Module: parallel.client.asyncresult — IPython 2.3.1 documentation

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# Module: `parallel.client.asyncresult`

AsyncResult objects for the client

Authors:

- MinRK

## 3 Classes

class `IPython.parallel.client.asyncresult.` `AsyncResult`(client, msg_ids, fname='unknown', targets=None, tracker=None)

  Bases: `object`

  Class for representing results of non-blocking calls.

  Provides the same interface as `multiprocessing.pool.AsyncResult`.

  `__init__`(client, msg_ids, fname='unknown', targets=None, tracker=None)
  `abort`()

    abort my tasks.

  `display_outputs`(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.

  `elapsed`

    elapsed time since initial submission

  `get`(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.

  `get_dict`(timeout=-1)

    Get the results as a dict, keyed by engine_id.

    timeout behavior is described in get().

  `metadata`

    property for accessing execution metadata.

  `progress`

    the number of tasks which have been completed at this point.

    Fractional progress would be given by 1.0 * ar.progress / len(ar)

  `r`

    result property wrapper for get(timeout=-1).

  `ready`()

    Return whether the call has completed.

  `result`

    result property wrapper for get(timeout=-1).

  `result_dict`

    result property as a dict.

  `sent`

    check whether my messages have been sent.

  `serial_time`

    serial computation time of a parallel calculation

    Computed as the sum of (completed-started) of each task

  `successful`()

    Return whether the call completed without raising an exception.

    Will raise `AssertionError` if the result is not ready.

  `timedelta`(start, end, start_key=<built-in function min>, end_key=<built-in function 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.

  `wait`(timeout=-1)

    Wait until the result is available or until timeout seconds pass.

    This method always returns None.

  `wait_for_send`(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.

  `wait_interactive`(interval=1.0, timeout=-1)

    interactive wait, printing progress at regular intervals

  `wall_time`

    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.

class `IPython.parallel.client.asyncresult.` `AsyncMapResult`(client, msg_ids, mapObject, fname='', ordered=True)

  Bases: `IPython.parallel.client.asyncresult.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.

  `__init__`(client, msg_ids, mapObject, fname='', ordered=True)

class `IPython.parallel.client.asyncresult.` `AsyncHubResult`(client, msg_ids, fname='unknown', targets=None, tracker=None)

  Bases: `IPython.parallel.client.asyncresult.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.

  `wait`(timeout=-1)

    wait for result to complete.

## 1 Function

`IPython.parallel.client.asyncresult.` `check_ready`(f)

  Call spin() to sync state prior to calling the method.

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