How do I find out what parts of my code are inefficient in Python

Question

On a previous question I was asking about multiprocessing, using multiple cores to make a program run faster, and someone told me this:

More often than not, you can get a 100x+ optimization with better code compared to a 4x improvement and additional complexities with multiprocessing

They then recommended that I should:

Use a profiler to understand what is slow, then focus on optimizing that.

So I went to this question: How can you profile a script?

Here I found cProfile and implemented it into some testing code to see how it works.

This is my code:

import cProfile  def foo():     for i in range(10000):         a = i**i         if i % 1000 == 0:             print(i)  cProfile.run('foo()') 

However, after running it, this was what I got:

0 1000 2000 3000 4000 5000 6000 7000 8000 9000          1018 function calls in 20.773 seconds     Ordered by: standard name     ncalls  tottime  percall  cumtime  percall filename:lineno(function)         1    0.000    0.000   20.773   20.773 <string>:1(<module>)       147    0.000    0.000    0.000    0.000 rpc.py:150(debug)        21    0.000    0.000    0.050    0.002 rpc.py:213(remotecall)        21    0.000    0.000    0.002    0.000 rpc.py:223(asynccall)        21    0.000    0.000    0.048    0.002 rpc.py:243(asyncreturn)        21    0.000    0.000    0.000    0.000 rpc.py:249(decoderesponse)        21    0.000    0.000    0.048    0.002 rpc.py:287(getresponse)        21    0.000    0.000    0.000    0.000 rpc.py:295(_proxify)        21    0.001    0.000    0.048    0.002 rpc.py:303(_getresponse)        21    0.000    0.000    0.000    0.000 rpc.py:325(newseq)        21    0.000    0.000    0.002    0.000 rpc.py:329(putmessage)        21    0.000    0.000    0.000    0.000 rpc.py:55(dumps)        20    0.000    0.000    0.001    0.000 rpc.py:556(__getattr__)         1    0.000    0.000    0.001    0.001 rpc.py:574(__getmethods)        20    0.000    0.000    0.000    0.000 rpc.py:598(__init__)        20    0.000    0.000    0.050    0.002 rpc.py:603(__call__)        20    0.000    0.000    0.051    0.003 run.py:340(write)         1   20.722   20.722   20.773   20.773 test.py:3(foo)        42    0.000    0.000    0.000    0.000 threading.py:1226(current_thread)        21    0.000    0.000    0.000    0.000 threading.py:215(__init__)        21    0.000    0.000    0.047    0.002 threading.py:263(wait)        21    0.000    0.000    0.000    0.000 threading.py:74(RLock)        21    0.000    0.000    0.000    0.000 {built-in method _struct.pack}        21    0.000    0.000    0.000    0.000 {built-in method _thread.allocate_lock}        42    0.000    0.000    0.000    0.000 {built-in method _thread.get_ident}         1    0.000    0.000   20.773   20.773 {built-in method builtins.exec}        42    0.000    0.000    0.000    0.000 {built-in method builtins.isinstance}        63    0.000    0.000    0.000    0.000 {built-in method builtins.len}        10    0.000    0.000    0.051    0.005 {built-in method builtins.print}        21    0.000    0.000    0.000    0.000 {built-in method select.select}        21    0.000    0.000    0.000    0.000 {method '_acquire_restore' of '_thread.RLock' objects}        21    0.000    0.000    0.000    0.000 {method '_is_owned' of '_thread.RLock' objects}        21    0.000    0.000    0.000    0.000 {method '_release_save' of '_thread.RLock' objects}        21    0.000    0.000    0.000    0.000 {method 'acquire' of '_thread.RLock' objects}        42    0.047    0.001    0.047    0.001 {method 'acquire' of '_thread.lock' objects}        21    0.000    0.000    0.000    0.000 {method 'append' of 'collections.deque' objects}         1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}        21    0.000    0.000    0.000    0.000 {method 'dump' of '_pickle.Pickler' objects}        20    0.000    0.000    0.000    0.000 {method 'get' of 'dict' objects}        21    0.000    0.000    0.000    0.000 {method 'getvalue' of '_io.BytesIO' objects}        21    0.000    0.000    0.000    0.000 {method 'release' of '_thread.RLock' objects}        21    0.001    0.000    0.001    0.000 {method 'send' of '_socket.socket' objects} 

I was expecting it to show me what parts of my code were taking the longest, for example for it to show that a = i**i was taking the longest to compute, however all I can gather from what it has told me is that the foo() function took the longest, however what took the longest inside that function I have no idea from the data.

Also, when I implement this into my actual code, it does the same thing. Everything is in functions, and it only tells me what functions are taking the longest rather than what in the function is taking so long.

So here are my main questions:

1. How do I see what inside the function is making the code take so long (Should I even use cProfile?)

2. What is the best way to set about optimizing my code once I know what is using the most CPU

Note: My RAM and disk etc is absolutely fine, it is just the CPU which is maxed out (12% CPU because it only runs on a single core)

Answer 1

How do I see what inside the function is making the code take so long (Should I even use cProfile?)

Yes, you can use cProfile but the way you asked the question makes me wonder if line_profiler (third party module, you need to install it) wouldn't be a better tool.

I'm using the IPython/Jupyter bindings of this package when I want to profile a function:

%load_ext line_profiler 

To actually profile a function:

%lprun -f foo foo() #             ^^^^^---- this call will be profiled #         ^^^-----------function to profile 

Which produces this output:

Timer unit: 5.58547e-07 s  Total time: 17.1189 s File: <ipython-input-1-21b5a5f52f66> Function: foo at line 1  Line #      Hits         Time  Per Hit   % Time  Line Contents ==============================================================      1                                           def foo():      2     10001        31906      3.2      0.1      for i in range(10000):      3     10000     30534065   3053.4     99.6          a = i**i      4     10000        75998      7.6      0.2          if i % 1000 == 0:      5        10         6953    695.3      0.0              print(i) 

That includes several things that might be interesting. For example 99.6% of the time is spent in the i**i line.

2. What is the best way to set about optimising my code once I know what is using the most CPU

That depends. Sometimes you need to use different functions/datastructures/algorithms - sometimes you can't do anything. But at least you know where your bottleneck is and you can estimate how much impact a change at the bottleneck or somewhere else would have.

Answer 2

As you have noticed in the profiling log, the cProfile maximum resolution is the function.

So:

• if your function is small you may be able to figure out which part is taking a long time (although it's sometimes difficult with built-in calls like in)
• if your function is big, maybe it's time to cut it down to smaller functions, which become "profileable", however function call overhead may slow things down