docs/source/parallelz/parallel_multiengine.txt

.. _parallelmultiengine:

==========================
IPython's Direct interface
==========================

The direct, or multiengine, interface represents one possible way of working with a set of
IPython engines. The basic idea behind the multiengine interface is that the
capabilities of each engine are directly and explicitly exposed to the user.
Thus, in the multiengine interface, each engine is given an id that is used to
identify the engine and give it work to do. This interface is very intuitive
and is designed with interactive usage in mind, and is thus the best place for
new users of IPython to begin.

Starting the IPython controller and engines
===========================================

To follow along with this tutorial, you will need to start the IPython
controller and four IPython engines. The simplest way of doing this is to use
the :command:`ipclusterz` command::

    $ ipclusterz start -n 4
    
For more detailed information about starting the controller and engines, see
our :ref:`introduction <ip1par>` to using IPython for parallel computing.

Creating a ``Client`` instance
==============================

The first step is to import the IPython :mod:`IPython.zmq.parallel.client`
module and then create a :class:`.Client` instance:

.. sourcecode:: ipython

    In [1]: from IPython.zmq.parallel import client
    
    In [2]: rc = client.Client()

This form assumes that the default connection information (stored in
:file:`ipcontroller-client.json` found in :file:`IPYTHON_DIR/clusterz_default/security`) is
accurate. If the controller was started on a remote machine, you must copy that connection
file to the client machine, or enter its contents as arguments to the Client constructor:

.. sourcecode:: ipython

    # If you have copied the json connector file from the controller:
    In [2]: rc = client.Client('/path/to/ipcontroller-client.json')
    # or for a remote controller at 10.0.1.5, visible from my.server.com:
    In [3]: rc = client.Client('tcp://10.0.1.5:12345', sshserver='my.server.com')
    

To make sure there are engines connected to the controller, users can get a list
of engine ids:

.. sourcecode:: ipython

    In [3]: rc.ids
    Out[3]: [0, 1, 2, 3]

Here we see that there are four engines ready to do work for us.

For direct execution, we will make use of a :class:`DirectView` object, which can be
constructed via list-access to the client:

.. sourcecode::

    In [4]: dview = rc[:] # use all engines

.. seealso::

    For more information, see the in-depth explanation of :ref:`Views <parallel_details>`.


Quick and easy parallelism
==========================

In many cases, you simply want to apply a Python function to a sequence of
objects, but *in parallel*. The client interface provides a simple way
of accomplishing this: using the DirectView's :meth:`~DirectView.map` method.

Parallel map
------------

Python's builtin :func:`map` functions allows a function to be applied to a
sequence element-by-element. This type of code is typically trivial to
parallelize. In fact, since IPython's interface is all about functions anyway,
you can just use the builtin :func:`map` with a :class:`RemoteFunction`, or a 
DirectView's :meth:`map` method:

.. sourcecode:: ipython

    In [62]: serial_result = map(lambda x:x**10, range(32))
    
    In [63]: dview.block = True
    
    In [66]: parallel_result = dview.map(lambda x: x**10, range(32))

    In [67]: serial_result==parallel_result
    Out[67]: True


.. note::

    The :class:`DirectView`'s version of :meth:`map` does
    not do dynamic load balancing. For a load balanced version, use a
    :class:`LoadBalancedView`, or a :class:`ParallelFunction` with
    `balanced=True`.

.. seealso::
    
    :meth:`map` is implemented via :class:`ParallelFunction`.

Remote function decorators
--------------------------

Remote functions are just like normal functions, but when they are called,
they execute on one or more engines, rather than locally. IPython provides
two decorators:

.. sourcecode:: ipython

    In [10]: @rc.remote(block=True, targets='all')
        ...: def getpid():
        ...:     import os
        ...:     return os.getpid()
        ...: 

    In [11]: getpid()
    Out[11]: [12345, 12346, 12347, 12348]

A ``@parallel`` decorator creates parallel functions, that break up an element-wise
operations and distribute them, reconstructing the result.

.. sourcecode:: ipython

    In [12]: import numpy as np
    
    In [13]: A = np.random.random((64,48))
    
    In [14]: @rc.parallel(block=True, targets='all')
        ...: def pmul(A,B):
        ...:     return A*B
    
    In [15]: C_local = A*A
    
    In [16]: C_remote_partial = pmul(A,A)
    
    In [17]: (C_local == C_remote).all()
    Out[17]: True

.. seealso::

    See the docstrings for the :func:`parallel` and :func:`remote` decorators for
    options.

