Revert "feature: Decouple model.right_size() from model registry (#3688)"

This reverts commit 1ad9d9c.

Author: claytonparnell

src/sagemaker/inference_recommender/inference_recommender_mixin.py

@@ -38,7 +38,7 @@ class Phase:
     """
 
     def __init__(self, duration_in_seconds: int, initial_number_of_users: int, spawn_rate: int):
-        """Initialize a `Phase`"""
+        """Initialze a `Phase`"""
         self.to_json = {
             "DurationInSeconds": duration_in_seconds,
             "InitialNumberOfUsers": initial_number_of_users,
@@ -53,7 +53,7 @@ class ModelLatencyThreshold:
     """
 
     def __init__(self, percentile: str, value_in_milliseconds: int):
-        """Initialize a `ModelLatencyThreshold`"""
+        """Initialze a `ModelLatencyThreshold`"""
         self.to_json = {"Percentile": percentile, "ValueInMilliseconds": value_in_milliseconds}
 
 
@@ -79,12 +79,6 @@ def right_size(
     ):
         """Recommends an instance type for a SageMaker or BYOC model.
 
-        Create a SageMaker ``Model`` or use a registered ``ModelPackage``,
-        to start an Inference Recommender job.
-
-        The name of the created model is accessible in the ``name`` field of
-        this ``Model`` after right_size returns.
-
         Args:
             sample_payload_url (str): The S3 path where the sample payload is stored.
             supported_content_types: (list[str]): The supported MIME types for the input data.
@@ -125,6 +119,8 @@ def right_size(
             sagemaker.model.Model: A SageMaker ``Model`` object. See
             :func:`~sagemaker.model.Model` for full details.
         """
+        if not isinstance(self, sagemaker.model.ModelPackage):
+            raise ValueError("right_size() is currently only supported with a registered model")
 
         if not framework and self._framework():
             framework = INFERENCE_RECOMMENDER_FRAMEWORK_MAPPING.get(self._framework(), framework)
@@ -153,36 +149,12 @@ def right_size(
 
         self._init_sagemaker_session_if_does_not_exist()
 
-        if isinstance(self, sagemaker.model.Model) and not isinstance(
-            self, sagemaker.model.ModelPackage
-        ):
-            primary_container_def = self.prepare_container_def()
-            if not self.name:
-                self._ensure_base_name_if_needed(
-                    image_uri=primary_container_def["Image"],
-                    script_uri=self.source_dir,
-                    model_uri=self.model_data,
-                )
-                self._set_model_name_if_needed()
-
-            create_model_args = dict(
-                name=self.name,
-                role=self.role,
-                container_defs=None,
-                primary_container=primary_container_def,
-                vpc_config=self.vpc_config,
-                enable_network_isolation=self.enable_network_isolation(),
-            )
-            LOGGER.warning("Attempting to create new model with name %s", self.name)
-            self.sagemaker_session.create_model(**create_model_args)
-
         ret_name = self.sagemaker_session.create_inference_recommendations_job(
             role=self.role,
             job_name=job_name,
             job_type=job_type,
             job_duration_in_seconds=job_duration_in_seconds,
-            model_name=self.name,
-            model_package_version_arn=getattr(self, "model_package_arn", None),
+            model_package_version_arn=self.model_package_arn,
             framework=framework,
             framework_version=framework_version,
             sample_payload_url=sample_payload_url,

src/sagemaker/session.py

@@ -4820,7 +4820,6 @@ def _create_inference_recommendations_job_request(
         framework: str,
         sample_payload_url: str,
         supported_content_types: List[str],
-        model_name: str = None,
         model_package_version_arn: str = None,
         job_duration_in_seconds: int = None,
         job_type: str = "Default",
@@ -4844,7 +4843,6 @@ def _create_inference_recommendations_job_request(
             framework (str): The machine learning framework of the Image URI.
             sample_payload_url (str): The S3 path where the sample payload is stored.
             supported_content_types (List[str]): The supported MIME types for the input data.
-            model_name (str): Name of the Amazon SageMaker ``Model`` to be used.
             model_package_version_arn (str): The Amazon Resource Name (ARN) of a
                 versioned model package.
             job_duration_in_seconds (int): The maximum job duration that a job
@@ -4892,15 +4890,10 @@ def _create_inference_recommendations_job_request(
             "RoleArn": role,
             "InputConfig": {
                 "ContainerConfig": containerConfig,
+                "ModelPackageVersionArn": model_package_version_arn,
             },
         }
 
