easy to use multiprocessing

pip install multiwrapper==0.1.1




git clone
cd MultiWrapper
pip install . --upgrade

Usage contains functionality to execute functions (that do not need to communicate with each other) in parallel, i.e. the same functions gets called with different parameter sets.

The function func needs to accept an array of arguments only. Example:

def _add_layer_thread(args):
    dev_mode, layer_id, chunk_coords = args

    cg = chunkedgraph.ChunkedGraph(dev_mode=dev_mode)
    cg.add_layer(layer_id, chunk_coords)

Hence args needs to be a list of lists - number of jobs x number of arguments.

There are three ways to start processes in The standard approach uses Python's multiprocessing.pool to start multiple processes or threads, the other creates subprocesses via the command line. In general, the former approach should be chosen if there are no significant reasons to choose 'subprocesses' (such as library limitations).

The functions can be called with

multiprocessing_utils.multiprocess_func(func, args, n_threads=n_threads)

multiprocessing_utils.multithread_func(func, args, n_threads=n_threads)

multiprocessing_utils.multisubprocess_func(func, args, n_threads=n_threads)

To use multisubprocess_func the targeted modules have to be installed in the python environment/ The current implementation waits until all jobs have finished (using pool.close() and pool.join() or p.wait()).