First, I'd like to thank the StackOverflow community for the tremendous help it provided me over the years, without me having to ask a single question.
I could not find anything that I can relate to my problem, though it is probably due to my lack of understanding of the subject, rather than the absence of a response on the website. My apologies in advance if this is a duplicate.
I am relatively new to multiprocess; some time ago I succeeded in using multiprocessing.pools in a very simple way, where I didn't need any feedback between the child processes.
Now I am facing a much more complicated problem, and I am just lost in the documentation about multiprocessing. I hence ask for you help, your kindness and your patience.
I am trying to build a parallel tempering monte-carlo algorithm, from a class.
The basic class very roughly goes as follows:
import numpy as np
class monte_carlo:
def __init__(self):
self.x=np.ones((1000,3))
self.E=np.mean(self.x)
self.Elist=[]
def simulation(self,temperature):
self.T=temperature
for i in range(3000):
self.MC_step()
if i%10==0:
self.Elist.append(self.E)
return
def MC_step(self):
x=self.x.copy()
k = np.random.randint(1000)
x[k] = (x[k] + np.random.uniform(-1,1,3))
temp_E=np.mean(self.x)
if np.random.random()<np.exp((self.E-temp_E)/self.T):
self.E=temp_E
self.x=x
return
Obviously, I simplified a great deal (actual class is 500 lines long!), and built fake functions for simplicity: __init__ takes a bunch of parameters as arguments, there are many more lists of measurement else than self.Elist, and also many arrays derived from self.X that I use to compute them. The key point is that each instance of the class contains a lot of informations that I want to keep in memory, and that I don't want to copy over and over again, to avoid dramatic slowing down. Else I would just use the multiprocessing.pool module.
Now, the parallelization I want to do, in pseudo-code:
def proba(dE,pT):
return np.exp(-dE/pT)
Tlist=[1.1,1.2,1.3]
N=len(Tlist)
G=[]
for _ in range(N):
G.append(monte_carlo())
for _ in range(5):
for i in range(N): # this loop should be ran in multiprocess
G[i].simulation(Tlist[i])
for i in range(N//2):
dE=G[i].E-G[i+1].E
pT=G[i].T + G[i+1].T
p=proba(dE,pT) # (proba is a function, giving a probability depending on dE)
if np.random.random() < p:
T_temp = G[i].T
G[i].T = G[i+1].T
G[i+1].T = T_temp
Synthesis: I want to run several instances of my monte-carlo class in parallel child processes, with different values for a parameter T, then periodically pause everything to change the different T's, and run again the child processes/class instances, from where they paused.
Doing this, I want each class-instance/child-process to stay independent from one another, save its current state with all internal variables while it is paused, and do as few copies as possible. This last point is critical, as the arrays inside the class are quite big (some are 1000x1000), and a copy will therefore very quickly become quite time-costly.
Thanks in advance, and sorry if I am not clear...
Edit:
I am using a distant machine with many (64) CPUs, running on Debian GNU/Linux 10 (buster).
Edit2:
I made a mistake in my original post: in the end, the temperatures must be exchanged between the class-instances, and not inside the global Tlist.
Edit3: Charchit answer works perfectly for the test code, on both my personal machine and the distant machine I am usually using for running my codes. I hence check this as the accepted answer.
However, I want to report here that, inserting the actual, more complicated code, instead of the oversimplified monte_carlo class, the distant machine gives me some strange errors:
Unable to init server: Could not connect: Connection refused
(CMC_temper_all.py:55509): Gtk-WARNING **: ##:##:##:###: Locale not supported by C library.
Using the fallback 'C' locale.
Unable to init server: Could not connect: Connection refused
(CMC_temper_all.py:55509): Gdk-CRITICAL **: ##:##:##:###:
gdk_cursor_new_for_display: assertion 'GDK_IS_DISPLAY (display)' failed
(CMC_temper_all.py:55509): Gdk-CRITICAL **: ##:##:##:###: gdk_cursor_new_for_display: assertion 'GDK_IS_DISPLAY (display)' failed
The "##:##:##:###" are (or seems like) IP adresses.
