# mplite
[](https://codecov.io/gh/root-11/mplite)
[](https://pepy.tech/project/mplite)
[](https://pepy.tech/project/mplite/month)
[](https://badge.fury.io/py/mplite)
A light weight wrapper for pythons multiprocessing module that makes multiprocessing easy.
In case anyone is looking for a very easy way to use multiprocessing with args and kwargs, here is a neat wrapper as [mplite](https://pypi.org/project/mplite/):
The [test](https://github.com/root-11/mplite/blob/main/tests/test_basics.py) is also the showcase:
*1. get the imports*
```
from mplite import TaskManager, Task
import time
```
*2. Create the function that each cpu should work on individually.*
```
def f(*args, **kwargs):
time.sleep(args[0])
return args[0]/kwargs['hello']
```
*2.1. I also add a function that will fail to illustrate that the TaskManager doesn't crash...*
```
def broken(*args, **kwargs):
raise NotImplementedError("this task must fail!")
```
*3. create the main function you'd like to run everything from:*
```
def main():
args = list(range(10)) * 5
start = time.time()
with TaskManager() as tm:
# add the first tasks
tasks = [Task(f, *(arg/10,), **{'hello': arg}) for arg in args]
print("an example of a tasks is available as string:\n\t", str(tasks[0]), '\n\t', repr(tasks[0]))
results = tm.execute(tasks) # this will contain results and tracebacks!
end = time.time()
print(f"did nothing for {end-start} seconds, producing {len(results)} results")
print(f"hereof {len([result for result in results if isinstance(result, str) ])} had errors.")
print(f"the rest where results: {[i for i in results if not isinstance(i,str)]}")
# add more tasks to the SAME pool of workers:
tasks = [Task(broken, *(i,)) for i in range(3)]
results = tm.execute(tasks)
print("More expected errors:")
for result in results:
print("expected -->", result)
if __name__ == "__main__":
main()
```
*Expected outputs*
```
an example of a tasks is available as string:
Task(f=f, *(0.0,), **{'hello': 0})
Task(f=f, *(0.0,), **{'hello': 0})
0%| | 0/50 [00:00<?, ?tasks/s]
2%|▏ | 1/50 [00:00<00:07, 6.96tasks/s]
4%|▍ | 2/50 [00:00<00:06, 7.75tasks/s]
6%|▌ | 3/50 [00:00<00:05, 8.15tasks/s]
14%|█▍ | 7/50 [00:00<00:03, 14.16tasks/s]
18%|█▊ | 9/50 [00:00<00:02, 14.36tasks/s]
24%|██▍ | 12/50 [00:00<00:02, 14.13tasks/s]
32%|███▏ | 16/50 [00:01<00:01, 17.34tasks/s]
38%|███▊ | 19/50 [00:01<00:01, 18.03tasks/s]
42%|████▏ | 21/50 [00:01<00:01, 16.66tasks/s]
46%|████▌ | 23/50 [00:01<00:01, 15.06tasks/s]
52%|█████▏ | 26/50 [00:01<00:01, 17.60tasks/s]
56%|█████▌ | 28/50 [00:01<00:01, 16.86tasks/s]
62%|██████▏ | 31/50 [00:02<00:01, 16.72tasks/s]
66%|██████▌ | 33/50 [00:02<00:00, 17.37tasks/s]
70%|███████ | 35/50 [00:02<00:00, 17.72tasks/s]
74%|███████▍ | 37/50 [00:02<00:00, 17.52tasks/s]
80%|████████ | 40/50 [00:02<00:00, 19.88tasks/s]
86%|████████▌ | 43/50 [00:02<00:00, 15.19tasks/s]
90%|█████████ | 45/50 [00:02<00:00, 13.69tasks/s]
94%|█████████▍| 47/50 [00:03<00:00, 14.46tasks/s]
98%|█████████▊| 49/50 [00:03<00:00, 10.98tasks/s]
100%|██████████| 50/50 [00:03<00:00, 14.40tasks/s]
did nothing for 3.601374387741089 seconds, producing 50 results
hereof 5 had errors.
the rest where results: [0.1, 0.1, 0.0999..., 0.1, 0.1, 0.1, 0.1, 0.0999..., 0.0999..., 0.0999..., 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.0999..., 0.0999..., 0.0999..., 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.0999..., 0.0999..., 0.0999..., 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.0999..., 0.0999..., 0.0999..., 0.1, 0.1, 0.1, 0.1, 0.0999..., 0.0999..., 0.1, 0.1]
0%| | 0/3 [00:00<?, ?tasks/s]
100%|██████████| 3/3 [00:00<00:00, 80.66tasks/s]
More expected errors:
expected --> Traceback (most recent call last):
File "d:\github\mplite\mplite\__init__.py", line 97, in execute
return self.f(*self.args,**self.kwargs)
File "d:\github\mplite\tests\test_basics.py", line 36, in broken
raise NotImplementedError("this task must fail!")
