feat: custom base job name for jumpstart models/estimators

doc/overview.rst

@@ -773,11 +773,10 @@ Deployment may take about 5 minutes.
        instance_type=instance_type,
    )
 
-Because ``catboost`` and ``lightgbm`` rely on the PyTorch Deep Learning Containers
-image, the corresponding Models and Endpoints display the “pytorch”
-prefix when viewed in the AWS console. To verify that these models
-were created successfully with your desired base model, refer to
-the ``Tags`` section.
+Because the model and script URIs are owned by JumpStart, the endpoint,
+endpoint config and model resources will be prefixed with
+``sagemaker-jumpstart``. Refer to the model ``Tags`` to inspect the
+JumpStart artifacts involved in the model creation.
 
 Perform Inference
 -----------------

src/sagemaker/estimator.py

@@ -50,6 +50,7 @@
 from sagemaker.job import _Job
 from sagemaker.jumpstart.utils import (
     add_jumpstart_tags,
+    get_jumpstart_base_name_if_jumpstart_model,
     update_inference_tags_with_jumpstart_training_tags,
 )
 from sagemaker.local import LocalSession
@@ -570,7 +571,9 @@ def _ensure_base_job_name(self):
         """Set ``self.base_job_name`` if it is not set already."""
         # honor supplied base_job_name or generate it
         if self.base_job_name is None:
-            self.base_job_name = base_name_from_image(self.training_image_uri())
+            self.base_job_name = get_jumpstart_base_name_if_jumpstart_model(
+                self.source_dir, self.model_uri
+            ) or base_name_from_image(self.training_image_uri())
 
     def _get_or_create_name(self, name=None):
         """Generate a name based on the base job name or training image if needed.
@@ -1208,7 +1211,15 @@ def deploy(
         is_serverless = serverless_inference_config is not None
         self._ensure_latest_training_job()
         self._ensure_base_job_name()
-        default_name = name_from_base(self.base_job_name)
+
+        jumpstart_base_name = get_jumpstart_base_name_if_jumpstart_model(
+            kwargs.get("source_dir"), self.source_dir, kwargs.get("model_data"), self.model_uri
+        )
+        default_name = (
+            name_from_base(jumpstart_base_name)
+            if jumpstart_base_name
+            else name_from_base(self.base_job_name)
+        )
         endpoint_name = endpoint_name or default_name
         model_name = model_name or default_name
 

src/sagemaker/jumpstart/constants.py

@@ -124,3 +124,5 @@
 SUPPORTED_JUMPSTART_SCOPES = set(scope.value for scope in JumpStartScriptScope)
 
 ENV_VARIABLE_JUMPSTART_CONTENT_BUCKET_OVERRIDE = "AWS_JUMPSTART_CONTENT_BUCKET_OVERRIDE"
+
+JUMPSTART_RESOURCE_BASE_NAME = "sagemaker-jumpstart"

src/sagemaker/jumpstart/utils.py

@@ -232,6 +232,19 @@ def add_single_jumpstart_tag(
     return curr_tags
 
 
+def get_jumpstart_base_name_if_jumpstart_model(
+    *uris,
+) -> Optional[str]:
+    """Return default JumpStart base name if a URI belongs to JumpStart.
+
+    If no URIs belong to JumpStart, return None.
+    """
+    for uri in uris:
+        if is_jumpstart_model_uri(uri):
+            return constants.JUMPSTART_RESOURCE_BASE_NAME
+    return None
+
+
 def add_jumpstart_tags(
     tags: Optional[List[Dict[str, str]]] = None,
     inference_model_uri: Optional[str] = None,

src/sagemaker/model.py

@@ -33,7 +33,7 @@
 from sagemaker.predictor import PredictorBase
 from sagemaker.serverless import ServerlessInferenceConfig
 from sagemaker.transformer import Transformer
-from sagemaker.jumpstart.utils import add_jumpstart_tags
+from sagemaker.jumpstart.utils import add_jumpstart_tags, get_jumpstart_base_name_if_jumpstart_model
 from sagemaker.utils import unique_name_from_base
 from sagemaker.async_inference import AsyncInferenceConfig
 from sagemaker.predictor_async import AsyncPredictor
@@ -514,7 +514,9 @@ def _create_sagemaker_model(self, instance_type=None, accelerator_type=None, tag
         """
         container_def = self.prepare_container_def(instance_type, accelerator_type=accelerator_type)
 
