MXNet

AimCallback for MXNet 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.

To track MXNet experiments use Aim callback designed for MXNet fit method. It takes two steps to integrate Aim into your training script.

Step 1: Import the AimLoggingHandler for tracking training metadata.

from aimstack.experiment_tracker.mxnet import LoggingHandler as AimLoggingHandler

Step 2: Pass a callback instance to event_handlers list upon initiating your training.

aim_log_handler = AimLoggingHandler(experiment_name="test_experiment",
                                    log_interval=1, metrics=[train_acc, train_loss, val_acc])

est.fit(train_data=train_data_loader, val_data=val_data_loader,
        epochs=num_epochs, event_handlers=[aim_log_handler])

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