ONNX Tutorials
Open Neural Network Exchange (ONNX) is an open standard format for representing machine learning models. ONNX is supported by a community of partners who have implemented it in many frameworks and tools.
These images are available for convenience to get started with ONNX and tutorials on this page
- Docker image for ONNX and Caffe2/PyTorch
- Docker image for ONNX, ONNX Runtime, and various converters
Getting ONNX models
- Pre-trained models: Many pre-trained ONNX models are provided for common scenarios in the ONNX Model Zoo.
- Services: Customized ONNX models are generated for your data by cloud based services (see below)
- Convert models from various frameworks (see below)
Services
Below is a list of services that can output ONNX models customized for your data.
Converting to ONNX format
Scoring ONNX Models
Once you have an ONNX model, it can be scored with a variety of tools.
| Framework / Tool | Installation | Tutorial |
|---|---|---|
| Caffe2 | Caffe2 | Example |
| Cognitive Toolkit (CNTK) | built-in | Example |
| CoreML (Apple) | onnx/onnx-coreml | Example |
| MATLAB | Deep Learning Toolbox Converter | Documentation and Examples |
| Menoh | Github Packages or from Nuget | Example |
| ML.NET | Microsoft.ML Nuget Package | Example |
| MXNet (Apache) - Github | MXNet | API Example |
| ONNX Runtime | See onnxruntime.ai | Documentation |
| SINGA (Apache) - Github [experimental] | built-in | Example |
| Tensorflow | onnx-tensorflow | Example |
| TensorRT | onnx-tensorrt | Example |
| Windows ML | Pre-installed on Windows 10 | API Tutorials - C++ Desktop App, C# UWP App Examples |
| Vespa.ai | Vespa Getting Started Guide | Real Time ONNX Inference Distributed Real Time ONNX Inference for Search and Passage Ranking |
End-to-End Tutorials
Tutorials demonstrating how to use ONNX in practice for varied scenarios across frameworks, platforms, and device types
General
- AI-Serving
- AWS Lambda
- Cortex
- MXNet
- ONNX Runtime
Mobile
- 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
- Deploy ONNX Runtime on Mobile/Edge devices
ONNX Quantization
ONNX as an intermediary format
ONNX Custom Operators
Visualizing ONNX Models
- Netdrawer: Visualizing ONNX models
- Netron: Viewer for ONNX models
- Zetane: 3D visualizer for ONNX models and internal tensors
Other ONNX tools
- Verifying correctness and comparing performance
- Example of operating on ONNX protobuf
- Float16 <-> Float32 converter
- Version conversion
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.