Version: 2.6.x



run(model: "Text", endpoints: "Text", connector: "Text" = None, credentials: "Text" = None, **kwargs: "Dict[Text, Any]", ,) -> "NoReturn"

Runs a Rasa model.


  • model - Path to model archive.
  • endpoints - Path to endpoints file.
  • connector - Connector which should be use (overwrites credentials field).
  • credentials - Path to channel credentials file.
  • **kwargs - Additional arguments which are passed to


train(domain: "Text", config: "Text", training_files: "Union[Text, List[Text]]", output: "Text" = rasa.shared.constants.DEFAULT_MODELS_PATH, dry_run: bool = False, force_training: bool = False, fixed_model_name: "Optional[Text]" = None, persist_nlu_training_data: bool = False, core_additional_arguments: "Optional[Dict]" = None, nlu_additional_arguments: "Optional[Dict]" = None, loop: "Optional[asyncio.AbstractEventLoop]" = None, model_to_finetune: "Optional[Text]" = None, finetuning_epoch_fraction: float = 1.0) -> "TrainingResult"

Runs Rasa Core and NLU training in async loop.


  • domain - Path to the domain file.
  • config - Path to the config for Core and NLU.
  • training_files - Paths to the training data for Core and NLU.
  • output - Output path.
  • dry_run - If True then no training will be done, and the information about whether the training needs to be done will be printed.
  • force_training - If True retrain model even if data has not changed.
  • fixed_model_name - Name of model to be stored.
  • persist_nlu_training_data - True if the NLU training data should be persisted with the model.
  • core_additional_arguments - Additional training parameters for core training.
  • nlu_additional_arguments - Additional training parameters forwarded to training method of each NLU component.
  • loop - Optional EventLoop for running coroutines.
  • model_to_finetune - Optional path to a model which should be finetuned or a directory in case the latest trained model should be used.
  • finetuning_epoch_fraction - The fraction currently specified training epochs in the model configuration which should be used for finetuning.


An instance of TrainingResult.


test(model: "Text", stories: "Text", nlu_data: "Text", output: "Text" = rasa.shared.constants.DEFAULT_RESULTS_PATH, additional_arguments: "Optional[Dict]" = None) -> None

Test a Rasa model against a set of test data.


  • model - model to test
  • stories - path to the dialogue test data
  • nlu_data - path to the NLU test data
  • output - path to folder where all output will be stored
  • additional_arguments - additional arguments for the test call