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350 lines
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ReStructuredText
350 lines
12 KiB
ReStructuredText
.. _tune-search-alg:
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Search Algorithms (tune.suggest)
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================================
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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.
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You can utilize these search algorithms as follows:
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.. code-block:: python
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from ray.tune.suggest.hyperopt import HyperOptSearch
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tune.run(my_function, search_alg=HyperOptSearch(...))
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Summary
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-------
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.. list-table::
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:header-rows: 1
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* - SearchAlgorithm
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- Summary
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- Website
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- Code Example
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* - :ref:`Random search/grid search <tune-basicvariant>`
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- Random search/grid search
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-
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- :doc:`/tune/examples/tune_basic_example`
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* - :ref:`AxSearch <tune-ax>`
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- Bayesian/Bandit Optimization
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- [`Ax <https://ax.dev/>`__]
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- :doc:`/tune/examples/ax_example`
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* - :ref:`BlendSearch <BlendSearch>`
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- Blended Search
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- [`Bs <https://github.com/microsoft/FLAML/tree/main/flaml/tune>`__]
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- :doc:`/tune/examples/blendsearch_example`
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* - :ref:`CFO <CFO>`
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- Cost-Frugal hyperparameter Optimization
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- [`Cfo <https://github.com/microsoft/FLAML/tree/main/flaml/tune>`__]
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- :doc:`/tune/examples/cfo_example`
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* - :ref:`DragonflySearch <Dragonfly>`
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- Scalable Bayesian Optimization
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- [`Dragonfly <https://dragonfly-opt.readthedocs.io/>`__]
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- :doc:`/tune/examples/dragonfly_example`
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* - :ref:`SkoptSearch <skopt>`
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- Bayesian Optimization
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- [`Scikit-Optimize <https://scikit-optimize.github.io>`__]
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- :doc:`/tune/examples/skopt_example`
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* - :ref:`HyperOptSearch <tune-hyperopt>`
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- Tree-Parzen Estimators
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- [`HyperOpt <http://hyperopt.github.io/hyperopt>`__]
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- :doc:`/tune/examples/hyperopt_example`
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* - :ref:`BayesOptSearch <bayesopt>`
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- Bayesian Optimization
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- [`BayesianOptimization <https://github.com/fmfn/BayesianOptimization>`__]
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- :doc:`/tune/examples/bayesopt_example`
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* - :ref:`TuneBOHB <suggest-TuneBOHB>`
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- Bayesian Opt/HyperBand
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- [`BOHB <https://github.com/automl/HpBandSter>`__]
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- :doc:`/tune/examples/bohb_example`
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* - :ref:`NevergradSearch <nevergrad>`
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- Gradient-free Optimization
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- [`Nevergrad <https://github.com/facebookresearch/nevergrad>`__]
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- :doc:`/tune/examples/nevergrad_example`
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* - :ref:`OptunaSearch <tune-optuna>`
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- Optuna search algorithms
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- [`Optuna <https://optuna.org/>`__]
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- :doc:`/tune/examples/optuna_example`
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* - :ref:`ZOOptSearch <zoopt>`
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- Zeroth-order Optimization
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- [`ZOOpt <https://github.com/polixir/ZOOpt>`__]
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- :doc:`/tune/examples/zoopt_example`
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* - :ref:`SigOptSearch <sigopt>`
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- Closed source
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- [`SigOpt <https://sigopt.com/>`__]
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- :doc:`/tune/examples/sigopt_example`
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* - :ref:`HEBOSearch <tune-hebo>`
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- Heteroscedastic Evolutionary Bayesian Optimization
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- [`HEBO <https://github.com/huawei-noah/noah-research/tree/master/HEBO>`__]
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- :doc:`/tune/examples/hebo_example`
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.. note:: Unlike :ref:`Tune's Trial Schedulers <tune-schedulers>`, Tune SearchAlgorithms cannot affect or stop training processes. However, you can use them together to **early stop the evaluation of bad trials**.
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**Want to use your own algorithm?** The interface is easy to implement. :ref:`Read instructions here <byo-algo>`.
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Tune also provides helpful utilities to use with Search Algorithms:
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* :ref:`repeater`: Support for running each *sampled hyperparameter* with multiple random seeds.
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* :ref:`limiter`: Limits the amount of concurrent trials when running optimization.
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* :ref:`shim`: Allows creation of the search algorithm object given a string.
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Saving and Restoring
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--------------------
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Certain search algorithms have ``save/restore`` implemented,
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allowing reuse of learnings across multiple tuning runs.
