pycharm单卡多机训练

在PyCharm中进行单卡多机训练，你可以使用PyTorch的分布式训练功能。下面是一个简单的示例代码：

import torch
import torch.distributed as dist
from torch.multiprocessing import Process

def train(rank, world_size):
    # 初始化进程组
    dist.init_process_group(backend='nccl', init_method='tcp://localhost:23456', rank=rank, world_size=world_size)
    
    # 创建模型和优化器
    model = YourModel()
    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
    
    # 将模型和优化器放到GPU上
    model = model.to(rank)
    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[rank])
    
    # 定义训练数据
    train_dataset = YourDataset()
    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, sampler=train_sampler)
    
    # 训练循环
    for epoch in range(10):
        train_sampler.set_epoch(epoch)
        for data, target in train_loader:
            data = data.to(rank)
            target = target.to(rank)
            
            optimizer.zero_grad()
            output = model(data)
            loss = torch.nn.functional.cross_entropy(output, target)
            loss.backward()
            optimizer.step()

def main():
    # 设置进程数量和主机数量
    world_size = 2
    processes = []
    
    # 启动多个进程进行训练
    for rank in range(world_size):
        p = Process(target=train, args=(rank, world_size))
        p.start()
        processes.append(p)
    
    # 等待所有进程结束
    for p in processes:
        p.join()

if __name__ == '__main__':
    main()

请注意，上述代码中的`YourModel`和`YourDataset`需要根据你的实际情况进行替换。此外，你还需要根据你的网络设置来修改`init_method`参数中的IP地址和端口号。