EvalutationResults`
`logger`

ride.runner.EvalutationResults[source]¶

ride.runner.logger[source]¶

ride.runner.is_runnable(cls)[source]¶

class ride.runner.Runner(Module: Type[ride.core.RideModule])[source]¶

trained_model: ride.core.RideModule[source]¶

train(args: pytorch_lightning.utilities.parsing.AttributeDict, trainer_callbacks: List[Callable] = [], tune_checkpoint_dir: str = None, experiment_logger: ride.logging.ExperimentLoggerCreator = experiment_logger) → ride.core.RideModule[source]¶

evaluate(args: pytorch_lightning.utilities.parsing.AttributeDict, mode='val') → EvalutationResults[source]¶

validate(args: pytorch_lightning.utilities.parsing.AttributeDict) → EvalutationResults[source]¶

test(args: pytorch_lightning.utilities.parsing.AttributeDict) → EvalutationResults[source]¶

train_and_val(args: pytorch_lightning.utilities.parsing.AttributeDict, trainer_callbacks: List[Callable] = [], tune_checkpoint_dir: str = None, experiment_logger: ride.logging.ExperimentLoggerCreator = experiment_logger) → EvalutationResults[source]¶

static static_train_and_val(Module: Type[ride.core.RideModule], args: pytorch_lightning.utilities.parsing.AttributeDict, trainer_callbacks: List[Callable] = [], tune_checkpoint_dir: str = None, experiment_logger: ride.logging.ExperimentLoggerCreator = experiment_logger) → EvalutationResults[source]¶

profile_model(args: pytorch_lightning.utilities.parsing.AttributeDict, num_runs: int = 100) → Dict[str, Any][source]¶

abstract find_learning_rate()[source]¶

abstract find_batch_size()[source]¶

Read the Docs v: latest

Versions: latest; stable

Downloads: pdf; html; epub

On Read the Docs: Project Home; Builds

Free document hosting provided by Read the Docs.