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.. code-block:: python
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search_alg = HyperOptSearch()
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experiment_1 = tune.run(
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trainable,
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search_alg=search_alg)
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search_alg.save("./my-checkpoint.pkl")
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# Restore the saved state onto another search algorithm
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search_alg2 = HyperOptSearch()
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search_alg2.restore("./my-checkpoint.pkl")
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experiment_2 = tune.run(
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trainable,
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search_alg=search_alg2)
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Further, Tune automatically saves its state inside the current experiment folder ("Result Dir") during tuning.
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Note that if you have two Tune runs with the same experiment folder,
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the previous state checkpoint will be overwritten. You can
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avoid this by making sure ``tune.run(name=...)`` is set to a unique
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identifier.
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.. code-block:: python
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search_alg = HyperOptSearch()
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experiment_1 = tune.run(
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cost,
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num_samples=5,
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search_alg=search_alg,
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verbose=0,
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name="my-experiment-1",
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local_dir="~/my_results")
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search_alg2 = HyperOptSearch()
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search_alg2.restore_from_dir(
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os.path.join("~/my_results", "my-experiment-1"))
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.. note:: This is currently not implemented for: AxSearch, TuneBOHB, SigOptSearch, and DragonflySearch.
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.. _tune-basicvariant:
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Random search and grid search (tune.suggest.basic_variant.BasicVariantGenerator)
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--------------------------------------------------------------------------------
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The default and most basic way to do hyperparameter search is via random and grid search.
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Ray Tune does this through the :class:`BasicVariantGenerator <ray.tune.suggest.basic_variant.BasicVariantGenerator>`
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class that generates trial variants given a search space definition.
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The :class:`BasicVariantGenerator <ray.tune.suggest.basic_variant.BasicVariantGenerator>` is used per
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default if no search algorithm is passed to
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:func:`tune.run() <ray.tune.run>`.
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.. autoclass:: ray.tune.suggest.basic_variant.BasicVariantGenerator
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.. _tune-ax:
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Ax (tune.suggest.ax.AxSearch)
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-----------------------------
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.. autoclass:: ray.tune.suggest.ax.AxSearch
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.. _bayesopt:
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Bayesian Optimization (tune.suggest.bayesopt.BayesOptSearch)
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------------------------------------------------------------
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.. autoclass:: ray.tune.suggest.bayesopt.BayesOptSearch
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:members: save, restore
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.. _`BayesianOptimization search space specification`: https://github.com/fmfn/BayesianOptimization/blob/master/examples/advanced-tour.ipynb
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.. _suggest-TuneBOHB:
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BOHB (tune.suggest.bohb.TuneBOHB)
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---------------------------------
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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 <https://github.com/automl/HpBandSter>`_.
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Importantly, BOHB is intended to be paired with a specific scheduler class: :ref:`HyperBandForBOHB <tune-scheduler-bohb>`.
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This algorithm requires using the `ConfigSpace search space specification <https://automl.github.io/HpBandSter/build/html/quickstart.html#searchspace>`_. In order to use this search algorithm, you will need to install ``HpBandSter`` and ``ConfigSpace``:
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.. code-block:: bash
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$ pip install hpbandster ConfigSpace
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See the `BOHB paper <https://arxiv.org/abs/1807.01774>`_ for more details.
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.. autoclass:: ray.tune.suggest.bohb.TuneBOHB
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.. _BlendSearch:
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BlendSearch (tune.suggest.flaml.BlendSearch)
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--------------------------------------------
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BlendSearch is an economical hyperparameter optimization algorithm that combines combines local search with global search. It is backed by the `FLAML library <https://github.com/microsoft/FLAML>`_.
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It allows the users to specify a low-cost initial point as input if such point exists.
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In order to use this search algorithm, you will need to install ``flaml``:
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.. code-block:: bash
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$ pip install 'flaml[blendsearch]'
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See the `BlendSearch paper <https://openreview.net/pdf?id=VbLH04pRA3>`_ and documentation in FLAML `BlendSearch documentation <https://github.com/microsoft/FLAML/tree/main/flaml/tune>`_ for more details.
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.. autoclass:: ray.tune.suggest.flaml.BlendSearch
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.. _CFO:
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CFO (tune.suggest.flaml.CFO)
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----------------------------
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CFO (Cost-Frugal hyperparameter Optimization) is a hyperparameter search algorithm based on randomized local search. It is backed by the `FLAML library <https://github.com/microsoft/FLAML>`_.
