Counter per second with ray

multicore_count_per_second.py

import ray
# Start ray cluster with 3 cpu's
ray.init(num_cpus=3)

# @ray.remote decorator enables to use this 
# function in distributed setting
@ray.remote
def count_per_second(x):
    start = time.time()
    counter = 0
    while time.time() - start < 1:
        counter += 1
    return counter

start = time.time()
# Run the function 9 times using multiple cores and gather the results
results = ray.get([count_per_second.remote(x) for x in range(9)])
print("duration =", time.time() - start)
print("results =", results)

# Output:
# duration = 3.0135035514831543
# results = [711212, 702964, 708731, 667317, 665449, 667194, 643504, 641866, 647487]