Basic Ray tasks usage examples

The purpose of this article is to show you an example of a Ray app. It shows how Ray tasks are executed on different machines.

We have also prepared a couple of other example Ray apps to make it easier for you to play with Ray on Golem and to help you see the power of Ray. You can find the examples in ray-on-golem repository.

Have a look at the code (but you can find it also in the repository:

from collections import Counter
import socket
import time

import ray
ray.init()

print('''This cluster consists of
    {} nodes in total
    {} CPU resources in total
'''.format(len(ray.nodes()), ray.cluster_resources()['CPU']))

@ray.remote
def f():
    time.sleep(0.5)

    # Return IP address.
    return socket.gethostbyname(socket.gethostname())

object_ids = [f.remote() for _ in range(1000)]
ip_addresses = ray.get(object_ids)

print('Tasks executed')
for ip_address, num_tasks in Counter(ip_addresses).items():
    print('    {} tasks on {}'.format(num_tasks, ip_address))


print('''This cluster consists of
    {} nodes in total
    {} CPU resources in total
'''.format(len(ray.nodes()), ray.cluster_resources()['CPU']))

You can run the app on your local machine with

python3 simple-task.py

Or, you can execute it on Ray on the Golem cluster with ray submit. Let's have a look at ray usage in the app.

You need to run certain steps:

• initialize ray, so your code would be able to use ray infrastructure
• declare the methods that can be run remotely by ray
• apply ray pattern to execute tasks remote with remote() and await the results with ray.get()

import ray
ray.init()

This is what Ray initialization looks like. ray.init() without parameters tells Ray to look for an existing Ray cluster or (if not found) to start up a local Ray instance.

The ray module exposes methods we use to print the information about the cluster (before and after ray computation). When you run the script on a fresh Ray on the Golem cluster, the number of nodes will increase as a result of the computation. This happens because Ray autoscaler monitors the amount of work pending and requests additional nodes as the queues get longer.

It also decommissions the nodes when they start to idle (5 mins in the example config we are providing).

@ray.remote
def f:

Ray's remote decorator turns a regular local function into Ray's object, which enables the function to be executed remotely (on a different node). When you subsequently call its .remote method, the Ray scheduler will queue its execution. In our case, the function just sleeps for a moment and then returns the IP address of the node it was executed on.

object_ids = [f.remote() for _ in range(1000)]

What happens when you call the remote method added by the @ray.remote decorator, is that it immediately returns an id - a kind of future promise - that can be used to get the results later.

ip_addresses = ray.get(object_ids)

The future(s) can be awaited with ray.get. It returns only when all the remote tasks are executed.

And that's it - the code parallelization with Ray is done by choosing which parts of code can be executed remotely, decorating them with @ray.remote, then changing their execution to .remote(), and finally waiting for the results with ray.get(). Of course, remote Ray tasks can call other Ray remote tasks.

You can now proceed to run your app on a Ray on Golem cluster

Read more information about Ray tasks on Ray Core docs website