def test_predict(self, get_endpoint_mock, predict_client_predict_mock):
aiplatform.init(project=_TEST_PROJECT, location=_TEST_LOCATION)
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_constructor_with_custom_credentials(self,
create_endpoint_client_mock):
creds = auth_credentials.AnonymousCredentials()
models.Endpoint(_TEST_ENDPOINT_NAME, credentials=creds)
create_endpoint_client_mock.assert_has_calls([
mock.call(
client_class=utils.EndpointClientWithOverride,
credentials=creds,
location_override=_TEST_LOCATION,
),
mock.call(
client_class=utils.PredictionClientWithOverride,
credentials=creds,
location_override=_TEST_LOCATION,
prediction_client=True,
),
])
def test_deploy_with_explanations(self,
deploy_model_with_explanations_mock,
sync):
aiplatform.init(project=_TEST_PROJECT, location=_TEST_LOCATION)
test_endpoint = models.Endpoint(_TEST_ENDPOINT_NAME)
test_model = models.Model(_TEST_ID)
test_endpoint.deploy(
model=test_model,
machine_type=_TEST_MACHINE_TYPE,
accelerator_type=_TEST_ACCELERATOR_TYPE,
accelerator_count=_TEST_ACCELERATOR_COUNT,
explanation_metadata=_TEST_EXPLANATION_METADATA,
explanation_parameters=_TEST_EXPLANATION_PARAMETERS,
sync=sync,
)
if not sync:
test_endpoint.wait()
expected_machine_spec = gca_machine_resources_v1beta1.MachineSpec(
machine_type=_TEST_MACHINE_TYPE,
accelerator_type=_TEST_ACCELERATOR_TYPE,
accelerator_count=_TEST_ACCELERATOR_COUNT,
)
expected_dedicated_resources = gca_machine_resources_v1beta1.DedicatedResources(
machine_spec=expected_machine_spec,
min_replica_count=1,
max_replica_count=1,
)
expected_deployed_model = gca_endpoint_v1beta1.DeployedModel(
dedicated_resources=expected_dedicated_resources,
model=test_model.resource_name,
display_name=None,
explanation_spec=gca_endpoint_v1beta1.explanation.ExplanationSpec(
metadata=_TEST_EXPLANATION_METADATA,
parameters=_TEST_EXPLANATION_PARAMETERS,
),
)
deploy_model_with_explanations_mock.assert_called_once_with(
endpoint=test_endpoint.resource_name,
deployed_model=expected_deployed_model,
traffic_split={"0": 100},
metadata=(),
)
def test_constructor(self, create_endpoint_client_mock):
aiplatform.init(
project=_TEST_PROJECT,
location=_TEST_LOCATION,
credentials=_TEST_CREDENTIALS,
)
models.Endpoint(_TEST_ENDPOINT_NAME)
create_endpoint_client_mock.assert_has_calls([
mock.call(
client_class=utils.EndpointClientWithOverride,
credentials=initializer.global_config.credentials,
location_override=_TEST_LOCATION,
),
mock.call(
client_class=utils.PredictionClientWithOverride,
credentials=None,
location_override=_TEST_LOCATION,
prediction_client=True,
),
])
def test_undeploy(self, undeploy_model_mock, sync):
with mock.patch.object(endpoint_service_client.EndpointServiceClient,
"get_endpoint") as get_endpoint_mock:
get_endpoint_mock.return_value = gca_endpoint.Endpoint(
display_name=_TEST_DISPLAY_NAME,
name=_TEST_ENDPOINT_NAME,
traffic_split={"model1": 100},
)
test_endpoint = models.Endpoint(_TEST_ENDPOINT_NAME)
assert dict(test_endpoint.traffic_split) == {"model1": 100}
test_endpoint.undeploy("model1", sync=sync)
if not sync:
test_endpoint.wait()
undeploy_model_mock.assert_called_once_with(
endpoint=test_endpoint.resource_name,
deployed_model_id="model1",
traffic_split={},
# traffic_split={"model1": 0},
metadata=(),
)
def test_deploy_with_max_replica_count(self, deploy_model_mock, sync):
test_endpoint = models.Endpoint(_TEST_ENDPOINT_NAME)
test_model = models.Model(_TEST_ID)
test_endpoint.deploy(model=test_model, max_replica_count=2, sync=sync)
if not sync:
test_endpoint.wait()
automatic_resources = gca_machine_resources.AutomaticResources(
min_replica_count=1,
max_replica_count=2,
)
deployed_model = gca_endpoint.DeployedModel(
automatic_resources=automatic_resources,
model=test_model.resource_name,
display_name=None,
)
deploy_model_mock.assert_called_once_with(
endpoint=test_endpoint.resource_name,
deployed_model=deployed_model,
traffic_split={"0": 100},
metadata=(),
)
def test_deploy_with_traffic_split(self, deploy_model_mock, sync):
aiplatform.init(project=_TEST_PROJECT, location=_TEST_LOCATION)
with mock.patch.object(endpoint_service_client.EndpointServiceClient,
"get_endpoint") as get_endpoint_mock:
get_endpoint_mock.return_value = gca_endpoint.Endpoint(
display_name=_TEST_DISPLAY_NAME,
name=_TEST_ENDPOINT_NAME,
traffic_split={"model1": 100},
)
test_endpoint = models.Endpoint(_TEST_ENDPOINT_NAME)
test_model = models.Model(_TEST_ID)
test_endpoint.deploy(model=test_model,
traffic_split={
"model1": 30,
"0": 70
},
sync=sync)
if not sync:
test_endpoint.wait()
automatic_resources = gca_machine_resources.AutomaticResources(
