pytorc使用多个GPU同时训练模型

pytorch使用同一设备上多个GPU同时训练模型，只需在原有代码中稍作修改即可。

改动1：

os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,3' # 这里输入你的GPU_id

　　

改动2：

if torch.cuda.device_count() > 1:
  model = nn.DataParallel(model)

model.to(device)

　　

 

使用多GPU训练，速度明显得到提升。

 

 

 

 

 

 

 

官方示例代码

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

os.environ['CUDA_VISIBLE_DEVICES'] = '2,3' # 这里输入你的GPU_id

# 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")

# Dummy DataSet
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)

# Simple Model
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

# Create Model and DataParallel
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)


#Run the Model
for data in rand_loader:
    input = data.to(device)
    output = model(input)
    print("Outside: input size", input.size(),
          "output_size", output.size())