fix: populate default config name to model

src/sagemaker/jumpstart/estimator.py

@@ -739,7 +739,12 @@ def attach(
 
         model_version = model_version or "*"
 
-        additional_kwargs = {"model_id": model_id, "model_version": model_version}
+        additional_kwargs = {
+            "model_id": model_id,
+            "model_version": model_version,
+            "tolerate_vulnerable_model": True,  # model is already trained
+            "tolerate_deprecated_model": True,  # model is already trained
+        }
 
         model_specs = verify_model_region_and_return_specs(
             model_id=model_id,

src/sagemaker/jumpstart/factory/model.py

@@ -543,6 +543,31 @@ def _add_resources_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModel
     return kwargs
 
 
+def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
+    """Sets default config name to the kwargs. Returns full kwargs."""
+
+    specs = verify_model_region_and_return_specs(
+        model_id=kwargs.model_id,
+        version=kwargs.model_version,
+        scope=JumpStartScriptScope.INFERENCE,
+        region=kwargs.region,
+        tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
+        tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
+        sagemaker_session=kwargs.sagemaker_session,
+        model_type=kwargs.model_type,
+        config_name=kwargs.config_name,
+    )
+    if (
+        specs.inference_configs
+        and specs.inference_configs.get_top_config_from_ranking().config_name
+    ):
+        kwargs.config_name = (
+            kwargs.config_name or specs.inference_configs.get_top_config_from_ranking().config_name
+        )
+
+    return kwargs
+
+
 def get_deploy_kwargs(
     model_id: str,
     model_version: Optional[str] = None,
@@ -808,5 +833,6 @@ def get_init_kwargs(
     model_init_kwargs = _add_model_package_arn_to_kwargs(kwargs=model_init_kwargs)
 
     model_init_kwargs = _add_resources_to_kwargs(kwargs=model_init_kwargs)
+    model_init_kwargs = _add_config_name_to_kwargs(kwargs=model_init_kwargs)
 
     return model_init_kwargs

src/sagemaker/jumpstart/model.py

@@ -351,7 +351,7 @@ def _validate_model_id_and_type():
         self.tolerate_deprecated_model = model_init_kwargs.tolerate_deprecated_model
         self.region = model_init_kwargs.region
         self.sagemaker_session = model_init_kwargs.sagemaker_session
-        self.config_name = config_name
+        self.config_name = model_init_kwargs.config_name
 
         if self.model_type == JumpStartModelType.PROPRIETARY:
             self.log_subscription_warning()

src/sagemaker/jumpstart/types.py

@@ -1076,10 +1076,12 @@ class JumpStartMetadataConfig(JumpStartDataHolderType):
         "benchmark_metrics",
         "config_components",
         "resolved_metadata_config",
+        "config_name",
     ]
 
     def __init__(
         self,
+        config_name: str,
         base_fields: Dict[str, Any],
         config_components: Dict[str, JumpStartConfigComponent],
         benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]],
@@ -1098,6 +1100,7 @@ def __init__(
         self.config_components: Dict[str, JumpStartConfigComponent] = config_components
         self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = benchmark_metrics
         self.resolved_metadata_config: Optional[Dict[str, Any]] = None
+        self.config_name: Optional[str] = config_name
 
     def to_json(self) -> Dict[str, Any]:
         """Returns json representation of JumpStartMetadataConfig object."""
@@ -1251,6 +1254,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
         inference_configs_dict: Optional[Dict[str, JumpStartMetadataConfig]] = (
             {
                 alias: JumpStartMetadataConfig(
+                    alias,
                     json_obj,
                     (
                         {
@@ -1303,6 +1307,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
             training_configs_dict: Optional[Dict[str, JumpStartMetadataConfig]] = (
                 {
                     alias: JumpStartMetadataConfig(
+                        alias,
                         json_obj,
                         (
                             {

src/sagemaker/predictor.py

@@ -43,6 +43,7 @@ def retrieve_default(
     tolerate_vulnerable_model: bool = False,
     tolerate_deprecated_model: bool = False,
     model_type: JumpStartModelType = JumpStartModelType.OPEN_WEIGHTS,
+    config_name: Optional[str] = None,
 ) -> Predictor:
     """Retrieves the default predictor for the model matching the given arguments.
 
@@ -65,6 +66,8 @@ def retrieve_default(
         tolerate_deprecated_model (bool): True if deprecated models should be tolerated
             (exception not raised). False if these models should raise an exception.
             (Default: False).
+        config_name (Optional[str]): The name of the configuration to use for the
+            predictor. (Default: None)
     Returns:
         Predictor: The default predictor to use for the model.
 
@@ -91,10 +94,9 @@ def retrieve_default(
         model_id = inferred_model_id
         model_version = model_version or inferred_model_version or "*"
         inference_component_name = inference_component_name or inferred_inference_component_name
-        config_name = inferred_config_name or None
+        config_name = config_name or inferred_config_name or None
     else:
         model_version = model_version or "*"
-        config_name = None
 
     predictor = Predictor(
         endpoint_name=endpoint_name,

tests/integ/sagemaker/jumpstart/estimator/test_jumpstart_estimator.py

@@ -150,6 +150,7 @@ def test_gated_model_training_v2(setup):
         tags=[{"Key": JUMPSTART_TAG, "Value": os.environ[ENV_VAR_JUMPSTART_SDK_TEST_SUITE_ID]}],
         environment={"accept_eula": "true"},
         max_run=259200,  # avoid exceeding resource limits
+        tolerate_vulnerable_model=True,  # TODO: remove once vulnerbility is patched
     )
 
