def _load_model(
model_path: Union[Text,
Path]) -> Tuple[Text, ModelMetadata, GraphRunner]:
"""Unpacks a model from a given path using the graph model loader."""
try:
if os.path.isfile(model_path):
model_tar = model_path
else:
model_tar = get_latest_model(model_path)
if not model_tar:
raise ModelNotFound(
f"No model found at path '{model_path}'.")
except TypeError:
raise ModelNotFound(f"Model {model_path} can not be loaded.")
logger.info(f"Loading model {model_tar}...")
with tempfile.TemporaryDirectory() as temporary_directory:
try:
metadata, runner = loader.load_predict_graph_runner(
Path(temporary_directory),
Path(model_tar),
LocalModelStorage,
DaskGraphRunner,
)
return os.path.basename(model_tar), metadata, runner
except tarfile.ReadError:
raise ModelNotFound(f"Model {model_path} can not be loaded.")
def test_loader_loads_graph_runner(
default_model_storage: ModelStorage,
temp_cache: TrainingCache,
tmp_path: Path,
tmp_path_factory: TempPathFactory,
domain_path: Path,
):
graph_trainer = GraphTrainer(
model_storage=default_model_storage,
cache=temp_cache,
graph_runner_class=DaskGraphRunner,
)
test_value = "test_value"
train_schema = GraphSchema(
{
"train": SchemaNode(
needs={},
uses=PersistableTestComponent,
fn="train",
constructor_name="create",
config={"test_value": test_value},
is_target=True,
),
"load": SchemaNode(
needs={"resource": "train"},
uses=PersistableTestComponent,
fn="run_inference",
constructor_name="load",
config={},
),
}
)
predict_schema = GraphSchema(
{
"load": SchemaNode(
needs={},
uses=PersistableTestComponent,
fn="run_inference",
constructor_name="load",
config={},
is_target=True,
resource=Resource("train"),
)
}
)
output_filename = tmp_path / "model.tar.gz"
importer = TrainingDataImporter.load_from_dict(
training_data_paths=[], domain_path=str(domain_path)
)
trained_at = datetime.utcnow()
with freezegun.freeze_time(trained_at):
model_metadata = graph_trainer.train(
GraphModelConfiguration(
train_schema=train_schema,
predict_schema=predict_schema,
training_type=TrainingType.BOTH,
language=None,
core_target=None,
nlu_target=None,
),
importer=importer,
output_filename=output_filename,
)
assert isinstance(model_metadata, ModelMetadata)
assert output_filename.is_file()
loaded_model_storage_path = tmp_path_factory.mktemp("loaded model storage")
model_metadata, loaded_predict_graph_runner = loader.load_predict_graph_runner(
storage_path=loaded_model_storage_path,
model_archive_path=output_filename,
model_storage_class=LocalModelStorage,
graph_runner_class=DaskGraphRunner,
)
assert loaded_predict_graph_runner.run() == {"load": test_value}
assert model_metadata.predict_schema == predict_schema
assert model_metadata.train_schema == train_schema
assert model_metadata.model_id
assert model_metadata.domain.as_dict() == Domain.from_path(domain_path).as_dict()
assert model_metadata.rasa_open_source_version == rasa.__version__
assert model_metadata.trained_at == trained_at