Optimize and accelerate machine learning inferencing across the cloud and the edge

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Easily optimize and accelerate inferencing

Reduce latency and inferencing costs across the cloud and the edge using built-in graph optimizations and hardware accelerators.

Plug-in to your technology stack

Cross-platform support and convenient APIs make inferencing with ONNX Runtime easy.

Leverage open source innovation

With innovation and support from its open source community, ONNX Runtime continuously improves while delivering the reliability you need.

Get Started Easily

Select your requirements and use the resources provided to get started quickly

OS

OS list contains three items

Windows
Linux
Mac

Language

Language list contains four items

Python (3.5-3.7)
C++
C#
C

Architecture

Architecture list contains five items

X64
X86
ARM64
ARM32

Hardware Acceleration

Hardware Acceleration list contains nine items

Default CPU
CUDA
TensorRT
DirectML
MKL-DNN
MKL-ML
nGraph
NUPHAR
OpenVINO

Installation Instructions

Please select a combination of resources.

Build an ONNX Model

Build and train a machine learning model to meet your project goals using the tools that best meet your needs:

Deploy your ONNX Model

Deploy your ONNX model across hardware devices:

Improved performance by 14x

Microsoft Word Online includes a grammar checker that identifies missing determiners. This feature infers missing determiners in real-time on billions of sentences each month.

Using ONNX and ONNX Runtime, inference speed improved by 14.2x

Improved performance by 3x

Computer Vision, an Azure Cognitive Service, uses optical character recognition to detect text in an image and extract the recognized words into a machine-readable character stream.

Using ONNX and ONNX Runtime, inference speed improved by 3.7x

Improved performance by 2x

Bing Visual Search allows users to search the web using an image instead of text.

Using ONNX and ONNX Runtime, inference speed improved by 2x