ium_430705/lab_09_predict_coop.py

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import json
import mlflow
import pandas as pd
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from pprint import pprint
from mlflow.tracking import MlflowClient
model_name = "s430705"
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model_version = 30
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mlflow.set_tracking_uri("http://172.17.0.1:5000")
model = mlflow.pyfunc.load_model(
model_uri=f"models:/{model_name}/{model_version}"
)
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client = MlflowClient()
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models_version = client.search_model_versions("name='s430705'"):
print(type(models_version))
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with open('/tmp/mlruns/0/6be4f90846214df8913a553bc53b1019/artifacts/movies_imdb2/input_example.json', 'r') as datafile:
data = json.load(datafile)
example_input = data["inputs"]
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input_dictionary = {i: x for i, x in enumerate(example_input)}
input_ex = pd.DataFrame(input_dictionary, index=[0])
print(model.predict(input_ex))