Comments (3)
ok, debugged further, the call is only during the initialisation actually from https://github.com/pytorch/ignite/blob/8ec312c466c0f7456ac18746859d25d6232c12f1/ignite/distributed/comp_models/native.py#L153-L156
(in my opinion this initial attribute reading could be done lazily to avoid some latencies/confusions... cc @vfdev-5)
closing this ticket as the main logic is indeed all using nccl.
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@wyli how do you know that evenly_divisible_all_gather
would require gloo and nccl for idist.all_gather(length)
and idist.all_gather(data)
?
For all_gather the length it will use the global processing group's backend and wrap the int into a tensor on default device (which will be GPU if cuda is available). So, I would say both idist calls will use the same backend.
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ok, I don't have a min example for now but I remember I was trying to gather GPU tensors, there was a warning about gloo local hostname etc and it was slow, but using a pure torch.distributed API doesn't have this warning..
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