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A high-performance distributed execution engine

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Ray

https://travis-ci.org/ray-project/ray.svg?branch=master https://readthedocs.org/projects/ray/badge/?version=latest

Ray is a flexible, high-performance distributed execution framework.

Ray comes with libraries that accelerate deep learning and reinforcement learning development:

  • Ray.tune: Efficient Distributed Hyperparameter Search
  • Ray RLlib: A Composable and 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)

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A high-performance distributed execution engine

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  • Python 57.1%
  • C++ 35.8%
  • C 3.9%
  • Shell 1.5%
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