70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
import torch
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from torch.nn.parallel.distributed import DistributedDataParallel
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import lightning as L
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from lightning.app.components import MultiNode
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def distributed_train(local_rank: int, main_address: str, main_port: int, num_nodes: int, node_rank: int, nprocs: int):
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# 1. Setting distributed environment
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global_rank = local_rank + node_rank * nprocs
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world_size = num_nodes * nprocs
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if torch.distributed.is_available() and not torch.distributed.is_initialized():
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torch.distributed.init_process_group(
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"nccl" if torch.cuda.is_available() else "gloo",
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rank=global_rank,
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world_size=world_size,
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init_method=f"tcp://{main_address}:{main_port}",
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)
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# 2. Prepare the model
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model = torch.nn.Sequential(
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torch.nn.Linear(1, 1),
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torch.nn.ReLU(),
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torch.nn.Linear(1, 1),
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)
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# 3. Setup distributed training
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device = torch.device(f"cuda:{local_rank}") if torch.cuda.is_available() else torch.device("cpu")
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model = DistributedDataParallel(model.to(device), device_ids=[local_rank] if torch.cuda.is_available() else None)
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# 4. Prepare loss and optimizer
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criterion = torch.nn.MSELoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
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# 5. Train the model for 1000 steps.
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for step in range(1000):
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model.zero_grad()
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x = torch.tensor([0.8]).to(device)
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target = torch.tensor([1.0]).to(device)
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output = model(x)
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loss = criterion(output, target)
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print(f"global_rank: {global_rank} step: {step} loss: {loss}")
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loss.backward()
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optimizer.step()
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class PyTorchDistributed(L.LightningWork):
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def run(
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self,
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main_address: str,
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main_port: int,
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num_nodes: int,
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node_rank: int,
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):
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nprocs = torch.cuda.device_count() if torch.cuda.is_available() else 1
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torch.multiprocessing.spawn(
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distributed_train, args=(main_address, main_port, num_nodes, node_rank, nprocs), nprocs=nprocs
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)
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# Run over 2 nodes of 4 x V100
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app = L.LightningApp(
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MultiNode(
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PyTorchDistributed,
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num_nodes=2,
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cloud_compute=L.CloudCompute("gpu-fast-multi"), # 4 x V100
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)
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)
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