[tune] Put examples under proper version control (ray-project#9427)

Co-authored-by: krfricke <[email protected]>

doc/source/tune/_tutorials/overview.rst

@@ -148,55 +148,55 @@ If any example is broken, or if you'd like to add an example to this page, feel
 General Examples
 ~~~~~~~~~~~~~~~~
 
-- `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 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.
-- `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.
+- :doc:`/tune/examples/async_hyperband_example`: Example of using a Trainable class with AsyncHyperBandScheduler.
+- :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.
+- :doc:`/tune/examples/pbt_example`: Example of using a Trainable class with PopulationBasedTraining scheduler.
+- :doc:`/tune/examples/pbt_function`: Example of using the function API with a PopulationBasedTraining scheduler.
+- :doc:`/tune/examples/pbt_ppo_example`: Example of optimizing a distributed RLlib algorithm (PPO) with the PopulationBasedTraining scheduler.
+- :doc:`/tune/examples/logging_example`: Example of custom loggers and custom trial directory naming.
 
 Search Algorithm Examples
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
-- `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.
+- :doc:`/tune/examples/ax_example`: Optimize a Hartmann function with `Ax <https://ax.dev>`_ with 4 parallel workers.
+- :doc:`/tune/examples/hyperopt_example`: Optimizes a basic function using the function-based API and the HyperOptSearch (SearchAlgorithm wrapper for HyperOpt TPE).
+- :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.
+- :doc:`/tune/examples/bayesopt_example`: Optimize a simple toy function using `Bayesian Optimization <https://github.com/fmfn/BayesianOptimization>`_ with 4 parallel workers.
 
 Tensorflow/Keras Examples
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
-- `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.
-- `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.
-- `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
+- :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
 
 
 PyTorch Examples
 ~~~~~~~~~~~~~~~~
 
-- `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.
+- :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/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.
 
 
 XGBoost Example
 ~~~~~~~~~~~~~~~
 
 - :ref:`XGBoost tutorial <tune-xgboost>`: A guide to tuning XGBoost parameters with Tune.
-- `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.
+- :doc:`/tune/examples/xgboost_example`: Trains a basic XGBoost model with Tune with the function-based API and an 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.
+- :doc:`/tune/examples/lightgbm_example`: Trains a basic LightGBM model with Tune with the function-based API and a LightGBM callback.
 
 
 Contributed Examples
 ~~~~~~~~~~~~~~~~~~~~
 
-- `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 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.
+- :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.
+- :doc:`/tune/examples/tune_cifar10_gluon`: MXNet Gluon example to use Tune with the function-based API on CIFAR-10 dataset.
 
 Open Source Projects using Tune
 -------------------------------

doc/source/tune/_tutorials/tune-usage.rst

@@ -50,7 +50,7 @@ To leverage GPUs, you must set ``gpu`` in ``resources_per_trial``. This will aut
  # If you have 4 CPUs on your machine and 1 GPU, this will run 1 trial at a time.
  tune.run(trainable, num_samples=10, resources_per_trial={"cpu": 2, "gpu": 1})
 
-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>`__.
+You can find an example of this in the :doc:`Keras MNIST example </tune/examples/tune_mnist_keras>`.
 
 .. warning:: If 'gpu' is not set, ``CUDA_VISIBLE_DEVICES`` environment variable will be set as empty, disallowing GPU access.
 

doc/source/tune/api_docs/logging.rst

@@ -58,7 +58,7 @@ You can then pass in your own logger as follows:
 
 These loggers will be called along with the default Tune loggers. You can also check out `logger.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/logger.py>`__ for implementation details.
 
-An example of creating a custom logger can be found in `logging_example.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/logging_example.py>`__.
+An example of creating a custom logger can be found in :doc:`/tune/examples/logging_example`.
 
 .. _trainable-logging:
 
@@ -164,7 +164,7 @@ CSVLogger
 MLFLowLogger
 ------------
 
-Tune also provides a default logger for `MLFlow <https://mlflow.org>`_. You can install MLFlow via ``pip install mlflow``. An example can be found `mlflow_example.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/mlflow_example.py>`__. Note that this currently does not include artifact logging support. For this, you can use the native MLFlow APIs inside your Trainable definition.
+Tune also provides a default logger for `MLFlow <https://mlflow.org>`_. You can install MLFlow via ``pip install mlflow``. An example can be found in :doc:`/tune/examples/mlflow_example`. Note that this currently does not include artifact logging support. For this, you can use the native MLFlow APIs inside your Trainable definition.
 
