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# parallel.client.client

## Module: `parallel.client.client`

Inheritance diagram for `IPython.parallel.client.client`:

A semi-synchronous Client for the ZMQ cluster

Authors:

- MinRK

## Classes

### `Client`

class `IPython.parallel.client.client.` `Client`(url_or_file=None, profile=None, profile_dir=None, ipython_dir=None, context=None, debug=False, exec_key=None, sshserver=None, sshkey=None, password=None, paramiko=None, timeout=10, **extra_args)

  Bases: `IPython.utils.traitlets.HasTraits`

  A semi-synchronous client to the IPython ZMQ cluster

  Parameters : | url_or_file : bytes or unicode; zmq url or path to ipcontroller-client.json Connection information for the Hub’s registration. If a json connector file is given, then likely no further configuration is necessary. [Default: use profile] profile : bytes The name of the Cluster profile to be used to find connector information. If run from an IPython application, the default profile will be the same as the running application, otherwise it will be ‘default’. context : zmq.Context Pass an existing zmq.Context instance, otherwise the client will create its own. debug : bool flag for lots of message printing for debug purposes timeout : int/float time (in seconds) to wait for connection replies from the Hub [Default: 10] #————– session related args —————- : config : Config object If specified, this will be relayed to the Session for configuration username : str set username for the session object packer : str (import_string) or callable Can be either the simple keyword ‘json’ or ‘pickle’, or an import_string to a function to serialize messages. Must support same input as JSON, and output must be bytes. You can pass a callable directly as pack unpacker : str (import_string) or callable The inverse of packer. Only necessary if packer is specified as not one of ‘json’ or ‘pickle’. #————– ssh related args —————- : # These are args for configuring the ssh tunnel to be used : # credentials are used to forward connections over ssh to the Controller : # Note that the ip given in `addr` needs to be relative to sshserver : # The most basic case is to leave addr as pointing to localhost (127.0.0.1), : # and set sshserver as the same machine the Controller is on. However, : # the only requirement is that sshserver is able to see the Controller : # (i.e. is within the same trusted network). : sshserver : str A string of the form passed to ssh, i.e. ‘server.tld’ or 'user@server.tld:port’ If keyfile or password is specified, and this is not, it will default to the ip given in addr. sshkey : str; path to public ssh key file This specifies a key to be used in ssh login, default None. Regular default ssh keys will be used without specifying this argument. password : str Your ssh password to sshserver. Note that if this is left None, you will be prompted for it if passwordless key based login is unavailable. paramiko : bool flag for whether to use paramiko instead of shell ssh for tunneling. [default: True on win32, False else] ——- exec authentication args ——- : If even localhost is untrusted, you can have some protection against : unauthorized execution by signing messages with HMAC digests. : Messages are still sent as cleartext, so if someone can snoop your : loopback traffic this will not protect your privacy, but will prevent : unauthorized execution. : exec_key : str an authentication key or file containing a key default: None
  Attributes : | ids : list of int engine IDs requesting the ids attribute always synchronizes the registration state. To request ids without synchronization, use semi-private _ids attributes. history : list of msg_ids a list of msg_ids, keeping track of all the execution messages you have submitted in order. outstanding : set of msg_ids a set of msg_ids that have been submitted, but whose results have not yet been received. results : dict a dict of all our results, keyed by msg_id block : bool determines default behavior when block not specified in execution methods
  Methods : | spin : flushes incoming results and registration state changes control methods spin, and requesting ids also ensures up to date wait : wait on one or more msg_ids execution methods : apply legacy: execute, run data movement : push, pull, scatter, gather query methods : queue_status, get_result, purge, result_status control methods : abort, shutdown

  `__init__`(url_or_file=None, profile=None, profile_dir=None, ipython_dir=None, context=None, debug=False, exec_key=None, sshserver=None, sshkey=None, password=None, paramiko=None, timeout=10, **extra_args)
  `abort`(jobs=None, targets=None, block=None)

    Abort specific jobs from the execution queues of target(s).

    This is a mechanism to prevent jobs that have already been submitted from executing.

    Parameters : | jobs : msg_id, list of msg_ids, or AsyncResult The jobs to be aborted

  `block`

    A boolean (True, False) trait.

  classmethod `class_trait_names`(**metadata)

    Get a list of all the names of this classes traits.

