Add supported inference and incremental training configs

src/sagemaker/jumpstart/estimator.py

@@ -504,7 +504,7 @@ def __init__(
             enable_remote_debug (bool or PipelineVariable): Optional.
                 Specifies whether RemoteDebug is enabled for the training job
             config_name (Optional[str]):
-                Name of the JumpStart Model config to apply. (Default: None).
+                Name of the training configuration to apply to the Estimator. (Default: None).
 
         Raises:
             ValueError: If the model ID is not recognized by JumpStart.
@@ -686,6 +686,7 @@ def attach(
         model_version: Optional[str] = None,
         sagemaker_session: session.Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
         model_channel_name: str = "model",
+        config_name: Optional[str] = None,
     ) -> "JumpStartEstimator":
         """Attach to an existing training job.
 
@@ -721,6 +722,8 @@ def attach(
                 model data will be downloaded (default: 'model'). If no channel
                 with the same name exists in the training job, this option will
                 be ignored.
+            config_name (str): Optional. Name of the training configuration to use
+                when attaching to the training job. (Default: None).
 
         Returns:
             Instance of the calling ``JumpStartEstimator`` Class with the attached
@@ -732,7 +735,6 @@ def attach(
         """
         config_name = None
         if model_id is None:
-
             model_id, model_version, _, config_name = get_model_info_from_training_job(
                 training_job_name=training_job_name, sagemaker_session=sagemaker_session
             )
@@ -746,6 +748,9 @@ def attach(
             "tolerate_deprecated_model": True,  # model is already trained
         }
 
+        if config_name:
+            additional_kwargs.update({"config_name": config_name})
+
         model_specs = verify_model_region_and_return_specs(
             model_id=model_id,
             version=model_version,
@@ -804,6 +809,7 @@ def deploy(
         dependencies: Optional[List[str]] = None,
         git_config: Optional[Dict[str, str]] = None,
         use_compiled_model: bool = False,
+        inference_config_name: Optional[str] = None,
     ) -> PredictorBase:
         """Creates endpoint from training job.
 
@@ -1039,6 +1045,8 @@ def deploy(
                 (Default: None).
             use_compiled_model (bool): Flag to select whether to use compiled
                 (optimized) model. (Default: False).
+            inference_config_name (Optional[str]): Name of the inference configuration to
+                be used in the model. (Default: None).
         """
         self.orig_predictor_cls = predictor_cls
 
@@ -1091,7 +1099,8 @@ def deploy(
             git_config=git_config,
             use_compiled_model=use_compiled_model,
             training_instance_type=self.instance_type,
-            config_name=self.config_name,
+            training_config_name=self.config_name,
+            inference_config_name=inference_config_name,
         )
 
         predictor = super(JumpStartEstimator, self).deploy(
@@ -1108,7 +1117,7 @@ def deploy(
                 tolerate_deprecated_model=self.tolerate_deprecated_model,
                 tolerate_vulnerable_model=self.tolerate_vulnerable_model,
                 sagemaker_session=self.sagemaker_session,
-                config_name=self.config_name,
+                config_name=estimator_deploy_kwargs.config_name,
             )
 
         # If a predictor class was passed, do not mutate predictor
@@ -1140,7 +1149,9 @@ def set_training_config(self, config_name: str) -> None:
             config_name (str): The name of the config.
         """
         self.__init__(
-            model_id=self.model_id, model_version=self.model_version, config_name=config_name
+            model_id=self.model_id,
+            model_version=self.model_version,
+            config_name=config_name,
         )
 
     def __str__(self) -> str:

src/sagemaker/jumpstart/factory/estimator.py

@@ -207,6 +207,7 @@ def get_init_kwargs(
     estimator_init_kwargs = _add_role_to_kwargs(estimator_init_kwargs)
     estimator_init_kwargs = _add_env_to_kwargs(estimator_init_kwargs)
     estimator_init_kwargs = _add_tags_to_kwargs(estimator_init_kwargs)
+    estimator_init_kwargs = _add_config_name_to_kwargs(estimator_init_kwargs)
 
     return estimator_init_kwargs
 
@@ -291,7 +292,8 @@ def get_deploy_kwargs(
     use_compiled_model: Optional[bool] = None,
     model_name: Optional[str] = None,
     training_instance_type: Optional[str] = None,
-    config_name: Optional[str] = None,
+    training_config_name: Optional[str] = None,
+    inference_config_name: Optional[str] = None,
 ) -> JumpStartEstimatorDeployKwargs:
     """Returns kwargs required to call `deploy` on `sagemaker.estimator.Estimator` object."""
 
