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35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
import pickle
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import json
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import numpy as np
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from sklearn.externals import joblib
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from sklearn.linear_model import Ridge
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from azureml.core.model import Model
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from inference_schema.schema_decorators import input_schema, output_schema
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from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType
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def init():
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global model
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# note here "sklearn_regression_model.pkl" is the name of the model registered under
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# this is a different behavior than before when the code is run locally, even though the code is the same.
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model_path = Model.get_model_path('sklearn_regression_model.pkl')
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# deserialize the model file back into a sklearn model
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model = joblib.load(model_path)
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input_sample = np.array([[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]])
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output_sample = np.array([3726.995])
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@input_schema('data', NumpyParameterType(input_sample))
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@output_schema(NumpyParameterType(output_sample))
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def run(data):
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try:
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result = model.predict(data)
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# you can return any datatype as long as it is JSON-serializable
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return result.tolist()
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except Exception as e:
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error = str(e)
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return error
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