cascade.trainers#

class cascade.trainers.BasicTrainer(repo: Repo | str, *args: Any, **kwargs: Any)[source]#

The most common of concrete Trainers. Trains a model for a certain amount of epochs. Can start from checkpoint if model file exists.

__init__(repo: Repo | str, *args: Any, **kwargs: Any) → None[source]#

get_meta() → List[Dict[Any, Any]][source]#

Returns:

meta – A list where first element is this object’s metadata. All other elements represent the other stages of pipeline if present.

Meta can be anything that is worth to document about the object and its properties.

Meta is a list (see Meta type alias) to allow the formation of pipelines.

Return type:

Meta

Trains, evaluates and saves given model. If specified, loads model from checkpoint.

Parameters:

model – Model a model to be trained or which to load from line specified in start_from
train_data – Iterable train data to be passed to model’s fit()
test_data – Iterable, optional test data to be passed to model’s evaluate()
train_kwargs – Dict, optional arguments for fit()
test_kwargs – Dict, optional arguments for evaluate() - the most common is the dict of metrics
epochs – int, optional how many times to repeat training on data
start_from – str, optional name or index of line from which to start starts from the latest model in line
eval_strategy – int, optional Evaluation will take place every eval_strategy epochs. If None - the strategy is no evaluation.
save_strategy – int, optional Saving will take place every save_strategy epochs. Meta will be saved anyway. If None - the strategy is ‘save only meta’.
save_meta_callback – bool, optional By default True - adds line.save(model, only_meta=True) as a callback when model.log() is called

class cascade.trainers.Trainer(repo: Repo | str, *args: Any, **kwargs: Any)[source]#