Use separate tags for inference and training configs

src/sagemaker/jumpstart/enums.py

@@ -92,7 +92,9 @@ class JumpStartTag(str, Enum):
     MODEL_ID = "sagemaker-sdk:jumpstart-model-id"
     MODEL_VERSION = "sagemaker-sdk:jumpstart-model-version"
     MODEL_TYPE = "sagemaker-sdk:jumpstart-model-type"
-    MODEL_CONFIG_NAME = "sagemaker-sdk:jumpstart-model-config-name"
+
+    INFERENCE_CONFIG_NAME = "sagemaker-sdk:jumpstart-inference-config-name"
+    TRAINING_CONFIG_NAME = "sagemaker-sdk:jumpstart-training-config-name"
 
 
 class SerializerType(str, Enum):

src/sagemaker/jumpstart/estimator.py

@@ -733,7 +733,7 @@ def attach(
         config_name = None
         if model_id is None:
 
-            model_id, model_version, config_name = get_model_info_from_training_job(
+            model_id, model_version, _, config_name = get_model_info_from_training_job(
                 training_job_name=training_job_name, sagemaker_session=sagemaker_session
             )
 
@@ -1139,7 +1139,9 @@ def set_training_config(self, config_name: str) -> None:
         Args:
             config_name (str): The name of the config.
         """
-        self.__init__(**self.init_kwargs, config_name=config_name)
+        self.__init__(
+            model_id=self.model_id, model_version=self.model_version, config_name=config_name
+        )
 
     def __str__(self) -> str:
         """Overriding str(*) method to make more human-readable."""

src/sagemaker/jumpstart/factory/estimator.py

@@ -61,7 +61,7 @@
     JumpStartModelInitKwargs,
 )
 from sagemaker.jumpstart.utils import (
-    add_jumpstart_model_id_version_tags,
+    add_jumpstart_model_info_tags,
     get_eula_message,
     update_dict_if_key_not_present,
     resolve_estimator_sagemaker_config_field,
@@ -477,11 +477,12 @@ def _add_tags_to_kwargs(kwargs: JumpStartEstimatorInitKwargs) -> JumpStartEstima
     ).version
 
     if kwargs.sagemaker_session.settings.include_jumpstart_tags:
-        kwargs.tags = add_jumpstart_model_id_version_tags(
+        kwargs.tags = add_jumpstart_model_info_tags(
             kwargs.tags,
             kwargs.model_id,
             full_model_version,
             config_name=kwargs.config_name,
+            scope=JumpStartScriptScope.TRAINING,
         )
     return kwargs
 

src/sagemaker/jumpstart/factory/model.py

@@ -44,7 +44,7 @@
     JumpStartModelRegisterKwargs,
 )
 from sagemaker.jumpstart.utils import (
-    add_jumpstart_model_id_version_tags,
+    add_jumpstart_model_info_tags,
     update_dict_if_key_not_present,
     resolve_model_sagemaker_config_field,
     verify_model_region_and_return_specs,
@@ -495,8 +495,13 @@ def _add_tags_to_kwargs(kwargs: JumpStartModelDeployKwargs) -> Dict[str, Any]:
     ).version
 
     if kwargs.sagemaker_session.settings.include_jumpstart_tags:
-        kwargs.tags = add_jumpstart_model_id_version_tags(
-            kwargs.tags, kwargs.model_id, full_model_version, kwargs.model_type, kwargs.config_name
+        kwargs.tags = add_jumpstart_model_info_tags(
+            kwargs.tags,
+            kwargs.model_id,
+            full_model_version,
+            kwargs.model_type,
+            config_name=kwargs.config_name,
+            scope=JumpStartScriptScope.INFERENCE,
         )
 
     return kwargs

src/sagemaker/jumpstart/session_utils.py

@@ -17,7 +17,7 @@
 from typing import Optional, Tuple
 from sagemaker.jumpstart.constants import DEFAULT_JUMPSTART_SAGEMAKER_SESSION
 
-from sagemaker.jumpstart.utils import get_jumpstart_model_id_version_from_resource_arn
+from sagemaker.jumpstart.utils import get_jumpstart_model_info_from_resource_arn
 from sagemaker.session import Session
 from sagemaker.utils import aws_partition
 
@@ -26,7 +26,7 @@ def get_model_info_from_endpoint(
     endpoint_name: str,
     inference_component_name: Optional[str] = None,
     sagemaker_session: Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
-) -> Tuple[str, str, Optional[str], Optional[str]]:
+) -> Tuple[str, str, Optional[str], Optional[str], Optional[str]]:
     """Optionally inference component names, return the model ID, version and config name.
 
