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richardliaw [tune] Exercises fixup (#124)
* Fix up exercises

* Deprecate old tutorial

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* Update README.rst
Latest commit 07c0db6 Jun 21, 2019

README.rst

Tune Tutorial

Tuning hyperparameters is often the most expensive part of the machine learning workflow. Tune is built to address this, demonstrating an efficient and scalable solution for this pain point.

Code: https://github.com/ray-project/ray/tree/master/python/ray/tune

Examples: https://github.com/ray-project/ray/tree/master/python/ray/tune/examples

Documentation: http://ray.readthedocs.io/en/latest/tune.html

Mailing List https://groups.google.com/forum/#!forum/ray-dev

Notebooks

  1. exercise_1_basics.ipynb covers basics of using Tune - creating your first training function and using Tune. This tutorial uses Keras.
  2. exercise_2_optimize.ipynb covers Search algorithms and Trial Schedulers. This tutorial uses PyTorch.

Concepts that are generally useful but have not been covered:

  1. Using PBT
  2. Creating a Trainable with save and restore functions and checkpointing
  3. Distributed execution on a larger cluster

Please open an issue if you have any questions or identify any issues. All suggestions and contributions welcome!

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