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

Co-authored-by: Raymond Liu <tzujui@amazon.com>

Author: SSRraymond

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):
-        """Initialze a `Phase`"""
+        """Initialize 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):
-        """Initialze a `ModelLatencyThreshold`"""
+        """Initialize a `ModelLatencyThreshold`"""
         self.to_json = {"Percentile": percentile, "ValueInMilliseconds": value_in_milliseconds}
 
 
@@ -79,6 +79,12 @@ 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.
@@ -119,8 +125,6 @@ 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)
@@ -149,12 +153,36 @@ 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_package_version_arn=self.model_package_arn,
+            model_name=self.name,
+            model_package_version_arn=getattr(self, "model_package_arn", None),
             framework=framework,
             framework_version=framework_version,
             sample_payload_url=sample_payload_url,

src/sagemaker/session.py

@@ -4820,6 +4820,7 @@ 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",
@@ -4843,6 +4844,7 @@ 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
@@ -4890,10 +4892,15 @@ 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:
@@ -4918,6 +4925,7 @@ 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,
@@ -4938,6 +4946,7 @@ 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.
@@ -4964,6 +4973,12 @@ 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)
@@ -4972,6 +4987,7 @@ 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

@@ -16,6 +16,7 @@
 
 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
@@ -154,6 +155,120 @@ 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
@@ -176,6 +291,72 @@ 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