# Source code for ray.tune.schedulers.hyperband

```
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import logging
from ray.tune.schedulers.trial_scheduler import FIFOScheduler, TrialScheduler
from ray.tune.trial import Trial
from ray.tune.error import TuneError
logger = logging.getLogger(__name__)
# Implementation notes:
# This implementation contains 3 logical levels.
# Each HyperBand iteration is a "band". There can be multiple
# bands running at once, and there can be 1 band that is incomplete.
#
# In each band, there are at most `s` + 1 brackets.
# `s` is a value determined by given parameters, and assigned on
# a cyclic basis.
#
# In each bracket, there are at most `n(s)` trials, indicating that
# `n` is a function of `s`. These trials go through a series of
# halving procedures, dropping lowest performers. Multiple
# brackets are running at once.
#
# Trials added will be inserted into the most recent bracket
# and band and will spill over to new brackets/bands accordingly.
#
# This maintains the bracket size and max trial count per band
# to 5 and 117 respectively, which correspond to that of
# `max_attr=81, eta=3` from the blog post. Trials will fill up
# from smallest bracket to largest, with largest
# having the most rounds of successive halving.
[docs]class HyperBandScheduler(FIFOScheduler):
"""Implements the HyperBand early stopping algorithm.
HyperBandScheduler early stops trials using the HyperBand optimization
algorithm. It divides trials into brackets of varying sizes, and
periodically early stops low-performing trials within each bracket.
To use this implementation of HyperBand with Tune, all you need
to do is specify the max length of time a trial can run `max_t`, the time
units `time_attr`, the name of the reported objective value `metric`,
and if `metric` is to be maximized or minimized (`mode`).
We automatically determine reasonable values for the other
HyperBand parameters based on the given values.
For example, to limit trials to 10 minutes and early stop based on the
`episode_mean_reward` attr, construct:
``HyperBand('time_total_s', 'episode_reward_mean', max_t=600)``
Note that Tune's stopping criteria will be applied in conjunction with
HyperBand's early stopping mechanisms.
See also: https://people.eecs.berkeley.edu/~kjamieson/hyperband.html
Args:
time_attr (str): 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 (str): The training result objective value attribute. Stopping
procedures will use this attribute.
mode (str): One of {min, max}. Determines whether objective is
minimizing or maximizing the metric attribute.
max_t (int): max time units per trial. Trials will be stopped after
max_t time units (determined by time_attr) have passed.
The scheduler will terminate trials after this time has passed.
Note that this is different from the semantics of `max_t` as
mentioned in the original HyperBand paper.
reduction_factor (float): Same as `eta`. Determines how sharp
the difference is between bracket space-time allocation ratios.
"""
def __init__(self,
time_attr="training_iteration",
reward_attr=None,
metric="episode_reward_mean",
mode="max",
max_t=81,
reduction_factor=3):
assert max_t > 0, "Max (time_attr) not valid!"
assert mode in ["min", "max"], "`mode` must be 'min' or 'max'!"
if reward_attr is not None:
mode = "max"
metric = reward_attr
logger.warning(
"`reward_attr` is deprecated and will be removed in a future "
"version of Tune. "
"Setting `metric={}` and `mode=max`.".format(reward_attr))
FIFOScheduler.__init__(self)
self._eta = reduction_factor
self._s_max_1 = int(
np.round(np.log(max_t) / np.log(reduction_factor))) + 1
self._max_t_attr = max_t
# bracket max trials
self._get_n0 = lambda s: int(
np.ceil(self._s_max_1 / (s + 1) * self._eta**s))
# bracket initial iterations
self._get_r0 = lambda s: int((max_t * self._eta**(-s)))
self._hyperbands = [[]] # list of hyperband iterations
self._trial_info = {} # Stores Trial -> Bracket, Band Iteration
# Tracks state for new trial add
self._state = {"bracket": None, "band_idx": 0}
self._num_stopped = 0
self._metric = metric
if mode == "max":
self._metric_op = 1.
elif mode == "min":
self._metric_op = -1.
self._time_attr = time_attr
[docs] def on_trial_add(self, trial_runner, trial):
"""Adds new trial.
On a new trial add, if current bracket is not filled,
add to current bracket. Else, if current band is not filled,
create new bracket, add to current bracket.
