I have a list of objects and need to call a member function of every object. Is it possible to use multiprocessing for that?
I wrote a short example of what i want to do.
import multiprocessing as mp
class Example:
data1 = 0
data2 = 3
def compute():
self.val3 = 6
listofobjects = []
for i in range(5):
listofobjects.append(Example())
pool = mp.Pool()
pool.map(listofobjects[range(5)].compute())
There are two conceptual issues that #abarnert has pointed out, beyond the syntactic and usage problems in your "pseudocode". The first is that map works with a function that is applied to the elements of your input. The second is that each sub-process gets a copy of you object, so changes to attributes are not automatically seen in the originals. Both issues can be worked around.
To answer your immediate question, here is how you would apply a method to your list:
with mp.Pool() as pool:
pool.map(Example.compute, listofobjects)
Example.compute is an unbound method. That means that it is just a regular function that accepts self as a first argument, making it a perfect fit for map. I also use the pool as a context manager, which is recommended to ensure that cleanup is done properly whether or not an error occurs.
The code above would not work because the effects of compute would be local to the subprocess. The only way to pass them back to the original process would be to return them from the function you passed to map. If you don't want to modify compute, you could do something like this:
def get_val3(x):
x.compute()
return x.val3
with mp.Pool() as pool:
for value, obj in zip(pool.map(get_val3, listofobjects), listofobjects):
obj.val3 = value
If you were willing to modify compute to return the object it is operating on (self), you could use it to replace the original objects much more efficiently:
class Example:
...
def compute():
...
return self
with mp.Pool() as pool:
listofobjects = list(pool.map(Example.compute, listofobjects))
Update
If your object or some part of its reference tree does not support pickling (which is the form of serialization normally used to pass objects between processes), you can at least get rid of the wrapper function by returning the updated value directly from compute:
class Example:
...
def compute():
self.val3 = ...
return self.val3
with mp.Pool() as pool:
for value, obj in zip(pool.map(Example.compute, listofobjects), listofobjects):
obj.val3 = value
Related
I have a Look Up Table LUT which is a very large dictionary (24G).
And I have millions of inputs to perform query on it.
I want to split the millions of inputs across 32 jobs, and run them in parallel.
Due to the space contraint, I cannot run multiple python scripts, because that will result in memory overload.
I want to use the multiprocessing module to only load the LUT just once, and then have different processes look it up, while sharing it as a global variable, without having to duplicate it.
However when I look at the htop, it seems each subprocess are re-creating the LUT? I made this claim because under the VIRT, RES, SHR. The numbers are very high.
But at the same time I dont see the additional memory used in the Mem row, it increased from 11Gb to 12.3G and just hovers there.
So im confused, is it, or is it not re-creating the LUT within each sub process ?
How should i proceed to make sure i am running parallel works, without duplicating LUT in each subprocess ?
Code is shown below the picture.
(In this experiment I'm only using 1Gb of LUT so, dont worry about it not being 24Gb)
import os, sys, time, pprint, pdb, datetime
import threading, multiprocessing
## Print the process/thread details
def getDetails(idx):
pid = os.getpid()
threadName = threading.current_thread().name
processName = multiprocessing.current_process().name
print(f"{idx})\tpid={pid}\tprocessName={processName}\tthreadName={threadName} ")
return pid, threadName, processName
def ComplexAlgorithm(value):
# Instead of just lookup like this
# the real algorithm is some complex algorithm that performs some search
return value in LUT
## Querying the 24Gb LUT from my millions of lines of input
def PerformMatching(idx, NumberOfLines):
pid, threadName, processName = getDetails(idx)
NumberMatches = 0
for _ in range(NumberOfLines):
# I will actually read the contents from my file live,
# but here just assume i generate random numbers
value = random.randint(-100, 100)
if ComplexAlgorithm(value): NumberMatches += 1
print(f"\t{idx}) | LUT={len(LUT)} | NumberMatches={NumberMatches} | done")
if __name__ == "__main__":
## Init
num_processes = 9
# this is just a pseudo-call to show you the structure of my LUT, the real one is larger
LUT = (dict(i,set([i])) for i in range(1000))
## Store the multiple filenames
ListOfLists = []
for idx in range(num_processes):
NumberOfLines = 10000
ListOfLists.append( NumberOfLines )
## Init the processes
ProcessList = []
for processIndex in range(num_processes):
ProcessList.append(
multiprocessing.Process(
target=PerformMatching,
args=(processIndex, ListOfLists[processIndex])
)
)
ProcessList[processIndex].start()
