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PyTorch Ignite TensorboardLogger

The example script integrates ClearML into code that uses PyTorch Ignite.

The example script does the following:

  • Trains a neural network on the CIFAR10 dataset for image classification.
  • Creates a ClearML Task named image classification CIFAR10 in the examples project.
  • Calls the Task.connect method to track experiment configuration.
  • Uses ignite's TensorboardLogger and attaches handlers to it. See TensorboardLogger.

ClearML's automatic logging captures information and outputs logged with TensorboardLogger.


Parameters are explicitly reported to ClearML using the Task.connect method.

params = {'number_of_epochs': 20, 'batch_size': 64, 'dropout': 0.25, 'base_lr': 0.001, 'momentum': 0.9, 'loss_report': 100}
params = task.connect(params) # enabling configuration override by clearml

The hyperparameter configurations can be viewed in the WebApp in the experiment's CONFIGURATION tab.


Ignite TensorboardLogger

TensorboardLogger is a handler to log metrics, parameters, and gradients when training a model. When ClearML is integrated into a script which uses TensorboardLogger, all information logged through the handler is automatically captured by ClearML.


ClearML automatically captures scalars logged through TensorboardLogger.

View the scalars in the experiment's page in the ClearML Web UI, in SCALARS.


Model Snapshots

ClearML automatically captures the model logged with Torch, and saves it as an artifact.

View saved snapshots in the experiment's ARTIFACTS tab.


To view the model, in the ARTIFACTS tab, click the model name (or download it).


Debug Samples

ClearML automatically tracks images logged to TensorboardLogger. They appear in DEBUG SAMPLES.


Ignite ClearMLLogger

PyTorch Ignite also supports a dedicated ClearMLLogger handler to log metrics, text, model / optimizer parameters, plots, and model checkpoints during training and validation.

For more information, see the PyTorch Ignite ClearMLLogger example.