Note
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Metadata#
ONNX format contains metadata related to how the model was produced. It is useful when the model is deployed to production to keep track of which instance was used at a specific time. Let’s see how to do that with a simple logistic regression model trained with scikit-learn.
import skl2onnx
import onnxruntime
import sklearn
import numpy
from onnxruntime import InferenceSession
import onnx
from onnxruntime.datasets import get_example
example = get_example("logreg_iris.onnx")
model = onnx.load(example)
print("doc_string={}".format(model.doc_string))
print("domain={}".format(model.domain))
print("ir_version={}".format(model.ir_version))
print("metadata_props={}".format(model.metadata_props))
print("model_version={}".format(model.model_version))
print("producer_name={}".format(model.producer_name))
print("producer_version={}".format(model.producer_version))
doc_string=
domain=onnxml
ir_version=3
metadata_props=[]
model_version=0
producer_name=OnnxMLTools
producer_version=1.2.0.0116
With ONNX Runtime:
sess = InferenceSession(example)
meta = sess.get_modelmeta()
print("custom_metadata_map={}".format(meta.custom_metadata_map))
print("description={}".format(meta.description))
print("domain={}".format(meta.domain))
print("graph_name={}".format(meta.graph_name))
print("producer_name={}".format(meta.producer_name))
print("version={}".format(meta.version))
custom_metadata_map={}
description=
domain=onnxml
graph_name=3c59201b940f410fa29dc71ea9d5767d
producer_name=OnnxMLTools
version=0
Versions used for this example
print("numpy:", numpy.__version__)
print("scikit-learn:", sklearn.__version__)
print("onnx: ", onnx.__version__)
print("onnxruntime: ", onnxruntime.__version__)
print("skl2onnx: ", skl2onnx.__version__)
numpy: 1.23.5
scikit-learn: 1.3.dev0
onnx: 1.14.0
onnxruntime: 1.15.0+cpu
skl2onnx: 1.14.0
Total running time of the script: ( 0 minutes 0.077 seconds)