mirror of
https://github.com/wassname/ray.git
synced 2026-06-27 20:53:14 +08:00
Kai Fricke
120 lines
4.0 KiB
Python
120 lines
4.0 KiB
Python
import itertools
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import os
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import random
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import uuid
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from typing import Dict, List, Union
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from ray.tune.error import TuneError
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from ray.tune.experiment import Experiment, convert_to_experiment_list
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from ray.tune.config_parser import make_parser, create_trial_from_spec
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from ray.tune.suggest.variant_generator import (generate_variants, format_vars,
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flatten_resolved_vars)
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from ray.tune.suggest.search import SearchAlgorithm
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class BasicVariantGenerator(SearchAlgorithm):
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"""Uses Tune's variant generation for resolving variables.
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See also: `ray.tune.suggest.variant_generator`.
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Parameters:
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shuffle (bool): Shuffles the generated list of configurations.
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User API:
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.. code-block:: python
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from ray import tune
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from ray.tune.suggest import BasicVariantGenerator
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searcher = BasicVariantGenerator()
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tune.run(my_trainable_func, algo=searcher)
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Internal API:
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.. code-block:: python
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from ray.tune.suggest import BasicVariantGenerator
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searcher = BasicVariantGenerator()
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searcher.add_configurations({"experiment": { ... }})
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list_of_trials = searcher.next_trials()
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searcher.is_finished == True
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"""
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def __init__(self, shuffle: bool = False):
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"""Initializes the Variant Generator.
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"""
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self._parser = make_parser()
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self._trial_generator = []
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self._counter = 0
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self._finished = False
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self._shuffle = shuffle
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# Unique prefix for all trials generated, e.g., trial ids start as
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# 2f1e_00001, 2f1ef_00002, 2f1ef_0003, etc. Overridable for testing.
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force_test_uuid = os.environ.get("_TEST_TUNE_TRIAL_UUID")
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if force_test_uuid:
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self._uuid_prefix = force_test_uuid + "_"
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else:
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self._uuid_prefix = str(uuid.uuid1().hex)[:5] + "_"
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def add_configurations(
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self,
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experiments: Union[Experiment, List[Experiment], Dict[str, Dict]]):
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"""Chains generator given experiment specifications.
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Arguments:
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experiments (Experiment | list | dict): Experiments to run.
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"""
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experiment_list = convert_to_experiment_list(experiments)
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for experiment in experiment_list:
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self._trial_generator = itertools.chain(
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self._trial_generator,
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self._generate_trials(
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experiment.spec.get("num_samples", 1), experiment.spec,
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experiment.name))
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def next_trials(self):
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"""Provides Trial objects to be queued into the TrialRunner.
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Returns:
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trials (list): Returns a list of trials.
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"""
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trials = list(self._trial_generator)
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if self._shuffle:
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random.shuffle(trials)
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self.set_finished()
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return trials
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def _generate_trials(self, num_samples, unresolved_spec, output_path=""):
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"""Generates Trial objects with the variant generation process.
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Uses a fixed point iteration to resolve variants. All trials
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should be able to be generated at once.
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See also: `ray.tune.suggest.variant_generator`.
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Yields:
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Trial object
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"""
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if "run" not in unresolved_spec:
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raise TuneError("Must specify `run` in {}".format(unresolved_spec))
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for _ in range(num_samples):
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for resolved_vars, spec in generate_variants(unresolved_spec):
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trial_id = self._uuid_prefix + ("%05d" % self._counter)
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experiment_tag = str(self._counter)
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if resolved_vars:
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experiment_tag += "_{}".format(format_vars(resolved_vars))
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self._counter += 1
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yield create_trial_from_spec(
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spec,
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output_path,
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self._parser,
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evaluated_params=flatten_resolved_vars(resolved_vars),
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trial_id=trial_id,
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experiment_tag=experiment_tag)
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