Comments (4)
Thanks for the question. Have you tried setting NCCL_BLOCKING_WAIT
(or if you are using pytorch nightly - NCCL_ASYNC_ERROR_HANDLING
env var on your trainers?
https://pytorch.org/docs/stable/distributed.html
from elastic.
Hey @kiukchung thanks for the pointer! Setting the environment variable export NCCL_BLOCKING_WAIT=1
makes the previously-hanging worker throw the following error, which is subsequently caught by the elastic agent.
Traceback (most recent call last):
File "worker.py", line 251, in <module>
parse_args()
File "worker.py", line 247, in parse_args
init_processes(0, args)
File "worker.py", line 220, in init_processes
train(args)
File "worker.py", line 130, in train
update_gradients(model)
File "worker.py", line 55, in update_gradients
dist.all_reduce(param.grad.data, op=dist.ReduceOp.SUM)
File "/usr/local/lib/python3.8/dist-packages/torch/distributed/distributed_c10d.py", line 948, in all_reduce
work.wait()
RuntimeError: NCCL error: unhandled system error, NCCL version 2.7.8
[ERROR] 2020-11-16 21:08:51,160 local_elastic_agent: [default] Worker group failed
Thanks again for the quick help! Closing this issue.
from elastic.
Hey there,
I think I have the same trouble.
Any updates ?
Best regards,
Thomas Chaton.
from elastic.
@tchaton Unfortunately I haven't been able to resolve this issue :(
from elastic.
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