clean kfsering class

kubeflow/fairing/deployers/kfserving/kfserving.py

@@ -15,7 +15,6 @@
 import logging
 import uuid
 
-from kfserving import V1alpha2ONNXSpec
 from kfserving import V1beta1InferenceService
 from kfserving import V1beta1InferenceServiceSpec
 from kfserving import V1beta1PredictorSpec
@@ -37,7 +36,7 @@
 class KFServing(DeployerInterface):
     """Serves a prediction endpoint using Kubeflow KFServing."""
 
-    def __init__(self, framework, default_storage_uri=None, canary_storage_uri=None,
+    def __init__(self, framework, default_storage_uri=None,
                  canary_traffic_percent=0, namespace=None, labels=None, annotations=None,
                  custom_default_container=None, custom_canary_container=None,
                  isvc_name=None, stream_log=False, cleanup=False, config_file=None,
@@ -46,7 +45,6 @@ def __init__(self, framework, default_storage_uri=None, canary_storage_uri=None,
         :param framework: The framework for the InferenceService, such as Tensorflow,
             XGBoost and ScikitLearn etc.
         :param default_storage_uri: URI pointing to Saved Model assets for default service.
-        :param canary_storage_uri: URI pointing to Saved Model assets for canary service.
         :param canary_traffic_percent: The amount of traffic to sent to the canary, defaults to 0.
         :param namespace: The k8s namespace where the InferenceService will be deployed.
         :param labels: Labels for the InferenceService, separate with commas if have more than one.
@@ -69,7 +67,6 @@ def __init__(self, framework, default_storage_uri=None, canary_storage_uri=None,
         self.framework = framework
         self.isvc_name = isvc_name
         self.default_storage_uri = default_storage_uri
-        self.canary_storage_uri = canary_storage_uri
         self.canary_traffic_percent = canary_traffic_percent
         self.annotations = annotations
         self.set_labels(labels)
@@ -131,24 +128,9 @@ def deploy(self, isvc):  # pylint:disable=arguments-differ,unused-argument
 
     def generate_isvc(self):
         """ generate InferenceService """
-
         api_version = constants.KFSERVING_GROUP + '/' + constants.KFSERVING_VERSION
-        default_predictor, canary_predictor = None, None
-
-        if self.framework == 'custom':
-            default_predictor = self.generate_predictor_spec(
-                self.framework, container=self.custom_default_container)
-        else:
-            default_predictor = self.generate_predictor_spec(
-                self.framework, storage_uri=self.default_storage_uri)
-
-        if self.framework != 'custom' and self.canary_storage_uri is not None:
-            canary_predictor = self.generate_predictor_spec(
-                self.framework, storage_uri=self.canary_storage_uri)
-        if self.framework == 'custom' and self.custom_canary_container is not None:
-            canary_predictor = self.generate_predictor_spec(
-                self.framework, container=self.custom_canary_container)
-
+        default_predictor = self.generate_predictor_spec(
+            self.framework, storage_uri=self.default_storage_uri)
         return V1beta1InferenceService(api_version=api_version,
                                        kind=constants.KFSERVING_KIND,
                                        metadata=k8s_client.V1ObjectMeta(
@@ -159,16 +141,13 @@ def generate_isvc(self):
                                            predictor=default_predictor
                                        ))
 
-    def generate_predictor_spec(self, framework, storage_uri=None, container=None):
+    def generate_predictor_spec(self, framework, storage_uri=None):
         '''Generate predictor spec according to framework and
            default_storage_uri or custom container.
         '''
         if self.framework == 'tensorflow':
             predictor = V1beta1PredictorSpec(
                 tensorflow=V1beta1TFServingSpec(storage_uri=storage_uri))
-        elif self.framework == 'onnx':
-            predictor = V1beta1PredictorSpec(
-                onnx=V1alpha2ONNXSpec(storage_uri=storage_uri))
         elif self.framework == 'pytorch':
             predictor = V1beta1PredictorSpec(
                 pytorch=V1beta1TorchServeSpec(storage_uri=storage_uri))
@@ -181,9 +160,6 @@ def generate_predictor_spec(self, framework, storage_uri=None, container=None):
         elif self.framework == 'xgboost':
             predictor = V1beta1PredictorSpec(
                 xgboost=V1beta1XGBoostSpec(storage_uri=storage_uri))
-        # elif self.framework == 'custom':
-        #     predictor = V1beta1PredictorSpec(
-        #         custom=V1alpha2CustomSpec(container=container))
         else:
             raise RuntimeError("Unsupported framework {}".format(framework))
         return predictor