PaddlePaddle

AimCallback for PaddlePaddle 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 provides a built-in callback to easily track PaddlePaddle 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.paddle import Callback as AimCallback

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

callback = AimCallback(experiment="test_experiment")
model.fit(train_dataset, eval_dataset, batch_size=64, callbacks=callback)

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