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[tune] Fix links (#8477)

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This commit is contained in:
Richard Liaw
2020-05-18 10:08:29 -07:00
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parent 9a721ed71a
commit b6c4f45ae0
2 changed files with 3 additions and 6 deletions
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doc/source/tune.rst
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@@ -11,14 +11,11 @@ Tune is a Python library for experiment execution and hyperparameter tuning at a
* Launch a multi-node :ref:`distributed hyperparameter sweep <tune-distributed>` in less than 10 lines of code.
* Supports any machine learning framework, :ref:`including PyTorch, XGBoost, MXNet, and Keras<tune-guides-overview>`.
* Natively `integrates with optimization libraries <tune-searchalg.html>`_ such as `HyperOpt <https://github.com/hyperopt/hyperopt>`_, `Bayesian Optimization <https://github.com/fmfn/BayesianOptimization>`_, and `Facebook Ax <http://ax.dev>`_.
* Choose among `scalable algorithms <tune-schedulers.html>`_ such as `Population Based Training (PBT)`_, `Vizier's Median Stopping Rule`_, `HyperBand/ASHA`_.
* Natively :ref:`integrates with optimization libraries <tune-search-alg>` such as `HyperOpt <https://github.com/hyperopt/hyperopt>`_, `Bayesian Optimization <https://github.com/fmfn/BayesianOptimization>`_, and `Facebook Ax <http://ax.dev>`_.
* Choose among :ref:`scalable algorithms <tune-schedulers>` such as :ref:`Population Based Training (PBT) <tune-scheduler-pbt>`, :ref:`Vizier's Median Stopping Rule <tune-scheduler-msr>`, :ref:`HyperBand/ASHA <tune-scheduler-hyperband>`.
* Visualize results with `TensorBoard <https://www.tensorflow.org/get_started/summaries_and_tensorboard>`__.
* Move your models from training to serving on the same infrastructure with `Ray Serve`_.
.. _`Population Based Training (PBT)`: tune-schedulers.html#population-based-training-pbt
.. _`Vizier's Median Stopping Rule`: tune-schedulers.html#median-stopping-rule
.. _`HyperBand/ASHA`: tune-schedulers.html#asynchronous-hyperband
.. _`Ray Serve`: rayserve/overview.html
**Want to get started?** Head over to the :ref:`60 second Tune tutorial <tune-60-seconds>`.
doc/source/tune/api_docs/suggestion.rst
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@@ -97,7 +97,7 @@ BOHB (tune.suggest.bohb.TuneBOHB)
BOHB (Bayesian Optimization HyperBand) is an algorithm that both terminates bad trials and also uses Bayesian Optimization to improve the hyperparameter search. It is backed by the `HpBandSter library <https://github.com/automl/HpBandSter>`_.
Importantly, BOHB is intended to be paired with a specific scheduler class: `HyperBandForBOHB <tune-schedulers.html#hyperband-bohb>`__.
Importantly, BOHB is intended to be paired with a specific scheduler class: :ref:`HyperBandForBOHB <tune-scheduler-bohb>`.
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``: