Acme

AimCallback for Acme 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 Acme trainings. It takes few simple steps to integrate Aim into your training script.

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

from aimstack.experiment_tracker.acme import Writer as AimWriter, Callback as AimCallback

Step 2: Initialize an Aim Run via AimCallback, and create a log factory using the Run.

aim_run = AimCallback(experiment_name="test_experiment")
def logger_factory(
    name: str,
    steps_key: Optional[str] = None,
    task_id: Optional[int] = None,
) -> loggers.Logger:
    return AimWriter(aim_run, name, steps_key, task_id)

Step 3: Pass the logger factory to logger_factory upon initiating your training.

experiment_config = experiments.ExperimentConfig(
    builder=d4pg_builder,
    environment_factory=make_environment,
    network_factory=network_factory,
    logger_factory=logger_factory,
    seed=0,
    max_num_actor_steps=5000)

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