def test_explain(self, get_endpoint_mock, predict_client_explain_mock):
aiplatform.init(project=_TEST_PROJECT, location=_TEST_LOCATION)
test_endpoint = models.Endpoint(_TEST_ID)
test_prediction = test_endpoint.explain(
instances=_TEST_INSTANCES,
parameters={"param": 3.0},
deployed_model_id=_TEST_MODEL_ID,
)
expected_explanations = _TEST_EXPLANATIONS
expected_explanations[0].attributions.extend(_TEST_ATTRIBUTIONS)
expected_prediction = models.Prediction(
predictions=_TEST_PREDICTION,
deployed_model_id=_TEST_ID,
explanations=expected_explanations,
)
assert expected_prediction == test_prediction
predict_client_explain_mock.assert_called_once_with(
endpoint=_TEST_ENDPOINT_NAME,
instances=_TEST_INSTANCES,
parameters={"param": 3.0},
deployed_model_id=_TEST_MODEL_ID,
)
def test_predict(self, get_endpoint_mock, predict_client_predict_mock):
test_endpoint = models.Endpoint(_TEST_ID)
test_prediction = test_endpoint.predict(instances=_TEST_INSTANCES,
parameters={"param": 3.0})
true_prediction = models.Prediction(predictions=_TEST_PREDICTION,
deployed_model_id=_TEST_ID)
assert true_prediction == test_prediction
predict_client_predict_mock.assert_called_once_with(
endpoint=_TEST_ENDPOINT_NAME,
instances=_TEST_INSTANCES,
parameters={"param": 3.0},
)
def test_dataset_create_to_model_predict(
self,
create_dataset_mock, # noqa: F811
import_data_mock, # noqa: F811
predict_client_predict_mock, # noqa: F811
mock_python_package_to_gcs, # noqa: F811
mock_pipeline_service_create, # noqa: F811
mock_model_service_get, # noqa: F811
mock_pipeline_service_get, # noqa: F811
sync,
):
aiplatform.init(
project=test_datasets._TEST_PROJECT,
staging_bucket=test_training_jobs._TEST_BUCKET_NAME,
credentials=test_training_jobs._TEST_CREDENTIALS,
)
my_dataset = aiplatform.ImageDataset.create(
display_name=test_datasets._TEST_DISPLAY_NAME,
encryption_spec_key_name=_TEST_ENCRYPTION_KEY_NAME,
sync=sync,
)
my_dataset.import_data(
gcs_source=test_datasets._TEST_SOURCE_URI_GCS,
import_schema_uri=test_datasets._TEST_IMPORT_SCHEMA_URI,
data_item_labels=test_datasets._TEST_DATA_LABEL_ITEMS,
sync=sync,
)
job = aiplatform.CustomTrainingJob(
display_name=test_training_jobs._TEST_DISPLAY_NAME,
script_path=test_training_jobs._TEST_LOCAL_SCRIPT_FILE_NAME,
container_uri=test_training_jobs._TEST_TRAINING_CONTAINER_IMAGE,
model_serving_container_image_uri=test_training_jobs.
_TEST_SERVING_CONTAINER_IMAGE,
model_serving_container_predict_route=test_training_jobs.
_TEST_SERVING_CONTAINER_PREDICTION_ROUTE,
model_serving_container_health_route=test_training_jobs.
_TEST_SERVING_CONTAINER_HEALTH_ROUTE,
)
model_from_job = job.run(
dataset=my_dataset,
base_output_dir=test_training_jobs._TEST_BASE_OUTPUT_DIR,
args=test_training_jobs._TEST_RUN_ARGS,
replica_count=1,
machine_type=test_training_jobs._TEST_MACHINE_TYPE,
accelerator_type=test_training_jobs._TEST_ACCELERATOR_TYPE,
accelerator_count=test_training_jobs._TEST_ACCELERATOR_COUNT,
model_display_name=test_training_jobs._TEST_MODEL_DISPLAY_NAME,
training_fraction_split=test_training_jobs.
_TEST_TRAINING_FRACTION_SPLIT,
validation_fraction_split=test_training_jobs.
_TEST_VALIDATION_FRACTION_SPLIT,
test_fraction_split=test_training_jobs._TEST_TEST_FRACTION_SPLIT,
sync=sync,
)
created_endpoint = models.Endpoint.create(
display_name=test_endpoints._TEST_DISPLAY_NAME,
encryption_spec_key_name=_TEST_ENCRYPTION_KEY_NAME,
sync=sync,
)
my_endpoint = model_from_job.deploy(
encryption_spec_key_name=_TEST_ENCRYPTION_KEY_NAME, sync=sync)
endpoint_deploy_return = created_endpoint.deploy(model_from_job,
sync=sync)
assert endpoint_deploy_return is None
if not sync:
my_endpoint.wait()
created_endpoint.wait()
test_prediction = created_endpoint.predict(instances=[[1.0, 2.0, 3.0],
[1.0, 3.0, 4.0]],
parameters={"param": 3.0})
true_prediction = models.Prediction(
predictions=test_endpoints._TEST_PREDICTION,
deployed_model_id=test_endpoints._TEST_ID,
)
assert true_prediction == test_prediction
predict_client_predict_mock.assert_called_once_with(
endpoint=test_endpoints._TEST_ENDPOINT_NAME,
instances=[[1.0, 2.0, 3.0], [1.0, 3.0, 4.0]],
parameters={"param": 3.0},
)
expected_dataset = gca_dataset.Dataset(
display_name=test_datasets._TEST_DISPLAY_NAME,
metadata_schema_uri=test_datasets.
