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