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An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

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Ray

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

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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An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

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