min_replica_count=1,
max_replica_count=1,
)
deployed_model = gca_endpoint.DeployedModel(
automatic_resources=automatic_resources,
model=test_model.resource_name,
display_name=None,
)
deploy_model_mock.assert_called_once_with(
endpoint=test_endpoint.resource_name,
deployed_model=deployed_model,
traffic_split={
"model1": 30,
"0": 70
},
metadata=(),
)
def test_deploy_with_dedicated_resources(self, deploy_model_mock, sync):
aiplatform.init(project=_TEST_PROJECT, location=_TEST_LOCATION)
test_endpoint = models.Endpoint(_TEST_ENDPOINT_NAME)
test_model = models.Model(_TEST_ID)
test_endpoint.deploy(
model=test_model,
machine_type=_TEST_MACHINE_TYPE,
accelerator_type=_TEST_ACCELERATOR_TYPE,
accelerator_count=_TEST_ACCELERATOR_COUNT,
service_account=_TEST_SERVICE_ACCOUNT,
sync=sync,
)
if not sync:
test_endpoint.wait()
expected_machine_spec = gca_machine_resources.MachineSpec(
machine_type=_TEST_MACHINE_TYPE,
accelerator_type=_TEST_ACCELERATOR_TYPE,
accelerator_count=_TEST_ACCELERATOR_COUNT,
)
expected_dedicated_resources = gca_machine_resources.DedicatedResources(
machine_spec=expected_machine_spec,
min_replica_count=1,
max_replica_count=1,
)
expected_deployed_model = gca_endpoint.DeployedModel(
dedicated_resources=expected_dedicated_resources,
model=test_model.resource_name,
display_name=None,
service_account=_TEST_SERVICE_ACCOUNT,
)
deploy_model_mock.assert_called_once_with(
endpoint=test_endpoint.resource_name,
deployed_model=expected_deployed_model,
traffic_split={"0": 100},
metadata=(),
)
def test_undeploy_raises_error_on_zero_leftover_traffic(self, sync):
"""
Attempting to undeploy model with 100% traffic on an Endpoint with
multiple models deployed without an updated traffic_split should
raise an informative error.
"""
traffic_remaining = _TEST_TRAFFIC_SPLIT[_TEST_ID_2]
assert traffic_remaining == 100 # Confirm this model has all traffic
assert sum(
_TEST_TRAFFIC_SPLIT.values()) == 100 # Mock traffic sums to 100%
with pytest.raises(ValueError) as e:
test_endpoint = models.Endpoint(_TEST_ENDPOINT_NAME)
test_endpoint.undeploy(
deployed_model_id=_TEST_ID_2,
sync=sync,
)
assert e.match(
f"Undeploying deployed model '{_TEST_ID_2}' would leave the remaining "
f"traffic split at 0%.")
def test_explain(self, get_endpoint_mock, predict_client_explain_mock):
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_constructor_with_custom_project(self, get_endpoint_mock):
models.Endpoint(endpoint_name=_TEST_ID, project=_TEST_PROJECT_2)
test_endpoint_resource_name = endpoint_service_client.EndpointServiceClient.endpoint_path(
_TEST_PROJECT_2, _TEST_LOCATION, _TEST_ID)
get_endpoint_mock.assert_called_with(name=test_endpoint_resource_name)
def test_constructor_with_endpoint_name(self, get_endpoint_mock):
models.Endpoint(_TEST_ENDPOINT_NAME)
get_endpoint_mock.assert_called_with(name=_TEST_ENDPOINT_NAME)
def test_deploy_raise_error_traffic_split(self, sync):
with pytest.raises(ValueError):
aiplatform.init(project=_TEST_PROJECT, location=_TEST_LOCATION)
test_endpoint = models.Endpoint(_TEST_ENDPOINT_NAME)
test_model = models.Model(_TEST_ID)
test_endpoint.deploy(model=test_model, traffic_split={"a": 99}, sync=sync)
def test_deploy_raise_error_max_replica(self, sync):
with pytest.raises(ValueError):
aiplatform.init(project=_TEST_PROJECT, location=_TEST_LOCATION)
test_endpoint = models.Endpoint(_TEST_ENDPOINT_NAME)
test_model = models.Model(_TEST_ID)
test_endpoint.deploy(model=test_model, max_replica_count=-2, sync=sync)
def test_constructor_with_endpoint_name(self, get_endpoint_mock):
aiplatform.init(project=_TEST_PROJECT, location=_TEST_LOCATION)
models.Endpoint(_TEST_ENDPOINT_NAME)
get_endpoint_mock.assert_called_with(name=_TEST_ENDPOINT_NAME)
def test_lazy_constructor_with_endpoint_name(self, get_endpoint_mock):
ep = models.Endpoint(_TEST_ENDPOINT_NAME)
assert ep._gca_resource.name == _TEST_ENDPOINT_NAME
assert ep._skipped_getter_call()
assert not get_endpoint_mock.called
def test_undeploy_raise_error_traffic_split_total(self, sync):
with pytest.raises(ValueError):
test_endpoint = models.Endpoint(_TEST_ENDPOINT_NAME)
test_endpoint.undeploy(deployed_model_id="model1",
traffic_split={"model2": 99},
sync=sync)
def test_constructor_with_custom_location(self, get_endpoint_mock):
aiplatform.init(project=_TEST_PROJECT, location=_TEST_LOCATION)
models.Endpoint(endpoint_name=_TEST_ID, location=_TEST_LOCATION_2)
test_endpoint_resource_name = endpoint_service_client.EndpointServiceClient.endpoint_path(
_TEST_PROJECT, _TEST_LOCATION_2, _TEST_ID)
get_endpoint_mock.assert_called_with(name=test_endpoint_resource_name)
