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make AutoML.deploy use self.sagemaker_session by default #1311

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Merged
merged 1 commit into from
Feb 25, 2020
Merged

make AutoML.deploy use self.sagemaker_session by default #1311

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5 changes: 3 additions & 2 deletions src/sagemaker/automl/automl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -216,8 +216,7 @@ def deploy(
created from it.
sagemaker_session (sagemaker.session.Session): A SageMaker Session
object, used for SageMaker interactions (default: None). If not
specified, one is created using the default AWS configuration
chain.
specified, the one originally associated with the ``AutoML`` instance is used.
name (str): The pipeline model name. If None, a default model name will
be selected on each ``deploy``.
endpoint_name (str): The name of the endpoint to create (default:
Expand Down Expand Up @@ -248,6 +247,8 @@ def deploy(
If ``predictor_cls`` is specified, the invocation of ``self.predictor_cls`` on
the created endpoint name. Otherwise, ``None``.
"""
sagemaker_session = sagemaker_session or self.sagemaker_session

if candidate is None:
candidate_dict = self.best_candidate()
candidate = CandidateEstimator(candidate_dict, sagemaker_session=sagemaker_session)
Expand Down
9 changes: 7 additions & 2 deletions tests/unit/sagemaker/automl/test_auto_ml.py
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Expand Up @@ -477,16 +477,19 @@ def test_deploy(sagemaker_session, candidate_mock):
)


def test_deploy_optional_args(sagemaker_session, candidate_mock):
@patch("sagemaker.automl.automl.CandidateEstimator")
def test_deploy_optional_args(candidate_estimator, sagemaker_session, candidate_mock):
candidate_estimator.return_value = candidate_mock

auto_ml = AutoML(
role=ROLE, target_attribute_name=TARGET_ATTRIBUTE_NAME, sagemaker_session=sagemaker_session
)
auto_ml.best_candidate = Mock(name="best_candidate", return_value=CANDIDATE_DICT)
auto_ml._deploy_inference_pipeline = Mock("_deploy_inference_pipeline", return_value=None)

auto_ml.deploy(
initial_instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
candidate=CANDIDATE_DICT,
sagemaker_session=sagemaker_session,
name=JOB_NAME,
endpoint_name=JOB_NAME,
Expand Down Expand Up @@ -515,6 +518,8 @@ def test_deploy_optional_args(sagemaker_session, candidate_mock):
predictor_cls=RealTimePredictor,
)

candidate_estimator.assert_called_with(CANDIDATE_DICT, sagemaker_session=sagemaker_session)


def test_candidate_estimator_get_steps(sagemaker_session):
candidate_estimator = CandidateEstimator(CANDIDATE_DICT, sagemaker_session=sagemaker_session)
Expand Down