I have an ML model deployed on Azure ML Studio and I was updating it with an inference schema to allow compatibility with Power BI as described here.
When sending data up to the model via REST api (before adding this inference schema), everything works fine and I get results returned. However, once adding the schema as described in the instructions linked above and personalising to my data, the same data sent via REST api only returns the error "list index out of range". The deployment goes ahead fine and is designated as "healthy" with no error messages.
Any help would be greatly appreciated. Thanks.
EDIT:
Entry script:
import numpy as np
import pandas as pd
import joblib
from azureml.core.model import Model
from inference_schema.schema_decorators import input_schema, output_schema
from inference_schema.parameter_types.standard_py_parameter_type import StandardPythonParameterType
from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType
from inference_schema.parameter_types.pandas_parameter_type import PandasParameterType
def init():
global model
#Model name is the name of the model registered under the workspace
model_path = Model.get_model_path(model_name = 'databricksmodelpowerbi2')
model = joblib.load(model_path)
#Provide 3 sample inputs for schema generation for 2 rows of data
numpy_sample_input = NumpyParameterType(np.array([[2400.0, 78.26086956521739, 11100.0, 3.612565445026178, 3.0, 0.0], [368.55, 96.88311688311687, 709681.1600000012, 73.88059701492537, 44.0, 0.0]], dtype = 'float64'))
pandas_sample_input = PandasParameterType(pd.DataFrame({'1': [2400.0, 368.55], '2': [78.26086956521739, 96.88311688311687], '3': [11100.0, 709681.1600000012], '4': [3.612565445026178, 73.88059701492537], '5': [3.0, 44.0], '6': [0.0, 0.0]}))
standard_sample_input = StandardPythonParameterType(0.0)
# This is a nested input sample, any item wrapped by `ParameterType` will be described by schema
sample_input = StandardPythonParameterType({'input1': numpy_sample_input,
'input2': pandas_sample_input,
'input3': standard_sample_input})
sample_global_parameters = StandardPythonParameterType(1.0) #this is optional
sample_output = StandardPythonParameterType([1.0, 1.0])
@input_schema('inputs', sample_input)
@input_schema('global_parameters', sample_global_parameters) #this is optional
@output_schema(sample_output)
def run(inputs, global_parameters):
try:
data = inputs['input1']
# data will be convert to target format
assert isinstance(data, np.ndarray)
result = model.predict(data)
return result.tolist()
except Exception as e:
error = str(e)
return error
Prediction script:
import requests
import json
from ast import literal_eval
# URL for the web service
scoring_uri = ''
## If the service is authenticated, set the key or token
#key = '<your key or token>'
# Two sets of data to score, so we get two results back
data = {"data": [[2400.0, 78.26086956521739, 11100.0, 3.612565445026178, 3.0, 0.0], [368.55, 96.88311688311687, 709681.1600000012, 73.88059701492537, 44.0, 0.0]]}
# Convert to JSON string
input_data = json.dumps(data)
# Set the content type
headers = {'Content-Type': 'application/json'}
## If authentication is enabled, set the authorization header
#headers['Authorization'] = f'Bearer {key}'
# Make the request and display the response
resp = requests.post(scoring_uri, input_data, headers=headers)
print(resp.text)
result = literal_eval(resp.text)
The Microsoft documentation say's: "In order to generate conforming swagger for automated web service consumption, scoring script run() function must have API shape of:
A first parameter of type "StandardPythonParameterType", named Inputs and nested.
An optional second parameter of type "StandardPythonParameterType", named GlobalParameters.
Return a dictionary of type "StandardPythonParameterType" named Results and nested."
I've already test this and it is case sensitive So it will be like this:
import numpy as np
import pandas as pd
import joblib
from azureml.core.model import Model
from inference_schema.schema_decorators import input_schema, output_schema
from inference_schema.parameter_types.standard_py_parameter_type import
StandardPythonParameterType
from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType
from inference_schema.parameter_types.pandas_parameter_type import PandasParameterType
def init():
global model
# Model name is the name of the model registered under the workspace
model_path = Model.get_model_path(model_name = 'databricksmodelpowerbi2')
model = joblib.load(model_path)
# Provide 3 sample inputs for schema generation for 2 rows of data
numpy_sample_input = NumpyParameterType(np.array([[2400.0, 78.26086956521739, 11100.0,
3.612565445026178, 3.0, 0.0], [368.55, 96.88311688311687, 709681.1600000012,
73.88059701492537, 44.0, 0.0]], dtype = 'float64'))
pandas_sample_input = PandasParameterType(pd.DataFrame({'value': [2400.0, 368.55],
'delayed_percent': [78.26086956521739, 96.88311688311687], 'total_value_delayed':
[11100.0, 709681.1600000012], 'num_invoices_per30_dealing_days': [3.612565445026178,
73.88059701492537], 'delayed_streak': [3.0, 44.0], 'prompt_streak': [0.0, 0.0]}))
standard_sample_input = StandardPythonParameterType(0.0)
# This is a nested input sample, any item wrapped by `ParameterType` will be described
by schema
sample_input = StandardPythonParameterType({'input1': numpy_sample_input,
'input2': pandas_sample_input,
'input3': standard_sample_input})
sample_global_parameters = StandardPythonParameterType(1.0) #this is optional
numpy_sample_output = NumpyParameterType(np.array([1.0, 2.0]))
# 'Results' is case sensitive
sample_output = StandardPythonParameterType({'Results': numpy_sample_output})
# 'Inputs' is case sensitive
@input_schema('Inputs', sample_input)
@input_schema('global_parameters', sample_global_parameters) #this is optional
@output_schema(sample_output)
def run(Inputs, global_parameters):
try:
data = inputs['input1']
# data will be convert to target format
assert isinstance(data, np.ndarray)
result = model.predict(data)
return result.tolist()
except Exception as e:
error = str(e)
return error
`