Calling Python functions
========================

The most basic type of operation that can be performed on the engines is to
execute Python code or call Python functions. Executing Python code can be
done in blocking or non-blocking mode (non-blocking is default) using the
:meth:`execute` method, and calling functions can be done via the
:meth:`.View.apply` method.

apply
-----

The main method for doing remote execution (in fact, all methods that
communicate with the engines are built on top of it), is :meth:`Client.apply`.
Ideally, :meth:`apply` would have the signature ``apply(f,*args,**kwargs)``,
which would call ``f(*args,**kwargs)`` remotely.  However, since :class:`Clients`
require some more options, they cannot easily provide this interface.
Instead, they provide the signature:

.. sourcecode:: python

    c.apply(f, args=None, kwargs=None, bound=True, block=None, targets=None,
                    after=None, follow=None, timeout=None)

Where various behavior is controlled via keyword arguments. This means that in the client,
you must pass `args` as a tuple, and `kwargs` as a dict.

In order to provide the nicer interface, we have :class:`View` classes, which wrap
:meth:`Client.apply` by using attributes and extra :meth:`apply_x` methods to determine
the extra keyword arguments.  This means that the views can have the desired pattern:

.. sourcecode:: python

    v.apply(f, *args, **kwargs)


For instance, performing index-access on a client creates a
:class:`.DirectView`.

.. sourcecode:: ipython

    In [4]: view = rc[1:3]
    Out[4]: <DirectView [1, 2]>

    In [5]: view.apply<tab>
    view.apply  view.apply_async  view.apply_async_bound  view.apply_sync  view.apply_sync_bound

A :class:`DirectView` always uses its `targets` attribute, and it will use its `bound`
and `block` attributes in its :meth:`apply` method, but the suffixed :meth:`apply_x`
methods allow specifying `bound` and `block` via the different methods.

==================  ==========  ==========
method              block       bound
==================  ==========  ==========
apply               self.block  self.bound
apply_sync          True        False
apply_async         False       False
apply_sync_bound    True        True
apply_async_bound   False       True
==================  ==========  ==========

For explanation of these values, read on.

Blocking execution
------------------

In blocking mode, the :class:`.DirectView` object (called ``dview`` in
these examples) submits the command to the controller, which places the
command in the engines' queues for execution. The :meth:`apply` call then
blocks until the engines are done executing the command:

.. sourcecode:: ipython

    In [2]: dview = rc[:] # A DirectView of all engines
    In [3]: dview.block=True
    In [4]: dview['a'] = 5

    In [5]: dview['b'] = 10

    In [6]: dview.apply_sync(lambda x: a+b+x, 27)
    Out[6]: [42, 42, 42, 42]

Python commands can be executed on specific engines by calling execute using the ``targets``
keyword argument in :meth:`client.execute`, or creating a :class:`DirectView` instance by
index-access to the client:

.. sourcecode:: ipython

    In [6]: rc.execute('c=a+b', targets=[0,2])

    In [7]: rc[1::2].execute('c=a-b') # shorthand for rc.execute('c=a-b',targets=[1,3])

    In [8]: rc[:]['c'] # shorthand for rc.pull('c',targets='all')
    Out[8]: [15, -5, 15, -5]

.. note::

    Note that every call to ``rc.<meth>(...,targets=x)`` can be made via
    ``rc[<x>].<meth>(...)``, which constructs a View object. The only place
    where this differs in in :meth:`apply`. The :class:`Client` takes many
    arguments to apply, so it requires `args` and `kwargs` to be passed as
    individual arguments. Extended options such as `bound`,`targets`, and
    `block` are controlled by the attributes of the :class:`View` objects, so
    they can provide the much more convenient
    :meth:`View.apply(f,*args,**kwargs)`, which simply calls
    ``f(*args,**kwargs)`` remotely.

Bound and unbound execution
---------------------------

The previous example also shows one of the most important things about the IPython
engines: they have a persistent user namespaces. The :meth:`apply` method can
be run in either a bound or unbound manner.

When applying a function in a `bound` manner, the first argument to that function
will be the Engine's namespace, which is a :class:`Namespace` object, a dictionary
also providing attribute-access to keys.

In all (unbound and bound) execution

.. sourcecode:: ipython

    In [9]: dview['b'] = 5 # assign b to 5 everywhere
    
    In [10]: v0 = rc[0]
    
    # multiply b*2 inplace
    In [12]: v0.apply_sync_bound(lambda ns: ns.b*=2)
    
    # b is still available in globals during unbound execution
    In [13]: v0.apply_sync(lambda a: a*b, 3)
    Out[13]: 30

`bound=True` specifies that the engine's namespace is to be passed as the first argument when
the function is called, and the default `bound=False` specifies that the normal behavior, but
the engine's namespace will be available as the globals() when the function is called.

Non-blocking execution
----------------------

In non-blocking mode, :meth:`apply` submits the command to be executed and
then returns a :class:`AsyncResult` object immediately. The
:class:`AsyncResult` object gives you a way of getting a result at a later
time through its :meth:`get` method.

