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