Reinvent keynote3 v2

CHANGELOG.md

@@ -1,5 +1,15 @@
 # Changelog
 
+## v2.255.0 (2025-12-03)
+
+### Features
+
+* Extracts reward Lambda ARN from Nova recipes
+* Passes it as training job hyperparameter
+* Added LLMFT recipe support with standardized recipe handling
+* Enhanced recipe validation and multi-model type compatibility
+
+
 ## v2.254.1 (2025-10-31)
 
 ### Bug Fixes and Other Changes

VERSION

@@ -1 +1 @@
-2.254.2.dev0
+2.254.2.dev0

pyproject.toml

@@ -32,7 +32,7 @@ classifiers = [
 ]
 dependencies = [
   "attrs>=24,<26",
-  "boto3>=1.39.5,<2.0",
+  "boto3>=1.42.2,<2.0",
   "cloudpickle>=2.2.1",
   "docker",
   "fastapi",
@@ -49,7 +49,7 @@ dependencies = [
   "psutil",
   "PyYAML>=6.0.1",
   "requests",
-  "sagemaker-core>=1.0.17,<2.0.0",
+  "sagemaker-core>=1.0.71,<2.0.0",
   "schema",
   "smdebug_rulesconfig==1.0.1",
   "tblib>=1.7.0,<4",

requirements/extras/test_requirements.txt

@@ -62,4 +62,4 @@ mypy-boto3-s3==1.35.76
 mypy-extensions==1.0.0
 mypy==1.9.0
 # apache-airflow transitive dependancy
-google-re2<1.1.20250805; python_version < "3.10"
+google-re2<1.1.20250805; python_version < "3.10"

src/sagemaker/__init__.py

@@ -48,6 +48,7 @@
 from sagemaker.analytics import TrainingJobAnalytics, HyperparameterTuningJobAnalytics  # noqa: F401
 from sagemaker.local.local_session import LocalSession  # noqa: F401
 
+from sagemaker.container_base_model import ContainerBaseModel  # noqa: F401
 from sagemaker.model import Model, ModelPackage  # noqa: F401
 from sagemaker.model_metrics import ModelMetrics, MetricsSource, FileSource  # noqa: F401
 from sagemaker.pipeline import PipelineModel  # noqa: F401

src/sagemaker/chainer/model.py

@@ -17,7 +17,7 @@
 from typing import Callable, Optional, Union, List, Dict
 
 import sagemaker
-from sagemaker import image_uris, ModelMetrics
+from sagemaker import image_uris, ModelMetrics, ContainerBaseModel
 from sagemaker.drift_check_baselines import DriftCheckBaselines
 from sagemaker.fw_utils import (
     model_code_key_prefix,
@@ -182,6 +182,8 @@ def register(
         source_uri: Optional[Union[str, PipelineVariable]] = None,
         model_card: Optional[Union[ModelPackageModelCard, ModelCard]] = None,
         model_life_cycle: Optional[ModelLifeCycle] = None,
+        model_package_registration_type: Optional[Union[str, PipelineVariable]] = None,
+        base_model: Optional[ContainerBaseModel] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -236,6 +238,9 @@ def register(
             model_card (ModeCard or ModelPackageModelCard): document contains qualitative and
                 quantitative information about a model (default: None).
             model_life_cycle (ModelLifeCycle): ModelLifeCycle object (default: None).
+            model_package_registration_type (str or PipelineVariable): Model Package Registration
+                Type (default: None).
+            base_model (ContainerBaseModel): ContainerBaseModel object (default: None).
 
         Returns:
             str: A string of SageMaker Model Package ARN.
@@ -278,6 +283,8 @@ def register(
             source_uri=source_uri,
             model_card=model_card,
             model_life_cycle=model_life_cycle,
+            model_package_registration_type=model_package_registration_type,
+            base_model=base_model,
         )
 
     def prepare_container_def(

src/sagemaker/container_base_model.py

@@ -0,0 +1,52 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express oXr implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""This file contains code related to base model for containers."""
+from __future__ import absolute_import
+
+from typing import Optional, Union
+
+from sagemaker.workflow.entities import PipelineVariable
+
+
+class ContainerBaseModel(object):
+    """Accepts Base Model parameters for conversion to request dict."""
+
+    def __init__(
+        self,
+        hub_content_name: Union[str, PipelineVariable] = None,
+        hub_content_version: Optional[Union[str, PipelineVariable]] = None,
+        recipe_name: Optional[Union[str, PipelineVariable]] = None,
+    ):
+        """Initialize a ``ContainerBaseModel`` instance and turn parameters into dict.
+
+        Args:
+            hub_content_name (str or PipelineVariable): The hub content name
+            hub_content_version (str or PipelineVariable): The hub content version
+                (default: None)
+            recipe_name (str or PipelineVariable): The Recipe name
+               (default: None)
+        """
+        self.hub_content_name = hub_content_name
+        self.hub_content_version = hub_content_version
+        self.recipe_name = recipe_name
+
+    def _to_request_dict(self):
+        """Generates a request dictionary using the parameters provided to the class."""
+        base_model_request = {}
+        if self.hub_content_name is not None:
+            base_model_request["HubContentName"] = self.hub_content_name
+        if self.hub_content_version is not None:
+            base_model_request["HubContentVersion"] = self.hub_content_version
+        if self.recipe_name is not None:
+            base_model_request["RecipeName"] = self.recipe_name
+        return base_model_request

src/sagemaker/estimator.py

@@ -1817,6 +1817,8 @@ def register(
         source_uri=None,
         model_life_cycle=None,
         model_card=None,
+        model_package_registration_type=None,
+        base_model=None,
         **kwargs,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
@@ -1868,6 +1870,9 @@ def register(
             model_card (ModeCard or ModelPackageModelCard): document contains qualitative and
                 quantitative information about a model (default: None).
             model_life_cycle (ModelLifeCycle): ModelLifeCycle object (default: None).
+            model_package_registration_type (str): Model Package Registration
+                Type (default: None).
+            base_model (ContainerBaseModel): ContainerBaseModel object (default: None).
             **kwargs: Passed to invocation of ``create_model()``. Implementations may customize
                 ``create_model()`` to accept ``**kwargs`` to customize model creation during
                 deploy. For more, see the implementation docs.
@@ -1924,6 +1929,8 @@ def register(
             source_uri=source_uri,
             model_card=model_card,
             model_life_cycle=model_life_cycle,
+            model_package_registration_type=model_package_registration_type,
+            base_model=base_model,
         )
 
     @property

src/sagemaker/huggingface/model.py

@@ -17,7 +17,7 @@
 from typing import Callable, Optional, Union, List, Dict
 
 import sagemaker
-from sagemaker import image_uris, ModelMetrics
+from sagemaker import image_uris, ModelMetrics, ContainerBaseModel
 from sagemaker.deserializers import JSONDeserializer
 from sagemaker.drift_check_baselines import DriftCheckBaselines
 from sagemaker.fw_utils import (
@@ -372,6 +372,8 @@ def register(
         source_uri: Optional[Union[str, PipelineVariable]] = None,
         model_life_cycle: Optional[ModelLifeCycle] = None,
         model_card: Optional[Union[ModelPackageModelCard, ModelCard]] = None,
+        model_package_registration_type: Optional[Union[str, PipelineVariable]] = None,
+        base_model: Optional[ContainerBaseModel] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -427,6 +429,9 @@ def register(
             model_card (ModeCard or ModelPackageModelCard): document contains qualitative and
                 quantitative information about a model (default: None).
             model_life_cycle (ModelLifeCycle): ModelLifeCycle object (default: None).
+            model_package_registration_type (str or PipelineVariable): Model Package Registration
+                Type (default: None).
+            base_model (ContainerBaseModel): ContainerBaseModel object (default: None).
 
         Returns:
             A `sagemaker.model.ModelPackage` instance.
@@ -477,6 +482,8 @@ def register(
             source_uri=source_uri,
             model_life_cycle=model_life_cycle,
             model_card=model_card,
+            model_package_registration_type=model_package_registration_type,
+            base_model=base_model,
         )
 
     def prepare_container_def(

src/sagemaker/model.py

@@ -52,6 +52,7 @@
 from sagemaker.model_card.helpers import _hash_content_str
 from sagemaker.model_card.schema_constraints import ModelApprovalStatusEnum
 from sagemaker.session import Session
+from sagemaker.container_base_model import ContainerBaseModel
 from sagemaker.model_metrics import ModelMetrics
 from sagemaker.drift_check_baselines import DriftCheckBaselines
 from sagemaker.explainer import ExplainerConfig
@@ -477,6 +478,8 @@ def register(
         model_life_cycle: Optional[ModelLifeCycle] = None,
         accept_eula: Optional[bool] = None,
         model_type: Optional[JumpStartModelType] = None,
+        model_package_registration_type: Optional[Union[str, PipelineVariable]] = None,
+        base_model: Optional[ContainerBaseModel] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -531,6 +534,9 @@ def register(
             model_card (ModeCard or ModelPackageModelCard): document contains qualitative and
                 quantitative information about a model (default: None).
             model_life_cycle (ModelLifeCycle): ModelLifeCycle object (default: None).
+            model_package_registration_type (str or PipelineVariable): Model Package Registration
+                Type (default: None).
+            base_model (ContainerBaseModel): ContainerBaseModel object (default: None).
 
         Returns:
             A `sagemaker.model.ModelPackage` instance or pipeline step arguments
@@ -578,6 +584,9 @@ def register(
             if self.model_data is not None:
                 container_def["ModelDataUrl"] = self.model_data
 
+        if base_model is not None and hasattr(base_model, "_to_request_dict"):
+            container_def["BaseModel"] = base_model._to_request_dict()
+
         model_pkg_args = sagemaker.get_model_package_args(
             self.content_types,
             self.response_types,
@@ -601,6 +610,8 @@ def register(
             source_uri=source_uri,
             model_card=model_card,
             model_life_cycle=model_life_cycle,
+            model_package_registration_type=model_package_registration_type,
+            base_model=base_model,
         )
         model_package = self.sagemaker_session.create_model_package_from_containers(
             **model_pkg_args
@@ -2150,6 +2161,7 @@ def __init__(
             You can find additional parameters for initializing this class at
             :class:`~sagemaker.model.Model`.
         """
+
         super(FrameworkModel, self).__init__(
             image_uri,
             model_data,

src/sagemaker/modules/train/model_trainer.py

@@ -108,6 +108,7 @@
     _get_args_from_recipe,
     _determine_device_type,
     _is_nova_recipe,
+    _is_llmft_recipe,
     _load_base_recipe,
 )
 
@@ -252,6 +253,7 @@ class ModelTrainer(BaseModel):
     _metric_definitions: Optional[List[MetricDefinition]] = PrivateAttr(default=None)
 
     _is_nova_recipe: Optional[bool] = PrivateAttr(default=None)
+    _is_llmft_recipe: Optional[bool] = PrivateAttr(default=None)
     _temp_recipe_train_dir: Optional[TemporaryDirectory] = PrivateAttr(default=None)
     _temp_code_dir: Optional[TemporaryDirectory] = PrivateAttr(default=None)
 
@@ -632,12 +634,13 @@ def _create_training_job_args(
 
             final_input_data_config = list(existing_channels.values()) + new_channels
 
-        if self._is_nova_recipe:
+        if self._is_nova_recipe or self._is_llmft_recipe:
+
             for input_data in final_input_data_config:
                 if input_data.channel_name == SM_RECIPE:
                     raise ValueError(
                         "Cannot use reserved channel name 'recipe' as an input channel name "
-                        " for Nova Recipe"
+                        " for Nova or LLMFT Recipe"
                     )
             recipe_file_path = os.path.join(self._temp_recipe_train_dir.name, SM_RECIPE_YAML)
             recipe_channel = self.create_input_data_channel(
@@ -646,7 +649,10 @@ def _create_training_job_args(
                 key_prefix=input_data_key_prefix,
             )
             final_input_data_config.append(recipe_channel)
-            self.hyperparameters.update({"sagemaker_recipe_local_path": SM_RECIPE_CONTAINER_PATH})
+            if self._is_nova_recipe:
+                self.hyperparameters.update(
+                    {"sagemaker_recipe_local_path": SM_RECIPE_CONTAINER_PATH}
+                )
 
         if final_input_data_config:
             final_input_data_config = self._get_input_data_config(
@@ -1201,14 +1207,15 @@ def from_recipe(
             training_recipe=training_recipe, recipe_overrides=recipe_overrides
         )
         is_nova = _is_nova_recipe(recipe=recipe)
+        is_llmft = _is_llmft_recipe(recipe=recipe)
 
-        if device_type == "cpu" and not is_nova:
+        if device_type == "cpu" and not (is_nova or is_llmft):
             raise ValueError(
                 "Training recipe is not supported for CPU instances. "
                 + "Please provide a GPU or Tranium instance type."
             )
-        if training_image is None and is_nova:
-            raise ValueError("training_image must be provided when using recipe for Nova.")
+        if training_image is None and (is_nova or is_llmft):
+            raise ValueError("training_image must be provided when using recipe for Nova or LLMFT")
 
         if training_image_config and training_image is None:
             raise ValueError("training_image must be provided when using training_image_config.")
@@ -1238,7 +1245,7 @@ def from_recipe(
             model_trainer_args["training_image"] = training_image
         if hyperparameters and not is_nova:
             logger.warning(
-                "Hyperparameters are not supported for general training recipes. "
+                "Hyperparameters are not supported for general and LLMFT training recipes. "
                 + "Ignoring hyperparameters input."
             )
         if is_nova:
@@ -1264,6 +1271,7 @@ def from_recipe(
             **model_trainer_args,
         )
         model_trainer._is_nova_recipe = is_nova
+        model_trainer._is_llmft_recipe = is_llmft
         model_trainer._temp_recipe_train_dir = tmp_dir
         return model_trainer