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SivananthamandGitHub
2a534af9fd
* Add client sdk for localmodelcache, localmodelnodegroup Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Add e2e test for modelcache Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Use docker driver and minikube tunnel Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Merge "Allow multiple node groups in the model cache CR (#4134)" Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Try mounting image dir Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Add local model agent to image scan Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Debug Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Create model root directory beforehand Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Restart kserve controller after patch Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Enablepvc direct mount in e2e test Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Reduce pv storage to 1GB Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Update modelcache test Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Update status-check to include modelcache logs Signed-off-by: Sivanantham Chinnaiyan <[email protected]> --------- Signed-off-by: Sivanantham Chinnaiyan <[email protected]>
265 lines
8.3 KiB
Python
265 lines
8.3 KiB
Python
# Copyright 2022 The KServe Authors.
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#
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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 kserve import (
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constants,
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KServeClient,
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V1beta1PredictorSpec,
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V1alpha1TrainedModel,
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V1beta1InferenceService,
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V1beta1InferenceServiceSpec,
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V1alpha1ModelSpec,
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V1alpha1TrainedModelSpec,
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V1beta1SKLearnSpec,
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V1beta1XGBoostSpec,
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)
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from ..common.utils import predict_isvc, get_cluster_ip
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from ..common.utils import KSERVE_TEST_NAMESPACE
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@pytest.mark.parametrize(
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"protocol_version,storage_uri",
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[
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(
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"v1",
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"gs://kfserving-examples/models/sklearn/1.0/model",
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),
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(
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"v2",
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"gs://seldon-models/sklearn/mms/lr_model",
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),
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],
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)
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@pytest.mark.mms
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@pytest.mark.asyncio(scope="session")
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async def test_mms_sklearn_kserve(
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protocol_version: str, storage_uri: str, rest_v1_client, rest_v2_client
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):
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service_name = f"isvc-sklearn-mms-{protocol_version}"
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# Define an inference service
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predictor = V1beta1PredictorSpec(
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min_replicas=1,
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sklearn=V1beta1SKLearnSpec(
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protocol_version=protocol_version,
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resources=client.V1ResourceRequirements(
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requests={"cpu": "50m", "memory": "128Mi"},
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limits={"cpu": "100m", "memory": "1024Mi"},
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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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# Create an instance of inference service with isvc
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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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cluster_ip = get_cluster_ip()
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model_names = [
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f"model1-sklearn-{protocol_version}",
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f"model2-sklearn-{protocol_version}",
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]
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for model_name in model_names:
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model_spec = V1alpha1ModelSpec(
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storage_uri=storage_uri,
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memory="128Mi",
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framework="sklearn",
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)
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model = V1alpha1TrainedModel(
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api_version=constants.KSERVE_V1ALPHA1,
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kind=constants.KSERVE_KIND_TRAINEDMODEL,
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metadata=client.V1ObjectMeta(
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name=model_name, namespace=KSERVE_TEST_NAMESPACE
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),
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spec=V1alpha1TrainedModelSpec(
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inference_service=service_name, model=model_spec
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),
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)
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# Create instances of trained models using model1 and model2
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kserve_client.create_trained_model(model, KSERVE_TEST_NAMESPACE)
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kserve_client.wait_model_ready(
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service_name,
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model_name,
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isvc_namespace=KSERVE_TEST_NAMESPACE,
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isvc_version=constants.KSERVE_V1BETA1_VERSION,
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protocol_version=protocol_version,
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cluster_ip=cluster_ip,
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)
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if protocol_version == "v1":
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responses = [
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await predict_isvc(
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rest_v1_client,
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service_name,
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"./data/iris_input.json",
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model_name=model_name,
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)
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for model_name in model_names
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]
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assert responses[0]["predictions"] == [1, 1]
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assert responses[1]["predictions"] == [1, 1]
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elif protocol_version == "v2":
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responses = [
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await predict_isvc(
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rest_v2_client,
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service_name,
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"./data/iris_input_v2.json",
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model_name=model_name,
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)
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for model_name in model_names
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]
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assert responses[0].outputs[0].data == [1, 1]
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assert responses[1].outputs[0].data == [1, 1]
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# Clean up inference service and trained models
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for model_name in model_names:
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kserve_client.delete_trained_model(model_name, KSERVE_TEST_NAMESPACE)
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kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)
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@pytest.mark.parametrize(
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"protocol_version,storage_uri",
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[
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(
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"v1",
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"gs://kfserving-examples/models/xgboost/1.5/model",
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),
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(
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"v2",
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"gs://seldon-models/xgboost/mms/iris",
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),
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],
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)
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@pytest.mark.mms
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@pytest.mark.asyncio(scope="session")
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async def test_mms_xgboost_kserve(
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protocol_version: str, storage_uri: str, rest_v1_client, rest_v2_client
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):
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service_name = f"isvc-xgboost-mms-{protocol_version}"
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# Define an inference service
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predictor = V1beta1PredictorSpec(
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min_replicas=1,
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xgboost=V1beta1XGBoostSpec(
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env=[client.V1EnvVar(name="MLSERVER_MODEL_PARALLEL_WORKERS", value="0")],
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protocol_version=protocol_version,
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resources=client.V1ResourceRequirements(
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requests={"cpu": "50m", "memory": "128Mi"},
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limits={"cpu": "100m", "memory": "1024Mi"},
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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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# Create an instance of inference service with isvc
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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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cluster_ip = get_cluster_ip()
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model_names = [
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f"model1-xgboost-{protocol_version}",
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f"model2-xgboost-{protocol_version}",
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]
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for model_name in model_names:
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# Define trained models
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model_spec = V1alpha1ModelSpec(
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storage_uri=storage_uri,
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memory="128Mi",
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framework="xgboost",
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)
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model = V1alpha1TrainedModel(
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api_version=constants.KSERVE_V1ALPHA1,
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kind=constants.KSERVE_KIND_TRAINEDMODEL,
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metadata=client.V1ObjectMeta(
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name=model_name, namespace=KSERVE_TEST_NAMESPACE
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),
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spec=V1alpha1TrainedModelSpec(
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inference_service=service_name, model=model_spec
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),
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)
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# Create instances of trained models using model1 and model2
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kserve_client.create_trained_model(model, KSERVE_TEST_NAMESPACE)
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kserve_client.wait_model_ready(
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service_name,
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model_name,
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isvc_namespace=KSERVE_TEST_NAMESPACE,
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isvc_version=constants.KSERVE_V1BETA1_VERSION,
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protocol_version=protocol_version,
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cluster_ip=cluster_ip,
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)
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if protocol_version == "v1":
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responses = [
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await predict_isvc(
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rest_v1_client,
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service_name,
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"./data/iris_input.json",
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model_name=model_name,
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)
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for model_name in model_names
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]
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assert responses[0]["predictions"] == [1, 1]
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assert responses[1]["predictions"] == [1, 1]
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elif protocol_version == "v2":
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responses = [
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await predict_isvc(
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rest_v2_client,
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service_name,
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"./data/iris_input_v2.json",
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model_name=model_name,
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)
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for model_name in model_names
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]
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assert responses[0].outputs[0].data == [1.0, 1.0]
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assert responses[1].outputs[0].data == [1.0, 1.0]
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# Clean up inference service and trained models
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for model_name in model_names:
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kserve_client.delete_trained_model(model_name, KSERVE_TEST_NAMESPACE)
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kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)
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