ray.tune.logger.aim.AimLoggerCallback
ray.tune.logger.aim.AimLoggerCallback#
- class ray.tune.logger.aim.AimLoggerCallback(repo: Optional[str] = None, experiment_name: Optional[str] = None, metrics: Optional[List[str]] = None, **aim_run_kwargs)[source]#
Bases:
ray.tune.logger.logger.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: https://github.com/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 byRunConfig(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
PublicAPI: This API is stable across Ray releases.
Methods
__init__
([repo, experiment_name, metrics])See help(AimLoggerCallback) for more information about parameters.
Get the state of the callback.
log_trial_restore
(trial)Handle logging when a trial restores.
log_trial_save
(trial)Handle logging when a trial saves a checkpoint.
on_checkpoint
(iteration, trials, trial, ...)Called after a trial saved a checkpoint with Tune.
on_experiment_end
(trials, **info)Called after experiment is over and all trials have concluded.
on_step_begin
(iteration, trials, **info)Called at the start of each tuning loop step.
on_step_end
(iteration, trials, **info)Called at the end of each tuning loop step.
set_state
(state)Set the state of the callback.
setup
([stop, num_samples, total_num_samples])Called once at the very beginning of training.
Attributes