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TensorBoardX with PyTorch

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

The script does the following:

  • Trains a simple deep neural network on the PyTorch built-in MNIST dataset
  • Creates an experiment named pytorch with tensorboardX in the examples project
  • ClearML automatically captures scalars and text logged using the TensorBoardX SummaryWriter object, and the model created by PyTorch

Scalars​

The loss and accuracy metric scalar plots appear in the experiment's page in the ClearML web UI, under 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 CONFIGURATION > HYPERPARAMETERS > Args.

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Console​

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

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Artifacts​

Models created by the experiment appear in the experiment's ARTIFACTS tab. ClearML automatically logs and tracks models and any snapshots created using PyTorch.

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Clicking on the model's name takes you to the model's page, where you can view the model's details and access the model.

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