"""AsyncResult objects for the client Authors: * MinRK """ #----------------------------------------------------------------------------- # Copyright (C) 2010-2011 The IPython Development Team # # Distributed under the terms of the BSD License. The full license is in # the file COPYING, distributed as part of this software. #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- import sys import time from datetime import datetime from zmq import MessageTracker from IPython.core.display import clear_output, display from IPython.external.decorator import decorator from IPython.parallel import error #----------------------------------------------------------------------------- # Functions #----------------------------------------------------------------------------- def _total_seconds(td): """timedelta.total_seconds was added in 2.7""" try: # Python >= 2.7 return td.total_seconds() except AttributeError: # Python 2.6 return 1e-6 * (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6) #----------------------------------------------------------------------------- # Classes #----------------------------------------------------------------------------- # global empty tracker that's always done: finished_tracker = MessageTracker() @decorator def check_ready(f, self, *args, **kwargs): """Call spin() to sync state prior to calling the method.""" self.wait(0) if not self._ready: raise error.TimeoutError("result not ready") return f(self, *args, **kwargs) class AsyncResult(object): """Class for representing results of non-blocking calls. Provides the same interface as :py:class:`multiprocessing.pool.AsyncResult`. """ msg_ids = None _targets = None _tracker = None _single_result = False def __init__(self, client, msg_ids, fname='unknown', targets=None, tracker=None): if isinstance(msg_ids, basestring): # always a list msg_ids = [msg_ids] if tracker is None: # default to always done tracker = finished_tracker self._client = client self.msg_ids = msg_ids self._fname=fname self._targets = targets self._tracker = tracker self._ready = False self._success = None self._metadata = None if len(msg_ids) == 1: self._single_result = not isinstance(targets, (list, tuple)) else: self._single_result = False def __repr__(self): if self._ready: return "<%s: finished>"%(self.__class__.__name__) else: return "<%s: %s>"%(self.__class__.__name__,self._fname) def _reconstruct_result(self, res): """Reconstruct our result from actual result list (always a list) Override me in subclasses for turning a list of results into the expected form. """ if self._single_result: return res[0] else: return res def get(self, timeout=-1): """Return the result when it arrives. If `timeout` is not ``None`` and the result does not arrive within `timeout` seconds then ``TimeoutError`` is raised. If the remote call raised an exception then that exception will be reraised by get() inside a `RemoteError`. """ if not self.ready(): self.wait(timeout) if self._ready: if self._success: return self._result else: raise self._exception else: raise error.TimeoutError("Result not ready.") def ready(self): """Return whether the call has completed.""" if not self._ready: self.wait(0) return self._ready def wait(self, timeout=-1): """Wait until the result is available or until `timeout` seconds pass. This method always returns None. """ if self._ready: return self._ready = self._client.wait(self.msg_ids, timeout) if self._ready: try: results = map(self._client.results.get, self.msg_ids) self._result = results if self._single_result: r = results[0] if isinstance(r, Exception): raise r else: results = error.collect_exceptions(results, self._fname) self._result = self._reconstruct_result(results) except Exception, e: self._exception = e self._success = False else: self._success = True finally: self._metadata = map(self._client.metadata.get, self.msg_ids) def successful(self): """Return whether the call completed without raising an exception. Will raise ``AssertionError`` if the result is not ready. """ assert self.ready() return self._success #---------------------------------------------------------------- # Extra methods not in mp.pool.AsyncResult #---------------------------------------------------------------- def get_dict(self, timeout=-1): """Get the results as a dict, keyed by engine_id. timeout behavior is described in `get()`. """ results = self.get(timeout) engine_ids = [ md['engine_id'] for md in self._metadata ] bycount = sorted(engine_ids, key=lambda k: engine_ids.count(k)) maxcount = bycount.count(bycount[-1]) if maxcount > 1: raise ValueError("Cannot build dict, %i jobs ran on engine #%i"%( maxcount, bycount[-1])) return dict(zip(engine_ids,results)) @property def result(self): """result property wrapper for `get(timeout=0)`.""" return self.get() # abbreviated alias: r = result @property @check_ready def metadata(self): """property for accessing execution metadata.""" if self._single_result: return self._metadata[0] else: return self._metadata @property def result_dict(self): """result property as a dict.""" return self.get_dict() def __dict__(self): return self.get_dict(0) def abort(self): """abort my tasks.""" assert not self.ready(), "Can't abort, I am already done!" return self._client.abort(self.msg_ids, targets=self._targets, block=True) @property def sent(self): """check whether my messages have been sent.""" return self._tracker.done def wait_for_send(self, timeout=-1): """wait for pyzmq send to complete. This is necessary when sending arrays that you intend to edit in-place. `timeout` is