async def test_rasa_packaging(
trained_rasa_model: Text, project: Text, use_fingerprint: bool, tmp_path: Path
):
unpacked_model_path = get_model(trained_rasa_model)
os.remove(os.path.join(unpacked_model_path, FINGERPRINT_FILE_PATH))
if use_fingerprint:
fingerprint = await model_fingerprint(_project_files(project))
else:
fingerprint = None
output_path = str(tmp_path / "test.tar.gz")
create_package_rasa(unpacked_model_path, output_path, fingerprint)
unpacked = get_model(output_path)
assert (
os.path.exists(os.path.join(unpacked, FINGERPRINT_FILE_PATH)) == use_fingerprint
)
assert os.path.exists(os.path.join(unpacked, DEFAULT_CORE_SUBDIRECTORY_NAME))
assert os.path.exists(os.path.join(unpacked, "nlu-en")) # bf
assert os.path.exists(os.path.join(unpacked, "nlu-fr")) # bf
assert not os.path.exists(unpacked_model_path)
def train_nlu(config: Text, nlu_data: Text, output: Text,
train_path: Optional[Text]) -> Optional["Interpreter"]:
"""Trains a NLU model.
Args:
config: Path to the config file for NLU.
nlu_data: Path to the NLU training data.
output: Output path.
train_path: If `None` the model will be trained in a temporary
directory, otherwise in the provided directory.
Returns:
If `train_path` is given it returns the path to the model archive,
otherwise the path to the directory with the trained model files.
"""
import rasa_nlu
_train_path = train_path or tempfile.mkdtemp()
_, nlu_model, _ = rasa_nlu.train(config, nlu_data, _train_path,
project="",
fixed_model_name="nlu")
if not train_path:
nlu_data = data.get_nlu_directory(nlu_data)
output_path = create_output_path(output, prefix="nlu-")
new_fingerprint = model.model_fingerprint(config, nlu_data=nlu_data)
model.create_package_rasa(_train_path, output_path, new_fingerprint)
print_success("Your Rasa NLU model is trained and saved at '{}'."
"".format(output_path))
return nlu_model
def trained_nlu_model(request):
cfg = RasaNLUModelConfig({"pipeline": "keyword"})
trainer = Trainer(cfg)
td = training_data.load_data(DEFAULT_DATA_PATH)
trainer.train(td)
model_path = trainer.persist(NLU_MODEL_PATH)
nlu_data = data.get_nlu_directory(DEFAULT_DATA_PATH)
output_path = os.path.join(NLU_MODEL_PATH, NLU_MODEL_NAME)
new_fingerprint = model.model_fingerprint(NLU_DEFAULT_CONFIG_PATH,
nlu_data=nlu_data)
model.create_package_rasa(model_path, output_path, new_fingerprint)
def fin():
if os.path.exists(NLU_MODEL_PATH):
shutil.rmtree(NLU_MODEL_PATH)
if os.path.exists(output_path):
shutil.rmtree(output_path)
request.addfinalizer(fin)
return output_path
async def train(request):
# if set will not generate a model name but use the passed one
model_name = request.args.get("model", None)
try:
model_config, data_dict = extract_data_and_config(request)
except Exception as e:
logger.debug(traceback.format_exc())
raise ErrorResponse(
500,
"ServerError",
"An unexpected error occurred.",
details={"error": str(e)},
)
data_file = dump_to_data_file(data_dict)
config_file = dump_to_data_file(model_config, "_config")
try:
path_to_model = await data_router.start_train_process(
data_file, RasaNLUModelConfig(model_config), model_name)
# store trained model as tar.gz file
output_path = create_model_path(model_name, path_to_model)
nlu_data = data.get_nlu_directory(data_file)
new_fingerprint = model.model_fingerprint(config_file,
nlu_data=nlu_data)
model.create_package_rasa(path_to_model, output_path,
new_fingerprint)
logger.info("Rasa NLU model trained and persisted to '{}'.".format(
output_path))
await data_router.load_model(output_path)
return await response.file(output_path)
except MaxWorkerProcessError as e:
raise ErrorResponse(
403,
"NoFreeProcess",
"No process available for training.",
details={"error": str(e)},
)
except InvalidModelError as e:
raise ErrorResponse(
404,
"ModelNotFound",
"Model '{}' not found.".format(model_name),
details={"error": str(e)},
)
except TrainingException as e:
logger.debug(traceback.format_exc())
raise ErrorResponse(
500,
"ServerError",
"An unexpected error occurred.",
details={"error": str(e)},
)
def train_nlu(config: Text, nlu_data: Text, output: Text,
train_path: Optional[Text]) -> Optional[Text]:
"""Trains a NLU model.
