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 skl2onnx
import onnxruntime
import onnx
import sklearn
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
import numpy
from onnxruntime import get_device
import numpy as np
import onnxruntime.backend as backend

Let’s create an ONNX graph first.

data = load_iris()
X, Y = data.data, data.target
logreg = LogisticRegression(C=1e5).fit(X, Y)
model = skl2onnx.to_onnx(logreg, X.astype(np.float32))
name = "logreg_iris.onnx"
with open(name, "wb") as f:
    f.write(model.SerializeToString())

Let’s use ONNX backend API to test it.

model = onnx.load(name)
rep = backend.prepare(model, 'CPU')
x = np.array([[-1.0, -2.0, 5.0, 6.0],
              [-1.0, -2.0, -3.0, -4.0],
              [-1.0, -2.0, 7.0, 8.0]],
             dtype=np.float32)
label, proba = rep.run(x)
print("label={}".format(label))
print("probabilities={}".format(proba))

label=[2 1 2]
probabilities=[{0: 0.0, 1: 0.0, 2: 1.0}, {0: 0.04301583021879196, 1: 0.9569841623306274, 2: 0.0}, {0: 0.0, 1: 0.0, 2: 1.0}]

The device depends on how the package was compiled, GPU or CPU.

print(get_device())

CPU

The backend can also directly load the model without using onnx.

rep = backend.prepare(name, 'CPU')
x = np.array([[-1.0, -2.0, -3.0, -4.0],
              [-1.0, -2.0, -3.0, -4.0],
              [-1.0, -2.0, -3.0, -4.0]],
             dtype=np.float32)
label, proba = rep.run(x)
print("label={}".format(label))
print("probabilities={}".format(proba))

label=[1 1 1]
probabilities=[{0: 0.04301583021879196, 1: 0.9569841623306274, 2: 0.0}, {0: 0.04301583021879196, 1: 0.9569841623306274, 2: 0.0}, {0: 0.04301583021879196, 1: 0.9569841623306274, 2: 0.0}]

The backend API is implemented by other frameworks and makes it easier to switch between multiple runtimes with the same API.

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