Pytorch进行数据并行计算

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GPU并行计算,可以将数据分割分发给不同的GPU,每个GPU计算之后再将结果汇总返回

这里面我们做一个实验

1 定义一些参数

input_size = 5
output_size = 2

batch_size = 30
data_size = 100

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

 2 自己做一个数据集

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)

3 创建模型

class Model(nn.Module):
    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

4 开始训练

使用nn.DataParallel封装模型然后放在GPU上model.to(device)

每次训练时先将数据放到GPU data.to(device)再训练

model = Model(input_size,output_size)
if(torch.cuda.device_count() > 1):
    print("Let's use",torch.cuda.device_count(),"CPUs!")
    model = nn.DataParallel(model)
else:
    print("can not use parallel")
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())

5 完整代码

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

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 Model(nn.Module):
    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(),"CPUs!")
    model = nn.DataParallel(model)
else:
    print("can not use parallel")
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())