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PyTorch with TensorBoard

The pytorch_tensorboard.py example demonstrates the integration of ClearML into code that uses PyTorch and TensorBoard.

The example does the following:

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

Scalars​

In the example script, the train and test functions call the TensorBoard SummaryWriter.add_scalar method to log loss. These scalars, along with the resource utilization plots, which are titled :monitor: machine, appear in the experiment's page in the ClearML web UI under SCALARS.

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Debug Samples​

ClearML automatically tracks images and text output to TensorFlow. They appear in DEBUG SAMPLES.

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Hyperparameters​

ClearML automatically logs TensorFlow Definitions. They appear in CONFIGURATION > HYPERPARAMETERS > TF_DEFINE.

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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 a 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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