mirror of
https://github.com/clearml/clearml-serving
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33 lines
1.3 KiB
Python
33 lines
1.3 KiB
Python
from typing import Any, List
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# Notice Preprocess class Must be named "Preprocess"
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class Preprocess(object):
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def __init__(self):
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# set internal state, this will be called only once. (i.e. not per request)
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pass
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def postprocess(self, data: List[dict]) -> dict:
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# we will here average the results and return the new value
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# assume data is a list of dicts greater than 1
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# average result
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return dict(y=0.5 * data[0]['y'][0] + 0.5 * data[1]['y'][0])
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def process(self, data: Any) -> Any:
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"""
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do something with the actual data, return any type of object.
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The returned object will be passed as is to the postprocess function engine
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"""
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predict_a = self.send_request(endpoint="/test_model_sklearn_a/", version=None, data=data)
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predict_b = self.send_request(endpoint="/test_model_sklearn_b/", version=None, data=data)
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if not predict_b or not predict_a:
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raise ValueError("Error requesting inference endpoint test_model_sklearn a/b")
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return [predict_a, predict_b]
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def send_request(self, endpoint, version, data) -> List[dict]:
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# Mock Function!
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# replaced by real send request function when constructed by the inference service
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pass
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