pytorch加速训练过程（单机多卡）

第一种方式:nn.DataParallel方式

# main.py
import torch
import torch.distributed as dist

gpus = [0, 1, 2, 3]#指定有哪些gpu
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])#output_device为指定哪一张卡进行汇总梯度，这张卡内存要大一些一般

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)#将数据放入cuda 
　　　　target = target.cuda(non_blocking=True)#将标签放入cuda 
　　　　... 
　　　　output = model(images) 
　　　　loss = criterion(output, target) 
　　　　... 
　　　　optimizer.zero_grad() 
　　　　loss.backward() 
　　　　optimizer.step() 

#训练的话直接python main.py

第二种方式：使用torch.distributed加速
# main.py
import torch
import argparse
import torch.distributed as dist
#获取当前Gpu进程的index
parser = argparse.ArgumentParser()
parser.add_argument('--local_rank', default=-1, type=int,
                    help='node rank for distributed training')
args = parser.parse_args()
#设置GPU之间通信使用的后端和端口
dist.init_process_group(backend='nccl')
torch.cuda.set_device(args.local_rank)
#使用DistributedSampler对数据集进行划分

train_dataset = ... 
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) 
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=..., sampler=train_sampler) 
#使用 DistributedDataParallel 包装模型，汇总不同GPU算得的梯度，并同步计算结果
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()
#启动方法

CUDA_VISIBLE_DEVICES=0,1,2,3 #指定使用哪些GPU，--nproc_per_node表示有几个进程，有几个GPU就有几个进程

python -m torch.distributed.launch --nproc_per_node=4 main.py

#使用 torch.multiprocessing 取代启动器

# main.py
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
args=parser.parse_args()
args.nprocs=torch.cuda.device_count()
mp.spawn(main_worker, nprocs=args.nprocs, args=(args.nprocs, args))

def main_worker(local_rank, nprocs, args):

   dist.init_process_group(backend='nccl', init_method='tcp://127.0.0.1:23456', world_size=args.nprocs, rank=local_rank)
   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()

 #启动方法：python main.py

第三种：使用Apex加速
# main.py
import torch
import argparse
import torch.distributed as dist

from apex.parallel import DistributedDataParallel

parser = 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, optimizer = amp.initialize(model, optimizer)
model = 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()
      with amp.scale_loss(loss, optimizer) as scaled_loss:
         scaled_loss.backward()
      optimizer.step()
#　启动方法

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 main.py

标签：...,args,单机,torch,多卡,pytorch,train,cuda,model
来源： https://www.cnblogs.com/zhaojianhui/p/16684209.html