allennlp.training.checkpointer

class allennlp.training.checkpointer.Checkpointer(serialization_dir: str = None, keep_serialized_model_every_num_seconds: int = None, num_serialized_models_to_keep: int = 20)[source]

Bases: allennlp.common.registrable.Registrable

This class implements the functionality for checkpointing your model and trainer state during training. It is agnostic as to what those states look like (they are typed as Dict[str, Any]), but they will be fed to torch.save so they should be serializable in that sense. They will also be restored as Dict[str, Any], which means the calling code is responsible for knowing what to do with them.

best_model_state(self) → Dict[str, Any][source]
find_latest_checkpoint(self) → Tuple[str, str][source]

Return the location of the latest model and training state files. If there isn’t a valid checkpoint then return None.

restore_checkpoint(self) → Tuple[Dict[str, Any], Dict[str, Any]][source]

Restores a model from a serialization_dir to the last saved checkpoint. This includes a training state (typically consisting of an epoch count and optimizer state), which is serialized separately from model parameters. This function should only be used to continue training - if you wish to load a model for inference/load parts of a model into a new computation graph, you should use the native Pytorch functions: `` model.load_state_dict(torch.load(“/path/to/model/weights.th”))``

If self._serialization_dir does not exist or does not contain any checkpointed weights, this function will do nothing and return empty dicts.

Returns
states: Tuple[Dict[str, Any], Dict[str, Any]]

The model state and the training state.

save_checkpoint(self, epoch:Union[int, str], model_state:Dict[str, Any], training_states:Dict[str, Any], is_best_so_far:bool) → None[source]