What is the method for deploying models in Torch?

There are several common methods for deploying models in PyTorch.

1. Save the model as a .pth file and load the model: You can save the model as a .pth file using the torch.save() method, then load the model using the torch.load() method, and finally use the model for prediction or inference.
2. Convert the model to ONNX format: You can use the torch.onnx.export() method to convert the PyTorch model to ONNX format, and then load and run the model using the ONNX runtime.
3. Using TorchScript: You can convert a PyTorch model to TorchScript using the torch.jit.script() method, then load the TorchScript model and make predictions using the torch.jit.load() method.
4. Utilize TorchServe: TorchServe is an open-source PyTorch model deployment framework that allows for quick deployment of PyTorch models, with features including model loading, predicting, and monitoring.

These are common PyTorch model deployment methods, and you can choose the most suitable method for deployment according to specific requirements.