## Wait until the process terminates.
for processIndex in range(num_processes):
ProcessList[processIndex].join()
## Done
If you want to go the route of using a multiprocessing.Manager, this is how you could do it. The trade-off is that the dictionary is represented by a reference to a proxy for the actual dictionary that exists in a different address space and consequently every dictionary reference results in the equivalent of a remote procedure call. In other words, access is much slower compared with a "regular" dictionary.
In the demo program below, I have only defined a couple of methods for my managed dictionary, but you can define whatever you need. I have also used a multiprocessing pool instead of explicitly starting individual processes; you might consider doing likewise.
from multiprocessing.managers import BaseManager, BaseProxy
from multiprocessing import Pool
from functools import partial
def worker(LUT, key):
return LUT[key]
class MyDict:
def __init__(self):
""" initialize the dictionary """
# the very large dictionary reduced for demo purposes:
self._dict = {i: i for i in range(100)}
def get(self, obj, default=None):
""" delegates to underlying dict """
return self._dict.get(obj, default)
def __getitem__(self, obj):
""" delegates to underlying dict """
return self._dict[obj]
class MyDictManager(BaseManager):
pass
class MyDictProxy(BaseProxy):
_exposed_ = ('get', '__getitem__')
def get(self, *args, **kwargs):
return self._callmethod('get', args, kwargs)
def __getitem__(self, *args, **kwargs):
return self._callmethod('__getitem__', args, kwargs)
def main():
MyDictManager.register('MyDict', MyDict, MyDictProxy)
with MyDictManager() as manager:
my_dict = manager.MyDict()
pool = Pool()
# pass proxy instead of actual LUT:
results = pool.map(partial(worker, my_dict), range(100))
print(sum(results))
if __name__ == '__main__':
main()
Prints:
4950
Discussion
Python comes with a managed dict class built in obtainable with multiprocessing.Manager().dict(). But initializing such a large number of entries with such a dictionary would be very inefficient based on my prior comment that each access would be relatively expensive. It seemed to me that it would be less expensive to create our own managed class that had an underlying "regular" dictionary that could be initialized directly when the managed class is constructed and not via the proxy reference. And while it is true that the managed dict that comes with Python can be instantiated with an already built dictionary, which avoids that inefficiency problem, my concern is that memory efficiency would suffer because you would have two instances of the dictionary, i.e. the "regular" dictionary and the "managed" dictionary.
Lets say we have the following code:
class Pipe:
def run(self, id):
return f'Running pipe {id}'
pipes = [Pipe() for n in range(5)]
ids = [n for n in range(5)]
How can we execute method Run() for each Pipe object passin ids into each Run() method?
Kind of below makes no sense:
with f.ThreadPoolExecutor(max_workers=5) as executor:
results = executor.map([p.run for p in pipes], ids)
A method is a function that expects an instance to be passed in as its first argument (typically named self). Normally that happens automatically, obj.method(x) gets translated into type(obj).method(obj, x) behind the scenes. But you can do that kind of call yourself if you need to.
