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Which model checkpoint does the Deci platform support?

The model’s checkpoint file should meet the following requirements


Checkpoint File Requirements

How to generate?

ONNX 1.8.0 (.onnx)

The checkpoint should be saved as a .onnx file, and must support inference with dynamic batch size.

We currently support Opsets 9-11, (Opset 12 is in beta)

See link for instructions.

TensorFlow 2.3 (zip with saved_model.pb)

The checkpoint should be a TF2 saved-model format. Make sure to zip the file's directory before uploading.

See link for instructions.
An example of how the folder should be zipped is shown below –

Keras (.h5)

Save your Keras model in .h5 format. Make sure to save both the architecture and the weights.

See link for instructions.

PyTorch (pth)

To enable the upload of a PyTorch model into Deci, convert it to an ONNX 1.8.0 format ( .onnx file) that supports inferencing with dynamic batch size.

See link for instructions.

TorchScript what we want to remember what we want

Save your traced TorchScript module as a .pth file.

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