async def train_async( domain: Text, config: Text, training_files: Union[Text, List[Text]], output: Text = DEFAULT_MODELS_PATH, force_training: bool = False, kwargs: Optional[Dict] = None, ) -> Optional[Text]: """Trains a Rasa model (Core and NLU). Args: 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. force_training: If `True` retrain model even if data has not changed. kwargs: Additional training parameters. Returns: Path of the trained model archive. """ config = get_valid_config(config, CONFIG_MANDATORY_KEYS) train_path = tempfile.mkdtemp() old_model = model.get_latest_model(output) retrain_core = True retrain_nlu = True story_directory, nlu_data_directory = data.get_core_nlu_directories(training_files) new_fingerprint = model.model_fingerprint( config, domain, nlu_data_directory, story_directory ) dialogue_data_not_present = not os.listdir(story_directory) nlu_data_not_present = not os.listdir(nlu_data_directory) if dialogue_data_not_present and nlu_data_not_present: print_error( "No training data given. Please provide dialogue and NLU data in " "order to train a Rasa model." ) return if dialogue_data_not_present: print_warning( "No dialogue data present. Just a Rasa NLU model will be trained." ) return train_nlu(config, nlu_data_directory, output, None) if nlu_data_not_present: print_warning("No NLU data present. Just a Rasa Core model will be trained.") return await train_core_async( domain, config, story_directory, output, None, kwargs ) if not force_training and old_model: unpacked = model.unpack_model(old_model) old_core, old_nlu = model.get_model_subdirectories(unpacked) last_fingerprint = model.fingerprint_from_path(unpacked) if not model.core_fingerprint_changed(last_fingerprint, new_fingerprint): target_path = os.path.join(train_path, "core") retrain_core = not model.merge_model(old_core, target_path) if not model.nlu_fingerprint_changed(last_fingerprint, new_fingerprint): target_path = os.path.join(train_path, "nlu") retrain_nlu = not model.merge_model(old_nlu, target_path) if force_training or retrain_core: await train_core_async( domain, config, story_directory, output, train_path, kwargs ) else: print ( "Dialogue data / configuration did not change. " "No need to retrain dialogue model." ) if force_training or retrain_nlu: train_nlu(config, nlu_data_directory, output, train_path) else: print ("NLU data / configuration did not change. No need to retrain NLU model.") if retrain_core or retrain_nlu: output = create_output_path(output) model.create_package_rasa(train_path, output, new_fingerprint) print_success("Your bot is trained and ready to take for a spin!") return output else: print_success( "Nothing changed. You can use the old model stored at '{}'" "".format(os.path.abspath(old_model)) ) return old_model
async def train_async(domain: Text, config: Text, training_files: Union[Text, List[Text]], output: Text = DEFAULT_MODELS_PATH, force_training: bool = False) -> Optional[Text]: """Trains a Rasa model (Core and NLU). Args: 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. force_training: If `True` retrain model even if data has not changed. Returns: Path of the trained model archive. """ train_path = tempfile.mkdtemp() old_model = model.get_latest_model(output) retrain_core = True retrain_nlu = True story_directory, nlu_data_directory = data.get_core_nlu_directories( training_files) new_fingerprint = model.model_fingerprint(config, domain, nlu_data_directory, story_directory) if not force_training and old_model: unpacked = model.unpack_model(old_model) old_core, old_nlu = model.get_model_subdirectories(unpacked) last_fingerprint = model.fingerprint_from_path(unpacked) if not model.core_fingerprint_changed(last_fingerprint, new_fingerprint): target_path = os.path.join(train_path, "core") retrain_core = not model.merge_model(old_core, target_path) if not model.nlu_fingerprint_changed(last_fingerprint, new_fingerprint): target_path = os.path.join(train_path, "nlu") retrain_nlu = not model.merge_model(old_nlu, target_path) if force_training or retrain_core: await train_core_async(domain, config, story_directory, output, train_path) else: print("Dialogue data / configuration did not change. " "No need to retrain dialogue model.") if force_training or retrain_nlu: train_nlu(config, nlu_data_directory, output, train_path) else: print("NLU data / configuration did not change. " "No need to retrain NLU model.") if retrain_core or retrain_nlu: output = create_output_path(output) model.create_package_rasa(train_path, output, new_fingerprint) print("Train path: '{}'.".format(train_path)) print_success("Your bot is trained and ready to take for a spin!") return output else: print("Nothing changed. You can use the old model stored at {}" "".format(os.path.abspath(old_model))) return old_model
