mirror of
https://github.com/hanzo-ml/kserve.git
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b18ce24f71
Signed-off-by: Abolfazl Shahbazi <[email protected]> Co-authored-by: Dan Sun <[email protected]>
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import pytest
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from kubernetes import client
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from kubernetes.client import V1Container, V1ResourceRequirements, V1ContainerPort
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from kserve import (
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V1beta1PredictorSpec,
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V1beta1InferenceService,
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V1beta1InferenceServiceSpec,
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KServeClient,
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)
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from kserve.constants import constants
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from ..common.utils import KSERVE_TEST_NAMESPACE, predict_isvc
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@pytest.mark.predictor
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@pytest.mark.asyncio(scope="session")
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async def test_custom_model_http_ray(rest_v1_client):
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service_name = "custom-model-http-ray"
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model_name = "custom-model"
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predictor = V1beta1PredictorSpec(
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containers=[
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V1Container(
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name="kserve-container",
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image=os.environ.get("CUSTOM_MODEL_GRPC_IMG_TAG"),
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# Override the entrypoint to run the model using ray
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command=["python", "-m", "custom_model.model_remote"],
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resources=V1ResourceRequirements(
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requests={"cpu": "1", "memory": "1Gi"},
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limits={"cpu": "1", "memory": "4Gi"},
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),
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ports=[V1ContainerPort(container_port=8080, protocol="TCP")],
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)
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]
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)
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isvc = V1beta1InferenceService(
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api_version=constants.KSERVE_V1BETA1,
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kind=constants.KSERVE_KIND_INFERENCESERVICE,
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metadata=client.V1ObjectMeta(
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name=service_name, namespace=KSERVE_TEST_NAMESPACE
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),
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spec=V1beta1InferenceServiceSpec(predictor=predictor),
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)
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kserve_client = KServeClient(
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config_file=os.environ.get("KUBECONFIG", "~/.kube/config")
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)
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kserve_client.create(isvc)
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kserve_client.wait_isvc_ready(service_name, namespace=KSERVE_TEST_NAMESPACE)
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response = await predict_isvc(
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rest_v1_client,
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service_name=service_name,
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input="./data/custom_model_input.json",
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model_name=model_name,
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)
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outputs = response["predictions"]
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points = ["%.3f" % (point) for point in outputs[0]]
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assert points == ["14.976", "14.037", "13.966", "12.252", "12.086"]
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kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)
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