.. Note::

    The :class:`AsyncResult` object provides a superset of the interface in 
    :py:class:`multiprocessing.pool.AsyncResult`.  See the 
    `official Python documentation <http://docs.python.org/library/multiprocessing#multiprocessing.pool.AsyncResult>`_
    for more.


This allows you to quickly submit long running commands without blocking your
local Python/IPython session:

.. sourcecode:: ipython
    
    # define our function
    In [6]: def wait(t):
       ...:     import time
       ...:     tic = time.time()
       ...:     time.sleep(t)
       ...:     return time.time()-tic
    
    # In non-blocking mode
    In [7]: ar = dview.apply_async(wait, 2)

    # Now block for the result
    In [8]: ar.get()
    Out[8]: [2.0006198883056641, 1.9997570514678955, 1.9996809959411621, 2.0003249645233154]

    # Again in non-blocking mode
    In [9]: ar = dview.apply_async(wait, 10)

    # Poll to see if the result is ready
    In [10]: ar.ready()
    Out[10]: False
    
    # ask for the result, but wait a maximum of 1 second:
    In [45]: ar.get(1)
    ---------------------------------------------------------------------------
    TimeoutError                              Traceback (most recent call last)
    /home/you/<ipython-input-45-7cd858bbb8e0> in <module>()
    ----> 1 ar.get(1)

    /path/to/site-packages/IPython/zmq/parallel/asyncresult.pyc in get(self, timeout)
         62                 raise self._exception
         63         else:
    ---> 64             raise error.TimeoutError("Result not ready.")
         65 
         66     def ready(self):

    TimeoutError: Result not ready.

.. Note::

    Note the import inside the function. This is a common model, to ensure
    that the appropriate modules are imported where the task is run.

Often, it is desirable to wait until a set of :class:`AsyncResult` objects
are done. For this, there is a the method :meth:`barrier`. This method takes a
tuple of :class:`AsyncResult` objects (or `msg_ids` or indices to the client's History),
and blocks until all of the associated results are ready:

.. sourcecode:: ipython

    In [72]: rc.block=False

    # A trivial list of AsyncResults objects
    In [73]: pr_list = [dview.apply_async(wait, 3) for i in range(10)]

    # Wait until all of them are done
    In [74]: rc.barrier(pr_list)

    # Then, their results are ready using get() or the `.r` attribute
    In [75]: pr_list[0].get()
    Out[75]: [2.9982571601867676, 2.9982588291168213, 2.9987530708312988, 2.9990990161895752]
    


The ``block`` keyword argument and attributes
---------------------------------------------

Most client methods(like :meth:`apply`) accept
``block`` as a keyword argument. As we have seen above, these
keyword arguments control the blocking mode. The :class:`Client` class also has 
a :attr:`block` attribute that controls the default behavior when the keyword
argument is not provided. Thus the following logic is used for :attr:`block`:

* If no keyword argument is provided, the instance attributes are used.
* Keyword argument, if provided override the instance attributes for
  the duration of a single call.
  
DirectView objects also have a ``bound`` attribute, which is used in the same way.

The following examples demonstrate how to use the instance attributes:

.. sourcecode:: ipython

    In [17]: rc.block = False

    In [18]: ar = rc.apply(lambda : 10, targets=[0,2])

    In [19]: ar.get()
    Out[19]: [10,10]

    In [21]: rc.block = True

    # Note targets='all' means all engines
    In [22]: rc.apply(lambda : 42, targets='all')
    Out[22]: [42, 42, 42, 42]

The :attr:`block`, :attr:`bound`, and :attr:`targets` instance attributes of the
:class:`.DirectView` also determine the behavior of the parallel magic commands.


Parallel magic commands
-----------------------

.. warning::

    The magics have not been changed to work with the zeromq system. ``%px``
    and ``%autopx`` do work, but ``%result`` does not. %px and %autopx *do
    not* print stdin/out.

We provide a few IPython magic commands (``%px``, ``%autopx`` and ``%result``)
that make it more pleasant to execute Python commands on the engines
interactively. These are simply shortcuts to :meth:`execute` and
:meth:`get_result` of the :class:`DirectView`. The ``%px`` magic executes a single 
Python command on the engines specified by the :attr:`targets` attribute of the
:class:`DirectView` instance:

.. sourcecode:: ipython

    # Create a DirectView for all targets
    In [22]: dv = rc[:]
    
    # Make this DirectView active for parallel magic commands
    In [23]: dv.activate()

    In [24]: dv.block=True

    In [25]: import numpy

    In [26]: %px import numpy
    Parallel execution on engines: [0, 1, 2, 3]

