Why Ray Core?

Learn why thousands of engineers and scientists are using Ray for creating scalable, distributed Python applications.


Simple primitives

Flexibly compose distributed applications with tasks, actors, and objects in native Python code.



Run the same Ray code on any cloud — AWS, GCP, Azure — or even on-prem.


Dynamic scaling

Ray Core can automatically scale (using Ray Autoscaler) up or down to smoothly handle changing compute load.


Massive scalability envelope

Ray Core can easily scale to thousands of cores, and is getting more scalable with every release.


Open community / ecosystem

With a vibrant dedicated community and rich ecosystem of integrations, fixes and best practices are easy to find.


Laptop -> Cluster with ease

With Ray Client, going from laptop to cluster is as easy as changing 1 variable.

Try it yourself

Install Ray with pip install ray and give this example a try.

# Approximate pi using random sampling. Generate x and y randomly between 0 and 1. 
#  if x^2 + y^2 < 1 it's inside the quarter circle. x 4 to get pi. 
import ray
from random import random

# Let's start Ray

SAMPLES = 1000000; 
# By adding the `@ray.remote` decorator, a regular Python function
# becomes a Ray remote function.
def pi4_sample():
    in_count = 0
    for _ in range(SAMPLES):
        x, y = random(), random()
        if x*x + y*y <= 1:
            in_count += 1
    return in_count

# To invoke this remote function, use the `remote` method.
# This will immediately return an object ref (a future) and then create
# a task that will be executed on a worker process. Get retreives the result. 
future = pi4_sample.remote()
pi = ray.get(future) * 4.0 / SAMPLES
print(f'{pi} is an approximation of pi') 

# Now let's do this 100,000 times. 
# With regular python this would take 11 hours
# Ray on a modern laptop, roughly 2 hours
# On a 10-node Ray cluster, roughly 10 minutes 
BATCHES = 100000
results = [] 
for _ in range(BATCHES):
output = ray.get(results)
pi = sum(output) * 4.0 / BATCHES / SAMPLES
print(f'{pi} is a way better approximation of pi') 
Code sample background image

Do more with Ray libraries

Learn how you can scale other components of your machine learning pipelines, such as training, data processing, and serving, just as easily using Ray.

Ray Train

Scale deep learning

Ray Serve

Scale model serving

Ray Tune

Scale hyperparameter search

O'Reilly Learning Ray Book

Get your free copy of early release chapters of Learning Ray, the first and only comprehensive book on Ray and its ecosystem, authored by members on the Ray engineering team

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