| Name | onnxconverter-common JSON |
| Version |
1.16.0
JSON |
| download |
| home_page | https://github.com/microsoft/onnxconverter-common |
| Summary | ONNX Converter and Optimization Tools |
| upload_time | 2025-08-28 19:37:46 |
| maintainer | None |
| docs_url | None |
| author | Microsoft Corporation |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) Microsoft Corporation. All rights reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE
|
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
onnx
packaging
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
| Linux | Windows |
|-------|---------|
| [](https://aiinfra.visualstudio.com/ONNX%20Converters/_build/latest?definitionId=689&branchName=master)| [](https://aiinfra.visualstudio.com/ONNX%20Converters/_build/latest?definitionId=690&branchName=master)|
# Introduction
The onnxconverter-common package provides common functions and utilities for use in converters from various AI frameworks to ONNX. It also enables the different converters to work together to convert a model from mixed frameworks, like a scikit-learn pipeline embedding a xgboost model.
# License
[MIT License](LICENSE)
# Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
# Document
Please reference the simple [document](https://onnxruntime.ai/docs/performance/model-optimizations/float16.html) here.
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