Question

I have a data analysis script that takes an argument specifying the segments of the analysis to perform. I want to run up to ‘n’ instances of the script at a time where ‘n’ is the number of cores on the machine. The complication is that there are more segments of the analysis than there are cores so I want to run at most, ‘n’ processes at once, and one of them finishes, kick off another one. Has anyone done something like this before using the subprocess module?

Answer 1

I do think that multiprocessing module will help you achieve what you need.
Take look at the example technique.

import multiprocessing

def do_calculation(data):
    """
    @note: you can define your calculation code
    """
    return data * 2

def start_process():
    print 'Starting', multiprocessing.current_process().name

if __name__ == '__main__':
    analsys_jobs = list(range(10))  # could be your analysis work
    print 'analsys_jobs :', analsys_jobs

    pool_size = multiprocessing.cpu_count() * 2
    pool = multiprocessing.Pool(processes=pool_size,
                                initializer=start_process,
                                maxtasksperchild=2, ) 
    #maxtasksperchild = tells the pool to restart a worker process \
    # after it has finished a few tasks. This can be used to avoid \ 
    # having long-running workers consume ever more system resources

    pool_outputs = pool.map(do_calculation, analsys_jobs)   
    #The result of the map() method is functionally equivalent to the \
    # built-in map(), except that individual tasks run in parallel. \
    # Since the pool is processing its inputs in parallel, close() and join()\
    # can be used to synchronize the main process with the \
    # task processes to ensure proper cleanup.  

    pool.close() # no more tasks
    pool.join() # wrap up current tasks


    print 'Pool :', pool_outputs

You could find good multiprocessing techniques here to start with