Scalene: a high-performance, high-precision CPU and memory profiler for Python
1–10 of 19 posts
Re: Scalene: a high-performance, high-precision CPU and memory profiler for Python
#2[deleted]
Re: Scalene: a high-performance, high-precision CPU and memory profiler for Python
#3I love the idea of fast Python profilers that don't require modified source code to run.
One profiler I used recently which isn't mentioned in the readme is pyflame (developed at uber):
https://pyflame.readthedocs.io/
pyflame likewise claims to run on unmodified source code and be fast enough to run in production, so it might be worth adding to the comparison. It generates flamegraphs, which greatly sped up debugging the other day when I needed to figure out why something was slow somewhere in a Django request-response callstack.
Re: Scalene: a high-performance, high-precision CPU and memory profiler for Python
#4I love the idea of fast Python profilers that don't require modified source code to run. One profiler I used recently which isn't mentioned in the readme is pyflame (developed at uber): https://pyflame.readthedocs.io/ pyflame likewise claims to run on unmodified source code and be fast enough to run in production, so it might be worth adding to the comparison. It generates flamegraphs, which greatly sped up debugging…
Similar to PyFlame is also py-spy: https://github.com/benfred/py-spy
> While pyflame is a great project, it doesn't support Python 3.7 yet and doesn't work on OSX, Windows or FreeBSD.
I wonder how the CPU profiling in Scalene is different. It does not mention PyFlame or py-spy at all in the Readme. Of course, the memory profiler is some nice extra.
Re: Scalene: a high-performance, high-precision CPU and memory profiler for Python
#5I love the idea of fast Python profilers that don't require modified source code to run. One profiler I used recently which isn't mentioned in the readme is pyflame (developed at uber): https://pyflame.readthedocs.io/ pyflame likewise claims to run on unmodified source code and be fast enough to run in production, so it might be worth adding to the comparison. It generates flamegraphs, which greatly sped up debugging…
You can fairly easily write helper scripts using eg pkg_util to automatically add profiler decorators (eg with kernprof) so that profiling never requires modifying code.
My team has a large body of profiling code written with kernprof and none of it modifies the underlying source. Profiler annotations are solely added automatically by the little profiler runner tooling we wrote.
Not to say other profiling tools aren’t worth it.
Re: Scalene: a high-performance, high-precision CPU and memory profiler for Python
#6Another python profile that isn't mentioned is pprofile[0]. It is also a statistical profiler with line-level granularity. It doesn't have the memory profiling abilities (that is pretty slick...), but it also doesn't require the dynamic library injection.
I don't know how it's statistical profiling speed compares to scalene, but it would be great to see the comparison.
Also, does anyone know how the malloc interacts with pytorch?
Re: Scalene: a high-performance, high-precision CPU and memory profiler for Python
#7I love the idea of fast Python profilers that don't require modified source code to run. One profiler I used recently which isn't mentioned in the readme is pyflame (developed at uber): https://pyflame.readthedocs.io/ pyflame likewise claims to run on unmodified source code and be fast enough to run in production, so it might be worth adding to the comparison. It generates flamegraphs, which greatly sped up debugging…
Similar to PyFlame is also py-spy: https://github.com/benfred/py-spy > While pyflame is a great project, it doesn't support Python 3.7 yet and doesn't work on OSX, Windows or FreeBSD. I wonder how the CPU profiling in Scalene is different. It does not mention PyFlame or py-spy at all in the Readme. Of course, the memory profiler is some nice extra.
Scalene author here. I was not aware of either tool - many thanks for the pointers! I just tried py-spy (I will try PyFlame on a Linux box momentarily). It's pretty cool, though having to run it as root on OS X isn't great (scalene runs without the need for root privileges; the CPU profiling part is pure Python). Py-spy does appear to efficiently track CPU perf at the line granularity (and does a lot of other stuff). It does a lot of things that scalene does not do, but not memory profiling. Also, I personally prefer scalene's line-level display of annotated source code vs. the flame graphs, but YMMV.
Re: Scalene: a high-performance, high-precision CPU and memory profiler for Python
#8Another similar sampling profiler which has decent performance is pyinstrument: https://github.com/joerick/pyinstrument
Re: Scalene: a high-performance, high-precision CPU and memory profiler for Python
#9Earlier quoted context omitted.
Similar to PyFlame is also py-spy: https://github.com/benfred/py-spy > While pyflame is a great project, it doesn't support Python 3.7 yet and doesn't work on OSX, Windows or FreeBSD. I wonder how the CPU profiling in Scalene is different. It does not mention PyFlame or py-spy at all in the Readme. Of course, the memory profiler is some nice extra.
Scalene author here. I was not aware of either tool - many thanks for the pointers! I just tried py-spy (I will try PyFlame on a Linux box momentarily). It's pretty cool, though having to run it as root on OS X isn't great (scalene runs without the need for root privileges; the CPU profiling part is pure Python). Py-spy does appear to efficiently track CPU perf at the line granularity (and does a lot of other stuff).…
The possibility to run py-spy on an already running Python program in production is pretty awesome. Do you think Scalene could do this?
Re: Scalene: a high-performance, high-precision CPU and memory profiler for Python
#10Sadly, relying on Python's signal handling is not enough to get robust profiling information the moment your code is spending a significant chunk of time outside of simple Python calls. This is because signals don't get delivered to the Python level until the interpreter comes back from C land, and it's possible to get stuck in C land even with pure Python code.
To wit:
$ python -m scalene trace.py
f1 1.3852 312499987500000
f2 1.2420 2812499962500000
f3 1.5018 1.5
trace.py: % of CPU time = 33.66% out of 4.13s.
Line | CPU % | [trace.py]
1 | | #!/usr/bin/env python
2 | |
3 | | import time
4 | |
5 | | def timed(f):
6 | | def f_timed(*args, **kwargs):
7 | | t = time.time()
8 | | r = f(*args, **kwargs)
9 | | t = time.time() - t
10 | | print('%s %.4f %s' % (f.__name__, t, r))
11 | | return f_timed
12 | |
13 | | @timed
14 | | def f1(n):
15 | | s = 0
16 | 17.99% | for i in range(n):
17 | 81.29% | s += i
18 | | return s
19 | |
20 | | @timed
21 | | def f2(n):
22 | 0.72% | return sum(range(n))
23 | |
24 | | @timed
25 | | def f3(t):
26 | | time.sleep(t)
27 | | return t
28 | |
29 | | if __name__ == '__main__':
30 | | f1(25_000_000)
31 | | f2(75_000_000)
32 | | f3(1.5)