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from memory_profiler import memory_usage
# size = 50000
size = 3000
def test_simple():
import numpy as np
def func():
a = np.random.random((size, size))
return a
rss = memory_usage(proc=func, max_usage=True, backend="psutil")
uss = memory_usage(proc=func, max_usage=True, backend="psutil_uss")
pss = memory_usage(proc=func, max_usage=True, backend="psutil_pss")
print(rss, uss, pss)
def test_multiprocessing():
import numpy as np
import joblib
import time
def func():
n_jobs = 4
a = np.random.random((size, size))
def subprocess(i):
time.sleep(2)
return a[i,i]
results = joblib.Parallel(n_jobs=n_jobs)(
joblib.delayed(subprocess)(i)
for i in range(n_jobs))
return results
rss = memory_usage(proc=func, max_usage=True, backend="psutil", include_children=True, multiprocess=True)
uss = memory_usage(proc=func, max_usage=True, backend="psutil_uss", include_children=True, multiprocess=True)
pss = memory_usage(proc=func, max_usage=True, backend="psutil_pss", include_children=True, multiprocess=True)
print(rss, uss, pss)
def test_multiprocessing_write():
import numpy as np
import joblib
import time
def func():
n_jobs = 4
a = np.random.random((size, size))
def subprocess(i):
aa = a.copy()
time.sleep(2)
return aa[i,i]
results = joblib.Parallel(n_jobs=n_jobs)(
joblib.delayed(subprocess)(i)
for i in range(n_jobs))
return results
rss = memory_usage(proc=func, max_usage=True, backend="psutil", include_children=True, multiprocess=True)
uss = memory_usage(proc=func, max_usage=True, backend="psutil_uss", include_children=True, multiprocess=True)
pss = memory_usage(proc=func, max_usage=True, backend="psutil_pss", include_children=True, multiprocess=True)
print(rss, uss, pss)
def test_multiprocessing_showcase():
import numpy as np
import joblib
import time
import datetime
def func():
# n_jobs = 32
# size = 25000
# Creating data: 25000x25000 ... done (4.66 Gb). Starting processing: n_jobs=32 ... done (0:00:37.581291). RSS: 353024.01
# Creating data: 25000x25000 ... done (4.66 Gb). Starting processing: n_jobs=32 ... done (0:00:38.867385). USS: 148608.62
# Creating data: 25000x25000 ... done (4.66 Gb). Starting processing: n_jobs=32 ... done (0:00:29.049754). PSS: 169253.91
# n_jobs = 64
# size = 10000
# Creating data: 10000x10000 ... done (0.75 Gb). Starting processing: n_jobs=64 ... done (0:00:14.701243). RSS: 111362.79
# Creating data: 10000x10000 ... done (0.75 Gb). Starting processing: n_jobs=64 ... done (0:00:15.020202). USS: 56108.69
# Creating data: 10000x10000 ... done (0.75 Gb). Starting processing: n_jobs=64 ... done (0:00:15.072918). PSS: 54826.61
# Conclusion:
# * RSS is overestimating like crazy (I checked the actual memory usage using htop)
n_jobs = 8
size = 3000
print("Creating data: {size}x{size} ... ".format(size=size), end="")
a = np.random.random((size, size))
print("done ({size:.02f} Gb). ".format(size=a.size * a.itemsize / 1024**3), end="")
def subprocess(i):
aa = a.copy()
r = aa[1,1]
aa = a.copy()
time.sleep(10)
return r
# r = a[1,1]
# # time.sleep(10)
# return r
pass
start = datetime.datetime.now()
print("Starting processing: n_jobs={n_jobs} ... ".format(n_jobs=n_jobs), end="")
results = joblib.Parallel(n_jobs=n_jobs)(
joblib.delayed(subprocess)(i)
for i in range(n_jobs))
print("done ({}). ".format(datetime.datetime.now() - start), end="")
return results
rss = memory_usage(proc=func, max_usage=True, backend="psutil", include_children=True, multiprocess=True)
print("RSS: {rss:.02f}".format(rss=rss))
uss = memory_usage(proc=func, max_usage=True, backend="psutil_uss", include_children=True, multiprocess=True)
print("USS: {uss:.02f}".format(uss=uss))
pss = memory_usage(proc=func, max_usage=True, backend="psutil_pss", include_children=True, multiprocess=True)
print("PSS: {pss:.02f}".format(pss=pss))
if __name__ == "__main__":
test_simple()
test_multiprocessing()
test_multiprocessing_write()
test_multiprocessing_showcase()
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