-:doc:`/tune/examples/async_hyperband_example`: Example of using a simple tuning function with AsyncHyperBandScheduler.
-:doc:`/tune/examples/hyperband_function_example`: Example of using a Trainable function with HyperBandScheduler. Also uses the AsyncHyperBandScheduler.
-:doc:`/tune/examples/hyperopt_conditional_search_space_example`: Example script showing usage of :ref:`HyperOptSearch <tune-hyperopt>` [`HyperOpt website <http://hyperopt.github.io/hyperopt>`__] with a conditional search space
-:doc:`/tune/examples/optuna_define_by_run_example`: Example script showing usage of :ref:`OptunaSearch <tune-optuna>` [`Optuna website <https://optuna.org/>`__] with a define-by-run function
-:doc:`/tune/examples/optuna_multiobjective_example`: Example script showing usage of :ref:`OptunaSearch <tune-optuna>` [`Optuna website <https://optuna.org/>`__] for multi-objective optimization
See the `ray-project/tune-sklearn examples <https://github.com/ray-project/tune-sklearn/tree/master/examples>`__ for a comprehensive list of examples leveraging Tune's sklearn interface.
-`tune-sklearn with xgboost <https://github.com/ray-project/tune-sklearn/blob/master/examples/xgbclassifier.py>`__
-`tune-sklearn with sklearn pipelines <https://github.com/ray-project/tune-sklearn/blob/master/examples/sklearn_pipeline.py>`__
-`tune-sklearn with Bayesian Optimization <https://github.com/ray-project/tune-sklearn/blob/master/examples/hyperopt_sgd.py>`__
-:doc:`/tune/examples/mnist_pytorch`: 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.
-:doc:`/tune/examples/ddp_mnist_torch`: An example showing how to use DistributedDataParallel with Ray Tune. This enables both distributed training and distributed hyperparameter tuning.
-:doc:`/tune/examples/cifar10_pytorch`: Uses Pytorch to tune a simple model on CIFAR10.
-:doc:`/tune/examples/pbt_convnet_function_example`: Example training a ConvNet with checkpointing in function API.
.. - :doc:`/tune/examples/pbt_convnet_example`: Example of training a Memory NN on bAbI with Keras using PBT.
.. - :doc:`/tune/examples/mnist_pytorch_trainable`: Converts the PyTorch MNIST example to use Tune with Trainable API. Also uses the HyperBandScheduler and checkpoints the model at the end.
-:doc:`/tune/examples/mnist_ptl_mini`: A minimal example of using `Pytorch Lightning <https://github.com/PyTorchLightning/pytorch-lightning>`_ to train a MNIST model. This example utilizes the Ray Tune-provided :ref:`PyTorch Lightning callbacks <tune-integration-pytorch-lightning>`. See also :ref:`this tutorial for a full walkthrough <tune-pytorch-lightning>`.
-:doc:`/tune/examples/mnist_pytorch_lightning`: A comprehensive example using `Pytorch Lightning <https://github.com/PyTorchLightning/pytorch-lightning>`_ to train a MNIST model. This example showcases how to use various search optimization techniques. It utilizes the Ray Tune-provided :ref:`PyTorch Lightning callbacks <tune-integration-pytorch-lightning>`.
-:ref:`A walkthrough tutorial for using Ray Tune with Pytorch-Lightning <tune-pytorch-lightning>`.
-:doc:`/tune/examples/mlflow_example`: Example for using `MLflow <https://github.com/mlflow/mlflow/>`__ with Ray Tune.
-:doc:`/tune/examples/mlflow_ptl_example`: Example for using `MLflow <https://github.com/mlflow/mlflow/>`__ and `Pytorch Lightning <https://github.com/PyTorchLightning/pytorch-lightning>`_ with Ray Tune.
-:doc:`/tune/examples/tune_mnist_keras`: 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.
-:doc:`/tune/examples/pbt_memnn_example`: Example of training a Memory NN on bAbI with Keras using PBT.
-:doc:`/tune/examples/tf_mnist_example`: 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
MXNet
~~~~~
-:doc:`/tune/examples/mxnet_example`: Simple example for using MXNet with Tune.
-:doc:`/tune/examples/tune_cifar10_gluon`: MXNet Gluon example to use Tune with the function-based API on CIFAR-10 dataset.
Horovod
~~~~~~~
-:doc:`/tune/examples/horovod_simple`: Leverages the :ref:`Horovod-Tune <tune-integration-horovod>` integration to launch a distributed training + tuning job.
-:doc:`/tune/examples/xgboost_dynamic_resources_example`: Trains a basic XGBoost model with Tune with the class-based API and a ResourceChangingScheduler, ensuring all resources are being used at all time.
-:doc:`/tune/examples/pbt_ppo_example`: Example of optimizing a distributed RLlib algorithm (PPO) with the PopulationBasedTraining scheduler.
-:doc:`/tune/examples/pb2_ppo_example`: Example of optimizing a distributed RLlib algorithm (PPO) with the PB2 scheduler. Uses a small population size of 4, so can train on a laptop.
-:doc:`/tune/examples/pbt_tune_cifar10_with_keras`: A contributed example of tuning a Keras model on CIFAR10 with the PopulationBasedTraining scheduler.
-:doc:`/tune/examples/genetic_example`: Optimizing the michalewicz function using the contributed GeneticSearch algorithm with AsyncHyperBandScheduler.
Here are some of the popular open source repositories and research projects that leverage Tune. Feel free to submit a pull-request adding (or requesting a removal!) of a listed project.
-`Softlearning <https://github.com/rail-berkeley/softlearning>`_: Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.
-`Flambe <https://github.com/asappresearch/flambe>`_: An ML framework to accelerate research and its path to production. See `flambe.ai <https://flambe.ai>`_.
-`Population Based Augmentation <https://github.com/arcelien/pba>`_: Population Based Augmentation (PBA) is a algorithm that quickly and efficiently learns data augmentation functions for neural network training. PBA matches state-of-the-art results on CIFAR with one thousand times less compute.
-`Fast AutoAugment by Kakao <https://github.com/kakaobrain/fast-autoaugment>`_: Fast AutoAugment (Accepted at NeurIPS 2019) learns augmentation policies using a more efficient search strategy based on density matching.
-`Allentune <https://github.com/allenai/allentune>`_: Hyperparameter Search for AllenNLP from AllenAI.
-`machinable <https://github.com/frthjf/machinable>`_: A modular configuration system for machine learning research. See `machinable.org <https://machinable.org>`_.
-`NeuroCard <https://github.com/neurocard/neurocard>`_: NeuroCard (Accepted at VLDB 2021) is a neural cardinality estimator for multi-table join queries. It uses state of the art deep density models to learn correlations across relational database tables.