    This method is just like the `trait_names()` method, but is unbound.

  classmethod `class_traits`(**metadata)

    Get a list of all the traits of this class.

    This method is just like the `traits()` method, but is unbound.

    The TraitTypes returned don’t know anything about the values that the various HasTrait’s instances are holding.

    This follows the same algorithm as traits does and does not allow for any simple way of specifying merely that a metadata name exists, but has any value. This is because get_metadata returns None if a metadata key doesn’t exist.

  `clear`(targets=None, block=None)

    Clear the namespace in target(s).

  `close`()
  `db_query`(query, keys=None)

    Query the Hub’s TaskRecord database

    This will return a list of task record dicts that match query

    Parameters : | query : mongodb query dict The search dict. See mongodb query docs for details. keys : list of strs [optional] The subset of keys to be returned. The default is to fetch everything but buffers. ‘msg_id’ will always be included.

  `debug`

    A boolean (True, False) trait.

  `direct_view`(targets='all')

    construct a DirectView object.

    If no targets are specified, create a DirectView using all engines.

    Parameters : | targets: list,slice,int,etc. [default: use all engines] : The engines to use for the View

  `get_result`(indices_or_msg_ids=None, block=None)

    Retrieve a result by msg_id or history index, wrapped in an AsyncResult object.

    If the client already has the results, no request to the Hub will be made.

    This is a convenient way to construct AsyncResult objects, which are wrappers that include metadata about execution, and allow for awaiting results that were not submitted by this Client.

    It can also be a convenient way to retrieve the metadata associated with blocking execution, since it always retrieves

    Parameters : | indices_or_msg_ids : integer history index, str msg_id, or list of either The indices or msg_ids of indices to be retrieved block : bool Whether to wait for the result to be done
    Returns : | AsyncResult : A single AsyncResult object will always be returned. AsyncHubResult : A subclass of AsyncResult that retrieves results from the Hub

    Examples

        In [10]: r = client.apply()

  `history`

    An instance of a Python list.

  `hub_history`()

    Get the Hub’s history

    Just like the Client, the Hub has a history, which is a list of msg_ids. This will contain the history of all clients, and, depending on configuration, may contain history across multiple cluster sessions.

    Any msg_id returned here is a valid argument to get_result.

    Returns : | msg_ids : list of strs list of all msg_ids, ordered by task submission time.

  `ids`

    Always up-to-date ids property.

  `load_balanced_view`(targets=None)

    construct a DirectView object.

    If no arguments are specified, create a LoadBalancedView using all engines.

    Parameters : | targets: list,slice,int,etc. [default: use all engines] : The subset of engines across which to load-balance

  `metadata`

    A trait whose value must be an instance of a specified class.

    The value can also be an instance of a subclass of the specified class.

  `on_trait_change`(handler, name=None, remove=False)

    Setup a handler to be called when a trait changes.

    This is used to setup dynamic notifications of trait changes.

    Static handlers can be created by creating methods on a HasTraits subclass with the naming convention ‘_[traitname]_changed’. Thus, to create static handler for the trait ‘a’, create the method _a_changed(self, name, old, new) (fewer arguments can be used, see below).

    Parameters : | handler : callable A callable that is called when a trait changes. Its signature can be handler(), handler(name), handler(name, new) or handler(name, old, new). name : list, str, None If None, the handler will apply to all traits. If a list of str, handler will apply to all names in the list. If a str, the handler will apply just to that name. remove : bool If False (the default), then install the handler. If True then unintall it.

  `outstanding`

    An instance of a Python set.

  `profile`

    A trait for unicode strings.

  `purge_results`(jobs=[], targets=[])

    Tell the Hub to forget results.

    Individual results can be purged by msg_id, or the entire history of specific targets can be purged.

    Use purge_results(‘all’) to scrub everything from the Hub’s db.

    Parameters : | jobs : str or list of str or AsyncResult objects the msg_ids whose results should be forgotten. targets : int/str/list of ints/strs The targets, by int_id, whose entire history is to be purged. default : None

  `queue_status`(targets='all', verbose=False)

    Fetch the status of engine queues.