Without the call to set_start_method('spawn') this error shows only once, in the very beginning, while when I use this method, it seems to show at every occurrence of result.get()...
The strangest thing is that the code seems otherwise to work fine, does not crash, produces the datafiles I then ask it to, etc...
I think this would deserve to publish a new question, but I put it here nonetheless in case someone has a quick answer.
If not, I will resort to add one by one the variables, methods, etc... that are present in my actual code but not in the test example, to try and find the origin of the bug. My best guess for now is that the memory space required by each child-process with the actual code, is too large for the distant machine to accept it, due to some restrictions implemented by the admin.
What you are looking for is sharing state between processes. As per the documentation, you can either create shared memory, which is restrictive about the data it can store and is not thread-safe, but offers better speed and performance; or you can use server processes through managers. The latter is what we are going to use since you want to share whole objects of user-defined datatypes. Keep in mind that using managers will impact speed of your code depending on the complexity of the arguments that you pass and receive, to and from the managed objects.
Managers, proxies and pickling
As mentioned, managers create server processes to store objects, and allow access to them through proxies. I have answered a question with better details on how they work, and how to create a suitable proxy here. We are going to use the same proxy defined in the linked answer, with some variations. Namely, I have replaced the factory functions inside the __getattr__ to something that can be pickled using pickle. This means that you can run instance methods of managed objects created with this proxy without resorting to using multiprocess. The result is this modified proxy:
from multiprocessing.managers import NamespaceProxy, BaseManager
import types
import numpy as np
class A:
def __init__(self, name, method):
self.name = name
self.method = method
def get(self, *args, **kwargs):
return self.method(self.name, args, kwargs)
class ObjProxy(NamespaceProxy):
"""Returns a proxy instance for any user defined data-type. The proxy instance will have the namespace and
functions of the data-type (except private/protected callables/attributes). Furthermore, the proxy will be
pickable and can its state can be shared among different processes. """
def __getattr__(self, name):
result = super().__getattr__(name)
if isinstance(result, types.MethodType):
return A(name, self._callmethod).get
return result
Solution
Now we only need to make sure that when we are creating objects of monte_carlo, we do so using managers and the above proxy. For that, we create a class constructor called create. All objects for monte_carlo should be created with this function. With that, the final code looks like this:
from multiprocessing import Pool
from multiprocessing.managers import NamespaceProxy, BaseManager
import types
import numpy as np
class A:
def __init__(self, name, method):
self.name = name
self.method = method
def get(self, *args, **kwargs):
return self.method(self.name, args, kwargs)
class ObjProxy(NamespaceProxy):
"""Returns a proxy instance for any user defined data-type. The proxy instance will have the namespace and
functions of the data-type (except private/protected callables/attributes). Furthermore, the proxy will be
pickable and can its state can be shared among different processes. """
def __getattr__(self, name):
result = super().__getattr__(name)
if isinstance(result, types.MethodType):
return A(name, self._callmethod).get
return result
class monte_carlo:
def __init__(self, ):
self.x = np.ones((1000, 3))
self.E = np.mean(self.x)
self.Elist = []
self.T = None
def simulation(self, temperature):
self.T = temperature
for i in range(3000):
self.MC_step()
if i % 10 == 0:
self.Elist.append(self.E)
return
def MC_step(self):
x = self.x.copy()
k = np.random.randint(1000)
x[k] = (x[k] + np.random.uniform(-1, 1, 3))
temp_E = np.mean(self.x)
if np.random.random() < np.exp((self.E - temp_E) / self.T):
self.E = temp_E
self.x = x
return
#classmethod
def create(cls, *args, **kwargs):
# Register class
class_str = cls.__name__
BaseManager.register(class_str, cls, ObjProxy, exposed=tuple(dir(cls)))
# Start a manager process
manager = BaseManager()
manager.start()
