def inner(
train_schema: GraphSchema,
cache: Optional[TrainingCache] = None,
model_storage: Optional[ModelStorage] = None,
path: Optional[Path] = None,
force_retraining: bool = False,
) -> Path:
if not path:
path = tmp_path_factory.mktemp("model_storage_path")
if not model_storage:
model_storage = LocalModelStorage.create(path)
if not cache:
cache = local_cache_creator(path)
graph_trainer = GraphTrainer(
model_storage=model_storage, cache=cache, graph_runner_class=DaskGraphRunner
)
output_filename = path / "model.tar.gz"
graph_trainer.train(
GraphModelConfiguration(
train_schema=train_schema,
predict_schema=GraphSchema({}),
language=None,
core_target=None,
nlu_target="nlu",
training_type=TrainingType.BOTH,
),
importer=TrainingDataImporter.load_from_dict(domain_path=str(domain_path)),
output_filename=output_filename,
force_retraining=force_retraining,
)
assert output_filename.is_file()
return output_filename
def inner(
train_schema: GraphSchema,
cache: Optional[TrainingCache] = None,
model_storage: Optional[ModelStorage] = None,
path: Optional[Path] = None,
) -> Path:
if not path:
path = tmp_path_factory.mktemp("model_storage_path")
if not model_storage:
model_storage = LocalModelStorage.create(path)
if not cache:
cache = local_cache_creator(path)
graph_trainer = GraphTrainer(
model_storage=model_storage,
cache=cache,
graph_runner_class=DaskGraphRunner,
)
output_filename = path / "model.tar.gz"
graph_trainer.train(
train_schema=train_schema,
predict_schema=GraphSchema({}),
domain_path=domain_path,
output_filename=output_filename,
)
assert output_filename.is_file()
return output_filename
def test_create_package_with_non_existing_parent(tmp_path: Path):
storage = LocalModelStorage.create(tmp_path)
model_file = tmp_path / "new" / "sub" / "dir" / "file.tar.gz"
storage.create_model_package(
model_file,
GraphModelConfiguration(GraphSchema({}), GraphSchema({}),
TrainingType.BOTH, None, None, "nlu"),
Domain.empty(),
)
assert model_file.is_file()
def trained_ted(
tmp_path_factory: TempPathFactory, moodbot_domain_path: Path,
) -> TEDPolicyGraphComponent:
training_files = "data/test_moodbot/data/stories.yml"
domain = Domain.load(moodbot_domain_path)
trackers = training.load_data(str(training_files), domain)
policy = TEDPolicyGraphComponent.create(
{**TEDPolicyGraphComponent.get_default_config(), EPOCHS: 1},
LocalModelStorage.create(tmp_path_factory.mktemp("storage")),
Resource("ted"),
ExecutionContext(GraphSchema({})),
)
policy.train(trackers, domain)
return policy
def default_model_storage(tmp_path: Path) -> ModelStorage:
return LocalModelStorage.create(tmp_path)
def default_model_storage(tmp_path: Path,
monkeypatch: MonkeyPatch) -> ModelStorage:
return LocalModelStorage.create(tmp_path)