I have an class called Experiment and another called Case. One Experiment is made of many individual cases. See Class definitions below,
from multiprocessing import Process
class Experiment (object):
def __init__(self, name):
self.name = name
self.cases = []
self.cases.append(Case('a'))
self.cases.append(Case('b'))
self.cases.append(Case('c'))
def sr_execute(self):
for c in self.cases:
c.setVars(6)
class Case(object):
def __init__(self, name):
self.name = name
def setVars(self, var):
self.var = var
In my Experiment Class, I have a function called sr_execute. This function shows the desired behavior. I am interested in parsing thru all cases and set an attribute for each of the cases. When I run the following code,
if __name__ == '__main__':
#multiprocessing.freeze_support()
e = Experiment('exp')
e.sr_execute()
for c in e.cases: print c.name, c.var
I get,
a 6
b 6
c 6
This is the desired behavior.
However, I would like to do this in parallel using multiprocessing. To do this, I add a mp_execute() function to the Experiment Class,
def mp_execute(self):
processes = []
for c in self.cases:
processes.append(Process(target= c.setVars, args = (6,)))
[p.start() for p in processes]
[p.join() for p in processes]
However, this does not work. When I execute the following,
if __name__ == '__main__':
#multiprocessing.freeze_support()
e = Experiment('exp')
e.mp_execute()
for c in e.cases: print c.name, c.var
I get an error,
AttributeError: 'Case' object has no attribute 'var'
Apparently, I am unable to set class attribute using multiprocessing.
Any clues what is going on,
When you call:
def mp_execute(self):
processes = []
for c in self.cases:
processes.append(Process(target= c.setVars, args = (6,)))
[p.start() for p in processes]
[p.join() for p in processes]
when you create the Process it will use a copy of your object and the modifications to such object are not passed to the main program because different processes have different adress spaces. It would work if you used Threads
since in that case no copy is created.
Also note that your code will probably fail in Windows because you are passing a method as target and Windows requires the target to be picklable (and instance methods are not pickable).
The target should be a function defined at the top level of a module in order to work on all Oses.
If you want to communicate to the main process the changes you could:
Use a Queue to pass the result
Use a Manager to built a shared object
Anyway you must handle the communication "explicitly" either by setting up a "channel" (like a Queue) or setting up a shared state.
Style note: Do not use list-comprehensions in this way:
[p.join() for p in processes]
it's simply wrong. You are only wasting space creating a list of Nones. It is also probably slower compared to the right way:
for p in processes:
p.join()
Since it has to append the elements to the list.
Some say that list-comprehensions are slightly faster than for loops, however:
The difference in performance is so small that it generally doesn't matter
They are faster if and only if you consider this kind of loops:
a = []
for element in something:
a.append(element)
If the loop, like in this case, does not create a list, then the for loop will be faster.
By the way: some use map in the same way to perform side-effects. This again is wrong because you wont gain much in speed for the same reason as before and it fails completely in python3 where map returns an iterator and hence it will not execute the functions at all, thus making the code less portable.
#Bakuriu's answer offers good styling and efficiency suggestions. And true that each process gets a copy of the master process stack, hence the changes made by forked processes will not be reflected in address space of the master process unless you utilize some form of IPC (e.g. Queue, Pipe, Manager).
But the particular AttributeError: 'Case' object has no attribute 'var' error that you are getting has an additional reason, namely that your Case objects do not yet have the var attribute at the time you launch your processes. Instead, the var attribute is created in the setVars() method.
Your forked processes do indeed create the variable when they call setVars() (and actually even set it to 6), but alas, this change is only in the copies of Case objects, i.e. not reflected in the master process's memory space (where the variable still does not exist).
To see what I mean, change your Case class to this:
class Case(object):
def __init__(self, name):
self.name = name
self.var = 7 # Create var in the constructor.
def setVars(self, var):
self.var = var
By adding the var member variable in the constructor, your master process will have access to it. Of course, the changes in the forked processes will still not be reflected in the master process, but at least you don't get the error:
a 7
b 7
c 7
Hope this sheds light on what's going on. =)
SOLUTION:
The least-intrusive (to original code) thing to do is use ctypes object from shared memory:
from multiprocessing import Value
class Case(object):
def __init__(self, name):
self.name = name
self.var = Value('i', 7) # Use ctypes "int" from shared memory.
def setVars(self, var):
self.var.value = var # Set the variable's "value" attribute.
and change your main() to print c.var.value:
for c in e.cases: print c.name, c.var.value # Print the "value" attribute.
