-`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.
-`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.
-`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.
-`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.
-`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.
-`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.
-`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.
-`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).
-`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.
-`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.
-`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.
-`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.
-`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.
-`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.
-`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.
-`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.
-`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.
LightGBM Example
----------------
-`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.
-`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.
-`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.
-`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.