NotImplementedError: this task must fail!
expected --> Traceback (most recent call last):
File "d:\github\mplite\mplite\__init__.py", line 97, in execute
return self.f(*self.args,**self.kwargs)
File "d:\github\mplite\tests\test_basics.py", line 36, in broken
raise NotImplementedError("this task must fail!")
NotImplementedError: this task must fail!
expected --> Traceback (most recent call last):
File "d:\github\mplite\mplite\__init__.py", line 97, in execute
return self.f(*self.args,**self.kwargs)
File "d:\github\mplite\tests\test_basics.py", line 36, in broken
raise NotImplementedError("this task must fail!")
NotImplementedError: this task must fail!
```
Note that tasks **can't crash**! In case of exceptions during
task execution, the traceback is captured and the compute
core continues to execute the next task.
### How to test worker functions
Also, if you want to check that the inputs to the task
are formed correctly, you can do the check from the interpreter,
by calling `.execute()` on the task:
```
>>> t = Task(f, *(1,2,3), **{"this":42})
>>> t.execute()
```
### How to handle incremental tasks
From version 1.1.0 it is possible to add tasks incrementally.
Let's say I'd like to solve the pyramid task where I add up all numbers
```
1+2 3+4 5+6 7+8 9+10
= = = = =
3 + 7 11 + 15 19
= = =
10 26 + 19
= =
10 + 45
=
55
```
This requires that I:
1. create a queue with 1,2,3,...,10
2. add tasks for the numbers to be added pairwise
3. receive the result
4. when I have a pair of numbers submit them AGAIN.
Here is an example of what the code can look like:
```
def test_incremental_workload():
with TaskManager() as tm:
# 1. create initial workload
checksum = 55
for a in range(1,10,2):
t = Task(adder, a, a+1)
print(t)
tm.submit(t)
# 2. create incremental workload
a,b = None,None
while True:
result = tm.take()
if result is None:
if tm.open_tasks == 0:
break
else:
continue
if a is None:
a = result
else:
b = result
if a and b:
t = Task(adder, a,b)
print(t)
tm.submit(t)
a,b = None,None
print(a,b,flush=True)
assert a == checksum or b == checksum,(a,b,checksum)
```
Output:
```
Task(f=adder, *(1, 2), **{})
Task(f=adder, *(3, 4), **{})
Task(f=adder, *(5, 6), **{})
Task(f=adder, *(7, 8), **{})
Task(f=adder, *(9, 10), **{})
Task(f=adder, *(3, 7), **{})
Task(f=adder, *(11, 15), **{})
Task(f=adder, *(19, 10), **{})
Task(f=adder, *(26, 29), **{})
55 None
```
Use mplite wisely. Executing each tasks has a certain overhead associated with it.
The fewer the number of tasks and the heavier (computationally) each of them the better.