@@ -319,7 +321,8 @@ def get_deploy_kwargs(
         tolerate_vulnerable_model=tolerate_vulnerable_model,
         tolerate_deprecated_model=tolerate_deprecated_model,
         sagemaker_session=sagemaker_session,
-        config_name=config_name,
+        training_config_name=training_config_name,
+        config_name=inference_config_name,
     )
 
     model_init_kwargs: JumpStartModelInitKwargs = model.get_init_kwargs(
@@ -348,7 +351,7 @@ def get_deploy_kwargs(
         tolerate_deprecated_model=tolerate_deprecated_model,
         training_instance_type=training_instance_type,
         disable_instance_type_logging=True,
-        config_name=config_name,
+        config_name=model_deploy_kwargs.config_name,
     )
 
     estimator_deploy_kwargs: JumpStartEstimatorDeployKwargs = JumpStartEstimatorDeployKwargs(
@@ -393,7 +396,7 @@ def get_deploy_kwargs(
         tolerate_vulnerable_model=model_init_kwargs.tolerate_vulnerable_model,
         tolerate_deprecated_model=model_init_kwargs.tolerate_deprecated_model,
         use_compiled_model=use_compiled_model,
-        config_name=config_name,
+        config_name=model_deploy_kwargs.config_name,
     )
 
     return estimator_deploy_kwargs
@@ -793,3 +796,27 @@ def _add_fit_extra_kwargs(kwargs: JumpStartEstimatorFitKwargs) -> JumpStartEstim
             setattr(kwargs, key, value)
 
     return kwargs
+
+
+def _add_config_name_to_kwargs(
+    kwargs: JumpStartEstimatorInitKwargs,
+) -> JumpStartEstimatorInitKwargs:
+    """Sets tags in kwargs based on default or override, returns full kwargs."""
+
+    specs = verify_model_region_and_return_specs(
+        model_id=kwargs.model_id,
+        version=kwargs.model_version,
+        scope=JumpStartScriptScope.TRAINING,
+        region=kwargs.region,
+        tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
+        tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
+        sagemaker_session=kwargs.sagemaker_session,
+        config_name=kwargs.config_name,
+    )
+
+    if specs.training_configs and specs.training_configs.get_top_config_from_ranking():
+        kwargs.config_name = (
+            kwargs.config_name or specs.training_configs.get_top_config_from_ranking().config_name
+        )
+
+    return kwargs

src/sagemaker/jumpstart/factory/model.py

@@ -42,6 +42,7 @@
     JumpStartModelDeployKwargs,
     JumpStartModelInitKwargs,
     JumpStartModelRegisterKwargs,
+    JumpStartModelSpecs,
 )
 from sagemaker.jumpstart.utils import (
     add_jumpstart_model_info_tags,
@@ -548,7 +549,27 @@ def _add_resources_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModel
     return kwargs
 
 
-def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
+def _select_inference_config_from_training_config(
+    specs: JumpStartModelSpecs, training_config_name: str
+) -> Optional[str]:
+    """Selects the inference config from the training config.
+
+    Args:
+        specs (JumpStartModelSpecs): The specs for the model.
+        training_config_name (str): The name of the training config.
+
+    Returns:
+        str: The name of the inference config.
+    """
+    if specs.training_configs:
+        resolved_training_config = specs.training_configs.configs.get(training_config_name)
+        if resolved_training_config:
+            return resolved_training_config.default_inference_config
+
+    return None
+
+
+def _add_config_name_to_init_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
     """Sets default config name to the kwargs. Returns full kwargs.
 
     Raises:
@@ -566,13 +587,9 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
         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
-        )
+    if specs.inference_configs:
+        default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
+        kwargs.config_name = kwargs.config_name or default_config_name
 
         if not kwargs.config_name:
             return kwargs
@@ -593,6 +610,42 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
     return kwargs
 
 
+def _add_config_name_to_deploy_kwargs(
+    kwargs: JumpStartModelDeployKwargs, training_config_name: Optional[str] = None
+) -> JumpStartModelInitKwargs:
+    """Sets default config name to the kwargs. Returns full kwargs.
+
+    If a training_config_name is passed, then choose the inference config
+    based on the supported inference configs in that training config.
+
+    Raises:
+        ValueError: If the instance_type is not supported with the current config.
+    """
+
+    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 training_config_name:
+        kwargs.config_name = _select_inference_config_from_training_config(
+            specs=specs, training_config_name=training_config_name
+        )
+
+    if specs.inference_configs:
+        default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
+        kwargs.config_name = kwargs.config_name or default_config_name
+
+    return kwargs
+
+
 def get_deploy_kwargs(
     model_id: str,
     model_version: Optional[str] = None,
@@ -623,6 +676,7 @@ def get_deploy_kwargs(
     resources: Optional[ResourceRequirements] = None,
     managed_instance_scaling: Optional[str] = None,
     endpoint_type: Optional[EndpointType] = None,
+    training_config_name: Optional[str] = None,
     config_name: Optional[str] = None,
 ) -> JumpStartModelDeployKwargs:
     """Returns kwargs required to call `deploy` on `sagemaker.estimator.Model` object."""
@@ -664,6 +718,10 @@ def get_deploy_kwargs(
 
