在PyTorch中实现多GPU并行训练可以通过使用torch.nn.DataParallel
模块或torch.nn.parallel.DistributedDataParallel
模块来实现。下面分别介绍这两种方法的实现步骤:
- 使用
torch.nn.DataParallel
模块:
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
# 构建模型
model = nn.Sequential(
nn.Linear(10, 100),
nn.ReLU(),
nn.Linear(100, 1)
)
# 将模型放到多个GPU上
model = nn.DataParallel(model)
# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
# 构建数据加载器
train_loader = DataLoader(dataset, batch_size=64, shuffle=True)
# 开始训练
for epoch in range(num_epochs):
for inputs, targets in train_loader:
outputs = model(inputs)
loss = criterion(outputs, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
- 使用
torch.nn.parallel.DistributedDataParallel
模块:
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.distributed as dist
# 初始化进程组
dist.init_process_group(backend='nccl')
# 构建模型
model = nn.Sequential(
nn.Linear(10, 100),
nn.ReLU(),
nn.Linear(100, 1)
)
# 将模型放到多个GPU上
model = nn.parallel.DistributedDataParallel(model)
# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
# 构建数据加载器
train_loader = DataLoader(dataset, batch_size=64, shuffle=True)
# 开始训练
for epoch in range(num_epochs):
for inputs, targets in train_loader:
outputs = model(inputs)
loss = criterion(outputs, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
以上是使用torch.nn.DataParallel
和torch.nn.parallel.DistributedDataParallel
模块在PyTorch中实现多GPU并行训练的方法。根据具体需求选择合适的模块来实现多GPU训练。