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https://github.com/hanzo-ml/kserve.git
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0cc7b33d97
Signed-off-by: ZabinskiMichal <[email protected]> Signed-off-by: Michał Żabiński <[email protected]> Signed-off-by: Spolti <[email protected]> Co-authored-by: Spolti <[email protected]>
96 lines
3.2 KiB
Python
96 lines
3.2 KiB
Python
# Copyright 2026 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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"""
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E2E tests for AutoGluon TimeSeriesPredictor on kserve-autogluonserver.
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The InferenceService ``storage_uri`` must refer to an AutoGluon TimeSeries
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predictor save directory (the path you would pass to ``TimeSeriesPredictor.load``).
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By default, tests use a sample model artifact in a **public** GCS bucket. Override
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``AUTOGLUON_TIMESERIES_STORAGE_URI`` if you need a different ``storage_uri``.
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"""
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import os
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import pytest
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from kubernetes.client import V1ResourceRequirements
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from kserve import (
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V1beta1ModelFormat,
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V1beta1ModelSpec,
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V1beta1PredictorSpec,
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)
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from .autogluon_helpers import deploy_and_predict
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# Public sample TimeSeriesPredictor artifact for e2e (override via env if needed).
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_AUTOGLUON_TS_DEFAULT_STORAGE_URI = (
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"gs://test-project-frog-ml-artifacts/timeseries-artifacts/predictor/"
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)
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AUTOGLUON_TS_STORAGE_URI = os.getenv(
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"AUTOGLUON_TIMESERIES_STORAGE_URI",
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_AUTOGLUON_TS_DEFAULT_STORAGE_URI,
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)
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AUTOGLUON_TS_RESOURCES = V1ResourceRequirements(
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requests={"cpu": "100m", "memory": "2Gi"},
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limits={"cpu": "2", "memory": "4Gi"},
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)
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def _create_ts_predictor(service_name: str, storage_uri: str = None):
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model = V1beta1ModelSpec(
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model_format=V1beta1ModelFormat(name="autogluon"),
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runtime="kserve-autogluonserver",
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storage_uri=storage_uri or AUTOGLUON_TS_STORAGE_URI,
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resources=AUTOGLUON_TS_RESOURCES,
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)
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return V1beta1PredictorSpec(min_replicas=1, model=model)
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async def _deploy_and_predict_v1(
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service_name: str, rest_v1_client, input_path: str, storage_uri: str = None
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):
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predictor = _create_ts_predictor(service_name, storage_uri=storage_uri)
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return await deploy_and_predict(service_name, predictor, rest_v1_client, input_path)
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@pytest.mark.predictor
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@pytest.mark.asyncio(scope="session")
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async def test_autogluon_timeseries_runtime_kserve_v1(rest_v1_client):
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service_name = "isvc-autogluon-ts-v1"
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response = await _deploy_and_predict_v1(
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service_name,
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rest_v1_client,
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"./data/autogluon_timeseries_input.json",
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)
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assert "predictions" in response
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assert len(response["predictions"]) > 0
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@pytest.mark.predictor
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@pytest.mark.asyncio(scope="session")
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async def test_autogluon_timeseries_runtime_kserve_v1_storage_uri_without_trailing_slash(
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rest_v1_client,
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):
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service_name = "isvc-autogluon-ts-v1-noslash"
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response = await _deploy_and_predict_v1(
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service_name,
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rest_v1_client,
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"./data/autogluon_timeseries_input_long.json",
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storage_uri=AUTOGLUON_TS_STORAGE_URI.rstrip("/"),
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)
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assert "predictions" in response
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assert len(response["predictions"]) > 0
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