 .. autoclass:: ray.tune.logger.MLFLowLogger
 

doc/source/tune/api_docs/schedulers.rst

@@ -32,23 +32,23 @@ When using schedulers, you may face compatibility issues, as shown in the below
  * - :ref:`ASHA <tune-scheduler-hyperband>`
  - No
  - Yes
- - `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/async_hyperband_example.py>`__
+ - :doc:`Link </tune/examples/async_hyperband_example>`
  * - :ref:`Median Stopping Rule <tune-scheduler-msr>`
  - No
  - Yes
  - :ref:`Link <tune-scheduler-msr>`
  * - :ref:`HyperBand <tune-original-hyperband>`
  - Yes
  - Yes
- - `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/hyperband_example.py>`__
+ - :doc:`Link </tune/examples/hyperband_example>`
  * - :ref:`BOHB <tune-scheduler-bohb>`
  - Yes
  - Only TuneBOHB
- - `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/bohb_example.py>`__
+ - :doc:`Link </tune/examples/bohb_example>`
  * - :ref:`Population Based Training <tune-scheduler-pbt>`
  - Yes
  - Not Compatible
- - `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_example.py>`__
+ - :doc:`Link </tune/examples/pbt_example>`
 
 .. _tune-scheduler-hyperband:
 
@@ -69,7 +69,7 @@ The `ASHA <https://openreview.net/forum?id=S1Y7OOlRZ>`__ scheduler can be used b
  brackets=1)
  tune.run( ... , scheduler=asha_scheduler)
 
-Compared to the original version of HyperBand, this implementation provides better parallelism and avoids straggler issues during eliminations. **We recommend using this over the standard HyperBand scheduler.** An example of this can be `found here <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/async_hyperband_example.py>`_.
+Compared to the original version of HyperBand, this implementation provides better parallelism and avoids straggler issues during eliminations. **We recommend using this over the standard HyperBand scheduler.** An example of this can be found here: :doc:`/tune/examples/async_hyperband_example`.
 
 Even though the original paper mentions a bracket count of 3, discussions with the authors concluded that the value should be left to 1 bracket. This is the default used if no value is provided for the ``brackets`` argument.
 
@@ -141,7 +141,7 @@ Tune includes a distributed implementation of `Population Based Training (PBT) <
 
 When the PBT scheduler is enabled, each trial variant is treated as a member of the population. Periodically, top-performing trials are checkpointed (this requires your Trainable to support :ref:`save and restore <tune-checkpoint>`). Low-performing trials clone the checkpoints of top performers and perturb the configurations in the hope of discovering an even better variation.
 
-You can run this `toy PBT example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_example.py>`__ to get an idea of how how PBT operates. When training in PBT mode, a single trial may see many different hyperparameters over its lifetime, which is recorded in its ``result.json`` file. The following figure generated by the example shows PBT with optimizing a LR schedule over the course of a single experiment:
+You can run this :doc:`toy PBT example </tune/examples/pbt_function>` to get an idea of how how PBT operates. When training in PBT mode, a single trial may see many different hyperparameters over its lifetime, which is recorded in its ``result.json`` file. The following figure generated by the example shows PBT with optimizing a LR schedule over the course of a single experiment:
 
 .. image:: /pbt.png
 
@@ -157,7 +157,7 @@ This class is a variant of HyperBand that enables the `BOHB Algorithm <https://a
 
 This is to be used in conjunction with the Tune BOHB search algorithm. See :ref:`TuneBOHB <suggest-TuneBOHB>` for package requirements, examples, and details.
 
-An example of this in use can be found in `bohb_example.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/bohb_example.py>`_.
+An example of this in use can be found here: :doc:`/tune/examples/bohb_example`.
 