Args:
config: Path to the config file for NLU.
nlu_data: Path to the NLU training data.
output: Output path.
train_path: If `None` the model will be trained in a temporary
directory, otherwise in the provided directory.
Returns:
If `train_path` is given it returns the path to the model archive,
otherwise the path to the directory with the trained model files.
"""
import rasa.nlu.train
config = get_valid_config(config, CONFIG_MANDATORY_KEYS_NLU)
if not train_path:
# training NLU only hence the training files still have to be selected
skill_imports = SkillSelector.load(config)
nlu_data_directory = data.get_nlu_directory(nlu_data, skill_imports)
else:
nlu_data_directory = nlu_data
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:
output_path = create_output_path(output, prefix="nlu-")
new_fingerprint = model.model_fingerprint(config,
nlu_data=nlu_data_directory)
model.create_package_rasa(_train_path, output_path, new_fingerprint)
print_success(
"Your Rasa NLU model is trained and saved at '{}'.".format(
output_path))
return output_path
return _train_path
def set_fingerprint(trained_rasa_model: Text, fingerprint: Fingerprint,
tmp_path: Path) -> Text:
unpacked_model_path = get_model(trained_rasa_model)
os.remove(os.path.join(unpacked_model_path, FINGERPRINT_FILE_PATH))
output_path = str(tmp_path / "test.tar.gz")
create_package_rasa(unpacked_model_path, output_path, fingerprint)
return output_path
def set_fingerprint(trained_rasa_model: Text, fingerprint: Fingerprint) -> Text:
unpacked_model_path = get_model(trained_rasa_model)
os.remove(os.path.join(unpacked_model_path, FINGERPRINT_FILE_PATH))
tempdir = tempfile.mkdtemp()
output_path = os.path.join(tempdir, "test.tar.gz")
create_package_rasa(unpacked_model_path, output_path, fingerprint)
return output_path
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
async def train_core_async(
domain: Text,
config: Text,
stories: Text,
output: Text,
train_path: Optional[Text] = None,
kwargs: Optional[Dict] = None,
) -> Optional[Text]:
"""Trains a Core model.
Args:
domain: Path to the domain file.
config: Path to the config file for Core.
stories: Path to the Core training data.
output: Output path.
train_path: If `None` the model will be trained in a temporary
directory, otherwise in the provided directory.
kwargs: Additional training parameters.
Returns:
If `train_path` is given it returns the path to the model archive,
otherwise the path to the directory with the trained model files.
"""
import rasa.core.train
config = get_valid_config(config, CONFIG_MANDATORY_KEYS_CORE)
_train_path = train_path or tempfile.mkdtemp()
# normal (not compare) training
core_model = await rasa.core.train(
domain_file=domain,
stories_file=data.get_core_directory(stories),
output_path=os.path.join(_train_path, "core"),
policy_config=config,
kwargs=kwargs,
)
if not train_path:
