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Co-authored-by: will <will@anyscale.com> Co-authored-by: Antoni Baum <antoni.baum@protonmail.com> Co-authored-by: Kai Fricke <kai@anyscale.com>
122 lines
6 KiB
ReStructuredText
122 lines
6 KiB
ReStructuredText
.. _tune-main:
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Tune: Scalable Hyperparameter Tuning
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====================================
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.. tip:: We'd love to hear your feedback on using Tune - `get in touch <https://forms.gle/PTRvGLbKRdUfuzQo9>`_!
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.. image:: /images/tune.png
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:scale: 30%
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:align: center
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Tune is a Python library for experiment execution and hyperparameter tuning at any scale. Core features:
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* Launch a multi-node :ref:`distributed hyperparameter sweep <tune-distributed>` in less than 10 lines of code.
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* Supports any machine learning framework, :ref:`including PyTorch, XGBoost, MXNet, and Keras <tune-guides>`.
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* Automatically manages :ref:`checkpoints <tune-checkpoint-syncing>` and logging to :ref:`TensorBoard <tune-logging>`.
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* Choose among state of the art algorithms such as :ref:`Population Based Training (PBT) <tune-scheduler-pbt>`, :ref:`BayesOptSearch <bayesopt>`, :ref:`HyperBand/ASHA <tune-scheduler-hyperband>`.
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* Move your models from training to serving on the same infrastructure with `Ray Serve`_.
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.. _`Ray Serve`: ../serve/index.html
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**Want to get started?** Head over to the :doc:`Key Concepts page </tune/key-concepts>`.
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Quick Start
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-----------
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To run this example, install the following: ``pip install "ray[tune]"``.
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This example runs a parallel grid search to optimize an example objective function.
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.. literalinclude:: ../../../python/ray/tune/tests/example.py
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:language: python
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:start-after: __quick_start_begin__
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:end-before: __quick_start_end__
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If TensorBoard is installed, automatically visualize all trial results:
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.. code-block:: bash
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tensorboard --logdir ~/ray_results
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.. image:: /images/tune-start-tb.png
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:scale: 30%
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:align: center
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If using TF2 and TensorBoard, Tune will also automatically generate TensorBoard HParams output:
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.. image:: /images/tune-hparams-coord.png
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:scale: 20%
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:align: center
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Why choose Tune?
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----------------
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There are many other hyperparameter optimization libraries out there. If you're new to Tune, you're probably wondering, "what makes Tune different?"
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Cutting-edge optimization algorithms
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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As a user, you're probably looking into hyperparameter optimization because you want to quickly increase your model performance.
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Tune enables you to leverage a variety of these cutting edge optimization algorithms, reducing the cost of tuning by `aggressively terminating bad hyperparameter evaluations <tune-scheduler-hyperband>`_, intelligently :ref:`choosing better parameters to evaluate <tune-search-alg>`, or even :ref:`changing the hyperparameters during training <tune-scheduler-pbt>` to optimize hyperparameter schedules.
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First-class Developer Productivity
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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A key problem with machine learning frameworks is the need to restructure all of your code to fit the framework.
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With Tune, you can optimize your model just by :ref:`adding a few code snippets <tune-tutorial>`.
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Further, Tune actually removes boilerplate from your code training workflow, automatically :ref:`managing checkpoints <tune-checkpoint-syncing>` and :ref:`logging results to tools <tune-logging>` such as MLflow and TensorBoard.
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Multi-GPU & distributed training out of the box
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Hyperparameter tuning is known to be highly time-consuming, so it is often necessary to parallelize this process. Most other tuning frameworks require you to implement your own multi-process framework or build your own distributed system to speed up hyperparameter tuning.
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However, Tune allows you to transparently :ref:`parallelize across multiple GPUs and multiple nodes <tune-parallelism>`. Tune even has seamless :ref:`fault tolerance and cloud support <tune-distributed>`, allowing you to scale up your hyperparameter search by 100x while reducing costs by up to 10x by using cheap preemptible instances.
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What if I'm already doing hyperparameter tuning?
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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You might be already using an existing hyperparameter tuning tool such as HyperOpt or Bayesian Optimization.
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In this situation, Tune actually allows you to power up your existing workflow. Tune's :ref:`Search Algorithms <tune-search-alg>` integrate with a variety of popular hyperparameter tuning libraries (such as Nevergrad or HyperOpt) and allow you to seamlessly scale up your optimization process -- without sacrificing performance.
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Reference Materials
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-------------------
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Here are some reference materials for Tune:
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* :doc:`/tune/user-guide`
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* :ref:`Frequently asked questions <tune-faq>`
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* `Code <https://github.com/ray-project/ray/tree/master/python/ray/tune>`__: GitHub repository for Tune
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Below are some blog posts and talks about Tune:
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- [blog] `Tune: a Python library for fast hyperparameter tuning at any scale <https://towardsdatascience.com/fast-hyperparameter-tuning-at-scale-d428223b081c>`_
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- [blog] `Cutting edge hyperparameter tuning with Ray Tune <https://medium.com/riselab/cutting-edge-hyperparameter-tuning-with-ray-tune-be6c0447afdf>`_
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- [blog] `Simple hyperparameter and architecture search in tensorflow with Ray Tune <http://louiskirsch.com/ai/ray-tune>`_
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- [slides] `Talk given at RISECamp 2019 <https://docs.google.com/presentation/d/1v3IldXWrFNMK-vuONlSdEuM82fuGTrNUDuwtfx4axsQ/edit?usp=sharing>`_
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- [video] `Talk given at RISECamp 2018 <https://www.youtube.com/watch?v=38Yd_dXW51Q>`_
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- [video] `A Guide to Modern Hyperparameter Optimization (PyData LA 2019) <https://www.youtube.com/watch?v=10uz5U3Gy6E>`_ (`slides <https://speakerdeck.com/richardliaw/a-modern-guide-to-hyperparameter-optimization>`_)
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Citing Tune
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-----------
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If Tune helps you in your academic research, you are encouraged to cite `our paper <https://arxiv.org/abs/1807.05118>`__. Here is an example bibtex:
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.. code-block:: tex
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@article{liaw2018tune,
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title={Tune: A Research Platform for Distributed Model Selection and Training},
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author={Liaw, Richard and Liang, Eric and Nishihara, Robert
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and Moritz, Philipp and Gonzalez, Joseph E and Stoica, Ion},
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journal={arXiv preprint arXiv:1807.05118},
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year={2018}
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}
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