Here you will find various samples, tutorials, and reference implementations for using ONNX Runtime. For a list of available dockerfiles and published images to help with getting started, see this page.

General

• Python
• C#
• C/C++
• Java
• Node.js

Integrations

• Azure Machine Learning
• Azure IoT Edge
• Azure Media Services
• Azure SQL Edge and Managed Instance
• Windows Machine Learning
• ML.NET

Inference only

• CPU: Basic
• CPU: Resnet50
• ONNX-Ecosystem Docker image
• ONNX Runtime Server: SSD Single Shot MultiBox Detector
• NUPHAR EP samples

Inference with model conversion

• SKL Pipeline: Train, Convert, and Inference
• Keras: Convert and Inference

Other

• Running ONNX model tests
• Common Errors with explanations

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

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

• Inference Tutorial
• MNIST inference

• Inference with Nodejs

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
• Inferencing on CPU with model conversion for existing (CoreML) model:
  • TinyYolo
• Inferencing on GPU with TensorRT Execution Provider (AKS):
  • FER+

Inference and Deploy with Azure IoT Edge

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

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

Deploy ONNX model in Azure SQL Edge

Examples of inferencing with ONNX Runtime through Windows Machine Learning

Object Detection with ONNX Runtime in ML.NET