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, which is associated with the
- ClearML automatically logs
argparsecommand line options, and models (and their snapshots) created by PyTorch
- Additional metrics are logged by calling the Logger.report_scalar method.
In the example script's
train function, the following code explicitly reports scalars to ClearML:
"train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item()
test method, the code explicitly reports
"test", "loss", iteration=epoch, value=test_loss
"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.
ClearML automatically logs command line options defined with
argparse. They appear in CONFIGURATION > HYPER PARAMETERS > Args.
Text printed to the console for training progress, as well as all other console output, appear in CONSOLE.
Models created by the experiment appear in the experiment’s ARTIFACTS tab. ClearML automatically logs and tracks models and any snapshots created using PyTorch.
Clicking on the model name takes you to the model’s page, where you can view the model’s details and access the model.