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Tutorials for creating and using ONNX models
https://github.com/onnx/tutorials
Last synced: 29 days ago
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Tutorials for creating and using ONNX models
Host: GitHub
URL: https://github.com/onnx/tutorials
Owner: onnx
License: apache-2.0
Created: 2017-11-15T18:59:18.000Z (almost 7 years ago)
Default Branch: main
Last Pushed: 2024-07-15T13:12:48.000Z (4 months ago)
Last Synced: 2024-10-01T23:21:11.769Z (about 1 month ago)
Language: Jupyter Notebook
Homepage:
Size: 35.7 MB
Stars: 3,349
Watchers: 130
Forks: 627
Open Issues: 111
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

awesome-deep-model-compression - Open Neural Network Exchange (ONNX)
README

        

# [ONNX](https://github.com/onnx/onnx) Tutorials

[Open Neural Network Exchange (ONNX)](https://onnx.ai/) is an open standard format for representing machine learning models. ONNX is supported by [a community of partners](https://onnx.ai/supported-tools) who have implemented it in many frameworks and tools.

## Getting ONNX models

* Pre-trained models (validated): Many pre-trained ONNX models are provided for common scenarios in the [ONNX Model Zoo](https://github.com/onnx/models/tree/main/validated)

* Pre-trained models (non-validated): Many pre-trained ONNX models are provided for common scenarios in the [ONNX Model Zoo](https://github.com/onnx/models).

* 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.

* [Azure Custom Vision service](https://docs.microsoft.com/en-us/azure/cognitive-services/Custom-Vision-Service/custom-vision-onnx-windows-ml)

* [Azure Machine Learning automated ML](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-automated-ml#use-with-onnx-in-c-apps)

* [Lobe desktop app](https://lobe.ai)

### Converting to ONNX format

| Framework / Tool | Installation | Tutorial |

| --- | --- | --- |

| [Caffe](https://github.com/BVLC/caffe) | [apple/coremltools](https://github.com/apple/coremltools) and [onnx/onnxmltools](https://github.com/onnx/onnxmltools) | [Example](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/converter_scripts/caffe_coreml_onnx.ipynb) |

| [Caffe2](https://caffe2.ai) | [part of caffe2 package](https://github.com/pytorch/pytorch/tree/master/caffe2/python/onnx) | [Example](tutorials/Caffe2OnnxExport.ipynb) |

| [Chainer](https://chainer.org/) | [chainer/onnx-chainer](https://github.com/chainer/onnx-chainer) | [Example](tutorials/ChainerOnnxExport.ipynb) |

| [Cognitive Toolkit (CNTK)](https://learn.microsoft.com/en-us/cognitive-toolkit//) | [built-in](https://docs.microsoft.com/en-us/cognitive-toolkit/setup-cntk-on-your-machine) | [Example](tutorials/CntkOnnxExport.ipynb) |

| [CoreML (Apple)](https://developer.apple.com/documentation/coreml) | [onnx/onnxmltools](https://github.com/onnx/onnxmltools) | [Example](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/converter_scripts/coreml_onnx.ipynb) |

| [Keras](https://github.com/keras-team/keras) | [onnx/tensorflow-onnx](https://github.com/onnx/tensorflow-onnx) | [Example](https://github.com/onnx/tensorflow-onnx/blob/master/tutorials/keras-resnet50.ipynb) | n/a |

| [LibSVM](https://github.com/cjlin1/libsvm) | [onnx/onnxmltools](https://github.com/onnx/onnxmltools) | [Example](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/converter_scripts/libsvm_onnx.ipynb) | n/a |

| [LightGBM](https://github.com/Microsoft/LightGBM) | [onnx/onnxmltools](https://github.com/onnx/onnxmltools) | [Example](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/converter_scripts/lightgbm_onnx.ipynb) | n/a |

| [MATLAB](https://www.mathworks.com/) | [Deep Learning Toolbox](https://www.mathworks.com/matlabcentral/fileexchange/67296) | [Example](https://www.mathworks.com/help/deeplearning/ref/exportonnxnetwork.html) |

| [ML.NET](https://github.com/dotnet/machinelearning/) | [built-in](https://www.nuget.org/packages/Microsoft.ML/) | [Example](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/OnnxConversionTest.cs) |

| [MXNet (Apache)](https://mxnet.incubator.apache.org/) | part of mxnet package [docs](https://mxnet.incubator.apache.org/api/python/contrib/onnx.html) [github](https://github.com/apache/incubator-mxnet/tree/master/python/mxnet/contrib/onnx) | [Example](tutorials/MXNetONNXExport.ipynb) |

