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A simple framework to run tasks in parallel. It’s similar to multiprocessing.Pool, but has a few enhancements over that. For example, mp.Pool is only useful for multiprocessing functions (not objects). You can wrap a function around the object, but it’s nicer just to deal with task objects themselves.

Polymer is mostly useful for its Worker error logging and run-time statistics. It also restarts crashed multiprocessing workers automatically (not true with multiprocessing.Pool). When a worker crashes, Polymer knows what the worker was doing and resubmits that task as well. This definitely is not fool-proof; however, it’s a helpful feature.

Once TaskMgr().supervise() finishes, a list of object instances is returned. You can store per-task results as an attribute of each object instance.


import time

from polymer.Polymer import ControllerQueue, TaskMgr
from polymer.abc_task import BaseTask

class SimpleTask(BaseTask):
   def __init__(self, text="", wait=0.0):
       super(SimpleTask, self).__init__()
       self.text = text
       self.wait = wait

   def run(self):
       """run() is where all the work is done; this is called by TaskMgr()"""
       ## WARNING... using try / except in run() could squash Polymer's
       ##      internal error logging...
       print self.text

   def __eq__(self, other):
       """Define how tasks are uniquely identified"""
       if other.text==self.text:
           return True
       return False

   def __repr__(self):
       return """<{0}, wait: {1}>""".format(self.text, self.wait)

def Controller():
    """Controller() builds a list of tasks, and queues them to the TaskMgr
    There is nothing special about the name Controller()... it's just some
    code to build a list of SimpleTask() instances."""

    tasks = list()

    ## Build ten tasks... do *not* depend on execution order...
    num_tasks = 10
    for ii in range(0, num_tasks):
        tasks.append(SimpleTask(text="Task {0}".format(ii), wait=ii))

    targs = {
        'work_todo': tasks,  # a list of SimpleTask() instances
        'hot_loop': False,   # If True, continuously loop over the tasks
        'worker_count': 3,           # Number of workers (default: 5)
        'resubmit_on_error': False,  # Do not retry errored jobs...
        'queue': ControllerQueue(),
        'worker_cycle_sleep': 0.001, # Worker sleep time after a task
        'log_stdout': False,         # Don't log to stdout (default: True)
        'log_path':  "taskmgr.log",  # Log file name
        'log_level': 0,              # Logging off is 0 (debugging=3)
        'log_interval': 10,          # Statistics logging interval

    ## task_mgr reads and executes the queued tasks
    task_mgr = TaskMgr(**targs)

    ## a set() of completed task objects are returned after supervise()
    results = task_mgr.supervise()
    return results

if __name__=='__main__':



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