ray.tune.schedulers.MedianStoppingRule#

class ray.tune.schedulers.MedianStoppingRule(time_attr: str = 'time_total_s', metric: str | None = None, mode: str | None = None, grace_period: float = 60.0, min_samples_required: int = 3, min_time_slice: int = 0, hard_stop: bool = True)[source]#

Bases: FIFOScheduler

Implements the median stopping rule as described in the Vizier paper:

https://research.google.com/pubs/pub46180.html

Parameters:

time_attr – The training result attr to use for comparing time. Note that you can pass in something non-temporal such as training_iteration as a measure of progress, the only requirement is that the attribute should increase monotonically.
metric – The training result objective value attribute. Stopping procedures will use this attribute. If None but a mode was passed, the ray.tune.result.DEFAULT_METRIC will be used per default.
mode – One of {min, max}. Determines whether objective is minimizing or maximizing the metric attribute.
grace_period – Only stop trials at least this old in time. The mean will only be computed from this time onwards. The units are the same as the attribute named by time_attr.
min_samples_required – Minimum number of trials to compute median over.
min_time_slice – Each trial runs at least this long before yielding (assuming it isn’t stopped). Note: trials ONLY yield if there are not enough samples to evaluate performance for the current result AND there are other trials waiting to run. The units are the same as the attribute named by time_attr.
hard_stop – If False, pauses trials instead of stopping them. When all other trials are complete, paused trials will be resumed and allowed to run FIFO.

Methods

`on_trial_result`	Callback for early stopping.
`restore`	Restore trial scheduler from checkpoint.
`save`	Save trial scheduler to a checkpoint

Attributes

`CONTINUE`	Status for continuing trial execution
`NOOP`
`PAUSE`	Status for pausing trial execution
`STOP`	Status for stopping trial execution
`metric`
`supports_buffered_results`