Python 如何将azureml中注册的模型加载到azure函数应用程序中

我同意@Muhammad Pathan，您不需要在此函数中定义全局模型变量.您可以按如下方式更新代码:-

100

import logging
import azure.functions as func
from azureml.core import Workspace, Model
import joblib
import pandas as pd

def load_model(model_name):
    try:
        # Load the model from Azure ML
        model = Model(workspace=ws, name=model_name)
        model_path = model.download(exist_ok=True)  # Download the model
        loaded_model = joblib.load(model_path)
        return loaded_model
    except Exception as e:
        return None

# Load your Azure ML Workspace from a configuration file
ws = Workspace.from_config('./config.json')

def main(req: func.HttpRequest):
    logging.info("Python HTTP trigger function processed a request.")

    try:
        # Check if request contains JSON
        if not req.get_json():
            return func.HttpResponse(
                "JSON data expected in the request.",
                status_code=400
            )
        dat = req.get_json()
    except ValueError:
        return func.HttpResponse(
            "Invalid JSON received.",
            status_code=400
        )

    model_name = "sample_model3"  # Replace with the model name in your workspace

    # Load the model
    model = load_model(model_name)
    if model is None:
        return func.HttpResponse(
            f"Model '{model_name}' not found.",
            status_code=404
        )

    # Perform inference using the loaded model
    try:
        result = model.predict(pd.DataFrame([dat]))
        return func.HttpResponse(f"Inference result: {result}")
    except Exception as e:
        return func.HttpResponse(f"An error occurred during inference: {str(e)}", status_code=500)

Output:-个个

100

{
    "sepal length (cm)": 5.1,
    "sepal width (cm)": 3.5,
    "petal length (cm)": 1.4,
    "petal width (cm)": 0.2
}

Postman Output:-个

As an alternative, In order to load your Azure ML model you can make use of the HttpTrigger code below:-

我有loaded and downloaded the model in a folder called temp. This folder is also created by Function code in the same directory as my Function code个，下面是Azure Functions个:

确保你已经登录到你的Azure帐户，该帐户包含你的ML工作区和模型，并在你的VS代码终端中使用100命令，以使以下功能代码正常工作.

My HttpTrigger code:-个

import os
import azure.functions as func
from azureml.core import Workspace, Model

# Function to load and download models
def load_and_download_models():
    try:
        ws = Workspace.from_config('./config.json')
        # Load your Azure ML Workspace. Replace with your own values.
        ws = Workspace(subscription_id="xxxxxxxxaxxxx",
                       resource_group="xxxx",
                       workspace_name="xxx")

        
        downloaded_models_path = "temp"

        loaded_and_downloaded_models = []

        for model_name in ws.models:
            # Iterate through model names in the workspace

            # Load the model using its name
            model = Model(ws, model_name)

            

            # Download the model to a specified directory
            model_path = model.download(target_dir=downloaded_models_path)

            loaded_and_downloaded_models.append(model_name)

        return loaded_and_downloaded_models

    except Exception as e:
        return str(e)

# Azure Function entry point with HTTP trigger
def main(req: func.HttpRequest) -> func.HttpResponse:
    try:
        loaded_and_downloaded_models = load_and_download_models()
        if isinstance(loaded_and_downloaded_models, list):
            return func.HttpResponse(f"Models loaded and downloaded successfully: {', '.join(loaded_and_downloaded_models)}", status_code=200)
        else:
            return func.HttpResponse(f"An error occurred: {loaded_and_downloaded_models}", status_code=500)
    except Exception as e:
        return func.HttpResponse(f"An error occurred: {str(e)}", status_code=500)

Output:-个个

Reference:-个

Python Azure function timer trigger log info not showing when deployed - Stack Overflow个