CatBoost#
AimCallback for CatBoost is designed to enhance your experiment logging and monitoring. It thoroughly records essential information, including hyperparameters, training, validation, and test time metrics like loss and accuracy. Moreover, it offers comprehensive system usage tracking, keeping an eye on CPU and GPU memory utilization.
It only takes two steps to integrate Aim with CatBoost.
Step 1: Import AimLogger to track the training metadata.
# call SDK aim.catboost
from aimstack.experiment_tracker.catboost import Logger as AimLogger
Step 2: Pass the logger to the trainer.
Trainings in CatBoost are initiated with fit method. The method can be supplied with log_cout parameter to redirect output logs into a custom handler. Pass AimLogger to automatically track metrics and hyper-parameters with Aim. Depending on the training log output, an additional argument logging_level could be passed to make Catboost yield more logs to track test & best values.
model.fit(train_data, train_labels, log_cout=AimLogger(loss_function='Logloss'), logging_level='Info')
AimLogger also accepts log_cout parameter to preserve the default functionality of Catboost’s log handling. You can pass your own handler, else it defaults to sys.stdout.