{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"Converts machine learning models from multiple frameworks (TensorFlow, scikit-learn, Core ML, LightGBM, XGBoost, H2O, CatBoost, Spark ML, libsvm) into ONNX format for cross-platform inference.","skillfed_tags":["model-conversion","onnx","ml-interop"],"use_cases":["Export a scikit-learn classifier to ONNX for deployment on a web service or edge device that only supports ONNX Runtime.","Convert a TensorFlow model to ONNX to enable inference on platforms that lack TensorFlow support.","Standardize model formats across a team using different ML frameworks by converting all models to ONNX for unified inference pipelines.","Prepare a LightGBM or XGBoost model for production by exporting to ONNX to decouple inference from the original training library.","Validate model behavior across frameworks by converting to ONNX and running inference to verify numerical consistency."],"what_it_does":"ONNXMLTools is a conversion toolkit that translates trained machine learning models from their native framework formats into ONNX (Open Neural Network Exchange), a standardized model interchange format. It wraps or implements converters for TensorFlow, scikit-learn, Core ML, LightGBM, XGBoost, H2O, CatBoost, Spark ML, and libsvm, allowing models trained in any of these frameworks to be exported as ONNX files that can then run on any ONNX-compatible runtime.\n\nThe package depends on numpy, onnx, protobuf, and skl2onnx as core runtime dependencies. Conversion is controlled via a target_opset parameter to ensure compatibility with specific ONNX versions; the converter respects operator set versioning by selecting the maximum opset required by all operators in the model. It is actively maintained, tested with Python 3.9 through 3.13, and has no known security vulnerabilities.","worth_installing":"Yes. The package is actively maintained, has low install friction, carries a permissive license, and solves a real problem\u2014enabling model portability across ML frameworks. It is well-suited for teams needing to standardize model formats or deploy models on runtimes that require ONNX. Install it when you need to convert models from any of its supported frameworks; skip it if your workflow stays within a single framework's native inference ecosystem."},"id":"onnxmltools","links":{"html":"https://skillfed.io/packages/onnxmltools","md":"https://skillfed.io/packages/onnxmltools.md","pypi":"https://pypi.org/project/onnxmltools/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-01-30","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"onnxmltools","python_support":"supports_current","summary":"Converts Machine Learning models to ONNX"},"popularity":{"monthly_downloads":902456,"position":4769,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.16.0"}