tutorial/tune_exercises at master · ray-project/tutorial · GitHub

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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: https://ray.readthedocs.io/en/latest/tune.html

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

Notebooks

Exercise 1 covers basics of using Tune - creating your first training function and using Tune. This tutorial uses Keras.

Tune Tutorial

Exercise 2 covers Search algorithms and Trial Schedulers. This tutorial uses PyTorch.

Tune Tutorial

Exercise 3 covers using Population-Based Training and uses the advanced Trainable API with save and restore functions and checkpointing.

Tune Tutorial

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