fix: allow predictor to be returned from AutoML.deploy() (aws#1220)

Author: laurenyu

src/sagemaker/automl/automl.py

@@ -201,6 +201,7 @@ def deploy(
         vpc_config=None,
         enable_network_isolation=False,
         model_kms_key=None,
+        predictor_cls=None,
     ):
         """Deploy a candidate to a SageMaker Inference Pipeline and return a Predictor
 
@@ -237,10 +238,15 @@ def deploy(
                 training cluster for distributed training. Default: False
             model_kms_key (str): KMS key ARN used to encrypt the repacked
                 model archive file if the model is repacked
+            predictor_cls (callable[string, sagemaker.session.Session]): A
+                function to call to create a predictor (default: None). If
+                specified, ``deploy()``  returns the result of invoking this
+                function on the created endpoint name.
 
         Returns:
-            callable[string, sagemaker.session.Session]: Invocation of
-            ``self.predictor_cls`` on the created endpoint name.
+            callable[string, sagemaker.session.Session] or ``None``:
+                If ``predictor_cls`` is specified, the invocation of ``self.predictor_cls`` on
+                the created endpoint name. Otherwise, ``None``.
         """
         if candidate is None:
             candidate_dict = self.best_candidate()
@@ -264,6 +270,7 @@ def deploy(
             vpc_config=vpc_config,
             enable_network_isolation=enable_network_isolation,
             model_kms_key=model_kms_key,
+            predictor_cls=predictor_cls,
         )
 
     def _check_problem_type_and_job_objective(self, problem_type, job_objective):
@@ -299,6 +306,7 @@ def _deploy_inference_pipeline(
         vpc_config=None,
         enable_network_isolation=False,
         model_kms_key=None,
+        predictor_cls=None,
     ):
         """Deploy a SageMaker Inference Pipeline.
 
@@ -329,6 +337,10 @@ def _deploy_inference_pipeline(
                 contains "SecurityGroupIds", "Subnets"
             model_kms_key (str): KMS key ARN used to encrypt the repacked
                 model archive file if the model is repacked
+            predictor_cls (callable[string, sagemaker.session.Session]): A
+                function to call to create a predictor (default: None). If
+                specified, ``deploy()``  returns the result of invoking this
+                function on the created endpoint name.
         """
         # construct Model objects
         models = []
@@ -352,6 +364,7 @@ def _deploy_inference_pipeline(
         pipeline = PipelineModel(
             models=models,
             role=self.role,
+            predictor_cls=predictor_cls,
             name=name,
             vpc_config=vpc_config,
             sagemaker_session=sagemaker_session or self.sagemaker_session,

tests/unit/sagemaker/automl/test_auto_ml.py

@@ -15,6 +15,7 @@
 import pytest
 from mock import Mock, patch
 from sagemaker import AutoML, AutoMLJob, AutoMLInput, CandidateEstimator
+from sagemaker.predictor import RealTimePredictor
 
 MODEL_DATA = "s3://bucket/model.tar.gz"
 MODEL_IMAGE = "mi"
@@ -472,6 +473,46 @@ def test_deploy(sagemaker_session, candidate_mock):
         vpc_config=None,
         enable_network_isolation=False,
         model_kms_key=None,
+        predictor_cls=None,
+    )
+
+
+def test_deploy_optional_args(sagemaker_session, 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,
+        sagemaker_session=sagemaker_session,
+        name=JOB_NAME,
+        endpoint_name=JOB_NAME,
+        tags=TAGS,
+        wait=False,
+        update_endpoint=True,
+        vpc_config=VPC_CONFIG,
+        enable_network_isolation=True,
+        model_kms_key=OUTPUT_KMS_KEY,
+        predictor_cls=RealTimePredictor,
+    )
+    auto_ml._deploy_inference_pipeline.assert_called_once()
+    auto_ml._deploy_inference_pipeline.assert_called_with(
+        candidate_mock.containers,
+        initial_instance_count=INSTANCE_COUNT,
+        instance_type=INSTANCE_TYPE,
+        name=JOB_NAME,
+        sagemaker_session=sagemaker_session,
+        endpoint_name=JOB_NAME,
+        tags=TAGS,
+        wait=False,
+        update_endpoint=True,
+        vpc_config=VPC_CONFIG,
+        enable_network_isolation=True,
+        model_kms_key=OUTPUT_KMS_KEY,
+        predictor_cls=RealTimePredictor,
     )