{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"Provides common utilities and functions for converting machine learning models from various AI frameworks to ONNX format, enabling interoperability between different framework converters.","skillfed_tags":["model-conversion","onnx","ml-interop"],"use_cases":["Build a converter that translates scikit-learn models to ONNX using shared utility functions","Enable a pipeline that combines xgboost and scikit-learn models and exports them together to ONNX","Develop a custom framework converter that leverages common ONNX conversion patterns and helpers","Optimize model export workflows by reusing standardized conversion logic across multiple frameworks","Integrate model format conversion into a production ML serving pipeline that targets ONNX runtime"],"what_it_does":"onnxconverter-common is a utility library that sits at the intersection of multiple machine learning framework converters, providing shared functions and abstractions for translating models to ONNX format. Rather than being a converter itself, it acts as a foundation that different framework-specific converters build upon\u2014allowing them to work together when a model combines components from multiple frameworks (like a scikit-learn pipeline that includes an xgboost model). The package depends on numpy, onnx, packaging, and protobuf, making it a lightweight addition to existing ONNX workflows.\n\nThe library is maintained by Microsoft, actively developed, and supports Python 3.8 through 3.13 on Linux, Windows, and macOS. It carries an MIT License, making it suitable for both open-source and commercial use. With nearly 2 million monthly downloads and a position in the top 5000 PyPI packages, it is a stable, widely-adopted component in the ONNX ecosystem.","worth_installing":"Yes. onnxconverter-common is a stable, actively maintained library with low install friction, no known vulnerabilities, and permissive licensing. Install it if you are building or using ONNX converters, especially when working with multiple frameworks or planning to leverage existing converter ecosystems. It is a foundational dependency rather than a standalone tool, so evaluate it in the context of your specific converter needs."},"id":"onnxconverter-common","links":{"html":"https://skillfed.io/packages/onnxconverter-common","md":"https://skillfed.io/packages/onnxconverter-common.md","pypi":"https://pypi.org/project/onnxconverter-common/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-08-28","license_spdx":null,"license_treatment":"permissive","name":"onnxconverter-common","python_support":"supports_current","summary":"ONNX Converter and Optimization Tools"},"popularity":{"monthly_downloads":1897193,"position":3450,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.16.0"}