mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
No description
![]() This also removes the async resetting code in VectorEnv. While that improves benchmark performance slightly, it substantially complicates env configuration and probably isn't worth it for most envs. This makes it easy to efficiently support setups like Joint PPO: https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/retro-contest/gotta_learn_fast_report.pdf For example, for 188 envs, you could do something like num_envs: 10, num_envs_per_worker: 19. |
||
---|---|---|
.github | ||
.travis | ||
cmake/Modules | ||
doc | ||
docker | ||
examples | ||
java | ||
python | ||
site | ||
src | ||
test | ||
thirdparty/scripts | ||
.clang-format | ||
.gitignore | ||
.style.yapf | ||
.travis.yml | ||
build-docker.sh | ||
build.sh | ||
CMakeLists.txt | ||
CONTRIBUTING.rst | ||
LICENSE | ||
pylintrc | ||
README.rst | ||
scripts | ||
setup_thirdparty.sh |
Ray === .. image:: https://travis-ci.com/ray-project/ray.svg?branch=master :target: https://travis-ci.com/ray-project/ray .. image:: https://readthedocs.org/projects/ray/badge/?version=latest :target: http://ray.readthedocs.io/en/latest/?badge=latest | Ray is a flexible, high-performance distributed execution framework. Ray is easy to install: ``pip install ray`` Example Use ----------- +------------------------------------------------+----------------------------------------------------+ | **Basic Python** | **Distributed with Ray** | +------------------------------------------------+----------------------------------------------------+ |.. code-block:: python |.. code-block:: python | | | | | # Execute f serially. | # Execute f in parallel. | | | | | | @ray.remote | | def f(): | def f(): | | time.sleep(1) | time.sleep(1) | | return 1 | return 1 | | | | | | | | | ray.init() | | results = [f() for i in range(4)] | results = ray.get([f.remote() for i in range(4)]) | +------------------------------------------------+----------------------------------------------------+ Ray comes with libraries that accelerate deep learning and reinforcement learning development: - `Ray Tune`_: Hyperparameter Optimization Framework - `Ray RLlib`_: Scalable Reinforcement Learning .. _`Ray Tune`: http://ray.readthedocs.io/en/latest/tune.html .. _`Ray RLlib`: http://ray.readthedocs.io/en/latest/rllib.html Installation ------------ Ray can be installed on Linux and Mac with ``pip install ray``. To build Ray from source or to install the nightly versions, see the `installation documentation`_. .. _`installation documentation`: http://ray.readthedocs.io/en/latest/installation.html More Information ---------------- - `Documentation`_ - `Tutorial`_ - `Blog`_ - `Ray paper`_ - `Ray HotOS paper`_ .. _`Documentation`: http://ray.readthedocs.io/en/latest/index.html .. _`Tutorial`: https://github.com/ray-project/tutorial .. _`Blog`: https://ray-project.github.io/ .. _`Ray paper`: https://arxiv.org/abs/1712.05889 .. _`Ray HotOS paper`: https://arxiv.org/abs/1703.03924 Getting Involved ---------------- - Ask questions on our mailing list `ray-dev@googlegroups.com`_. - Please report bugs by submitting a `GitHub issue`_. - Submit contributions using `pull requests`_. .. _`ray-dev@googlegroups.com`: https://groups.google.com/forum/#!forum/ray-dev .. _`GitHub issue`: https://github.com/ray-project/ray/issues .. _`pull requests`: https://github.com/ray-project/ray/pulls