# Create and return this proxy instance. Using this proxy allows sharing of state between processes.
inst = eval("manager.{}(*args, **kwargs)".format(class_str))
return inst
def proba(dE,pT):
return np.exp(-dE/pT)
if __name__ == "__main__":
Tlist = [1.1, 1.2, 1.3]
N = len(Tlist)
G = []
# Create our managed instances
for _ in range(N):
G.append(monte_carlo.create())
for _ in range(5):
# Run simulations in the manager server
results = []
with Pool(8) as pool:
for i in range(N): # this loop should be ran in multiprocess
results.append(pool.apply_async(G[i].simulation, (Tlist[i], )))
# Wait for the simulations to complete
for result in results:
result.get()
for i in range(N // 2):
dE = G[i].E - G[i + 1].E
pT = G[i].T + G[i + 1].T
p = proba(dE, pT) # (proba is a function, giving a probability depending on dE)
if np.random.random() < p:
T_temp = Tlist[i]
Tlist[i] = Tlist[i + 1]
Tlist[i + 1] = T_temp
print(Tlist)
This meets the criteria you wanted. It does not create any copies at all, rather, all arguments to the simulation method call are serialized inside the pool and sent to the manager server where the object is actually stored. It gets executed there, and the results (if any) are serialized and returned in the main process. All of this, with only using the builtins!
Output
[1.2, 1.1, 1.3]
Edit
Since you are using Linux, I encourage you to use multiprocessing.set_start_method inside the if __name__ ... clause to set the start method to "spawn". Doing this will ensure that the child processes do not have access to variables defined inside the clause.
Consider the following peice of code.
from multiprocessing import Process
class A(Process):
def __init__(self):
self.a = None
super(A, self).__init__()
def run(self) -> None:
self.a = 4
def print_a(self):
print(self.a)
if __name__ == '__main__':
b = A()
b.start()
b.print_a()
I want output to be :-
4
But after running the code, the output is :-
None
What am I doing wrong and how to fix it ?
My final Remarks :-
I am a noob in Python. I used to use Thread in java, and we never had such issue there. Initially, i didn't knew there is a godly class like threading in python too.
Simply put, what i wanted to do, is just the thing for which we use threading's Thread class. After using that, all my issues are fixed. The code I provided above is just a simple enact of the actual code where the shared variable is a non-Picklable class Web3 and it was giving me tons of problems with multiprocessing. It's just that my understanding and knowledge of python was shallow. I didn't realise that having a seperate memory for variables is what Process is for...
How do i write a test, to Test for the default behavior (of a method ) of printing a range that we give it? Below is my attempt. Pasted code from my implementation file and the test case file.
`class FizzBuzzService:
def print_number(self, num):
for i in range(num):
print(i, end=' ')
import unittest
from app.logic import FizzBuzzService
class FizzBuzzServiceTestCases(unittest.TestCase):
def setUp(self):
"""
Create an instance of fizz_buzz_service
"""
self.fizzbuzz = FizzBuzzService()
def test_it_prints_a_number(self):
"""
Test for the default behavior of printing the range that we give
fizz_buzz_service
"""
number_range = range(10)
self.assertEqual(self.fizzbuzz.print_number(10), print(*number_range))
For me at least TDD is about finding a good design as much as it's about testing. As you've seen, testing for things like output is hard.
printing like this is known as a side effect - put simply it's doing something not based solely on the input parameter to the method. My solution would be to make print_number less side effecty, then test it like that. If you need to print it you can write another function higher up that prints, the output of print_number, but contains no meaningful logic other than that, which doesn't really need testing. Here's an example with your code changed to not have a side effect (it's one of several possible alternatives)
class FizzBuzzService:
def print_number(self, num):
for i in range(num):
yield i
import unittest
class FizzBuzzServiceTestCases(unittest.TestCase):
def setUp(self):
"""
Create an instance of fizz_buzz_service
"""
self.fizzbuzz = FizzBuzzService()
def test_it_prints_a_number(self):
"""
Test for the default behavior of printing the range that we give
fizz_buzz_service
"""
number_range = range(10)
output = []
for x in self.fizzbuzz.print_number(10):
output.append(x)
self.assertEqual(range(10), output)
You need to capture standard outputs in your tests to do that -
import sys
import cStringIO
def test_it_prints_a_number(self):
inital_stdout = sys.stdout
sys.stdout = cStringIO()
self.fizzbuzz.print_number(10)
value = sys.stdout.getvalue()
self.assertEqual(value, str(range(10)))
As you can see it's really messy, thus I'd highly recommend against it. Tests written on the based on string contents, especially standard outputs are utterly fragile. Besides the whole point of TDD is to write well-designed isolated code that is easily testable. If your code is difficult to test, than it is a sure shot indication that there's a problem in your design.
