callback

allennlp.training.callbacks.callback

TrainerCallback¶

class TrainerCallback(Registrable):
 | def __init__(self, serialization_dir: str) -> None

A general callback object that handles multiple events.

This class has on_batch, on_epoch, and on_end methods, corresponding to each callback type. Each one receives the state of the wrapper object as self. This enables easier state sharing between related callbacks.

Also, this callback type is instantiated with serialization_dir and on_start is called with the trainer instance as an argument. This might be handy in case of callback logging and saving its own files next to the config/checkpoints/logs/etc.

on_start¶

class TrainerCallback(Registrable):
 | ...
 | def on_start(
 |     self,
 |     trainer: "GradientDescentTrainer",
 |     is_primary: bool = True,
 |     **kwargs
 | ) -> None

This callback hook is called before the training is started.

on_batch¶

class TrainerCallback(Registrable):
 | ...
 | def on_batch(
 |     self,
 |     trainer: "GradientDescentTrainer",
 |     batch_inputs: List[TensorDict],
 |     batch_outputs: List[Dict[str, Any]],
 |     batch_metrics: Dict[str, Any],
 |     epoch: int,
 |     batch_number: int,
 |     is_training: bool,
 |     is_primary: bool = True,
 |     batch_grad_norm: Optional[float] = None,
 |     **kwargs
 | ) -> None

This callback hook is called after the end of each batch.

on_epoch¶

class TrainerCallback(Registrable):
 | ...
 | def on_epoch(
 |     self,
 |     trainer: "GradientDescentTrainer",
 |     metrics: Dict[str, Any],
 |     epoch: int,
 |     is_primary: bool = True,
 |     **kwargs
 | ) -> None

This callback hook is called after the end of each epoch.

on_end¶

class TrainerCallback(Registrable):
 | ...
 | def on_end(
 |     self,
 |     trainer: "GradientDescentTrainer",
 |     metrics: Dict[str, Any] = None,
 |     epoch: int = None,
 |     is_primary: bool = True,
 |     **kwargs
 | ) -> None

This callback hook is called after the final training epoch.