Note
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ONNX Runtime Backend for ONNX¶
ONNX Runtime extends the onnx backend API to run predictions using this runtime. Let’s use the API to compute the prediction of a simple logistic regression model.
import numpy as np
from onnxruntime import datasets
import onnxruntime.backend as backend
from onnx import load
name = datasets.get_example("logreg_iris.onnx")
model = load(name)
rep = backend.prepare(model, 'CPU')
x = np.array([[-1.0, -2.0]], dtype=np.float32)
label, proba = rep.run(x)
print("label={}".format(label))
print("probabilities={}".format(proba))
Out:
label=[1]
probabilities=[{0: 0.02731134556233883, 1: 0.5175684094429016, 2: 0.4551202654838562}]
The device depends on how the package was compiled, GPU or CPU.
from onnxruntime import get_device
print(get_device())
Out:
CPU
The backend can also directly load the model without using onnx.
rep = backend.prepare(name, 'CPU')
x = np.array([[-1.0, -2.0]], dtype=np.float32)
label, proba = rep.run(x)
print("label={}".format(label))
print("probabilities={}".format(proba))
Out:
label=[1]
probabilities=[{0: 0.02731134556233883, 1: 0.5175684094429016, 2: 0.4551202654838562}]
The backend API is implemented by other frameworks and makes it easier to switch between multiple runtimes with the same API.
Total running time of the script: ( 0 minutes 0.078 seconds)