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39 lines
1.5 KiB
Python
39 lines
1.5 KiB
Python
import joblib
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import numpy as np
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import os
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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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# The init() method is called once, when the web service starts up.
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#
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# Typically you would deserialize the model file, as shown here using joblib,
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# and store it in a global variable so your run() method can access it later.
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def init():
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global model
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# The AZUREML_MODEL_DIR environment variable indicates
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# a directory containing the model file you registered.
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model_filename = 'sklearn_regression_model.pkl'
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model_path = os.path.join(os.environ['AZUREML_MODEL_DIR'], model_filename)
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model = joblib.load(model_path)
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# The run() method is called each time a request is made to the scoring API.
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#
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# Shown here are the optional input_schema and output_schema decorators
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# from the inference-schema pip package. Using these decorators on your
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# run() method parses and validates the incoming payload against
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# the example input you provide here. This will also generate a Swagger
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# API document for your web service.
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@input_schema('data', NumpyParameterType(np.array([[0.1, 1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9, 9.0]])))
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@output_schema(NumpyParameterType(np.array([4429.929236457418])))
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def run(data):
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# Use the model object loaded by init().
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result = model.predict(data)
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# You can return any JSON-serializable object.
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return result.tolist()
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