chore: always include inference script if available

Author: evakravi

src/sagemaker/jumpstart/artifacts.py

@@ -177,7 +177,6 @@ def _retrieve_model_uri(
     region: Optional[str] = None,
     tolerate_vulnerable_model: bool = False,
     tolerate_deprecated_model: bool = False,
-    include_script: bool = False,
 ):
     """Retrieves the model artifact S3 URI for the model matching the given arguments.
 
@@ -198,8 +197,6 @@ def _retrieve_model_uri(
         tolerate_deprecated_model (bool): True if deprecated versions of model
             specifications should be tolerated (exception not raised). If False, raises
             an exception if the version of the model is deprecated.
-        include_script (bool): True if script artifact should be packaged alongside model
-            tarball. (Default: False).
     Returns:
         str: the model artifact S3 URI for the corresponding model.
 
@@ -221,24 +218,14 @@ def _retrieve_model_uri(
         tolerate_deprecated_model=tolerate_deprecated_model,
     )
 
-    error_msg_no_combined_artifact = (
-        "No combined script and model tarball available "
-        f"for {model_id} with version {model_version} for {model_scope}."
-    )
-
     if model_scope == JumpStartScriptScope.INFERENCE:
-        if not include_script:
-            model_artifact_key = model_specs.hosting_artifact_key
-        else:
-            model_artifact_key = getattr(model_specs, "hosting_prepacked_artifact_key", None)
-            if model_artifact_key is None:
-                raise ValueError(error_msg_no_combined_artifact)
+        model_artifact_key = (
+            getattr(model_specs, "hosting_prepacked_artifact_key", None)
+            or model_specs.hosting_artifact_key
+        )
 
     elif model_scope == JumpStartScriptScope.TRAINING:
-        if not include_script:
-            model_artifact_key = model_specs.training_artifact_key
-        else:
-            raise ValueError(error_msg_no_combined_artifact)
+        model_artifact_key = model_specs.training_artifact_key
 
     bucket = os.environ.get(
         ENV_VARIABLE_JUMPSTART_MODEL_ARTIFACT_BUCKET_OVERRIDE

src/sagemaker/model_uris.py

@@ -30,7 +30,6 @@ def retrieve(
     model_scope: Optional[str] = None,
     tolerate_vulnerable_model: bool = False,
     tolerate_deprecated_model: bool = False,
-    include_script: bool = False,
 ) -> str:
     """Retrieves the model artifact Amazon S3 URI for the model matching the given arguments.
 
@@ -49,8 +48,6 @@ def retrieve(
         tolerate_deprecated_model (bool): ``True`` if deprecated versions of model
             specifications should be tolerated without raising an exception. If ``False``, raises
             an exception if the version of the model is deprecated. (Default: False).
-        include_script (bool): True if script artifact should be packaged alongside model
-            tarball. (Default: False).
     Returns:
         str: The model artifact S3 URI for the corresponding model.
 
@@ -71,5 +68,4 @@ def retrieve(
         region,
         tolerate_vulnerable_model,
         tolerate_deprecated_model,
-        include_script,
     )

tests/unit/sagemaker/model_uris/jumpstart/test_combined_artifact.py

@@ -15,9 +15,8 @@
 from mock.mock import patch
 
 from sagemaker import model_uris
-import pytest
 
-from tests.unit.sagemaker.jumpstart.utils import get_prototype_model_spec, get_special_model_spec
+from tests.unit.sagemaker.jumpstart.utils import get_special_model_spec
 
 
 @patch("sagemaker.jumpstart.accessors.JumpStartModelsAccessor.get_model_specs")
@@ -32,31 +31,8 @@ def test_jumpstart_combined_artifacts(patched_get_model_specs):
         model_scope="inference",
         model_id=model_id_combined_model_artifact,
         model_version="*",
-        include_script=True,
     )
     assert (
         uri == "s3://jumpstart-cache-prod-us-west-2/huggingface-infer/"
         "prepack/v1.0.0/infer-prepack-huggingface-text2text-flan-t5-xxl-fp16.tar.gz"
     )
-
-    with pytest.raises(ValueError):
-        model_uris.retrieve(
-            region="us-west-2",
-            model_scope="training",
-            model_id=model_id_combined_model_artifact,
-            model_version="*",
-            include_script=True,
-        )
-
-    patched_get_model_specs.side_effect = get_prototype_model_spec
-
-    model_id_combined_model_artifact_unsupported = "xgboost-classification-model"
-
-    with pytest.raises(ValueError):
-        model_uris.retrieve(
-            region="us-west-2",
-            model_scope="inference",
-            model_id=model_id_combined_model_artifact_unsupported,
-            model_version="*",
-            include_script=True,
-        )