Following imagenet-example: https://github.com/pytorch/examples/blob/master/imagenet/main.py 6, It seems that  seed  is not set in default (default is None):
parser.add_argument('--seed', default=None, type=int, help='seed for initializing training. ')
But when we use DistributedDataParallel mode, if seed is not set, the initialized parameters across multi-gpu will be different, resulting in different model param is kept in different gpus during training process (although we only save ckpt in rank0 gpu).
I am not sure whether this phenomenon will cause unknown errors, or may lead to an unstable results? Is it safe for me not to set the initialization seed？