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[tune] Add command line support for choosing early stopping schedulers (#1209)

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* command line support

* add checkpoint freq

* fix other flags

* fix

* docs

* doc
This commit is contained in:
Eric Liang
2017-11-12 12:05:18 -08:00
committed by Richard Liaw
parent afdc87323f
commit 7c38f964b7
11 changed files with 218 additions and 101 deletions
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python/ray/tune/trial_scheduler.py
+4 -85
View File
@@ -1,9 +1,6 @@
from __future__ import absolute_import
from __future__ import division
import collections
import numpy as np
from ray.tune.trial import Trial
@@ -73,89 +70,11 @@ class FIFOScheduler(TrialScheduler):
if (trial.status == Trial.PENDING and
trial_runner.has_resources(trial.resources)):
return trial
for trial in trial_runner.get_trials():
if (trial.status == Trial.PAUSED and
trial_runner.has_resources(trial.resources)):
return trial
return None
def debug_string(self):
return "Using FIFO scheduling algorithm."
# TODO(ekl) expose this in the command line API
class MedianStoppingRule(FIFOScheduler):
"""Implements the median stopping rule as described in the Vizier paper:
https://research.google.com/pubs/pub46180.html
Args:
time_attr (str): The TrainingResult 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.
reward_attr (str): The TrainingResult objective value attribute. As
with `time_attr`, this may refer to any objective value that
is supposed to increase with time.
grace_period (float): Only stop trials at least this old in time.
The units are the same as the attribute named by `time_attr`.
min_samples_required (int): Min samples to compute median over.
"""
def __init__(
self, time_attr='time_total_s', reward_attr='episode_reward_mean',
grace_period=60.0, min_samples_required=3):
FIFOScheduler.__init__(self)
self._completed_trials = set()
self._results = collections.defaultdict(list)
self._grace_period = grace_period
self._min_samples_required = min_samples_required
self._reward_attr = reward_attr
self._time_attr = time_attr
self._num_stopped = 0
def on_trial_result(self, trial_runner, trial, result):
"""Callback for early stopping.
This stopping rule stops a running trial if the trial's best objective
value by step `t` is strictly worse than the median of the running
averages of all completed trials' objectives reported up to step `t`.
"""
time = getattr(result, self._time_attr)
self._results[trial].append(result)
median_result = self._get_median_result(time)
best_result = self._best_result(trial)
print("Trial {} best res={} vs median res={} at t={}".format(
trial, best_result, median_result, time))
if best_result < median_result and time > self._grace_period:
print("MedianStoppingRule: early stopping {}".format(trial))
self._num_stopped += 1
return TrialScheduler.STOP
else:
return TrialScheduler.CONTINUE
def on_trial_complete(self, trial_runner, trial, result):
self._results[trial].append(result)
self._completed_trials.add(trial)
def debug_string(self):
return "Using MedianStoppingRule: num_stopped={}.".format(
self._num_stopped)
def _get_median_result(self, time):
scores = []
for trial in self._completed_trials:
scores.append(self._running_result(trial, time))
if len(scores) >= self._min_samples_required:
return np.median(scores)
else:
return float('-inf')
def _running_result(self, trial, t_max=float('inf')):
results = self._results[trial]
# TODO(ekl) we could do interpolation to be more precise, but for now
# assume len(results) is large and the time diffs are roughly equal
return np.mean(
[getattr(r, self._reward_attr)
for r in results if getattr(r, self._time_attr) <= t_max])
def _best_result(self, trial):
results = self._results[trial]
return max([getattr(r, self._reward_attr) for r in results])