objectproxypool 0.0.1

Simple object-based multiprocessing

ObjectProxyPool - simple object-based multiprocessing

This package provides the implementation of an ObjectProxyPool, a multiprocessing pool of instances of a specified class. The pool object features all methods that the original class provides. Whenever a methods is called, this call is applied to all remote copies; the results are computed in parallel, collected, and returned as an array. This makes it very easy to implement object-based parallelism.

Motivation

Object-based parallelism is useful if an object-based workflow should be repeated several times, e.g. to assess the impact of stochasticity. In modelling, for example, we may want to repeat a simulation with multiple instances of the model and compare the results.

The approach has significant advantages if the object used for the computations is expensive to initialize. As the instances can be reused for multiple tasks, initialization needs to happen only once for each process.

Example usage

from objectproxypool import ProxyPool
import numpy as np
import os
import time


# Define some class providing the
# the functionality we are interested in
class MyClass:
    def __init__(self) -> None:
        np.random.seed(os.getpid())
        self.property = None
    
    def get_normal_sample(self, mean, std):
        return mean + np.random.randn(10) * std
    
    def set_property(self, value):
        self.property = value
        
        # we add some delay to prevent that one worker 
        # does this task twice whereas another worker has 
        # an unspecified property.
        time.sleep(0.1)
    
    def add_and_get_property(self, *args):
        return self.property, sum(args)



# Create a pool of 4 instances of MyClass, each running
# in a separate process. Set `separateProcesses=False`
# to work with threads instead of processes.
# (Caution: if numWorkers is larger than the number of
# available CPUs, the performance can be bad!)
with ProxyPool(MyClass, numWorkers=4, separateProcesses=False) as pool:
    
    # We can easily parallelize a task by letting
    # each remote instance do the same. For example, 
    # we obtain a sample of normally distributed random
    # numbers in parallel. 
    print(pool.get_normal_sample(10, 1))
    
    # We can change the state of the remote instances.
    # `map_args=True` makes that each worker receives
    # one particular number of the range 1:4.
    # Without this argument, each worker would receive the
    # argument `range(4)`, i.e., all four numbers
    pool.set_property(range(4), map_args=True)
    
    # Add numbers to the property
    # Even though we have only four workers, we can reuse
    # the workers to do multiple tasks until all the 
    # work is done.
    print(pool.add_and_get_property(range(20), range(20), map_args=True))