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[tune] ASHA xgboost and lightgbm examples (#5500)
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Richard Liaw
@@ -11,54 +11,56 @@ If any example is broken, or if you'd like to add an example to this page, feel
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General Examples
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- `async_hyperband_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/async_hyperband_example.py>`__:
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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>`__:
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Example of using a Trainable class with HyperBandScheduler.
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- `hyperopt_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/hyperopt_example.py>`__:
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Optimizes a basic function using the function-based API and the HyperOptSearch (SearchAlgorithm wrapper for HyperOpt TPE).
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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>`__:
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Example of using a Trainable class with 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>`__:
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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>`__:
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Example of custom loggers and custom trial directory naming.
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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_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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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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- `HyperOpt Example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/hyperopt_example.py>`__: Optimizes a basic function using the function-based API and the HyperOptSearch (SearchAlgorithm wrapper for HyperOpt TPE).
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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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Keras Examples
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--------------
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- `tune_mnist_keras <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_keras.py>`__:
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Converts the Keras MNIST example to use Tune with the function-based API and a Keras callback.
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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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PyTorch Examples
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- `mnist_pytorch <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/mnist_pytorch.py>`__:
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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>`__:
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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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- `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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TensorFlow Examples
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-------------------
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- `tune_mnist_ray <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_ray.py>`__:
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A basic example of tuning a TensorFlow model on MNIST using the Trainable class.
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- `tune_mnist_ray_hyperband <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_ray_hyperband.py>`__:
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A basic example of tuning a TensorFlow model on MNIST using the Trainable class and the HyperBand scheduler.
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- `tune_mnist_async_hyperband <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_async_hyperband.py>`__:
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Example of tuning a TensorFlow model on MNIST using AsyncHyperBand.
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- `tune_mnist_ray <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_ray.py>`__: A basic example of tuning a TensorFlow model on MNIST using the Trainable class.
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- `tune_mnist_ray_hyperband <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_ray_hyperband.py>`__: A basic example of tuning a TensorFlow model on MNIST using the Trainable class and the HyperBand scheduler.
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- `tune_mnist_async_hyperband <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_async_hyperband.py>`__: Example of tuning a TensorFlow model on MNIST using AsyncHyperBand.
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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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LightGBM Example
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----------------
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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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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>`__:
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A contributed example of tuning a Keras model on CIFAR10 with the PopulationBasedTraining scheduler.
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- `genetic_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/genetic_example.py>`__:
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Optimizing the michalewicz function using the contributed GeneticSearch search algorithm with AsyncHyperBandScheduler.
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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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- `genetic_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/genetic_example.py>`__: Optimizing the michalewicz function using the contributed GeneticSearch search algorithm with AsyncHyperBandScheduler.
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- `tune_cifar10_gluon <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_cifar10_gluon.py>`__: MXNet Gluon example to use Tune with the function-based API on CIFAR-10 dataset.
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