def execute(self, context: "Context"):
self.hook = AutoMLHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
model = self.hook.create_auto_ml_video_training_job(
project_id=self.project_id,
region=self.region,
display_name=self.display_name,
dataset=datasets.VideoDataset(dataset_name=self.dataset_id),
prediction_type=self.prediction_type,
model_type=self.model_type,
labels=self.labels,
training_encryption_spec_key_name=self.training_encryption_spec_key_name,
model_encryption_spec_key_name=self.model_encryption_spec_key_name,
training_fraction_split=self.training_fraction_split,
test_fraction_split=self.test_fraction_split,
training_filter_split=self.training_filter_split,
test_filter_split=self.test_filter_split,
model_display_name=self.model_display_name,
model_labels=self.model_labels,
sync=self.sync,
)
result = Model.to_dict(model)
model_id = self.hook.extract_model_id(result)
VertexAIModelLink.persist(context=context, task_instance=self, model_id=model_id)
return result
def test_import_data(self, import_data_mock, sync):
aiplatform.init(project=_TEST_PROJECT)
my_dataset = datasets.VideoDataset(dataset_name=_TEST_NAME)
my_dataset.import_data(
gcs_source=[_TEST_SOURCE_URI_GCS],
import_schema_uri=_TEST_IMPORT_SCHEMA_URI_VIDEO,
sync=sync,
)
if not sync:
my_dataset.wait()
expected_import_config = gca_dataset.ImportDataConfig(
gcs_source=gca_io.GcsSource(uris=[_TEST_SOURCE_URI_GCS]),
import_schema_uri=_TEST_IMPORT_SCHEMA_URI_VIDEO,
)
import_data_mock.assert_called_once_with(
name=_TEST_NAME, import_configs=[expected_import_config])
def test_init_dataset_non_video(self):
aiplatform.init(project=_TEST_PROJECT)
with pytest.raises(ValueError):
datasets.VideoDataset(dataset_name=_TEST_NAME)
def test_init_dataset_video(self, get_dataset_video_mock):
aiplatform.init(project=_TEST_PROJECT)
datasets.VideoDataset(dataset_name=_TEST_NAME)
get_dataset_video_mock.assert_called_once_with(name=_TEST_NAME)