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aaronmk
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# Parallel processing
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import cPickle
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import itertools
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import Queue
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import rand
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import types
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import warnings
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import collection
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import dicts
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import exc
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from Runnable import Runnable
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def try_pickle(value):
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try: cPickle.dumps(value)
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except Exception, e:
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exc.add_msg(e, 'Tried to pickle: '+repr(value))
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raise
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def prepickle(value, vars_id_dict_):
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def filter_(value, is_leaf):
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id_ = id(value)
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if id_ in vars_id_dict_: value = id_
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# Try pickling the value. If it fails, we'll get a full traceback here,
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# which is not provided with pickling errors in multiprocessing's Pool.
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elif is_leaf: try_pickle(value)
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return value
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return collection.rmap(filter_, value)
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def post_unpickle(value, vars_id_dict_):
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def filter_(value, is_leaf):
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if type(value) == int: value = vars_id_dict_.get(value, value)
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# get() returns the value itself if it isn't a known id()
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return value
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return collection.rmap(filter_, value)
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class SyncPool:
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'''A dummy synchronous Pool to use if multiprocessing is not available'''
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def __init__(self, processes=None): pass
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class Result:
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def __init__(self, value): self.value = value
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def get(timeout=None): return self.value
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def wait(timeout=None): pass
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def ready(): return True
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def successful(): return True # TODO: False if raised exception
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def apply_async(self, func, args=(), kw_args={}, callback=None):
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if callback == None: callback = lambda v: None
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value = func(*args, **kw_args)
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callback(value)
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return self.Result(value)
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class MultiProducerPool:
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'''A multi-producer pool. You must call pool.main_loop() in the thread that
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created this to process new tasks.'''
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def __init__(self, processes=None, locals_={}, *shared):
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'''
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@param processes If 0, uses SyncPool
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@post The # processes actually used is made available in self.process_ct
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'''
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try:
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if processes == 0: raise ImportError('turned off')
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import multiprocessing
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import multiprocessing.pool
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except ImportError, e:
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warnings.warn(UserWarning('Not using parallel processing: '+str(e)))
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processes = 1
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Pool_ = SyncPool
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Queue_ = Queue.Queue
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else:
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if processes == None: processes = multiprocessing.cpu_count()
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Pool_ = multiprocessing.pool.Pool
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Queue_ = multiprocessing.Queue
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self.process_ct = processes
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self.pool = Pool_(processes)
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self.queue = Queue_()
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self.active_tasks = 0
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# Values that may be pickled by id()
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self.vars_id_dict = dicts.IdDict()
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self.share(self, *shared).share_vars(locals_).share_vars(globals())
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def share(self, *values):
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'''Call this on all values that that should be pickled by id()'''
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self.vars_id_dict.add(*values)
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return self
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def share_vars(self, vars_):
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'''Call this on all vars that that should be pickled by id().
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Usage: self.share_vars(locals())
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'''
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self.vars_id_dict.add_vars(vars_)
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return self
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def main_loop(self):
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'''Prime the task queue with at least one task before calling this'''
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while True:
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try: call = self.queue.get(timeout=0.1) # sec
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except Queue.Empty:
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if self.active_tasks == 0: break # program done
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else: continue
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def handle_result(*args, **kw_args):
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self.active_tasks -= 1
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if call.callback != None: call.callback(*args, **kw_args)
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self.active_tasks += 1
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self.pool.apply_async(call.func, self.post_unpickle(call.args),
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self.post_unpickle(call.kw_args), handle_result)
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class Result:
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def get(timeout=None): raise NotImplementedError()
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def wait(timeout=None): raise NotImplementedError()
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def ready(): raise NotImplementedError()
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def successful(): raise NotImplementedError()
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def apply_async(self, func, args=(), kw_args={}, callback=None):
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assert callback == None, 'Callbacks not supported'
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call = Runnable(func, *self.prepickle(args), **self.prepickle(kw_args))
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call.callback = callback # store this inside the Runnable
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self.queue.put_nowait(call)
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return self.Result()
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def prepickle(self, value): return prepickle(value, self.vars_id_dict)
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def post_unpickle(self, value):
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return post_unpickle(value, self.vars_id_dict)
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