Note
Click here to download the full example code
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 and converted with sklearn-onnx.
from onnxruntime.datasets import get_example
example = get_example("logreg_iris.onnx")
import 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))
Out:
doc_string=
domain=onnxml
ir_version=3
metadata_props=[]
model_version=0
producer_name=OnnxMLTools
producer_version=1.2.0.0116
With ONNX Runtime:
import onnxruntime as rt
sess = rt.InferenceSession(example, providers=rt.get_available_providers())
meta = sess.get_modelmeta()
print("custom_metadata_map={}".format(meta.custom_metadata_map))
print("description={}".format(meta.description))
print("domain={}".format(meta.domain, meta.domain))
print("graph_name={}".format(meta.graph_name))
print("producer_name={}".format(meta.producer_name))
print("version={}".format(meta.version))
Out:
custom_metadata_map={}
description=
domain=onnxml
graph_name=3c59201b940f410fa29dc71ea9d5767d
producer_name=OnnxMLTools
version=0
Total running time of the script: ( 0 minutes 0.008 seconds)