-        self._ensure_base_name_if_needed(container_def["Image"])
+        self._ensure_base_name_if_needed(
+            image_uri=container_def["Image"], script_uri=self.source_dir, model_uri=self.model_data
+        )
         self._set_model_name_if_needed()
 
         enable_network_isolation = self.enable_network_isolation()
@@ -529,10 +531,14 @@ def _create_sagemaker_model(self, instance_type=None, accelerator_type=None, tag
             tags=tags,
         )
 
-    def _ensure_base_name_if_needed(self, image_uri):
+    def _ensure_base_name_if_needed(self, image_uri, script_uri, model_uri):
         """Create a base name from the image URI if there is no model name provided."""
         if self.name is None:
-            self._base_name = self._base_name or utils.base_name_from_image(image_uri)
+            self._base_name = (
+                self._base_name
+                or get_jumpstart_base_name_if_jumpstart_model(script_uri, model_uri)
+                or utils.base_name_from_image(image_uri)
+            )
 
     def _set_model_name_if_needed(self):
         """Generate a new model name if ``self._base_name`` is present."""
@@ -963,7 +969,9 @@ def deploy(
 
         compiled_model_suffix = None if is_serverless else "-".join(instance_type.split(".")[:-1])
         if self._is_compiled_model and not is_serverless:
-            self._ensure_base_name_if_needed(self.image_uri)
+            self._ensure_base_name_if_needed(
+                image_uri=self.image_uri, script_uri=self.source_dir, model_uri=self.model_data
+            )
             if self._base_name is not None:
                 self._base_name = "-".join((self._base_name, compiled_model_suffix))
 

tests/integ/sagemaker/jumpstart/script_mode_class/test_transfer_learning.py

@@ -21,7 +21,7 @@
     TRAINING_ENTRY_POINT_SCRIPT_NAME,
 )
 from sagemaker.jumpstart.utils import get_jumpstart_content_bucket
-from sagemaker.utils import name_from_base
+from sagemaker.predictor import Predictor
 from tests.integ.sagemaker.jumpstart.constants import (
     ENV_VAR_JUMPSTART_SDK_TEST_SUITE_ID,
     JUMPSTART_TAG,
@@ -106,19 +106,17 @@ def test_jumpstart_transfer_learning_estimator_class(setup):
         model_id=model_id, model_version=model_version, model_scope="inference"
     )
 
-    endpoint_name = name_from_base(f"{model_id}-transfer-learning")
-
-    estimator.deploy(
+    predictor: Predictor = estimator.deploy(
         initial_instance_count=instance_count,
         instance_type=inference_instance_type,
         entry_point=INFERENCE_ENTRY_POINT_SCRIPT_NAME,
         image_uri=image_uri,
         source_dir=script_uri,
-        endpoint_name=endpoint_name,
+        tags=[{"Key": JUMPSTART_TAG, "Value": os.environ[ENV_VAR_JUMPSTART_SDK_TEST_SUITE_ID]}],
     )
 
     endpoint_invoker = EndpointInvoker(
-        endpoint_name=endpoint_name,
+        endpoint_name=predictor.endpoint_name,
     )
 
     response = endpoint_invoker.invoke_spc_endpoint(["hello", "world"])

tests/unit/sagemaker/jumpstart/test_utils.py

@@ -20,6 +20,7 @@
     ENV_VARIABLE_JUMPSTART_CONTENT_BUCKET_OVERRIDE,
     JUMPSTART_BUCKET_NAME_SET,
     JUMPSTART_REGION_NAME_SET,
+    JUMPSTART_RESOURCE_BASE_NAME,
     JumpStartScriptScope,
 )
 from sagemaker.jumpstart.enums import JumpStartTag
@@ -874,3 +875,11 @@ def make_deprecated_spec(*largs, **kwargs):
             "pytorch-eqa-bert-base-cased",
             "*",
         )
+
+
+def test_get_jumpstart_base_name_if_jumpstart_model():
+    uris = [random_jumpstart_s3_uri("random_key") for _ in range(random.randint(1, 10))]
+    assert JUMPSTART_RESOURCE_BASE_NAME == utils.get_jumpstart_base_name_if_jumpstart_model(*uris)
+
+    uris = ["s3://not-jumpstart-bucket/some-key" for _ in range(random.randint(0, 10))]
+    assert utils.get_jumpstart_base_name_if_jumpstart_model(*uris) is None

