chore: address PR comments, fix failing tests

Author: evakravi

src/sagemaker/base_predictor.py

@@ -157,15 +157,15 @@ def predict(
             custom_attributes (str): Provides additional information about a request for an
                 inference submitted to a model hosted at an Amazon SageMaker endpoint.
                 The information is an opaque value that is forwarded verbatim. You could use this
-                value, for example, to provide an ID that you can use to track a request or to provide
-                other metadata that a service endpoint was programmed to process. The value must
-                consist of no more than 1024 visible US-ASCII characters.
-
-                The code in your model is responsible for setting or updating any custom attributes in
-                the response. If your code does not set this value in the response, an empty value is
-                returned. For example, if a custom attribute represents the trace ID, your model can
-                prepend the custom attribute with Trace ID: in your post-processing function
-                (Default: None).
+                value, for example, to provide an ID that you can use to track a request or to
+                provide other metadata that a service endpoint was programmed to process. The value
+                must consist of no more than 1024 visible US-ASCII characters.
+
+                The code in your model is responsible for setting or updating any custom attributes
+                in the response. If your code does not set this value in the response, an empty
+                value is returned. For example, if a custom attribute represents the trace ID, your
+                model can prepend the custom attribute with Trace ID: in your post-processing
+                function (Default: None).
 
         Returns:
             object: Inference for the given input. If a deserializer was specified when creating

src/sagemaker/jumpstart/artifacts/model_packages.py

@@ -16,9 +16,6 @@
 from sagemaker.jumpstart.constants import (
     JUMPSTART_DEFAULT_REGION_NAME,
 )
-from sagemaker.jumpstart.enums import (
-    JumpStartScriptScope,
-)
 from sagemaker.jumpstart.utils import (
     verify_model_region_and_return_specs,
 )
@@ -28,6 +25,7 @@ def _retrieve_model_package_arn(
     model_id: str,
     model_version: str,
     region: Optional[str],
+    scope: Optional[str] = None,
     tolerate_vulnerable_model: bool = False,
     tolerate_deprecated_model: bool = False,
 ) -> Optional[str]:
@@ -39,6 +37,7 @@ def _retrieve_model_package_arn(
         model_version (str): Version of the JumpStart model for which to retrieve the
             model package arn.
         region (Optional[str]): Region for which to retrieve the model package arn.
+        scope (Optional[str]): Scope for which to retrieve the model package arn.
         tolerate_vulnerable_model (bool): True if vulnerable versions of model
             specifications should be tolerated (exception not raised). If False, raises an
             exception if the script used by this version of the model has dependencies with known
@@ -57,7 +56,7 @@ def _retrieve_model_package_arn(
     model_specs = verify_model_region_and_return_specs(
         model_id=model_id,
         version=model_version,
-        scope=JumpStartScriptScope.TRAINING,
+        scope=scope,
         region=region,
         tolerate_vulnerable_model=tolerate_vulnerable_model,
         tolerate_deprecated_model=tolerate_deprecated_model,

src/sagemaker/jumpstart/factory/model.py

@@ -296,6 +296,7 @@ def _add_model_package_arn_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpSt
     model_package_arn = kwargs.model_package_arn or _retrieve_model_package_arn(
         model_id=kwargs.model_id,
         model_version=kwargs.model_version,
+        scope=JumpStartScriptScope.INFERENCE,
         region=kwargs.region,
         tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
         tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,

src/sagemaker/jumpstart/types.py

@@ -351,7 +351,7 @@ class JumpStartModelSpecs(JumpStartDataHolderType):
         "inference_enable_network_isolation",
         "training_enable_network_isolation",
         "resource_name_base",
-        "eula_model",
+        "eula_key",
         "model_package_arn",
     ]
 
@@ -421,7 +421,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
         )
         self.resource_name_base: bool = json_obj.get("resource_name_base")
 
-        self.eula_model: bool = json_obj.get("eula_model", False)
+        self.eula_key: Optional[str] = json_obj.get("eula_key")
 
         self.model_package_arn: Optional[str] = json_obj.get("model_package_arn")
 

src/sagemaker/jumpstart/utils.py

@@ -402,10 +402,13 @@ def verify_model_region_and_return_specs(
             f"JumpStart model ID '{model_id}' and version '{version}' " "does not support training."
         )
 
-    if model_specs.eula_model:
+    if model_specs.eula_key:
         LOGGER.info(
             "Using model with end-user license agreement (EULA). "
-            "Deploying this model requires accepting EULA terms."
+            "See https://%s.s3.%s.amazonaws.com/%s for terms of use.",
+            get_jumpstart_content_bucket(region=region),
+            region,
+            model_specs.eula_key,
         )
 
     if model_specs.deprecated:

tests/unit/sagemaker/jumpstart/constants.py

@@ -2300,7 +2300,7 @@
     "training_prepacked_script_key": None,
     "hosting_prepacked_artifact_key": None,
     "model_package_arn": None,
-    "eula_model": False,
+    "eula_key": False,
     "hyperparameters": [
         {
             "name": "epochs",