要在PyTorch中实现分布式训练,可以使用torch.distributed包提供的工具和函数。下面是一个简单的示例代码,演示如何在PyTorch中设置并运行分布式训练:
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
def setup(rank, world_size):
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '12355'
# 初始化进程组
dist.init_process_group("gloo", rank=rank, world_size=world_size)
def cleanup():
dist.destroy_process_group()
def train(rank, world_size):
setup(rank, world_size)
# 创建模型和优化器
model = MyModel()
model = DDP(model)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
# 加载数据
train_dataset = MyDataset()
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, sampler=train_sampler)
# 训练
for epoch in range(10):
for data, target in train_loader:
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
cleanup()
if __name__ == '__main__':
world_size = 4
mp.spawn(train, args=(world_size,), nprocs=world_size)
在这个示例中,我们首先设置了进程组,然后创建了模型、优化器和数据加载器。然后在train
函数中,我们使用torch.multiprocessing.spawn
函数来启动多个进程,每个进程运行train
函数。在train
函数中,我们将模型包装成DistributedDataParallel
对象来实现分布式训练,同时使用torch.utils.data.distributed.DistributedSampler
来分配数据。最后,我们在训练循环中进行模型训练,并在训练结束后清理进程组。