The PyTorch example could use some improvement:

• It doesn't reflect the latest API anymore: the experiment.log_metric1 signature has swapped xandy`.
• 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

watch_torch_model() does not work entirely. For all images, I somehow only see the very first training instance the model encountered, though the distributions and also some stuff in the weights still seem to get updated.

Setup:

• Windows 10
• Deepkit Release 2020.1.5
• Deepkit SDK 1.0.1
• Running experiment on anything, from local script run, to local UI start to deployed docker run, eg. on pytorch/pytorch:1.4-cuda10.1-cudnn7-runtime

When I launch an experiment from the Deepkit "Start" dialog using a yml config, I don't see any hardware resource monitoring metrics. I do see metrics when either launching a remote docker experiment from the Deepkit "Start" dialog or when launching locally by "Script run".

Setup:

• Windows 10
• Deepkit Release 2020.1.5
• Deepkit SDK 1.0.1

When using the account localhost, STDOUT print logs do not appear in any case (launching from console, starting from Deepkit UI or starting in remote docker).

Setup:

• Windows 10
• Deepkit Release 2020.1.5
• Deepkit SDK 1.0.1
• Python 3.7.x (local and remote)

They start showing up, however, when the Deepkit App is connected to a remote-hosted Team Server (eg. a non-localhost account)