Session Abstract

Microsoft and a community of partners created ONNX as an open standard for representing machine learning models. Models from many frameworks including TensorFlow, PyTorch, SciKit-Learn, Keras, Chainer, MXNet, and MATLAB can be exported or converted to the standard ONNX format. Once the models are in the ONNX format, they can be run on a variety of platforms and devices.

ONNX Runtime is a high-performance inference engine for deploying ONNX models to production. Its optimized for both cloud and edge and works on Linux, Windows, and Mac. Written in C++, it also has C, Python, and C# APIs. ONNX Runtime provides support for all of the ONNX-ML specification and also integrates with accelerators on different hardware such as TensorRT on NVidia GPUs.

The ONNX Runtime is used in high scale Microsoft services such as Bing, Office, and Cognitive Services. Performance gains are dependent on a number of factors but these Microsoft services have seen an average 2x performance gain on CPU. ONNX Runtime is also used as part of Windows ML on hundreds of millions of devices. You can use the runtime with Azure Machine Learning services. By using ONNX Runtime, you can benefit from the extensive production-grade optimizations, testing, and ongoing improvements.

Session Speakers

Weixing Zhang

Senior Software Engineer (Microsoft)

Weixing Zhang is a Senior Software Engineer working in AI Framework Architecture team at Microsoft. His focus is optimization of AI framework, code generation and training in ONNX Runtime.