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.deff():
time.sleep(1)
return1results= [f() foriinrange(4)] | # Execute f in parallel.@ray.remotedeff():
time.sleep(1)
return1ray.init()
results=ray.get([f.remote() foriinrange(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.
- Ask questions on our mailing list ray-dev@googlegroups.com.
- Please report bugs by submitting a GitHub issue.
- Submit contributions using pull requests.