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test: add REST and gRPC test cases for Sklearn framework with MLServer runtime deployment
Signed-off-by: Snomaan6846 <syedali@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED
1 parent de11de9 commit 9d5d969

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‎conftest.py‎

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@@ -102,6 +102,11 @@ def pytest_addoption(parser: Parser) -> None:
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default=os.environ.get("VLLM_RUNTIME_IMAGE"),
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help="Specify the runtime image to use for the tests",
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)
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runtime_group.addoption(
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"--mlserver-runtime-image",
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default=os.environ.get("MLSERVER_RUNTIME_IMAGE"),
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help="Specify the runtime image to use for the tests",
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)
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# Upgrade options
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upgrade_group.addoption(

‎tests/conftest.py‎

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@@ -236,6 +236,14 @@ def vllm_runtime_image(pytestconfig: pytest.Config) -> str | None:
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return runtime_image
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@pytest.fixture(scope="session")
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def mlserver_runtime_image(pytestconfig: pytest.Config) -> str | None:
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runtime_image = pytestconfig.option.mlserver_runtime_image
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if not runtime_image:
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return None
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return runtime_image
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@pytest.fixture(scope="session")
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def use_unprivileged_client(pytestconfig: pytest.Config) -> bool:
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_use_unprivileged_client = py_config.get("use_unprivileged_client")

‎tests/model_serving/model_runtime/mlserver/__init__.py‎

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‎tests/model_serving/model_runtime/mlserver/basic_model_deployment/__init__.py‎

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‎tests/model_serving/model_runtime/mlserver/basic_model_deployment/__snapshots__/test_sklearn_basic_model_deployment/TestSkLearnModel.test_sklearn_model_inference[sklearn-iris-raw-grpc-deployment].json‎

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{
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"modelName": "sklearn-iris",
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"modelVersion": "v1.0.0",
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"outputs": [
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{
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"contents": {
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"int64Contents": [
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"1",
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"1"
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]
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},
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"datatype": "INT64",
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"name": "predict",
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"parameters": {
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"content_type": {
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"stringParam": "np"
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}
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},
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"shape": [
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"2",
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"1"
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]
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}
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]
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}

‎tests/model_serving/model_runtime/mlserver/basic_model_deployment/__snapshots__/test_sklearn_basic_model_deployment/TestSkLearnModel.test_sklearn_model_inference[sklearn-iris-raw-rest-deployment].json‎

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{
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"id": "sklearn-iris",
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"model_name": "sklearn-iris",
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"model_version": "v1.0.0",
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"outputs": [
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{
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"data": [
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1,
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1
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],
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"datatype": "INT64",
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"name": "predict",
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"parameters": {
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"content_type": "np"
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},
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"shape": [
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2,
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1
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]
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}
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],
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"parameters": {}
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}

‎tests/model_serving/model_runtime/mlserver/basic_model_deployment/__snapshots__/test_sklearn_basic_model_deployment/TestSkLearnModel.test_sklearn_model_inference[sklearn-iris-serverless-grpc-deployment].json‎

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{
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"modelName": "sklearn-iris",
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"modelVersion": "v1.0.0",
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"outputs": [
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{
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"contents": {
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"int64Contents": [
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"1",
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"1"
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]
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},
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"datatype": "INT64",
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"name": "predict",
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"parameters": {
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"content_type": {
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"stringParam": "np"
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}
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},
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"shape": [
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"2",
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"1"
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]
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}
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]
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}

‎tests/model_serving/model_runtime/mlserver/basic_model_deployment/__snapshots__/test_sklearn_basic_model_deployment/TestSkLearnModel.test_sklearn_model_inference[sklearn-iris-serverless-rest-deployment].json‎

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{
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"id": "sklearn-iris",
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"model_name": "sklearn-iris",
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"model_version": "v1.0.0",
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"outputs": [
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{
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"data": [
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1,
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1
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],
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"datatype": "INT64",
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"name": "predict",
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"parameters": {
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"content_type": "np"
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},
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"shape": [
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2,
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1
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]
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}
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],
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"parameters": {}
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}

‎tests/model_serving/model_runtime/mlserver/basic_model_deployment/test_sklearn_basic_model_deployment.py‎

