Check out the Tune User Guides To learn more about Tune’s features in depth.
Practical How-To Guides¶
Ray Tune integrates with many popular machine learning frameworks. Here you find a few practical examples showing you how to tune your models. At the end of these guides you will often find links to even more examples.
Search Algorithm Examples¶
SigOpt Multi-Objective Example: Example using Sigopt’s multi-objective functionality (contributed).
SigOpt Prior Belief Example: Example using Sigopt’s support for prior beliefs (contributed).
tune_basic_example: Simple example for doing a basic random and grid search.
Asynchronous HyperBand Example: Example of using a simple tuning function with AsyncHyperBandScheduler.
HyperBand Function Example: Example of using a Trainable function with HyperBandScheduler. Also uses the AsyncHyperBandScheduler.
PBT Function Example: Example of using the function API with a PopulationBasedTraining scheduler.
PB2 Example: Example of using the Population-based Bandits (PB2) scheduler.
Logging Example: Example of custom loggers and custom trial directory naming.
Genetic Search Example: Optimizing the michalewicz function using the contributed GeneticSearch algorithm with AsyncHyperBandScheduler.
Learn how to use Tune in your browser with the following Colab-based exercises.
Tutorial source files can be found here.