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[tune] Put examples under proper version control (#9427)
Co-authored-by: krfricke <krfricke@users.noreply.github.com>
This commit is contained in:
Richard Liaw
@@ -58,7 +58,7 @@ You can then pass in your own logger as follows:
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These loggers will be called along with the default Tune loggers. You can also check out `logger.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/logger.py>`__ for implementation details.
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An example of creating a custom logger can be found in `logging_example.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/logging_example.py>`__.
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An example of creating a custom logger can be found in :doc:`/tune/examples/logging_example`.
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.. _trainable-logging:
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@@ -164,7 +164,7 @@ CSVLogger
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MLFLowLogger
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------------
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Tune also provides a default logger for `MLFlow <https://mlflow.org>`_. You can install MLFlow via ``pip install mlflow``. An example can be found `mlflow_example.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/mlflow_example.py>`__. Note that this currently does not include artifact logging support. For this, you can use the native MLFlow APIs inside your Trainable definition.
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Tune also provides a default logger for `MLFlow <https://mlflow.org>`_. You can install MLFlow via ``pip install mlflow``. An example can be found in :doc:`/tune/examples/mlflow_example`. Note that this currently does not include artifact logging support. For this, you can use the native MLFlow APIs inside your Trainable definition.
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.. autoclass:: ray.tune.logger.MLFLowLogger
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@@ -32,7 +32,7 @@ When using schedulers, you may face compatibility issues, as shown in the below
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* - :ref:`ASHA <tune-scheduler-hyperband>`
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- No
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- Yes
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- `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/async_hyperband_example.py>`__
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- :doc:`Link </tune/examples/async_hyperband_example>`
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* - :ref:`Median Stopping Rule <tune-scheduler-msr>`
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- No
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- Yes
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@@ -40,15 +40,15 @@ When using schedulers, you may face compatibility issues, as shown in the below
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* - :ref:`HyperBand <tune-original-hyperband>`
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- Yes
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- Yes
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- `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/hyperband_example.py>`__
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- :doc:`Link </tune/examples/hyperband_example>`
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* - :ref:`BOHB <tune-scheduler-bohb>`
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- Yes
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- Only TuneBOHB
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- `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/bohb_example.py>`__
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- :doc:`Link </tune/examples/bohb_example>`
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* - :ref:`Population Based Training <tune-scheduler-pbt>`
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- Yes
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- Not Compatible
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- `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_example.py>`__
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- :doc:`Link </tune/examples/pbt_example>`
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.. _tune-scheduler-hyperband:
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@@ -69,7 +69,7 @@ The `ASHA <https://openreview.net/forum?id=S1Y7OOlRZ>`__ scheduler can be used b
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brackets=1)
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tune.run( ... , scheduler=asha_scheduler)
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Compared to the original version of HyperBand, this implementation provides better parallelism and avoids straggler issues during eliminations. **We recommend using this over the standard HyperBand scheduler.** An example of this can be `found here <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/async_hyperband_example.py>`_.
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Compared to the original version of HyperBand, this implementation provides better parallelism and avoids straggler issues during eliminations. **We recommend using this over the standard HyperBand scheduler.** An example of this can be found here: :doc:`/tune/examples/async_hyperband_example`.
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Even though the original paper mentions a bracket count of 3, discussions with the authors concluded that the value should be left to 1 bracket. This is the default used if no value is provided for the ``brackets`` argument.
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@@ -141,7 +141,7 @@ Tune includes a distributed implementation of `Population Based Training (PBT) <
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When the PBT scheduler is enabled, each trial variant is treated as a member of the population. Periodically, top-performing trials are checkpointed (this requires your Trainable to support :ref:`save and restore <tune-checkpoint>`). Low-performing trials clone the checkpoints of top performers and perturb the configurations in the hope of discovering an even better variation.
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You can run this `toy PBT example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_example.py>`__ to get an idea of how how PBT operates. When training in PBT mode, a single trial may see many different hyperparameters over its lifetime, which is recorded in its ``result.json`` file. The following figure generated by the example shows PBT with optimizing a LR schedule over the course of a single experiment:
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You can run this :doc:`toy PBT example </tune/examples/pbt_function>` to get an idea of how how PBT operates. When training in PBT mode, a single trial may see many different hyperparameters over its lifetime, which is recorded in its ``result.json`` file. The following figure generated by the example shows PBT with optimizing a LR schedule over the course of a single experiment:
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.. image:: /pbt.png
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@@ -157,7 +157,7 @@ This class is a variant of HyperBand that enables the `BOHB Algorithm <https://a
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This is to be used in conjunction with the Tune BOHB search algorithm. See :ref:`TuneBOHB <suggest-TuneBOHB>` for package requirements, examples, and details.
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An example of this in use can be found in `bohb_example.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/bohb_example.py>`_.
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An example of this in use can be found here: :doc:`/tune/examples/bohb_example`.
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.. autoclass:: ray.tune.schedulers.HyperBandForBOHB
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@@ -25,39 +25,39 @@ Summary
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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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- `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/ax_example.py>`__
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- :doc:`/tune/examples/ax_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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- `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/dragonfly_example.py>`__
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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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- `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/skopt_example.py>`__
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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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- `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/hyperopt_example.py>`__
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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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- `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/bayesopt_example.py>`__
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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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- `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/bohb_example.py>`__
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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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- `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/nevergrad_example.py>`__
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- :doc:`/tune/examples/nevergrad_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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- `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/zoopt_example.py>`__
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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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- `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/sigopt_example.py>`__
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- :doc:`/tune/examples/sigopt_example`
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.. note::Search algorithms will require a different search space declaration than the default Tune format - meaning that you will not be able to combine ``tune.grid_search`` with the below integrations.
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