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"""
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Test module for sklearn model deployment using MLServer runtime.
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This module contains parameterized tests that validate sklearn model inference
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across different protocols (REST/gRPC) and deployment types (raw/serverless).
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"""
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from typing import Any
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import pytest
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from simple_logger.logger import get_logger
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from ocp_resources.inference_service import InferenceService
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from ocp_resources.pod import Pod
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from utilities.constants import Protocols
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from tests.model_serving.model_runtime.mlserver.constant import (
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BASE_RAW_DEPLOYMENT_CONFIG,
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BASE_SERVERLESS_DEPLOYMENT_CONFIG,
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MODEL_PATH_PREFIX,
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SKLEARN_GRPC_INPUT_QUERY,
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SKLEARN_REST_INPUT_QUERY,
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)
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from tests.model_serving.model_runtime.mlserver.utils import validate_inference_request
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LOGGER = get_logger(name=__name__)
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MODEL_NAME: str = "sklearn-iris"
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MODEL_VERSION: str = "v1.0.0"
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MODEL_NAME_DICT: dict[str, str] = {"name": MODEL_NAME}
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MODEL_STORAGE_URI_DICT: dict[str, str] = {"model-dir": f"{MODEL_PATH_PREFIX}/sklearn"}
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pytestmark = pytest.mark.usefixtures(
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"root_dir", "valid_aws_config", "mlserver_rest_serving_runtime_template", "mlserver_grpc_serving_runtime_template"
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)
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@pytest.mark.parametrize(
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"protocol, model_namespace, mlserver_inference_service, s3_models_storage_uri, serving_runtime",
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[
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pytest.param(
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{"protocol_type": Protocols.REST},
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{"name": "sklearn-iris-raw-rest"},
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{
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**BASE_RAW_DEPLOYMENT_CONFIG,
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**MODEL_NAME_DICT,
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},
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MODEL_STORAGE_URI_DICT,
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BASE_RAW_DEPLOYMENT_CONFIG,
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id="sklearn-iris-raw-rest-deployment",
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),
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pytest.param(
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{"protocol_type": Protocols.GRPC},
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{"name": "sklearn-iris-raw-grpc"},
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{
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**BASE_RAW_DEPLOYMENT_CONFIG,
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**MODEL_NAME_DICT,
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},
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MODEL_STORAGE_URI_DICT,
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BASE_RAW_DEPLOYMENT_CONFIG,
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id="sklearn-iris-raw-grpc-deployment",
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),
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pytest.param(
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{"protocol_type": Protocols.REST},
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{"name": "sklearn-iris-serverless-rest"},
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{**BASE_SERVERLESS_DEPLOYMENT_CONFIG, **MODEL_NAME_DICT},
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MODEL_STORAGE_URI_DICT,
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BASE_SERVERLESS_DEPLOYMENT_CONFIG,
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id="sklearn-iris-serverless-rest-deployment",
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),
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pytest.param(
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{"protocol_type": Protocols.GRPC},
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{"name": "sklearn-iris-serverless-grpc"},
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{**BASE_SERVERLESS_DEPLOYMENT_CONFIG, **MODEL_NAME_DICT},
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MODEL_STORAGE_URI_DICT,
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BASE_SERVERLESS_DEPLOYMENT_CONFIG,
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id="sklearn-iris-serverless-grpc-deployment",
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),
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],
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indirect=True,
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)
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class TestSkLearnModel:
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"""Test class for sklearn model inference with MLServer runtime.
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Tests cover multiple deployment scenarios:
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- REST and gRPC protocols
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- Raw and serverless deployment modes
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- Response validation against snapshots
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"""
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def test_sklearn_model_inference(
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self,
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mlserver_inference_service: InferenceService,
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mlserver_pod_resource: Pod,
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response_snapshot: Any,
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protocol: str,
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root_dir: str,
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):
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"""Test sklearn model inference across different protocols and deployment types.
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Args:
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mlserver_inference_service: The deployed inference service
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mlserver_pod_resource: Pod running the model server
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response_snapshot: Expected response for validation
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protocol: Communication protocol (REST or gRPC)
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root_dir: Test root directory path
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"""
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input_query = SKLEARN_REST_INPUT_QUERY if protocol == Protocols.REST else SKLEARN_GRPC_INPUT_QUERY
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validate_inference_request(
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pod_name=mlserver_pod_resource.name,
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isvc=mlserver_inference_service,
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response_snapshot=response_snapshot,
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input_query=input_query,
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model_version=MODEL_VERSION,
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protocol=protocol,
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root_dir=root_dir,
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)

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