mirror of
https://github.com/vale981/ray
synced 2025-03-10 05:16:49 -04:00
4043769ba2
* putting large objects * add more checks * support large objects * fix test * fix linting * upgrade to latest arrow version * check malloc return code * print mmap file sizes * printing * revert to dlmalloc * add prints * more prints * add printing * printing * fix * update * fix * update * print * initialization * temp * fix * update * fix linting * comment out object_store_full tests * fix test * fix test * evict objects if dlmalloc fails * fix stresstests * Fix linting. * Uncomment large-memory tests. * Increase memory for docker image for jenkins tests. * Reduce large memory tests. * Further reduce large memory tests.
28 lines
704 B
Python
28 lines
704 B
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import time
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import ray
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@ray.remote
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def f():
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time.sleep(0.1)
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return ray.services.get_node_ip_address()
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if __name__ == "__main__":
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ray.init(redis_address=os.environ["RAY_REDIS_ADDRESS"])
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# Check that tasks are scheduled on all nodes.
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num_attempts = 30
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for i in range(num_attempts):
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ip_addresses = ray.get([f.remote() for i in range(1000)])
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distinct_addresses = set(ip_addresses)
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counts = [ip_addresses.count(address) for address in distinct_addresses]
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print("Counts are {}".format(counts))
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if len(counts) == 5:
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break
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assert len(counts) == 5
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