Fastai

AimCallback for Fastai 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 fastai 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.fastai import Callback as AimCallback

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

learn = cnn_learner(dls, resnet18, pretrained=True,
                    loss_func=CrossEntropyLossFlat(),
                    metrics=accuracy, model_dir="/tmp/model/",
                    cbs=AimCallback(experiment="test_experiment"))

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