Ray Tune Examples

Tip

See Package overview to learn more about Tune features.

Below are examples for using Ray Tune for a variety 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 Horovod parameters with Tune

Tuning Hugging Face Transformers with Tune

End-to-end example for tuning a TensorFlow model

End-to-end example for tuning a PyTorch model with PBT

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

Configuring and running (synchronous) PBT and understanding the underlying algorithm behavior with a simple example

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.