update kubeflow dip-catalog

2025-01-13 02:31:27 +00:00
parent 1dc1181a03
commit 5451f16d72
1959 changed files with 602337 additions and 0 deletions
@@ -0,0 +1,60 @@
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+from kubernetes import client
+from kubernetes.client import V1ResourceRequirements
+
+from kserve import (
+    constants,
+    KServeClient,
+    V1beta1InferenceService,
+    V1beta1InferenceServiceSpec,
+    V1beta1PredictorSpec,
+    V1beta1SKLearnSpec,
+)
+from utils import KSERVE_TEST_NAMESPACE
+from utils import predict
+
+
+def test_sklearn_kserve():
+    service_name = "isvc-sklearn"
+    predictor = V1beta1PredictorSpec(
+        min_replicas=1,
+        sklearn=V1beta1SKLearnSpec(
+            storage_uri="gs://kfserving-examples/models/sklearn/1.0/model",
+            resources=V1ResourceRequirements(
+                requests={"cpu": "50m", "memory": "128Mi"},
+                limits={"cpu": "100m", "memory": "256Mi"},
+            ),
+        ),
+    )
+
+    isvc = V1beta1InferenceService(
+        api_version=constants.KSERVE_V1BETA1,
+        kind=constants.KSERVE_KIND,
+        metadata=client.V1ObjectMeta(
+            name=service_name, namespace=KSERVE_TEST_NAMESPACE
+        ),
+        spec=V1beta1InferenceServiceSpec(predictor=predictor),
+    )
+
+    kserve_client = KServeClient(
+        config_file=os.environ.get("KUBECONFIG", "~/.kube/config")
+    )
+    kserve_client.create(isvc)
+    kserve_client.wait_isvc_ready(service_name, namespace=KSERVE_TEST_NAMESPACE)
+    res = predict(service_name, "./data/iris_input.json")
+    assert res["predictions"] == [1, 1]
+    kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)