Starmap Interface

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Starmap Interface#

In general, pymoo allows passing a starmap object to be used for parallelization. The starmap interface is defined in the Python standard library multiprocessing.Pool.starmap function. This allows for excellent and flexible parallelization opportunities.

IMPORTANT: Please note that the problem needs to have set elementwise_evaluation=True, which indicates one call of _evaluate only takes care of a single solution.

[1]:

from pymoo.core.problem import ElementwiseProblem

class MyProblem(ElementwiseProblem):

    def __init__(self, **kwargs):
        super().__init__(n_var=10, n_obj=1, n_ieq_constr=0, xl=-5, xu=5, **kwargs)

    def _evaluate(self, x, out, *args, **kwargs):
         out["F"] = (x ** 2).sum()

Then, we can pass a starmap object to be used for parallelization.

Threads#

[2]:

from multiprocessing.pool import ThreadPool
from pymoo.parallelization.starmap import StarmapParallelization
from pymoo.algorithms.soo.nonconvex.ga import GA
from pymoo.optimize import minimize


# initialize the thread pool and create the runner
n_threads = 4
pool = ThreadPool(n_threads)
runner = StarmapParallelization(pool.starmap)

# define the problem by passing the starmap interface of the thread pool
problem = MyProblem(elementwise_runner=runner)

res = minimize(problem, GA(), termination=("n_gen", 50), seed=1)
print('Threads:', res.exec_time)

pool.close()

Threads: 0.12570619583129883

Processes#

import multiprocessing
from pymoo.parallelization.starmap import StarmapParallelization
from pymoo.algorithms.soo.nonconvex.ga import GA
from pymoo.optimize import minimize


# initialize the process pool and create the runner
n_processes = 4
pool = multiprocessing.Pool(n_processes)
runner = StarmapParallelization(pool.starmap)

# define the problem by passing the starmap interface of the process pool
problem = MyProblem(elementwise_runner=runner)

res = minimize(problem, GA(), termination=("n_gen", 50), seed=1)
print('Processes:', res.exec_time)

pool.close()
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