Today I want to use DISTRIBUTED COMMUNICATION PACKAGE to train the imagenet, however I found there are many processes have been created on the node.
script.py:
def main():
    ...
    model.to(device)
    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=None, output_device=None, find_unused_parameters=True)
    ...

if __name__ == '__main__':
    print('Use {back} as backend.'.format(back=args.backend))
    dist.init_process_group(backend=args.backend, init_method='env://', timeout=datetime.timedelta(seconds=1000))
    main()


bash.sh:
CUDA_VISIBLE_DEVICES=0,1,2,3
export GLOO_SOCKET_IFNAME=ib0
export OMP_NUM_THREADS=24
NPROC_PER_NODE=4
SLURM_JOB_NUM_NODES=4
...

COMMAND="script.py -a inception_v3 --print-freq 1000 --backend gloo --nproc-per-node 4 --pretrained  --multiprocessing-distributed  $HOME/ImageNet"

python -m torch.distributed.launch \
--nproc_per_node=$NPROC_PER_NODE \
--nnodes=$SLURM_JOB_NUM_NODES \
--node_rank=$SLURM_NODEID \
--master_addr=$MIP \
--master_port=$MPORT \
$COMMAND > $HOME/thesis/PCL/log/"log_v1_inception_"${SLURM_JOB_ID}"_"${SLURM_NODEID}".out"

I have trained it in 4 nodes and there are 4 gpus on each node.
I log in the node0 then I found there are 16 processes in this node

Is it abnormal? Or DDP would create nproc_per_nodee * node_num processes on each node?