# Only Core was trained.
stories = data.get_core_directory(stories)
output_path = create_output_path(output, prefix="core-")
new_fingerprint = model.model_fingerprint(config,
domain,
stories=stories)
model.create_package_rasa(_train_path, output_path, new_fingerprint)
print_success(
"Your Rasa Core model is trained and saved at '{}'.".format(
output_path))
return core_model
def _async_train(self, config, nlu_data, model_name):
training_start = timer()
with self.lock:
self.training_status[model_name] = {
"status": "TRAINING",
}
data = self.data_reader.read_from_json({'rasa_nlu_data': nlu_data})
with self.interpreter_cache.lock:
trainer = Trainer(RasaNLUModelConfig(config), self.interpreter_cache.component_builder)
interpreter = trainer.train(data)
tempdir = tempfile.mkdtemp()
trainer.persist(tempdir, None, "nlu")
_model_package = create_package_rasa(tempdir, os.path.join("models", model_name))
self.interpreter_cache.store(model_name, interpreter)
with self.lock:
training_end = timer()
self.training_status[model_name] = {
"status": "READY",
"training_time": f"{training_end - training_start:.2f}"
}
def _package_model(
new_fingerprint: Fingerprint,
output_path: Text,
train_path: Text,
fixed_model_name: Optional[Text] = None,
model_prefix: Text = "",
):
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)
print_success("Your Rasa model is trained and saved at '{}'.".format(
os.path.abspath(output_path)))
return output_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 test_rasa_packaging(trained_model, project, use_fingerprint):
unpacked_model_path = get_model(trained_model)
os.remove(os.path.join(unpacked_model_path, FINGERPRINT_FILE_PATH))
if use_fingerprint:
fingerprint = model_fingerprint(**_project_files(project))
else:
fingerprint = None
tempdir = tempfile.mkdtemp()
output_path = os.path.join(tempdir, "test.tar.gz")
create_package_rasa(unpacked_model_path, output_path, fingerprint)
unpacked = get_model(output_path)
assert (os.path.exists(os.path.join(
unpacked, FINGERPRINT_FILE_PATH)) == use_fingerprint)
assert os.path.exists(os.path.join(unpacked, "core"))
assert os.path.exists(os.path.join(unpacked, "nlu"))
assert not os.path.exists(unpacked_model_path)
async def train_core_async(domain: Text, config: Text, stories: Text,
output: Text,
train_path: Optional[Text]) -> Optional[Text]:
"""Trains a Core model.
Args:
domain: Path to the domain file.
config: Path to the config file for Core.
stories: Path to the Core training data.
output: Output path.
train_path: If `None` the model will be trained in a temporary
directory, otherwise in the provided directory.
Returns:
If `train_path` is given it returns the path to the model archive,
otherwise the path to the directory with the trained model files.
"""
import rasa_core.train
# normal (not compare) training
core_model = await rasa_core.train(domain_file=domain,
stories_file=stories,
output_path=os.path.join(
train_path, "core"),
policy_config=config)
if not train_path:
# Only Core was trained.
stories = data.get_core_directory(stories)
output_path = create_output_path(output, prefix="core-")
new_fingerprint = model.model_fingerprint(config,
domain,
stories=stories)
model.create_package_rasa(train_path, output_path, new_fingerprint)
print_success("Your Rasa Core model is trained and saved at '{}'."
"".format(output_path))
return core_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: 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_core_async(
domain: Union[Domain, Text],
config: Text,
stories: Text,
output: Text,
train_path: Optional[Text] = None,
kwargs: Optional[Dict] = None,
) -> Optional[Text]:
"""Trains a Core model.
Args:
domain: Path to the domain file.
config: Path to the config file for Core.
stories: Path to the Core training data.
output: Output path.
train_path: If `None` the model will be trained in a temporary
directory, otherwise in the provided directory.
kwargs: Additional training parameters.
Returns:
If `train_path` is given it returns the path to the model archive,
otherwise the path to the directory with the trained model files.
"""
import rasa.core.train
config = get_valid_config(config, CONFIG_MANDATORY_KEYS_CORE)
_train_path = train_path or tempfile.mkdtemp()
if isinstance(Domain, str) or not train_path:
skill_imports = SkillSelector.load(config)
domain = Domain.load(domain, skill_imports)
story_directory = data.get_core_directory(stories, skill_imports)
else:
story_directory = stories
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
# 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-")
new_fingerprint = model.model_fingerprint(config,
domain,
stories=story_directory)
model.create_package_rasa(_train_path, output_path, new_fingerprint)
print_success(
"Your Rasa Core model is trained and saved at '{}'.".format(
output_path))
return output_path
return _train_path
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
