Text Classification - Jupyter Notebook

The example text_classification_AG_NEWS.ipynb demonstrates using Jupyter Notebook for ClearML, and the integration of ClearML into code which trains a network to classify text in the torchtext AG_NEWS dataset, and then applies the model to predict the classification of sample text. ClearML automatically logs the scalar and console output by calling TensorBoard methods. In the example, we explicitly log parameters with the Task. When the script runs, it creates an experiment named text classifier which is associated with the Text Example project.


Accuracy, learning rate, and training loss appear in RESULTS > SCALARS, along with the resource utilization plots, which are titled :monitor: machine.



ClearML automatically logs the command line options, because the example code uses argparse. A parameter dictionary is logged by connecting it to the Task using a call to the Task.connect method.

configuration_dict = {'number_of_epochs': 6, 'batch_size': 16, 'ngrams': 2, 'base_lr': 1.0}
configuration_dict = task.connect(configuration_dict)  # enabling configuration override by clearml

Command line options appear in CONFIGURATIONS > HYPER PARAMETERS > Args.


Parameter dictionaries appear in the General subsection.



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