feat: infer framework and version

src/sagemaker/chainer/model.py

@@ -140,6 +140,108 @@ def __init__(
 
         self.model_server_workers = model_server_workers
 
+    def register(
+        self,
+        content_types,
+        response_types,
+        inference_instances,
+        transform_instances,
+        model_package_name=None,
+        model_package_group_name=None,
+        image_uri=None,
+        model_metrics=None,
+        metadata_properties=None,
+        marketplace_cert=False,
+        approval_status=None,
+        description=None,
+        drift_check_baselines=None,
+        customer_metadata_properties=None,
+        domain=None,
+        sample_payload_url=None,
+        task=None,
+        framework=None,
+        framework_version=None,
+        nearest_model_name=None,
+        data_input_configuration=None,
+    ):
+        """Creates a model package for creating SageMaker models or listing on Marketplace.
+
+        Args:
+            content_types (list): The supported MIME types for the input data.
+            response_types (list): The supported MIME types for the output data.
+            inference_instances (list): A list of the instance types that are used to
+                generate inferences in real-time.
+            transform_instances (list): A list of the instance types on which a transformation
+                job can be run or on which an endpoint can be deployed.
+            model_package_name (str): Model Package name, exclusive to `model_package_group_name`,
+                using `model_package_name` makes the Model Package un-versioned (default: None).
+            model_package_group_name (str): Model Package Group name, exclusive to
+                `model_package_name`, using `model_package_group_name` makes the Model Package
+                versioned (default: None).
+            image_uri (str): Inference image uri for the container. Model class' self.image will
+                be used if it is None (default: None).
+            model_metrics (ModelMetrics): ModelMetrics object (default: None).
+            metadata_properties (MetadataProperties): MetadataProperties (default: None).
+            marketplace_cert (bool): A boolean value indicating if the Model Package is certified
+                for AWS Marketplace (default: False).
+            approval_status (str): Model Approval Status, values can be "Approved", "Rejected",
+                or "PendingManualApproval" (default: "PendingManualApproval").
+            description (str): Model Package description (default: None).
+            drift_check_baselines (DriftCheckBaselines): DriftCheckBaselines object (default: None).
+            customer_metadata_properties (dict[str, str]): A dictionary of key-value paired
+                metadata properties (default: None).
+            domain (str): Domain values can be "COMPUTER_VISION", "NATURAL_LANGUAGE_PROCESSING",
+                "MACHINE_LEARNING" (default: None).
+            sample_payload_url (str): The S3 path where the sample payload is stored
+                (default: None).
+            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).
+            framework (str): Machine learning framework of the model package container image
+                (default: None).
+            framework_version (str): Framework version of the Model Package Container Image
+                (default: None).
+            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).
+
+        Returns:
+            str: A string of SageMaker Model Package ARN.
+        """
+        instance_type = inference_instances[0]
+        self._init_sagemaker_session_if_does_not_exist(instance_type)
+
+        if image_uri:
+            self.image_uri = image_uri
+        if not self.image_uri:
+            self.image_uri = self.serving_image_uri(
+                region_name=self.sagemaker_session.boto_session.region_name,
+                instance_type=instance_type,
+            )
+        return super(ChainerModel, self).register(
+            content_types,
+            response_types,
+            inference_instances,
+            transform_instances,
+            model_package_name,
+            model_package_group_name,
+            image_uri,
+            model_metrics,
+            metadata_properties,
+            marketplace_cert,
+            approval_status,
+            description,
+            drift_check_baselines=drift_check_baselines,
+            customer_metadata_properties=customer_metadata_properties,
+            domain=domain,
+            sample_payload_url=sample_payload_url,
+            task=task,
+            framework=(framework or self._framework_name).upper(),
+            framework_version=framework_version or self.framework_version,
+            nearest_model_name=nearest_model_name,
+            data_input_configuration=data_input_configuration,
+        )
+
     def prepare_container_def(
         self, instance_type=None, accelerator_type=None, serverless_inference_config=None
     ):

src/sagemaker/huggingface/model.py

@@ -85,6 +85,14 @@ def _validate_pt_tf_versions(pytorch_version, tensorflow_version, image_uri):
         )
 
 
+def fetch_framework_and_framework_version(tensorflow_version, pytorch_version):
+    """Function to check the framework used in HuggingFace class"""
+
+    if tensorflow_version is not None:  # pylint: disable=no-member
+        return ("tensorflow", tensorflow_version)  # pylint: disable=no-member
+    return ("pytorch", pytorch_version)  # pylint: disable=no-member
+
+
 class HuggingFaceModel(FrameworkModel):
     """A Hugging Face SageMaker ``Model`` that can be deployed to a SageMaker ``Endpoint``."""
 
