async def start_training_process(
self,
team: Text = config.team_name,
project_id: Text = config.project_name) -> Any:
url = "/model/train"
nlu_training_data = self.data_service.get_nlu_training_data_object(
should_include_lookup_table_entries=True, )
responses: Optional[Dict] = None
if self.data_service.training_data_contains_retrieval_intents(
nlu_training_data):
try:
from rasa.utils import io as io_utils
responses = io_utils.read_yaml(self._get_responses_file_name())
except ValueError as e:
rasa_cli_utils.print_error(
"Could not complete training request as your training data contains "
"retrieval intents of the form 'intent/response_key' but there is no "
"responses file found.")
raise ValueError(
f"Unable to train on data containing retrieval intents. "
f"Details:\n{e}")
nlu_training_data = nlu_training_data.filter_training_examples(
lambda ex: ex.get(RESPONSE_KEY_ATTRIBUTE) is None)
md_formatted_data = nlu_training_data.nlu_as_markdown().strip()
stories = self.story_services.fetch_stories(None)
combined_stories = self.story_services.get_stories_markdown(stories)
domain = self.domain_service.get_or_create_domain()
domain_yaml = dump_obj_as_yaml_to_string(domain)
_config = self.settings_service.get_config(team, project_id)
config_yaml = dump_obj_as_yaml_to_string(_config)
payload = dict(
domain=domain_yaml,
config=config_yaml,
nlu=md_formatted_data,
stories=combined_stories,
responses=dump_obj_as_yaml_to_string(responses),
force=False,
save_to_default_model_directory=False,
)
async with self._session() as session:
response = await session.post(
self._request_url(url),
params=self._query_parameters(),
json=payload,
timeout=24 * 60 * 60, # 24 hours
)
return await response.read()
def as_yaml(self, clean_before_dump=False):
if clean_before_dump:
domain_data = self.cleaned_domain()
else:
domain_data = self.as_dict()
return utils.dump_obj_as_yaml_to_string(domain_data)
def as_yaml(self):
domain_data = self.as_dict()
return utils.dump_obj_as_yaml_to_string(domain_data)
