Examples#

Tip

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:

Other Examples#

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 Tune Tutorial
Using Search algorithms and Trial Schedulers to optimize your model. Pytorch Tune Tutorial
Using Population-Based Training (PBT). Pytorch Tune Tutorial
Fine-tuning Huggingface Transformers with PBT. Huggingface Transformers/Pytorch Tune Tutorial
Logging Tune Runs to Comet ML. Comet Tune Tutorial

Tutorial source files can be found here.