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History

Tianlei Wu c00e13a291 Cherry pick (batch 2) to rel-1.5.1 (#5290 ) * remove implicit linking of tensorrt and dnnl ep shared libs (#5262) * Update DirectML Nuget to 1.3.0 (#5274) * Update PyTorch TransformerModel sample (#5275) * Insert telemetry template into GPU build, add telemry build switches. (#5278) * Synchronize training dependency versions between Docker image and Python wheel (#5261) * Downgrade GCC (#5269) * Remove --enable_symbolic_shape_infer_tests to fix linux ci pipeline build error. Co-authored-by: Edward Chen Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: Dwayne Robinson <dwayner@microsoft.com> Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com> Co-authored-by: Dmitri Smirnov <yuslepukhin@users.noreply.github.com> Co-authored-by: edgchen1 <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Changming Sun <chasun@microsoft.com>		2020-09-25 09:26:40 -07:00
..
c_cxx	Add missing header file to MNIST.cpp (#4773 )	2020-08-12 21:46:11 -07:00
nodejs	Add nodejs samples README (#5005 )	2020-09-03 15:58:44 -07:00
python	Cherry pick (batch 2) to rel-1.5.1 (#5290 )	2020-09-25 09:26:40 -07:00
swift	Add Swift/macOS sample, a port of the Windows MNist sample	2020-06-05 21:16:41 -07:00
README.md	Add nodejs samples README (#5005 )	2020-09-03 15:58:44 -07:00

README.md

ONNX Runtime Samples and Tutorials

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

Python

Inference only

Inference with model conversion

Other

C#

C/C++

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

Java

Node.js

Inference with Nodejs

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:
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+

Azure IoT Edge

Inference and Deploy with Azure IoT Edge

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