Note

Ray 2.10.0 introduces the alpha stage of RLlib’s “new API stack”. The Ray Team plans to transition algorithms, example scripts, and documentation to the new code base thereby incrementally replacing the “old API stack” (e.g., ModelV2, Policy, RolloutWorker) throughout the subsequent minor releases leading up to Ray 3.0.

See here for more details on how to use the new API stack.

Install RLlib for Development#

You can develop RLlib locally without needing to compile Ray by using the setup-dev.py script. This sets up symlinks between the ray/rllib dir in your local git clone and the respective directory bundled with the pip-installed ray package. This way, every change you make in the source files in your local git clone will immediately be reflected in your installed ray as well.

However if you have installed ray from source using these instructions then don’t use this, as these steps should have already created the necessary symlinks.

When using the setup-dev.py script, make sure that your git branch is in sync with the installed Ray binaries, meaning you are up-to-date on master and have the latest wheel installed.

# Clone your fork onto your local machine, e.g.:
git clone https://github.com/[your username]/ray.git
cd ray
# Only enter 'Y' at the first question on linking RLlib.
# This leads to the most stable behavior and you won't have to re-install ray as often.
# If you anticipate making changes to e.g. Tune or Train quite often, consider also symlinking Ray Tune or Train here
# (say 'Y' when asked by the script about creating the Tune or Train symlinks).
python python/ray/setup-dev.py

Contributing to RLlib#

Contributing Fixes and Enhancements#

Feel free to file new RLlib-related PRs through Ray’s github repo. The RLlib team is very grateful for any external help they can get from the open-source community. If you are unsure about how to structure your bug-fix or enhancement-PRs, create a small PR first, then ask us questions within its conversation section. See here for an example of a good first community PR.

Contributing Algorithms#

These are the guidelines for merging new algorithms into RLlib. We distinguish between two levels of contributions: As an example script (possibly with additional classes in other files) or as a fully-integrated RLlib Algorithm in rllib/algorithms.

  • Example Algorithms:

    • must subclass Algorithm and implement the training_step() method

    • must include the main example script, in which the algo is demoed, in a CI test, which proves that the algo is learning a certain task.

    • should offer functionality not present in existing algorithms

  • Fully integrated Algorithms have the following additional requirements:

    • must offer substantial new functionality not possible to add to other algorithms

    • should support custom RLModules

    • should use RLlib abstractions and support distributed execution

    • should include at least one tuned hyperparameter example, testing of which is part of the CI

Both integrated and contributed algorithms ship with the ray PyPI package, and are tested as part of Ray’s automated tests.

New Features#

New feature developments, discussions, and upcoming priorities are tracked on the GitHub issues page (note that this may not include all development efforts).

API Stability#

New API stack vs Old API stack#

Starting in Ray 2.10, you can opt-in to the alpha version of a “new API stack”, a fundamental overhaul from the ground up with respect to architecture, design principles, code base, and user facing APIs.

See here for more details on this effort and how to activate the new API stack through your config.

API Decorators in the Codebase#

Objects and methods annotated with @PublicAPI (new API stack), @DeveloperAPI (new API stack), or @OldAPIStack (old API stack) have the following API compatibility guarantees:

ray.util.annotations.PublicAPI(*args, **kwargs)[source]

Annotation for documenting public APIs.

Public APIs are classes and methods exposed to end users of Ray.

If stability="alpha", the API can be used by advanced users who are tolerant to and expect breaking changes.

If stability="beta", the API is still public and can be used by early users, but are subject to change.

If stability="stable", the APIs will remain backwards compatible across minor Ray releases (e.g., Ray 1.4 -> 1.8).

For a full definition of the stability levels, please refer to the Ray API Stability definitions.

Parameters:

  • stability – One of {“stable”, “beta”, “alpha”}.

  • api_group – Optional. Used only for doc rendering purpose. APIs in the same group will be grouped together in the API doc pages.

Examples

>>> from ray.util.annotations import PublicAPI
>>> @PublicAPI
... def func(x):
...     return x

>>> @PublicAPI(stability="beta")
... def func(y):
...     return y
ray.util.annotations.DeveloperAPI(*args, **kwargs)[source]

Annotation for documenting developer APIs.

Developer APIs are lower-level methods explicitly exposed to advanced Ray users and library developers. Their interfaces may change across minor Ray releases.

Examples

>>> from ray.util.annotations import DeveloperAPI
>>> @DeveloperAPI
... def func(x):
...     return x
ray.rllib.utils.annotations.OldAPIStack(obj)[source]

Decorator for classes/methods/functions belonging to the old API stack.

These should be deprecated at some point after Ray 3.0 (RLlib GA). It is recommended for users to start exploring (and coding against) the new API stack instead.

Benchmarks#

A number of training run results are available in the rl-experiments repo, and there is also a list of working hyperparameter configurations in 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.

Debugging RLlib#

Finding Memory Leaks In Workers#

Keeping the memory usage of long running workers stable can be challenging. The MemoryTrackingCallbacks class can be used to track memory usage of workers.

class ray.rllib.algorithms.callbacks.MemoryTrackingCallbacks[source]#

MemoryTrackingCallbacks can be used to trace and track memory usage in rollout workers.

The Memory Tracking Callbacks uses tracemalloc and psutil to track python allocations during rollouts, in training or evaluation.

The tracking data is logged to the custom_metrics of an episode and can therefore be viewed in tensorboard (or in WandB etc..)

Add MemoryTrackingCallbacks callback to the tune config e.g. { …’callbacks’: MemoryTrackingCallbacks …}

Note

This class is meant for debugging and should not be used in production code as tracemalloc incurs a significant slowdown in execution speed.

The objects with the top 20 memory usage in the workers are added as custom metrics. These can then be monitored using tensorboard or other metrics integrations like Weights & Biases:

../_images/MemoryTrackingCallbacks.png

Troubleshooting#

If you encounter errors like blas_thread_init: pthread_create: Resource temporarily unavailable when using many workers, try setting OMP_NUM_THREADS=1. Similarly, check configured system limits with ulimit -a for other resource limit errors.

For debugging unexpected hangs or performance problems, you can run ray stack to dump the stack traces of all Ray workers on the current node, ray timeline to dump a timeline visualization of tasks to a file, and ray memory to list all object references in the cluster.