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[tune] Tune Facelift (#2472)

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This PR introduces the following changes:

 * Ray Tune -> Tune 
 * [breaking] Creation of `schedulers/`, moving PBT, HyperBand into a submodule
 * [breaking] Search Algorithms now must take in experiment configurations via `add_configurations` rather through initialization
 * Support `"run": (function | class | str)` with automatic registering of trainable
 * Documentation Changes
This commit is contained in:
Richard Liaw
2018-08-19 11:00:55 -07:00
committed by GitHub
parent 78b6bfb7f9
commit 62d0698097
44 changed files with 1050 additions and 664 deletions
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python/ray/tune/trainable.py
+42 -33
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@@ -36,26 +36,23 @@ class Trainable(object):
Note that, if you don't require checkpoint/restore functionality, then
instead of implementing this class you can also get away with supplying
just a `my_train(config, reporter)` function and calling:
``register_trainable("my_func", train)``
to register it for use with Tune. The function will be automatically
converted to this interface (sans checkpoint functionality).
Attributes:
config (obj): The hyperparam configuration for this trial.
logdir (str): Directory in which training outputs should be placed.
just a ``my_train(config, reporter)`` function to the config.
The function will be automatically converted to this interface
(sans checkpoint functionality).
"""
def __init__(self, config=None, logger_creator=None):
"""Initialize an Trainable.
Sets up logging and points ``self.logdir`` to a directory in which
training outputs should be placed.
Subclasses should prefer defining ``_setup()`` instead of overriding
``__init__()`` directly.
Args:
config (dict): Trainable-specific configuration data.
config (dict): Trainable-specific configuration data. By default
will be saved as ``self.config``.
logger_creator (func): Function that creates a ray.tune.Logger
object. If unspecified, a default logger is created.
"""
@@ -102,28 +99,36 @@ class Trainable(object):
"""Runs one logical iteration of training.
Subclasses should override ``_train()`` instead to return results.
This class automatically fills the following fields in the result:
done (bool): training is terminated. Filled only if not provided.
time_this_iter_s (float): Time in seconds
this iteration took to run. This may be overriden in order to
override the system-computed time difference.
time_total_s (float): Accumulated time in seconds
for this entire experiment.
experiment_id (str): Unique string identifier
for this experiment. This id is preserved
across checkpoint / restore calls.
training_iteration (int): The index of this
training iteration, e.g. call to train().
pid (str): The pid of the training process.
date (str): A formatted date of
when the result was processed.
timestamp (str): A UNIX timestamp of
when the result was processed.
hostname (str): The hostname of the machine
hosting the training process.
node_ip (str): The node ip of the machine
hosting the training process.
`done` (bool): training is terminated. Filled only if not provided.
`time_this_iter_s` (float): Time in seconds this iteration
took to run. This may be overriden in order to override the
system-computed time difference.
`time_total_s` (float): Accumulated time in seconds for this
entire experiment.
`experiment_id` (str): Unique string identifier
for this experiment. This id is preserved
across checkpoint / restore calls.
`training_iteration` (int): The index of this
training iteration, e.g. call to train().
`pid` (str): The pid of the training process.
`date` (str): A formatted date of when the result was processed.
`timestamp` (str): A UNIX timestamp of when the result
was processed.
`hostname` (str): Hostname of the machine hosting the training
process.
`node_ip` (str): Node ip of the machine hosting the training
process.
Returns:
A dict that describes training progress.
@@ -283,7 +288,11 @@ class Trainable(object):
raise NotImplementedError
def _setup(self):
"""Subclasses should override this for custom initialization."""
"""Subclasses should override this for custom initialization.
Subclasses can access the hyperparameter configuration via
``self.config``.
"""
pass
def _stop(self):