ONNX model exporter
Find the best ONNX model exporters for your machine learning workflow. Compare top-rated tools by activity and features to pick the right one.
ONNX model exporter
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- onnx/onnx
onnx/onnx
20,358View on GitHubONNX is an open-source standard for machine learning interoperability that provides a unified format for representing neural network models. By defining a common set of operators and a standardized file structure, it enables models to be shared, exported, and executed consistently across different training frameworks and software ecosystems. The project functions as an intermediate representation layer that decouples model development from deployment. It utilizes a language-neutral binary serialization format to store model structures and weights, ensuring that computational graphs remain por
The official ONNX repository is the core standard and reference implementation for exporting models to the unified ONNX format, directly addressing the need for multi‑framework conversion and interoperability across PyTorch, TensorFlow, and other ecosystems.
PythonMachine Learning Model PortabilityDirected Acyclic Graph Execution EnginesModel Interoperability Formats - onnx/onnxmltools
onnx/onnxmltools
1,160View on GitHubThis project is a machine learning interoperability tool designed to translate models from various training frameworks into the standardized open neural network exchange format. It functions as a model deployment pipeline that enables consistent execution across diverse inference engines and hardware environments. The tool utilizes graph-based translation and an operator mapping layer to convert framework-specific mathematical functions into a common intermediate representation. It distinguishes itself through a pluggable converter architecture, which allows developers to register custom tran
ONNXMLTools converts models to ONNX from non-PyTorch/TensorFlow frameworks like scikit-learn and Keras, so it fits the ONNX export category but may not cover the deep-learning frameworks your search primarily asks for.
PythonModel Interoperability ToolsModel Deployment PipelinesModel Format Converters
