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82 lines
6.2 KiB
ReStructuredText
Tune Examples
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=============
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.. Keep this in sync with ray/doc/tune-examples.rst
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In our repository, we provide a variety of examples for the various use cases and features of Tune.
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If any example is broken, or if you'd like to add an example to this page, feel free to raise an issue on our Github repository.
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General Examples
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----------------
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- `async_hyperband_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/async_hyperband_example.py>`__: Example of using a Trainable class with AsyncHyperBandScheduler.
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- `hyperband_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/hyperband_example.py>`__: Example of using a Trainable class with HyperBandScheduler. Also uses the Experiment class API for specifying the experiment configuration. Also uses the AsyncHyperBandScheduler.
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- `pbt_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_example.py>`__: Example of using a Trainable class with PopulationBasedTraining scheduler.
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- `PBT with Function API <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_function.py>`__: Example of using the function API with a PopulationBasedTraining scheduler.
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- `pbt_ppo_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_ppo_example.py>`__: Example of optimizing a distributed RLlib algorithm (PPO) with the PopulationBasedTraining scheduler.
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- `logging_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/logging_example.py>`__: Example of custom loggers and custom trial directory naming.
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- `custom_func_checkpointing <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/logging_example.py>`__: Example of custom checkpointing logic using the function API.
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Search Algorithm Examples
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-------------------------
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- `Ax example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/ax_example.py>`__: Optimize a Hartmann function with `Ax <https://ax.dev>`_ with 4 parallel workers.
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- `Nevergrad example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/nevergrad_example.py>`__: Optimize a simple toy function with the gradient-free optimization package `Nevergrad <https://github.com/facebookresearch/nevergrad>`_ with 4 parallel workers.
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- `Bayesian Optimization example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/bayesopt_example.py>`__: Optimize a simple toy function using `Bayesian Optimization <https://github.com/fmfn/BayesianOptimization>`_ with 4 parallel workers.
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Tensorflow/Keras Examples
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- `tune_mnist_keras <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_keras.py>`__: Converts the Keras MNIST example to use Tune with the function-based API and a Keras callback. Also shows how to easily convert something relying on argparse to use Tune.
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- `pbt_memnn_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_memnn_example.py>`__: Example of training a Memory NN on bAbI with Keras using PBT.
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- `Tensorflow 2 Example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tf_mnist_example.py>`__: Converts the Advanced TF2.0 MNIST example to use Tune with the Trainable. This uses `tf.function`. Original code from tensorflow: https://www.tensorflow.org/tutorials/quickstart/advanced
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PyTorch Examples
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- `mnist_pytorch <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/mnist_pytorch.py>`__: Converts the PyTorch MNIST example to use Tune with the function-based API. Also shows how to easily convert something relying on argparse to use Tune.
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- `mnist_pytorch_trainable <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/mnist_pytorch_trainable.py>`__: Converts the PyTorch MNIST example to use Tune with Trainable API. Also uses the HyperBandScheduler and checkpoints the model at the end.
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PyTorch Lightning Examples
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--------------------------
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For a full walkthrough of tuning a PyTorch Lightning model with Ray Tune, see the
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`Using PyTorch Lightning with Tune <https://docs.ray.io/en/latest/tune/examples/tune-pytorch-lightning.html>`__ tutorial.
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- `mnist_ptl_mini <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/mnist_ptl_mini.py>`__: A minimal example of tuning a PyTorch Lightning MNIST classifier with Ray Tune.
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- `mlflow_ptl <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/mlflow_ptl.py>`__: Example for using `MLflow <https://github.com/mlflow/mlflow/>`__ and PyTorch Lightning with Ray Tune.
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XGBoost Example
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---------------
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- `xgboost_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/xgboost_example.py>`__: Trains a basic XGBoost model with Tune with the function-based API and a XGBoost callback.
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XGBoost with Dynamic Resources Example
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- `xgboost_dynamic_resources_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/xgboost_dynamic_resources_example.py>`__: Trains a basic XGBoost model with Tune with the class-based API and a ResourceChangingScheduler, ensuring all resources are being used at all time.
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LightGBM Example
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- `lightgbm_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/lightgbm_example.py>`__: Trains a basic LightGBM model with Tune with the function-based API and a LightGBM callback.
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Huggingface Transformers Example
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- `pbt_transformers <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_transformers/pbt_transformers.py>`__: Fine-tunes a Huggingface transformer with Tune Population Based Training.
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Contributed Examples
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--------------------
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- `pbt_tune_cifar10_with_keras <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_tune_cifar10_with_keras.py>`__: A contributed example of tuning a Keras model on CIFAR10 with the PopulationBasedTraining scheduler.
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- `hyperopt_conditional_search_space_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/hyperopt_conditional_search_space_example.py>`__: Conditional search space example using HyperOpt.
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