code: def main():
      # Connecting ClearML with the current process,
      # from here on everything is logged automatically
      task = Task.init(
          project_name="examples", task_name="PyTorch MNIST train"
      )
  
      # 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",
      )
  
      parser.add_argument(
          "--save-model",
          action="store_true",
          default=True,
          help="For Saving the current Model",
      )
      args = parser.parse_args()
      use_cuda = not args.no_cuda and torch.cuda.is_available()
  
      torch.manual_seed(args.seed)
  
      device = torch.device("cuda" if use_cuda else "cpu")
  
      kwargs = (
          {"num_workers": 4, "pin_memory": True} if use_cuda else {}
      )
      train_loader = torch.utils.data.DataLoader(
          datasets.MNIST(
              os.path.join("..", "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(
              os.path.join("..", "data"),
              train=False,
              transform=transforms.Compose(
                  [
                      transforms.ToTensor(),
                      transforms.Normalize((0.1307,), (0.3081,)),
                  ]
              ),
          ),
          batch_size=args.test_batch_size,
          shuffle=True,
          **kwargs,
      )
  
      model = Net().to(device)
      optimizer = optim.SGD(
          model.parameters(), lr=args.lr, momentum=args.momentum
      )
  
      for epoch in range(1, args.epochs + 1):
          train(args, model, device, train_loader, optimizer, epoch)
          test(args, model, device, test_loader, epoch)
  
      if args.save_model:
          torch.save(
              model.state_dict(),
              os.path.join(gettempdir(), "mnist_cnn.pt"),
          )
  
file_path: clearml/examples/frameworks/pytorch/pytorch_mnist.py
imports: from __future__ import print_function
  import argparse
  import os
  from tempfile import gettempdir
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torchvision import datasets, transforms
  from clearml import Task, Logger
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types