Ray is an experimental distributed execution framework with a Python-like programming model. It is under development and not ready for general use.
The goal of Ray is to make it easy to write machine learning applications that run on a cluster while providing the development and debugging experience of working on a single machine.
Before jumping into the details, here's a simple Python example for doing a Monte Carlo estimation of pi (using multiple cores or potentially multiple machines).
import ray
import functions # See definition below
results = []
for _ in range(10):
results.append(functions.estimate_pi(100))
estimate = np.mean([ray.get(ref) for ref in results])
print "Pi is approximately {}.".format(estimate)This assumes that we've defined the file functions.py as below.
import ray
import numpy as np
@ray.remote([int], [float])
def estimate_pi(n):
x = np.random.uniform(size=n)
y = np.random.uniform(size=n)
return 4 * np.mean(x ** 2 + y ** 2 < 1)Within the for loop, each call to functions.estimate_pi(100) sends a message
to the scheduler asking it to schedule the task of running
functions.estimate_pi with the argument 100. This call returns right away
without waiting for the actual estimation of pi to take place. Instead of
returning a float, it returns an object reference, which represents the
eventual output of the computation.
The call to ray.get(ref) takes an object reference and returns the actual
estimate of pi (waiting until the computation has finished if necessary).
- Installation on Ubuntu, Mac OS X, Windows
- Basic Usage
- Tutorial
- About the System
- Using Ray on a Cluster