model integration with DT

src/machinelearningservices/CHANGELOG.rst

@@ -1,3 +1,9 @@
+## 2025-12-19
+
+### Azure Machine Learning CLI (v2) v 2.41.0
+- `az ml model create`
+  - Add support for `default_deployment_template` parameter to associate a deployment template with a model.
+
 ## 2025-11-04
 
 ### Azure Machine Learning CLI (v2) v 2.40.0

src/machinelearningservices/azext_mlv2/manual/_help/_model_help.py

@@ -28,7 +28,7 @@ def get_model_help():
         short-summary: Create a model.
         long-summary: >
             Models can be created from a local file, local directory, datastore or job outputs. The created model will be
-            tracked in the workspace/registry under the specified name and version. If you are using a registry, replace `--workspace-name my-workspace` with the `--registry-name <registry-name>` option.
+            tracked in the workspace/registry under the specified name and version. If you are using a registry, replace `--workspace-name my-workspace` with the `--registry-name my-registry` option.
         examples:
         - name: Create a model from a YAML specification file
           text: az ml model create --file model.yml --resource-group my-resource-group --workspace-name my-workspace
@@ -40,6 +40,8 @@ def get_model_help():
           text: az ml model create --name my-model --version 1 --path azureml://jobs/c42d2507-4953-4a7c-a4c1-2b5bfe0ac64e/outputs/artifacts/paths/model/ --resource-group my-resource-group --workspace-name my-workspace
         - name: Create a model from a datastore 'azureml://datastores/<datastore-name>/paths/<path-to-model-relative-to-the-root-of-the-datastore-location>' using command options
           text: az ml model create --name my-model --version 1 --path azureml://datastores/myblobstore/paths/models/cifar10/cifar.pt --resource-group my-resource-group --workspace-name my-workspace
+        - name: Create a model from a YAML file and override the default deployment template
+          text: az ml model create --registry-name my-registry --resource-group my-resource-group --file model.yaml --set default_deployment_template.asset_id="azureml://registries/my-registry/deploymenttemplates/my-template/versions/1" --version 3
     """
     helps[
         "ml model show"
@@ -83,7 +85,7 @@ def get_model_help():
         type: command
         short-summary: Update a model in a workspace/registry.
         long-summary: >
-            The 'description', and 'tags' properties can be updated. If you are using a registry, replace `--workspace-name my-workspace` with the `--registry-name <registry-name>` option.
+            The 'description', and 'tags' properties can be updated. If you are using a registry, replace `--workspace-name my-workspace` with the `--registry-name my-registry` option.
         examples:
         - name: Update a model's flavors
           text: az ml model update --name my-model --version 1 --set flavors.python_function.python_version=3.8 --resource-group my-resource-group --workspace-name my-workspace

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

@@ -8,6 +8,8 @@
 # regenerated.
 # --------------------------------------------------------------------------
 
+from azure.ai.ml.constants._common import YAMLRefDocLinks
+
 from ._common_params import (
     add_common_params,
     add_description_param,
@@ -60,7 +62,7 @@ def load_deployment_template_params(self):
         # Optional for create since they can come from file
         add_deployment_template_common_param(c, name_required=False, version_required=False)
         add_lro_param(c)
-        add_file_param(c, "deployment-template", "https://aka.ms/ml-cli-v2-deployment-template-yaml")
+        add_file_param(c, "deployment-template", YAMLRefDocLinks.DEPLOYMENT_TEMPLATE)
         add_override_param(c)
         c.argument(
             "registry_name",

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

@@ -61,6 +61,13 @@ 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", "-d"],
+            help="Default deployment template for the model. Can be overridden using --set with the format: "
+                 "--set default_deployment_template.asset_id="
+                 "\"azureml://registries/my-registry/deploymenttemplates/template-name/versions/version\"",
+        )
 
     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(
@@ -61,6 +62,8 @@ def ml_model_create(
         params_override.append({"stage": stage})
     if datastore:
         params_override.append({"datastore": datastore})
+    if default_deployment_template:
+        params_override.append({"default_deployment_template": default_deployment_template})
 
     model_info = ModelInfo()
     try:
@@ -81,6 +84,7 @@ 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