     Infers the model ID and version based on the resource tags. Returns a tuple of the model ID
@@ -46,7 +46,8 @@ def get_model_info_from_endpoint(
             (
                 model_id,
                 model_version,
-                config_name,
+                inference_config_name,
+                training_config_name,
             ) = _get_model_info_from_inference_component_endpoint_with_inference_component_name(  # noqa E501  # pylint: disable=c0301
                 inference_component_name, sagemaker_session
             )
@@ -55,17 +56,29 @@ def get_model_info_from_endpoint(
             (
                 model_id,
                 model_version,
-                config_name,
+                inference_config_name,
+                training_config_name,
                 inference_component_name,
             ) = _get_model_info_from_inference_component_endpoint_without_inference_component_name(  # noqa E501  # pylint: disable=c0301
                 endpoint_name, sagemaker_session
             )
 
     else:
-        model_id, model_version, config_name = _get_model_info_from_model_based_endpoint(
+        (
+            model_id,
+            model_version,
+            inference_config_name,
+            training_config_name,
+        ) = _get_model_info_from_model_based_endpoint(
             endpoint_name, inference_component_name, sagemaker_session
         )
-    return model_id, model_version, inference_component_name, config_name
+    return (
+        model_id,
+        model_version,
+        inference_component_name,
+        inference_config_name,
+        training_config_name,
+    )
 
 
 def _get_model_info_from_inference_component_endpoint_without_inference_component_name(
@@ -125,9 +138,12 @@ def _get_model_info_from_inference_component_endpoint_with_inference_component_n
         f"inference-component/{inference_component_name}"
     )
 
-    model_id, model_version, config_name = get_jumpstart_model_id_version_from_resource_arn(
-        inference_component_arn, sagemaker_session
-    )
+    (
+        model_id,
+        model_version,
+        inference_config_name,
+        training_config_name,
+    ) = get_jumpstart_model_info_from_resource_arn(inference_component_arn, sagemaker_session)
 
     if not model_id:
         raise ValueError(
@@ -136,14 +152,14 @@ def _get_model_info_from_inference_component_endpoint_with_inference_component_n
             "when retrieving default predictor for this inference component."
         )
 
-    return model_id, model_version, config_name
+    return model_id, model_version, inference_config_name, training_config_name
 
 
 def _get_model_info_from_model_based_endpoint(
     endpoint_name: str,
     inference_component_name: Optional[str],
     sagemaker_session: Session,
-) -> Tuple[str, str, Optional[str]]:
+) -> Tuple[str, str, Optional[str], Optional[str]]:
     """Returns the model ID, version and config name inferred from a model-based endpoint.
 
     Raises:
@@ -163,9 +179,12 @@ def _get_model_info_from_model_based_endpoint(
 
     endpoint_arn = f"arn:{partition}:sagemaker:{region}:{account_id}:endpoint/{endpoint_name}"
 
-    model_id, model_version, config_name = get_jumpstart_model_id_version_from_resource_arn(
-        endpoint_arn, sagemaker_session
-    )
+    (
+        model_id,
+        model_version,
+        inference_config_name,
+        training_config_name,
+    ) = get_jumpstart_model_info_from_resource_arn(endpoint_arn, sagemaker_session)
 
     if not model_id:
         raise ValueError(
@@ -174,13 +193,13 @@ def _get_model_info_from_model_based_endpoint(
             "predictor for this endpoint."
         )
 
-    return model_id, model_version, config_name
+    return model_id, model_version, inference_config_name, training_config_name
 
 
 def get_model_info_from_training_job(
     training_job_name: str,
     sagemaker_session: Optional[Session] = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
-) -> Tuple[str, str, Optional[str]]:
+) -> Tuple[str, str, Optional[str], Optional[str]]:
     """Returns the model ID and version and config name inferred from a training job.
 
     Raises:
@@ -199,8 +218,9 @@ def get_model_info_from_training_job(
     (
         model_id,
         inferred_model_version,
-        config_name,
-    ) = get_jumpstart_model_id_version_from_resource_arn(training_job_arn, sagemaker_session)
+        inference_config_name,
+        trainig_config_name,
+    ) = get_jumpstart_model_info_from_resource_arn(training_job_arn, sagemaker_session)
 
     model_version = inferred_model_version or None
 
@@ -211,4 +231,4 @@ def get_model_info_from_training_job(
             "for this training job."
         )
 
