'''
主要语句:
model.gpu()
tensor.gpu()
#多GPU的情况下
nn.DataParallel(model)
'''
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils.data import DataLoader,Dataset
#一些参数
input_size = 5
output_size = 2
batch_size = 30
data_size = 100
#get the data
class RandomDataset(Dataset):
def __init__(self,size,length):
self.len = length
self.data = torch.randn(length,size)
def __len__(self):
return self.len
def __getitem__(self, item):
return self.data[item]
random_loader = DataLoader(dataset=RandomDataset(input_size,100),batch_size=batch_size,shuffle=True)
#网路模型定义
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(" In Model: input size", input.size(),
"output size", output.size())
return output
'''
首先,我们要多模型进行实例化然后检查是不是有多个GPUs,
如果是的话就要先用nn.DataParallel语句,然后就可以调用model.gpu()将模型放到GPUs上面。
如果只有一个GPU那就直接调用model.gpu()就可以了。
'''
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)
if torch.cuda.is_available():
model.cuda()
for data in random_loader:
if torch.cuda.is_available():
input_var = Variable(data.cuda())
else:
input_var = Variable(data)
output = model(input_var)
print("Outside: input size", input_var.size(),
"output_size", output.size())
0011-pytorch入门-数据并行
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转载自blog.csdn.net/zhonglongshen/article/details/112727133
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