Example with number of calls with a number of iterations in the call:
```
import multiprocessing
import time
from mplite import TaskManager, Task
def run_calcs_calls(mp_enabled=True, rng=50_000_000, calls=20, cpus=1):
start = time.perf_counter()
L = []
if mp_enabled:
with TaskManager(cpu_count=cpus) as tm:
tasks = []
for call in range(1, calls+1):
tasks.append(Task(fun, *(call, rng)))
L = tm.execute(tasks)
else:
for call in range(1, calls+1):
res = fun(call, rng)
L.append(res)
task_times = [tm for res, tm in L]
cpu_count = cpus if mp_enabled else 1
cpu_task_time = sum(task_times)/cpu_count
if mp_enabled:
print('mplite - enabled')
else:
print('mplite - disabled')
print('cpu_count: ', cpu_count)
print(f'avg. time taken per cpu: ', cpu_task_time)
end = time.perf_counter()
total_time = end - start
print('total time taken: ', total_time)
print()
return total_time, cpu_task_time, cpu_count
def fun(call_id, rng):
# burn some time iterating thru
start = time.perf_counter()
t = 0
for i in range(rng):
t = i/call_id
end = time.perf_counter()
return t, end - start
def test_mplite_performance():
# change calls and range to see the knock on effect on performance
print('========CALLS TEST===========')
for cpus in [1, multiprocessing.cpu_count()]:
for ix, (calls, rng) in enumerate([(10, 50_000_000), (2000, 50)], start=1):
print('calls: ', calls, ', range: ', rng)
total_time_mp_e, cpu_task_time_mp_e, cpu_count_mp_e = run_calcs_calls(True, rng, calls, cpus)
total_time_mp_d, cpu_task_time_mp_d, cpu_count_mp_d = run_calcs_calls(False, rng, calls, cpus)
artifacts = [cpus, calls, rng, total_time_mp_e, cpu_task_time_mp_e, cpu_count_mp_e, total_time_mp_d, cpu_task_time_mp_d, cpu_count_mp_d]
if cpu_count_mp_e > cpu_count_mp_d:
if ix == 1: # assert mplite is faster for less calls and heavier process
assert total_time_mp_e < total_time_mp_d, artifacts
else:
assert True
```
Output:
```
========CALLS TEST===========
calls: 10 , range: 50000000
mplite - enabled
cpu_count: 1
avg. time taken per cpu: 18.5264333
total time taken: 18.8809622
mplite - disabled
cpu_count: 1
avg. time taken per cpu: 18.912037
total time taken: 18.9126078
calls: 2000 , range: 50
mplite - enabled
cpu_count: 1
avg. time taken per cpu: 0.005216900000000357
total time taken: 0.490177800000005
mplite - disabled
cpu_count: 1
avg. time taken per cpu: 0.003248700000142435
total time taken: 0.003983699999999146
calls: 10 , range: 50000000
mplite - enabled
cpu_count: 12
avg. time taken per cpu: 3.410191883333333
total time taken: 4.978601699999999
mplite - disabled
cpu_count: 1
avg. time taken per cpu: 19.312383399999995
total time taken: 19.312710600000003
calls: 2000 , range: 50
mplite - enabled
cpu_count: 12
avg. time taken per cpu: 0.0005722500000000056
total time taken: 0.9079466999999966
mplite - disabled
cpu_count: 1
avg. time taken per cpu: 0.0038669999999427773
total time taken: 0.004872100000000046
```
Example with sleep time in each adder function:
```
import multiprocessing
import time
from mplite import TaskManager, Task
def run_calcs_sleep(mp_enabled, sleep=2, cpus=1):
args = list(range(20))
start = time.perf_counter()
prev_mem = 0
L = []
if mp_enabled:
with TaskManager(cpus) as tm:
tasks = []
for arg in args:
tasks.append(Task(adder, *(prev_mem, arg, sleep)))
prev_mem = arg
L = tm.execute(tasks)
else:
for arg in args:
res = adder(prev_mem, arg, sleep)
L.append(res)
prev_mem = arg
end = time.perf_counter()
cpu_count = cpus if mp_enabled else 1
if mp_enabled:
print('mplite - enabled')
else:
print('mplite - disabled')
total_time = end - start
print('cpu_count: ', cpu_count)
print('total time taken: ', total_time)
print()
return total_time, cpu_count
def adder(a, b, sleep):
time.sleep(sleep)
return a+b
def test_mplite_performance():
# change sleep times to see the knock on effect on performance
print('========SLEEP TEST===========')
