Tune's Search Algorithms are wrappers around open-source optimization libraries for efficient hyperparameter selection.
Each library has a specific way of defining the search space - please refer to their documentation for more details.
Tune will automatically convert search spaces passed to Tuner to the library format in most cases.
You can utilize these search algorithms as follows:
from ray import train, tune
from ray.train import RunConfig
from ray.tune.search.optuna import OptunaSearch
def train_fn(config):
# This objective function is just for demonstration purposes
train.report({"loss": config["param"]})
tuner = tune.Tuner(
train_fn,
tune_config=tune.TuneConfig(
search_alg=OptunaSearch(),
num_samples=100,
metric="loss",
mode="min",
),
param_space={"param": tune.uniform(0, 1)},
)
results = tuner.fit()Certain search algorithms have save/restore implemented,
allowing reuse of searchers that are fitted on the results of multiple tuning runs.
search_alg = HyperOptSearch()
tuner_1 = tune.Tuner(
train_fn,
tune_config=tune.TuneConfig(search_alg=search_alg)
)
results_1 = tuner_1.fit()
search_alg.save("./my-checkpoint.pkl")
# Restore the saved state onto another search algorithm,
# in a new tuning script
search_alg2 = HyperOptSearch()
search_alg2.restore("./my-checkpoint.pkl")
tuner_2 = tune.Tuner(
train_fn,
tune_config=tune.TuneConfig(search_alg=search_alg2)
)
results_2 = tuner_2.fit()Tune automatically saves searcher state inside the current experiment folder during tuning.
See Result logdir: ... in the output logs for this location.
Note that if you have two Tune runs with the same experiment folder,
the previous state checkpoint will be overwritten. You can
avoid this by making sure RunConfig(name=...) is set to a unique
identifier:
search_alg = HyperOptSearch()
tuner_1 = tune.Tuner(
train_fn,
tune_config=tune.TuneConfig(
num_samples=5,
search_alg=search_alg,
),
run_config=RunConfig(
name="my-experiment-1",
storage_path="~/my_results",
)
)
results = tuner_1.fit()
search_alg2 = HyperOptSearch()
search_alg2.restore_from_dir(
os.path.join("~/my_results", "my-experiment-1")
)The default and most basic way to do hyperparameter search is via random and grid search. Ray Tune does this through the :class:`BasicVariantGenerator <ray.tune.search.basic_variant.BasicVariantGenerator>` class that generates trial variants given a search space definition.
The :class:`BasicVariantGenerator <ray.tune.search.basic_variant.BasicVariantGenerator>` is used per default if no search algorithm is passed to :func:`Tuner <ray.tune.Tuner>`.
.. currentmodule:: ray.tune.search
.. autosummary::
:nosignatures:
:toctree: doc/
basic_variant.BasicVariantGenerator
.. autosummary::
:nosignatures:
:toctree: doc/
ax.AxSearch
.. autosummary::
:nosignatures:
:toctree: doc/
bayesopt.BayesOptSearch
BOHB (Bayesian Optimization HyperBand) is an algorithm that both terminates bad trials and also uses Bayesian Optimization to improve the hyperparameter search. It is available from the HpBandSter library.
Importantly, BOHB is intended to be paired with a specific scheduler class: :ref:`HyperBandForBOHB <tune-scheduler-bohb>`.
In order to use this search algorithm, you will need to install HpBandSter and ConfigSpace:
$ pip install hpbandster ConfigSpaceSee the BOHB paper for more details.
.. autosummary::
:nosignatures:
:toctree: doc/
bohb.TuneBOHB
.. autosummary::
:nosignatures:
:toctree: doc/
hebo.HEBOSearch
.. autosummary::
:nosignatures:
:toctree: doc/
hyperopt.HyperOptSearch
.. autosummary::
:nosignatures:
:toctree: doc/
nevergrad.NevergradSearch
.. autosummary::
:nosignatures:
:toctree: doc/
optuna.OptunaSearch
.. autosummary::
:nosignatures:
:toctree: doc/
zoopt.ZOOptSearch
Use ray.tune.search.Repeater to average over multiple evaluations of the same
hyperparameter configurations. This is useful in cases where the evaluated
training procedure has high variance (i.e., in reinforcement learning).
By default, Repeater will take in a repeat parameter and a search_alg.
The search_alg will suggest new configurations to try, and the Repeater
will run repeat trials of the configuration. It will then average the
search_alg.metric from the final results of each repeated trial.
Warning
It is recommended to not use Repeater with a TrialScheduler.
Early termination can negatively affect the average reported metric.
.. autosummary::
:nosignatures:
:toctree: doc/
Repeater
Use ray.tune.search.ConcurrencyLimiter to limit the amount of concurrency when using a search algorithm.
This is useful when a given optimization algorithm does not parallelize very well (like a naive Bayesian Optimization).
.. autosummary::
:nosignatures:
:toctree: doc/
ConcurrencyLimiter
If you are interested in implementing or contributing a new Search Algorithm, provide the following interface:
.. autosummary::
:nosignatures:
:toctree: doc/
Searcher
.. autosummary::
:nosignatures:
:toctree: doc/
Searcher.suggest
Searcher.save
Searcher.restore
Searcher.on_trial_result
Searcher.on_trial_complete
If contributing, make sure to add test cases and an entry in the function described below.
There is also a shim function that constructs the search algorithm based on the provided string. This can be useful if the search algorithm you want to use changes often (e.g., specifying the search algorithm via a CLI option or config file).
.. autosummary::
:nosignatures:
:toctree: doc/
create_searcher