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Ray Tune Examples

Ray Tune Examples

Tip

See Ray Tune: Hyperparameter Tuning to learn more about Tune features.

Below are examples for using Ray Tune for a variety of use cases and sorted by categories:

  • ML frameworks

  • Experiment tracking tools

  • Hyperparameter optimization frameworks

  • Others

  • Exercises

ML frameworks

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.

How to use Tune with Keras and TensorFlow models

How to use Tune with PyTorch models
How to tune PyTorch Lightning models
Tuning RL experiments with Ray Tune and Ray Serve
Tuning XGBoost parameters with Tune
Tuning LightGBM parameters with Tune
Tuning Hugging Face Transformers with Tune

Experiment tracking tools

Ray Tune integrates with some popular Experiment tracking and management tools, such as CometML, or Weights & Biases. For how to use Ray Tune with Tensorboard, see Guide to logging and outputs.

Using Aim with Ray Tune for experiment management

Using Comet with Ray Tune for experiment management
Tracking your experiment process Weights & Biases
Using MLflow tracking and auto logging with Tune

Hyperparameter optimization frameworks

Tune integrates with a wide variety of hyperparameter optimization frameworks and their respective search algorithms. See the following detailed examples for each integration:

Running Tune experiments with AxSearch

Running Tune experiments with HyperOpt
Running Tune experiments with BayesOpt
Running Tune experiments with BOHB
Running Tune experiments with Nevergrad
Running Tune experiments with Optuna

Others

Simple example for doing a basic random and grid search

Example of using a simple tuning function with AsyncHyperBandScheduler
Example of using a trainable function with HyperBandScheduler and the AsyncHyperBandScheduler
PBT Function Example
PB2 Example
Logging Example

Exercises

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

Description Library Colab link
Basics of using Tune PyTorch
Using search algorithms and trial schedulers to optimize your model PyTorch
Using Population-Based Training (PBT) PyTorch
Fine-tuning Hugging Face Transformers with PBT Hugging Face Transformers and PyTorch
Logging Tune runs to Comet ML Comet

Tutorial source files are on GitHub.