Update PyTorch Example

[stocyr]

The PyTorch example could use some improvement:

• It doesn't reflect the latest API anymore: the experiment.log_metric1 signature has swappedxandy`.
• When using multiprocessing for data loading (that's common practice with PyTorch's torch.utils.data.DataLoader), its problematic to execute expressions outside an if __name__ == '__main__' clause (at least on Windows): it starts multiple experiments...
• I usually want to have my training script also be possible to launch from console. What do you think of the proposed snipped for merging both sets of arguments/options?

  from collections import ChainMap
  ​
  def main():
  # Training settings
  parser = argparse.ArgumentParser(description='PyTorch MNIST Example',
                                   formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                      help='input batch size for training')
  # More arguments...
  args = parser.parse_args()
  experiment = deepkit.experiment()
  # Merge CLI arguments (low priority) with experiment hyperparameters (high priority)
  args = argparse.Namespace(**ChainMap(experiment.full_config(), vars(args)))
  # Update merged arguments back to experiment server
  for key, val in vars(args).items():
      experiment.set_config(key, val)
  # Capsule the experiment handle into `args` as well, so distribution to train() and test() methods is easy
  args.experiment = experiment

[marcj]

Looks good, thanks! If you would like to provide a PR I'd love to merge it.