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 […]
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