Weird outputs in Azure Machine Learning with advanced entry script - ONLY in Azure ML Studio

Question

Hi,

I have recently redeployed a model and while its output is fine when I use - for example - Postman - the outputs of 'test' tab and 'consume' codes is very weird. It was not an issue couple of days ago - what might have happened?

azureml.core.__version__
'1.45.0'

Entry script:

import joblib
from azureml.core.model import Model
import json
import pandas as pd
import numpy as np
from oremoval import outlier_removal

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

def init():
    global model
    # Example when the model is a file
    model_path = Model.get_model_path('hd_otr_f') # logistic
    print('Model Path is  ', model_path)
    model = joblib.load(model_path)
    
data_sample = PandasParameterType(pd.DataFrame({'age': pd.Series([71], dtype='int64'),
                                                'sex': pd.Series(['0'], dtype='object'),
                                                'cp': pd.Series(['0'], dtype='object'),
                                                'trestbps': pd.Series([112], dtype='int64'),
                                                'chol': pd.Series([203], dtype='int64'),
                                                'fbs': pd.Series(['0'], dtype='object'),
                                                'restecg': pd.Series(['1'], dtype='object'),
                                                'thalach': pd.Series([185], dtype='int64'),
                                                'exang': pd.Series(['0'], dtype='object'),
                                                'oldpeak': pd.Series([0.1], dtype='float64'),
                                                'slope': pd.Series(['2'], dtype='object'),
                                                'ca': pd.Series(['0'], dtype='object'),
                                                'thal': pd.Series(['2'], dtype='object')}))

input_sample = StandardPythonParameterType({'data': data_sample})
result_sample = NumpyParameterType(np.array([0]))
output_sample = StandardPythonParameterType({'Results': result_sample})

@input_schema('Inputs', input_sample)

@output_schema(output_sample)

def run(Inputs):
    try:
        data = Inputs['data']
        #result = model.predict_proba(data)
        result = np.round(model.predict_proba(data)[0][0], 2)
        return result.tolist()
    except Exception as e:
        error = str(e)
        return error

Script

import json
import requests
key = key
headers = {'Content-Type':'application/json'}
headers['Authorization'] = f'Bearer {key}'

new_data = {
  "Inputs": {
    "data": [
      {
       'age': 71,
       'sex': 0,
       'cp': 0,
       'trestbps': 112,
       'chol': 203,
       'fbs': 0,
       'restecg': 1,
       'thalach': 185,
       'exang': 0,
       'oldpeak': 0.1,
       'slope': 2,
       'ca': 0,
       'thal': 2
      }
    ]
  }
}


data = new_data

r = requests.post(url, str.encode(json.dumps(data)), headers = headers)
print(r.status_code)
print(r.json())

returns

200

0.02

Script from consumption - also in Python:

// This code requires the Nuget package Microsoft.AspNet.WebApi.Client to be installed.
// Instructions for doing this in Visual Studio:
// Tools -> Nuget Package Manager -> Package Manager Console
// Install-Package Newtonsoft.Json
// .NET Framework 4.7.1 or greater must be used

using System;
using System.Collections.Generic;
using System.IO;
using System.Net.Http;
using System.Net.Http.Headers;
using System.Text;
using System.Threading.Tasks;
using Newtonsoft.Json;

namespace CallRequestResponseService
{
    class Program
    {
        static void Main(string[] args)
        {
            InvokeRequestResponseService().Wait();
        }

        static async Task InvokeRequestResponseService()
        {
            var handler = new HttpClientHandler()
            {
                ClientCertificateOptions = ClientCertificateOption.Manual,
                ServerCertificateCustomValidationCallback =
                        (httpRequestMessage, cert, cetChain, policyErrors) => { return true; }
            };
            using (var client = new HttpClient(handler))
            {
                // Request data goes here
                // The example below assumes JSON formatting which may be updated
                // depending on the format your endpoint expects.
                // More information can be found here:
                // https://docs.microsoft.com/azure/machine-learning/how-to-deploy-advanced-entry-script
                var requestBody = @"{
                  ""Inputs"": {
                    ""data"": [
                      {
                        ""age"": 71,
                        ""sex"": ""0"",
                        ""cp"": ""0"",
                        ""trestbps"": 112,
                        ""chol"": 203,
                        ""fbs"": ""0"",
                        ""restecg"": ""1"",
                        ""thalach"": 185,
                        ""exang"": ""0"",
                        ""oldpeak"": 0.1,
                        ""slope"": ""2"",
                        ""ca"": ""0"",
                        ""thal"": ""2""
                      }
                    ]
                  }
                }";
                
                // Replace this with the primary/secondary key or AMLToken for the endpoint
                const string apiKey = "";
                if (string.IsNullOrEmpty(apiKey))  
                {
                    throw new Exception("A key should be provided to invoke the endpoint");
                }
                client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue( "Bearer", apiKey);
                client.BaseAddress = new Uri("http://b5cbe4c2-f4e1-4d11-ba2f-dcffd29c9a79.westeurope.azurecontainer.io/score");

                var content = new StringContent(requestBody);
                content.Headers.ContentType = new MediaTypeHeaderValue("application/json");

                // WARNING: The 'await' statement below can result in a deadlock
                // if you are calling this code from the UI thread of an ASP.Net application.
                // One way to address this would be to call ConfigureAwait(false)
                // so that the execution does not attempt to resume on the original context.
                // For instance, replace code such as:
                //      result = await DoSomeTask()
                // with the following:
                //      result = await DoSomeTask().ConfigureAwait(false)
                HttpResponseMessage response = await client.PostAsync("", content);

                if (response.IsSuccessStatusCode)
                {
                    string result = await response.Content.ReadAsStringAsync();
                    Console.WriteLine("Result: {0}", result);
                }
                else
                {
                    Console.WriteLine(string.Format("The request failed with status code: {0}", response.StatusCode));

                    // Print the headers - they include the requert ID and the timestamp,
                    // which are useful for debugging the failure
                    Console.WriteLine(response.Headers.ToString());

                    string responseContent = await response.Content.ReadAsStringAsync();
                    Console.WriteLine(responseContent);
                }
            }
        }
    }
}

returns b'0.02'

And clicking simply 'test' tab in ML Studio throws an error:

What happened and how to cope with these issues?

Answer 1

EDIT: it seems to be working just fine right now (Monday, 8:51 GMT+2).