PyBenchmarks
Automate the creation of benchmark tables.
The benchmark utility times one or more code snippets or functions by iterating through input arguments or keyed variables. It returns a dictionary containing the elapsed time (all platforms) and memory usage (linux only) for each combination of the input variables. An argument or keyed variable is iterated if and only if it is a list, a tuple, a generator or a range.
Examples
>>> import numpy as np
>>> from pybenchmarks import benchmark
>>> b = benchmark((np.empty, np.ones), (100, 10000, 1000000),
... dtype=(int, complex))
empty: 100 dtype=int 1.62 us
ones : 100 dtype=int 3.61 us
empty: 100 dtype=complex 1.70 us
ones : 100 dtype=complex 5.42 us
empty: 10000 dtype=int 1.53 us
ones : 10000 dtype=int 7.53 us
empty: 10000 dtype=complex 2.33 us
ones : 10000 dtype=complex 16.29 us
empty: 1000000 dtype=int 1.87 us
ones : 1000000 dtype=int 1.84 ms
empty: 1000000 dtype=complex 2.19 us
ones : 1000000 dtype=complex 4.20 ms>>> b = benchmark(['np.empty(n, dtype=dtype)', 'np.ones(n, dtype=dtype)'],
... dtype=(int, complex), n=(100, 10000, 1000000),
... setup='from __main__ import np')
'np.empty(n, dt...: dtype=int n=100 1.36 us
'np.ones(n, dty...: dtype=int n=100 2.83 us
'np.empty(n, dt...: dtype=complex n=100 1.44 us
'np.ones(n, dty...: dtype=complex n=100 3.50 us
'np.empty(n, dt...: dtype=int n=10000 1.22 us
'np.ones(n, dty...: dtype=int n=10000 7.05 us
'np.empty(n, dt...: dtype=complex n=10000 1.35 us
'np.ones(n, dty...: dtype=complex n=10000 23.78 us
'np.empty(n, dt...: dtype=int n=1000000 1.47 us
'np.ones(n, dty...: dtype=int n=1000000 2.04 ms
'np.empty(n, dt...: dtype=complex n=1000000 2.91 us
'np.ones(n, dty...: dtype=complex n=1000000 4.26 ms>>> import time
>>> benchmark(time.sleep, (1, 2, 3))
1 1.00 s
2 2.00 s
3 3.00 s>>> shapes = (100, 10000, 1000000)
>>> setup = """
... import numpy as np
... a = np.random.random_sample(shape)
... """
>>> b = benchmark('np.dot(a, a)', shape=shapes, setup=setup)
shape=100 1.38 us
shape=10000 6.33 us
shape=1000000 855.44 us>>> shapes = (10, 100, 1000)
>>> setup="""
... import numpy as np
... a = np.random.random_sample((m, n))
... b = np.random.random_sample(n)
... """
>>> b = benchmark('np.dot(a, b)', m=shapes, n=shapes, setup=setup)
m=10 n=10 1.08 us
m=100 n=10 1.61 us
m=1000 n=10 6.91 us
m=10 n=100 1.48 us
m=100 n=100 4.16 us
m=1000 n=100 20.69 us
m=10 n=1000 4.42 us
m=100 n=1000 39.23 us
m=1000 n=1000 931.04 us>>> def f(x, n, start=1):
... out = start
... for i in range(n):
... out *= x
... return out
>>> b = benchmark(f, np.full(10, 2), xrange(10), start=2.)
0 1.09 us
1 4.15 us
2 5.25 us
3 5.53 us
4 13.10 us
5 9.23 us
6 9.69 us
7 10.46 us
8 13.03 us
9 10.77 us