    Parameters : | targets : int/str/list of ints/strs the engines whose states are to be queried. default : all verbose : bool Whether to return lengths only, or lists of ids for each element

  `resubmit`(indices_or_msg_ids=None, subheader=None, block=None)

    Resubmit one or more tasks.

    in-flight tasks may not be resubmitted.

    Parameters : | indices_or_msg_ids : integer history index, str msg_id, or list of either The indices or msg_ids of indices to be retrieved block : bool Whether to wait for the result to be done
    Returns : | AsyncHubResult : A subclass of AsyncResult that retrieves results from the Hub

  `result_status`(msg_ids, status_only=True)

    Check on the status of the result(s) of the apply request with msg_ids.

    If status_only is False, then the actual results will be retrieved, else only the status of the results will be checked.

    Parameters : | msg_ids : list of msg_ids if int: Passed as index to self.history for convenience. status_only : bool (default: True) if False: Retrieve the actual results of completed tasks.
    Returns : | results : dict There will always be the keys ‘pending’ and ‘completed’, which will be lists of msg_ids that are incomplete or complete. If status_only is False, then completed results will be keyed by their msg_id.

  `results`

    A trait whose value must be an instance of a specified class.

    The value can also be an instance of a subclass of the specified class.

  `send_apply_message`(socket, f, args=None, kwargs=None, subheader=None, track=False, ident=None)

    construct and send an apply message via a socket.

    This is the principal method with which all engine execution is performed by views.

  `shutdown`(targets=None, restart=False, hub=False, block=None)

    Terminates one or more engine processes, optionally including the hub.

  `spin`()

    Flush any registration notifications and execution results waiting in the ZMQ queue.

  `trait_metadata`(traitname, key)

    Get metadata values for trait by key.

  `trait_names`(**metadata)

    Get a list of all the names of this classes traits.

  `traits`(**metadata)

    Get a list of all the traits of this class.

    The TraitTypes returned don’t know anything about the values that the various HasTrait’s instances are holding.

    This follows the same algorithm as traits does and does not allow for any simple way of specifying merely that a metadata name exists, but has any value. This is because get_metadata returns None if a metadata key doesn’t exist.

  `wait`(jobs=None, timeout=-1)

    waits on one or more jobs, for up to timeout seconds.

    Parameters : | jobs : int, str, or list of ints and/or strs, or one or more AsyncResult objects ints are indices to self.history strs are msg_ids default: wait on all outstanding messages timeout : float a time in seconds, after which to give up. default is -1, which means no timeout
    Returns : | True : when all msg_ids are done False : timeout reached, some msg_ids still outstanding

### `Metadata`

class `IPython.parallel.client.client.` `Metadata`(*args, **kwargs)

  Bases: `dict`

  Subclass of dict for initializing metadata values.

  Attribute access works on keys.

  These objects have a strict set of keys - errors will raise if you try to add new keys.

  `__init__`(*args, **kwargs)
  `clear`

    D.clear() -> None. Remove all items from D.

  `copy`

    D.copy() -> a shallow copy of D

  static `fromkeys`(S[, v]) → New dict with keys from S and values equal to v.

    v defaults to None.

  `get`

    D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.

  `has_key`

    D.has_key(k) -> True if D has a key k, else False

  `items`

    D.items() -> list of D’s (key, value) pairs, as 2-tuples

  `iteritems`

    D.iteritems() -> an iterator over the (key, value) items of D

  `iterkeys`

    D.iterkeys() -> an iterator over the keys of D

  `itervalues`

    D.itervalues() -> an iterator over the values of D

  `keys`

    D.keys() -> list of D’s keys

  `pop`

    D.pop(k[,d]) -> v, remove specified key and return the corresponding value. If key is not found, d is returned if given, otherwise KeyError is raised

  `popitem`

    D.popitem() -> (k, v), remove and return some (key, value) pair as a 2-tuple; but raise KeyError if D is empty.

  `setdefault`

    D.setdefault(k[,d]) -> D.get(k,d), also set D[k]=d if k not in D

  `update`

    D.update(E, **F) -> None. Update D from dict/iterable E and F. If E has a .keys() method, does: for k in E: D[k] = E[k] If E lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

  `values`

    D.values() -> list of D’s values

## Function

`IPython.parallel.client.client.` `spin_first`(f)

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

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