onnxruntime/docs/tutorials/samples_catalog.md

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Samples catalog

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This page catalogs code samples for ONNX Runtime, running locally, and on Azure, both cloud and edge.

Contents

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Python

• Basic inference
• Resnet50 inference
• Inference samples with ONNX-Ecosystem Docker image
• ONNX Runtime Server: SSD Single Shot MultiBox Detector
• NUPHAR Execution Provider samples
• SKL tutorials
• Keras - Basic
• SSD Mobilenet (Tensorflow)
• BERT-SQuAD (PyTorch) on CPU
• BERT-SQuAD (PyTorch) on GPU
• BERT-SQuAD (Keras)
• BERT-SQuAD (Tensorflow)
• GPT2 (PyTorch)
• EfficientDet (Tensorflow)
• EfficientNet-Edge (Tensorflow)
• EfficientNet-Lite (Tensorflow)
• EfficientNet(Keras)
• MNIST (Keras)
• BERT Quantization on CPU
• Get started with training
• Train NVIDIA BERT transformer model
• Train HuggingFace GPT-2 model

C/C++

• C: SqueezeNet
• C++: model-explorer - single and batch processing
• C++: SqueezeNet

C#

• Inference Tutorial
• ResNet50 v2 Tutorial
• Faster R-CNN Tutorial

Java

• MNIST inference

Node.js

• Inference with Nodejs

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Azure Machine Learning

Inference and deploy through AzureML

For aditional information on training in AzureML, please see AzureML Training Notebooks

• Inferencing on CPU using ONNX Model Zoo models:
  • Facial Expression Recognition
  • MNIST Handwritten Digits
  • Resnet50 Image Classification
• Inferencing on CPU with PyTorch model training:
  • MNIST
  • BERT
• Inferencing on CPU with model conversion for existing (CoreML) model:
  • TinyYolo
• Inferencing on GPU with TensorRT Execution Provider (AKS):
  • FER+

Huggingface

Export Tranformer models

Azure IoT Edge

• Intel OpenVINO
• NVIDIA TensorRT on Jetson Nano (ARM64)
• ONNX Runtime with Azure ML

Azure Media Services

Video Analysis through Azure Media Services using using Yolov3 to build an IoT Edge module for object detection

Azure SQL

Deploy ONNX model in Azure SQL Edge

Windows Machine Learning

Examples of inferencing with ONNX Runtime through Windows Machine Learning

ML.NET

Object Detection with ONNX Runtime in ML.NET