Else, create new iteration, create new bracket, add to bracket."""
cur_bracket = self._state["bracket"]
cur_band = self._hyperbands[self._state["band_idx"]]
if cur_bracket is None or cur_bracket.filled():
retry = True
while retry:
# if current iteration is filled, create new iteration
if self._cur_band_filled():
cur_band = []
self._hyperbands.append(cur_band)
self._state["band_idx"] += 1
# cur_band will always be less than s_max_1 or else filled
s = len(cur_band)
assert s < self._s_max_1, "Current band is filled!"
if self._get_r0(s) == 0:
logger.info("Bracket too small - Retrying...")
cur_bracket = None
else:
retry = False
cur_bracket = Bracket(self._time_attr, self._get_n0(s),
self._get_r0(s), self._max_t_attr,
self._eta, s)
cur_band.append(cur_bracket)
self._state["bracket"] = cur_bracket
self._state["bracket"].add_trial(trial)
self._trial_info[trial] = cur_bracket, self._state["band_idx"]
def _cur_band_filled(self):
"""Checks if the current band is filled.
The size of the current band should be equal to s_max_1"""
cur_band = self._hyperbands[self._state["band_idx"]]
return len(cur_band) == self._s_max_1
[docs] def on_trial_result(self, trial_runner, trial, result):
"""If bracket is finished, all trials will be stopped.
If a given trial finishes and bracket iteration is not done,
the trial will be paused and resources will be given up.
This scheduler will not start trials but will stop trials.
The current running trial will not be handled,
as the trialrunner will be given control to handle it."""
bracket, _ = self._trial_info[trial]
bracket.update_trial_stats(trial, result)
if bracket.continue_trial(trial):
return TrialScheduler.CONTINUE
action = self._process_bracket(trial_runner, bracket)
return action
def _process_bracket(self, trial_runner, bracket):
"""This is called whenever a trial makes progress.
When all live trials in the bracket have no more iterations left,
Trials will be successively halved. If bracket is done, all
non-running trials will be stopped and cleaned up,
and during each halving phase, bad trials will be stopped while good
trials will return to "PENDING"."""
action = TrialScheduler.PAUSE
if bracket.cur_iter_done():
if bracket.finished():
bracket.cleanup_full(trial_runner)
return TrialScheduler.STOP
good, bad = bracket.successive_halving(self._metric,
self._metric_op)
# kill bad trials
self._num_stopped += len(bad)
for t in bad:
if t.status == Trial.PAUSED:
trial_runner.stop_trial(t)
elif t.status == Trial.RUNNING:
bracket.cleanup_trial(t)
action = TrialScheduler.STOP
else:
raise TuneError("Trial with unexpected status encountered")
# ready the good trials - if trial is too far ahead, don't continue
for t in good:
if t.status not in [Trial.PAUSED, Trial.RUNNING]:
raise TuneError("Trial with unexpected status encountered")
if bracket.continue_trial(t):
if t.status == Trial.PAUSED:
self._unpause_trial(trial_runner, t)
elif t.status == Trial.RUNNING:
action = TrialScheduler.CONTINUE
return action
[docs] def on_trial_remove(self, trial_runner, trial):
"""Notification when trial terminates.
Trial info is removed from bracket. Triggers halving if bracket is
not finished."""
bracket, _ = self._trial_info[trial]
bracket.cleanup_trial(trial)
if not bracket.finished():
self._process_bracket(trial_runner, bracket)
[docs] def on_trial_complete(self, trial_runner, trial, result):
"""Cleans up trial info from bracket if trial completed early."""
self.on_trial_remove(trial_runner, trial)
[docs] def on_trial_error(self, trial_runner, trial):
"""Cleans up trial info from bracket if trial errored early."""
self.on_trial_remove(trial_runner, trial)
[docs] def choose_trial_to_run(self, trial_runner):
"""Fair scheduling within iteration by completion percentage.
List of trials not used since all trials are tracked as state
of scheduler. If iteration is occupied (ie, no trials to run),
then look into next iteration.
"""
for hyperband in self._hyperbands:
# band will have None entries if no resources
# are to be allocated to that bracket.
scrubbed = [b for b in hyperband if b is not None]
for bracket in sorted(
scrubbed, key=lambda b: b.completion_percentage()):
for trial in bracket.current_trials():
if (trial.status == Trial.PENDING
and trial_runner.has_resources(trial.resources)):
return trial
return None
[docs] def debug_string(self):
"""This provides a progress notification for the algorithm.
For each bracket, the algorithm will output a string as follows:
Bracket(Max Size (n)=5, Milestone (r)=33, completed=14.6%):
{PENDING: 2, RUNNING: 3, TERMINATED: 2}
"Max Size" indicates the max number of pending/running experiments
set according to the Hyperband algorithm.
"Milestone" indicates the iterations a trial will run for before
the next halving will occur.
"Completed" indicates an approximate progress metric. Some brackets,
like ones that are unfilled, will not reach 100%.
