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139 lines
4.6 KiB
Python
139 lines
4.6 KiB
Python
from typing import Dict, Optional
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from ray.tune import trial_runner
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from ray.tune.result import DEFAULT_METRIC
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from ray.tune.trial import Trial
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class TrialScheduler:
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"""Interface for implementing a Trial Scheduler class."""
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CONTINUE = "CONTINUE" #: Status for continuing trial execution
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PAUSE = "PAUSE" #: Status for pausing trial execution
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STOP = "STOP" #: Status for stopping trial execution
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_metric = None
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@property
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def metric(self):
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return self._metric
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def set_search_properties(self, metric: Optional[str],
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mode: Optional[str]) -> bool:
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"""Pass search properties to scheduler.
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This method acts as an alternative to instantiating schedulers
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that react to metrics with their own `metric` and `mode` parameters.
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Args:
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metric (str): Metric to optimize
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mode (str): One of ["min", "max"]. Direction to optimize.
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"""
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if self._metric and metric:
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return False
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if metric:
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self._metric = metric
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if self._metric is None:
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# Per default, use anonymous metric
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self._metric = DEFAULT_METRIC
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return True
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def on_trial_add(self, trial_runner: "trial_runner.TrialRunner",
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trial: Trial):
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"""Called when a new trial is added to the trial runner."""
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raise NotImplementedError
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def on_trial_error(self, trial_runner: "trial_runner.TrialRunner",
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trial: Trial):
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"""Notification for the error of trial.
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This will only be called when the trial is in the RUNNING state."""
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raise NotImplementedError
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def on_trial_result(self, trial_runner: "trial_runner.TrialRunner",
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trial: Trial, result: Dict) -> str:
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"""Called on each intermediate result returned by a trial.
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At this point, the trial scheduler can make a decision by returning
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one of CONTINUE, PAUSE, and STOP. This will only be called when the
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trial is in the RUNNING state."""
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raise NotImplementedError
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def on_trial_complete(self, trial_runner: "trial_runner.TrialRunner",
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trial: Trial, result: Dict):
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"""Notification for the completion of trial.
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This will only be called when the trial is in the RUNNING state and
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either completes naturally or by manual termination."""
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raise NotImplementedError
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def on_trial_remove(self, trial_runner: "trial_runner.TrialRunner",
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trial: Trial):
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"""Called to remove trial.
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This is called when the trial is in PAUSED or PENDING state. Otherwise,
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call `on_trial_complete`."""
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raise NotImplementedError
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def choose_trial_to_run(
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self, trial_runner: "trial_runner.TrialRunner") -> Optional[Trial]:
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"""Called to choose a new trial to run.
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This should return one of the trials in trial_runner that is in
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the PENDING or PAUSED state. This function must be idempotent.
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If no trial is ready, return None."""
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raise NotImplementedError
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def debug_string(self) -> str:
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"""Returns a human readable message for printing to the console."""
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raise NotImplementedError
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class FIFOScheduler(TrialScheduler):
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"""Simple scheduler that just runs trials in submission order."""
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def on_trial_add(self, trial_runner: "trial_runner.TrialRunner",
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trial: Trial):
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pass
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def on_trial_error(self, trial_runner: "trial_runner.TrialRunner",
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trial: Trial):
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pass
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def on_trial_result(self, trial_runner: "trial_runner.TrialRunner",
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trial: Trial, result: Dict) -> str:
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return TrialScheduler.CONTINUE
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def on_trial_complete(self, trial_runner: "trial_runner.TrialRunner",
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trial: Trial, result: Dict):
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pass
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def on_trial_remove(self, trial_runner: "trial_runner.TrialRunner",
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trial: Trial):
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pass
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def choose_trial_to_run(
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self, trial_runner: "trial_runner.TrialRunner") -> Optional[Trial]:
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for trial in trial_runner.get_trials():
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if (trial.status == Trial.PENDING
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and trial_runner.has_resources(trial.resources)):
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return trial
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for trial in trial_runner.get_trials():
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if (trial.status == Trial.PAUSED
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and trial_runner.has_resources(trial.resources)):
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return trial
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return None
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def debug_string(self) -> str:
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return "Using FIFO scheduling algorithm."
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