| [PyTorch](https://pytorch.org/) | [part of pytorch package](https://pytorch.org/docs/master/onnx.html) | [Example1](https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html), [Example2](tutorials/PytorchOnnxExport.ipynb), [export for Windows ML](tutorials/ExportModelFromPyTorchForWinML.md), [Extending support](tutorials/PytorchAddExportSupport.md) |

| [SciKit-Learn](https://scikit-learn.org/) | [onnx/sklearn-onnx](https://github.com/onnx/sklearn-onnx) | [Example](https://onnx.ai/sklearn-onnx/index.html) | n/a |

| [SINGA (Apache)](https://singa.apache.org/) - [Github](https://github.com/apache/incubator-singa/blob/master/python/singa/sonnx.py) (experimental) | [built-in](https://singa.apache.org/docs/installation/) | [Example](https://github.com/apache/incubator-singa/tree/master/examples/onnx) |

| [TensorFlow](https://www.tensorflow.org/) | [onnx/tensorflow-onnx](https://github.com/onnx/tensorflow-onnx) | [Examples](https://github.com/onnx/tutorials/blob/master/tutorials/TensorflowToOnnx-1.ipynb) |

## Scoring ONNX Models

Once you have an ONNX model, it can be scored with a variety of tools.

| Framework / Tool | Installation | Tutorial |

| --- | --- | --- |

| [Caffe2](https://caffe2.ai) | [Caffe2](https://github.com/pytorch/pytorch/tree/master/caffe2/python/onnx) | [Example](tutorials/OnnxCaffe2Import.ipynb) |

| [Cognitive Toolkit (CNTK)](https://learn.microsoft.com/en-us/cognitive-toolkit//) | [built-in](https://docs.microsoft.com/en-us/cognitive-toolkit/setup-cntk-on-your-machine) | [Example](tutorials/OnnxCntkImport.ipynb)|

| [CoreML (Apple)](https://developer.apple.com/documentation/coreml) | [onnx/onnx-coreml](https://github.com/onnx/onnx-coreml) | [Example](tutorials/OnnxCoremlImport.ipynb)|

| [MATLAB](https://www.mathworks.com/) | [Deep Learning Toolbox Converter](https://www.mathworks.com/matlabcentral/fileexchange/67296) | [Documentation and Examples](https://www.mathworks.com/help/deeplearning/ref/importonnxnetwork.html) |

| [Menoh](https://github.com/pfnet-research/menoh) | [Github Packages](https://github.com/pfnet-research/menoh/releases) or from [Nuget](https://www.nuget.org/packages/Menoh/) | [Example](tutorials/OnnxMenohHaskellImport.ipynb) |

| [ML.NET](https://github.com/dotnet/machinelearning/) | [Microsoft.ML Nuget Package](https://www.nuget.org/packages/Microsoft.ML/) | [Example](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.OnnxTransformerTest/OnnxTransformTests.cs) |

| [MXNet (Apache)](https://mxnet.incubator.apache.org/) - [Github](https://github.com/apache/incubator-mxnet/tree/master/python/mxnet/contrib/onnx) | [MXNet](https://mxnet.incubator.apache.org/versions/master/install/index.html?platform=Linux&language=Python&processor=CPU) |  [API](https://mxnet.incubator.apache.org/api/python/contrib/onnx.html) 
[Example](tutorials/OnnxMxnetImport.ipynb) |

[ONNX Runtime](https://github.com/microsoft/onnxruntime) | See [onnxruntime.ai](https://onnxruntime.ai)| [Documentation](https://onnxruntime.ai/docs/) |

| [SINGA (Apache)](https://singa.apache.org/) - [Github](https://github.com/apache/incubator-singa/blob/master/python/singa/sonnx.py) [experimental]| [built-in](https://singa.apache.org/docs/installation/) | [Example](https://github.com/apache/incubator-singa/tree/master/examples/onnx) |

| [Tensorflow](https://www.tensorflow.org/) | [onnx-tensorflow](https://github.com/onnx/onnx-tensorflow) | [Example](tutorials/OnnxTensorflowImport.ipynb)|

| [TensorRT](https://developer.nvidia.com/tensorrt) | [onnx-tensorrt](https://github.com/onnx/onnx-tensorrt) | [Example](https://github.com/onnx/onnx-tensorrt/blob/master/README.md) |

