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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 vars_id_dict(locals_, globals_, *misc):
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'''Usage: vars_id_dict(locals(), globals(), misc...)'''
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vars_ = map(lambda v: v.values(), [locals_, globals_]) + list(misc)
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return dicts.id_dict(vars_)
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def prepickle(value, vars_id_dict_):
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def filter_(value):
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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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else: 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):
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try: return vars_id_dict_[value] # value is an id()
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except KeyError: 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_=None, globals_=None, *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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if locals_ == None: locals_ = locals()
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if globals_ == None: globals_ = globals()
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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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# Values that may be pickled by id()
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self.vars_id_dict = vars_id_dict(locals_, globals_, *shared)
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def share(self, value):
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'''Call this on every value that that may be pickled by id()'''
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self.vars_id_dict[id(value)] = value
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def main_loop(self):
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try:
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while True:
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# block=False raises Empty immediately if the queue is empty,
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# which indicates that the program is done
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call = self.queue.get(block=False)
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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), call.callback)
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except Queue.Empty: pass
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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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