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A system for parallel and distributed Python that unifies the ML ecosystem.

Project description

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

Ray is easy to install: pip install ray

Example Use

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:

Installation

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.

Getting Involved

Project details


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