Which model checkpoint does the Deci platform support?

The model’s checkpoint file should meet the following requirements

FrameworkCheckpoint File RequirementsHow 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 wantSave your traced TorchScript module as a .pth file.