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import os
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
def setup(rank, world_size):
os.environ['MASTER_ADDR'] = 'localhost' # Replace with master node's IP
os.environ['MASTER_PORT'] = '12355'
dist.init_process_group("nccl", rank=rank, world_size=world_size)
def cleanup():
dist.destroy_process_group()
class MyDiffusionModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = torch.nn.Conv2d(3, 16, 3, 1)
# self.conv2 = torch.nn.Conv2d(16, 32, 3, 1)
self.fc1 = torch.nn.Linear(32 * 6 * 6, 128)
# self.fc2 = torch.nn.Linear(128, 10)
def forward(self, x):
x = torch.nn.functional.relu(self.conv1(x))
x = torch.nn.functional.max_pool2d(x, 2)
# x = torch.nn.functional.relu(self.conv2(x))
# x = torch.nn.functional.max_pool2d(x, 2)
x = torch.flatten(x, 1)
x = torch.nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
def main(rank, world_size):
setup(rank, world_size)
if torch.cuda.is_available():
num_gpus = torch.cuda.device_count()
print(f"Rank {rank}, Number of GPUs available: {num_gpus}")
for i in range(num_gpus):
print(f"Rank {rank}, GPU {i}: {torch.cuda.get_device_name(i)}")
else:
print(f"Rank {rank}, No GPUs available")
cleanup()
if __name__ == "__main__":
world_size = 1 # Number of nodes
mp.spawn(main, args=(world_size,), nprocs=torch.cuda.device_count(), join=True)
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