model integration with DT

Author: kshitij-microsoft

src/machinelearningservices/azext_mlv2/manual/_params/_model_params.py

@@ -61,6 +61,11 @@ def load_model_params(self):
                  "The default type is custom_model.",
         )
         add_description_param(c, help_message="Description of the model.")
+        c.argument(
+            "default_deployment_template",
+            options_list=["--default-deployment-template"],
+            help="Default deployment template for the model.",
+        )
 
     with self.argument_context("ml model show") as c:
         add_common_params(c)

src/machinelearningservices/azext_mlv2/manual/custom/model.py

@@ -35,6 +35,7 @@ def ml_model_create(
     registry_name=None,
     params_override=None,
     datastore=None,
+    default_deployment_template=None,
     no_wait=None,  # pylint: disable=unused-argument
 ):
     ml_client, debug = get_ml_client(
@@ -81,6 +82,7 @@ def ml_model_create(
                 stage=stage,
                 type=type,
                 datastore=datastore,
+                default_deployment_template=default_deployment_template,
             )
             if model and model.type:
                 model_info.model_type = model.type
@@ -267,3 +269,9 @@ def ml_model_share(cmd, name, version, share_with_name, share_with_version,
                                    share_with_name=share_with_name,
                                    share_with_version=share_with_version)
     return _dump_entity_with_warnings(model)
+
+c.argument(
+    "default_deployment_template",
+    options_list=["--default-deployment-template"],
+    help="Default deployment template for the model.",
+)