Refactor cuda device assignation

[sacdallago]

Currently, in one form or another, in various parts of the pipeline, this is used:

"cuda:0" if torch.cuda.is_available() and not self._use_cpu else "cpu"

The problem here is that cuda:0 will always refer to the 0 card. In systems hosting multiple cards, this will be painful. workoarounds are:

• NOT defining the device number, aka, just cuda (see: https://pytorch.org/docs/stable/tensor_attributes.html#torch.torch.device), or

• allow the device number to be passed via a param, e.g.:

  "cuda:{cuda_device}" if torch.cuda.is_available() and not self._use_cpu else "cpu"
  

  where cuda_device is an integer that defaults to 0.

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Examples where it's used:

• https://github.com/sacdallago/bio_embeddings/blob/master/bio_embeddings/embed/albert/AlbertEmbedder.py#L39
• https://github.com/sacdallago/bio_embeddings/blob/master/bio_embeddings/embed/seqvec/SeqVecEmbedder.py#L39

[sacdallago]

Only relevant once we can test on GPU server