in seconds, and will raise TimeoutError if it is reached before the send completes. """ return self._tracker.wait(timeout) #------------------------------------- # dict-access #------------------------------------- @check_ready def __getitem__(self, key): """getitem returns result value(s) if keyed by int/slice, or metadata if key is str. """ if isinstance(key, int): return error.collect_exceptions([self._result[key]], self._fname)[0] elif isinstance(key, slice): return error.collect_exceptions(self._result[key], self._fname) elif isinstance(key, basestring): values = [ md[key] for md in self._metadata ] if self._single_result: return values[0] else: return values else: raise TypeError("Invalid key type %r, must be 'int','slice', or 'str'"%type(key)) def __getattr__(self, key): """getattr maps to getitem for convenient attr access to metadata.""" try: return self.__getitem__(key) except (error.TimeoutError, KeyError): raise AttributeError("%r object has no attribute %r"%( self.__class__.__name__, key)) # asynchronous iterator: def __iter__(self): if self._single_result: raise TypeError("AsyncResults with a single result are not iterable.") try: rlist = self.get(0) except error.TimeoutError: # wait for each result individually for msg_id in self.msg_ids: ar = AsyncResult(self._client, msg_id, self._fname) yield ar.get() else: # already done for r in rlist: yield r def __len__(self): return len(self.msg_ids) #------------------------------------- # Sugar methods and attributes #------------------------------------- def timedelta(self, start, end, start_key=min, end_key=max): """compute the difference between two sets of timestamps The default behavior is to use the earliest of the first and the latest of the second list, but this can be changed by passing a different Parameters ---------- start : one or more datetime objects (e.g. ar.submitted) end : one or more datetime objects (e.g. ar.received) start_key : callable Function to call on `start` to extract the relevant entry [defalt: min] end_key : callable Function to call on `end` to extract the relevant entry [default: max] Returns ------- dt : float The time elapsed (in seconds) between the two selected timestamps. """ if not isinstance(start, datetime): # handle single_result AsyncResults, where ar.stamp is single object, # not a list start = start_key(start) if not isinstance(end, datetime): # handle single_result AsyncResults, where ar.stamp is single object, # not a list end = end_key(end) return _total_seconds(end - start) @property def progress(self): """the number of tasks which have been completed at this point. Fractional progress would be given by 1.0 * ar.progress / len(ar) """ self.wait(0) return len(self) - len(set(self.msg_ids).intersection(self._client.outstanding)) @property def elapsed(self): """elapsed time since initial submission""" if self.ready(): return self.wall_time now = submitted = datetime.now() for msg_id in self.msg_ids: if msg_id in self._client.metadata: stamp = self._client.metadata[msg_id]['submitted'] if stamp and stamp < submitted: submitted = stamp return _total_seconds(now-submitted) @property @check_ready def serial_time(self): """serial computation time of a parallel calculation Computed as the sum of (completed-started) of each task """ t = 0 for md in self._metadata: t += _total_seconds(md['completed'] - md['started']) return t @property @check_ready def wall_time(self): """actual computation time of a parallel calculation Computed as the time between the latest `received` stamp and the earliest `submitted`. Only reliable if Client was spinning/waiting when the task finished, because the `received` timestamp is created when a result is pulled off of the zmq queue, which happens as a result of `client.spin()`. For similar comparison of other timestamp pairs, check out AsyncResult.timedelta. """ return self.timedelta(self.submitted, self.received) def wait_interactive(self, interval=1., timeout=None): """interactive wait, printing progress at regular intervals""" N = len(self) tic = time.time() while not self.ready() and (timeout is None or time.time() - tic <= timeout): self.wait(interval) clear_output() print "%4i/%i tasks finished after %4i s" % (self.progress, N, self.elapsed), sys.stdout.flush() print print "done" def _republish_displaypub(self, content, eid): """republish individual displaypub content dicts""" try: ip = get_ipython() except NameError: # displaypub is meaningless outside IPython return md = content['metadata'] or {} md['engine'] = eid ip.display_pub.publish(content['source'], content['data'], md) def _display_single_result(self): print self.stdout print >> sys.stderr, self.stderr try: get_ipython() except NameError: # displaypub is meaningless outside IPython return for output in self.outputs: self._republish_displaypub(output, self.engine_id) if self.pyout is not None: display(self.get()) @check_ready def display_outputs(self, groupby="type"): """republish the outputs of the computation Parameters ---------- groupby : str [default: type] if 'type': Group outputs by type (show all stdout, then all stderr, etc.): [stdout:1] foo [stdout:2] foo [stderr:1] bar [stderr:2] bar if 'engine': Display outputs for each engine before moving on to the next: [stdout:1] foo [stderr:1] bar [stdout:2] foo [stderr:2] bar if 'order': Like 'type', but further collate individual displaypub outputs. This is meant for cases of each command producing several plots, and you would like to see all of the first plots together, then all of the second plots, and