Ray is a flexible, high-performance distributed execution framework.
Ray is easy to install: pip install ray
Example Use
| Basic Python | Distributed with Ray |
# Execute f serially. def f(): time.sleep(1) return 1 results = [f() for i in range(4)] |
# Execute f in parallel. @ray.remote def f(): time.sleep(1) return 1 ray.init() results = ray.get([f.remote() for i in range(4)]) |
Ray comes with libraries that accelerate deep learning and reinforcement learning development:
- Tune: Hyperparameter Optimization Framework
- RLlib: Scalable Reinforcement Learning
- Distributed Training
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.
More Information
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.