@@ -387,8 +395,16 @@ def register(
             domain=domain,
             sample_payload_url=sample_payload_url,
             task=task,
-            framework=framework,
-            framework_version=framework_version,
+            framework=(
+                framework
+                or fetch_framework_and_framework_version(
+                    self.tensorflow_version, self.pytorch_version
+                )[0]
+            ).upper(),
+            framework_version=framework_version
+            or fetch_framework_and_framework_version(self.tensorflow_version, self.pytorch_version)[
+                1
+            ],
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
         )

src/sagemaker/model.py

@@ -374,12 +374,12 @@ def register(
 
         if model_package_group_name is not None:
             container_def = self.prepare_container_def()
-            update_container_with_inference_params(
+            container_def = update_container_with_inference_params(
                 framework=framework,
                 framework_version=framework_version,
                 nearest_model_name=nearest_model_name,
                 data_input_configuration=data_input_configuration,
-                container_obj=container_def,
+                container_def=container_def,
             )
         else:
             container_def = {

src/sagemaker/mxnet/model.py

@@ -238,8 +238,8 @@ def register(
             domain=domain,
             sample_payload_url=sample_payload_url,
             task=task,
-            framework=framework,
-            framework_version=framework_version,
+            framework=(framework or self._framework_name).upper(),
+            framework_version=framework_version or self.framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
         )

src/sagemaker/pipeline.py

@@ -340,12 +340,12 @@ def register(
             container_def = self.pipeline_container_def(
                 inference_instances[0] if inference_instances else None
             )
-            update_container_with_inference_params(
+            container_def = update_container_with_inference_params(
                 framework=framework,
                 framework_version=framework_version,
                 nearest_model_name=nearest_model_name,
                 data_input_configuration=data_input_configuration,
-                container_list=container_def,
+                container_def=container_def,
             )
         else:
             container_def = [

src/sagemaker/pytorch/model.py

@@ -239,8 +239,8 @@ def register(
             domain=domain,
             sample_payload_url=sample_payload_url,
             task=task,
-            framework=framework,
-            framework_version=framework_version,
+            framework=(framework or self._framework_name).upper(),
+            framework_version=framework_version or self.framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
         )

src/sagemaker/sklearn/model.py

@@ -233,8 +233,8 @@ def register(
             domain=domain,
             sample_payload_url=sample_payload_url,
             task=task,
-            framework=framework,
-            framework_version=framework_version,
+            framework=(framework or self._framework_name).upper(),
+            framework_version=framework_version or self.framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
         )

src/sagemaker/tensorflow/model.py

@@ -285,8 +285,8 @@ def register(
             domain=domain,
             sample_payload_url=sample_payload_url,
             task=task,
-            framework=framework,
-            framework_version=framework_version,
+            framework=(framework or self._framework_name).upper(),
+            framework_version=framework_version or self.framework_version,
             nearest_model_name=nearest_model_name,
             data_input_configuration=data_input_configuration,
         )

src/sagemaker/utils.py

@@ -739,7 +739,7 @@ def update_container_with_inference_params(
     framework_version=None,
     nearest_model_name=None,
     data_input_configuration=None,
-    container_obj=None,
+    container_def=None,
     container_list=None,
 ):
     """Function to check if inference recommender parameters exist and update container.
@@ -752,28 +752,30 @@ def update_container_with_inference_params(
         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).
-        container_obj (dict): object to be updated.
+        container_def (dict): object to be updated.
         container_list (list): list to be updated.
 
     Returns:
         dict: dict with inference recommender params
     """
 
-    if framework is not None and framework_version is not None and nearest_model_name is not None:
-        if container_list is not None:
-            for obj in container_list:
-                construct_container_object(
-                    obj, data_input_configuration, framework, framework_version, nearest_model_name
-                )
-        if container_obj is not None:
+    if container_list is not None:
+        for obj in container_list:
             construct_container_object(
-                container_obj,
-                data_input_configuration,
-                framework,
-                framework_version,
-                nearest_model_name,
+                obj, data_input_configuration, framework, framework_version, nearest_model_name
             )
 
+    if container_def is not None:
+        construct_container_object(
+            container_def,
+            data_input_configuration,
+            framework,
+            framework_version,
+            nearest_model_name,
+        )
+
+    return container_list or container_def
+
 
 def construct_container_object(
     obj, data_input_configuration, framework, framework_version, nearest_model_name
@@ -788,20 +790,32 @@ def construct_container_object(
         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).
-        container_obj (dict): object to be updated.
-        container_list (list): list to be updated.
+        obj (dict): object to be updated.
 
     Returns:
         dict: container object
     """
 
-    obj.update(
-        {
-            "Framework": framework,
-            "FrameworkVersion": framework_version,
-            "NearestModelName": nearest_model_name,
-        }
-    )
+    if framework is not None:
+        obj.update(
+            {
+                "Framework": framework,
+            }
+        )
+
+    if framework_version is not None:
+        obj.update(
+            {
+                "FrameworkVersion": framework_version,
+            }
+        )
+
+    if nearest_model_name is not None:
+        obj.update(
+            {
+                "NearestModelName": nearest_model_name,
+            }
+        )
 
     if data_input_configuration is not None:
         obj.update(
@@ -811,3 +825,5 @@ def construct_container_object(
                 },
             }
         )
+
+    return obj

src/sagemaker/workflow/step_collections.py

@@ -250,7 +250,7 @@ def __init__(
                     )
                 ]
 
-            update_container_with_inference_params(
+            self.container_def_list = update_container_with_inference_params(
                 framework=framework,
                 framework_version=framework_version,
                 nearest_model_name=nearest_model_name,