-        request.get("InputConfig").update(
-            {"ModelPackageVersionArn": model_package_version_arn}
-            if model_package_version_arn
-            else {"ModelName": model_name}
-        )
-
         if job_description:
             request["JobDescription"] = job_description
         if job_duration_in_seconds:
@@ -4925,7 +4918,6 @@ def create_inference_recommendations_job(
         supported_content_types: List[str],
         job_name: str = None,
         job_type: str = "Default",
-        model_name: str = None,
         model_package_version_arn: str = None,
         job_duration_in_seconds: int = None,
         nearest_model_name: str = None,
@@ -4946,7 +4938,6 @@ def create_inference_recommendations_job(
                 You must grant sufficient permissions to this role.
             sample_payload_url (str): The S3 path where the sample payload is stored.
             supported_content_types (List[str]): The supported MIME types for the input data.
-            model_name (str): Name of the Amazon SageMaker ``Model`` to be used.
             model_package_version_arn (str): The Amazon Resource Name (ARN) of a
                 versioned model package.
             job_name (str): The name of the job being run.
@@ -4973,12 +4964,6 @@ def create_inference_recommendations_job(
             str: The name of the job created. In the form of `SMPYTHONSDK-<timestamp>`
         """
 
-        if model_name is None and model_package_version_arn is None:
-            raise ValueError("Please provide either model_name or model_package_version_arn.")
-
-        if model_name is not None and model_package_version_arn is not None:
-            raise ValueError("Please provide either model_name or model_package_version_arn.")
-
         if not job_name:
             unique_tail = uuid.uuid4()
             job_name = "SMPYTHONSDK-" + str(unique_tail)
@@ -4987,7 +4972,6 @@ def create_inference_recommendations_job(
         create_inference_recommendations_job_request = (
             self._create_inference_recommendations_job_request(
                 role=role,
-                model_name=model_name,
                 model_package_version_arn=model_package_version_arn,
                 job_name=job_name,
                 job_type=job_type,

tests/integ/test_inference_recommender.py

@@ -16,7 +16,6 @@
 
 import pytest
 
-from sagemaker.model import Model
 from sagemaker.sklearn.model import SKLearnModel, SKLearnPredictor
 from sagemaker.utils import unique_name_from_base
 from tests.integ import DATA_DIR
@@ -155,120 +154,6 @@ def advanced_right_sized_model(sagemaker_session, cpu_instance_type):
             )
 
 
-@pytest.fixture(scope="module")
-def default_right_sized_unregistered_model(sagemaker_session, cpu_instance_type):
-    with timeout(minutes=45):
-        try:
-            ir_job_name = unique_name_from_base("test-ir-right-size-job-name")
-            model_data = sagemaker_session.upload_data(path=IR_SKLEARN_MODEL)
-            payload_data = sagemaker_session.upload_data(path=IR_SKLEARN_PAYLOAD)
-
-            iam_client = sagemaker_session.boto_session.client("iam")
-            role_arn = iam_client.get_role(RoleName="SageMakerRole")["Role"]["Arn"]
-
-            sklearn_model = SKLearnModel(
-                model_data=model_data,
-                role=role_arn,
-                entry_point=IR_SKLEARN_ENTRY_POINT,
-                framework_version=IR_SKLEARN_FRAMEWORK_VERSION,
-            )
-
-            return (
-                sklearn_model.right_size(
-                    job_name=ir_job_name,
-                    sample_payload_url=payload_data,
-                    supported_content_types=IR_SKLEARN_CONTENT_TYPE,
-                    supported_instance_types=[cpu_instance_type],
-                    framework=IR_SKLEARN_FRAMEWORK,
-                    log_level="Quiet",
-                ),
-                ir_job_name,
-            )
-        except Exception:
-            sagemaker_session.delete_model(ModelName=sklearn_model.name)
-
-
-@pytest.fixture(scope="module")
-def advanced_right_sized_unregistered_model(sagemaker_session, cpu_instance_type):
-    with timeout(minutes=45):
-        try:
-            model_data = sagemaker_session.upload_data(path=IR_SKLEARN_MODEL)
-            payload_data = sagemaker_session.upload_data(path=IR_SKLEARN_PAYLOAD)
-
-            iam_client = sagemaker_session.boto_session.client("iam")
-            role_arn = iam_client.get_role(RoleName="SageMakerRole")["Role"]["Arn"]
-
-            sklearn_model = SKLearnModel(
-                model_data=model_data,
-                role=role_arn,
-                entry_point=IR_SKLEARN_ENTRY_POINT,
-                framework_version=IR_SKLEARN_FRAMEWORK_VERSION,
-            )
-
-            hyperparameter_ranges = [
-                {
-                    "instance_types": CategoricalParameter([cpu_instance_type]),
-                    "TEST_PARAM": CategoricalParameter(
-                        ["TEST_PARAM_VALUE_1", "TEST_PARAM_VALUE_2"]
-                    ),
-                }
-            ]
-
-            phases = [
-                Phase(duration_in_seconds=300, initial_number_of_users=2, spawn_rate=2),
-                Phase(duration_in_seconds=300, initial_number_of_users=14, spawn_rate=2),
-            ]
-
-            model_latency_thresholds = [
-                ModelLatencyThreshold(percentile="P95", value_in_milliseconds=100)
-            ]
-
-            return sklearn_model.right_size(
-                sample_payload_url=payload_data,
-                supported_content_types=IR_SKLEARN_CONTENT_TYPE,
-                framework=IR_SKLEARN_FRAMEWORK,
-                job_duration_in_seconds=3600,
-                hyperparameter_ranges=hyperparameter_ranges,
-                phases=phases,
-                model_latency_thresholds=model_latency_thresholds,
-                max_invocations=100,
-                max_tests=5,
-                max_parallel_tests=5,
-                log_level="Quiet",
-            )
-
-        except Exception:
-            sagemaker_session.delete_model(ModelName=sklearn_model.name)
-
-
-@pytest.fixture(scope="module")
-def default_right_sized_unregistered_base_model(sagemaker_session, cpu_instance_type):
-    with timeout(minutes=45):
-        try:
-            ir_job_name = unique_name_from_base("test-ir-right-size-job-name")
-            model_data = sagemaker_session.upload_data(path=IR_SKLEARN_MODEL)
-            payload_data = sagemaker_session.upload_data(path=IR_SKLEARN_PAYLOAD)
-
-            iam_client = sagemaker_session.boto_session.client("iam")
-            role_arn = iam_client.get_role(RoleName="SageMakerRole")["Role"]["Arn"]
-
-            model = Model(model_data=model_data, role=role_arn, entry_point=IR_SKLEARN_ENTRY_POINT)
-
-            return (
-                model.right_size(
-                    job_name=ir_job_name,
-                    sample_payload_url=payload_data,
-                    supported_content_types=IR_SKLEARN_CONTENT_TYPE,
-                    supported_instance_types=[cpu_instance_type],
-                    framework=IR_SKLEARN_FRAMEWORK,
-                    log_level="Quiet",
-                ),
-                ir_job_name,
-            )
-        except Exception:
-            sagemaker_session.delete_model(ModelName=model.name)
-
-
 @pytest.mark.slow_test
 def test_default_right_size_and_deploy_registered_model_sklearn(
     default_right_sized_model, sagemaker_session
@@ -291,72 +176,6 @@ def test_default_right_size_and_deploy_registered_model_sklearn(
             predictor.delete_endpoint()
 
