Check out the Tune User Guides To learn more about Tune’s features in depth.
ML Framework Examples#
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.
Experiment Tracking Examples#
Ray Tune integrates with some popular Experiment tracking and management tools, such as CometML, or Weights & Biases. If you’re interested in learning how to use Ray Tune with Tensorboard, you can find more information in our Guide to logging and outputs.
Hyperparameter Optimization Framework Examples#
Tune integrates with a wide variety of hyperparameter optimization frameworks and their respective search algorithms. Here you can find detailed examples on each of our integrations:
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.
Visualizing Population Based Training (PBT) Hyperparameter Optimization: Configuring and running (synchronous) PBT and understanding the underlying algorithm behavior with a simple example.
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.