 .. autoclass:: ray.tune.schedulers.HyperBandForBOHB
 

doc/source/tune/api_docs/suggestion.rst

@@ -25,39 +25,39 @@ Summary
  * - :ref:`AxSearch <tune-ax>`
  - Bayesian/Bandit Optimization
  - [`Ax <https://ax.dev/>`__]
- - `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/ax_example.py>`__
+ - :doc:`/tune/examples/ax_example`
  * - :ref:`DragonflySearch <Dragonfly>`
  - Scalable Bayesian Optimization
  - [`Dragonfly <https://dragonfly-opt.readthedocs.io/>`__]
- - `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/dragonfly_example.py>`__
+ - :doc:`/tune/examples/dragonfly_example`
  * - :ref:`SkoptSearch <skopt>`
  - Bayesian Optimization
  - [`Scikit-Optimize <https://scikit-optimize.github.io>`__]
- - `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/skopt_example.py>`__
+ - :doc:`/tune/examples/skopt_example`
  * - :ref:`HyperOptSearch <tune-hyperopt>`
  - Tree-Parzen Estimators
  - [`HyperOpt <https://hyperopt.github.io/hyperopt>`__]
- - `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/hyperopt_example.py>`__
+ - :doc:`/tune/examples/hyperopt_example`
  * - :ref:`BayesOptSearch <bayesopt>`
  - Bayesian Optimization
  - [`BayesianOptimization <https://github.com/fmfn/BayesianOptimization>`__]
- - `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/bayesopt_example.py>`__
+ - :doc:`/tune/examples/bayesopt_example`
  * - :ref:`TuneBOHB <suggest-TuneBOHB>`
  - Bayesian Opt/HyperBand
  - [`BOHB <https://github.com/automl/HpBandSter>`__]
- - `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/bohb_example.py>`__
+ - :doc:`/tune/examples/bohb_example`
  * - :ref:`NevergradSearch <nevergrad>`
  - Gradient-free Optimization
  - [`Nevergrad <https://github.com/facebookresearch/nevergrad>`__]
- - `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/nevergrad_example.py>`__
+ - :doc:`/tune/examples/nevergrad_example`
  * - :ref:`ZOOptSearch <zoopt>`
  - Zeroth-order Optimization
  - [`ZOOpt <https://github.com/polixir/ZOOpt>`__]
- - `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/zoopt_example.py>`__
+ - :doc:`/tune/examples/zoopt_example`
  * - :ref:`SigOptSearch <sigopt>`
  - Closed source
  - [`SigOpt <https://sigopt.com/>`__]
- - `Link <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/sigopt_example.py>`__
+ - :doc:`/tune/examples/sigopt_example`
 
 
 .. note::Search algorithms will require a different search space declaration than the default Tune format - meaning that you will not be able to combine ``tune.grid_search`` with the below integrations.

doc/source/tune/examples/async_hyperband_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+async_hyperband_example
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/async_hyperband_example.py

doc/source/tune/examples/ax_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+ax_example
+~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/ax_example.py

doc/source/tune/examples/bayesopt_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+bayesopt_example
+~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/bayesopt_example.py

doc/source/tune/examples/bohb_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+bohb_example
+~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/bohb_example.py

doc/source/tune/examples/dragonfly_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+dragonfly_example
+~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/dragonfly_example.py

doc/source/tune/examples/genetic_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+genetic_example
+~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/genetic_example.py

doc/source/tune/examples/hyperband_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+hyperband_example
+=================
+
+.. literalinclude:: /../../python/ray/tune/examples/hyperband_example.py

doc/source/tune/examples/hyperopt_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+hyperopt_example
+~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/hyperopt_example.py

doc/source/tune/examples/lightgbm_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+lightgbm_example
+~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/lightgbm_example.py

doc/source/tune/examples/logging_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+logging_example
+~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/logging_example.py

doc/source/tune/examples/mlflow_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+mlflow_example
+~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/mlflow_example.py

doc/source/tune/examples/mnist_pytorch.rst

@@ -0,0 +1,7 @@
+:orphan:
+
+mnist_pytorch
+~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/mnist_pytorch.py
+

doc/source/tune/examples/mnist_pytorch_trainable.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+mnist_pytorch_trainable
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/mnist_pytorch_trainable.py

doc/source/tune/examples/nevergrad_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+nevergrad_example
+~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/nevergrad_example.py

doc/source/tune/examples/pbt_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+pbt_example
+~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/pbt_example.py

doc/source/tune/examples/pbt_function.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+pbt_function
+~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/pbt_function.py

doc/source/tune/examples/pbt_memnn_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+pbt_memnn_example
+~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/pbt_memnn_example.py

doc/source/tune/examples/pbt_ppo_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+pbt_ppo_example
+~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/pbt_ppo_example.py

doc/source/tune/examples/pbt_tune_cifar10_with_keras.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+pbt_tune_cifar10_with_keras
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/pbt_tune_cifar10_with_keras.py

doc/source/tune/examples/sigopt_example.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+sigopt_example
+~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/sigopt_example.py