Ray is a flexible, high-performance distributed execution framework.
Ray is easy to install: pip install ray
| 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
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.
- 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.