fix: Various fixes for Open API

client/verta/tests/registry/conftest.py

@@ -22,15 +22,15 @@ def docker_image():
 
 @pytest.fixture
 def make_model_schema_file(tmp_path, monkeypatch):
-    path = tmp_path / "model_schema.json"
+    path = tmp_path / "model_schema"
     schema = {"input": InputClass.schema(), "output": OutputClass.schema()}
     path.write_text(json.dumps(schema))
     monkeypatch.setenv(_MODEL_SCHEMA_PATH_ENV_VAR, str(path))
 
 
 @pytest.fixture
 def make_model_schema_file_no_output(tmp_path, monkeypatch):
-    path = tmp_path / "model_schema_no_output.json"
+    path = tmp_path / "model_schema_no_output"
     schema = {"input": InputClass.schema()}
     path.write_text(json.dumps(schema))
     monkeypatch.setenv(_MODEL_SCHEMA_PATH_ENV_VAR, str(path))

client/verta/tests/registry/test_validate_schema.py

@@ -19,7 +19,6 @@ class TestValidateSchema:
     )
     def test_validate_schema_allow(
         self,
-        recwarn,
         make_model_schema_file,
         matching_input_value,
         matching_output_value,
@@ -29,8 +28,6 @@ def predict(self, input):
             return matching_output_value.dict()
 
         predict(None, matching_input_value.dict())
-        # verify there were no warnings
-        assert len(recwarn) == 0
 
     @hypothesis.settings(
         suppress_health_check=[hypothesis.HealthCheck.function_scoped_fixture],
@@ -67,7 +64,9 @@ def predict(self, input):
             return input
 
         # when verify_io is first, it will raise a TypeError before validate_schema is called
-        with pytest.raises(TypeError, match="Object of type ndarray is not JSON serializable.*"):
+        with pytest.raises(
+            TypeError, match="Object of type ndarray is not JSON serializable.*"
+        ):
             predict(None, array)
 
     @hypothesis.settings(
@@ -76,16 +75,14 @@ def predict(self, input):
     )
     @hypothesis.given(matching_input_value=generate_object())
     def test_validate_schema_deny_output(
-        self, recwarn, make_model_schema_file, matching_input_value
+        self, make_model_schema_file, matching_input_value
     ):
         @validate_schema
         def predict(self, input):
             return input  # note that this does not match the output schema
 
-        predict(None, matching_input_value.dict())
-        assert len(recwarn) == 1
-        w = recwarn.pop(UserWarning)
-        assert str(w.message).startswith("output failed schema validation")
+        with pytest.raises(ValidationError, match="output failed schema validation.*"):
+            predict(None, matching_input_value.dict())
 
     @hypothesis.settings(
         suppress_health_check=[hypothesis.HealthCheck.function_scoped_fixture],

client/verta/verta/__about__.py

@@ -6,4 +6,4 @@
 __maintainer_email__ = "miliu@verta.ai"
 __title__ = "verta"
 __url__ = "https://www.verta.ai/"
-__version__ = "0.24.0a2"
+__version__ = "0.24.0a3"

client/verta/verta/registry/_validate_schema.py

@@ -80,7 +80,7 @@ def main():
     def wrapper(self, input: Dict):
         # fetch schema
         model_schema_path = os.environ.get(
-            _MODEL_SCHEMA_PATH_ENV_VAR, "/app/model_schema.json"
+            _MODEL_SCHEMA_PATH_ENV_VAR, "/app/model_schema"
         )
         try:
             with open(model_schema_path, "r") as file:
@@ -116,7 +116,9 @@ def wrapper(self, input: Dict):
         try:
             jsonschema.validate(instance=output, schema=output_schema)
         except jsonschema.exceptions.ValidationError as e:
-            warnings.warn("output failed schema validation: " + str(e))
+            raise jsonschema.exceptions.ValidationError(
+                "output failed schema validation"
+            ) from e
 
         return output
 

client/verta/verta/tracking/entities/_deployable_entity.py

@@ -92,7 +92,9 @@ def _get_artifact(self, key):
 
     def log_schema(self, input: dict, output: Optional[dict] = None) -> None:
         """
-        Sets the input and output schemas, which are stored as model artifacts.
+        Sets the input and output schemas, which are stored as model artifacts. To propagate this
+        change to any live endpoints, you must redeploy the model by calling
+        :func:`~verta.endpoint.Endpoint.update`.
 
         The output schema is optional.