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 ssh private 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

If unspecified/None: abort all outstanding jobs.

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.

rc.direct_view(‘all’) is distinguished from rc[:] in that ‘all’ will evaluate the target engines at each execution, whereas rc[:] will connect to all current engines, and that list will not change.

That is, ‘all’ will always use all engines, whereas rc[:] will not use engines added after the DirectView is constructed.

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()

dict.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

viewitems

D.viewitems() -> a set-like object providing a view on D’s items

viewkeys

D.viewkeys() -> a set-like object providing a view on D’s keys

viewvalues

D.viewvalues() -> an object providing a view on D’s values

Function

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

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