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
- ray-dev@googlegroups.com: For discussions about development or any general questions.
- StackOverflow: For questions about how to use Ray.
- GitHub Issues: For reporting bugs and feature requests.
- Pull Requests: For submitting code contributions.