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f9b71a8bf6
This PR consolidates both #21667 and #21759 (look there for features), but improves on them in the following way: - [x] we reverted renaming of existing projects `tune`, `rllib`, `train`, `cluster`, `serve`, `raysgd` and `data` so that links won't break. I think my consolidation efforts with the `ray-` prefix were a little overeager in that regard. It's better like this. Only the creation of `ray-core` was a necessity, and some files moved into the `rllib` folder, so that should be relatively benign. - [x] Additionally, we added Algolia `docsearch`, screenshot below. This is _much_ better than our current search. Caveat: there's a sphinx dependency that needs to be replaced (`sphinx-tabs`) by another, newer one (`sphinx-panels`), as the former prevents loading of the `algolia.js` library. Will follow-up in the next PR (hoping this one doesn't get re-re-re-re-reverted).
102 lines
5.3 KiB
ReStructuredText
102 lines
5.3 KiB
ReStructuredText
.. include:: we_are_hiring.rst
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Contributing to RLlib
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=====================
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Development Install
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-------------------
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You can develop RLlib locally without needing to compile Ray by using the `setup-dev.py <https://github.com/ray-project/ray/blob/master/python/ray/setup-dev.py>`__ script. This sets up links between the ``rllib`` dir in your git repo and the one bundled with the ``ray`` package. However if you have installed ray from source using `these instructions <https://docs.ray.io/en/master/installation.html>`__ then do not this as these steps should have already created this symlink. When using this script, make sure that your git branch is in sync with the installed Ray binaries (i.e., you are up-to-date on `master <https://github.com/ray-project/ray>`__ and have the latest `wheel <https://docs.ray.io/en/master/installation.html>`__ installed.)
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API Stability
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-------------
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Objects and methods annotated with ``@PublicAPI``, ``@DeveloperAPI``, or ``@ExperimentalAPI``
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have the following API compatibility guarantees:
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.. autofunction:: ray.rllib.utils.annotations.PublicAPI
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:noindex:
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.. autofunction:: ray.rllib.utils.annotations.DeveloperAPI
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:noindex:
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.. autofunction:: ray.rllib.utils.annotations.ExperimentalAPI
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:noindex:
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Features
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--------
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Feature development, discussion, and upcoming priorities are tracked on the `GitHub issues page <https://github.com/ray-project/ray/issues>`__ (note that this may not include all development efforts).
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Benchmarks
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----------
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A number of training run results are available in the `rl-experiments repo <https://github.com/ray-project/rl-experiments>`__, and there is also a list of working hyperparameter configurations in `tuned_examples <https://github.com/ray-project/ray/tree/master/rllib/tuned_examples>`__, sorted by algorithm. Benchmark results are extremely valuable to the community, so if you happen to have results that may be of interest, consider making a pull request to either repo.
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Contributing Algorithms
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-----------------------
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These are the guidelines for merging new algorithms into RLlib:
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* Contributed algorithms (`rllib/contrib <https://github.com/ray-project/ray/tree/master/rllib/contrib>`__):
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- must subclass Trainer and implement the ``step()`` method
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- must include a lightweight test (`example <https://github.com/ray-project/ray/blob/6bb110393008c9800177490688c6ed38b2da52a9/test/jenkins_tests/run_multi_node_tests.sh#L45>`__) to ensure the algorithm runs
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- should include tuned hyperparameter examples and documentation
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- should offer functionality not present in existing algorithms
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* Fully integrated algorithms (`rllib/agents <https://github.com/ray-project/ray/tree/master/rllib/agents>`__) have the following additional requirements:
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- must fully implement the Trainer API
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- must offer substantial new functionality not possible to add to other algorithms
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- should support custom models and preprocessors
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- should use RLlib abstractions and support distributed execution
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Both integrated and contributed algorithms ship with the ``ray`` PyPI package, and are tested as part of Ray's automated tests. The main difference between contributed and fully integrated algorithms is that the latter will be maintained by the Ray team to a much greater extent with respect to bugs and integration with RLlib features.
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How to add an algorithm to ``contrib``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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It takes just two changes to add an algorithm to `contrib <https://github.com/ray-project/ray/tree/master/rllib/contrib>`__. A minimal example can be found `here <https://github.com/ray-project/ray/tree/master/rllib/contrib/random_agent/random_agent.py>`__. First, subclass `Trainer <https://github.com/ray-project/ray/commits/master/rllib/agents/trainer.py>`__ and implement the ``_init`` and ``step`` methods:
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.. literalinclude:: ../../../rllib/contrib/random_agent/random_agent.py
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:language: python
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:start-after: __sphinx_doc_begin__
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:end-before: __sphinx_doc_end__
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Second, register the trainer with a name in `contrib/registry.py <https://github.com/ray-project/ray/blob/master/rllib/contrib/registry.py>`__.
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.. code-block:: python
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def _import_random_agent():
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from ray.rllib.contrib.random_agent.random_agent import RandomAgent
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return RandomAgent
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def _import_random_agent_2():
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from ray.rllib.contrib.random_agent_2.random_agent_2 import RandomAgent2
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return RandomAgent2
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CONTRIBUTED_ALGORITHMS = {
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"contrib/RandomAgent": _import_random_trainer,
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"contrib/RandomAgent2": _import_random_trainer_2,
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# ...
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}
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After registration, you can run and visualize training progress using ``rllib train``:
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.. code-block:: bash
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rllib train --run=contrib/RandomAgent --env=CartPole-v0
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tensorboard --logdir=~/ray_results
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Debugging your Algorithms
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-------------------------
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Finding Memory Leaks In Workers
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Keeping the memory usage of long running workers stable can be challenging. The ``MemoryTrackingCallbacks`` class can be used to track memory usage of workers.
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.. autoclass:: ray.rllib.agents.callbacks.MemoryTrackingCallbacks
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The objects with the top 20 memory usage in the workers will be added as custom metrics. These can then be monitored using tensorboard or other metrics integrations like Weights and Biases:
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.. image:: images/MemoryTrackingCallbacks.png
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