hiro/ray
1
0
Fork 0
mirror of https://github.com/vale981/ray synced 2025-03-06 02:21:39 -05:00
Code Issues Projects Releases Packages Wiki Activity Actions

Link to serve in tune overview (#8487)

Browse source
This commit is contained in:
Edward Oakes 2020-05-18 11:29:38 -05:00 • committed by GitHub
parent 796a834c48
commit 9a721ed71a
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
1 changed files with 2 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

2
doc/source/tune.rst
View file

@ -14,10 +14,12 @@ Tune is a Python library for experiment execution and hyperparameter tuning at a
* 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`_.
* 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>`.