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* 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]>
249 lines
8.1 KiB
Python
249 lines
8.1 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 json
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import os
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import time
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from kserve import KServeClient, InferOutput
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from kserve import V1beta1InferenceService
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from kserve import V1beta1InferenceServiceSpec
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from kserve import V1beta1PMMLSpec
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from kserve import V1beta1PredictorSpec
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from kserve import V1beta1ModelSpec, V1beta1ModelFormat
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from kserve import constants
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from kubernetes import client
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from kubernetes.client import V1ResourceRequirements, V1ContainerPort
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import pytest
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from kserve.logging import trace_logger as logger
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from ..common.utils import KSERVE_TEST_NAMESPACE, predict_grpc
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from ..common.utils import 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_pmml_kserve(rest_v1_client):
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service_name = "isvc-pmml"
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predictor = V1beta1PredictorSpec(
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min_replicas=1,
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pmml=V1beta1PMMLSpec(
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storage_uri="gs://kfserving-examples/models/pmml",
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resources=V1ResourceRequirements(
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requests={"cpu": "10m", "memory": "256Mi"},
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limits={"cpu": "100m", "memory": "512Mi"},
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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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start = time.perf_counter()
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res = await predict_isvc(rest_v1_client, service_name, "./data/pmml_input.json")
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end = time.perf_counter()
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print(f"Time taken: {end - start}")
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logger.info(f"Time taken: {end - start}")
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assert res["predictions"] == [
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{
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"Species": "setosa",
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"Probability_setosa": 1.0,
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"Probability_versicolor": 0.0,
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"Probability_virginica": 0.0,
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"Node_Id": "2",
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}
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]
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kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)
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@pytest.mark.predictor
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@pytest.mark.asyncio(scope="session")
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async def test_pmml_runtime_kserve(rest_v1_client):
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service_name = "isvc-pmml-runtime"
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predictor = V1beta1PredictorSpec(
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min_replicas=1,
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model=V1beta1ModelSpec(
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model_format=V1beta1ModelFormat(
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name="pmml",
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),
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storage_uri="gs://kfserving-examples/models/pmml",
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resources=V1ResourceRequirements(
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requests={"cpu": "10m", "memory": "256Mi"},
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limits={"cpu": "100m", "memory": "512Mi"},
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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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res = await predict_isvc(rest_v1_client, service_name, "./data/pmml_input.json")
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assert res["predictions"] == [
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{
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"Species": "setosa",
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"Probability_setosa": 1.0,
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"Probability_versicolor": 0.0,
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"Probability_virginica": 0.0,
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"Node_Id": "2",
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}
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]
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kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)
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@pytest.mark.predictor
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@pytest.mark.asyncio(scope="session")
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async def test_pmml_v2_kserve(rest_v2_client):
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service_name = "isvc-pmml-v2-kserve"
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predictor = V1beta1PredictorSpec(
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min_replicas=1,
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model=V1beta1ModelSpec(
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model_format=V1beta1ModelFormat(
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name="pmml",
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),
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runtime="kserve-pmmlserver",
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storage_uri="gs://kfserving-examples/models/pmml",
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resources=V1ResourceRequirements(
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requests={"cpu": "10m", "memory": "256Mi"},
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limits={"cpu": "100m", "memory": "512Mi"},
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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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res = await predict_isvc(
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rest_v2_client,
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service_name,
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"./data/pmml-input-v2.json",
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)
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assert res.outputs == [
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InferOutput(
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name="Species",
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shape=[1],
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datatype="BYTES",
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data=["setosa"],
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parameters=None,
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),
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InferOutput(
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name="Probability_setosa",
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shape=[1],
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datatype="FP64",
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data=[1.0],
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parameters=None,
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),
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InferOutput(
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name="Probability_versicolor",
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shape=[1],
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datatype="FP64",
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data=[0.0],
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parameters=None,
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),
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InferOutput(
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name="Probability_virginica",
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shape=[1],
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datatype="FP64",
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data=[0.0],
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parameters=None,
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),
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InferOutput(
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name="Node_Id", shape=[1], datatype="BYTES", data=["2"], parameters=None
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),
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]
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kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)
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@pytest.mark.predictor
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@pytest.mark.asyncio(scope="session")
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async def test_pmml_v2_grpc():
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service_name = "isvc-pmml-v2-grpc"
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model_name = "pmml"
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predictor = V1beta1PredictorSpec(
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min_replicas=1,
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model=V1beta1ModelSpec(
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model_format=V1beta1ModelFormat(
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name="pmml",
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),
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runtime="kserve-pmmlserver",
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storage_uri="gs://kfserving-examples/models/pmml",
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resources=V1ResourceRequirements(
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requests={"cpu": "10m", "memory": "256Mi"},
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limits={"cpu": "100m", "memory": "512Mi"},
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),
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ports=[V1ContainerPort(container_port=8081, name="h2c", protocol="TCP")],
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args=["--model_name", model_name],
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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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json_file = open("./data/pmml_input_v2_grpc.json")
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payload = json.load(json_file)["inputs"]
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response = await predict_grpc(
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service_name=service_name, payload=payload, model_name=model_name
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)
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assert response.outputs[0].data == [b"setosa"]
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assert response.outputs[1].data == [1.0]
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assert response.outputs[2].data == [0.0]
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assert response.outputs[3].data == [0.0]
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kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)
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