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32 lines
812 B
Python
32 lines
812 B
Python
import io
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import pickle
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import argparse
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import numpy as np
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from azureml.core.model import Model
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from sklearn.linear_model import LogisticRegression
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def init():
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global iris_model
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parser = argparse.ArgumentParser(description="Iris model serving")
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parser.add_argument('--model_name', dest="model_name", required=True)
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args, unknown_args = parser.parse_known_args()
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model_path = Model.get_model_path(args.model_name)
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with open(model_path, 'rb') as model_file:
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iris_model = pickle.load(model_file)
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def run(input_data):
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# make inference
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num_rows, num_cols = input_data.shape
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pred = iris_model.predict(input_data).reshape((num_rows, 1))
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# cleanup output
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result = input_data.drop(input_data.columns[4:], axis=1)
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result['variety'] = pred
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return result
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