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[tune] Put examples under proper version control (#9427)
Co-authored-by: krfricke <krfricke@users.noreply.github.com>
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Richard Liaw
@@ -148,55 +148,55 @@ 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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~~~~~~~~~~~~~~~~
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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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- :doc:`/tune/examples/async_hyperband_example`: Example of using a Trainable class with AsyncHyperBandScheduler.
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- :doc:`/tune/examples/hyperband_example`: 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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- :doc:`/tune/examples/pbt_example`: Example of using a Trainable class with PopulationBasedTraining scheduler.
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- :doc:`/tune/examples/pbt_function`: Example of using the function API with a PopulationBasedTraining scheduler.
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- :doc:`/tune/examples/pbt_ppo_example`: Example of optimizing a distributed RLlib algorithm (PPO) with the PopulationBasedTraining scheduler.
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- :doc:`/tune/examples/logging_example`: 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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- :doc:`/tune/examples/ax_example`: Optimize a Hartmann function with `Ax <https://ax.dev>`_ with 4 parallel workers.
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- :doc:`/tune/examples/hyperopt_example`: Optimizes a basic function using the function-based API and the HyperOptSearch (SearchAlgorithm wrapper for HyperOpt TPE).
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- :doc:`/tune/examples/nevergrad_example`: 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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- :doc:`/tune/examples/bayesopt_example`: 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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~~~~~~~~~~~~~~~~~~~~~~~~~
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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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- :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.
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- :doc:`/tune/examples/pbt_memnn_example`: Example of training a Memory NN on bAbI with Keras using PBT.
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- :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
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PyTorch Examples
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~~~~~~~~~~~~~~~~
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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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- :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.
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- :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.
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XGBoost Example
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~~~~~~~~~~~~~~~
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- :ref:`XGBoost tutorial <tune-xgboost>`: A guide to tuning XGBoost parameters with Tune.
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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 an XGBoost callback.
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- :doc:`/tune/examples/xgboost_example`: Trains a basic XGBoost model with Tune with the function-based API and an 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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- :doc:`/tune/examples/lightgbm_example`: 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>`__: 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 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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- :doc:`/tune/examples/pbt_tune_cifar10_with_keras`: A contributed example of tuning a Keras model on CIFAR10 with the PopulationBasedTraining scheduler.
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- :doc:`/tune/examples/genetic_example`: Optimizing the michalewicz function using the contributed GeneticSearch algorithm with AsyncHyperBandScheduler.
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- :doc:`/tune/examples/tune_cifar10_gluon`: MXNet Gluon example to use Tune with the function-based API on CIFAR-10 dataset.
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Open Source Projects using Tune
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-------------------------------
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@@ -50,7 +50,7 @@ To leverage GPUs, you must set ``gpu`` in ``resources_per_trial``. This will aut
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# If you have 4 CPUs on your machine and 1 GPU, this will run 1 trial at a time.
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tune.run(trainable, num_samples=10, resources_per_trial={"cpu": 2, "gpu": 1})
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You can find an example of this in the `Keras MNIST example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_keras.py>`__.
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You can find an example of this in the :doc:`Keras MNIST example </tune/examples/tune_mnist_keras>`.
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.. warning:: If 'gpu' is not set, ``CUDA_VISIBLE_DEVICES`` environment variable will be set as empty, disallowing GPU access.
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