LightGBM

AimCallback for LightGBM 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.

Aim comes with a built-in callback designed to automatically track LightGBM trainings. It takes two steps to integrate Aim into your training script.

Step 1: Explicitly import the AimCallback for tracking training metadata.

from aimstack.experiment_tracker.lightgbm import Callback as AimCallback

Step 2: Pass the callback to callbacks list upon initiating your training.

gbm = lgb.train(params,
                lgb_train,
                num_boost_round=20,
                valid_sets=lgb_eval,
                callbacks=[AimCallback(experiment="test_experiment")])

While your training is running you can start aim up in another terminal session and observe the information in real time.

See AimCallback source here. Check out a simple example here.