pytorch DataParallel 多GPU使用

import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
# Parameters and DataLoaders
input_size = 5
output_size = 2
batch_size = 30
data_size = 100
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
class RandomDataset(Dataset):
    def __init__(self, size, length):
        self.len = length
        self.data = torch.randn(length, size)
    def __getitem__(self, index):
        return self.data[index]
    def __len__(self):
        return self.len
rand_loader = DataLoader(dataset=RandomDataset(input_size, data_size), batch_size=batch_size, shuffle=True)
class Model(nn.Module):
    # Our model
    def __init__(self, input_size, output_size):
        super(Model, self).__init__()
        self.fc = nn.Linear(input_size, output_size)
    def forward(self, input):
        output = self.fc(input)
        print("\tIn Model: input size", input.size(),
              "output size", output.size())
        return output
model = Model(input_size, output_size)
if torch.cuda.device_count() > 1:
  print("Let's use", torch.cuda.device_count(), "GPUs!")
  # dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
  model = nn.DataParallel(model)
model.to(device)
for data in rand_loader:
    input = data.to(device)
    output = model(input)
    print("Outside: input size", input.size(),
          "output_size", output.size())

核心：

首先，确定使用GPU还是使用CPU，设置device

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

其次，我们需要制作一个模型实例，并检查是否有多个GPU。如果我们有多个GPU，则可以使用nn.DataParallel包装模型。然后我们可以通过model.to（device）将我们的模型放在GPU上

model = Model(input_size, output_size)
if torch.cuda.device_count() > 1:
  print("Let's use", torch.cuda.device_count(), "GPUs!")
  # dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
  model = nn.DataParallel(model)
model.to(device)