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Tutorials for using ONNX

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ONNX tutorials

Open Neural Network Exchange (ONNX) is an open standard format for representing machine learning models offering interoperability between various AI frameworks. With ONNX, AI developers can choose the best framework for training and switch to a different one for shipping.

ONNX is supported by a community of partners, and more and more AI frameworks are building ONNX support including PyTorch, Caffe2, Microsoft Cognitive Toolkit and Apache MXNet.

Getting ONNX models

  • Choose a pre-trained ONNX model from the ONNX Model Zoo. A lot of pre-trained ONNX models are provided for common scenarios.
  • Convert models from mainstream frameworks. More tutorials are below.
Framework / tool Installation Exporting to ONNX (frontend) Importing ONNX models (backend)
Caffe2 part of caffe2 package Exporting Importing
PyTorch part of pytorch package Exporting, Extending support coming soon
Cognitive Toolkit (CNTK) built-in Exporting Importing
Apache MXNet part of mxnet package docs github Exporting Importing
Chainer chainer/onnx-chainer Exporting coming soon
TensorFlow onnx/onnx-tensorflow and onnx/tensorflow-onnx Exporting Importing [experimental]
Apple CoreML onnx/onnx-coreml and onnx/onnxmltools Exporting Importing
SciKit-Learn onnx/onnxmltools Exporting n/a
ML.NET built-in Exporting Importing
Menoh pfnet-research/menoh n/a Importing
MATLAB onnx converter on matlab central file exchange Exporting Importing
TensorRT onnx/onnx-tensorrt n/a Importing

End-to-end tutorials

ONNX tools

Contributing

We welcome improvements to the convertor tools and contributions of new ONNX bindings. Check out contributor guide to get started.

Use ONNX for something cool? Send the tutorial to this repo by submitting a PR.

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Tutorials for using ONNX

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