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.
I am developing a data pipeline in python with around 7 or 8 modes. Basically the data pipeline will be calling many class. For simplicity, I have created a simple test script ((pseudo code))as below. But every function is getting imported from a class.
Some modes/tasks are independent steps and few can be combined and make as a datapipeline.
For example test_flow is an independant workflow. create_flow and monitor_flow can be called as independant tasks or sometimes can be called together also.
Is there a better way to design the pipeline as there are about 8 modes and I feel the design(calling --modes as below) is bit clumsy. Please let me know if there are any other elegant ways. Thanks.
def test_flow:
print(test_flow)
def create_flow:
print(create_flow)
def monitor_flow:
print(monitor_flow)
if __name__ == "__main__":
if args.mode == "test_flow":
test_flow
if args.mode == "create_flow":
create_flow
if args.mode == "monitor_flow":
monitor_flow
Your example code is full of syntax errors!
I would suggest something like this, but you probably would want to ensure further that only certain functions are reachable via the command line:
import sys
def test_flow():
print("called test_flow")
def create_flow():
print("called create_flow")
def monitor_flow():
print("called monitor_flow")
def main(argv):
if len(argv)>1:
specifiedCall = globals()[argv[1]]
if specifiedCall:
specifiedCall()
pass
if __name__ == "__main__":
main(sys.argv)
I am new to learn pytest. In bellow sample code.
how can i get A() object in test_one function when fixture is in autouse mode?
import pytest
import time
class A:
def __init__(self):
self.abc = 12
#pytest.fixture(autouse=True)
def test_foo():
print('connecting')
yield A()
print('disconnect')
def test_one():
#how can i get A() object?
print([locals()])
assert 1 == 1
You can always add the fixture as parameter despite the autouse:
def test_one(test_foo):
print(test_foo)
assert 1 == 1
If you don't want to use the fixture parameter for some reason, you have to save the object elsewhere to be able to access it from your test :
a = None
#pytest.fixture(autouse=True)
def test_foo():
global a
a = A()
yield
a = None
def test_one():
print(a)
assert 1 == 1
This could be made a little better if using a test class and put a in a class variable to avoid the use of the global var, but the first variant is still the preferred one, as it localizes the definition of the object.
Apart from that, there is no real point in yielding an object you don't have access to. You may consider if autouse is the right option for your use case. Autouse is often used for stateless setup / teardown.
If your use case is to do some setup/teardown regardless (as suggested by the connect/disconnect comments), and give optional access to an object, this is ok, of course.
I wrote a PyQt5 GUI (Python 3.5 on Win7). While it is running, its GUI is not responsive. To still be able to use the GUI, I tried to use QThread from this answer: https://stackoverflow.com/a/6789205/5877928
The module is now designed like this:
class MeasureThread(QThread):
def __init(self):
super().__init__()
def get_data(self):
data = auto.data
def run(self):
time.sleep(600)
# measure some things
with open(outputfile) as f:
write(things)
class Automation(QMainWindow):
[...]
def measure(self):
thread = MeasureThread()
thread.finished.connect(app.exit)
for line in open(inputfile):
thread.get_data()
thread.start()
measure() gets called once per measurement but starts the thread once per line in inputfile. The module now exits almost immediately after starting it (I guess it runs all thread at once and does not sleep) but I only want it to do all the measurements in another single thread so the GUI can still be accessed.
I also tried to apply this to my module, but could not connect the methods used there to my methods: http://www.xyzlang.com/python/PyQT5/pyqt_multithreading.html
The way I used to use the module:
class Automation(QMainWindow):
[...]
def measure(self):
param1, param2 = (1,2)
for line in open(inputfile):
self.measureValues(param1, param2)
def measureValues(self, param1, param2):
time.sleep(600)
# measure some things
with open(outputfile) as f:
write(things)
But that obviously used only one thread. Can you help me to find the right method to use here( QThread, QRunnable) or to map the example of the second link to my module?
Thanks!
When running an analysis under MPI with distributed components in a ParallelGroup, I get an error when adding a DumpRecorder to the analysis. Below is a small example that demonstrates this (this was run with the latest master branch commit aaa67a4d51f4081e9e41b250b0a76b077f6f0c21 from 28/10/2015):
import numpy as np
from openmdao.core.mpi_wrap import MPI
from openmdao.api import Component, Group, DumpRecorder, Problem, ParallelGroup
class Sliced(Component):
def __init__(self):
super(Sliced, self).__init__()
self.add_param('x', 0.)
self.add_output('y', 0.)
def solve_nonlinear(self, params, unknowns, resids):
unknowns['y'] = params['x'] * 2.
class VectorComp(Component):
def __init__(self, size):
super(VectorComp, self).__init__()
self.add_param('xin', np.zeros(size))
self.add_output('x', np.zeros(size))
def solve_nonlinear(self, params, unknowns, resids):
unknowns['x'] = params['xin'] * 2.
class Analysis(Group):
def __init__(self, size):
super(Analysis, self).__init__()
self.add('v', VectorComp(size), promotes=['*'])
par = self.add('par', ParallelGroup())
for i in range(size):
par.add('sec%02d' % i, Sliced())
self.connect('x', 'par.sec%02d.x' % i, src_indices=[i])
if __name__ == '__main__':
if MPI:
from openmdao.core.petsc_impl import PetscImpl as impl
else:
from openmdao.core.basic_impl import BasicImpl as impl
p = Problem(impl=impl, root=Analysis(4))
recorder = DumpRecorder('optimization.log')
# adding specific includes works, but leaving it out results in a crash
# recorder.options['includes'] = ['x']
p.driver.add_recorder(recorder)
p.setup()
p.run()
The error which is raised is:
RuntimeError: Cannot access remote Variable 'par.sec00.x' in this process.
I see that the recorder dumps a file per processor, so shouldn't the BaseRecorder._filter_vectors method filter out params not present on a specific processor? I'm not yet familiar enough with the code to propose a fix, so I hope the OpenMDAO devs can easily figure out what goes wrong.
Manually specifying the includes works since the Sliced parameters are then excluded, but it would be nice that this was not necessary, and dealt with under the hood.
I also want to let you guys know how excited we are about the new framework. It is so much faster that the 0.x version, and the parallel FD feature is much appreciated and works like a charm!
There were some recent changes that broke the dump recorder in parallel. We put a story up for someone to fix it, but in the meantime, you might want to try the SqliteRecorder recorder. It's what I have been using for performance testing on CADRE. You set it up the same way, but then to read the values using an sqlitedict. There is a small example in the docs, but a more practical example is here in the CADRE code:
https://github.com/OpenMDAO/CADRE/blob/master/plot_progress.py