Hardcoded GPU 0? about mlm-scoring

[mfelice]

Thanks! The values in ctxs seem to be ignored. However, I've been able to circumvent the issue by setting CUDA_VISIBLE_DEVICES. I believe the culprit is cuda:0 and/or device_ids=[0] in the following block:

mlm-scoring/src/mlm/scorers.py

Lines 561 to 568 in 6727297

	self._device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
	torch.manual_seed(0)
	torch.cuda.manual_seed_all(0)
	# TODO: This does not restrict to specific GPUs however, use CUDA_VISIBLE_DEVICES?
	# TODO: It also unnecessarily locks the GPUs to each other
	self._model.to(self._device)
	self._model = torch.nn.DataParallel(self._model, device_ids=[0])
	self._model.eval()

Maybe that should be set to whatever is specified by ctxs?

[DarrenAbramson]

From the fourth line of the readme:

ctxs = [mx.cpu()] # or, e.g., [mx.gpu(0), mx.gpu(1)]

Did you happen to try ctxs = [mx.gpu(0), mx.gpu(1), mx.gpu(2)]?

As for finding things that are hard-coded, are you aware that you can search the repository?