ONNX Runtime is an open-source scoring engine for Open Neural Network Exchange (ONNX) models.

ONNX is an open format for machine learning (ML) models that is supported by various ML and DNN frameworks and tools. This format makes it easier to interoperate between frameworks and to maximize the reach of your hardware optimization investments. Learn more about ONNX on https://onnx.ai or view the Github Repo.

Why use ONNX Runtime

ONNX Runtime is an open architecture that is continually evolving to adapt to and address the newest developments and challenges in AI and Deep Learning. We will keep ONNX Runtime up to date with the ONNX standard, supporting all ONNX releases with future compatibliity while maintaining backwards compatibility with prior releases.

ONNX Runtime continuously strives to provide top performance for a broad and growing number of usage scenarios in Machine Learning. Our investments focus on these 3 core areas:

Run any ONNX model
High performance
Cross platform

Run any ONNX model

Alignment with ONNX Releases

ONNX Runtime provides comprehensive support of the ONNX spec and can be used to run all models based on ONNX v1.2.1 and higher. See ONNX version release details here.

As of January 2019, ONNX Runtime supports ONNX 1.3. We will soon add support for the recently released ONNX 1.4.

Traditional ML support

ONNX Runtime fully supports the ONNX-ML profile of the ONNX spec for traditional ML scenarios.

High Performance

You can use ONNX Runtime with both CPU and GPU hardware. You can also plug in additional execution providers to ONNX Runtime. With many graph optimizations and various accelerators, ONNX Runtime can often provide lower latency and higher efficiency compared to other runtimes. This provides smoother end-to-end customer experiences and lower costs from improved machine utilization.

Currently ONNX Runtime supports CUDA and MKL-DNN (with option to build with MKL) for computation acceleration. To add an execution provider, please refer to this page.

We are continuously working to integrate new execution providers to provide improvements in latency and efficiency. We have ongoing collaborations to integrate the following with ONNX Runtime: * Intel MKL-DNN and nGraph * NVIDIA TensorRT

Cross Platform

ONNX Runtime offers:

APIs for Python, C#, and C
Available for Linux, Windows, and Mac

See API documentation and package installation instructions below.

Looking ahead: To broaden the reach of the runtime, we will continue investments to make ONNX Runtime available and compatible with more platforms. These include but are not limited to:

C# for Mac
ARM

Getting Started

If you need a model:

Check out the ONNX Model Zoo for ready-to-use pre-trained models.
To get an ONNX model by exporting from various frameworks, see ONNX Tutorials.

If you already have an ONNX model, just install the runtime for your machine to try it out. One easy way to deploy the model on the cloud is by using Azure Machine Learning. See detailed instructions here.

Installation

APIs and Official Builds

API Documentation	CPU package	GPU package
Python	Windows/Linux/Mac	Windows/Linux (Compatible with CUDA9.1 and cuDNN 7.3)
C#	Windows/Linux Mac - Coming Soon	Windows/Linux Mac - Coming Soon
C	Windows/Linux Mac - Coming Soon	Windows/Linux Mac - Coming Soon
C++	TBD	TBD

System Requirements

The OnnxRuntime binaries in CPU packages use OpenMP and depends on the library being available at runtime in the system. For Windows, OpenMP support comes as part of VC runtime. For Linux, the system must have the libgomp.so.1 installed.
The GPU builds require the CUDA9.1 and cuDNN 7.3 runtime libraries being installed in the system.

Build Details

For details on the build configurations and information on how to create a build, see Build ONNX Runtime.

Versioning

See more details on API and ABI Versioning and ONNX Compatibility in Versioning.

Design and Key Features

For an overview of the high level architecture and key decisions in the technical design of ONNX Runtime, see Engineering Design.

ONNX Runtime is built with an extensible design that makes it versatile to support a wide array of models with high performance.

Contribute

We welcome your contributions! Please see the contribution guidelines.

Feedback

For any feedback or to report a bug, please file a GitHub Issue.

Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

License

MIT License

License

kestrelm/onnxruntime

ErrorLooks like something went wrong!

About

Resources

License

Uh oh!

Stars

Watchers

Forks

Releases

Packages 0

Languages

Error
Looks like something went wrong!

Packages