cma.optimization_tools.EvalParallel2

A class and context manager for parallel evaluations.

This class is based on the Pool class of the multiprocessing module.

The interface in v2 changed, such that the fitness function can be given once in the constructor. Hence the number of processes has become the second (optional) argument of __init__ and the function has become the second and optional argument of __call__.

To be used with the with statement (otherwise terminate needs to be called to free resources):

with EvalParallel2(fitness_function) as eval_all:
    fvals = eval_all(solutions)

assigns a callable EvalParallel2 class instance to eval_all. The instance can be called with a list (or tuple or any sequence) of solutions and returns their fitness values. That is:

eval_all(solutions) == [fitness_function(x) for x in solutions]

EvalParallel2.__call__ may take three additional optional arguments, namely fitness_function (like this the function may change from call to call), args passed to fitness and timeout passed to the multiprocessing.pool.ApplyResult.get method which raises multiprocessing.TimeoutError in case.

eval_all = EvalParallel2(fitness_function, 0) bypasses multiprocessing, hence the construct can be used even when multiprocessing fails on this fitness_function instantiation.

Examples:

>>> from cma.optimization_tools import EvalParallel2
>>> for n_jobs in [None, -1, 0, 1, 2, 4]:
...     with EvalParallel2(cma.fitness_functions.elli, n_jobs) as eval_all:
...         res = eval_all([[1,2], [3,4]])
>>> # class usage, don't forget to call terminate
>>> ep = EvalParallel2(cma.fitness_functions.elli, 4)
>>> ep([[1,2], [3,4], [4, 5]])  # doctest:+ELLIPSIS
[4000000.944...
>>> ep.terminate()
...
>>> # use with `with` statement (context manager)
>>> es = cma.CMAEvolutionStrategy(3 * [1], 1, dict(verbose=-9))
>>> with EvalParallel2(cma.fitness_functions.elli,
...                    number_of_processes=12) as eval_all:
...     while not es.stop():
...         X = es.ask()
...         es.tell(X, eval_all(X, args=(1e1,)))  # `eval_all` also accepts
...                                               # `fitness_function` as
...                                               # (optional) keyword argument
>>> assert es.result[1] < 1e-13 and es.result[2] < 1500

Parameters: the EvalParallel2 constructor takes the number of processes as optional input argument, which is by default multiprocessing.cpu_count(). If number_of_processes <= 0, no multiprocessing is invoked and the fitness is computed directly in a regular loop.

Limitations: the multiprocessing module, on which this class is based upon, may not work with certain class instance methods or Cython instances, or class instances that contain modules as it uses pickle.

Details: in some cases the execution may be considerably slowed down, as for example in previous tests done with test suites from coco/bbob.

Comparing setting number_of_processes = 0 with number_of_processes = 1 evaluates the overhead introduced by multiprocessing.Pool.apply_async.