在 AzureML 中部署模型时如何将参数传递给评分文件

Question

我正在使用 Python SDK 将经过训练的模型部署到 Azure 机器学习上的 ACI 端点。我已经创建了我的 score.py 文件，但我希望调用该文件时传递一个我可以使用 argparse 解释的参数（就像训练文件一样）。但是，我似乎找不到如何传递参数这是我必须创建 InferenceConfig 环境的代码，但显然不起作用。我应该重新使用额外的 Docker 文件步骤吗？

from azureml.core.conda_dependencies import CondaDependencies
from azureml.core.environment import Environment
from azureml.core.model import InferenceConfig

env = Environment('my_hosted_environment')
env.python.conda_dependencies = CondaDependencies.create(
    conda_packages=['scikit-learn'],
    pip_packages=['azureml-defaults'])
scoring_script = 'score.py --model_name ' + model_name
inference_config = InferenceConfig(entry_script=scoring_script, environment=env)

添加 score.py 作为我希望如何使用该脚本中的参数的参考：

#removed imports
import argparse

def init():
    global model

    parser = argparse.ArgumentParser(description="Load sklearn model")
    parser.add_argument('--model_name', dest="model_name", required=True)
    args, _ = parser.parse_known_args()

    model_path = Model.get_model_path(model_name=args.model_name)
    model = joblib.load(model_path)

def run(raw_data):
    try:
        data = json.loads(raw_data)['data']
        data = np.array(data)
        result = model.predict(data)
        return result.tolist()

    except Exception as e:
        result = str(e)
        return result

有兴趣听听你的想法

Answer 1

可以在此处找到如何使用环境进行部署 model-register-and-deploy.ipynb . InferenceConfig class accepts source_directory and entry_script parameters，其中 source_directory 是包含要创建的所有文件（score.py 和任何其他附加文件）的文件夹的路径图片。

此 multi-model-register-and-deploy.ipynb 包含有关如何使用 source_directory 和 entry_script 创建 InferenceConfig 的代码片段。

from azureml.core.webservice import Webservice
from azureml.core.model import InferenceConfig
from azureml.core.environment import Environment

myenv = Environment.from_conda_specification(name="myenv", file_path="myenv.yml")
inference_config = InferenceConfig(entry_script="score.py", environment=myenv)

service = Model.deploy(workspace=ws,
                       name='sklearn-mnist-svc',
                       models=[model], 
                       inference_config=inference_config,
                       deployment_config=aciconfig)

service.wait_for_deployment(show_output=True)

print(service.scoring_uri)

Answer 2

这个问题已经有一年了。提供解决方案以帮助那些可能仍在寻找答案的人。我对类似问题的回答是 here。您可以将本机 python 数据类型变量传递到推理配置中，并在评分脚本中将它们作为环境变量访问。

Answer 3

我以不同的方式解决了这个问题。当 score.py 被 InferenceConfig 使用时，我找不到一种（正确且易于遵循的）方法来传递参数。相反，我所做的是遵循 4 个步骤：

已创建score_template.py并定义应分配的变量
读取 score_template.py 的内容并通过用所需值替换变量来修改它
将修改后的内容写入score.py
最后将score.py传给InferenceConfig

score_template.py 中的第 1 步：

import json
from azureml.core.model import Model
import os
import joblib
import pandas as pd
import numpy as np
def init():
    global model
    #model = joblib.load('recommender.pkl')
    model_name="#MODEL_NAME#"
    model_saved_file='#MODEL_SAVED_FILE#'
    try: 
        model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), model_saved_file)
        model = joblib.load(model_path)
    except:
        model_path = Model.get_model_path(model_name)
        model = joblib.load(model_path)

def run(raw_data):
    try:
        #data=pd.json_normalize(data) 
        #data=np.array(data['data'])
        data = json.loads(raw_data)["data"]
        data = np.array(data)
        result = model.predict(data)
        # you can return any datatype as long as it is JSON-serializable
        return {"result": result.tolist()}
    except Exception as e:
        error = str(e)
        #error= data
        return error

deploy_model.py 中的第 2-4 步：

#--Modify Entry Script/Pass Model Name--
entry_script="score.py"
entry_script_temp="score_template.py"

# Read in the entry script template
print("Prepare Entry Script")
with open(entry_script_temp, 'r') as file :
  entry_script_contents = file.read()

# Replace the target string
entry_script_contents = entry_script_contents.replace('#MODEL_NAME#', model_name)
entry_script_contents = entry_script_contents.replace('#MODEL_SAVED_FILE#', model_file_name)
# Write the file to entry script
with open(entry_script, 'w') as file:
  file.write(entry_script_contents)

#--Define configs for the deployment---
print("Get Environtment")
env = Environment.get(workspace=ws, name=env_name)
env.inferencing_stack_version = "latest"
print("Inference Configuration")
inference_config = InferenceConfig(entry_script=entry_script, environment=env, source_directory=base_path)
aci_config = AciWebservice.deploy_configuration(cpu_cores = int(cpu_cores), memory_gb = int(memory_gb),location=location)

#--Deloy the service---
print("Deploy Model")
print("model version:", model_artifact.version)
service = Model.deploy( workspace=ws,
                        name=service_name,
                        models=[model_artifact],
                        inference_config=inference_config,
                        deployment_config=aci_config,
                        overwrite=True )

service.wait_for_deployment(show_output=True)

在 AzureML 中部署模型时如何将参数传递给评分文件

How to pass arguments to scoring file when deploying a Model in AzureML

python

azure-machine-learning-service

azureml