for cpus in [1, multiprocessing.cpu_count()]:
for ix, sleep in enumerate([2, 0.02, 0.01], start=1):
print('sleep timer value: ', sleep)
total_time_mp_e, cpu_count_mp_e = run_calcs_sleep(True, sleep, cpus)
total_time_mp_d, cpu_count_mp_d = run_calcs_sleep(False, sleep, cpus)
artifacts = [cpus, total_time_mp_e, cpu_count_mp_e, total_time_mp_d, cpu_count_mp_d]
if cpu_count_mp_e > cpu_count_mp_d:
if ix == 1: # assert mplite is faster for longer sleep
assert total_time_mp_e < total_time_mp_d, artifacts
else:
assert True
```
Output:
```
========SLEEP TEST===========
sleep timer value: 2
mplite - enabled
cpu_count: 1
total time taken: 40.4222287
mplite - disabled
cpu_count: 1
total time taken: 40.006973200000004
sleep timer value: 0.02
mplite - enabled
cpu_count: 1
total time taken: 0.7628226999999868
mplite - disabled
cpu_count: 1
total time taken: 0.4116598999999894
sleep timer value: 0.01
mplite - enabled
cpu_count: 1
total time taken: 0.5629501999999889
mplite - disabled
cpu_count: 1
total time taken: 0.21054430000000934
sleep timer value: 2
mplite - enabled
cpu_count: 12
total time taken: 4.821827799999994
mplite - disabled
cpu_count: 1
total time taken: 40.011519899999996
sleep timer value: 0.02
mplite - enabled
cpu_count: 12
total time taken: 0.713870500000013
mplite - disabled
cpu_count: 1
total time taken: 0.41133019999998055
sleep timer value: 0.01
mplite - enabled
cpu_count: 12
total time taken: 0.6938743000000045
Ran 1 test in 192.739s
mplite - disabled
cpu_count: 1
total time taken: 0.20631170000001475
```
Raw data
{
"_id": null,
"home_page": "https://github.com/root-11/mplite",
"name": "mplite",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "multiprocessing, tasks",
"author": "root-11",
"author_email": null,
"download_url": null,
"platform": "any",
"description": "# mplite\n\n\n[](https://codecov.io/gh/root-11/mplite)\n[](https://pepy.tech/project/mplite)\n[](https://pepy.tech/project/mplite/month)\n[](https://badge.fury.io/py/mplite)\n\nA light weight wrapper for pythons multiprocessing module that makes multiprocessing easy.\n\nIn case anyone is looking for a very easy way to use multiprocessing with args and kwargs, here is a neat wrapper as [mplite](https://pypi.org/project/mplite/):\n\nThe [test](https://github.com/root-11/mplite/blob/main/tests/test_basics.py) is also the showcase:\n\n*1. get the imports*\n\n```\nfrom mplite import TaskManager, Task\nimport time\n```\n\n*2. Create the function that each cpu should work on individually.*\n\n```\ndef f(*args, **kwargs):\n time.sleep(args[0])\n return args[0]/kwargs['hello']\n```\n\n*2.1. I also add a function that will fail to illustrate that the TaskManager doesn't crash...*\n```\ndef broken(*args, **kwargs):\n raise NotImplementedError(\"this task must fail!\")\n```\n\n\n*3. create the main function you'd like to run everything from:*\n\n```\ndef main():\n args = list(range(10)) * 5\n start = time.time()\n \n with TaskManager() as tm:\n # add the first tasks\n tasks = [Task(f, *(arg/10,), **{'hello': arg}) for arg in args]\n\n print(\"an example of a tasks is available as string:\\n\\t\", str(tasks[0]), '\\n\\t', repr(tasks[0]))\n\n results = tm.execute(tasks) # this will contain results and tracebacks!\n \n end = time.time()\n print(f\"did nothing for {end-start} seconds, producing {len(results)} results\")\n print(f\"hereof {len([result for result in results if isinstance(result, str) ])} had errors.