How severe does this issue affect your experience of using Ray?
- High: It blocks me to complete my task.
Hello,
I have a VDS (virtual dedicated server with 24 logical cores) and have rented a completely dedicated server (say DS) (with 64 logical cores) newly. And I’ve faced a really bad performance in my code although DS resources are way better than those of VDS. To confirm the issue, I’ve tested the simple code below:
import time
import random
import ray
ray.init()
@ray.remote
def do_some_work(x):
time.sleep(random.uniform(0, 10) / 100)
return x
def process_incremental(sum, result):
return sum + result
start = time.time()
result_ids = [do_some_work.remote(x) for x in range(10000)]
sum = 0
while len(result_ids):
done_id, result_ids = ray.wait(result_ids)
sum = process_incremental(sum, ray.get(done_id[0]))
print("duration =", time.time() - start, "\nresult = ", sum)
This code runs as expected. VDS finishes it in ~21.5 seconds. DS finishes it in ~8.5 seconds. Do you have any idea why my code (~6 sec in VDS but ~40 sec in DS) shows worse performance in better machine although the code above runs as expected? I would ask the VDS and DS provider (same company, Contabo) about this issue but once the code above runs as expected, this may be meaningless.
Thanks for the help.