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30 lines
881 B
Python
30 lines
881 B
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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def init():
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global model
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# note here "best_model" is the name of the model registered under the workspace
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# this call should return the path to the model.pkl file on the local disk.
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model_path = Model.get_model_path(model_name='best_model')
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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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# note you can pass in multiple rows for scoring
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def run(raw_data):
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try:
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data = json.loads(raw_data)['data']
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data = np.array(data)
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result = model.predict(data)
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# you can return any data type 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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result = str(e)
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return result
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