30 lines
895 B
Python
30 lines
895 B
Python
# import mlflow
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# import numpy as np
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# import json
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#
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# logged_model = '/mlruns/14/80fe21a0804844088147d15a3cebb3e5/artifacts/lego-model'
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#
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# # Load model as a PyFuncModel.
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# loaded_model = mlflow.pyfunc.load_model(logged_model)
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#
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# with open(f'{loaded_model}/input_example.json') as f:
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# input_example_data = json.load(f)
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#
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# # Predictions
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# print(f'input: {input_example_data}')
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# print(f'predictions: {loaded_model.predict(input_example_data)}')
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import mlflow
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import numpy as np
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import json
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registry_path = '/mlruns/14/80fe21a0804844088147d15a3cebb3e5/artifacts/lego-model'
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model = mlflow.pyfunc.load_model(registry_path)
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with open(f'{registry_path}/input_example.json') as f:
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input_example_data = json.load(f)
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input_example = np.array(input_example_data['inputs']).reshape(-1,)
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print(f'Input: {input_example}')
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print(f'Prediction: {model.predict(input_example)}') |