async def _train_core_with_validated_data( domain: Domain, config: Text, story_directory: Text, output: Text, train_path: Optional[Text] = None, fixed_model_name: Optional[Text] = None, uncompress: bool = False, kwargs: Optional[Dict] = None, ) -> Optional[Text]: """Train Core with validated training and config data.""" import rasa.core.train if not os.listdir(story_directory): print_error( "No dialogue data given. Please provide dialogue data in order to " "train a Rasa Core model.") return _train_path = train_path or tempfile.mkdtemp() # normal (not compare) training print_color("Start training dialogue model ...", color=bcolors.OKBLUE) await rasa.core.train( domain_file=domain, stories_file=story_directory, output_path=os.path.join(_train_path, "core"), policy_config=config, kwargs=kwargs, ) print_color("Done.", color=bcolors.OKBLUE) if not train_path: # Only Core was trained. output_path = create_output_path(output, prefix="core-", fixed_name=fixed_model_name) new_fingerprint = model.model_fingerprint(config, domain, stories=story_directory) model.create_package_rasa(_train_path, output_path, new_fingerprint) if uncompress: output_path = decompress(output_path) print_success( "Your Rasa Core model is trained and saved at '{}'.".format( output_path)) return output_path return _train_path
def _train_nlu_with_validated_data( config: Text, nlu_data_directory: Text, output: Text, train_path: Optional[Text] = None, fixed_model_name: Optional[Text] = None, uncompress: bool = False, ) -> Optional[Text]: """Train NLU with validated training and config data.""" import rasa.nlu.train if not os.listdir(nlu_data_directory): print_error( "No NLU data given. Please provide NLU data in order to train " "a Rasa NLU model.") return _train_path = train_path or tempfile.mkdtemp() print_color("Start training NLU model ...", color=bcolors.OKBLUE) _, nlu_model, _ = rasa.nlu.train(config, nlu_data_directory, _train_path, fixed_model_name="nlu") print_color("Done.", color=bcolors.OKBLUE) if not train_path: # Only NLU was trained output_path = create_output_path(output, prefix="nlu-", fixed_name=fixed_model_name) new_fingerprint = model.model_fingerprint(config, nlu_data=nlu_data_directory) model.create_package_rasa(_train_path, output_path, new_fingerprint) if uncompress: output_path = decompress(output_path) print_success( "Your Rasa NLU model is trained and saved at '{}'.".format( output_path)) return output_path return _train_path
def _package_model( new_fingerprint: Fingerprint, output_path: Text, train_path: Text, fixed_model_name: Optional[Text] = None, model_prefix: Text = "", uncompress: bool = False, ): output_path = create_output_path(output_path, prefix=model_prefix, 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( os.path.abspath(output_path))) return output_path
def test_decompress(trained_model): output_path = decompress(trained_model) assert os.path.exists(output_path) assert os.path.isdir(output_path) assert not os.path.exists(trained_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