Hey guys,
I've been trying to make my code faster for a while using parallel and had
limited success, so if someone could help I would be very grateful!
So, I've 2 different RNNs networks that are not very large (input for one
sample is 2000 time points x 20 features, and I have 2 layers of 30-50
neurons), and a code that basically looks like this:
for i in range(3):
# do some stuff
for j in range(30):
# predict with RNN_1
# do some other stuff
# predict with RNN_2
The whole code takes about 30 seconds to run.
After some google-research (I'm quite new to parallel computing), I found a
way to make it a bit faster using the multiprocessing like this :
pool = Pool(processes=3)
multiresults = pool.map(partial(function_for_one_i,arg_a), list_of_arg_b)
def function_for_one_i(arg_a,arg_b):
# do some stuff
#
for j in range(30):
# predict with RNN_1
# do some other stuff
#
# predict with RNN_2
Now I try to see if there is no other way to make it run faster, but I
found so many different things to try that trying all of them would take me
weeks =)
My problem is that I can't figure out what would be really helpful in my
case.
So my question is: what would be my best bet to make my code run faster?
- Should I better look at sparse RNN models?
- Multi-theading on the 'j-loop'?
- GPU computations?
- or something else?
I've been trying to use multithreading on the 'j-loop' (using pool.map()
and a ThreadPool) but without success so far, and I suspect most of the
answers I found on the internet to be outdated as theano is evolving quite
fast.
I use theano 0.8.2 (I've some memory leak problems with theano 0.9) on a
mac book pro.
Any tip is welcome! =)
And on another note, many thanks to all theano developers, it's really an
impressive work!
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