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PyTorch MNIST

The pytorch_mnist.py example demonstrates the integration of ClearML into code that uses PyTorch.

The example script does the following:

  • Trains a simple deep neural network on the PyTorch built-in MNIST dataset.
  • Creates an experiment named pytorch mnist train in the examples project.
  • ClearML automatically logs argparse command line options, and models (and their snapshots) created by PyTorch
  • Additional metrics are logged by calling Logger.report_scalar().

Scalars​

In the example script's train function, the following code explicitly reports scalars to ClearML:

Logger.current_logger().report_scalar(
    "train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item()
)

In the test method, the code explicitly reports loss and accuracy scalars.

Logger.current_logger().report_scalar(
    "test", "loss", iteration=epoch, value=test_loss
)
Logger.current_logger().report_scalar(
    "test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset))
)

These scalars can be visualized in plots, which appear in the ClearML web UI, in the experiment's page > SCALARS.

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

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