text: # TRAINS - Example of pytorch with tensorboard>=v1.14
  #
  from __future__ import print_function
  
  import argparse
  import os
  from tempfile import gettempdir
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.optim as optim
  from torchvision import datasets, transforms
  from torch.autograd import Variable
  from torch.utils.tensorboard import SummaryWriter
  
  from trains import Task
  
  
  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(model, epoch, train_loader, args, optimizer, writer):
      model.train()
      for batch_idx, (data, target) in enumerate(train_loader):
          if args.cuda:
              data, target = data.cuda(), target.cuda()
          data, target = Variable(data), Variable(target)
          optimizer.zero_grad()
          output = model(data)
          loss = F.nll_loss(output, target)
          loss.backward()
          optimizer.step()
          if batch_idx % args.log_interval == 0:
              print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                  epoch, batch_idx * len(data), len(train_loader.dataset),
                  100. * batch_idx / len(train_loader), loss.data.item()))
              niter = epoch*len(train_loader)+batch_idx
              writer.add_scalar('Train/Loss', loss.data.item(), niter)
  
  
  def test(model, test_loader, args, optimizer, writer):
      model.eval()
      test_loss = 0
      correct = 0
      for niter, (data, target) in enumerate(test_loader):
          if args.cuda:
              data, target = data.cuda(), target.cuda()
          data, target = Variable(data), Variable(target)
          output = model(data)
          test_loss += F.nll_loss(output, target, reduction='sum').data.item()  # sum up batch loss
          pred = output.data.max(1)[1] # get the index of the max log-probability
          pred = pred.eq(target.data).cpu().sum()
          writer.add_scalar('Test/Loss', pred, niter)
          correct += pred
          if niter % 100 == 0:
              writer.add_image('test', data[0, :, :, :], niter)
  
      test_loss /= len(test_loader.dataset)
      print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
          test_loss, correct, len(test_loader.dataset),
          100. * correct / len(test_loader.dataset)))
  
  
  def main():
      # 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('--no-cuda', action='store_true', default=False,
                          help='disables CUDA training')
      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')
      args = parser.parse_args()
      task = Task.init(project_name='examples', task_name='pytorch with tensorboard', output_uri='/tmp/blah')
      writer = SummaryWriter('runs')
      writer.add_text('TEXT', 'This is some text', 0)
      args.cuda = not args.no_cuda and torch.cuda.is_available()
  
      torch.manual_seed(args.seed)
      if args.cuda:
          torch.cuda.manual_seed(args.seed)
  
      kwargs = {'num_workers': 4, 'pin_memory': True} if args.cuda else {}
      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, **kwargs)
      test_loader = torch.utils.data.DataLoader(datasets.MNIST('../data', train=False,
                                                               transform=transforms.Compose([
                                                                   transforms.ToTensor(),
                                                                   transforms.Normalize((0.1307,), (0.3081,))])),
                                                batch_size=args.batch_size, shuffle=True, **kwargs)
  
      model = Net()
      if args.cuda:
          model.cuda()
      optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
  
      for epoch in range(1, args.epochs + 1):
          train(model, epoch, train_loader, args, optimizer, writer)
          torch.save(model, os.path.join(gettempdir(), 'model{}'.format(epoch)))
      test(model, test_loader, args, optimizer, writer)
  
  
  if __name__ == "__main__":
      # Hack for supporting Windows OS - https://pytorch.org/docs/stable/notes/windows.html#usage-multiprocessing
      main()
  