from __future__ import print_function
import argparse
import os
import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import torch.multiprocessing as mp
from torchvision import datasets, transforms
from tensorboardX import GlobalSummaryWriter
# 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=2, 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)
def train(rank, args, model, device, dataloader_kwargs):
torch.manual_seed(args.seed + rank)
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.batch_size, shuffle=True, num_workers=1,
**dataloader_kwargs)
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
for epoch in range(1, args.epochs + 1):
train_epoch(epoch, args, model, device, train_loader, optimizer)
def train_epoch(epoch, args, model, device, data_loader, optimizer):
model.train()
pid = os.getpid()
for batch_idx, (data, target) in enumerate(data_loader):
optimizer.zero_grad()
output = model(data.to(device))
loss = F.nll_loss(output, target.to(device))
loss.backward()
optimizer.step()
if batch_idx % args.log_interval == 0:
writer.add_scalar("Loss", loss)
print('{}\tTrain Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
pid, epoch, batch_idx * len(data), len(data_loader.dataset),
100. * batch_idx / len(data_loader), loss.item()))
writer = GlobalSummaryWriter()
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()
