New method to create/deploy trained model object

go.mod

@@ -18,8 +18,8 @@ require (
 	github.com/google/uuid v1.1.1
 	github.com/googleapis/google-cloud-go-testing v0.0.0-20200911160855-bcd43fbb19e8
 	github.com/json-iterator/go v1.1.10
-	github.com/mattbaird/jsonpatch v0.0.0-20171005235357-81af80346b1a // indirect
 	github.com/kelseyhightower/envconfig v1.4.0
+	github.com/mattbaird/jsonpatch v0.0.0-20171005235357-81af80346b1a // indirect
 	github.com/onsi/ginkgo v1.14.0
 	github.com/onsi/gomega v1.10.2
 	github.com/pkg/errors v0.9.1
@@ -39,6 +39,7 @@ require (
 	istio.io/gogo-genproto v0.0.0-20191029161641-f7d19ec0141d // indirect
 	k8s.io/api v0.18.8
 	k8s.io/apimachinery v0.18.8
+	k8s.io/client-go v11.0.1-0.20190805182717-6502b5e7b1b5+incompatible
 	k8s.io/klog v1.0.0
 	k8s.io/kube-openapi v0.0.0-20200410145947-bcb3869e6f29
 	knative.dev/networking v0.0.0-20200922180040-a71b40c69b15

python/kfserving/kfserving/api/kf_serving_client.py

@@ -373,6 +373,30 @@ def wait_isvc_ready(self, name, namespace=None,  # pylint:disable=too-many-argum
             raise RuntimeError("Timeout to start the InferenceService {}. \
                                The InferenceService is as following: {}".format(name, current_isvc))
 
+    def create_trained_model(self, trainedmodel, namespace):
+            """
+            Create a trained model
+            :param trainedmodel: trainedmodel object
+            :param namespace: defaults to current or default namespace
+            :return:
+            """
+
+            if trainedmodel.api_version == constants.KFSERVING_V1ALPHA2:
+                logging.warning("The version v1alpha2 will be deprecated from KFServing 0.6 release.")
+            version = trainedmodel.api_version.split("/")[1]
+
+            try:
+                outputs = self.api_instance.create_namespaced_custom_object(
+                    constants.KFSERVING_GROUP,
+                    version,
+                    namespace,
+                    constants.KFSERVING_PLURAL_TRAINEDMODEL,
+                    trainedmodel)
+            except client.rest.ApiException as e:
+                raise RuntimeError(
+                    "Exception when calling CustomObjectsApi->create_namespaced_custom_object:\
+                     %s\n" % e)
+
     def wait_model_ready(self, service_name, model_name, isvc_namespace=None, # pylint:disable=too-many-arguments
                             isvc_version=constants.KFSERVING_V1BETA1_VERSION,
                             cluster_ip=None,

test/e2e/predictor/test_multi_model_serving.py

@@ -32,7 +32,6 @@
 from ..common.utils import predict, get_cluster_ip
 from ..common.utils import KFSERVING_TEST_NAMESPACE
 
-api_v1alpha1_version = constants.KFSERVING_GROUP + "/v1alpha1"
 api_v1beta1_version = constants.KFSERVING_GROUP + "/" + constants.KFSERVING_V1BETA1_VERSION
 
 KFServing = KFServingClient(config_file=os.environ.get("KUBECONFIG", "~/.kube/config"))
@@ -50,7 +49,7 @@ def test_mms_sklearn_kfserving():
         )
     )
 
