mirror of
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46 lines
1.3 KiB
Python
46 lines
1.3 KiB
Python
# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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"""
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Metadata
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========
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ONNX format contains metadata related to how the
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model was produced. It is useful when the model
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is deployed to production to keep track of which
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instance was used at a specific time.
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Let's see how to do that with a simple
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logistic regression model trained with
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*scikit-learn* and converted with *sklearn-onnx*.
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"""
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from onnxruntime.datasets import get_example
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example = get_example("logreg_iris.onnx")
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import onnx # noqa: E402
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model = onnx.load(example)
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print(f"doc_string={model.doc_string}")
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print(f"domain={model.domain}")
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print(f"ir_version={model.ir_version}")
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print(f"metadata_props={model.metadata_props}")
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print(f"model_version={model.model_version}")
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print(f"producer_name={model.producer_name}")
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print(f"producer_version={model.producer_version}")
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#############################
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# With *ONNX Runtime*:
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import onnxruntime as rt # noqa: E402
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sess = rt.InferenceSession(example, providers=rt.get_available_providers())
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meta = sess.get_modelmeta()
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print(f"custom_metadata_map={meta.custom_metadata_map}")
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print(f"description={meta.description}")
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print(f"domain={meta.domain}")
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print(f"graph_name={meta.graph_name}")
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print(f"producer_name={meta.producer_name}")
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print(f"version={meta.version}")
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