_TEST_METADATA_SCHEMA_URI_NONTABULAR,
metadata=test_datasets._TEST_NONTABULAR_DATASET_METADATA,
encryption_spec=_TEST_ENCRYPTION_SPEC,
)
expected_import_config = gca_dataset.ImportDataConfig(
gcs_source=gca_io.GcsSource(
uris=[test_datasets._TEST_SOURCE_URI_GCS]),
import_schema_uri=test_datasets._TEST_IMPORT_SCHEMA_URI,
data_item_labels=test_datasets._TEST_DATA_LABEL_ITEMS,
)
create_dataset_mock.assert_called_once_with(
parent=test_datasets._TEST_PARENT,
dataset=expected_dataset,
metadata=test_datasets._TEST_REQUEST_METADATA,
)
import_data_mock.assert_called_once_with(
name=test_datasets._TEST_NAME,
import_configs=[expected_import_config])
expected_dataset.name = test_datasets._TEST_NAME
assert my_dataset._gca_resource == expected_dataset
mock_python_package_to_gcs.assert_called_once_with(
gcs_staging_dir=test_training_jobs._TEST_BUCKET_NAME,
project=test_training_jobs._TEST_PROJECT,
credentials=initializer.global_config.credentials,
)
true_args = test_training_jobs._TEST_RUN_ARGS
true_worker_pool_spec = {
"replica_count": test_training_jobs._TEST_REPLICA_COUNT,
"machine_spec": {
"machine_type": test_training_jobs._TEST_MACHINE_TYPE,
"accelerator_type": test_training_jobs._TEST_ACCELERATOR_TYPE,
"accelerator_count":
test_training_jobs._TEST_ACCELERATOR_COUNT,
},
"python_package_spec": {
"executor_image_uri":
test_training_jobs._TEST_TRAINING_CONTAINER_IMAGE,
"python_module":
source_utils._TrainingScriptPythonPackager.module_name,
"package_uris":
[test_training_jobs._TEST_OUTPUT_PYTHON_PACKAGE_PATH],
"args":
true_args,
},
}
true_fraction_split = gca_training_pipeline.FractionSplit(
training_fraction=test_training_jobs._TEST_TRAINING_FRACTION_SPLIT,
validation_fraction=test_training_jobs.
_TEST_VALIDATION_FRACTION_SPLIT,
test_fraction=test_training_jobs._TEST_TEST_FRACTION_SPLIT,
)
true_container_spec = gca_model.ModelContainerSpec(
image_uri=test_training_jobs._TEST_SERVING_CONTAINER_IMAGE,
predict_route=test_training_jobs.
_TEST_SERVING_CONTAINER_PREDICTION_ROUTE,
health_route=test_training_jobs.
_TEST_SERVING_CONTAINER_HEALTH_ROUTE,
)
true_managed_model = gca_model.Model(
display_name=test_training_jobs._TEST_MODEL_DISPLAY_NAME,
container_spec=true_container_spec,
)
true_input_data_config = gca_training_pipeline.InputDataConfig(
fraction_split=true_fraction_split,
dataset_id=my_dataset.name,
gcs_destination=gca_io.GcsDestination(
output_uri_prefix=test_training_jobs._TEST_BASE_OUTPUT_DIR),
)
true_training_pipeline = gca_training_pipeline.TrainingPipeline(
display_name=test_training_jobs._TEST_DISPLAY_NAME,
training_task_definition=schema.training_job.definition.
custom_task,
training_task_inputs=json_format.ParseDict(
{
"worker_pool_specs": [true_worker_pool_spec],
"base_output_directory": {
"output_uri_prefix":
test_training_jobs._TEST_BASE_OUTPUT_DIR
},
},
struct_pb2.Value(),
),
model_to_upload=true_managed_model,
input_data_config=true_input_data_config,
)
mock_pipeline_service_create.assert_called_once_with(
parent=initializer.global_config.common_location_path(),
training_pipeline=true_training_pipeline,
)
assert job._gca_resource is mock_pipeline_service_get.return_value
mock_model_service_get.assert_called_once_with(
name=test_training_jobs._TEST_MODEL_NAME)
assert model_from_job._gca_resource is mock_model_service_get.return_value
assert job.get_model(
)._gca_resource is mock_model_service_get.return_value
assert not job.has_failed
assert job.state == gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED