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https://github.com/vale981/ray
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87bb7a8f67
* Revert "Make tests more informative (#372)" This reverts commitfd353250c8. * fix bugs, in particular deactivate worker service on driver and remove condition variables * changes to minimize the changes in this PR * switch from faulty mutex synchronization to using atomics * Increase the default size of the message queues, to accommodate exporting large numbers of remote functions. This is a temporary fix, but not a long term solution. * Reorganize the scheduler export code to queue up exports. This does not solve the underlying problem yet, but sets up a solution. * Start a separate thread on driver to print error messages by constantly querying the scheduler. This is a temporary solution because the solution based on starting a worker service for the driver which the scheduler can push error messages to is buggy. * Fix segfault in taskcapsule destructor. * Move tests for catching errors into a separate test file. * Revert "roll back grpc (#368)" This reverts commitc01ef95d04.
83 lines
3.2 KiB
Python
83 lines
3.2 KiB
Python
import unittest
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import ray
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import time
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import os
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import numpy as np
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import test_functions
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class MicroBenchmarkTest(unittest.TestCase):
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def testTiming(self):
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reload(test_functions)
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ray.init(start_ray_local=True, num_workers=3)
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# measure the time required to submit a remote task to the scheduler
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elapsed_times = []
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for _ in range(1000):
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start_time = time.time()
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test_functions.empty_function.remote()
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end_time = time.time()
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elapsed_times.append(end_time - start_time)
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elapsed_times = np.sort(elapsed_times)
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average_elapsed_time = sum(elapsed_times) / 1000
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print "Time required to submit an empty function call:"
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print " Average: {}".format(average_elapsed_time)
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print " 90th percentile: {}".format(elapsed_times[900])
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print " 99th percentile: {}".format(elapsed_times[990])
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print " worst: {}".format(elapsed_times[999])
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# average_elapsed_time should be about 0.00038
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# measure the time required to submit a remote task to the scheduler (where the remote task returns one value)
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elapsed_times = []
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for _ in range(1000):
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start_time = time.time()
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test_functions.trivial_function.remote()
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end_time = time.time()
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elapsed_times.append(end_time - start_time)
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elapsed_times = np.sort(elapsed_times)
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average_elapsed_time = sum(elapsed_times) / 1000
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print "Time required to submit a trivial function call:"
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print " Average: {}".format(average_elapsed_time)
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print " 90th percentile: {}".format(elapsed_times[900])
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print " 99th percentile: {}".format(elapsed_times[990])
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print " worst: {}".format(elapsed_times[999])
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# average_elapsed_time should be about 0.001
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# measure the time required to submit a remote task to the scheduler and get the result
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elapsed_times = []
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for _ in range(1000):
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start_time = time.time()
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x = test_functions.trivial_function.remote()
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ray.get(x)
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end_time = time.time()
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elapsed_times.append(end_time - start_time)
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elapsed_times = np.sort(elapsed_times)
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average_elapsed_time = sum(elapsed_times) / 1000
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print "Time required to submit a trivial function call and get the result:"
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print " Average: {}".format(average_elapsed_time)
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print " 90th percentile: {}".format(elapsed_times[900])
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print " 99th percentile: {}".format(elapsed_times[990])
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print " worst: {}".format(elapsed_times[999])
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# average_elapsed_time should be about 0.0013
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# measure the time required to do do a put
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elapsed_times = []
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for _ in range(1000):
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start_time = time.time()
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ray.put(1)
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end_time = time.time()
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elapsed_times.append(end_time - start_time)
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elapsed_times = np.sort(elapsed_times)
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average_elapsed_time = sum(elapsed_times) / 1000
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print "Time required to put an int:"
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print " Average: {}".format(average_elapsed_time)
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print " 90th percentile: {}".format(elapsed_times[900])
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print " 99th percentile: {}".format(elapsed_times[990])
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print " worst: {}".format(elapsed_times[999])
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# average_elapsed_time should be about 0.00087
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ray.worker.cleanup()
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if __name__ == "__main__":
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unittest.main(verbosity=2)
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