sklearn-onnx: Convert your scikit-learn model into ONNX

sklearn-onnx enables you to convert models from sklearn-learn toolkits into ONNX.

Issues, questions

You should look for existing issues or submit a new one. Sources are available on onnx/sklearn-onnx.

ONNX version

If you want the converted model is compatible with certain ONNX version, please specify the target_opset parameter on invoking convert function, and the following Keras converter example code shows how it works.


sklearn-onnx converts models in ONNX format which can be then used to compute predictions with the backend of your choice. However, there exists a way to automatically check every converter with onnxruntime, onnxruntime-gpu. Every converter is tested with this backend.

# Train a model.
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
iris = load_iris()
X, y =,
X_train, X_test, y_train, y_test = train_test_split(X, y)
clr = RandomForestClassifier(), y_train)

# Convert into ONNX format with onnxmltools
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
initial_type = [('float_input', FloatTensorType([1, 4]))]
onx = convert_sklearn(clr, initial_types=initial_type)
with open("rf_iris.onnx", "wb") as f:

# Compute the prediction with ONNX Runtime
import onnxruntime as rt
import numpy
sess = rt.InferenceSession("rf_iris.onnx")
input_name = sess.get_inputs()[0].name
label_name = sess.get_outputs()[0].name
pred_onx =[label_name], {input_name: X_test.astype(numpy.float32)})[0]

Related converters

sklearn-onnx only converts models from scikit-learn. It was initially part of onnxmltools which can still be used to convert models for xgboost and libsvm. Other converters can be found on github/onnx, torch.onnx, ONNX-MXNet API, Microsoft.ML.Onnx


The package was started by the following engineers and data scientists at Microsoft starting from winter 2017: Zeeshan Ahmed, Wei-Sheng Chin, Aidan Crook, Xavier Dupré, Costin Eseanu, Tom Finley, Lixin Gong, Scott Inglis, Pei Jiang, Ivan Matantsev, Prabhat Roy, M. Zeeshan Siddiqui, Shouheng Yi, Shauheen Zahirazami, Yiwen Zhu, Du Li, Xuan Li, Wenbing Li.


It is licensed with MIT License.