\")\n print(f\"the rest where results: {[i for i in results if not isinstance(i,str)]}\")\n \n # add more tasks to the SAME pool of workers:\n tasks = [Task(broken, *(i,)) for i in range(3)]\n results = tm.execute(tasks)\n print(\"More expected errors:\")\n for result in results:\n print(\"expected -->\", result) \n\nif __name__ == \"__main__\":\n main()\n```\n\n*Expected outputs*\n\n```\nan example of a tasks is available as string:\n\t Task(f=f, *(0.0,), **{'hello': 0}) \n\t Task(f=f, *(0.0,), **{'hello': 0})\n\n 0%| | 0/50 [00:00<?, ?tasks/s]\n 2%|\u258f | 1/50 [00:00<00:07, 6.96tasks/s]\n 4%|\u258d | 2/50 [00:00<00:06, 7.75tasks/s]\n 6%|\u258c | 3/50 [00:00<00:05, 8.15tasks/s]\n 14%|\u2588\u258d | 7/50 [00:00<00:03, 14.16tasks/s]\n 18%|\u2588\u258a | 9/50 [00:00<00:02, 14.36tasks/s]\n 24%|\u2588\u2588\u258d | 12/50 [00:00<00:02, 14.13tasks/s]\n 32%|\u2588\u2588\u2588\u258f | 16/50 [00:01<00:01, 17.34tasks/s]\n 38%|\u2588\u2588\u2588\u258a | 19/50 [00:01<00:01, 18.03tasks/s]\n 42%|\u2588\u2588\u2588\u2588\u258f | 21/50 [00:01<00:01, 16.66tasks/s]\n 46%|\u2588\u2588\u2588\u2588\u258c | 23/50 [00:01<00:01, 15.06tasks/s]\n 52%|\u2588\u2588\u2588\u2588\u2588\u258f | 26/50 [00:01<00:01, 17.60tasks/s]\n 56%|\u2588\u2588\u2588\u2588\u2588\u258c | 28/50 [00:01<00:01, 16.86tasks/s]\n 62%|\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 31/50 [00:02<00:01, 16.72tasks/s]\n 66%|\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 33/50 [00:02<00:00, 17.37tasks/s]\n 70%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 35/50 [00:02<00:00, 17.72tasks/s]\n 74%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 37/50 [00:02<00:00, 17.52tasks/s]\n 80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 40/50 [00:02<00:00, 19.88tasks/s]\n 86%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 43/50 [00:02<00:00, 15.19tasks/s]\n 90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 45/50 [00:02<00:00, 13.69tasks/s]\n 94%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d| 47/50 [00:03<00:00, 14.46tasks/s]\n 98%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a| 49/50 [00:03<00:00, 10.98tasks/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:03<00:00, 14.40tasks/s]\n\ndid nothing for 3.601374387741089 seconds, producing 50 results\nhereof 5 had errors.\nthe rest where results: [0.1, 0.1, 0.0999..., 0.1, 0.1, 0.1, 0.1, 0.0999..., 0.0999..., 0.0999..., 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.0999..., 0.0999..., 0.0999..., 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.0999..., 0.0999..., 0.0999..., 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.0999..., 0.0999..., 0.0999..., 0.1, 0.1, 0.1, 0.1, 0.0999..., 0.0999..., 0.1, 0.1]\n\n 0%| | 0/3 [00:00<?, ?tasks/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3/3 [00:00<00:00, 80.66tasks/s]\n\nMore expected errors:\n\nexpected --> Traceback (most recent call last):\n File \"d:\\github\\mplite\\mplite\\__init__.py\", line 97, in execute\n return self.f(*self.args,**self.kwargs)\n File \"d:\\github\\mplite\\tests\\test_basics.py\", line 36, in broken\n raise NotImplementedError(\"this task must fail!\")\nNotImplementedError: this task must fail!\n\nexpected --> Traceback (most recent call last):\n File \"d:\\github\\mplite\\mplite\\__init__.py\", line 97, in execute\n return self.f(*self.args,**self.kwargs)\n File \"d:\\github\\mplite\\tests\\test_basics.py\", line 36, in broken\n raise NotImplementedError(\"this task must fail!\")\nNotImplementedError: this task must fail!\n\nexpected --> Traceback (most recent call last):\n File \"d:\\github\\mplite\\mplite\\__init__.py\", line 97, in execute\n return self.f(*self.args,**self.kwargs)\n File \"d:\\github\\mplite\\tests\\test_basics.py\", line 36, in broken\n raise NotImplementedError(\"this task must fail!\")\nNotImplementedError: this task must fail!\n\n```\n\nNote that tasks **can't crash**! In case of exceptions during\ntask execution, the traceback is captured and the compute\ncore continues to execute the next task.