ONNX makes machine learning models portable, shareable


and  have announced a joint project to make it easier for data analysts to exchange trained models between different machine learning frameworks.

The format is meant to provide a common way to represent the data used by neural networks. Most frameworks have their own specific model format that will only work with models from other frameworks by way of a conversion tool.

ONNX allows models to be swapped freely between frameworks without the conversion process. A model trained on one framework can be used for inference by another framework.

Microsoft claims the ONNX format provides advantages above and beyond not having to convert between model formats. For instance, it allows developers to choose frameworks that reflect the job and workflow at hand, since each framework tends to be optimized for different use cases: “fast training, supporting flexible network architectures, inferencing on mobile devices, etc.”