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36 lines
1.2 KiB
Python
36 lines
1.2 KiB
Python
import os
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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 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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# AZUREML_MODEL_DIR is an environment variable created during deployment.
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# It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)
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# For multiple models, it points to the folder containing all deployed models (./azureml-models)
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model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), '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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