Only generate model uuid when logging model

mlflow/models/model.py

@@ -54,7 +54,7 @@ def __init__(
         self.flavors = flavors if flavors is not None else {}
         self.signature = signature
         self.saved_input_example_info = saved_input_example_info
-        self.model_uuid = uuid.uuid4().hex if model_uuid is None else model_uuid
+        self.model_uuid = model_uuid
         self.__dict__.update(kwargs)
 
     def __eq__(self, other):
@@ -186,7 +186,8 @@ def log(
         with TempDir() as tmp:
             local_path = tmp.path("model")
             run_id = mlflow.tracking.fluent._get_or_start_run().info.run_id
-            mlflow_model = cls(artifact_path=artifact_path, run_id=run_id)
+            model_uuid = uuid.uuid4().hex
+            mlflow_model = cls(artifact_path=artifact_path, run_id=run_id, model_uuid=model_uuid)
             flavor.save_model(path=local_path, mlflow_model=mlflow_model, **kwargs)
             mlflow.tracking.fluent.log_artifacts(local_path, artifact_path)
             try:

tests/pyfunc/test_model_export_with_loader_module_and_data_path.py

@@ -85,6 +85,16 @@ def model_path(tmpdir):
     return os.path.join(str(tmpdir), "model")
 
 
+def _is_valid_uuid(val):
+    import uuid
+
+    try:
+        uuid.UUID(str(val))
+        return True
+    except ValueError:
+        return False
+
+
 @pytest.mark.large
 def test_model_save_load(sklearn_knn_model, iris_data, tmpdir, model_path):
     sk_model_path = os.path.join(str(tmpdir), "knn.pkl")
@@ -102,13 +112,17 @@ def test_model_save_load(sklearn_knn_model, iris_data, tmpdir, model_path):
 
     reloaded_model_config = Model.load(os.path.join(model_path, "MLmodel"))
     assert model_config.__dict__ == reloaded_model_config.__dict__
+    assert model_config.model_uuid is not None and _is_valid_uuid(model_config)
     assert mlflow.pyfunc.FLAVOR_NAME in reloaded_model_config.flavors
     assert mlflow.pyfunc.PY_VERSION in reloaded_model_config.flavors[mlflow.pyfunc.FLAVOR_NAME]
     reloaded_model = mlflow.pyfunc.load_pyfunc(model_path)
     np.testing.assert_array_equal(
         sklearn_knn_model.predict(iris_data[0]), reloaded_model.predict(iris_data[0])
     )
 
+    reloaded_model_config2 = Model.load(os.path.join(model_path, "MLmodel"))
+    assert reloaded_model_config.model_uuid == reloaded_model_config2.model_uuid
+
 
 @pytest.mark.large
 def test_signature_and_examples_are_saved_correctly(sklearn_knn_model, iris_data):
@@ -556,26 +570,6 @@ def test_column_schema_enforcement_no_col_names():
     assert pyfunc_model.predict(d).equals(pd.DataFrame(d))
 
 
-def _is_valid_uuid(val):
-    import uuid
-
-    try:
-        uuid.UUID(str(val))
-        return True
-    except ValueError:
-        return False
-
-
-def test_model_uuid():
-    m = Model()
-    assert m.model_uuid is not None
-    assert _is_valid_uuid(m.model_uuid)
-    m_dict = m.to_dict()
-    assert m_dict["model_uuid"] == m.model_uuid
-    m2 = Model.from_dict(m_dict)
-    assert m2.model_uuid == m.model_uuid
-
-
 def test_tensor_schema_enforcement_no_col_names():
     m = Model()
     input_schema = Schema([TensorSpec(np.dtype(np.float32), (-1, 3))])