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TensorBoardX

The pytorch_tensorboardX.py example demonstrates the integration of ClearML into code that uses PyTorch and TensorBoardX.

The script does the following:

  1. Trains a simple deep neural network on the PyTorch built-in MNIST dataset.
  2. Creates a TensorBoardX SummaryWriter object to log:
    • Scalars during training
    • Scalars and debug samples during testing
    • A test text message to the console (a test message to demonstrate ClearML).
  3. Creates an experiment named pytorch with tensorboardX which is associated with the examples project in the ClearML Web UI.

Scalars#

The loss and accuracy metric scalar plots appear in the experiment's page in the ClearML web UI, under RESULTS > SCALARS. The also includes resource utilization plots, which are titled :monitor: machine.

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Hyperparameters#

ClearML automatically logs command line options defined with argparse. They appear in CONFIGURATIONS > HYPER PARAMETERS > Args.

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Console#

Text printed to the console for training progress, as well as all other console output, appear in RESULTS > CONSOLE.

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Artifacts#

Model artifacts associated with the experiment appear in the info panel of the EXPERIMENTS tab and in the info panel of the MODELS tab.

The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).

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The model info panel contains the model details, including:

  • Model URL
  • Framework
  • Snapshot locations.

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