\n\n### How to test worker functions\n\nAlso, if you want to check that the inputs to the task\nare formed correctly, you can do the check from the interpreter,\nby calling `.execute()` on the task:\n\n```\n>>> t = Task(f, *(1,2,3), **{\"this\":42})\n>>> t.execute()\n```\n\n### How to handle incremental tasks\n\nFrom version 1.1.0 it is possible to add tasks incrementally.\n\nLet's say I'd like to solve the pyramid task where I add up all numbers\n\n```\n1+2 3+4 5+6 7+8 9+10\n = = = = = \n 3 + 7 11 + 15 19\n = = =\n 10 26 + 19\n = =\n 10 + 45\n = \n 55\n```\n\nThis requires that I:\n\n1. create a queue with 1,2,3,...,10\n2. add tasks for the numbers to be added pairwise\n3. receive the result\n4. when I have a pair of numbers submit them AGAIN.\n\nHere is an example of what the code can look like:\n```\n\ndef test_incremental_workload():\n with TaskManager() as tm: \n # 1. create initial workload\n checksum = 55\n for a in range(1,10,2):\n t = Task(adder, a, a+1)\n print(t)\n tm.submit(t)\n \n # 2. create incremental workload\n a,b = None,None\n while True:\n result = tm.take()\n if result is None:\n if tm.open_tasks == 0:\n break\n else:\n continue\n \n if a is None:\n a = result\n else:\n b = result\n \n if a and b:\n t = Task(adder, a,b)\n print(t)\n tm.submit(t)\n a,b = None,None\n\n print(a,b,flush=True)\n assert a == checksum or b == checksum,(a,b,checksum)\n\n\n```\n\nOutput:\n```\nTask(f=adder, *(1, 2), **{})\nTask(f=adder, *(3, 4), **{})\nTask(f=adder, *(5, 6), **{})\nTask(f=adder, *(7, 8), **{})\nTask(f=adder, *(9, 10), **{})\nTask(f=adder, *(3, 7), **{})\nTask(f=adder, *(11, 15), **{})\nTask(f=adder, *(19, 10), **{})\nTask(f=adder, *(26, 29), **{})\n55 None\n\n```\n\nUse mplite wisely. Executing each tasks has a certain overhead associated with it. \nThe fewer the number of tasks and the heavier (computationally) each of them the better.\n\nExample with number of calls with a number of iterations in the call:\n```\nimport multiprocessing\nimport time\nfrom mplite import TaskManager, Task\n\n\ndef run_calcs_calls(mp_enabled=True, rng=50_000_000, calls=20, cpus=1):\n start = time.perf_counter()\n L = []\n if mp_enabled:\n with TaskManager(cpu_count=cpus) as tm:\n tasks = []\n for call in range(1, calls+1):\n tasks.append(Task(fun, *(call, rng)))\n L = tm.execute(tasks)\n else:\n for call in range(1, calls+1):\n res = fun(call, rng)\n L.append(res)\n\n task_times = [tm for res, tm in L]\n cpu_count = cpus if mp_enabled else 1\n cpu_task_time = sum(task_times)/cpu_count\n\n if mp_enabled:\n print('mplite - enabled')\n else:\n print('mplite - disabled')\n\n print('cpu_count: ', cpu_count)\n print(f'avg. time taken per cpu: ', cpu_task_time)\n end = time.perf_counter()\n total_time = end - start\n print('total time taken: ', total_time)\n print()\n return total_time, cpu_task_time, cpu_count\n\n\ndef fun(call_id, rng):\n # burn some time iterating thru\n start = time.perf_counter()\n t = 0\n for i in range(rng):\n t = i/call_id\n end = time.perf_counter()\n return t, end - start\n\n\ndef test_mplite_performance(): \n # change calls and range to see the knock on effect on performance\n print('========CALLS TEST===========')\n for cpus in [1, multiprocessing.cpu_count()]:\n for ix, (calls, rng) in enumerate([(10, 50_000_000), (2000, 50)], start=1):\n print('calls: ', calls, ', range: ', rng)\n total_time_mp_e, cpu_task_time_mp_e, cpu_count_mp_e = run_calcs_calls(True, rng, calls, cpus)\n total_time_mp_d, cpu_task_time_mp_d, cpu_count_mp_d = run_calcs_calls(False, rng, calls, cpus)\n artifacts = [cpus, calls, rng, total_time_mp_e, cpu_task_time_mp_e, cpu_count_mp_e, total_time_mp_d, cpu_task_time_mp_d, cpu_count_mp_d]\n if cpu_count_mp_e > cpu_count_mp_d:\n if ix == 1: # assert mplite is faster for less calls and heavier process\n assert total_time_mp_e < total_time_mp_d, artifacts\n else:\n assert True\n```\n\nOutput:\n```\n========CALLS