Comments (4)
This is bad. We'll look into it.
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This has long been the case and is basically related to the architecture of nn.DataParallel
, which uses Python threads and is limited by both the GIL and CUDA synchronization. So it's fine for networks like typical convnets that have few to no sync points and relatively few kernel launches (each of which is fairly large), but it doesn't work very well for NLP models with lots of tiny kernels.
It's worth also trying DistributedDataParallel
from torch.distributed
, which is available in master already. When running on a single machine, that uses Python multiprocessing rather than threads and should avoid at least the GIL-thrashing if you can get it to run.
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Thanks, we'll give this a try, or take a PR @nicolabertoldi if you are interested
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closing, now Multi GPU is implemented. x3 on 4 GPU.
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