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.