CODEWITHSUNDEEP
ray
Francesco Saracco
Francesco Saracco

Reputation: 63

How does Ray handles a number of jobs higher than the number of resources?

Pretty basic question, but I wasn't able to find the answer in the docs. I am developing a computationally intensive application in Python and I'm employing Ray to parallelize computation. I only use remote functions (thus no Actors) and I have 40 cores available.

What happens when the main script sends a number of tasks higher than 40? Is Ray able to handle it or should I always control the number of tasks in order to keep it under the number of available cores?

Upvotes: 4

Views: 3290

Answers (1)

Alex
Alex

Reputation: 1448

In this scenario, Ray will queue the tasks, and run them as CPUs become available.

For example,

import ray 
import time

ray.init(num_cpus=10)

@ray.remote
def outer_task():
 return ray.get([inner_task.remote() for _ in range(20)])

@ray.remote
def inner_task():
 return time.sleep(1)

ray.get([outer_task.remote() for _ in range(20)])

in this scenario, there are 420 tasks which each require a CPU. Ray will queue and run these tasks, so that at most 10 tasks are running at the same time, and will make sure they all finish (in roughly 40 seconds).

Upvotes: 2

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