 
-@pytest.mark.slow_test
-def test_default_right_size_and_deploy_unregistered_model_sklearn(
-    default_right_sized_unregistered_model, sagemaker_session
-):
-    endpoint_name = unique_name_from_base("test-ir-right-size-default-unregistered-sklearn")
-
-    right_size_model, ir_job_name = default_right_sized_unregistered_model
-    with timeout(minutes=45):
-        try:
-            right_size_model.predictor_cls = SKLearnPredictor
-            predictor = right_size_model.deploy(endpoint_name=endpoint_name)
-
-            payload = pd.read_csv(IR_SKLEARN_DATA, header=None)
-
-            inference = predictor.predict(payload)
-            assert inference is not None
-            assert 26 == len(inference)
-        finally:
-            predictor.delete_model()
-            predictor.delete_endpoint()
-
-
-@pytest.mark.slow_test
-def test_default_right_size_and_deploy_unregistered_base_model(
-    default_right_sized_unregistered_base_model, sagemaker_session
-):
-    endpoint_name = unique_name_from_base("test-ir-right-size-default-unregistered-base")
-
-    right_size_model, ir_job_name = default_right_sized_unregistered_base_model
-    with timeout(minutes=45):
-        try:
-            right_size_model.predictor_cls = SKLearnPredictor
-            predictor = right_size_model.deploy(endpoint_name=endpoint_name)
-
-            payload = pd.read_csv(IR_SKLEARN_DATA, header=None)
-
-            inference = predictor.predict(payload)
-            assert inference is not None
-            assert 26 == len(inference)
-        finally:
-            predictor.delete_model()
-            predictor.delete_endpoint()
-
-
-@pytest.mark.slow_test
-def test_advanced_right_size_and_deploy_unregistered_model_sklearn(
-    advanced_right_sized_unregistered_model, sagemaker_session
-):
-    endpoint_name = unique_name_from_base("test-ir-right-size-advanced-sklearn")
-
-    right_size_model = advanced_right_sized_unregistered_model
-    with timeout(minutes=45):
-        try:
-            right_size_model.predictor_cls = SKLearnPredictor
-            predictor = right_size_model.deploy(endpoint_name=endpoint_name)
-
-            payload = pd.read_csv(IR_SKLEARN_DATA, header=None)
-
-            inference = predictor.predict(payload)
-            assert inference is not None
-            assert 26 == len(inference)
-        finally:
-            predictor.delete_model()
-            predictor.delete_endpoint()
-
-
 @pytest.mark.slow_test
 def test_advanced_right_size_and_deploy_registered_model_sklearn(
     advanced_right_sized_model, sagemaker_session