ray.tune.logger.aim.AimLoggerCallback#

class ray.tune.logger.aim.AimLoggerCallback(repo: str | None = None, experiment_name: str | None = None, metrics: List[str] | None = None, **aim_run_kwargs)[source]#

Bases: LoggerCallback

Aim Logger: logs metrics in Aim format.

Aim is an open-source, self-hosted ML experiment tracking tool. It’s good at tracking lots (thousands) of training runs, and it allows you to compare them with a performant and well-designed UI.

Source: aimhubio/aim

Parameters:

repo – Aim repository directory or a Repo object that the Run object will log results to. If not provided, a default repo will be set up in the experiment directory (one level above trial directories).
experiment – Sets the experiment property of each Run object, which is the experiment name associated with it. Can be used later to query runs/sequences. If not provided, the default will be the Tune experiment name set by RunConfig(name=...).
metrics – List of metric names (out of the metrics reported by Tune) to track in Aim. If no metric are specified, log everything that is reported.
aim_run_kwargs – Additional arguments that will be passed when creating the individual Run objects for each trial. For the full list of arguments, please see the Aim documentation: https://aimstack.readthedocs.io/en/latest/refs/sdk.html

Methods

`__init__`	See help(AimLoggerCallback) for more information about parameters.
`get_state`	Get the state of the callback.
`log_trial_restore`	Handle logging when a trial restores.
`log_trial_save`	Handle logging when a trial saves a checkpoint.
`on_checkpoint`	Called after a trial saved a checkpoint with Tune.
`on_experiment_end`	Called after experiment is over and all trials have concluded.
`on_step_begin`	Called at the start of each tuning loop step.
`on_step_end`	Called at the end of each tuning loop step.
`on_trial_recover`	Called after a trial instance failed (errored) but the trial is scheduled for retry.
`set_state`	Set the state of the callback.
`setup`	Called once at the very beginning of training.

Attributes

`VALID_HPARAMS`
`VALID_NP_HPARAMS`