Often, I code on my laptop which is not equipped with a GPU (MacBook, if it makes a difference). Then files are transferred to a server with a GPU. I just want to perform a sanity-check on my code, before running it on the server, in order to avoid errors related to tensors being on different devices. I am looking for a gpu emulator, which takes in some tenosrs and outputs some other random tensors.
1 Answers
Just add .to('cuda:0') to your model which inherits nn.Module and any tensor you created within the forward/backward pass.
Note that cuda:0 means the gpu of index 0.
Moreover, I'd like to define a hyper parameter dictionary to pass to the model, one can easily define hparams['device'] = 'cpu'/'cuda:0'/'cuda:1' in the dict, and when initialize the model, the property self.device = hparams['device'] is set, so that any tensor/module in the model can be easily migrated to any device as configured by adding .to(self.device).
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