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52 lines
3.6 KiB
Markdown
52 lines
3.6 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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* [Python](#Python)
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* [C#](#C)
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* [C/C++](#CC)
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***
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## Python
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**Inference only**
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* [Basic Model Inferencing (single node Sigmoid) on CPU](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/inference_demos/simple_onnxruntime_inference.ipynb)
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* [Model Inferencing (Resnet50) on CPU](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/inference_demos/resnet50_modelzoo_onnxruntime_inference.ipynb)
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* [Model Inferencing on CPU](https://github.com/onnx/onnx-docker/tree/master/onnx-ecosystem/inference_demos) using [ONNX-Ecosystem Docker image](https://github.com/onnx/onnx-docker/tree/master/onnx-ecosystem)
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* [Model Inferencing on CPU using ONNX Runtime Server (SSD Single Shot MultiBox Detector)](https://github.com/onnx/tutorials/blob/master/tutorials/OnnxRuntimeServerSSDModel.ipynb)
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* [Model Inferencing using NUPHAR Execution Provider](../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/tutorial.html)
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* [Keras: Convert and Inference](https://microsoft.github.io/onnxruntime/auto_examples/plot_dl_keras.html#sphx-glr-auto-examples-plot-dl-keras-py)
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**Inference and deploy through AzureML**
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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 model conversion step for existing models:
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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 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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*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 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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**Inference and Deploy wtih 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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**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/auto_examples/plot_common_errors.html#sphx-glr-auto-examples-plot-common-errors-py)
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## C#
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* [Inferencing Tutorial](../docs/CSharp_API.md#getting-started)
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## C/C++
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* [C - Inferencing (SqueezeNet)](../csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/C_Api_Sample.cpp)
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* [C++ - Inferencing (SqueezeNet)](../csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/CXX_Api_Sample.cpp)
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* [C++ - Inferencing (MNIST)](../samples/c_cxx/MNIST)
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