Convert scikit-learn models to ONNX
Project description
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.
Documentation
Full documentation including tutorials is available at https://onnx.ai/sklearn-onnx/. Supported scikit-learn Models Last supported opset is 15.
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
Contribute
We welcome contributions in the form of feedback, ideas, or code.
License
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file skl2onnx-1.13.tar.gz.
File metadata
- Download URL: skl2onnx-1.13.tar.gz
- Upload date:
- Size: 872.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/30.0 requests/2.26.0 requests-toolbelt/0.9.1 urllib3/1.26.7 tqdm/4.62.3 importlib-metadata/4.11.2 keyring/23.2.1 rfc3986/1.5.0 colorama/0.4.4 CPython/3.9.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 5f352f6b9b855ffac6305a707f02c7d436f4368938ee9049092a95a3565c273d |
|
| MD5 | 003a2f4576340e70fb6686babfddba30 |
|
| BLAKE2b-256 | 9beeba3e16629b434d7082cbcbb0bf92fc134c34b211b0c699b481142eeb6209 |
File details
Details for the file skl2onnx-1.13-py2.py3-none-any.whl.
File metadata
- Download URL: skl2onnx-1.13-py2.py3-none-any.whl
- Upload date:
- Size: 288.7 kB
- Tags: Python 2, Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/30.0 requests/2.26.0 requests-toolbelt/0.9.1 urllib3/1.26.7 tqdm/4.62.3 importlib-metadata/4.11.2 keyring/23.2.1 rfc3986/1.5.0 colorama/0.4.4 CPython/3.9.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 51011c52d445ecef71967c67522ca7d1a57fc15576556beefeef40895b960830 |
|
| MD5 | 5eecc330788563d184395b119bbe1bba |
|
| BLAKE2b-256 | 095fa1d80847987588aba1335bfa5818f6c4264d4effa2bed20c2d30672e6b27 |
