Pytorch_dataparallel

%matplotlib inline

數據並行

Pytorch可以使用多個GPU，如把一個模型放置到GPU上

device = torch.device('cuda:0')
model.to(device)

GPU:然後複製所有的張量到GPU上：

mtensor = my_tensor.to(device)

只調用my_tensor.to(device)並沒有複製張量到GPU上，而是返回一個copy。所以需要賦值一個新的張量並在GPU上使用該張量。
Pytorch默認只使用一個GPU，使用DataParallel可以讓模型在多個GPU上運行

import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset

input_size = 5
output_size = 2
batch_size = 30
data_size = 100

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

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 Net(nn.Module):
    def __init__(self, input_size, output_size):
        super(Net, 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

創建實例並檢測是由有多個GPU，如有多個則使用nn.DataParallel把模型放到GPU上

model = Net(input_size, output_size)
if torch.cuda.device_count() > 1:
    print('Let us use:', torch.cuda.device_count(), 'GPUs!')
    model = nn.DataParallel(model)
model.to(device)

Net(
  (fc): Linear(in_features=5, out_features=2, bias=True)
)

for data in rand_loader:
    input = data.to(device)
    output = model(input)
    print('outside: input_size', input.size(), 'output_size:', output.size())

	In Model: input size: 5 output_size: 2
outside: input_size torch.Size([30, 5]) output_size: torch.Size([30, 2])
	In Model: input size: 5 output_size: 2
outside: input_size torch.Size([30, 5]) output_size: torch.Size([30, 2])
	In Model: input size: 5 output_size: 2
outside: input_size torch.Size([30, 5]) output_size: torch.Size([30, 2])
	In Model: input size: 5 output_size: 2
outside: input_size torch.Size([10, 5]) output_size: torch.Size([10, 2])