mirror of
https://github.com/wassname/ray.git
synced 2026-07-08 01:54:35 +08:00
[tune] Put examples under proper version control (#9427)
Co-authored-by: krfricke <krfricke@users.noreply.github.com>
This commit is contained in:
Richard Liaw
@@ -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
|
||||
-------------------------------
|
||||
|
||||
@@ -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.
|
||||
|
||||
|
||||
@@ -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
|
||||
|
||||
|
||||
@@ -32,7 +32,7 @@ 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
|
||||
@@ -40,15 +40,15 @@ When using schedulers, you may face compatibility issues, as shown in the below
|
||||
* - :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
|
||||
|
||||
|
||||
@@ -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 <http://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.
|
||||
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
async_hyperband_example
|
||||
~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/async_hyperband_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
ax_example
|
||||
~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/ax_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
bayesopt_example
|
||||
~~~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/bayesopt_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
bohb_example
|
||||
~~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/bohb_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
dragonfly_example
|
||||
~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/dragonfly_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
genetic_example
|
||||
~~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/genetic_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
hyperband_example
|
||||
=================
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/hyperband_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
hyperopt_example
|
||||
~~~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/hyperopt_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
lightgbm_example
|
||||
~~~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/lightgbm_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
logging_example
|
||||
~~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/logging_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
mlflow_example
|
||||
~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/mlflow_example.py
|
||||
@@ -0,0 +1,7 @@
|
||||
:orphan:
|
||||
|
||||
mnist_pytorch
|
||||
~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/mnist_pytorch.py
|
||||
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
mnist_pytorch_trainable
|
||||
~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/mnist_pytorch_trainable.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
nevergrad_example
|
||||
~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/nevergrad_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
pbt_example
|
||||
~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/pbt_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
pbt_function
|
||||
~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/pbt_function.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
pbt_memnn_example
|
||||
~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/pbt_memnn_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
pbt_ppo_example
|
||||
~~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/pbt_ppo_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
pbt_tune_cifar10_with_keras
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/pbt_tune_cifar10_with_keras.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
sigopt_example
|
||||
~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/sigopt_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
skopt_example
|
||||
~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/skopt_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
tf_mnist_example
|
||||
~~~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/tf_mnist_example.py
|
||||
@@ -0,0 +1,7 @@
|
||||
:orphan:
|
||||
|
||||
tune_cifar10_gluon
|
||||
~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/tune_cifar10_gluon.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
tune_mnist_keras
|
||||
~~~~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/tune_mnist_keras.py
|
||||
@@ -0,0 +1,7 @@
|
||||
:orphan:
|
||||
|
||||
xgboost_example
|
||||
~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/xgboost_example.py
|
||||
@@ -0,0 +1,6 @@
|
||||
:orphan:
|
||||
|
||||
zoopt_example
|
||||
~~~~~~~~~~~~~
|
||||
|
||||
.. literalinclude:: /../../python/ray/tune/examples/zoopt_example.py
|
||||
@@ -1,10 +1,9 @@
|
||||
Best Practices: Ray with Tensorflow
|
||||
===================================
|
||||
|
||||
This document describes best practices for using the Ray core APIs with TensorFlow. Ray also provides higher-level utilities for working with Tensorflow, such as distributed training APIs (`training tensorflow example`_), Tune for hyperparameter search (`Tune tensorflow example`_), RLlib for reinforcement learning (`RLlib tensorflow example`_).
|
||||
This document describes best practices for using the Ray core APIs with TensorFlow. Ray also provides higher-level utilities for working with Tensorflow, such as distributed training APIs (`training tensorflow example`_), Tune for hyperparameter search (:doc:`/tune/examples/tf_mnist_example`), RLlib for reinforcement learning (`RLlib tensorflow example`_).
|
||||
|
||||
.. _`training tensorflow example`: tf_distributed_training.html
|
||||
.. _`Tune tensorflow example`: https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tf_mnist_example.py
|
||||
.. _`RLlib tensorflow example`: rllib-models.html#tensorflow-models
|
||||
|
||||
Feel free to contribute if you think this document is missing anything.
|
||||
|
||||
Reference in New Issue
Block a user