| [Windows ML](https://docs.microsoft.com/en-us/windows/ai/windows-ml) | Pre-installed on [Windows 10](https://docs.microsoft.com/en-us/windows/ai/release-notes) | [API](https://docs.microsoft.com/en-us/windows/ai/api-reference) 
Tutorials - [C++ Desktop App](https://docs.microsoft.com/en-us/windows/ai/get-started-desktop), [C# UWP App](https://docs.microsoft.com/en-us/windows/ai/get-started-uwp) 
 [Examples](https://docs.microsoft.com/en-us/windows/ai/tools-and-samples) |

| [Vespa.ai](https://vespa.ai) | [Vespa Getting Started Guide](https://docs.vespa.ai/en/getting-started.html) | [Real Time ONNX Inference](https://github.com/vespa-engine/sample-apps/tree/master/model-inference) 
Distributed Real Time ONNX Inference for [Search and Passage Ranking](https://github.com/vespa-engine/sample-apps/blob/master/msmarco-ranking/README.md)|

## End-to-End Tutorials

Tutorials demonstrating how to use ONNX in practice for varied scenarios across frameworks, platforms, and device types

### General

  * [AI-Serving](https://github.com/autodeployai/ai-serving/blob/master/examples/AIServingMnistOnnxModel.ipynb)

  * [AWS Lambda](https://github.com/michaelulin/pytorch-caffe2-aws-lambda)

  * [Cortex](https://towardsdatascience.com/how-to-deploy-onnx-models-in-production-60bd6abfd3ae)

  * MXNet

    * [MXNet Model Server](tutorials/ONNXMXNetServer.ipynb)

    * [AWS SageMaker and MXNet](https://github.com/aws/amazon-sagemaker-examples/blob/v0.1.0/sagemaker-python-sdk/mxnet_onnx_eia/mxnet_onnx_eia.ipynb)

    * [MXNet to ONNX to ML.NET with SageMaker, ECS and ECR](https://cosminsanda.com/posts/mxnet-to-onnx-to-ml.net-with-sagemaker-ecs-and-ecr/) - external link

* ONNX Runtime

  * [ONNX Runtime Tutorials](https://onnxruntime.ai/docs/tutorials/)

  * [Azure ML and ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/deployment/onnx)

### Mobile

  * [Converting SuperResolution model from PyTorch to Caffe2 with ONNX and deploying on mobile device](tutorials/PytorchCaffe2SuperResolution.ipynb)

  * [Transferring SqueezeNet from PyTorch to Caffe2 with ONNX and to Android app](tutorials/PytorchCaffe2MobileSqueezeNet.ipynb)

  * [Converting Style Transfer model from PyTorch to CoreML with ONNX and deploying to an iPhone](https://github.com/onnx/tutorials/tree/master/examples/CoreML/ONNXLive)

  * [Deploy ONNX Runtime on Mobile/Edge devices](https://onnxruntime.ai/docs/tutorials/mobile/)

### ONNX Quantization

  * [HuggingFace Bert Quantization with ONNX Runtime](https://github.com/microsoft/onnxruntime-inference-examples/blob/main/quantization/notebooks/bert/Bert-GLUE_OnnxRuntime_quantization.ipynb)

### ONNX as an intermediary format

  * [Convert a PyTorch model to Tensorflow using ONNX](tutorials/PytorchTensorflowMnist.ipynb)

### ONNX Custom Operators

  * [How to export Pytorch model with custom op to ONNX and run it in ONNX Runtime](PyTorchCustomOperator/README.md)

## Visualizing ONNX Models

* [Netdrawer: Visualizing ONNX models](tutorials/VisualizingAModel.md)

* [Netron: Viewer for ONNX models](https://github.com/lutzroeder/Netron)

* [Zetane: 3D visualizer for ONNX models and internal tensors](https://github.com/zetane/viewer)

## Other ONNX tools

* [Verifying correctness and comparing performance](tutorials/CorrectnessVerificationAndPerformanceComparison.ipynb)

* [Example of operating on ONNX protobuf](https://github.com/onnx/onnx/blob/main/onnx/examples/Protobufs.ipynb)

* [Float16 <-> Float32 converter](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/converter_scripts/float32_float16_onnx.ipynb)

* [Version conversion](tutorials/VersionConversion.md)

## Application of ONNX

* [Explainable AI for ONNX models](tutorials/XAI4ONNX_dianna_overview.ipynb)

  

## Contributing

We welcome improvements to the convertor tools and contributions of new ONNX bindings. Check out [contributor guide](https://github.com/onnx/onnx/blob/main/CONTRIBUTING.md) to get started.

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