TEST===========\ncalls: 10 , range: 50000000\nmplite - enabled\ncpu_count: 1\navg. time taken per cpu: 18.5264333\ntotal time taken: 18.8809622\n\nmplite - disabled\ncpu_count: 1\navg. time taken per cpu: 18.912037\ntotal time taken: 18.9126078\n\ncalls: 2000 , range: 50\nmplite - enabled\ncpu_count: 1\navg. time taken per cpu: 0.005216900000000357\ntotal time taken: 0.490177800000005\n\nmplite - disabled\ncpu_count: 1\navg. time taken per cpu: 0.003248700000142435\ntotal time taken: 0.003983699999999146\n\ncalls: 10 , range: 50000000\nmplite - enabled\ncpu_count: 12\navg. time taken per cpu: 3.410191883333333\ntotal time taken: 4.978601699999999\n\nmplite - disabled\ncpu_count: 1\navg. time taken per cpu: 19.312383399999995\ntotal time taken: 19.312710600000003\n\ncalls: 2000 , range: 50\nmplite - enabled\ncpu_count: 12\navg. time taken per cpu: 0.0005722500000000056\ntotal time taken: 0.9079466999999966\n\nmplite - disabled\ncpu_count: 1\navg. time taken per cpu: 0.0038669999999427773\ntotal time taken: 0.004872100000000046\n\n```\n\nExample with sleep time in each adder function:\n```\nimport multiprocessing\nimport time\nfrom mplite import TaskManager, Task\n\n\ndef run_calcs_sleep(mp_enabled, sleep=2, cpus=1):\n args = list(range(20))\n start = time.perf_counter()\n prev_mem = 0\n L = []\n\n if mp_enabled:\n with TaskManager(cpus) as tm:\n tasks = []\n for arg in args:\n tasks.append(Task(adder, *(prev_mem, arg, sleep)))\n prev_mem = arg\n L = tm.execute(tasks)\n else:\n for arg in args:\n res = adder(prev_mem, arg, sleep)\n L.append(res)\n prev_mem = arg\n\n end = time.perf_counter()\n\n cpu_count = cpus if mp_enabled else 1\n\n if mp_enabled:\n print('mplite - enabled')\n else:\n print('mplite - disabled')\n\n total_time = end - start\n print('cpu_count: ', cpu_count)\n print('total time taken: ', total_time)\n print()\n return total_time, cpu_count\n\n\ndef adder(a, b, sleep):\n time.sleep(sleep)\n return a+b\n\n\ndef test_mplite_performance():\n # change sleep times to see the knock on effect on performance\n print('========SLEEP TEST===========')\n for cpus in [1, multiprocessing.cpu_count()]:\n for ix, sleep in enumerate([2, 0.02, 0.01], start=1):\n print('sleep timer value: ', sleep)\n total_time_mp_e, cpu_count_mp_e = run_calcs_sleep(True, sleep, cpus)\n total_time_mp_d, cpu_count_mp_d = run_calcs_sleep(False, sleep, cpus)\n artifacts = [cpus, total_time_mp_e, cpu_count_mp_e, total_time_mp_d, cpu_count_mp_d]\n if cpu_count_mp_e > cpu_count_mp_d:\n if ix == 1: # assert mplite is faster for longer sleep\n assert total_time_mp_e < total_time_mp_d, artifacts\n else:\n assert True\n```\n\nOutput:\n```\n========SLEEP TEST===========\nsleep timer value: 2\nmplite - enabled\ncpu_count: 1\ntotal time taken: 40.4222287\n\nmplite - disabled\ncpu_count: 1\ntotal time taken: 40.006973200000004\n\nsleep timer value: 0.02\nmplite - enabled\ncpu_count: 1\ntotal time taken: 0.7628226999999868\n\nmplite - disabled\ncpu_count: 1\ntotal time taken: 0.4116598999999894\n\nsleep timer value: 0.01\nmplite - enabled\ncpu_count: 1\ntotal time taken: 0.5629501999999889\n\nmplite - disabled\ncpu_count: 1\ntotal time taken: 0.21054430000000934\n\nsleep timer value: 2\nmplite - enabled\ncpu_count: 12\ntotal time taken: 4.821827799999994\n\nmplite - disabled\ncpu_count: 1\ntotal time taken: 40.011519899999996\n\nsleep timer value: 0.02\nmplite - enabled\ncpu_count: 12\ntotal time taken: 0.713870500000013\n\nmplite - disabled\ncpu_count: 1\ntotal time taken: 0.41133019999998055\n\nsleep timer value: 0.01\nmplite - enabled\ncpu_count: 12\ntotal time taken: 0.6938743000000045\n\nRan 1 test in 192.739s\nmplite - disabled\ncpu_count: 1\ntotal time taken: 0.20631170000001475\n\n\n\n```\n",
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