fix: add kwargs to create_model for 1p to work with kms

src/sagemaker/amazon/factorization_machines.py

@@ -235,7 +235,7 @@ def __init__(
         self.factors_init_sigma = factors_init_sigma
         self.factors_init_value = factors_init_value
 
-    def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
+    def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
         """Return a :class:`~sagemaker.amazon.FactorizationMachinesModel`
         referencing the latest s3 model data produced by this Estimator.
 
@@ -244,12 +244,14 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
                 the model. Default: use subnets and security groups from this Estimator.
                 * 'Subnets' (list[str]): List of subnet ids.
                 * 'SecurityGroupIds' (list[str]): List of security group ids.
+            **kwargs: Additional kwargs passed to the FactorizationMachinesModel constructor.
         """
         return FactorizationMachinesModel(
             self.model_data,
             self.role,
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
+            **kwargs
         )
 
 

src/sagemaker/amazon/ipinsights.py

@@ -131,7 +131,7 @@ def __init__(
         self.shuffled_negative_sampling_rate = shuffled_negative_sampling_rate
         self.weight_decay = weight_decay
 
-    def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
+    def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
         """Create a model for the latest s3 model produced by this estimator.
 
         Args:
@@ -140,6 +140,7 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
                 Default: use subnets and security groups from this Estimator.
                 * 'Subnets' (list[str]): List of subnet ids.
                 * 'SecurityGroupIds' (list[str]): List of security group ids.
+            **kwargs: Additional kwargs passed to the IPInsightsModel constructor.
         Returns:
             :class:`~sagemaker.amazon.IPInsightsModel`: references the latest s3 model
             data produced by this estimator.
@@ -149,6 +150,7 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
             self.role,
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
+            **kwargs
         )
 
     def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):

src/sagemaker/amazon/knn.py

@@ -145,7 +145,7 @@ def __init__(
                 '"dimension_reduction_target" is required when "dimension_reduction_type" is set.'
             )
 
-    def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
+    def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
         """Return a :class:`~sagemaker.amazon.KNNModel` referencing the latest
         s3 model data produced by this Estimator.
 
@@ -154,12 +154,14 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
                 the model. Default: use subnets and security groups from this Estimator.
                 * 'Subnets' (list[str]): List of subnet ids.
                 * 'SecurityGroupIds' (list[str]): List of security group ids.
+            **kwargs: Additional kwargs passed to the KNNModel constructor.
         """
         return KNNModel(
             self.model_data,
             self.role,
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
+            **kwargs
         )
 
     def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):

src/sagemaker/amazon/ntm.py

@@ -155,7 +155,7 @@ def __init__(
         self.weight_decay = weight_decay
         self.learning_rate = learning_rate
 
-    def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
+    def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
         """Return a :class:`~sagemaker.amazon.NTMModel` referencing the latest
         s3 model data produced by this Estimator.
 
@@ -164,12 +164,14 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
                 the model. Default: use subnets and security groups from this Estimator.
                 * 'Subnets' (list[str]): List of subnet ids.
                 * 'SecurityGroupIds' (list[str]): List of security group ids.
+            **kwargs: Additional kwargs passed to the NTMModel constructor.
         """
         return NTMModel(
             self.model_data,
             self.role,
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
+            **kwargs
         )
 
     def _prepare_for_training(  # pylint: disable=signature-differs

src/sagemaker/amazon/object2vec.py

@@ -295,7 +295,7 @@ def __init__(
         self.enc0_freeze_pretrained_embedding = enc0_freeze_pretrained_embedding
         self.enc1_freeze_pretrained_embedding = enc1_freeze_pretrained_embedding
 
-    def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
+    def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
         """Return a :class:`~sagemaker.amazon.Object2VecModel` referencing the
         latest s3 model data produced by this Estimator.
 
@@ -304,12 +304,14 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
                 the model. Default: use subnets and security groups from this Estimator.
                 * 'Subnets' (list[str]): List of subnet ids.
                 * 'SecurityGroupIds' (list[str]): List of security group ids.
+            **kwargs: Additional kwargs passed to the Object2VecModel constructor.
         """
         return Object2VecModel(
             self.model_data,
             self.role,
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
+            **kwargs
         )
 
     def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):

src/sagemaker/amazon/pca.py

@@ -120,7 +120,7 @@ def __init__(
         self.subtract_mean = subtract_mean
         self.extra_components = extra_components
 
-    def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
+    def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
         """Return a :class:`~sagemaker.amazon.pca.PCAModel` referencing the
         latest s3 model data produced by this Estimator.
 
@@ -129,12 +129,14 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
                 the model. Default: use subnets and security groups from this Estimator.
                 * 'Subnets' (list[str]): List of subnet ids.
                 * 'SecurityGroupIds' (list[str]): List of security group ids.
+            **kwargs: Additional kwargs passed to the PCAModel constructor.
         """
         return PCAModel(
             self.model_data,
             self.role,
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
+            **kwargs
         )
 
     def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):

src/sagemaker/amazon/randomcutforest.py

@@ -113,7 +113,7 @@ def __init__(
         self.num_trees = num_trees
         self.eval_metrics = eval_metrics
 
-    def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
+    def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
         """Return a :class:`~sagemaker.amazon.RandomCutForestModel` referencing
         the latest s3 model data produced by this Estimator.
 
@@ -122,12 +122,14 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
                 the model. Default: use subnets and security groups from this Estimator.
                 * 'Subnets' (list[str]): List of subnet ids.
                 * 'SecurityGroupIds' (list[str]): List of security group ids.
+            **kwargs: Additional kwargs passed to the RandomCutForestModel constructor.
         """
         return RandomCutForestModel(
             self.model_data,
             self.role,
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
+            **kwargs
         )
 
     def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):