使用 AML SDK V2 自动为 Azure ML 端点生成 Swagger

Question

参考示例：https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-advanced-entry-script?view=azureml-api-1#automatically-generate- a-swagger-模式

我希望通过包含模型和示例枚举的可选输入等详细信息来增强我的 Swagger 文档。这可以通过推理模式实现吗？我还没有遇到任何例子来证明这一点。当尝试使用 input_schema 装饰器以我的请求格式向 json_schema 提供枚举和必需字段时，Swagger 似乎会根据需要考虑所有内容。

任何有关如何实现这一目标的指导将不胜感激。

Answer 1

要创建可选参数，您需要将

GlobalParameters

类型的

StandardPythonParameterType

赋予

input_schema

装饰器。

以下是示例

score.py

我正在使用。

创建

StandardPythonParameterType

类型的参数：

method_sample  = StandardPythonParameterType("predict")
sample_global_params  = StandardPythonParameterType({"method": method_sample})

然后，在装饰器中，提供如下：

@input_schema('GlobalParameters', sample_global_params, convert_to_provided_type=False)

这里，

GlobalParameters

区分大小写。

代码：

import json
import logging
import os
import pickle
import numpy as np
import pandas as pd
import joblib

import azureml.automl.core
from azureml.automl.core.shared import logging_utilities, log_server
from azureml.telemetry import INSTRUMENTATION_KEY

from inference_schema.schema_decorators import input_schema, output_schema
from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType
from inference_schema.parameter_types.pandas_parameter_type import PandasParameterType
from inference_schema.parameter_types.standard_py_parameter_type import StandardPythonParameterType

data_sample = StandardPythonParameterType([1,2])
input_sample = StandardPythonParameterType({'data': data_sample})
method_sample = StandardPythonParameterType("predict")
sample_global_params = StandardPythonParameterType({"method": method_sample})

result_sample = NumpyParameterType(np.array(["example_value"]))
output_sample = StandardPythonParameterType({'Results':result_sample})

try:
    log_server.enable_telemetry(INSTRUMENTATION_KEY)
    log_server.set_verbosity('INFO')
    logger = logging.getLogger('azureml.automl.core.scoring_script_v2')
except:
    pass

def init():
    global model
    # This name is model.id of the model that we want to deploy; deserialize the model file back
    # into a sklearn model
    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.pkl')
    path = os.path.normpath(model_path)
    path_split = path.split(os.sep)
    log_server.update_custom_dimensions({'model_name': path_split[-3], 'model_version': path_split[-2]})
    try:
        logger.info("Loading model from path.")
        model = joblib.load(model_path)
        logger.info("Loading successful.")
    except Exception as e:
        logging_utilities.log_traceback(e, logger)
        raise

@input_schema('GlobalParameters', sample_global_params, convert_to_provided_type=False)
@input_schema('Inputs', input_sample)
@output_schema(output_sample)
def run(Inputs, GlobalParameters={"method": "predict"}):
    data = Inputs['data']
    if GlobalParameters.get("method", None) == "predict_proba":
        result = ["Method proba executed",sum(data)]
    elif GlobalParameters.get("method", None) == "predict":
        result = ["Method predict executed",sum(data)]
    else:
        raise Exception(f"Invalid predict method argument received. GlobalParameters: {GlobalParameters}")
    if isinstance(result, pd.DataFrame):
        result = result.values
    return {'Results':result.tolist()}

可以参考这个Stack解决方案