lint

runtimes/alibi-explain/mlserver_alibi_explain/common.py

@@ -21,15 +21,18 @@
 
 _TAG_TO_RT_IMPL = {
     _ANCHOR_IMAGE_TAG: (
-        "mlserver_alibi_explain.explainers.black_box_runtime.AlibiExplainBlackBoxRuntime",
+        "mlserver_alibi_explain.explainers.black_box_runtime."
+        "AlibiExplainBlackBoxRuntime",
         "alibi.explainers.AnchorImage",
     ),
     _ANCHOR_TEXT_TAG: (
-        "mlserver_alibi_explain.explainers.black_box_runtime.AlibiExplainBlackBoxRuntime",
+        "mlserver_alibi_explain.explainers.black_box_runtime."
+        "AlibiExplainBlackBoxRuntime",
         "alibi.explainers.AnchorText",
     ),
     _INTEGRATED_GRADIENTS_TAG: (
-        "mlserver_alibi_explain.explainers.integrated_gradients.IntegratedGradientsWrapper",
+        "mlserver_alibi_explain.explainers.integrated_gradients."
+        "IntegratedGradientsWrapper",
         "alibi.explainers.IntegratedGradients",
     ),
 }
@@ -70,7 +73,8 @@ def remote_predict(
     return InferenceResponse.parse_raw(response_raw.text)
 
 
-# TODO: this is very similar to `asyncio.to_thread` (python 3.9+), so lets use it at some point.
+# TODO: this is very similar to `asyncio.to_thread` (python 3.9+),
+# so lets use it at some point.
 def execute_async(
     loop: Optional[AbstractEventLoop], fn: Callable, *args, **kwargs
 ) -> Awaitable:

runtimes/alibi-explain/mlserver_alibi_explain/explainers/black_box_runtime.py

@@ -13,8 +13,8 @@
 
 class AlibiExplainBlackBoxRuntime(AlibiExplainRuntimeBase):
     """
-    Runtime for black box explainer runtime, i.e. explainer that would just need access to infer feature from the
-    underlying model (no gradients etc.)
+    Runtime for black box explainer runtime, i.e. explainer that would just need access
+    to infer feature from the underlying model (no gradients etc.)
     """
 
     def __init__(self, settings: ModelSettings, explainer_class: Type[Explainer]):
@@ -48,8 +48,8 @@ def _infer_impl(self, input_data: np.ndarray) -> np.ndarray:
 
         v2_request = InferenceRequest(
             parameters=Parameters(content_type=NumpyCodec.ContentType),
-            # TODO: we probably need to tell alibi about the expected types to use or even whether it is a
-            # proba or targets etc
+            # TODO: we probably need to tell alibi about the expected types to use
+            # or even whether it is a proba or targets etc
             inputs=[np_codec.encode_request_input(name="predict", payload=input_data)],
         )
 

runtimes/alibi-explain/mlserver_alibi_explain/explainers/white_box_runtime.py

@@ -2,7 +2,6 @@
 
 from alibi.api.interfaces import Explanation, Explainer
 from alibi.saving import load_explainer
-from pydantic import BaseSettings
 
 from mlserver import ModelSettings
 from mlserver_alibi_explain.common import AlibiExplainSettings
@@ -11,7 +10,8 @@
 
 class AlibiExplainWhiteBoxRuntime(AlibiExplainRuntimeBase):
     """
-    White box alibi explain requires access to the full inference model to compute gradients etc. usually in the same
+    White box alibi explain requires access to the full inference model
+    to compute gradients etc. usually in the same
     domain as the explainer itself. e.g. `IntegratedGradients`
     """
 

runtimes/alibi-explain/mlserver_alibi_explain/runtime.py

@@ -38,7 +38,10 @@ def __init__(
         super().__init__(settings)
 
     async def predict(self, payload: InferenceRequest) -> InferenceResponse:
-        """This is actually a call to explain as we are treating an explainer model as MLModel"""
+        """
+        This is actually a call to explain as we are treating
+        an explainer model as MLModel
+        """
 
