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107 lines
6.8 KiB
Markdown
107 lines
6.8 KiB
Markdown
---
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parent: Tutorials
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toc: true
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nav_order: 1
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---
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# Samples catalog
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{: .no_toc }
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This page catalogs code samples for ONNX Runtime, running locally, and on Azure, both cloud and edge.
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## Contents
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{: .no_toc }
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* TOC placeholder
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{:toc}
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## Python
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* [Basic inference](https://microsoft.github.io/onnxruntime/python/tutorial.html)
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* [Resnet50 inference](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/inference_demos/resnet50_modelzoo_onnxruntime_inference.ipynb)
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* [Inference samples with 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 Execution Provider samples](https://github.com/microsoft/onnxruntime/tree/master/docs/python/notebooks/onnxruntime-nuphar-tutorial.ipynb)
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* [SKL tutorials](http://onnx.ai/sklearn-onnx/index_tutorial.html)
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* [Keras - Basic](https://microsoft.github.io/onnxruntime/python/auto_examples/plot_dl_keras.html#sphx-glr-auto-examples-plot-dl-keras-py)
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* [SSD Mobilenet (Tensorflow)](https://github.com/onnx/tensorflow-onnx/blob/master/tutorials/ConvertingSSDMobilenetToONNX.ipynb)
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* [BERT-SQuAD (PyTorch) on CPU](https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers/notebooks/PyTorch_Bert-Squad_OnnxRuntime_CPU.ipynb)
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* [BERT-SQuAD (PyTorch) on GPU](https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers/notebooks/PyTorch_Bert-Squad_OnnxRuntime_GPU.ipynb)
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* [BERT-SQuAD (Keras)](https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers/notebooks/Tensorflow_Keras_Bert-Squad_OnnxRuntime_CPU.ipynb)
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* [BERT-SQuAD (Tensorflow)](https://github.com/onnx/tensorflow-onnx/blob/master/tutorials/BertTutorial.ipynb)
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* [GPT2 (PyTorch)](https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers/notebooks/Inference_GPT2_with_OnnxRuntime_on_CPU.ipynb)
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* [EfficientDet (Tensorflow)](https://github.com/onnx/tensorflow-onnx/blob/master/tutorials/efficientdet.ipynb)
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* [EfficientNet-Edge (Tensorflow)](https://github.com/onnx/tensorflow-onnx/blob/master/tutorials/efficientnet-edge.ipynb)
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* [EfficientNet-Lite (Tensorflow)](https://github.com/onnx/tensorflow-onnx/blob/master/tutorials/efficientnet-lite.ipynb)
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* [EfficientNet(Keras)](https://github.com/onnx/keras-onnx/blob/master/tutorial/TensorFlow_Keras_EfficientNet.ipynb)
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* [MNIST (Keras)](https://github.com/onnx/keras-onnx/blob/master/tutorial/TensorFlow_Keras_MNIST.ipynb)
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* [BERT Quantization on CPU](https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/quantization/notebooks/Bert-GLUE_OnnxRuntime_quantization.ipynb)
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* [Get started with training](https://github.com/microsoft/onnxruntime-training-examples/tree/master/getting-started)
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* [Train NVIDIA BERT transformer model](https://github.com/microsoft/onnxruntime-training-examples/tree/master/nvidia-bert)
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* [Train HuggingFace GPT-2 model](https://github.com/microsoft/onnxruntime-training-examples/tree/master/huggingface-gpt2)
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## C/C++
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* [C: SqueezeNet](https://github.com/microsoft/onnxruntime/tree/master/csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/C_Api_Sample.cpp)
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* [C++: model-explorer](https://github.com/microsoft/onnxruntime/tree/master/samples/c_cxx/model-explorer) - single and batch processing
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* [C++: SqueezeNet](https://github.com/microsoft/onnxruntime/tree/master/csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/CXX_Api_Sample.cpp)
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## C#
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* [Inference Tutorial](resnet50_csharp.md)
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* [ResNet50 v2 Tutorial](https://github.com/microsoft/onnxruntime/tree/master/csharp/sample/Microsoft.ML.OnnxRuntime.ResNet50v2Sample)
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* [Faster R-CNN Tutorial](https://github.com/microsoft/onnxruntime/tree/master/csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample)
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## Java
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* [MNIST inference](https://github.com/microsoft/onnxruntime/tree/master/java/src/test/java/sample/ScoreMNIST.java)
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## Node.js
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* [Inference with Nodejs](https://github.com/microsoft/onnxruntime/tree/master/samples/nodejs)
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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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* [BERT](https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers/notebooks/Inference_Bert_with_OnnxRuntime_on_AzureML.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+](https://github.com/microsoft/onnxruntime/tree/master/docs/python/notebooks/onnx-inference-byoc-gpu-cpu-aks.ipynb)
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## Huggingface
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[Export Tranformer models](https://github.com/huggingface/transformers/blob/master/notebooks/04-onnx-export.ipynb)
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## 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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