33 lines
910 B
Python
33 lines
910 B
Python
import json
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import mlflow
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import pandas as pd
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from pprint import pprint
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from mlflow.tracking import MlflowClient
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model_name = "s434804"
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model_version = 12
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mlflow.set_tracking_uri("http://172.17.0.1:5000")
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model = mlflow.keras.pyfunc.load_model(
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model_uri=f"models:/{model_name}/{model_version}",
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)
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"""
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Honestly I have no clue how to get model's json input example
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any other way, so just intialize MLFlow client, get latest
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version of choosen model, and then get path to model's files
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"""
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client = MlflowClient()
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models_version = client.search_model_versions("name='s434804'")
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path_to_input = models_version[-1].source
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with open(f'{path_to_input}/input_example.json', 'r') as datafile:
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data = json.load(datafile)
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example_input = data["inputs"]
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input_dictionary = {i: x for i, x in enumerate(example_input)}
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input_ex = pd.DataFrame(input_dictionary, index=[0])
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print(model.predict(input_ex))
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