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[tune] Documentation for Ray.tune (#1243)

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Eric Liang
2017-11-23 11:31:59 -08:00
committed by Richard Liaw
parent 477a40f76d
commit ddfe00b7e8
11 changed files with 221 additions and 208 deletions
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python/ray/tune/hyperband.py
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@@ -8,32 +8,47 @@ from ray.tune.trial_scheduler import FIFOScheduler, TrialScheduler
from ray.tune.trial import Trial
# 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.
class HyperBandScheduler(FIFOScheduler):
"""Implements HyperBand.
"""Implements the HyperBand early stopping algorithm.
Blog post: https://people.eecs.berkeley.edu/~kjamieson/hyperband.html
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.
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.
To use this implementation of HyperBand with Ray.tune, all you need
to do is specify the max length of time a trial can run `max_t`, the time
units `time_attr`, and the name of the reported objective value
`reward_attr`. We automatically determine reasonable values for the other
HyperBand parameters based on the given values.
In each band, there are at most `s` + 1 brackets.
`s` is a value determined by given parameters, and assigned on
a cyclic basis.
For example, to limit trials to 10 minutes and early stop based on the
`episode_mean_reward` attr, construct:
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.
``HyperBand('time_total_s', 'episode_reward_mean', 600)``
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.
See also: https://people.eecs.berkeley.edu/~kjamieson/hyperband.html
Args:
time_attr (str): The TrainingResult attr to use for comparing time.
@@ -46,7 +61,7 @@ class HyperBandScheduler(FIFOScheduler):
max_t (int): max time units per trial. Trials will be stopped after
max_t time units (determined by time_attr) have passed.
The HyperBand scheduler automatically tries to determine a
reasonable number of brackets based on this and eta.
reasonable number of brackets based on this.
"""
def __init__(