Change chart directory structure

manifests/kustomize/kubeflow/1.10.0/tests/gh-actions/kserve/data/iris_input.json

@@ -0,0 +1,6 @@
+{
+  "instances": [
+    [6.8,  2.8,  4.8,  1.4],
+    [6.0,  3.4,  4.5,  1.6]
+  ]
+}

manifests/kustomize/kubeflow/1.10.0/tests/gh-actions/kserve/requirements.txt

@@ -0,0 +1,4 @@
+pytest>=7.0.0
+kserve>=0.15.0
+kubernetes>=18.20.0
+requests>=2.18.4

manifests/kustomize/kubeflow/1.10.0/tests/gh-actions/kserve/test_sklearn.py

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+#
+# 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="InferenceService",
+        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)

manifests/kustomize/kubeflow/1.10.0/tests/gh-actions/kserve/utils.py

@@ -0,0 +1,125 @@
+#
+# 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 json
+import logging
+import os
+import time
+from urllib.parse import urlparse
+
+import requests
+from kubernetes import client
+
+from kserve import KServeClient
+from kserve import constants
+
+logging.basicConfig(level=logging.INFO)
+
+KSERVE_NAMESPACE = "kserve"
+KSERVE_TEST_NAMESPACE = "kubeflow-user-example-com"
+MODEL_CLASS_NAME = "modelClass"
+
+
+class M2mTokenNotAvailable(Exception):
+    pass
+
+
+def get_cluster_ip(name="istio-ingressgateway", namespace="istio-system"):
+    api_instance = client.CoreV1Api(client.ApiClient())
+    service = api_instance.read_namespaced_service(name, namespace)
+    if service.status.load_balancer.ingress is None:
+        cluster_ip = service.spec.cluster_ip
+    else:
+        if service.status.load_balancer.ingress[0].hostname:
+            cluster_ip = service.status.load_balancer.ingress[0].hostname
+        else:
+            cluster_ip = service.status.load_balancer.ingress[0].ip
+    return os.environ.get("KSERVE_INGRESS_HOST_PORT", cluster_ip)
+
+
+def get_m2m_auth_token(env_name="KSERVE_M2M_TOKEN"):
+    try:
+        return os.environ[env_name]
+    except KeyError:
+        raise M2mTokenNotAvailable(env_name)
+
+
+def predict(
+    service_name,
+    input_json,
+    protocol_version="v1",
+    version=constants.KSERVE_V1BETA1_VERSION,
+    model_name=None,
+):
+    with open(input_json) as json_file:
+        data = json.load(json_file)
+
+        return predict_str(
+            service_name=service_name,
+            input_json=json.dumps(data),
+            protocol_version=protocol_version,
+            version=version,
+            model_name=model_name,
+        )
+
+
+def predict_str(
+    service_name,
+    input_json,
+    protocol_version="v1",
+    version=constants.KSERVE_V1BETA1_VERSION,
+    model_name=None,
+):
+    kfs_client = KServeClient(
+        config_file=os.environ.get("KUBECONFIG", "~/.kube/config")
+    )
+    isvc = kfs_client.get(
+        service_name,
+        namespace=KSERVE_TEST_NAMESPACE,
+        version=version,
+    )
+    # temporary sleep until this is fixed https://github.com/kserve/kserve/issues/604
+    time.sleep(10)
+    cluster_ip = get_cluster_ip()
+    host = f"{service_name}.{KSERVE_TEST_NAMESPACE}.example.com"
+    headers = {
+        "Host": host,
+        "Content-Type": "application/json",
+    }
+
+    try:
+        token = get_m2m_auth_token()
+        headers.update({"Authorization": f"Bearer {token}"})
+        logging.info("M2M Token Found.")
+    except M2mTokenNotAvailable:
+        logging.warn("M2M Token Not found, client authentication disabled.")
+
+    if model_name is None:
+        model_name = service_name
+
+    url = f"http://{cluster_ip}/v1/models/{model_name}:predict"
+    if protocol_version == "v2":
+        url = f"http://{cluster_ip}/v2/models/{model_name}/infer"
+
+    logging.info("Sending Header = %s", headers)
+    logging.info("Sending url = %s", url)
+    logging.info("Sending request data: %s", input_json)
+    response = requests.post(url, input_json, headers=headers)
+    logging.info(
+        "Got response code %s, content %s", response.status_code, response.content
+    )
+    if response.status_code == 200:
+        preds = json.loads(response.content.decode("utf-8"))
+        return preds
+    else:
+        response.raise_for_status()