xiaotongnii / tutorials

Tutorials for using ONNX

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

• Use services like Azure Custom Vision service that generate customized ONNX models for your data

End-to-end tutorials

• For preparation

  • Docker image for Caffe2/PyTorch/ONNX tutorials
  • Caffe/Keras/CoreML Docker Converter Image

• For serving

  • Serving ONNX models with MXNet Model Server
  • ONNX model hosting with AWS SageMaker and MXNet

• For conversion

  • Convert a PyTorch model to Tensorflow using ONNX

• From conversion to deployment

  • Converting SuperResolution model from PyTorch to Caffe2 with ONNX and deploying on mobile device
  • Transferring SqueezeNet from PyTorch to Caffe2 with ONNX and to Android app
  • Converting Style Transfer model from PyTorch to CoreML with ONNX and deploying to an iPhone
  • Serving PyTorch Models on AWS Lambda with Caffe2 & ONNX
  • MXNet to ONNX to ML.NET with SageMaker, ECS and ECR - external link

ONNX tools

• Verifying correctness and comparing performance
• Visualizing an ONNX model (useful for debugging)
• Netron: a viewer for ONNX models
• Example of operating on ONNX protobuf

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.