onnx/onnxmltools

Health v2 · maintenance only

Adoption not part of the score

1170 stars · 218 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

ONNXMLTools is a Python library that converts machine learning models from various toolkits (Keras/TensorFlow, scikit-learn, Core ML, XGBoost, LightGBM, Spark ML, H2O, CatBoost, libsvm) into the ONNX format. It acts as a wrapper around specialized converters to enable model interoperability across frameworks and inference runtimes.

Use cases

• convert a keras model to onnx
• export xgboost model to onnx format
• convert scikit-learn model for onnx runtime inference
• deploy lightgbm model with onnx
• make machine learning models framework-agnostic
• convert core ml models to onnx
• run catboost models on onnx runtime

When to choose

• you need to deploy models trained in one framework using an ONNX runtime
• you want hardware-optimized inference via ONNX-compatible runtimes
• you need a single converter entry point for multiple ML toolkits

When to avoid

• you use PyTorch, which has a built-in ONNX exporter
• you need conversion for a toolkit not in the supported list
• you only work within a single framework end to end

Facets

library · maturity active

machine-learning serialization compiler machine-learning deep-learning developer-tools python cross-platform onnx model-conversion scikit-learn keras xgboost lightgbm interoperability

Member repositories

For agents

markdown · JSON · MCP: product_card(name="onnx/onnxmltools")