easy-multiprocess 1.0.1

Use all your cores with no extra code

easy_multiprocess

easy_multiprocess is a package that makes it extremely simple to multiprocess.

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Multiprocess your code with just 1 line!

# Before:
def func1(x):
	# some heavy computing
    ...

a = [func1(i) for i in range(16)]

# After:
@parallelize
def func1(x):
	# some heavy computing

a = [func1(i) for i in range(16)] # all calls run in parallel on 16 cores

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Other multiprocess libraries force a specific coding style/syntax.

Below is the same code from above, except using concurrent.futures:

def func1(x):
	# some heavy computation
    ...

# concurrent.futures
with ProcessPoolExecutor() as pool:
	a = list(pool.map(func1, range(16)))

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Other multiprocess libraries don't use all cores (when multiple operations occur).

On a 16 core machine, let's see how long the following two take:

# Our machine has 16 cores
# func1, func2... each take 10 seconds

# 1: concurrent.futures library:
a = list(pool.map(func1, range(4)))
b = list(pool.map(func2, range(4)))
c = list(pool.map(func3, range(4)))
d = list(pool.map(func4, range(4)))

# elapsed time = 40s


# 2: easy_multiprocess:
a = [func1(i) for i in range(4)]
b = [func2(i) for i in range(4)]
c = [func3(i) for i in range(4)]
d = [func4(i) for i in range(4)]

# elapsed time = 10s

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Parallelize simple code

You can even use easy_multiprocess for simple code (that needs parallelizing):

# func1, func2... each take 10 seconds
a = func1(0)
b = func2(1)
c = func3(2)
d = func4(3)
print(a, b, c, d)
# elapsed time = 10s

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Non Embarrasingly Parallel Code

It even works for the non-embarrassingly parallel case (but might be suboptimal):

# func1, func2... each take 10 seconds
a = func1(0)
b = func2(a)
c = func3(2) # c/d need to wait if after b
d = func4(3)
print(a, b, c, d)

# elapsed time = 30s

easy_multiprocess implicitly uses a DAG computation graph for this (other libraries have similar mechanisms, such as Ray's DAG). See Limitations for where this doesn't work.

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User Installation

On Mac/Linux/Unix-like:

pip install easy_multiprocess

(Windows not currently supported)

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Developer Installation

git clone <this_repo>
cd easymultiprocess
pip install -e .

Then, run tests:

python -m unittest tests.test

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Author Notes

I built easy_multiprocess simply to learn how to build a python package.

It's built on top of concurrent.futures, rather than being built from the ground up using OS-level primitives, since that would've taken me over 10x as much time and code to build. This means it has MANY limitations.

Limitations:

• is comparisons aren't supported for FutureResult objects due to python identity. This means that for is operations involving any output from any @parallelize-d function, the user should use == instead, or call .result() before using is (similar to any future object from other multiprocess libraries). Adding support for this would require the user to install/use an inefficient custom python interpreter fork, which I would also have to spend time to build. This would anyway defeat the purpose of user-friendliness.
• Standard IO streams are not guaranteed to work correctly
• The non-embarrassingly parallel case is suboptimally implemented (see the example, which should take 20s in the ideal case), but can be improved in the future
• Requires Copy-on-write, so only works on Mac/Linux/Unix-like (system with fork method)

General limitations of all common python multiprocessing libraries:

• Closure variables cannot be created/updated once processes are set up (for std library concurrent futures, this occurs upon first submission to executor). You can get around this by calling ProcessPoolManager.cleanup and get_executor again. (TODO: add code sample)
• Args must be pickle-able (some other cases also work, such as if the library is using dill or other serialization methods)
• If you have lots of state, can be expensive to create new processes (copy-on-write not guaranteed)
• Program correctness is not guaranteed when external state race-conditions exist (ex. parallel processes try to write/read from same file)

Other Notes:

• The @parallelize decorator will send off the code it wraps to another process
• parallelize sounds more intuitive (and cooler), but concurrent is technically "correct". If you want, you can use @concurrent instead

Future improvements:

• Avoid pickle-ing arguments. Instead, wrap the function and turn its args into closure variables (copy-on-write would apply). Even if you pass in a large arg (such as a large ML model), it would not delay, or need to copy the large arg over to, the subprocess.