Ray
Ray is a flexible, high-performance distributed execution framework.
Ray comes with libraries that accelerate deep learning and reinforcement learning development:
- Ray Tune: Hyperparameter Optimization Framework
- Ray RLlib: A Scalable Reinforcement Learning Library
Installation
- Ray can be installed on Linux and Mac with
pip install ray. - To build Ray from source, see the instructions for Ubuntu and Mac.
Example Program
| Basic Python | Distributed with Ray |
import time
def f():
time.sleep(1)
return 1
# Execute f serially.
results = [f() for i in range(4)] |
import time
import ray
ray.init()
@ray.remote
def f():
time.sleep(1)
return 1
# Execute f in parallel.
object_ids = [f.remote() for i in range(4)]
results = ray.get(object_ids) |
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.