def dotest(n):
with eventstats.BenchmarkLogSpan("string"):
x = n * "a"
ray.put(x)
with eventstats.BenchmarkLogSpan("array_of_pairs"):
x = {i: i for i in range(n)}
ray.put(x)
with eventstats.BenchmarkLogSpan("ntuple"):
x = n * (1, )
ray.put(x)
def TEST_PutAfterPut(numobj):
with eventstats.BenchmarkLogSpan("put_after_put", {"numobj" : numobj}):
l1,dt1 = plasma_create_objects(numobj)
time.sleep(1)
l2,dt2 = plasma_create_objects(numobj)
print("TEST_PutAfterPut: slowdown factor={}".format(dt2/dt1))
return dt2/dt1
def TEST_GetBeforeAfterPut(numobj_getperf, numobj_put):
with eventstats.BenchmarkLogSpan("get_before_after_put", {"numobj_getperf" : numobj_getperf, "numobj_put" : numobj_put}):
lst1,putdt1 = plasma_create_objects(numobj_getperf)
dt1 = plasma_get_objects(lst1)
lst2,putdt2 = plasma_create_objects(numobj_put)
#dt2 = plasma_get_objects(lst2) #known bug: this causes a crash
#now try to match dt1 get time again
dt2 = plasma_get_objects(lst1)
lst3,putdt3 = plasma_create_objects(numobj_getperf)
dt3 = plasma_get_objects(lst3)
print("relative time={} to get initial numobj={} objects".format(dt2/dt1, numobj_getperf))
print("relative time={} to get new numobj={} objects".format(dt3/dt1, numobj_getperf))
def test_blocking_tasks(num_tasks):
@ray.remote
def f(i, j):
return (i, j)
@ray.remote
def g(i):
# Each instance of g submits and blocks on the result of another remote
# task.
object_ids = [f.remote(i, j) for j in range(10)]
return ray.get(object_ids)
with eventstats.BenchmarkLogSpan("submit"):
ray.get([g.remote(i) for i in range(num_tasks)])
import json
import ray
import raybench
import numpy as np
import raybench.eventstats as eventstats
if __name__ == '__main__':
bench_env = raybench.Env()
bench_env.ray_init()
n = int(10**bench_env.benchmark_iteration)
with eventstats.BenchmarkLogSpan("randints"):
x = list(np.random.randint(0, 100, size=n))
ray.put(x)
print "BENCHMARK_STATS:", json.dumps({
"config": {
"scale": n
},
"events": eventstats.log_span_events()
})
import json
import ray
import raybench
import raybench.eventstats as eventstats
if __name__ == '__main__':
bench_env = raybench.Env()
bench_env.ray_init()
n = int(10**bench_env.benchmark_iteration)
with eventstats.BenchmarkLogSpan("ntuple"):
x = n * (1, )
ray.put(x)
print "BENCHMARK_STATS:", json.dumps({
"config": {
"scale": n
},
"events": eventstats.log_span_events()
})
import json
import ray
import raybench
import raybench.eventstats as eventstats
if __name__ == '__main__':
bench_env = raybench.Env()
bench_env.ray_init()
n = int(10 ** bench_env.benchmark_iteration)
with eventstats.BenchmarkLogSpan("array_of_pairs"):
x = {i:i for i in range(n)}
ray.put(x)
print "BENCHMARK_STATS:", json.dumps({
"config": { "scale" : n },
"events" : eventstats.log_span_events() })
def TEST_PutGetLinearScale():
for object_size in 10**3, 10**4, 10** 5, 10**6:
time.sleep(1)
with eventstats.BenchmarkLogSpan("put_get_linear_scale", {"object_size" : object_size}):
l, pdt = plasma_create_objects(object_size)
gdt1 = plasma_get_objects(l)
def benchmark_launchtasks(num_tasks):
with eventstats.BenchmarkLogSpan("total"):
with eventstats.BenchmarkLogSpan("submit"):
l = [sleep1s.remote() for _ in range(num_tasks)]
ray.wait(l, num_returns=len(l))
import json
import ray
import raybench
import raybench.eventstats as eventstats
if __name__ == '__main__':
bench_env = raybench.Env()
bench_env.ray_init()
n = int(10**bench_env.benchmark_iteration)
with eventstats.BenchmarkLogSpan("ones_array"):
x = n * [1]
ray.put(x)
print "BENCHMARK_STATS:", json.dumps({
"config": {
"scale": n
},
"events": eventstats.log_span_events()
})
import json
import ray
import raybench
import raybench.eventstats as eventstats
if __name__ == '__main__':
bench_env = raybench.Env()
bench_env.ray_init()
n = int(10 ** bench_env.benchmark_iteration)
with eventstats.BenchmarkLogSpan("nstring"):
x = n * "a"
ray.put(x)
print "BENCHMARK_STATS:", json.dumps({
"config": { "scale" : n },
"events" : eventstats.log_span_events() })