def do_train(cfg: RasaNLUModelConfig,
data: Text,
path: Optional[Text] = None,
project: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
storage: Optional[Text] = None,
component_builder: Optional[ComponentBuilder] = None,
training_data_endpoint: Optional[EndpointConfig] = None,
**kwargs: Any) -> Tuple[Trainer, Interpreter, Text]:
"""Loads the trainer and the data and runs the training of the model."""
# Ensure we are training a model that we can save in the end
# WARN: there is still a race condition if a model with the same name is
# trained in another subprocess
trainer = Trainer(cfg, component_builder)
persistor = create_persistor(storage)
if training_data_endpoint is not None:
training_data = load_data_from_endpoint(training_data_endpoint,
cfg.language)
else:
training_data = load_data(data, cfg.language)
interpreter = trainer.train(training_data, **kwargs)
if path:
persisted_path = trainer.persist(path, persistor, project,
fixed_model_name)
else:
persisted_path = None
return trainer, interpreter, persisted_path
def train(nlu_config: Union[Text, RasaNLUModelConfig],
data: Text,
path: Optional[Text] = None,
project: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
storage: Optional[Text] = None,
component_builder: Optional[ComponentBuilder] = None,
training_data_endpoint: Optional[EndpointConfig] = None,
**kwargs: Any
) -> Tuple[Trainer, Interpreter, Text]:
"""Loads the trainer and the data and runs the training of the model."""
if isinstance(nlu_config, str):
nlu_config = config.load(nlu_config)
# Ensure we are training a model that we can save in the end
# WARN: there is still a race condition if a model with the same name is
# trained in another subprocess
trainer = Trainer(nlu_config, component_builder)
persistor = create_persistor(storage)
if training_data_endpoint is not None:
training_data = load_data_from_endpoint(training_data_endpoint,
nlu_config.language)
else:
training_data = load_data(data, nlu_config.language)
interpreter = trainer.train(training_data, **kwargs)
if path:
persisted_path = trainer.persist(path,
persistor,
project,
fixed_model_name)
else:
persisted_path = None
return trainer, interpreter, persisted_path