         # TODO: convert and validate?
         model_input = payload.inputs[0]
@@ -79,7 +82,8 @@ class AlibiExplainRuntime(MLModel):
 
     def __init__(self, settings: ModelSettings):
         # TODO: we probably want to validate the enum more sanely here
-        # we do not want to construct a specific alibi settings here because it might be dependent on type
+        # we do not want to construct a specific alibi settings here because
+        # it might be dependent on type
         # although at the moment we only have one `AlibiExplainSettings`
         explainer_type = settings.parameters.extra[EXPLAINER_TYPE_TAG]
 

runtimes/alibi-explain/tests/conftest.py

@@ -23,7 +23,8 @@
 from .test_model import TFMNISTModel
 
 # allow nesting loop
-# in our case this allows multiple runtimes to execute in the same thread for testing reasons
+# in our case this allows multiple runtimes to execute
+# in the same thread for testing reasons
 nest_asyncio.apply()
 
 TESTS_PATH = os.path.dirname(__file__)
@@ -40,7 +41,8 @@ def pytest_collection_modifyitems(items):
         item.add_marker("asyncio")
 
 
-# TODO: there is a lot of common code here with testing rest calls, refactor perhaps to make it neater
+# TODO: there is a lot of common code here with testing rest calls,
+#  refactor perhaps to make it neater
 @pytest.fixture
 def pytorch_model_uri() -> str:
     pytorch_model_path = os.path.join(TESTDATA_PATH, "pytorch_model")
@@ -159,9 +161,11 @@ async def anchor_image_runtime_with_remote_predict_patch(
     with patch(remote_predict_mock_path) as remote_predict:
 
         def mock_predict(*args, **kwargs):
-            # note: sometimes the event loop is not running and in this case we create a new one otherwise
+            # note: sometimes the event loop is not running and in this case
+            # we create a new one otherwise
             # we use the existing one.
-            # this mock implementation is required as we dont want to spin up a server, we just use MLModel.predict
+            # this mock implementation is required as we dont want to spin up a server,
+            # we just use MLModel.predict
             try:
                 loop = asyncio.get_event_loop()
                 res = loop.run_until_complete(
@@ -184,7 +188,7 @@ def mock_predict(*args, **kwargs):
                 parameters=ModelParameters(
                     uri=f"{TESTS_PATH}/data/mnist_anchor_image",
                     extra=AlibiExplainSettings(
-                        explainer_type="anchor_image", infer_uri=f"dummy_call"
+                        explainer_type="anchor_image", infer_uri="dummy_call"
                     ),
                 ),
             )

runtimes/alibi-explain/tests/test_black_box.py

@@ -39,7 +39,8 @@ async def test_predict_impl(
 ):
     # note: custom_runtime_tf is the underlying inference runtime
     # we want to test that the underlying impl predict is functionally correct
-    # anchor_image_runtime fixture if already mocking `remote_predict` -> custom_runtime_tf.predict
+    # anchor_image_runtime fixture is already mocking
+    # `remote_predict` -> custom_runtime_tf.predict
 
     # [batch, image_x, image_y, channel]
     data = np.random.randn(10, 28, 28, 1) * 255
@@ -77,8 +78,8 @@ async def test_end_2_end(
     alibi_anchor_image_model,
     payload: InferenceRequest,
 ):
-    # in this test we are getting explanation and making sure that it the same one as returned by alibi
-    # directly
+    # in this test we are getting explanation and making sure that it the same one
+    # as returned by alibi directly
     runtime_result = await anchor_image_runtime_with_remote_predict_patch.predict(
         payload
     )

runtimes/alibi-explain/tests/test_integrated_gradients.py

@@ -44,7 +44,8 @@ async def test_end_2_end(
     alibi_integrated_gradients_model,
     payload: InferenceRequest,
 ):
-    # in this test we are getting explanation and making sure that it the same one as returned by alibi
+    # in this test we are getting explanation and making sure that it the same
+    # one as returned by alibi
     # directly
     runtime_result = await integrated_gradients_runtime.predict(payload)
     decoded_runtime_results = json.loads(

runtimes/alibi-explain/tests/test_runtime.py

@@ -90,7 +90,7 @@ def test_remote_predict__smoke(runtime_pytorch, rest_client):
 
 async def test_alibi_runtime_wrapper(custom_runtime_tf: MLModel):
     """
-    Checks that the wrappers returns back the expected valued from the underlying runtime
+    Checks that the wrappers returns back the expected valued from the underlying rt
     """
 
     class _MockInit(AlibiExplainRuntime):