Now you have the desired output:
a 6
b 6
c 6
Related
I have a class with a method which modifies its internal state, for instance:
class Example():
def __init__(self, value):
self.param = value
def example_method(self, m):
self.param = self.param * m
# By convention, these methods in my implementation return the object itself
return self
I wanna run example_method in parallel (I am using the mpire lib, but other options are welcome as well), for many instances of Example, and have their internal states altered in my instances. Something like:
import mpire
list_of_instances = [Example(i) for i in range(1, 6)]
def run_method(ex):
ex.example_method(10)
print("Before parallel calls, this should print <1>}")
print(f"<{list_of_instances[0]}>")
with mpire.WorkerPool(n_jobs=3) as pool:
pool.map_unordered(run_method, [(example,) for example in list_of_instances])
print("After parallel calls, this should print <10>}")
print(f"<{list_of_instances[0]}>")
However, the way that mpire works, what is being modified are copies of example, and not the objects within list_of_instances, making any changes to internal state not being kept after the parallel processing. So the second print will print <1> instead, because that object`s internal state was not changed, a copy of it was.
I am wondering if there are any solutions to have the internal state changes be applied to the original objects in list_of_instances.
The only solutions I can think about is:
replace list_of_instances by the result of pool.map_unordered (changing to pool.map_ordered if order is important).
Since in any other case (even when using shared_objects) I have a copy of the original objects being made, resulting in the state changes being lost.
Is there any way to solve this with parallel processing? I also accept answers using other libs.
Consider the following snippet:
import concurrent.futures
import time
from random import random
class Test(object):
def __init__(self):
self.my_set = set()
def worker(self, name):
temp_set = set()
temp_set.add(name)
temp_set.add(name*10)
time.sleep(random() * 5)
temp_set.add(name*10 + 1)
self.my_set = self.my_set.union(temp_set) # question 1
return name
def start(self):
result = []
names = [1,2,3,4,5,6,7]
with concurrent.futures.ThreadPoolExecutor(max_workers=len(names)) as executor:
futures = [executor.submit(self.worker, x) for x in names]
for future in concurrent.futures.as_completed(futures):
result.append(future.result()) # question 2
Is there a chance self.my_set can become corrupted via the line marked "question 1"? I believe union is atomic, but couldn't the assignment be a problem?
Is there a problem on the line marked "question 2"? I believe the list append is atomic, so perhaps this is ok.
I've read these docs:
https://docs.python.org/3/library/stdtypes.html#set
https://web.archive.org/web/20201101025814id_/http://effbot.org/zone/thread-synchronization.htm
Is Python variable assignment atomic?
https://docs.python.org/3/glossary.html#term-global-interpreter-lock
and executed the snippet code provided in this question, but I can't find a definitive answer to how concurrency should work in this case.
Regarding question 1: Think about what's going on here:
self.my_set = self.my_set.union(temp_set)
There's a sequence of at least three distinct steps
The worker call grabs a copy of self.my_set (a reference to Set object)
The union function constructs a new set.
The worker assigns self.my_set to refer to the newly constructed set.
So what happens if two or more workers concurrently try to do the same thing? (note: it's not guaranteed to happen this way, but it could happen this way.)
Each of them could grab a reference to the original my_set.
Each of them could compute a new set, consisting only of the original members of my_set plus its own contribution.
Each of them could assign its new set to the my_set variable.
The problem is in step three. If it happened this way, then each of those new sets only would contain the contribution from the one worker that created it. There would be no single set containing the new contributions from all of the workers. When it's all over, my_set would only refer to one of those new sets—whichever thread was the last to perform the assignment would "win"—and the other new sets all would be be thrown away.
