I need to meassure the time certain parts of my code take. While executing my code on a powerfull server, I get 10 diffrent results
I tried comparing time measured with time.time(), time.perf_counter(), time.perf_counter_ns(), time.process_time() and time.process_time_ns().
import time
for _ in range(10):
start = time.perf_counter()
i = 0
while i < 100000:
i = i + 1
time.sleep(1)
end = time.perf_counter()
print(end - start)
I'm expecting when executing the same code 10 times, to be the same (the results to have a resolution of at least 1ms) ex. 1.041XX and not 1.030sec - 1.046sec.
When executing my code on a 16 cpu, 32gb memory server I'm receiving this result:
1.045549364
1.030857833
1.0466020120000001
1.0309665050000003
1.0464690349999994
1.046397238
1.0309525370000001
1.0312070380000007
1.0307592159999999
1.046095523
Im expacting the result to be:
1.041549364
1.041857833
1.0416020120000001
1.0419665050000003
1.0414690349999994
1.041397238
1.0419525370000001
1.0412070380000007
1.0417592159999999
1.041095523
Your expectations are wrong. If you want to measure code average time consumption use the timeit module. It executes your code multiple times and averages over the times.
The reason your code has different runtimes lies in your code:
time.sleep(1) # ensures (3.5+) _at least_ 1000ms are waited, won't be less, might be more
You are calling it in a tight loop,resulting in accumulated differences:
Quote from time.sleep(..) documentation:
Suspend execution of the calling thread for the given number of seconds. The argument may be a floating point number to indicate a more precise sleep time. The actual suspension time may be less than that requested because any caught signal will terminate the sleep() following execution of that signal’s catching routine. Also, the suspension time may be longer than requested by an arbitrary amount because of the scheduling of other activity in the system.
Changed in version 3.5: The function now sleeps at least secs even if the sleep is interrupted by a signal, except if the signal handler raises an exception (see PEP 475 for the rationale).
Emphasis mine.
Perfoming a code do not take the same time at each loop iteration because of the scheduling of the system (system puts on hold your process to perform another process then back to it...).
I trying to use time to record running time of this function, but i think the result is not correct, sometimes it will only cost 0s and the result is not stable.The first two result is for N=10000， the third one is N=30000
import time
def sumOfN(n):
start=time.time()
theSum=0
for i in range(1,n+1):
theSum=theSum+i
end=time.time()
return theSum,end-start
for i in range(5):
print("Sum is %d required %10.7f seconds"%sumOfN(300000))
According to the Python manual:
time.time()
Return the time in seconds since the epoch as a floating
point number. Note that even though the time is always returned as a
floating point number, not all systems provide time with a better
precision than 1 second. While this function normally returns
non-decreasing values, it can return a lower value than a previous
call if the system clock has been set back between the two calls.
(emphasis mine)
It seems the timer resolution of your system is not enough to correctly measure the elapsed time of the function. It actually looks like the precision is about 0.016, about 1/60 of a second, which is typical of Windows systems.
your approach has the following two problems:
time.time() returns the current time (as in time of day), which can vary by auto-adjusting processes such as NTP or if someone modifies it (either by hand or via code). Use time.perf_counter() (or time.clock() in Python <3.3) instead.
You are measuring one execution of the function. This can give you very wrong results due to the non-deterministic nature of garbage collection, bytecode optimization, and other quirks of languages like Python. You should look into the timeit module instead.
Using Python 2.7.5 on Windows 7, I have a problem.
I am writing a program that's supposed to calculate the time it takes to calculate a few mathematical algorithms.
I use start=time.clock() and elapsed=time.clock()-start. That works, but when i try to do start=time.clock() a second time it does not work. I get this message:
start=time.time()
AttributeError: 'float' object has no attribute 'time'
I obviously cant use time.clock() several times, but what can i use instead?
It seems like your code somewhere has time = <some calculated value> replacing the name "time" from pointing to the correct module to a float object.
You should not use time.clock():
On Unix, return the current processor time as a floating point number
expressed in seconds. The precision, and in fact the very definition
of the meaning of “processor time”, depends on that of the C function
of the same name, but in any case, this is the function to use for
benchmarking Python or timing algorithms.
On Windows, this function returns wall-clock seconds elapsed since the
first call to this function, as a floating point number, based on the
Win32 function QueryPerformanceCounter(). The resolution is typically
better than one microsecond.
Instead use time.time():
Return the time in seconds since the epoch as a floating point number.
Note that even though the time is always returned as a floating point
number, not all systems provide time with a better precision than 1
second. While this function normally returns non-decreasing values, it
can return a lower value than a previous call if the system clock has
been set back between the two calls.
To put the difference into easier terms: The first time you call time.clock() it will start a timer, every subsequent call to time.clock() will yield the time since the first call. Whereas using time.time() will always give you the time since the epoch.
To clarify, time.clock() is a relative time measurement method, relative to when you first start timing using the time.clock() method, whereas when using time.time() you are getting the seconds since the epoch each time.
