OLive, meaning ONNX Go Live, is a sequence of docker images that automates the process of ONNX model shipping. It integrates model conversion, correctness test, and performance tuning into a single pipeline, while each component is a standalone docker image and can be scaled out.

There are three ways to use OLive:

1. Use With Command Line Tool: Run the OLive with command line using Python.

2. Use With Local Web App: A web application with visualization to use OLive on your local machine.

3. Use With Jupyter Notebook: Quickstart of the OLive with tutorial using Jupyter Notebook.

4. Use Pipeline With Kubeflow: Portable and rapid solution with Kubeflow on Kubernetes to deploy easily manageable

end-to-end workflow.

The backend of OLive mainly contains two docker images, ONNX converter and performance tuning image.

1. ONNX Converter Image: Converts models from different frameworks to ONNX, generates random inputs, and verifies the correctness of the converted model. The current supported frameworks are Tensorflow, PyTorch, Keras, Scikit-learn, CNTK, and CoreML.

2. Performance Tuning Image: Tunes different execution providers and environment variable options for the converted ONNX model with ONNX Runtime. Selects and outputs the option combinations with the best performance.

We’d love to embrace your contribution to OLive. Please refer to CONTRIBUTING.md.

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Licensed under the MIT License.