What are the differences between TorchScript and ONNX?

What are the differences between TorchScript and ONNX?

TorchScript is a way to create serializable and optimizable models from PyTorch code, while ONNX is an open format for representing machine learning models that allows interoperability between different frameworks.

Can you explain how each one is used in practice?

Sure! TorchScript is typically used for deploying PyTorch models in production, allowing for optimizations and running in a C++ runtime. ONNX, on the other hand, is used to convert models from various frameworks into a common format, enabling them to be run on different platforms.

Which one is better for model deployment?

It depends on your use case. If you're working within the PyTorch ecosystem, TorchScript might be more straightforward. However, if you need to deploy models across different frameworks or platforms, ONNX could be more beneficial.

Are there any limitations to using ONNX?

Yes, while ONNX supports many operations, not all PyTorch features are fully supported, which can lead to compatibility issues when converting models.