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. Itβs 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 β 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 β 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 β 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 β 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.
- log_trial_start(trial: Trial)[source]#
Handle logging when a trial starts.
- Parameters
trial β Trial object.