Why Ray Core?

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

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Simple primitives

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

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Multi-cloud

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

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Dynamic scaling

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

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Massive scalability envelope

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

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Open community / ecosystem

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

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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.

import ray 

# By adding the `@ray.remote` decorator, a regular Python function
# becomes a Ray remote function.
@ray.remote
def my_function():
   return 1
 
# 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.
obj_ref = my_function.remote()
 
# The result can be retrieved with ``ray.get``.
assert ray.get(obj_ref) == 1
 
@ray.remote
def slow_function():
   time.sleep(10)
   return 1
 
# Invocations of Ray remote functions happen in parallel.
# All computation is performed in the background, driven by Ray's internal event loop.
results = []
for _ in range(4):
   # this doesn't block
   results.append(slow_function.remote())

ray.get(results)
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 SGD

Scale deep learning

Ray Serve

Scale model serving

Ray Tune

Scale hyperparameter search