     deploy_kwargs = _add_endpoint_name_to_kwargs(kwargs=deploy_kwargs)
 
+    deploy_kwargs = _add_config_name_to_deploy_kwargs(
+        kwargs=deploy_kwargs, training_config_name=training_config_name
+    )
+
     deploy_kwargs = _add_instance_type_to_kwargs(kwargs=deploy_kwargs)
 
     deploy_kwargs.initial_instance_count = initial_instance_count or 1
@@ -858,6 +916,7 @@ 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)
+
+    model_init_kwargs = _add_config_name_to_init_kwargs(kwargs=model_init_kwargs)
 
     return model_init_kwargs

src/sagemaker/jumpstart/types.py

@@ -1077,30 +1077,52 @@ class JumpStartMetadataConfig(JumpStartDataHolderType):
         "config_components",
         "resolved_metadata_config",
         "config_name",
+        "default_inference_config",
+        "default_incremental_trainig_config",
+        "supported_inference_configs",
+        "supported_incremental_training_configs",
     ]
 
     def __init__(
         self,
         config_name: str,
+        config: Dict[str, Any],
         base_fields: Dict[str, Any],
         config_components: Dict[str, JumpStartConfigComponent],
-        benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]],
     ):
         """Initializes a JumpStartMetadataConfig object from its json representation.
 
         Args:
+            config_name (str): Name of the config,
+            config (Dict[str, Any]):
+                Dictionary representation of the config.
             base_fields (Dict[str, Any]):
                 The default base fields that are used to construct the final resolved config.
             config_components (Dict[str, JumpStartConfigComponent]):
                 The list of components that are used to construct the resolved config.
-            benchmark_metrics (Dict[str, List[JumpStartBenchmarkStat]]):
-                The dictionary of benchmark metrics with name being the key.
         """
         self.base_fields = base_fields
         self.config_components: Dict[str, JumpStartConfigComponent] = config_components
-        self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = benchmark_metrics
+        self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = (
+            {
+                stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
+                for stat_name, stats in config.get("benchmark_metrics").items()
+            }
+            if config and config.get("benchmark_metrics")
+            else None
+        )
         self.resolved_metadata_config: Optional[Dict[str, Any]] = None
         self.config_name: Optional[str] = config_name
+        self.default_inference_config: Optional[str] = config.get("default_inference_config")
+        self.default_incremental_trainig_config: Optional[str] = config.get(
+            "default_incremental_training_config"
+        )
+        self.supported_inference_configs: Optional[List[str]] = config.get(
+            "supported_inference_configs"
+        )
+        self.supported_incremental_training_configs: Optional[List[str]] = config.get(
+            "supported_incremental_training_configs"
+        )
 
     def to_json(self) -> Dict[str, Any]:
         """Returns json representation of JumpStartMetadataConfig object."""
@@ -1255,6 +1277,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
             {
                 alias: JumpStartMetadataConfig(
                     alias,
+                    config,
                     json_obj,
                     (
                         {
@@ -1264,14 +1287,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
                         if config and config.get("component_names")
                         else None
                     ),
-                    (
-                        {
-                            stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
-                            for stat_name, stats in config.get("benchmark_metrics").items()
-                        }
-                        if config and config.get("benchmark_metrics")
-                        else None
-                    ),
                 )
                 for alias, config in json_obj["inference_configs"].items()
             }
@@ -1308,6 +1323,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
                 {
                     alias: JumpStartMetadataConfig(
                         alias,
+                        config,
                         json_obj,
                         (
                             {
@@ -1317,14 +1333,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
                             if config and config.get("component_names")
                             else None
                         ),
-                        (
-                            {
-                                stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
-                                for stat_name, stats in config.get("benchmark_metrics").items()
-                            }
-                            if config and config.get("benchmark_metrics")
-                            else None
-                        ),
                     )
                     for alias, config in json_obj["training_configs"].items()
                 }