-
Notifications
You must be signed in to change notification settings - Fork 0
/
data_parallel_test.py
61 lines (42 loc) · 1.49 KB
/
data_parallel_test.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
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 = 1000
data_size = 1000000
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# es necesario implementar la función getitem
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
# se llama al dataset dummy de 100x5
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())