snorkel.classification.Checkpointer

class snorkel.classification.Checkpointer(counter_unit, evaluation_freq, **kwargs)

Bases: object

Manager for checkpointing model.

Parameters

• counter_unit (str) – The unit to use when determining when its time to checkpoint (one of [“epochs”, “batches”, “points”]); must match the counter_unit of LogManager

• evaluation_freq (float) – How frequently the model is being evaluated (this is the maximum frequency that checkpointing can occur, which will happen if checkpoint_factor==1)

• kwargs (Any) – Config merged with default_config["checkpointer_config"]

__init__(counter_unit, evaluation_freq, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Return type

None

Methods

__init__(counter_unit, evaluation_freq, **kwargs)	Initialize self.
checkpoint(iteration, model, metric_dict)	Check if iteration and current metrics necessitate a checkpoint.
clear()	Clear existing checkpoint files, besides the best-yet model.
load_best_model(model)	Load the best model from the checkpoint.

checkpoint(iteration, model, metric_dict)

Check if iteration and current metrics necessitate a checkpoint.

Parameters

• iteration (float) – Current training iteration

• model (MultitaskClassifier) – Model to checkpoint

• metric_dict (Dict[str, float]) – Current performance metrics for model

Return type

None

clear()

Clear existing checkpoint files, besides the best-yet model.

Return type

None

load_best_model(model)

Load the best model from the checkpoint.

Return type

MultitaskClassifier