"""
out = "Using HyperBand: "
out += "num_stopped={} total_brackets={}".format(
self._num_stopped, sum(len(band) for band in self._hyperbands))
for i, band in enumerate(self._hyperbands):
out += "\nRound #{}:".format(i)
for bracket in band:
out += "\n {}".format(bracket)
return out
def state(self):
return {
"num_brackets": sum(len(band) for band in self._hyperbands),
"num_stopped": self._num_stopped
}
def _unpause_trial(self, trial_runner, trial):
trial_runner.trial_executor.unpause_trial(trial)
class Bracket():
"""Logical object for tracking Hyperband bracket progress. Keeps track
of proper parameters as designated by HyperBand.
Also keeps track of progress to ensure good scheduling.
"""
def __init__(self, time_attr, max_trials, init_t_attr, max_t_attr, eta, s):
self._live_trials = {} # maps trial -> current result
self._all_trials = []
self._time_attr = time_attr # attribute to
self._n = self._n0 = max_trials
self._r = self._r0 = init_t_attr
self._max_t_attr = max_t_attr
self._cumul_r = self._r0
self._eta = eta
self._halves = s
self._total_work = self._calculate_total_work(self._n0, self._r0, s)
self._completed_progress = 0
def add_trial(self, trial):
"""Add trial to bracket assuming bracket is not filled.
At a later iteration, a newly added trial will be given equal
opportunity to catch up."""
assert not self.filled(), "Cannot add trial to filled bracket!"
self._live_trials[trial] = None
self._all_trials.append(trial)
def cur_iter_done(self):
"""Checks if all iterations have completed.
TODO(rliaw): also check that `t.iterations == self._r`"""
return all(
self._get_result_time(result) >= self._cumul_r
for result in self._live_trials.values())
def finished(self):
return self._halves == 0 and self.cur_iter_done()
def current_trials(self):
return list(self._live_trials)
def continue_trial(self, trial):
result = self._live_trials[trial]
if self._get_result_time(result) < self._cumul_r:
return True
else:
return False
def filled(self):
"""Checks if bracket is filled.
Only let new trials be added at current level minimizing the need
to backtrack and bookkeep previous medians."""
return len(self._live_trials) == self._n
def successive_halving(self, metric, metric_op):
assert self._halves > 0
self._halves -= 1
self._n /= self._eta
self._n = int(np.ceil(self._n))
self._r *= self._eta
self._r = int(min(self._r, self._max_t_attr - self._cumul_r))
self._cumul_r = self._r
sorted_trials = sorted(
self._live_trials,
key=lambda t: metric_op * self._live_trials[t][metric])
good, bad = sorted_trials[-self._n:], sorted_trials[:-self._n]
return good, bad
def update_trial_stats(self, trial, result):
"""Update result for trial. Called after trial has finished
an iteration - will decrement iteration count.
TODO(rliaw): The other alternative is to keep the trials
in and make sure they're not set as pending later."""
assert trial in self._live_trials
assert self._get_result_time(result) >= 0
delta = self._get_result_time(result) - \
self._get_result_time(self._live_trials[trial])
assert delta >= 0
self._completed_progress += delta
self._live_trials[trial] = result
def cleanup_trial(self, trial):
"""Clean up statistics tracking for terminated trials (either by force
or otherwise).
This may cause bad trials to continue for a long time, in the case
where all the good trials finish early and there are only bad trials
left in a bracket with a large max-iteration."""
assert trial in self._live_trials
del self._live_trials[trial]
def cleanup_full(self, trial_runner):
"""Cleans up bracket after bracket is completely finished.
Lets the last trial continue to run until termination condition
kicks in."""
for trial in self.current_trials():
if (trial.status == Trial.PAUSED):
trial_runner.stop_trial(trial)
def completion_percentage(self):
"""Returns a progress metric.
This will not be always finish with 100 since dead trials
are dropped."""
if self.finished():
return 1.0
return self._completed_progress / self._total_work
def _get_result_time(self, result):
if result is None:
return 0
return result[self._time_attr]
def _calculate_total_work(self, n, r, s):
work = 0
cumulative_r = r
for i in range(s + 1):
work += int(n) * int(r)
n /= self._eta
n = int(np.ceil(n))
r *= self._eta
r = int(min(r, self._max_t_attr - cumulative_r))
return work
def __repr__(self):
status = ", ".join([
"Max Size (n)={}".format(self._n),
"Milestone (r)={}".format(self._cumul_r),
"completed={:.1%}".format(self.completion_percentage())
])
counts = collections.Counter([t.status for t in self._all_trials])
trial_statuses = ", ".join(
sorted("{}: {}".format(k, v) for k, v in counts.items()))
return "Bracket({}): {{{}}} ".format(status, trial_statuses)
```