One way to prevent that would be to use mutual exclusion to keep other threads from trying to compute their new sets and update the shared variable at the same time:
class Test(object):
def __init__(self):
self.my_set = set()
self.my_set_mutex = threading.Lock()
def worker(self, name):
...
with self.my_set_mutex
self.my_set = self.my_set.union(temp_set)
return name
Regarding question 2: It doesn't matter whether or not appending to a list is "atomic." The result variable is local to the start method. In the code that you've shown, the list to which result refers is inaccessible to any other thread than the one that created it. There can't be any interference between threads unless you share the list with other threads.
I am using the Pool class from python's multiprocessing library write a program that will run on an HPC cluster.
Here is an abstraction of what I am trying to do:
def myFunction(x):
# myObject is a global variable in this case
return myFunction2(x, myObject)
def myFunction2(x,myObject):
myObject.modify() # here I am calling some method that changes myObject
return myObject.f(x)
poolVar = Pool()
argsArray = [ARGS ARRAY GOES HERE]
output = poolVar.map(myFunction, argsArray)
The function f(x) is contained in a *.so file, i.e., it is calling a C function.
The problem I am having is that the value of the output variable is different each time I run my program (even though the function myObject.f() is a deterministic function). (If I only have one process then the output variable is the same each time I run the program.)
I have tried creating the object rather than storing it as a global variable:
def myFunction(x):
myObject = createObject()
return myFunction2(x, myObject)
However, in my program the object creation is expensive, and thus, it is a lot easier to create myObject once and then modify it each time I call myFunction2(). Thus, I would like to not have to create the object each time.
Do you have any tips? I am very new to parallel programming so I could be going about this all wrong. I decided to use the Pool class since I wanted to start with something simple. But I am willing to try a better way of doing it.
I am using the Pool class from python's multiprocessing library to do
some shared memory processing on an HPC cluster.
Processes are not threads! You cannot simply replace Thread with Process and expect all to work the same. Processes do not share memory, which means that the global variables are copied, hence their value in the original process doesn't change.
If you want to use shared memory between processes then you must use the multiprocessing's data types, such as Value, Array, or use the Manager to create shared lists etc.
In particular you might be interested in the Manager.register method, which allows the Manager to create shared custom objects(although they must be picklable).
However I'm not sure whether this will improve the performance. Since any communication between processes requires pickling, and pickling takes usually more time then simply instantiating the object.
Note that you can do some initialization of the worker processes passing the initializer and initargs argument when creating the Pool.
For example, in its simplest form, to create a global variable in the worker process:
def initializer():
global data
data = createObject()
Used as:
pool = Pool(4, initializer, ())
Then the worker functions can use the data global variable without worries.
Style note: Never use the name of a built-in for your variables/modules. In your case object is a built-in. Otherwise you'll end up with unexpected errors which may be obscure and hard to track down.
Global keyword works on the same file only. Another way is to set value dynamically in pool process initialiser, somefile.py can just be an empty file:
import importlib
def pool_process_init():
m = importlib.import_module("somefile.py")
m.my_global_var = "some value"
pool = Pool(4, initializer=pool_process_init)
How to use the var in task:
def my_coroutine():
m = importlib.import_module("somefile.py")
print(m.my_global_var)
I am trying to parallelize operations on objects which are attributes of another object by using a simple top-level script to access methods contained within a module.
I have four classes in two modules: Host_Population and Host, contained in Host_Within_Population; and Vector_Population and Vector, contained in Vector_Within_Population. Host_Population.hosts is a list of Host objects, and Vector_Population.vectors is a list of Vector objects.
