Logging

A Lightweight Logger for Machine Learning Experiments

A Lightweight Logger for ML Experiments

Simple logging of statistics, model checkpoints, plots and other objects for your Machine Learning Experiments (MLE). Furthermore, the MLELogger comes with smooth multi-seed result aggregation and combination of multi-configuration runs.

The API 🎮

from mle_logging import MLELogger

# Instantiate logging to experiment_dir
log = MLELogger(time_to_track=['num_updates', 'num_epochs'],
                what_to_track=['train_loss', 'test_loss'],
                experiment_dir="experiment_dir/",
                model_type='torch')

time_tic = {'num_updates': 10, 'num_epochs': 1}
stats_tic = {'train_loss': 0.1234, 'test_loss': 0.1235}

# Update the log with collected data & save it to .hdf5
log.update(time_tic, stats_tic)
log.save()

You can also log model checkpoints, matplotlib figures and other .pkl compatible objects.

# Save a model (torch, tensorflow, sklearn, jax, numpy)

import torchvision.models as models

model = models.resnet18()

log.save_model(model)


# Save a matplotlib figure as .png

fig, ax = plt.subplots()

log.save_plot(fig)


 


 




 


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