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predict registry
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s444417 2022-05-15 10:55:01 +02:00
parent b7fc68abea
commit 329240227d
1 changed files with 23 additions and 8 deletions
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lab8/predictMlflow.py
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@ -1,15 +1,30 @@
# import mlflow
# import numpy as np
# import json
#
# logged_model = '/mlruns/14/80fe21a0804844088147d15a3cebb3e5/artifacts/lego-model'
#
# # Load model as a PyFuncModel.
# loaded_model = mlflow.pyfunc.load_model(logged_model)
#
# with open(f'{loaded_model}/input_example.json') as f:
# input_example_data = json.load(f)
#
# # Predictions
# print(f'input: {input_example_data}')
# print(f'predictions: {loaded_model.predict(input_example_data)}')
import mlflow import mlflow
import numpy as np import numpy as np
import json import json
logged_model = '/mlruns/14/80fe21a0804844088147d15a3cebb3e5/artifacts/lego-model' registry_path = '/mlruns/14/80fe21a0804844088147d15a3cebb3e5/artifacts/lego-model'
model = mlflow.pyfunc.load_model(registry_path)
# Load model as a PyFuncModel. with open(f'{registry_path}/input_example.json') as f:
loaded_model = mlflow.pyfunc.load_model(logged_model)
with open(f'{loaded_model}/input_example.json') as f:
input_example_data = json.load(f) input_example_data = json.load(f)
# Predictions input_example = np.array(input_example_data['inputs']).reshape(-1, 8)
print(f'input: {input_example_data}')
print(f'predictions: {loaded_model.predict(input_example_data)}') print(f'Input: {input_example}')
print(f'Prediction: {model.predict(input_example)}')