Question

我正在关注本教程VSCODE tensorflow model deployment on Azure。在这里，我试图部署一个简单的决策树模型来代替tensorflow模型。我创建了一个train.py文件，像这样

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
import pickle
import os
import joblib

data=pd.read_csv('CreditCardWeka.csv')
model=DecisionTreeClassifier()
Y=data['Class']
del data['Class']
X=data
model.fit(X,Y)
os.makedirs('./outputs/model', exist_ok = True)
joblib.dump(model, './outputs/model/dec_model.sav')

此后，我将创建一个计算，创建一个运行配置并选择此文件。此后，我将创建一个实验并运行它并下载输出。我能够下载输出，直到此为止。之后，我可以成功注册我的模型，并且当我尝试将其部署为“ Azure容器服务”时，它会要求问score.py，这是哪个

import os
import joblib
import json
import time
import sklearn
# Called when the deployed service starts
from azureml.core.model import Model

def init():
    global model

    # Get the path where the deployed model can be found.
    # load models
    model_root = Model.get_model_path('decision-tree-model')
    model = joblib.load(os.path.join(model_root, 'dec-model.sav'))

# Handle requests to the service
def run(data):
    try:
        # Pick out the text property of the JSON request.
        # This expects a request in the form of {"text": "some text to score for sentiment"}
        data = json.loads(data)
        prediction = model.predict(data['X'])
        #Return prediction
        return prediction
    except Exception as e:
        error = str(e)
        return error

它还会要求一个yml文件

name: decision-tree
channels:
  - defaults
dependencies:
  - python
  - sklearn
  - joblib
  - pip
  - pip:
    - azureml-defaults

此后，当它开始创建Docker映像时，它将失败，错误是“ Docker映像生成失败”。我该如何解决？

Answer 1

在yml文件中，尝试将sklearn更改为scikit-learn。

如果仍然失败，请尝试详细的故障排除说明以获取更多日志：https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

使用vscode部署ml模型时，出现错误docker映像构建失败

1 个答案: