mirror of
https://github.com/vale981/ray
synced 2025-03-06 18:41:40 -05:00
67 lines
1.8 KiB
Python
67 lines
1.8 KiB
Python
# This workload tests submitting and getting many tasks over and over.
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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 time
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import ray
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from ray.tests.cluster_utils import Cluster
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num_redis_shards = 5
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redis_max_memory = 10**8
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object_store_memory = 10**8
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num_nodes = 10
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message = ("Make sure there is enough memory on this machine to run this "
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"workload. We divide the system memory by 2 to provide a buffer.")
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assert (num_nodes * object_store_memory + num_redis_shards * redis_max_memory <
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ray.utils.get_system_memory() / 2)
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# Simulate a cluster on one machine.
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cluster = Cluster()
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for i in range(num_nodes):
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cluster.add_node(
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redis_port=6379 if i == 0 else None,
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num_redis_shards=num_redis_shards if i == 0 else None,
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num_cpus=2,
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num_gpus=0,
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resources={str(i): 2},
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object_store_memory=object_store_memory,
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redis_max_memory=redis_max_memory)
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ray.init(address=cluster.address)
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# Run the workload.
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@ray.remote
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def f(*xs):
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return 1
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iteration = 0
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ids = []
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start_time = time.time()
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previous_time = start_time
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while True:
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for _ in range(50):
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new_constrained_ids = [
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f._remote(args=[*ids], resources={str(i % num_nodes): 1})
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for i in range(25)
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]
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new_unconstrained_ids = [f.remote(*ids) for _ in range(25)]
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ids = new_constrained_ids + new_unconstrained_ids
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ray.get(ids)
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new_time = time.time()
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print("Iteration {}:\n"
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" - Iteration time: {}.\n"
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" - Absolute time: {}.\n"
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" - Total elapsed time: {}.".format(
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iteration, new_time - previous_time, new_time,
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new_time - start_time))
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previous_time = new_time
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iteration += 1
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