Skip to main content

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 scalars and text samples reported with TensorBoard methods. The example code explicitly logs parameters to the Task. When the script runs, it creates an experiment named text classifier in the Text Example project.

Scalars​

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

Scalars

Debug Samples​

ClearML automatically logs the text samples reported to TensorBoard. They are displayed in the experiment's DEBUG SAMPLES.

Debug samples

Hyperparameters​

A parameter dictionary is logged by connecting it to the Task using Task.connect():

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

The parameters are displayed in the experiment's CONFIGURATION > HYPERPARAMETERS > General section.

Hyperparameters

Console​

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

Console