Comments (6)
hi Alan,
Could you share some thoughts on the easiest way to distribute shots?
Best,
Vladimir
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Thanks a lot for getting back. I am trying to distribute FWI and LSRTM within a single node. I tried to replace prop=nn.DataParallel(prop), which appears to be the most basic option.
source_amplitudes.swapaxes(0,1).swapaxes(1,2) before the propagator and swapping back within the propagator allows distributing inputs seemingly properly. The model itself does not get replicated staying at the "cuda:0" device so even forward propagation does not work...
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Hi Vladimir,
I think I see the problem. The model is not being registered as a parameter of the propagator, and so PyTorch doesn't know that it needs to copy it to the other devices. I don't have multiple GPUs to test it on at the moment, but does manually registering the parameters as below work?
prop = deepwave.scalar.Propagator({'vp': model}, dx)
prop.register_parameter('vp', torch.nn.Parameter(model))
prop = torch.nn.DataParallel(prop)
If so, I will fix it in the next release of Deepwave so that the parameters are automatically registered.
from deepwave.
Hi Alan,
Registering the model as a parameter did not change the behavior. Sending it manually to the device with inputs inside the forward method lets nn.DataParallel run, but it looks like it runs sequentially the forward propagation for different GPUs)
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