implement load model somewhere in our class api (DAT-1060)

datamint/api/endpoints/model_types.py

@@ -13,6 +13,7 @@
 
 if TYPE_CHECKING:
     from datamint.entities.project import Project
+    from datamint.mlflow.flavors.model import DatamintModel
 
     from .models_api import ModelsApi
 
@@ -119,6 +120,11 @@ def get_metrics(self) -> dict[str, float]:
     def is_deployed(self) -> bool:
         return self._api._deploy_api.image_exists(self.name)
 
+    def load_model(self, device: str | None = None) -> 'DatamintModel':
+        """Load this model version via the ``datamint`` MLflow flavor."""
+        from datamint.mlflow.flavors import load_model
+        return load_model(f"models:/{self.name}/{self.version}", device=device)
+
 
 @dataclass
 class Model:

docs/source/client_api_content.rst

@@ -568,6 +568,8 @@ a Datamint :mod:`~datamint.lightning.trainers`), rather than raising.
 ``Model.get_supported_modes()``/``get_metrics()`` are shortcuts that delegate
 to the latest version when you don't need a specific one.
 
+``latest.load_model()`` loads the model itself, ready for local inference.
+
 Find which projects a model belongs to
 ++++++++++++++++++++++++++++++++++++++
 

notebooks/05_deployment/01_deploy_registered_model.ipynb

@@ -87,6 +87,31 @@
     "        # If you need aliases, use ``get_model_version(mv.name, mv.version)`` method instead."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ca5f9fad",
+   "metadata": {},
+   "source": [
+    "## Loading a Registered Model Locally\n",
+    "\n",
+    "Before deploying, you can load a model version directly in Python via `ModelVersion.load_model()` (a shortcut over `datamint.mlflow.flavors.load_model`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd55407f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load a model version locally, without deploying it\n",
+    "model = api.models.get_by_name(all_registered_models[1].name)\n",
+    "latest = model.get_latest_version()\n",
+    "\n",
+    "loaded_model = latest.load_model()\n",
+    "print(loaded_model)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "9da152da",

notebooks/05_deployment/02_deploy_external_model.ipynb

@@ -424,8 +424,8 @@
    "source": [
     "from datamint.mlflow import flavors as datamint_flavor\n",
     "\n",
-    "model_uri = f\"models:/{MODEL_NAME}@champion\" # or f\"models:/{MODEL_NAME}/latest\" or f\"models:/{MODEL_NAME}/1\"\n",
-    "loaded_model = datamint_flavor.load_model(model_uri)\n",
+    "model = api.models.get_by_name(MODEL_NAME)\n",
+    "loaded_model = model.get_latest_version(alias='champion').load_model()\n",
     "\n",
     "print(f\"Loaded model type : {type(loaded_model).__name__}\")\n",
     "print(f\"Task type         : {loaded_model.task_type}\")\n",