     # uses ml.g5.12xlarge instance

tests/unit/sagemaker/jumpstart/estimator/test_estimator.py

@@ -1011,6 +1011,8 @@ def test_jumpstart_estimator_attach_eula_model(
             additional_kwargs={
                 "model_id": "gemma-model",
                 "model_version": "*",
+                'tolerate_vulnerable_model': True,
+                'tolerate_deprecated_model': True,
                 "environment": {"accept_eula": "true"},
             },
         )
@@ -1056,6 +1058,8 @@ def test_jumpstart_estimator_attach_no_model_id_happy_case(
             additional_kwargs={
                 "model_id": "js-trainable-model-prepacked",
                 "model_version": "1.0.0",
+                'tolerate_vulnerable_model': True,
+                'tolerate_deprecated_model': True,
             },
         )
 

tests/unit/sagemaker/jumpstart/model/test_model.py

@@ -1552,6 +1552,8 @@ def test_model_initialization_with_config_name(
 
         model = JumpStartModel(model_id=model_id, config_name="neuron-inference")
 
+        assert model.config_name == "neuron-inference"
+
         model.deploy()
 
         mock_model_deploy.assert_called_once_with(
@@ -1594,6 +1596,8 @@ def test_model_set_deployment_config(
 
         model = JumpStartModel(model_id=model_id)
 
+        assert model.config_name is None
+
         model.deploy()
 
         mock_model_deploy.assert_called_once_with(
@@ -1612,6 +1616,8 @@ def test_model_set_deployment_config(
         mock_get_model_specs.side_effect = get_prototype_spec_with_configs
         model.set_deployment_config("neuron-inference")
 
+        assert model.config_name == "neuron-inference"
+
         model.deploy()
 
         mock_model_deploy.assert_called_once_with(
@@ -1654,6 +1660,8 @@ def test_model_unset_deployment_config(
 
         model = JumpStartModel(model_id=model_id, config_name="neuron-inference")
 
+        assert model.config_name == "neuron-inference"
+
         model.deploy()
 
         mock_model_deploy.assert_called_once_with(
@@ -1789,7 +1797,6 @@ def test_model_retrieve_deployment_config(
     ):
         model_id, _ = "pytorch-eqa-bert-base-cased", "*"
 
-        mock_get_init_kwargs.side_effect = lambda *args, **kwargs: get_mock_init_kwargs(model_id)
         mock_verify_model_region_and_return_specs.side_effect = (
             lambda *args, **kwargs: get_base_spec_with_prototype_configs_with_missing_benchmarks()
         )
@@ -1804,15 +1811,23 @@ def test_model_retrieve_deployment_config(
         )
         mock_model_deploy.return_value = default_predictor
 
+        expected = get_base_deployment_configs()[0]
+        config_name = expected.get("DeploymentConfigName")
+        mock_get_init_kwargs.side_effect = lambda *args, **kwargs: get_mock_init_kwargs(
+            model_id, config_name
+        )
+
         mock_session.return_value = sagemaker_session
 
         model = JumpStartModel(model_id=model_id)
 
-        expected = get_base_deployment_configs()[0]
-        model.set_deployment_config(expected.get("DeploymentConfigName"))
+        model.set_deployment_config(config_name)
 
         self.assertEqual(model.deployment_config, expected)
 
+        mock_get_init_kwargs.reset_mock()
+        mock_get_init_kwargs.side_effect = lambda *args, **kwargs: get_mock_init_kwargs(model_id)
+
         # Unset
         model.set_deployment_config(None)
         self.assertIsNone(model.deployment_config)

tests/unit/sagemaker/jumpstart/utils.py

@@ -12,7 +12,7 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 import copy
-from typing import List, Dict, Any
+from typing import List, Dict, Any, Optional
 import boto3
 
 from sagemaker.compute_resource_requirements import ResourceRequirements
@@ -237,7 +237,7 @@ def get_base_spec_with_prototype_configs_with_missing_benchmarks(
     copy_inference_configs = copy.deepcopy(INFERENCE_CONFIGS)
     copy_inference_configs["inference_configs"]["neuron-inference"]["benchmark_metrics"] = None
 
-    inference_configs = {**INFERENCE_CONFIGS, **INFERENCE_CONFIG_RANKINGS}
+    inference_configs = {**copy_inference_configs, **INFERENCE_CONFIG_RANKINGS}
     training_configs = {**TRAINING_CONFIGS, **TRAINING_CONFIG_RANKINGS}
 
     spec.update(inference_configs)
@@ -335,7 +335,9 @@ def get_base_deployment_configs_with_acceleration_configs() -> List[Dict[str, An
     return configs
 
 
-def get_mock_init_kwargs(model_id) -> JumpStartModelInitKwargs:
+def get_mock_init_kwargs(
+    model_id: str, config_name: Optional[str] = None
+) -> JumpStartModelInitKwargs:
     return JumpStartModelInitKwargs(
         model_id=model_id,
         model_type=JumpStartModelType.OPEN_WEIGHTS,
@@ -344,4 +346,5 @@ def get_mock_init_kwargs(model_id) -> JumpStartModelInitKwargs:
         instance_type=INIT_KWARGS.get("instance_type"),
         env=INIT_KWARGS.get("env"),
         resources=ResourceRequirements(),
+        config_name=config_name,
     )