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* Add examples * fix build instructions for linux users * fix header include * update documentation
106 lines
5.3 KiB
Markdown
106 lines
5.3 KiB
Markdown
# ONNX Runtime Samples and Tutorials
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Here you will find various samples, tutorials, and reference implementations for using ONNX Runtime.
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For a list of available dockerfiles and published images to help with getting started, see [this page](../dockerfiles/README.md).
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**General**
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* [Python](#Python)
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* [C#](#C)
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* [C/C++](#CC)
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* [Java](#Java)
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* [Node.js](#Nodejs)
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**Integrations**
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* [Azure Machine Learning](#azure-machine-learning)
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* [Azure IoT Edge](#azure-iot-edge)
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* [Azure Media Services](#azure-media-services)
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* [Azure SQL Edge and Managed Instance](#azure-sql)
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* [Windows Machine Learning](#windows-machine-learning)
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* [ML.NET](#mlnet)
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***
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## Python
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**Inference only**
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* [CPU: Basic](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/inference_demos/simple_onnxruntime_inference.ipynb)
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* [CPU: Resnet50](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/inference_demos/resnet50_modelzoo_onnxruntime_inference.ipynb)
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* [ONNX-Ecosystem Docker image](https://github.com/onnx/onnx-docker/tree/master/onnx-ecosystem/inference_demos)
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* [ONNX Runtime Server: SSD Single Shot MultiBox Detector](https://github.com/onnx/tutorials/blob/master/tutorials/OnnxRuntimeServerSSDModel.ipynb)
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* [NUPHAR EP samples](../docs/python/notebooks/onnxruntime-nuphar-tutorial.ipynb)
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**Inference with model conversion**
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* [SKL Pipeline: Train, Convert, and Inference](https://microsoft.github.io/onnxruntime/python/tutorial.html)
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* [Keras: Convert and Inference](https://microsoft.github.io/onnxruntime/python/auto_examples/plot_dl_keras.html#sphx-glr-auto-examples-plot-dl-keras-py)
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**Other**
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* [Running ONNX model tests](../docs/Model_Test.md)
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* [Common Errors with explanations](https://microsoft.github.io/onnxruntime/python/auto_examples/plot_common_errors.html#sphx-glr-auto-examples-plot-common-errors-py)
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## C#
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* [Inference Tutorial](../docs/CSharp_API.md#getting-started)
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## C/C++
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* [C: SqueezeNet](../csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/C_Api_Sample.cpp)
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* [C++: model-explorer](./c_cxx/model-explorer) - single and batch processing
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* [C++: SqueezeNet](../csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/CXX_Api_Sample.cpp)
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* [C++: MNIST](./c_cxx/MNIST)
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## Java
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* [Inference Tutorial](../docs/Java_API.md#getting-started)
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* [MNIST inference](../java/src/test/java/sample/ScoreMNIST.java)
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## Node.js
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### Samples
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In each sample's implementation subdirectory, run
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```
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npm install
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node ./
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```
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* [Basic Usage](./nodejs/01_basic-usage/) - a demonstration of basic usage of ONNX Runtime Node.js binding.
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* [Create Tensor](./nodejs/02_create-tensor/) - a demonstration of basic usage of creating tensors.
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<!--
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* [Create Tensor (Advanced)](./nodejs/03_create-tensor-advanced/) - a demonstration of advanced usage of creating tensors.
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-->
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* [Create InferenceSession](./nodejs/04_create-inference-session/) - shows how to create `InferenceSession` in different ways.
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---
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## Azure Machine Learning
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**Inference and deploy through AzureML**
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*For aditional information on training in AzureML, please see [AzureML Training Notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/training)*
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* Inferencing on **CPU** using [ONNX Model Zoo](https://github.com/onnx/models) models:
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* [Facial Expression Recognition](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb)
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* [MNIST Handwritten Digits](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb)
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* [Resnet50 Image Classification](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb)
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* Inferencing on **CPU** with **PyTorch** model training:
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* [MNIST](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb)
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* Inferencing on **CPU** with model conversion for existing (CoreML) model:
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* [TinyYolo](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb)
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* Inferencing on **GPU** with **TensorRT** Execution Provider (AKS):
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* [FER+](../docs/python/notebooks/onnx-inference-byoc-gpu-cpu-aks.ipynb)
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## Azure IoT Edge
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**Inference and Deploy with Azure IoT Edge**
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* [Intel OpenVINO](http://aka.ms/onnxruntime-openvino)
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* [NVIDIA TensorRT on Jetson Nano (ARM64)](http://aka.ms/onnxruntime-arm64)
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* [ONNX Runtime with Azure ML](https://github.com/Azure-Samples/onnxruntime-iot-edge/blob/master/AzureML-OpenVINO/README.md)
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## Azure Media Services
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[Video Analysis through Azure Media Services using using Yolov3 to build an IoT Edge module for object detection](https://github.com/Azure/live-video-analytics/tree/master/utilities/video-analysis/yolov3-onnx)
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## Azure SQL
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[Deploy ONNX model in Azure SQL Edge](https://docs.microsoft.com/en-us/azure/azure-sql-edge/deploy-onnx)
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## Windows Machine Learning
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[Examples of inferencing with ONNX Runtime through Windows Machine Learning](https://docs.microsoft.com/en-us/windows/ai/windows-ml/tools-and-samples#samples)
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## ML.NET
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[Object Detection with ONNX Runtime in ML.NET](https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-onnx)
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