How can I do the profiling of functions in python which have very low execution time? I am getting 0 in most of the functions.
Put a loop around it to run it some large number of times, like 10^6, so it takes at least several seconds.
Then the method I use to see how time is spent is this.
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I am working on an optimization algorithm which I need to run at least 100 times to see its performance. The entire script is a straight loop script, running a specific set of code multiple times over. The problem is that this entire thing takes up to 10 hours on a small dataset.
Is it possible to run this on a platform so that I can decrease this time? Can I run it faster on the cloud?
I suggest to you to "divide and conquer" your algo.
First, take a small sample of data in order to explore the code without lost too much time. After that you have a manageable piece of code, you can appy some kind of profiling tools to see where your time is spent.
The line_profiler is very handy to analyze code performance line by line. I have a python function that is called N times and I want to measure its time performance using line_profiler. The problem is, python takes extra time to compile the function during the first running loop. And in my case, I am not interested to take into consideration the compiling time when analyzing the performance. Is there a way to ignore the time overhead of compiling the code when profiling the code in python?
I am working on driving down the execution time on a program I've refactored, and I'm having trouble understanding the profiler output in PyCharm and how it relates to the output I would get if I run cProfile directly. (My output is shown below, with two lines of interest highlighted that I want to be sure I understand correctly before attempting to make fixes.) In particular, what do the Time and Own Time columns represent? I am guessing Own Time is the time consumed by the function, minus the time of any other calls made within that function, and time is the total time spent in each function (i.e. they just renamed tottime and cumtime, respectively), but I can't find anything that documents that clearly.
Also, what can I do to find more information about a particularly costly function using either PyCharm's profiler or vanilla cProfile? For example, _strptime seems to be costing me a lot of time, but I know it is being used in four different functions in my code. I'd like to see a breakdown of how those 2 million calls are spread across my various functions. I'm guessing there's a disproportionate number in the calc_near_geo_size_and_latency function, but I'd like more proof of that before I go rewriting code. (I realize that I could just profile the functions individually and compare, but I'm hoping for something more concise.)
I'm using Python 3.6 and PyCharm Professional 2018.3.
In particular, what do the Time and Own Time columns represent? I am guessing Own Time is the time consumed by the function, minus the time of any other calls made within that function, and time is the total time spent in each function (i.e. they just renamed tottime and cumtime, respectively), but I can't find anything that documents that clearly.
You can see definitions of own time and time here: https://www.jetbrains.com/help/profiler/Reference__Dialog_Boxes__Properties.html
Own time - Own execution time of the chosen function. The percentage of own time spent in this call related to overall time spent in this call in the parentheses.
Time - Execution time of the chosen function plus all time taken by functions called by this function. The percentage of time spent in this call related to time spent in all calls in the parentheses.
This is also confirmed by a small test:
Also, what can I do to find more information about a particularly costly function using either PyCharm's profiler or vanilla cProfile?
By default pycharm does use cProfile as a profiler. Perhaps you're asking about using cProfile on the command line? There are plenty of examples of doing so here: https://docs.python.org/3.6/library/profile.html
For example, _strptime seems to be costing me a lot of time, but I know it is being used in four different functions in my code. I'd like to see a breakdown of how those 2 million calls are spread across my various functions.
Note that the act of measuring something will have an impact on the measurement retrieved. For a function or method that is called many times, especially 2 million, the profiler itself will have a significant impact on the measured value.
So I have a function that takes a long time, and I want to speed it up. I did all I could to optimize it, but it still takes a very long time. I want to multiprocessing to speed it up, using 3 or 4 threads. Everything I found online either gave me a way to run 2 functions at the same time (this particular function you cannot split up into different parts and combine them at the end), or a way to run the same function with different inputs, which also doesn't help me.
Thank you!
I am a beginner just starting to profile my code and was confused why the elapsed time given by cProfile was so off from the time given by using time.time().
# Python 2.7.2
import cProfile
def f(n):
G = (i for i in xrange(n))
sum = 0
for i in G:
sum += i
num = 10**6
cProfile.run('f(num)')
This gives
1000004 function calls in 2.648 seconds
Yet with time.time(), I get 0.218000173569 seconds
import time
x = time.time()
f(num)
print time.time() - x
From what I have read, I guess this may be because of the overhead of cProfile. Are there any general tips for when cProfile timing is likely to be very off, or ways to get more accurate timing?
The point of profiling is to find out what parts of your program are taking the most time, and thus need the most attention. If 90% of the time is being used by one function, you should be looking there to see how you can make that function more efficient. It doesn't matter whether the entire run takes 10 seconds or 1000.
Perhaps the most important piece of information the profiler gives you is how many times something is called. Why this is useful is that it helps you find places where you are calling things unnecessarily often, especially if you have nested loops, or many functions that call other functions. The profiler helps you track this stuff down.
The profiling overhead is unavoidable, and large. But it is much easier to let the profiler do what it does than to insert your own timings and print statements all over the place.
Note that cProfile gives you CPU time, but Using time.time() gives you elapsed time (which isn't what you want).
maybe you can try using the unix time program.
➜ sandbox /usr/bin/time -p python profiler.py
real 0.17
user 0.14
sys 0.01
The CPU time should be user+sys