I think you want something like this, which uses the unbound Pipe.run as the function and lets the map code iterate over the pipes and ids sequences in parallel:
with f.ThreadPoolExecutor(max_workers=5) as executor:
results = executor.map(Pipe.run, pipes, ids)
I have an alogirithm that I am trying to parallelize, because of very long run times in serial. However, the function that needs to be parallelized is inside a class. multiprocessing.Pool seems to be the best and fastest way to do this, but there is a problem. It's target function can not be a function of an object instance. Meaning this; you declare a Pool in the following way:
import multiprocessing as mp
cpus = mp.cpu_count()
poolCount = cpus*2
pool = mp.Pool(processes = poolCount, maxtasksperchild = 2)
And then actually use it as so:
pool.map(self.TargetFunction, args)
But this throws an error, because object instances cannot be pickled, as the Pool function does to pass information to all of its child processes. But I have to use self.TargetFunction
So I had an idea, I would create a new Python file named parallel and simply write a couple of functions without putting them in a class, and call those functions from within my original class (of whose function I want to parallelize)
So I tried this:
import multiprocessing as mp
def MatrixHelper(args):
WM = args[0][0]
print(WM.CreateMatrixMp(*args))
return WM.CreateMatrixMp(*args)
def Start(sigmaI, sigmaX, numPixels, WM):
cpus = mp.cpu_count()
poolCount = cpus * 2
args = [(WM, sigmaI, sigmaX, i) for i in range(numPixels)]
print('Number of cpu\'s to process WM:%d'%cpus)
pool = mp.Pool(processes = poolCount, maxtasksperchild = 2)
tempData = pool.map(MatrixHelper, args)
return tempData
These functions are not part of a class, using MatrixHelper in Pools map function works fine. But I realized while doing this that it was no way out. The function in need of parallelization (CreateMatrixMp) expects an object to be passed to it (it is declared as def CreateMatrixMp(self, sigmaI, sigmaX, i))
Since it is not being called from within its class, it doesn't get a self passed to it. To solve this, I passed the Start funtion the calling object itself. As in, I say parallel.Start(sigmaI, sigmaX, self.numPixels, self). The object self then becomes WM so that I will be able to finally call the desired function as WM.CreateMatrixMp().
I'm sure that that is a very sloppy way of coding, but I just wanted to see if it would work. But nope, pickling error again, the map function cannot handle any objects instances at all.
So my question is, why is it designed this way? It seems useless, it seems to be completely disfunctional in any program that uses classes at all.
I tried using Process rather than Pool, but this requires the array that I am ultimately writing to to be shared, which requires processes waiting for eachother. If I don't want it to be shared, then I have each process write its own smaller array, and do one big write at the end. But both of these result in slower run times than when I was doing this serially! Pythons builtin multiprocessing seems absolutely useless!
Can someone please give me some guidance as to how to actually save time with multiprocessing, in the context of my tagret function being inside a class? I have read on posts here to use pathos.multiprocessing instead, but I am on Windows, and am working on this project with multiple people who all have different set ups. Having everyone try to install it would be inconveinient.
I was having a similar issue with trying to use multiprocessing within a class. I was able to solve it with a relatively easy workaround I found online. Basically you use a function outside of your class that unwraps/unpacks the method inside your function that you're trying to parallelize. Here are the two websites I found that explain how to do it.
Website 1 (joblib example)
Website 2 (multiprocessing module example)
For both, the idea is to do something like this:
rom multiprocessing import Pool
import time
def unwrap_self_f(arg, **kwarg):
return C.f(*arg, **kwarg)
class C:
def f(self, name):
print 'hello %s,'%name
time.sleep(5)
print 'nice to meet you.'
def run(self):
pool = Pool(processes=2)
names = ('frank', 'justin', 'osi', 'thomas')
pool.map(unwrap_self_f, zip([self]*len(names), names))
if __name__ == '__main__':
c = C()
c.run()
The essence of how multiprocessing works is that it spawns sub-processes that receive parameters to run a certain function. In order to pass these arguments, it needs that they are, well, passable: non-exclusive to the main process, s.a. sockets, file descriptors and other low-level, OS related stuff.
This translates into "need to be pickleable or serializable".
On the same topic, parallel processing works best when you (can) have self-contained divisions of a problem. I can tell you want to share some sort of input/stream/database source, but this will probably create a bottleneck that you'll have to tackle at some point (at least, from the "python script" side, rather than the "OS/database" side. Fortunately, you have to tackle it early now.
You can re-code your classes to spawn/create these non-pickable resources when neeeded rather than at start
def targetFunction(self, range_params):
if not self.ready():
self._init_source()
#rest of the code
You kinda tackled the problem the other way around (initialized an object based on params). And yes, parallel processing comes with a cost.
You can see the multiprocessing programming guidelines for an even more thorough insight on this matter.
this is an old post but it still is one of the top results when you search for the topic. Some good info for this question can be found at this stack overflow: python subclassing multiprocessing.Process
I tried some workarounds to try calling pool.starmap from inside of a class to another function in the class. Making it a staticmethod or having a function on the outside call it didn't work and gave the same error. A class instance just can't be pickled so we need to create the instance after we start the multiprocessing.
What I ended up doing that worked for me was to separate my class into two classes. Basically, the function you are calling the multiprocessing on needs to be called right after you instantiate a new object for the class it belongs to.
Something like this:
from multiprocessing import Pool
class B:
...
def process_feature(idx, feature):
# do stuff in the new process
pass
...
def multiprocess_feature(process_args):
b_instance = B()
return b_instance.process_feature(*process_args)
class A:
...
def process_stuff():
...
with Pool(processes=num_processes, maxtasksperchild=10) as pool:
results = pool.starmap(
multiprocess_feature,
[
(idx, feature)
for idx, feature in enumerate(features)
],
chunksize=100,
)
...
...
...
I have a function that does a calculation and saves the state of the calculation in the result dictionary (default default argument). I first run it, then run several processes using the multiprocessing module. I need to run the function again in each of those parallel processes, but after this function has run once, I need the cached state to be returned, the value must not be recalculated. This requirement doesn't make sense in my example, but I can't think of a simple realistic argument that would require this restriction. Using a dict as mutable default argument works, but
this doesn't work with the multiprocessing module. What approach can I use to get the same effect?
Note that the state value is something (a dictionary containing class values) that cannot be passed to the multiple processes as an argument afaik.
The SO question Python multiprocessing: How do I share a dict among multiple processes? seems to cover similar ground. Perhaps I can use a Manager to do what I need, but it is not obvious how. Alternatively, one could perhaps save the value to a global object, per https://stackoverflow.com/a/4534956/350713, but that doesn't seem very elegant.
def foo(result={}):
if result:
print "returning cached result"
return result
result[1] = 2
return result
def parafn():
from multiprocessing import Pool
pool = Pool(processes=2)
arglist = []
foo()
for i in range(4):
arglist.append({})
results = []
r = pool.map_async(foo, arglist, callback=results.append)
r.get()
r.wait()
pool.close()
pool.join()
return results
print parafn()
UPDATE: Thanks for the comments. I've got a working example now, posted below.
I think the safest way of exchange data between procesess is with a Queue, the multiprocessing module brings you 2 types of them Queue and JoinableQueue, see documentation:
http://docs.python.org/library/multiprocessing.html#exchanging-objects-between-processes
This code would not win any beauty prizes, but works for me.
This example is similar to the example in the question, but with some minor changes.
The add_to_d construct is a bit awkward, but I don't see a better way to do this.
Brief summary: I copy the state of foo's d, (which is a mutable default argument) back to foo,
but the foo in the new process spaces created by the pool. Once this is done, then foo in the new process spaces
will not recalculate the cached values.
