Tutorials¶

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In this section, you can find material on how to use Tune and its various features. If any of the materials is out of date or broken, or if you’d like to add an example to this page, feel free to raise an issue on our Github repository.

Take a look at any of the below tutorials to get started with Tune.

Key concepts in 60 seconds¶

A walkthrough to setup your first Tune experiment¶

A simple guide to Population-based Training¶

A guide to distributed hyperparameter tuning¶

Tune’s Scikit-Learn Adapters¶

How to use Tune with PyTorch¶

../../_images/pytorch_lightning_small.png

Tuning PyTorch Lightning modules¶

A guide to tuning XGBoost parameters with Tune¶

Track your experiment process with the Weights & Biases tools¶

Colab Exercises¶

Learn how to use Tune in your browser with the following Colab-based exercises.

Exercise Description	Library	Colab Link
Basics of using Tune.	TF/Keras
Using Search algorithms and Trial Schedulers to optimize your model.	Pytorch
Using Population-Based Training (PBT).	Pytorch

Tutorial source files can be found here.

What’s Next?¶

Check out:

User Guide & Configuring Tune: A comprehensive overview of Tune’s features.

Examples: End-to-end examples and templates for using Tune with your preferred machine learning library.