Dask For Loop In Parallel

Question

I am trying to find the correct syntax for using a for loop with dask delayed. I have found several tutorials and other questions but none fit my condition, which is extremely basic.

First, is this the correct way to run a for-loop in parallel?

%%time

list_names=['a','b','c','d']
keep_return=[]

@delayed
def loop_dummy(target):
    for i in range (1000000000):
        pass
    print('passed value is:'+target)
    return(1)


for i in list_names:
    c=loop_dummy(i)
    keep_return.append(c)


total = delayed(sum)(keep_return)
total.compute()

This produced

passed value is:a
passed value is:b
passed value is:c
passed value is:d
Wall time: 1min 53s

If I run this in serial,

%%time

list_names=['a','b','c','d']
keep_return=[]


def loop_dummy(target):
    for i in range (1000000000):
        pass
    print('passed value is:'+target)
    return(1)


for i in list_names:
    c=loop_dummy(i)
    keep_return.append(c)

it is actually faster.

passed value is:a
passed value is:b
passed value is:c
passed value is:d
Wall time: 1min 49s

I have seen examples where it was stated there is a small amount of overhead for Dask, but this seems to take long enough to justify, no?

My actual for loop involves heavier computation where I build a model for various targets.

Answer 1

This computation

for i in range(...):
    pass

Is bound by the global interpreter lock (GIL). You will want to use the multiprocessing or dask.distributed Dask backends rather than the default threading backend. I recommend the following:

total.compute(scheduler='multiprocessing')

However, if your actual computation is mostly Numpy/Pandas/Scikit-Learn/Other numeric package code, then the default threading backend is probably the right choice.

More information about choosing between schedulers is available here: http://dask.pydata.org/en/latest/scheduling.html