Microsoft announced the deployment of ONNX Runtime source code on GitHub. The project is a high-performance engine for machine learning models in the ONNX (Open Neural Network Exchange) format, ensuring compatibility of ML models with free AI frameworks (TensorFlow, Cognitive Toolkit, Caffe2, MXNet). Therefore, ONNX Runtime is used to optimize computations in models of deep learning of neural networks.
With the translation of the project into open source, the company hopes to attract more people to the development of machine learning. Moreover, Microsoft promised to respond quickly to commits.
To use ONNX Runtime, it is necessary to determine the ONNX model and select a tool for it. Their list and instructions are available on the GitHub page. Microsoft offers several options for those who do not know where to start:
- Download ready-made ResNet or TinyYOLO models from ONNX Model Zoo;
- Create your own computer vision models using Azure Custom Vision Service
- convert models created in TensorFlow, Keras, Scikit-Learn or CoreML using ONNXMLTools and TF2ONNX;
- train new models using Azure machine learning and save the result in ONNX format.
According to Microsoft spokesman Eric Boyd, the Bing Search, Bing Ads and Office services teams were able to achieve twice the performance of ML models using ONNX Runtime compared to standard solutions. Therefore, it is important to support the project by both users and large companies. As for the latter, while they embody the following projects:
- Microsoft and Intel are implementing the nGraph compiler;
- NVIDIA is working on TensorRT integration;
- Qualcomm is looking forward to developing the Snapdragon mobile platform.
In early December 2017, ONNX was transferred from the stage of early access to a project corresponding to the conditions of industrial operation. Companies urged the community to join the project and help create a unified platform for engaging with in-depth training tools.