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Update PyTorch Example
The PyTorch example could use some improvement:
- It doesn't reflect the latest API anymore: the
experiment.log_metric1 signature has swapped
xand
y`. - When using multiprocessing for data loading (that's common practice with PyTorch's
torch.utils.data.DataLoader
), its problematic to execute expressions outside anif __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
โ
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: only first training instance visible
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
No hardware resource monitoring in some cases
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
No STDOUT logs on Windows
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)
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