multiprocessing in python - sharing large object (e.g. pandas dataframe) between multiple processes
I am using Python multiprocessing, more precisely from multiprocessing import Pool p = Pool(15) args = [(df, config1), (df, config2), ...] #list of args - df is the same object in each tuple res = p.map_async(func, args) #func is some arbitrary function p.close() p.join() This approach has a huge memory consumption; eating up pretty much all my RAM (at which point it gets extremely slow, hence mak
Python multiprocessing - watch a process and restart it when fails
Consider this working code: from multiprocessing import Process from updaters import app1, app2 if __name__ == '__main__': apps = [ app1, app2] for app in apps: instance = app() p = Process(target=instance.start_listener) p.start() p.join() This works fine until one process fails (lets say some exception) - how Can I programmatically monitor the process and restart it if it fails
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Python multiprocessing exception handling
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