-    service_name = "isvc-sklearn"
+    service_name = "isvc-sklearn-mms"
     isvc = V1beta1InferenceService(
         api_version=api_v1beta1_version,
         kind=constants.KFSERVING_KIND,
@@ -77,7 +76,7 @@ def test_mms_sklearn_kfserving():
     model1_name = "model1-sklearn"
     model2_name = "model2-sklearn"
     model1 = V1alpha1TrainedModel(
-        api_version=api_v1alpha1_version,
+        api_version=constants.KFSERVING_V1ALPHA1,
         kind=constants.KFSERVING_KIND_TRAINEDMODEL,
         metadata=client.V1ObjectMeta(
             name=model1_name,
@@ -90,7 +89,7 @@ def test_mms_sklearn_kfserving():
     )
 
     model2 = V1alpha1TrainedModel(
-        api_version=api_v1alpha1_version,
+        api_version=constants.KFSERVING_V1ALPHA1,
         kind=constants.KFSERVING_KIND_TRAINEDMODEL,
         metadata=client.V1ObjectMeta(
             name=model2_name,
@@ -103,14 +102,12 @@ def test_mms_sklearn_kfserving():
     )
 
     # Create an instance of inference service with isvc
-    KFServing.create(isvc, version=constants.KFSERVING_V1BETA1_VERSION)
+    KFServing.create(isvc)
     KFServing.wait_isvc_ready(service_name, namespace=KFSERVING_TEST_NAMESPACE)
 
     # Create instances of trained models using model1 and model2
-    KFServing.create(model1, namespace=KFSERVING_TEST_NAMESPACE, version=constants.KFSERVING_V1ALPHA1_VERSION,
-                        plural=constants.KFSERVING_PLURAL_TRAINEDMODEL)
-    KFServing.create(model2, namespace=KFSERVING_TEST_NAMESPACE, version=constants.KFSERVING_V1ALPHA1_VERSION,
-                        plural=constants.KFSERVING_PLURAL_TRAINEDMODEL)
+    KFServing.create_trained_model(model1, KFSERVING_TEST_NAMESPACE)
+    KFServing.create_trained_model(model2, KFSERVING_TEST_NAMESPACE)
 
     cluster_ip = get_cluster_ip()
 
@@ -142,7 +139,7 @@ def test_mms_xgboost_kfserving():
         )
     )
 
-    service_name = "isvc-xgboost"
+    service_name = "isvc-xgboost-mms"
     isvc = V1beta1InferenceService(
         api_version=api_v1beta1_version,
         kind=constants.KFSERVING_KIND,
@@ -169,7 +166,7 @@ def test_mms_xgboost_kfserving():
     model1_name = "model1-xgboost"
     model2_name = "model2-xgboost"
     model1 = V1alpha1TrainedModel(
-        api_version=api_v1alpha1_version,
+        api_version=constants.KFSERVING_V1ALPHA1,
         kind=constants.KFSERVING_KIND_TRAINEDMODEL,
         metadata=client.V1ObjectMeta(
             name=model1_name,
@@ -182,7 +179,7 @@ def test_mms_xgboost_kfserving():
     )
 
     model2 = V1alpha1TrainedModel(
-        api_version=api_v1alpha1_version,
+        api_version=constants.KFSERVING_V1ALPHA1,
         kind=constants.KFSERVING_KIND_TRAINEDMODEL,
         metadata=client.V1ObjectMeta(
             name=model2_name,
@@ -195,14 +192,12 @@ def test_mms_xgboost_kfserving():
     )
 
     # Create an instance of inference service with isvc
-    KFServing.create(isvc, version=constants.KFSERVING_V1BETA1_VERSION)
+    KFServing.create(isvc)
     KFServing.wait_isvc_ready(service_name, namespace=KFSERVING_TEST_NAMESPACE)
 
     # Create instances of trained models using model1 and model2
-    KFServing.create(model1, namespace=KFSERVING_TEST_NAMESPACE, version=constants.KFSERVING_V1ALPHA1_VERSION,
-                        plural=constants.KFSERVING_PLURAL_TRAINEDMODEL)
-    KFServing.create(model2, namespace=KFSERVING_TEST_NAMESPACE, version=constants.KFSERVING_V1ALPHA1_VERSION,
-                        plural=constants.KFSERVING_PLURAL_TRAINEDMODEL)
+    KFServing.create_trained_model(model1, KFSERVING_TEST_NAMESPACE)
+    KFServing.create_trained_model(model2, KFSERVING_TEST_NAMESPACE)
 
     cluster_ip = get_cluster_ip()