It seems this is what the pool initializer does, though the documentation is not very explicit.
class bar(object):
def __init__(self, x):
self.x = x
def __repr__(self):
return "<bar "+ str(self.x) +">"
def foo(x=None, add_to_d=None, d = {}):
if add_to_d:
d.update(add_to_d)
if x is None:
return
if x in d:
print "returning cached result, d is %s, x is %s"%(d, x)
return d[x]
d[x] = bar(x)
return d[x]
def finit(cacheval):
foo(x=None, add_to_d=cacheval)
def parafn():
from multiprocessing import Pool
arglist = []
foo(1)
pool = Pool(processes=2, initializer=finit, initargs=[foo.func_defaults[2]])
arglist = range(4)
results = []
r = pool.map_async(foo, iterable=arglist, callback=results.append)
r.get()
r.wait()
pool.close()
pool.join()
return results
print parafn()
This question already has answers here:
Closed 10 years ago.
Possible Duplicate:
how do I parallelize a simple python loop?
I'm quite new to Python (using Python 3.2) and I have a question concerning parallelisation. I have a for-loop that I wish to execute in parallel using "multiprocessing" in Python 3.2:
def computation:
global output
for x in range(i,j):
localResult = ... #perform some computation as a function of i and j
output.append(localResult)
In total, I want to perform this computation for a range of i=0 to j=100. Thus I want to create a number of processes that each call the function "computation" with a subdomain of the total range. Any ideas of how do to this? Is there a better way than using multiprocessing?
More specific, I want to perform a domain decomposition and I have the following code:
from multiprocessing import Pool
class testModule:
def __init__(self):
self
def computation(self, args):
start, end = args
print('start: ', start, ' end: ', end)
testMod = testModule()
length = 100
np=4
p = Pool(processes=np)
p.map(yes tMod.computation, [(length, startPosition, length//np) for startPosition in range(0, length, length//np)])
I get an error message mentioning PicklingError. Any ideas what could be the problem here?
Joblib is designed specifically to wrap around multiprocessing for the purposes of simple parallel looping. I suggest using that instead of grappling with multiprocessing directly.
The simple case looks something like this:
from joblib import Parallel, delayed
Parallel(n_jobs=2)(delayed(foo)(i**2) for i in range(10)) # n_jobs = number of processes
The syntax is simple once you understand it. We are using generator syntax in which delayed is used to call function foo with its arguments contained in the parentheses that follow.
In your case, you should either rewrite your for loop with generator syntax, or define another function (i.e. 'worker' function) to perform the operations of a single loop iteration and place that into the generator syntax of a call to Parallel.
In the later case, you would do something like:
Parallel(n_jobs=2)(delayed(foo)(parameters) for x in range(i,j))
where foo is a function you define to handle the body of your for loop. Note that you do not want to append to a list, since Parallel is returning a list anyway.
In this case, you probably want to define a simple function to perform the calculation and get localResult.
def getLocalResult(args):
""" Do whatever you want in this func.
The point is that it takes x,i,j and
returns localResult
"""
x,i,j = args #unpack args
return doSomething(x,i,j)
Now in your computation function, you just create a pool of workers and map the local results:
import multiprocessing
def computation(np=4):
""" np is number of processes to fork """
p = multiprocessing.Pool(np)
output = p.map(getLocalResults, [(x,i,j) for x in range(i,j)] )
return output
I've removed the global here because it's unnecessary (globals are usually unnecessary). In your calling routine you should just do output.extend(computation(np=4)) or something similar.
EDIT
Here's a "working" example of your code:
from multiprocessing import Pool
def computation(args):
length, startPosition, npoints = args
print(args)
length = 100
np=4
p = Pool(processes=np)
p.map(computation, [(startPosition,startPosition+length//np, length//np) for startPosition in range(0, length, length//np)])
Note that what you had didn't work because you were using an instance method as your function. multiprocessing starts new processes and sends the information between processes via pickle, therefore, only objects which can be pickled can be used. Note that it really doesn't make sense to use an instance method anyway. Each process is a copy of the parent, so any changes to state which happen in the processes do not propagate back to the parent anyway.