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It allows the users to specify a low-cost initial point as input if such point exists.
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In order to use this search algorithm, you will need to install ``flaml``:
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.. code-block:: bash
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$ pip install flaml
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See the `CFO paper <https://arxiv.org/pdf/2005.01571.pdf>`_ and documentation in FLAML `CFO documentation <https://github.com/microsoft/FLAML/tree/main/flaml/tune>`_ for more details.
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.. autoclass:: ray.tune.suggest.flaml.CFO
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.. _Dragonfly:
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Dragonfly (tune.suggest.dragonfly.DragonflySearch)
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--------------------------------------------------
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.. autoclass:: ray.tune.suggest.dragonfly.DragonflySearch
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:members: save, restore
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.. _tune-hebo:
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HEBO (tune.suggest.hebo.HEBOSearch)
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-----------------------------------------------
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.. autoclass:: ray.tune.suggest.hebo.HEBOSearch
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:members: save, restore
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.. _tune-hyperopt:
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HyperOpt (tune.suggest.hyperopt.HyperOptSearch)
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-----------------------------------------------
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.. autoclass:: ray.tune.suggest.hyperopt.HyperOptSearch
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:members: save, restore
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.. _nevergrad:
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Nevergrad (tune.suggest.nevergrad.NevergradSearch)
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--------------------------------------------------
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.. autoclass:: ray.tune.suggest.nevergrad.NevergradSearch
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:members: save, restore
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.. _`Nevergrad README's Optimization section`: https://github.com/facebookresearch/nevergrad/blob/master/docs/optimization.rst#choosing-an-optimizer
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.. _tune-optuna:
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Optuna (tune.suggest.optuna.OptunaSearch)
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-----------------------------------------
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.. autoclass:: ray.tune.suggest.optuna.OptunaSearch
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.. _`Optuna samplers`: https://optuna.readthedocs.io/en/stable/reference/samplers.html
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.. _sigopt:
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SigOpt (tune.suggest.sigopt.SigOptSearch)
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-----------------------------------------
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You will need to use the `SigOpt experiment and space specification <https://app.sigopt.com/docs/overview/create>`__ to specify your search space.
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.. autoclass:: ray.tune.suggest.sigopt.SigOptSearch
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.. _skopt:
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Scikit-Optimize (tune.suggest.skopt.SkOptSearch)
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------------------------------------------------
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.. autoclass:: ray.tune.suggest.skopt.SkOptSearch
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:members: save, restore
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.. _`skopt Optimizer object`: https://scikit-optimize.github.io/#skopt.Optimizer
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.. _zoopt:
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ZOOpt (tune.suggest.zoopt.ZOOptSearch)
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--------------------------------------
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.. autoclass:: ray.tune.suggest.zoopt.ZOOptSearch
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:members: save, restore
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.. _repeater:
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Repeated Evaluations (tune.suggest.Repeater)
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--------------------------------------------
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Use ``ray.tune.suggest.Repeater`` to average over multiple evaluations of the same
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hyperparameter configurations. This is useful in cases where the evaluated
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training procedure has high variance (i.e., in reinforcement learning).
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By default, ``Repeater`` will take in a ``repeat`` parameter and a ``search_alg``.
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The ``search_alg`` will suggest new configurations to try, and the ``Repeater``
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will run ``repeat`` trials of the configuration. It will then average the
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``search_alg.metric`` from the final results of each repeated trial.
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.. warning:: It is recommended to not use ``Repeater`` with a TrialScheduler.
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Early termination can negatively affect the average reported metric.
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.. autoclass:: ray.tune.suggest.Repeater
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.. _limiter:
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ConcurrencyLimiter (tune.suggest.ConcurrencyLimiter)
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----------------------------------------------------
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Use ``ray.tune.suggest.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).
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.. autoclass:: ray.tune.suggest.ConcurrencyLimiter
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.. _byo-algo:
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Custom Search Algorithms (tune.suggest.Searcher)
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------------------------------------------------
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If you are interested in implementing or contributing a new Search Algorithm, provide the following interface:
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.. autoclass:: ray.tune.suggest.Searcher
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:members:
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:private-members:
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:show-inheritance:
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If contributing, make sure to add test cases and an entry in the function described below.
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.. _shim:
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Shim Instantiation (tune.create_searcher)
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-----------------------------------------
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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).
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.. automethod:: ray.tune.create_searcher
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