opendatahub-tests/tests/model_serving/model_runtime/mlserver/constant.py at 5024afb1d4fa823e27b7da6e6c6e963ef84482fa · Snomaan6846/opendatahub-tests · GitHub

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"""
Constants for MLServer model serving tests.

This module defines configuration values, resource specifications, deployment configs,
and input queries used across MLServer runtime tests for different frameworks.
"""

from typing import Any

from utilities.constants import KServeDeploymentType, ModelFormat


class OutputType:
    """Model output types for response validation."""

    DETERMINISTIC: str = "deterministic"
    NON_DETERMINISTIC: str = "non_deterministic"


LOCALHOST_URL: str = "http://localhost"
RAW_DEPLOYMENT_TYPE: str = "raw"
MODEL_PATH_PREFIX: str = "mlserver/model_repository"

PREDICT_RESOURCES: dict[str, list[dict[str, str | dict[str, str]]] | dict[str, dict[str, str]]] = {
    "volumes": [
        {"name": "shared-memory", "emptyDir": {"medium": "Memory", "sizeLimit": "2Gi"}},
        {"name": "tmp", "emptyDir": {}},
        {"name": "home", "emptyDir": {}},
    ],
    "volume_mounts": [
        {"name": "shared-memory", "mountPath": "/dev/shm"},
        {"name": "tmp", "mountPath": "/tmp"},
        {"name": "home", "mountPath": "/home/mlserver"},
    ],
    "resources": {"requests": {"cpu": "1", "memory": "2Gi"}, "limits": {"cpu": "2", "memory": "4Gi"}},
}

BASE_RAW_DEPLOYMENT_CONFIG: dict[str, Any] = {
    "deployment_type": KServeDeploymentType.RAW_DEPLOYMENT,
    "min-replicas": 1,
    "enable_external_route": False,
}

SKLEARN_REST_INPUT_QUERY: dict[str, Any] = {
    "id": "sklearn",
    "inputs": [
        {
            "name": "sklearn-input-0",
            "shape": [2, 4],
            "datatype": "FP32",
            "data": [[6.8, 2.8, 4.8, 1.4], [6, 3.4, 4.5, 1.6]],
        }
    ],
}

XGBOOST_REST_INPUT_QUERY: dict[str, Any] = {
    "id": "xgboost",
    "inputs": [
        {
            "name": "xgboost-input-0",
            "shape": [2, 4],
            "datatype": "FP32",
            "data": [[6.8, 2.8, 4.8, 1.4], [6, 3.4, 4.5, 1.6]],
        }
    ],
}

LIGHTGBM_REST_INPUT_QUERY: dict[str, Any] = {
    "id": "lightgbm",
    "inputs": [
        {
            "name": "lightgbm-input-0",
            "shape": [1, 4],
            "datatype": "FP32",
            "data": [[6.7, 3.3, 5.7, 2.1]],
        }
    ],
}

ONNX_REST_INPUT_QUERY = {
    "id": "onnx",
    "inputs": [
        {
            "name": "input",
            "shape": [1, 4],
            "datatype": "FP32",
            "data": [[-1.44964521969853, -0.6500239344068982, -0.08343796979036086, -1.496529255090079]],
        }
    ],
}

MODEL_CONFIGS: dict[str, dict[str, Any]] = {
    ModelFormat.LIGHTGBM: {
        "model_name": ModelFormat.LIGHTGBM,
        "model_version": "v0.1.0",
        "rest_query": LIGHTGBM_REST_INPUT_QUERY,
        "output_type": OutputType.DETERMINISTIC,
    },
    ModelFormat.ONNX: {
        "model_name": ModelFormat.ONNX,
        "model_version": "v1.0.0",
        "rest_query": ONNX_REST_INPUT_QUERY,
        "output_type": OutputType.DETERMINISTIC,
    },
    ModelFormat.SKLEARN: {
        "model_name": ModelFormat.SKLEARN,
        "model_version": "v1.0.0",
        "rest_query": SKLEARN_REST_INPUT_QUERY,
        "output_type": OutputType.DETERMINISTIC,
    },
    ModelFormat.XGBOOST: {
        "model_name": ModelFormat.XGBOOST,
        "model_version": "v1.0.0",
        "rest_query": XGBOOST_REST_INPUT_QUERY,
        "output_type": OutputType.DETERMINISTIC,
    },
}