How about you divide your code into two parts, one that produce the numbers and need to be tested and other that just print it.
def get_numbers(self, num):
return range(num)
def print_number(self, num):
print(get_numbers)
# Now you can easily test get_numbers method.
Now if you really want to test printing functionality, then the better way would be use mocking.
I am trying to determine which parts of my python code are running the slowest, so that I have a better understanding on what I need to fix. I recently discovered cProfile and gprof2dot which have been extremely helpful. My problem is that I'm not seeing any information about functions that I'm using as callbacks, which I believe might be running very slowly. From what I understand from this answer is that cProfile only works by default in the main thread, and I'm guessing that callbacks use a separate thread. That answer showed a way to get things working if you are using the threading library, but I couldn't get it to work for my case.
Here is roughly what my code looks like:
import rospy
import cv
import cProfile
from numpy import *
from collections import deque
class Bla():
def __init__( self ):
self.image_data = deque()
self.do_some_stuff()
def vis_callback( self, data ):
cv_im = self.bridge.imgmsg_to_cv( data, "mono8" )
im = asarray( cv_im )
self.play_with_data( im )
self.image_data.append( im )
def run( self ):
rospy.init_node( 'bla', anonymous=True )
sub_vis = rospy.Subscriber('navbot/camera/image',Image,self.vis_callback)
while not rospy.is_shutdown():
if len( self.image_data ) > 0:
im = self.image_data.popleft()
self.do_some_things( im )
def main():
bla = Bla()
bla.run()
if __name__ == "__main__":
cProfile.run( 'main()', 'pstats.out' ) # this could go here, or just run python with -m cProfile
#main()
Any ideas on how to get cProfile info on the vis_callback function? Either by modifying the script itself, or even better using python -m cProfile or some other command line method?
I'm guessing it is either the reading of images or appending them to the queue which slow. My gut feeling is that storing them on a queue is a horrible thing to do, but I need to display them with matplotlib, which refuses to work if it isn't in the main thread, so I want to see exactly how bad the damage is with this workaround
Today when I checked some code at the office, I found the following code. It shocked me.
class XXXX():
def __init__(self, k, v):
for i in range(len(k)):
setattr(self, k[i], v[i])
Then I found that most of the classes are written in the same way. That means all the classes are the same class,the only different is their name.
In this project setattr() is used to set attributes and getattr() is used to get attributes In profile log setattr was called 2700 times and getattr was called 3800 times. The time consume was 0.003sec and 0.005sec respectively (whole process: 0.069sec).
Though I do think setattr and getattr drag down the speed, I'm not sure if a rewrite of all the code would make it better.
Does obj.attribute = value run faster than setattr(obj,'attribute',value)?
Yes, getattr and setattr are much slower, at least at the CPU level.
Since __init__ is only called once per object I wouldn't worry about that unless you are creating many, many objects.
If the objects' attributes are accessed many times it could be worth it to rewrite those sections. You should do some careful profiling first, though.
I did a small test. It was roughly 2x slower.
Increment using member took 2.8221039772
Increment using attr took 5.94811701775
Here is my code
import timeit
class Dummy(object):
__slots__ = ['testVal']
def __init__(self):
self.testVal = 0
def increment_using_attr(self):
i = getattr(self, 'testVal')
setattr(self, 'testVal', i+1)
def increment(self):
self.testVal += 1
if __name__ == '__main__':
d = Dummy()
print "Increment using member took {0}".format(timeit.Timer(d.increment).timeit(10000000))
print "Increment using attr took {0}".format(timeit.Timer(d.increment_using_attr).timeit(10000000))
Run on machine Intel(R) Core(TM)2 Quad CPU Q9550 # 2.83GHz