so on. """ # flush iopub, just in case self._client._flush_iopub(self._client._iopub_socket) if self._single_result: self._display_single_result() return stdouts = [s.rstrip() for s in self.stdout] stderrs = [s.rstrip() for s in self.stderr] pyouts = [p for p in self.pyout] output_lists = self.outputs results = self.get() targets = self.engine_id if groupby == "engine": for eid,stdout,stderr,outputs,r,pyout in zip( targets, stdouts, stderrs, output_lists, results, pyouts ): if stdout: print '[stdout:%i]' % eid, stdout if stderr: print >> sys.stderr, '[stderr:%i]' % eid, stderr try: get_ipython() except NameError: # displaypub is meaningless outside IPython return for output in outputs: self._republish_displaypub(output, eid) if pyout is not None: display(r) elif groupby in ('type', 'order'): # republish stdout: if any(stdouts): for eid,stdout in zip(targets, stdouts): print '[stdout:%i]' % eid, stdout # republish stderr: if any(stderrs): for eid,stderr in zip(targets, stderrs): print >> sys.stderr, '[stderr:%i]' % eid, stderr try: get_ipython() except NameError: # displaypub is meaningless outside IPython return if groupby == 'order': output_dict = dict((eid, outputs) for eid,outputs in zip(targets, output_lists)) N = max(len(outputs) for outputs in output_lists) for i in range(N): for eid in targets: outputs = output_dict[eid] if len(outputs) >= N: self._republish_displaypub(outputs[i], eid) else: # republish displaypub output for eid,outputs in zip(targets, output_lists): for output in outputs: self._republish_displaypub(output, eid) # finally, add pyout: for eid,r,pyout in zip(targets, results, pyouts): if pyout is not None: display(r) else: raise ValueError("groupby must be one of 'type', 'engine', 'collate', not %r" % groupby) class AsyncMapResult(AsyncResult): """Class for representing results of non-blocking gathers. This will properly reconstruct the gather. This class is iterable at any time, and will wait on results as they come. If ordered=False, then the first results to arrive will come first, otherwise results will be yielded in the order they were submitted. """ def __init__(self, client, msg_ids, mapObject, fname='', ordered=True): AsyncResult.__init__(self, client, msg_ids, fname=fname) self._mapObject = mapObject self._single_result = False self.ordered = ordered def _reconstruct_result(self, res): """Perform the gather on the actual results.""" return self._mapObject.joinPartitions(res) # asynchronous iterator: def __iter__(self): it = self._ordered_iter if self.ordered else self._unordered_iter for r in it(): yield r # asynchronous ordered iterator: def _ordered_iter(self): """iterator for results *as they arrive*, preserving submission order.""" try: rlist = self.get(0) except error.TimeoutError: # wait for each result individually for msg_id in self.msg_ids: ar = AsyncResult(self._client, msg_id, self._fname) rlist = ar.get() try: for r in rlist: yield r except TypeError: # flattened, not a list # this could get broken by flattened data that returns iterables # but most calls to map do not expose the `flatten` argument yield rlist else: # already done for r in rlist: yield r # asynchronous unordered iterator: def _unordered_iter(self): """iterator for results *as they arrive*, on FCFS basis, ignoring submission order.""" try: rlist = self.get(0) except error.TimeoutError: pending = set(self.msg_ids) while pending: try: self._client.wait(pending, 1e-3) except error.TimeoutError: # ignore timeout error, because that only means # *some* jobs are outstanding pass # update ready set with those no longer outstanding: ready = pending.difference(self._client.outstanding) # update pending to exclude those that are finished pending = pending.difference(ready) while ready: msg_id = ready.pop() ar = AsyncResult(self._client, msg_id, self._fname) rlist = ar.get() try: for r in rlist: yield r except TypeError: # flattened, not a list # this could get broken by flattened data that returns iterables # but most calls to map do not expose the `flatten` argument yield rlist else: # already done for r in rlist: yield r class AsyncHubResult(AsyncResult): """Class to wrap pending results that must be requested from the Hub. Note that waiting/polling on these objects requires polling the Hubover the network, so use `AsyncHubResult.wait()` sparingly. """ def wait(self, timeout=-1): """wait for result to complete.""" start = time.time() if self._ready: return local_ids = filter(lambda msg_id: msg_id in self._client.outstanding, self.msg_ids) local_ready = self._client.wait(local_ids, timeout) if local_ready: remote_ids = filter(lambda msg_id: msg_id not in self._client.results, self.msg_ids) if not remote_ids: self._ready = True else: rdict = self._client.result_status(remote_ids, status_only=False) pending = rdict['pending'] while pending and (timeout < 0 or time.time() < start+timeout): rdict = self._client.result_status(remote_ids, status_only=False) pending = rdict['pending'] if pending: time.sleep(0.1) if not pending: self._ready = True if self._ready: try: results = map(self._client.results.get, self.msg_ids) self._result = results if self._single_result: r = results[0] if isinstance(r, Exception): raise r else: results = error.collect_exceptions(results, self._fname) self._result = self._reconstruct_result(results) except Exception, e: self._exception = e self._success = False else: self._success = True finally: self._metadata = map(self._client.metadata.get, self.msg_ids) __all__ = ['AsyncResult', 'AsyncMapResult', 'AsyncHubResult']