In both methods you can deduce the difference between them quite easily, but using time.clock() can have unforeseen consequences when more complex time measurements are needed, like multiple threads, or different functions that have to communicate with each other - if they are all timing themselves relative to themselves, it is hard to keep things constant.
I had the same error.
I found that I named a variable 'time' which is the same name that the time module.
So it answers you that your variable time has no attribute time because your variable overwrites your module time.
Check your variable names or change the module name !
You could to
start1 = time.clock()
Calculation
print "Runtime: " + str(time.clock() - start1)
start2 = time.clock()
Calculation
print "Runtime: " + str(time.clock() - start2)
I'm new to Python. It is my first interpreted language. I used to study Java so far.
So, when Java program runs for the first time, it is execueted slower than for the next times.
The reasi is caching.
import time
def procedure():
time.sleep(2.5)
# measure process time
t0 = time.clock()
procedure()
print (time.clock() - t0), "seconds process time"
I tried this for several times. The result is always equal. So, am I right that no cashe interferes and that the benchmark is pretty reliable?
it's OK to do benchmarks like this, the accuracy is good enough for functions which run "long" and pretty constant, like in your example.
But there are some pitfalls: for "quick" functions (like the empty one), you run into precision limits. And for functions which vary in execution time (like net i/o, for example), you have to measure multiple times to find min/max/avg runtime.
And in addition to that, the clock best used differs on platforms: on windows, time.clock() is preferred, on *nix, time.time().
luckily, there is a module which takes care of all that: timeit:
>>> import time
>>> def procedure():
pass
>>> def time_this(f):
t0=time.clock()
f()
print((time.clock() - t0), "seconds process time")
>>> time_this(procedure)
1.9555558310457855e-06 seconds process time
>>> time_this(procedure)
1.9555557742023666e-06 seconds process time
>>> time_this(procedure)
1.9555557742023666e-06 seconds process time
>>> import timeit
>>> timeit.Timer(procedure).timeit()
0.09460783423588737
>>> timeit.Timer(procedure).repeat()
[0.09791419021132697, 0.09721947901198291, 0.09598943441130814]
you might want to look at it's source. Or just use it ;)
As for caching: python code compiles to bytecode when first used. This bytecode is cached by default - but this won't affect your benchmark as long as you don't do imports in your function.
Which is better to use for timing in Python? time.clock() or time.time()? Which one provides more accuracy?
for example:
start = time.clock()
... do something
elapsed = (time.clock() - start)
vs.
start = time.time()
... do something
elapsed = (time.time() - start)
As of 3.3, time.clock() is deprecated, and it's suggested to use time.process_time() or time.perf_counter() instead.
Previously in 2.7, according to the time module docs:
time.clock()
On Unix, return the current processor time as a floating point number
expressed in seconds. The precision, and in fact the very definition
of the meaning of “processor time”, depends on that of the C function
of the same name, but in any case, this is the function to use for
benchmarking Python or timing algorithms.
On Windows, this function returns wall-clock seconds elapsed since the
first call to this function, as a floating point number, based on the
Win32 function QueryPerformanceCounter(). The resolution is typically
better than one microsecond.
Additionally, there is the timeit module for benchmarking code snippets.
The short answer is: most of the time time.clock() will be better.
However, if you're timing some hardware (for example some algorithm you put in the GPU), then time.clock() will get rid of this time and time.time() is the only solution left.
Note: whatever the method used, the timing will depend on factors you cannot control (when will the process switch, how often, ...), this is worse with time.time() but exists also with time.clock(), so you should never run one timing test only, but always run a series of test and look at mean/variance of the times.
Others have answered re: time.time() vs. time.clock().
However, if you're timing the execution of a block of code for benchmarking/profiling purposes, you should take a look at the timeit module.
One thing to keep in mind:
Changing the system time affects time.time() but not time.clock().
I needed to control some automatic tests executions. If one step of the test case took more than a given amount of time, that TC was aborted to go on with the next one.
But sometimes a step needed to change the system time (to check the scheduler module of the application under test), so after setting the system time a few hours in the future, the TC timeout expired and the test case was aborted. I had to switch from time.time() to time.clock() to handle this properly.
clock() -> floating point number
Return the CPU time or real time since the start of the process or since
the first call to clock(). This has as much precision as the system
records.
time() -> floating point number
Return the current time in seconds since the Epoch.
Fractions of a second may be present if the system clock provides them.
Usually time() is more precise, because operating systems do not store the process running time with the precision they store the system time (ie, actual time)
Depends on what you care about. If you mean WALL TIME (as in, the time on the clock on your wall), time.clock() provides NO accuracy because it may manage CPU time.
time() has better precision than clock() on Linux. clock() only has precision less than 10 ms. While time() gives prefect precision.
