main.py

from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.multiprocessing as mp

from train import train, test

# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                    help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                    help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=10, metavar='N',
                    help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                    help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                    help='SGD momentum (default: 0.5)')
parser.add_argument('--seed', type=int, default=1, metavar='S',
                    help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                    help='how many batches to wait before logging training status')
parser.add_argument('--num-processes', type=int, default=2, metavar='N',
                    help='how many training processes to use (default: 2)')
parser.add_argument('--cuda', action='store_true', default=False,
                    help='enables CUDA training')

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

if __name__ == '__main__':
    args = parser.parse_args()

    use_cuda = args.cuda and torch.cuda.is_available()
    device = torch.device("cuda" if use_cuda else "cpu")
    dataloader_kwargs = {'pin_memory': True} if use_cuda else {}

    torch.manual_seed(args.seed)
    mp.set_start_method('spawn')

    model = Net().to(device)
    model.share_memory() # gradients are allocated lazily, so they are not shared here

    processes = []
    for rank in range(args.num_processes):
        p = mp.Process(target=train, args=(rank, args, model, device, dataloader_kwargs))
        # We first train the model across `num_processes` processes
        p.start()
        processes.append(p)
    for p in processes:
        p.join()

    # Once training is complete, we can test the model
    test(args, model, device, dataloader_kwargs)