Finding the number of of nodes and gpus of DistributedDataParallel - deep-learning

Question

I would like to know what number should I select for nodes and gpus.
I use Tesla V100-SXM2 (8 boards).
I tried:
nodes = 1, gpus=1 (only the first gpu works)
nodes=1, gpus =8 (It took very long time and cannot execute)
Did I got wrong parameter for the nodes and gpus? or Is my code wrong ? I would appreciate if you could help me out. The code below is simplified sample code of DPP.
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-n', '--nodes', default=1, type=int, metavar='N')
parser.add_argument('-g', '--gpus', default=1, type=int,
help='number of gpus per node')
parser.add_argument('-nr', '--nr', default=0, type=int,
help='ranking within the nodes')
parser.add_argument('--epochs', default=200, type=int, metavar='N',
help='number of total epochs to run')
args = parser.parse_args()
args.world_size = args.gpus * args.nodes
os.environ['MASTER_ADDR'] = 'host1'
os.environ['MASTER_PORT'] = '7777'
mp.spawn(train, nprocs=args.gpus, args=(args,))
def train(gpu, args):
rank = args.nr * args.gpus + gpu
dist.init_process_group(
backend='nccl',
init_method='env://',
world_size=args.world_size,
rank=rank
)
torch.manual_seed(0)
model = ConvNet()
torch.cuda.set_device(gpu)
model.cuda(gpu)
batch_size = 100
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda(gpu)
optimizer = torch.optim.SGD(model.parameters(), 1e-4)
# Wrapper around our model to handle parallel training
model = nn.parallel.DistributedDataParallel(model, device_ids=[gpu])
# Data loading code
train_dataset = get_datasets()
# Sampler that takes care of the distribution of the batches such that
# the data is not repeated in the iteration and sampled accordingly
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=args.world_size,
rank=rank
)
# We pass in the train_sampler which can be used by the DataLoader
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=0,
pin_memory=True,
sampler=train_sampler)
start = datetime.now()
total_step = len(train_loader)
for epoch in range(args.epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.cuda(non_blocking=True)
labels = labels.cuda(non_blocking=True)
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % 100 == 0 and gpu == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(
epoch + 1,
args.epochs,
i + 1,
total_step,
loss.item())
)
if gpu == 0:
print("Training complete)