My test is on CentOS 6.4， python 2.6
using time():
1 requests, response time: 14.1749382019 ms
2 requests, response time: 8.01301002502 ms
3 requests, response time: 8.01491737366 ms
4 requests, response time: 8.41021537781 ms
5 requests, response time: 8.38804244995 ms
using clock():
1 requests, response time: 10.0 ms
2 requests, response time: 0.0 ms
3 requests, response time: 0.0 ms
4 requests, response time: 10.0 ms
5 requests, response time: 0.0 ms
6 requests, response time: 0.0 ms
7 requests, response time: 0.0 ms
8 requests, response time: 0.0 ms
As others have noted time.clock() is deprecated in favour of time.perf_counter() or time.process_time(), but Python 3.7 introduces nanosecond resolution timing with time.perf_counter_ns(), time.process_time_ns(), and time.time_ns(), along with 3 other functions.
These 6 new nansecond resolution functions are detailed in PEP 564:
time.clock_gettime_ns(clock_id)
time.clock_settime_ns(clock_id, time:int)
time.monotonic_ns()
time.perf_counter_ns()
time.process_time_ns()
time.time_ns()
These functions are similar to the version without the _ns suffix, but
return a number of nanoseconds as a Python int.
As others have also noted, use the timeit module to time functions and small code snippets.
The difference is very platform-specific.
clock() is very different on Windows than on Linux, for example.
For the sort of examples you describe, you probably want the "timeit" module instead.
I use this code to compare 2 methods .My OS is windows 8 , processor core i5 , RAM 4GB
import time
def t_time():
start=time.time()
time.sleep(0.1)
return (time.time()-start)
def t_clock():
start=time.clock()
time.sleep(0.1)
return (time.clock()-start)
counter_time=0
counter_clock=0
for i in range(1,100):
counter_time += t_time()
for i in range(1,100):
counter_clock += t_clock()
print "time() =",counter_time/100
print "clock() =",counter_clock/100
output:
time() = 0.0993799996376
clock() = 0.0993572257367
time.clock() was removed in Python 3.8 because it had platform-dependent behavior:
On Unix, return the current processor time as a floating point number expressed in seconds.
On Windows, this function returns wall-clock seconds elapsed since the first call to this function, as a floating point number
print(time.clock()); time.sleep(10); print(time.clock())
# Linux : 0.0382 0.0384 # see Processor Time
# Windows: 26.1224 36.1566 # see Wall-Clock Time
So which function to pick instead?
Processor Time: This is how long this specific process spends actively being executed on the CPU. Sleep, waiting for a web request, or time when only other processes are executed will not contribute to this.
Use time.process_time()
Wall-Clock Time: This refers to how much time has passed "on a clock hanging on the wall", i.e. outside real time.
Use time.perf_counter()
time.time() also measures wall-clock time but can be reset, so you could go back in time
time.monotonic() cannot be reset (monotonic = only goes forward) but has lower precision than time.perf_counter()
On Unix time.clock() measures the amount of CPU time that has been used by the current process, so it's no good for measuring elapsed time from some point in the past. On Windows it will measure wall-clock seconds elapsed since the first call to the function. On either system time.time() will return seconds passed since the epoch.
If you're writing code that's meant only for Windows, either will work (though you'll use the two differently - no subtraction is necessary for time.clock()). If this is going to run on a Unix system or you want code that is guaranteed to be portable, you will want to use time.time().
Short answer: use time.clock() for timing in Python.
On *nix systems, clock() returns the processor time as a floating point number, expressed in seconds. On Windows, it returns the seconds elapsed since the first call to this function, as a floating point number.
time() returns the the seconds since the epoch, in UTC, as a floating point number. There is no guarantee that you will get a better precision that 1 second (even though time() returns a floating point number). Also note that if the system clock has been set back between two calls to this function, the second function call will return a lower value.
To the best of my understanding, time.clock() has as much precision as your system will allow it.
Right answer : They're both the same length of a fraction.
But which faster if subject is time ?
A little test case :
import timeit
import time
clock_list = []
time_list = []
test1 = """
def test(v=time.clock()):
s = time.clock() - v
"""
test2 = """
def test(v=time.time()):
s = time.time() - v
"""
def test_it(Range) :
for i in range(Range) :
clk = timeit.timeit(test1, number=10000)
clock_list.append(clk)
tml = timeit.timeit(test2, number=10000)
time_list.append(tml)
test_it(100)
print "Clock Min: %f Max: %f Average: %f" %(min(clock_list), max(clock_list), sum(clock_list)/float(len(clock_list)))
print "Time Min: %f Max: %f Average: %f" %(min(time_list), max(time_list), sum(time_list)/float(len(time_list)))
I am not work an Swiss labs but I've tested..
Based of this question : time.clock() is better than time.time()
Edit : time.clock() is internal counter so can't use outside, got limitations max 32BIT FLOAT, can't continued counting if not store first/last values. Can't merge another one counter...
Comparing test result between Ubuntu Linux and Windows 7.
On Ubuntu
>>> start = time.time(); time.sleep(0.5); (time.time() - start)
0.5005500316619873
On Windows 7
>>> start = time.time(); time.sleep(0.5); (time.time() - start)
0.5