async def train_async( domain: Optional, config: Text, training_files: Optional[Union[Text, List[Text]]], output_path: Text = DEFAULT_MODELS_PATH, force_training: bool = False, fixed_model_name: Optional[Text] = None, uncompress: bool = False, kwargs: Optional[Dict] = None, ) -> Optional[Text]: """Trains a Rasa model (Core and NLU). Args: 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_path: Output path. force_training: If `True` retrain model even if data has not changed. fixed_model_name: Name of model to be stored. uncompress: If `True` the model will not be compressed. kwargs: Additional training parameters. Returns: Path of the trained model archive. """ config = get_valid_config(config, CONFIG_MANDATORY_KEYS) train_path = tempfile.mkdtemp() old_model = model.get_latest_model(output_path) retrain_core = True retrain_nlu = True skill_imports = SkillSelector.load(config) try: domain = Domain.load(domain, skill_imports) except InvalidDomain as e: print_error(e) return None story_directory, nlu_data_directory = data.get_core_nlu_directories( training_files, skill_imports) new_fingerprint = model.model_fingerprint(config, domain, nlu_data_directory, story_directory) dialogue_data_not_present = not os.listdir(story_directory) nlu_data_not_present = not os.listdir(nlu_data_directory) if dialogue_data_not_present and nlu_data_not_present: print_error( "No training data given. Please provide dialogue and NLU data in " "order to train a Rasa model.") return if dialogue_data_not_present: print_warning( "No dialogue data present. Just a Rasa NLU model will be trained.") return _train_nlu_with_validated_data( config=config, nlu_data_directory=nlu_data_directory, output=output_path, fixed_model_name=fixed_model_name, uncompress=uncompress, ) if nlu_data_not_present: print_warning( "No NLU data present. Just a Rasa Core model will be trained.") return await _train_core_with_validated_data( domain=domain, config=config, story_directory=story_directory, output=output_path, fixed_model_name=fixed_model_name, uncompress=uncompress, kwargs=kwargs, ) if not force_training and old_model: unpacked = model.unpack_model(old_model) old_core, old_nlu = model.get_model_subdirectories(unpacked) last_fingerprint = model.fingerprint_from_path(unpacked) if not model.core_fingerprint_changed(last_fingerprint, new_fingerprint): target_path = os.path.join(train_path, "core") retrain_core = not model.merge_model(old_core, target_path) if not model.nlu_fingerprint_changed(last_fingerprint, new_fingerprint): target_path = os.path.join(train_path, "nlu") retrain_nlu = not model.merge_model(old_nlu, target_path) if force_training or retrain_core: await _train_core_with_validated_data( domain=domain, config=config, story_directory=story_directory, output=output_path, train_path=train_path, fixed_model_name=fixed_model_name, uncompress=uncompress, kwargs=kwargs, ) else: print("Dialogue data / configuration did not change. " "No need to retrain dialogue model.") if force_training or retrain_nlu: _train_nlu_with_validated_data( config=config, nlu_data_directory=nlu_data_directory, output=output_path, train_path=train_path, fixed_model_name=fixed_model_name, uncompress=uncompress, ) else: print( "NLU data / configuration did not change. No need to retrain NLU model." ) if retrain_core or retrain_nlu: output_path = create_output_path(output_path, fixed_name=fixed_model_name) model.create_package_rasa(train_path, output_path, new_fingerprint) if uncompress: output_path = decompress(output_path) print_success("Your Rasa model is trained and saved at '{}'.".format( output_path)) return output_path else: print_success( "Nothing changed. You can use the old model stored at '{}'" "".format(os.path.abspath(old_model))) return old_model