-    return model_id, model_version, config_name
+    return model_id, model_version, inference_config_name, trainig_config_name

src/sagemaker/jumpstart/utils.py

@@ -320,7 +320,8 @@ def add_single_jumpstart_tag(
                     tag_key_in_array(enums.JumpStartTag.MODEL_ID, curr_tags)
                     or tag_key_in_array(enums.JumpStartTag.MODEL_VERSION, curr_tags)
                     or tag_key_in_array(enums.JumpStartTag.MODEL_TYPE, curr_tags)
-                    or tag_key_in_array(enums.JumpStartTag.MODEL_CONFIG_NAME, curr_tags)
+                    or tag_key_in_array(enums.JumpStartTag.INFERENCE_CONFIG_NAME, curr_tags)
+                    or tag_key_in_array(enums.JumpStartTag.TRAINING_CONFIG_NAME, curr_tags)
                 )
                 if is_uri
                 else False
@@ -351,12 +352,13 @@ def get_jumpstart_base_name_if_jumpstart_model(
     return None
 
 
-def add_jumpstart_model_id_version_tags(
+def add_jumpstart_model_info_tags(
     tags: Optional[List[TagsDict]],
     model_id: str,
     model_version: str,
     model_type: Optional[enums.JumpStartModelType] = None,
     config_name: Optional[str] = None,
+    scope: enums.JumpStartScriptScope = None,
 ) -> List[TagsDict]:
     """Add custom model ID and version tags to JumpStart related resources."""
     if model_id is None or model_version is None:
@@ -380,10 +382,17 @@ def add_jumpstart_model_id_version_tags(
             tags,
             is_uri=False,
         )
-    if config_name:
+    if config_name and scope == enums.JumpStartScriptScope.INFERENCE:
         tags = add_single_jumpstart_tag(
             config_name,
-            enums.JumpStartTag.MODEL_CONFIG_NAME,
+            enums.JumpStartTag.INFERENCE_CONFIG_NAME,
+            tags,
+            is_uri=False,
+        )
+    if config_name and scope == enums.JumpStartScriptScope.TRAINING:
+        tags = add_single_jumpstart_tag(
+            config_name,
+            enums.JumpStartTag.TRAINING_CONFIG_NAME,
             tags,
             is_uri=False,
         )
@@ -840,10 +849,10 @@ def _extract_value_from_list_of_tags(
     return resolved_value
 
 
-def get_jumpstart_model_id_version_from_resource_arn(
+def get_jumpstart_model_info_from_resource_arn(
     resource_arn: str,
     sagemaker_session: Session = constants.DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
-) -> Tuple[Optional[str], Optional[str], Optional[str]]:
+) -> Tuple[Optional[str], Optional[str], Optional[str], Optional[str]]:
     """Returns the JumpStart model ID, version and config name if in resource tags.
 
     Returns 'None' if model ID or version or config name cannot be inferred from tags.
@@ -853,7 +862,8 @@ def get_jumpstart_model_id_version_from_resource_arn(
 
     model_id_keys = [enums.JumpStartTag.MODEL_ID, *constants.EXTRA_MODEL_ID_TAGS]
     model_version_keys = [enums.JumpStartTag.MODEL_VERSION, *constants.EXTRA_MODEL_VERSION_TAGS]
-    model_config_name_keys = [enums.JumpStartTag.MODEL_CONFIG_NAME]
+    inference_config_name_keys = [enums.JumpStartTag.INFERENCE_CONFIG_NAME]
+    training_config_name_keys = [enums.JumpStartTag.TRAINING_CONFIG_NAME]
 
     model_id: Optional[str] = _extract_value_from_list_of_tags(
         tag_keys=model_id_keys,
@@ -869,14 +879,21 @@ def get_jumpstart_model_id_version_from_resource_arn(
         resource_arn=resource_arn,
     )
 
-    config_name: Optional[str] = _extract_value_from_list_of_tags(
-        tag_keys=model_config_name_keys,
+    inference_config_name: Optional[str] = _extract_value_from_list_of_tags(
+        tag_keys=inference_config_name_keys,
+        list_tags_result=list_tags_result,
+        resource_name="inference config name",
+        resource_arn=resource_arn,
+    )
+
+    training_config_name: Optional[str] = _extract_value_from_list_of_tags(
+        tag_keys=training_config_name_keys,
         list_tags_result=list_tags_result,
-        resource_name="model config name",
+        resource_name="training config name",
         resource_arn=resource_arn,
     )
 
-    return model_id, model_version, config_name
+    return model_id, model_version, inference_config_name, training_config_name
 
 
 def get_region_fallback(

src/sagemaker/predictor.py

@@ -82,6 +82,7 @@ def retrieve_default(
             inferred_model_version,
             inferred_inference_component_name,
             inferred_config_name,
+            _,
         ) = get_model_info_from_endpoint(endpoint_name, inference_component_name, sagemaker_session)
 
         if not inferred_model_id: