feat: SkipModelValidation in modelRegistry

src/sagemaker/chainer/model.py

@@ -169,6 +169,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -216,6 +217,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             str: A string of SageMaker Model Package ARN.
@@ -254,6 +257,7 @@ def register(
             framework_version=framework_version or self.framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
         )
 
     def prepare_container_def(

src/sagemaker/estimator.py

@@ -1684,6 +1684,7 @@ def register(
         framework_version=None,
         nearest_model_name=None,
         data_input_configuration=None,
+        skip_model_validation=None,
         **kwargs,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
@@ -1729,6 +1730,8 @@ def register(
             nearest_model_name (str): Name of a pre-trained machine learning benchmarked by
                 Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str): Input object for the model (default: None).
+            skip_model_validation (str): Indicates if you want to skip model validation.
+                Values can be "All" or "None" (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.
@@ -1772,6 +1775,7 @@ def register(
             framework_version=framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
         )
 
     @property

src/sagemaker/huggingface/model.py

@@ -353,6 +353,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -401,6 +402,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             A `sagemaker.model.ModelPackage` instance.
@@ -447,6 +450,7 @@ def register(
             ],
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
         )
 
     def prepare_container_def(

src/sagemaker/model.py

@@ -400,6 +400,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -447,6 +448,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             A `sagemaker.model.ModelPackage` instance or pipeline step arguments
@@ -497,6 +500,7 @@ def register(
             domain=domain,
             sample_payload_url=sample_payload_url,
             task=task,
+            skip_model_validation=skip_model_validation,
         )
         model_package = self.sagemaker_session.create_model_package_from_containers(
             **model_pkg_args

src/sagemaker/mxnet/model.py

@@ -171,6 +171,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -218,6 +219,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             A `sagemaker.model.ModelPackage` instance.
@@ -256,6 +259,7 @@ def register(
             framework_version=framework_version or self.framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
         )
 
     def prepare_container_def(

src/sagemaker/pipeline.py

@@ -356,6 +356,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -403,6 +404,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             A `sagemaker.model.ModelPackage` instance.
@@ -448,6 +451,7 @@ def register(
             domain=domain,
             sample_payload_url=sample_payload_url,
             task=task,
+            skip_model_validation=skip_model_validation,
         )
 
         self.sagemaker_session.create_model_package_from_containers(**model_pkg_args)

src/sagemaker/pytorch/model.py

@@ -173,6 +173,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -220,6 +221,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             A `sagemaker.model.ModelPackage` instance.
@@ -258,6 +261,7 @@ def register(
             framework_version=framework_version or self.framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
         )
 
     def prepare_container_def(

src/sagemaker/session.py

@@ -3648,6 +3648,7 @@ def create_model_package_from_containers(
         domain=None,
         sample_payload_url=None,
         task=None,
+        skip_model_validation="None",
     ):
         """Get request dictionary for CreateModelPackage API.
 
@@ -3682,6 +3683,8 @@ def create_model_package_from_containers(
             task (str): Task values which are supported by Inference Recommender are "FILL_MASK",
                 "IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION", "IMAGE_SEGMENTATION",
                 "CLASSIFICATION", "REGRESSION", "OTHER" (default: None).
+            skip_model_validation (str): Indicates if you want to skip model validation.
+                Values can be "All" or "None" (default: None).
         """
         if containers:
             # Containers are provided. Now we can merge missing entries from config.
@@ -3737,6 +3740,7 @@ def create_model_package_from_containers(
             domain=domain,
             sample_payload_url=sample_payload_url,
             task=task,
+            skip_model_validation=skip_model_validation,
         )
 
         def submit(request):
@@ -5764,6 +5768,7 @@ def get_model_package_args(
     domain=None,
     sample_payload_url=None,
     task=None,
+    skip_model_validation=None,
 ):
     """Get arguments for create_model_package method.
 
@@ -5800,6 +5805,8 @@ def get_model_package_args(
         task (str): Task values which are supported by Inference Recommender are "FILL_MASK",
             "IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION", "IMAGE_SEGMENTATION",
             "CLASSIFICATION", "REGRESSION", "OTHER" (default: None).
+        skip_model_validation (str): Indicates if you want to skip model validation.
+            Values can be "All" or "None" (default: None).
 
     Returns:
         dict: A dictionary of method argument names and values.
@@ -5848,6 +5855,8 @@ def get_model_package_args(
         model_package_args["sample_payload_url"] = sample_payload_url
     if task is not None:
         model_package_args["task"] = task
+    if skip_model_validation is not None:
+        model_package_args["skip_model_validation"] = skip_model_validation
     return model_package_args
 
 
@@ -5871,6 +5880,7 @@ def get_create_model_package_request(
     domain=None,
     sample_payload_url=None,
     task=None,
+    skip_model_validation="None",
 ):
     """Get request dictionary for CreateModelPackage API.
 
@@ -5905,6 +5915,8 @@ def get_create_model_package_request(
         task (str): Task values which are supported by Inference Recommender are "FILL_MASK",
             "IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION", "IMAGE_SEGMENTATION",
             "CLASSIFICATION", "REGRESSION", "OTHER" (default: None).
+        skip_model_validation (str): Indicates if you want to skip model validation.
+            Values can be "All" or "None" (default: None).
     """
 
     if all([model_package_name, model_package_group_name]):
@@ -5974,6 +5986,7 @@ def get_create_model_package_request(
         request_dict["InferenceSpecification"] = inference_specification
     request_dict["CertifyForMarketplace"] = marketplace_cert
     request_dict["ModelApprovalStatus"] = approval_status
+    request_dict["SkipModelValidation"] = skip_model_validation
     return request_dict
 
 

src/sagemaker/sklearn/model.py

@@ -166,6 +166,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -213,6 +214,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             A `sagemaker.model.ModelPackage` instance.
@@ -251,6 +254,7 @@ def register(
             framework_version=framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
         )
 
     def prepare_container_def(

src/sagemaker/tensorflow/model.py

@@ -228,6 +228,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -275,6 +276,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             A `sagemaker.model.ModelPackage` instance.
@@ -313,6 +316,7 @@ def register(
             framework_version=framework_version or self.framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
         )
 
     def deploy(

src/sagemaker/workflow/_utils.py

@@ -322,6 +322,7 @@ def __init__(
         domain=None,
         sample_payload_url=None,
         task=None,
+        skip_model_validation=None,
         **kwargs,
     ):
         """Constructor of a register model step.
@@ -371,6 +372,8 @@ def __init__(
             task (str): Task values which are supported by Inference Recommender are "FILL_MASK",
                 "IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION", "IMAGE_SEGMENTATION",
                 "CLASSIFICATION", "REGRESSION", "OTHER" (default: None).
+            skip_model_validation (str): Indicates if you want to skip model validation.
+                Values can be "All" or "None" (default: None).
             **kwargs: additional arguments to `create_model`.
         """
         super(_RegisterModelStep, self).__init__(
@@ -407,6 +410,7 @@ def __init__(
         self.tags = tags
         self.kwargs = kwargs
         self.container_def_list = container_def_list
+        self.skip_model_validation = skip_model_validation
 
         self._properties = Properties(step_name=name, shape_name="DescribeModelPackageOutput")
 
@@ -481,6 +485,7 @@ def arguments(self) -> RequestType:
                 domain=self.domain,
                 sample_payload_url=self.sample_payload_url,
                 task=self.task,
+                skip_model_validation=self.skip_model_validation,
             )
 
             request_dict = get_create_model_package_request(**model_package_args)

src/sagemaker/workflow/step_collections.py

@@ -90,6 +90,7 @@ def __init__(
         framework_version=None,
         nearest_model_name=None,
         data_input_configuration=None,
+        skip_model_validation=None,
         **kwargs,
     ):
         """Construct steps `_RepackModelStep` and `_RegisterModelStep` based on the estimator.
@@ -145,6 +146,8 @@ def __init__(
             nearest_model_name (str): Name of a pre-trained machine learning benchmarked by
                 Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str): Input object for the model (default: None).
+            skip_model_validation (str): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
             **kwargs: additional arguments to `create_model`.
         """
@@ -281,6 +284,7 @@ def __init__(
             domain=domain,
             sample_payload_url=sample_payload_url,
             task=task,
+            skip_model_validation=skip_model_validation,
             **kwargs,
         )
         if not repack_model:

src/sagemaker/xgboost/model.py

@@ -154,6 +154,7 @@ def register(
         framework_version: Optional[Union[str, PipelineVariable]] = None,
         nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
         data_input_configuration: Optional[Union[str, PipelineVariable]] = None,
+        skip_model_validation: Optional[Union[str, PipelineVariable]] = None,
     ):
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
@@ -201,6 +202,8 @@ def register(
                 benchmarked by Amazon SageMaker Inference Recommender (default: None).
             data_input_configuration (str or PipelineVariable): Input object for the model
                 (default: None).
+            skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
+                validation. Values can be "All" or "None" (default: None).
 
         Returns:
             str: A string of SageMaker Model Package ARN.
@@ -239,6 +242,7 @@ def register(
             framework_version=framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
         )
 
     def prepare_container_def(