testMultiGPU.py

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")

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())