郑州市工程建设信息网seo网络营销技术
参考几个不错的帖子（还没来得及整理）：
基于pytorch多GPU单机多卡训练实践_多卡训练效果不如单卡-CSDN博客
关于PyTorch单机多卡训练_能用torch.device()实现多卡训练吗-CSDN博客
Pytorch多机多卡分布式训练 - 知乎 (zhihu.com)
当代研究生应当掌握的并行训练方法（单机多卡） - 知乎 (zhihu.com)
Dataparallel 较慢，不推荐使用：
DataParallel 并行训练部分主要与如下代码段有关：
# main.py
import torch
import torch.distributed as distgpus = [0, 1, 2, 3] #
torch.cuda.set_device('cuda:{}'.format(gpus[0])) # train_dataset = ...train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=...)model = ...
model = nn.DataParallel(model.to(device), device_ids=gpus, output_device=gpus[0]) #参与训练的 GPU 有哪些，device_ids=gpus；用于汇总梯度的 GPU 是哪个，output_device=gpus[0] optimizer = optim.SGD(model.parameters())for epoch in range(100):for batch_idx, (data, target) in enumerate(train_loader):images = images.cuda(non_blocking=True) #target = target.cuda(non_blocking=True)...output = model(images)loss = criterion(output, target)...optimizer.zero_grad()loss.backward()optimizer.step()平时可以直接使用内置的 Distributed：
torch.distributed 并行训练部分主要与如下代码段有关
# main.py
import torch
import argparse
import torch.distributed as distparser = argparse.ArgumentParser()
parser.add_argument('--local_rank', default=-1, type=int,help='node rank for distributed training')
args = parser.parse_args()dist.init_process_group(backend='nccl')
torch.cuda.set_device(args.local_rank)train_dataset = ...
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=..., sampler=train_sampler)model = ...
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank])optimizer = optim.SGD(model.parameters())for epoch in range(100):for batch_idx, (data, target) in enumerate(train_loader):images = images.cuda(non_blocking=True)target = target.cuda(non_blocking=True)...output = model(images)loss = criterion(output, target)...optimizer.zero_grad()loss.backward()optimizer.step()在使用时，调用 torch.distributed.launch 启动器启动：
github完整代码：https://github.com/tczhangzhi/pytorch-distributed/blob/master/distributed.py
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 main.py以上主要参考：当代研究生应当掌握的并行训练方法（单机多卡） - 知乎 (zhihu.com)