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62d0698097
This PR introduces the following changes: * Ray Tune -> Tune * [breaking] Creation of `schedulers/`, moving PBT, HyperBand into a submodule * [breaking] Search Algorithms now must take in experiment configurations via `add_configurations` rather through initialization * Support `"run": (function | class | str)` with automatic registering of trainable * Documentation Changes
108 lines
3.8 KiB
ReStructuredText
108 lines
3.8 KiB
ReStructuredText
Tune: Scalable Hyperparameter Search
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====================================
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.. image:: images/tune.png
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:scale: 30%
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:align: center
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Tune is a scalable framework for hyperparameter search with a focus on deep learning and deep reinforcement learning.
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You can find the code for Tune `here on GitHub <https://github.com/ray-project/ray/tree/master/python/ray/tune>`__.
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Features
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--------
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* Supports any deep learning framework, including PyTorch, Tensorflow, and Keras.
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* Choose among scalable hyperparameter and model search techniques such as:
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- `Population Based Training (PBT) <tune-schedulers.html#population-based-training-pbt>`__
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- `Median Stopping Rule <tune-schedulers.html#median-stopping-rule>`__
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- `HyperBand <tune-schedulers.html#asynchronous-hyperband>`__
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* Mix and match different hyperparameter optimization approaches - such as using `HyperOpt with HyperBand`_.
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* Visualize results with `TensorBoard <https://www.tensorflow.org/get_started/summaries_and_tensorboard>`__, `parallel coordinates (Plot.ly) <https://plot.ly/python/parallel-coordinates-plot/>`__, and `rllab's VisKit <https://media.readthedocs.org/pdf/rllab/latest/rllab.pdf>`__.
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* Scale to running on a large distributed cluster without changing your code.
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* Parallelize training for models with GPU requirements or algorithms that may themselves be parallel and distributed, using Tune's `resource-aware scheduling <tune-usage.html#using-gpus-resource-allocation>`__,
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Take a look at `the User Guide <tune-usage.html>`__ for a comprehensive overview on how to use Tune's features.
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Getting Started
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---------------
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Installation
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~~~~~~~~~~~~
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You'll need to first `install ray <installation.html>`__ to import Tune.
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.. code-block:: bash
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pip install ray
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Quick Start
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~~~~~~~~~~~
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This example runs a small grid search over a neural network training function using Tune, reporting status on the command line until the stopping condition of ``mean_accuracy >= 99`` is reached. Tune works with any deep learning framework.
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Tune uses Ray as a backend, so we will first import and initialize Ray.
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.. code-block:: python
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import ray
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import ray.tune as tune
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ray.init()
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For the function you wish to tune, pass in a ``reporter`` object:
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.. code-block:: python
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:emphasize-lines: 1,9
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def train_func(config, reporter): # add a reporter arg
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model = ( ... )
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optimizer = SGD(model.parameters(),
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momentum=config["momentum"])
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dataset = ( ... )
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for idx, (data, target) in enumerate(dataset):
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accuracy = model.fit(data, target)
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reporter(mean_accuracy=accuracy) # report metrics
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**Finally**, configure your search and execute it on your Ray cluster:
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.. code-block:: python
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all_trials = tune.run_experiments({
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"my_experiment": {
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"run": train_func,
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"stop": {"mean_accuracy": 99},
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"config": {"momentum": tune.grid_search([0.1, 0.2])}
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}
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})
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Tune can be used anywhere Ray can, e.g. on your laptop with ``ray.init()`` embedded in a Python script, or in an `auto-scaling cluster <autoscaling.html>`__ for massive parallelism.
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Citing Tune
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-----------
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If Tune helps you in your academic research, you are encouraged to cite `our paper <https://arxiv.org/abs/1807.05118>`__. Here is an example bibtex:
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.. code-block:: tex
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@article{liaw2018tune,
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title={Tune: A Research Platform for Distributed Model Selection and Training},
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author={Liaw, Richard and Liang, Eric and Nishihara, Robert
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and Moritz, Philipp and Gonzalez, Joseph E and Stoica, Ion},
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journal={arXiv preprint arXiv:1807.05118},
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year={2018}
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}
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.. _HyperOpt with HyperBand: https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/hyperopt_example.py
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