tests/unit/sagemaker/model/test_model.py

@@ -19,7 +19,7 @@
 import sagemaker
 from sagemaker.model import FrameworkModel, Model
 from sagemaker.huggingface.model import HuggingFaceModel
-from sagemaker.jumpstart.constants import JUMPSTART_BUCKET_NAME_SET
+from sagemaker.jumpstart.constants import JUMPSTART_BUCKET_NAME_SET, JUMPSTART_RESOURCE_BASE_NAME
 from sagemaker.jumpstart.enums import JumpStartTag
 from sagemaker.mxnet.model import MXNetModel
 from sagemaker.pytorch.model import PyTorchModel
@@ -551,3 +551,93 @@ def test_all_framework_models_add_jumpstart_tags(
 
         sagemaker_session.create_model.reset_mock()
         sagemaker_session.endpoint_from_production_variants.reset_mock()
+
+
+@patch("sagemaker.utils.repack_model")
+def test_script_mode_model_uses_jumpstart_base_name(repack_model, sagemaker_session):
+
+    jumpstart_source_dir = f"s3://{list(JUMPSTART_BUCKET_NAME_SET)[0]}/source_dirs/source.tar.gz"
+    t = Model(
+        entry_point=ENTRY_POINT_INFERENCE,
+        role=ROLE,
+        sagemaker_session=sagemaker_session,
+        source_dir=jumpstart_source_dir,
+        image_uri=IMAGE_URI,
+        model_data=MODEL_DATA,
+    )
+    t.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)
+
+    assert sagemaker_session.create_model.call_args_list[0][0][0].startswith(
+        JUMPSTART_RESOURCE_BASE_NAME
+    )
+
+    assert sagemaker_session.endpoint_from_production_variants.call_args_list[0].startswith(
+        JUMPSTART_RESOURCE_BASE_NAME
+    )
+
+    sagemaker_session.create_model.reset_mock()
+    sagemaker_session.endpoint_from_production_variants.reset_mock()
+
+    non_jumpstart_source_dir = "s3://blah/blah/blah"
+    t = Model(
+        entry_point=ENTRY_POINT_INFERENCE,
+        role=ROLE,
+        sagemaker_session=sagemaker_session,
+        source_dir=non_jumpstart_source_dir,
+        image_uri=IMAGE_URI,
+        model_data=MODEL_DATA,
+    )
+    t.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)
+
+    assert not sagemaker_session.create_model.call_args_list[0][0][0].startswith(
+        JUMPSTART_RESOURCE_BASE_NAME
+    )
+
+    assert not sagemaker_session.endpoint_from_production_variants.call_args_list[0][1][
+        "name"
+    ].startswith(JUMPSTART_RESOURCE_BASE_NAME)
+
+
+@patch("sagemaker.utils.repack_model")
+@patch("sagemaker.fw_utils.tar_and_upload_dir")
+def test_all_framework_models_add_jumpstart_base_name(
+    repack_model, tar_and_uload_dir, sagemaker_session
+):
+    framework_model_classes_to_kwargs = {
+        PyTorchModel: {"framework_version": "1.5.0", "py_version": "py3"},
+        TensorFlowModel: {
+            "framework_version": "2.3",
+        },
+        HuggingFaceModel: {
+            "pytorch_version": "1.7.1",
+            "py_version": "py36",
+            "transformers_version": "4.6.1",
+        },
+        MXNetModel: {"framework_version": "1.7.0", "py_version": "py3"},
+        SKLearnModel: {
+            "framework_version": "0.23-1",
+        },
+        XGBoostModel: {
+            "framework_version": "1.3-1",
+        },
+    }
+    jumpstart_model_dir = f"s3://{list(JUMPSTART_BUCKET_NAME_SET)[0]}/model_dirs/model.tar.gz"
+    for framework_model_class, kwargs in framework_model_classes_to_kwargs.items():
+        framework_model_class(
+            entry_point=ENTRY_POINT_INFERENCE,
+            role=ROLE,
+            sagemaker_session=sagemaker_session,
+            model_data=jumpstart_model_dir,
+            **kwargs,
+        ).deploy(instance_type="ml.m2.xlarge", initial_instance_count=INSTANCE_COUNT)
+
+        assert sagemaker_session.create_model.call_args_list[0][0][0].startswith(
+            JUMPSTART_RESOURCE_BASE_NAME
+        )
+
+        assert sagemaker_session.endpoint_from_production_variants.call_args_list[0].startswith(
+            JUMPSTART_RESOURCE_BASE_NAME
+        )
+
+        sagemaker_session.create_model.reset_mock()
+        sagemaker_session.endpoint_from_production_variants.reset_mock()