📚 Documentation This is a useful function to de-init a PG in order to re-initialize it, for example during error handling/retries for distributed training. However, the function is not documented in the docs currently: https://pytorch.or...
dist.destroy_process_group() super(DistributedTorchRunner, self).shutdown() Example #17Source File: i3d_learner.py From deep-smoke-machine with BSD 3-Clause "New" or "Revised" License 5 votes def clean_mp(self): if self.can_parallel: dist.destroy_process_group() Example...
from torch.nn.parallel import DistributedDataParallel as DDP # DDP 在每个 GPU 上运行一个进程,其中都有一套完全相同的 Trainer 副本(包括model和optimizer) # 各个进程之间通过一个进程池进行通信,这两个方法来初始化和销毁进程池 from torch.distributed import init_process_group, destroy_process_group def d...
4. `from torch.distributed import init_process_group, destroy_process_group`: 这些函数用于初始化和销毁分布式进程组。在使用分布式训练时,需要使用`init_process_group`函数来初始化分布式环境,包括指定通信后端(如NCCL、Gloo)、排名、全局大小等信息。而`destroy_process_group`函数则用于在训练结束后清理分布式环境。
import torch.distributed as dist import torch.multiprocessing as mp import torch.nn as nn import torch.optim as optim from torch.nn.parallel import DistributedDataParallel as DDP def example(rank, world_size): # create default process group ...
if master_process: save_checkpoint() 5. 如果使用dataloader,要用DataLoader(..., shuffle=False, sampler = DistributedSampler(dataset)) 在每个epoch要调用train_data.sampler.set_poch(epoch), 这样每个epoch的数据才会被重新打乱 6. 程序结尾添加 destroy_process_group() ...
正如您提到的，it is not documented yet。但是，它在DDP tutorial中使用，并在每个训练步骤结束时调用...
# 需要导入模块: import torch [as 别名]# 或者: from torch importmultiprocessing[as 别名]defrun_in_process_group(world_size, filename, fn, inputs):iftorch.distributed.is_initialized(): torch.distributed.destroy_process_group() processes = [] ...
在进行分布式训练的最后,需要使用torch.distributed.destroy_process_group函数结束进程组。以下是一个示例: dist.destroy_process_group() 这将释放分布式训练环境所占用的资源,并停止分布式通信。 总结: 本文介绍了如何使用PyTorch的分布式all_reduce函数进行数据并行化处理。首先,需要初始化分布式训练环境,并将模型和优化...
import torch.distributed as dist #初始化进程 dist.init_process_group(backend='gloo') #创建本地张量 local_tensor = torch.tensor([1, 2, 3, 4]) #创建全局张量 global_tensor = torch.zeros_like(local_tensor) #使用all_reduce将本地张量的值累加到全局张量上 dist.all_reduce(local_tensor, op=di...