Introduction
sklearn-onnx converts scikit-learn models to ONNX. Once in the ONNX format, you can use tools like ONNX Runtime for high performance scoring. All converters are tested with onnxruntime. Any external converter can be registered to convert scikit-learn pipeline including models or transformers coming from external libraries.
Documentation
Full documentation including tutorials is available at https://onnx.ai/sklearn-onnx/. Supported scikit-learn Models Last supported opset is 21.
You may also find answers in existing issues or submit a new one.
Installation
You can install from PyPi:
pip install skl2onnx
Or you can install from the source with the latest changes.
pip install git+https://github.com/onnx/sklearn-onnx.git
Getting started
# Train a model.
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
iris = load_iris()
X, y = iris.data, iris.target
X = X.astype(np.float32)
X_train, X_test, y_train, y_test = train_test_split(X, y)
clr = RandomForestClassifier()
clr.fit(X_train, y_train)
# Convert into ONNX format.
from skl2onnx import to_onnx
onx = to_onnx(clr, X[:1])
with open("rf_iris.onnx", "wb") as f:
f.write(onx.SerializeToString())
# Compute the prediction with onnxruntime.
import onnxruntime as rt
sess = rt.InferenceSession("rf_iris.onnx", providers=["CPUExecutionProvider"])
input_name = sess.get_inputs()[0].name
label_name = sess.get_outputs()[0].name
pred_onx = sess.run([label_name], {input_name: X_test.astype(np.float32)})[0]
Contribute
We welcome contributions in the form of feedback, ideas, or code.
PR
Before you submit any PR, you should apply the following command lines to fix the style issues.
black .
ruff check .
License
Release files for skl2onnx 1.20.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| skl2onnx-1.20.0.tar.gz | 956.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| skl2onnx-1.20.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.3 MB
Release files / skl2onnx-1.20.0.tar.gz
| Download URL | skl2onnx-1.20.0.tar.gz |
|---|---|
| Size | 956.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / skl2onnx-1.20.0-py3-none-any.whl
| Download URL | skl2onnx-1.20.0-py3-none-any.whl |
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| Size | 317.2 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
30cac34803d1776c14b336ae945e48ef28debfc339215acde1cc04b963ed3f7b
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jan 30, 2026.
Transparency log