def train_core(args: argparse.Namespace,
train_path: Optional[Text] = None) -> Optional[Text]:
from rasa.train import train_core
import asyncio
loop = asyncio.get_event_loop()
output = train_path or args.out
args.domain = get_validated_path(args.domain, "domain", DEFAULT_DOMAIN_PATH)
stories = get_validated_path(args.stories, "stories", DEFAULT_DATA_PATH)
_train_path = train_path or tempfile.mkdtemp()
# Policies might be a list for the compare training. Do normal training
# if only list item was passed.
if not isinstance(args.config, list) or len(args.config) == 1:
if isinstance(args.config, list):
args.config = args.config[0]
config = get_validated_path(args.config, "config", DEFAULT_CONFIG_PATH)
return train_core(args.domain, config, stories, output, train_path)
else:
from rasa.core.train import do_compare_training
loop.run_until_complete(do_compare_training(args, stories, None))
return None
def run_core_training(args: argparse.Namespace,
train_path: Optional[Text] = None) -> Optional[Text]:
"""Trains a Rasa Core model only.
Args:
args: Command-line arguments to configure training.
train_path: Path where trained model but not unzipped model should be stored.
Returns:
Path to a trained model or `None` if training was not successful.
"""
from rasa.model_training import train_core
output = train_path or args.out
args.domain = rasa.cli.utils.get_validated_path(args.domain,
"domain",
DEFAULT_DOMAIN_PATH,
none_is_valid=True)
story_file = rasa.cli.utils.get_validated_path(args.stories,
"stories",
DEFAULT_DATA_PATH,
none_is_valid=True)
additional_arguments = extract_core_additional_arguments(args)
# Policies might be a list for the compare training. Do normal training
# if only list item was passed.
if not isinstance(args.config, list) or len(args.config) == 1:
if isinstance(args.config, list):
args.config = args.config[0]
config = _get_valid_config(args.config, CONFIG_MANDATORY_KEYS_CORE)
return train_core(
domain=args.domain,
config=config,
stories=story_file,
output=output,
train_path=train_path,
fixed_model_name=args.fixed_model_name,
additional_arguments=additional_arguments,
model_to_finetune=_model_for_finetuning(args),
finetuning_epoch_fraction=args.epoch_fraction,
)
else:
do_compare_training(args, story_file, additional_arguments)
return None
def train_core(args: argparse.Namespace,
train_path: Optional[Text] = None) -> Optional[Text]:
"""Trains a Core model.
Args:
args: Namespace arguments.
train_path: Directory where models should be stored.
Returns:
Path to a trained model or `None` if training was not successful.
"""
from rasa.train import train_core
output = train_path or args.out
args.domain = rasa.cli.utils.get_validated_path(args.domain,
"domain",
DEFAULT_DOMAIN_PATH,
none_is_valid=True)
story_file = rasa.cli.utils.get_validated_path(args.stories,
"stories",
DEFAULT_DATA_PATH,
none_is_valid=True)
additional_arguments = extract_core_additional_arguments(args)
# Policies might be a list for the compare training. Do normal training
# if only list item was passed.
if not isinstance(args.config, list) or len(args.config) == 1:
if isinstance(args.config, list):
args.config = args.config[0]
config = _get_valid_config(args.config, CONFIG_MANDATORY_KEYS_CORE)
return train_core(
domain=args.domain,
config=config,
stories=story_file,
output=output,
train_path=train_path,
fixed_model_name=args.fixed_model_name,
additional_arguments=additional_arguments,
)
else:
from rasa.core.train import do_compare_training
rasa.utils.common.run_in_loop(
do_compare_training(args, story_file, additional_arguments))
def train_core(
args: argparse.Namespace, train_path: Optional[Text] = None
) -> Optional[Text]:
from rasa.train import train_core
import asyncio
loop = asyncio.get_event_loop()
output = train_path or args.out
args.domain = get_validated_path(
args.domain, "domain", DEFAULT_DOMAIN_PATH, none_is_valid=True
)
stories = get_validated_path(
args.stories, "stories", DEFAULT_DATA_PATH, none_is_valid=True
)
_train_path = train_path or tempfile.mkdtemp()
# Policies might be a list for the compare training. Do normal training
# if only list item was passed.
if not isinstance(args.config, list) or len(args.config) == 1:
if isinstance(args.config, list):
args.config = args.config[0]
config = args.config or DEFAULT_CONFIG_PATH
return train_core(
domain=args.domain,
config=config,
stories=stories,
output=output,
train_path=train_path,
fixed_model_name=args.fixed_model_name,
uncompress=args.store_uncompressed,
kwargs=extract_additional_arguments(args),
)
else:
from rasa.core.train import do_compare_training
loop.run_until_complete(do_compare_training(args, stories, None))
return None