The top-level script looks something like this:
import Host_Within_Population
import Vector_Within_Population
host_pop = Host_Within_Population.Host_Population()
vect_pop = Vector_Within_Population.Vector_Population()
for time in range(5):
host_pop.host_cycle(time)
vect_pop.vector_cycle(time)
host_pop.calculate_variance()
This is a representation of the module, Host_Within_Population
class Host_Population(object):
def host_cycle(self, time):
for host in self.hosts:
host.lifecycle(time)
host.mort()
class Host(object):
def lifecycle(self, time):
#do stuff
def mort(self):
#do stuff
This is a representation of the module, Vector_Within_Population
class Vector_Population(object):
def vector_cycle(self, time):
for vect in self.vects:
vect.lifecycle(time)
vect.mort()
class Vector(object):
def lifecycle(self, time):
#do stuff
def mort(self):
#do stuff
I want parallelize the for loops in host_cycle() and vector_cycle() after calling the methods from the top-level script. The attributes of each Host object will be permanently changed by the methods acting on them in host_cycle(), and likewise for each Vector object in vector_cycle(). It doesn't matter what order the objects within each cycle are processed in (ie hosts are not affected by actions taken on other hosts), but host_cycle() must completely finish before vector_cycle() begins. Processes in vector_cycle need to be able to access each Host in the Host_Population, and the outcome of those processes will depend on the attributes of the Host. I will need to access methods in both modules at times other than host_cycle() and vector_cycle(). I have been trying to use multiprocessing.pool and map in many different permutations, but no luck even in highly simplified forms. One example of something I've tried:
class Host_Population:
def host_cycle(self):
with Pool() as q:
q.map(h.lifecycle, [h for h in self.hosts])
But of course, h is not defined.
I have been unable to adapt the response to similar questions, such as this one. Any help is appreciated.
So I got a tumbleweed badge for this incredibly unpopular question, but just in case anyone ever has the same issue, I found a solution.
Within the Host class, lifecycle() returns a Host:
def lifecycle(self, time):
#do stuff
return self
These are passed to the multiprocessing method in the Host_Within_Population class, which adds them to the population.
def host_pop_cycle(self, time):
p = Pool()
results = p.map_async(partial(Host.lifecycle, time = time), self.hosts)
p.close()
p.join()
self.hosts = []
for a in results.get():
self.hosts.append(a)
I am using the Pool class from python's multiprocessing library write a program that will run on an HPC cluster.
Here is an abstraction of what I am trying to do:
def myFunction(x):
# myObject is a global variable in this case
return myFunction2(x, myObject)
def myFunction2(x,myObject):
myObject.modify() # here I am calling some method that changes myObject
return myObject.f(x)
poolVar = Pool()
argsArray = [ARGS ARRAY GOES HERE]
output = poolVar.map(myFunction, argsArray)
The function f(x) is contained in a *.so file, i.e., it is calling a C function.
The problem I am having is that the value of the output variable is different each time I run my program (even though the function myObject.f() is a deterministic function). (If I only have one process then the output variable is the same each time I run the program.)
I have tried creating the object rather than storing it as a global variable:
def myFunction(x):
myObject = createObject()
return myFunction2(x, myObject)
However, in my program the object creation is expensive, and thus, it is a lot easier to create myObject once and then modify it each time I call myFunction2(). Thus, I would like to not have to create the object each time.
Do you have any tips? I am very new to parallel programming so I could be going about this all wrong. I decided to use the Pool class since I wanted to start with something simple. But I am willing to try a better way of doing it.
I am using the Pool class from python's multiprocessing library to do
some shared memory processing on an HPC cluster.
Processes are not threads! You cannot simply replace Thread with Process and expect all to work the same. Processes do not share memory, which means that the global variables are copied, hence their value in the original process doesn't change.
If you want to use shared memory between processes then you must use the multiprocessing's data types, such as Value, Array, or use the Manager to create shared lists etc.
In particular you might be interested in the Manager.register method, which allows the Manager to create shared custom objects(although they must be picklable).
However I'm not sure whether this will improve the performance. Since any communication between processes requires pickling, and pickling takes usually more time then simply instantiating the object.
Note that you can do some initialization of the worker processes passing the initializer and initargs argument when creating the Pool.
For example, in its simplest form, to create a global variable in the worker process:
def initializer():
global data
data = createObject()
Used as:
pool = Pool(4, initializer, ())
Then the worker functions can use the data global variable without worries.
Style note: Never use the name of a built-in for your variables/modules. In your case object is a built-in. Otherwise you'll end up with unexpected errors which may be obscure and hard to track down.
Global keyword works on the same file only. Another way is to set value dynamically in pool process initialiser, somefile.py can just be an empty file:
import importlib
def pool_process_init():
m = importlib.import_module("somefile.py")
m.my_global_var = "some value"
pool = Pool(4, initializer=pool_process_init)
How to use the var in task:
def my_coroutine():
m = importlib.import_module("somefile.py")
print(m.my_global_var)