    In [27]: %px a = numpy.random.rand(2,2)
    Parallel execution on engines: [0, 1, 2, 3]

    In [28]: %px ev = numpy.linalg.eigvals(a)
    Parallel execution on engines: [0, 1, 2, 3]

    In [28]: dv['ev']
    Out[28]: [ array([ 1.09522024, -0.09645227]), 
               array([ 1.21435496, -0.35546712]), 
               array([ 0.72180653,  0.07133042]), 
               array([  1.46384341e+00,   1.04353244e-04])
             ]

The ``%result`` magic gets the most recent result, or takes an argument
specifying the index of the result to be requested. It is simply a shortcut to the
:meth:`get_result` method:

.. sourcecode:: ipython
    
    In [29]: dv.apply_async(lambda : ev)
    
    In [30]: %result
    Out[30]: [ [ 1.28167017  0.14197338],
                [-0.14093616  1.27877273],
                [-0.37023573  1.06779409],
                [ 0.83664764 -0.25602658] ]

The ``%autopx`` magic switches to a mode where everything you type is executed
on the engines given by the :attr:`targets` attribute:

.. sourcecode:: ipython

    In [30]: dv.block=False

    In [31]: %autopx
    Auto Parallel Enabled
    Type %autopx to disable

    In [32]: max_evals = []
    <IPython.zmq.parallel.asyncresult.AsyncResult object at 0x17b8a70>

    In [33]: for i in range(100):
       ....:     a = numpy.random.rand(10,10)
       ....:     a = a+a.transpose()
       ....:     evals = numpy.linalg.eigvals(a)
       ....:     max_evals.append(evals[0].real)
       ....:
       ....:
    <IPython.zmq.parallel.asyncresult.AsyncResult object at 0x17af8f0>

    In [34]: %autopx
    Auto Parallel Disabled

    In [35]: dv.block=True

    In [36]: px ans= "Average max eigenvalue is: %f"%(sum(max_evals)/len(max_evals))
    Parallel execution on engines: [0, 1, 2, 3]
    
    In [37]: dv['ans']
    Out[37]: [ 'Average max eigenvalue is:  10.1387247332',
               'Average max eigenvalue is:  10.2076902286',
               'Average max eigenvalue is:  10.1891484655',
               'Average max eigenvalue is:  10.1158837784',]


Moving Python objects around
============================

In addition to calling functions and executing code on engines, you can
transfer Python objects to and from your IPython session and the engines. In
IPython, these operations are called :meth:`push` (sending an object to the
engines) and :meth:`pull` (getting an object from the engines). 

Basic push and pull
-------------------

Here are some examples of how you use :meth:`push` and :meth:`pull`:

.. sourcecode:: ipython

    In [38]: rc.push(dict(a=1.03234,b=3453))
    Out[38]: [None,None,None,None]

    In [39]: rc.pull('a')
    Out[39]: [ 1.03234, 1.03234, 1.03234, 1.03234]

    In [40]: rc.pull('b',targets=0)
    Out[40]: 3453

    In [41]: rc.pull(('a','b'))
    Out[41]: [ [1.03234, 3453], [1.03234, 3453], [1.03234, 3453], [1.03234, 3453] ]
    
    # zmq client does not have zip_pull
    In [42]: rc.zip_pull(('a','b'))
    Out[42]: [(1.03234, 1.03234, 1.03234, 1.03234), (3453, 3453, 3453, 3453)]

    In [43]: rc.push(dict(c='speed'))
    Out[43]: [None,None,None,None]

In non-blocking mode :meth:`push` and :meth:`pull` also return
:class:`AsyncResult` objects:

.. sourcecode:: ipython

    In [47]: rc.block=False

    In [48]: ar = rc.pull('a')

    In [49]: ar.get()
    Out[49]: [1.03234, 1.03234, 1.03234, 1.03234]


Dictionary interface
--------------------

Since a Python namespace is just a :class:`dict`, :class:`DirectView` objects provide
dictionary-style access by key and methods such as :meth:`get` and
:meth:`update` for convenience. This make the remote namespaces of the engines
appear as a local dictionary. Underneath, these methods call :meth:`apply`:

.. sourcecode:: ipython

    In [50]: dview.block=True

    In [51]: dview['a']=['foo','bar']

    In [52]: dview['a']
    Out[52]: [ ['foo', 'bar'], ['foo', 'bar'], ['foo', 'bar'], ['foo', 'bar'] ]

Scatter and gather
------------------

Sometimes it is useful to partition a sequence and push the partitions to
different engines. In MPI language, this is know as scatter/gather and we
follow that terminology. However, it is important to remember that in
IPython's :class:`Client` class, :meth:`scatter` is from the
interactive IPython session to the engines and :meth:`gather` is from the
engines back to the interactive IPython session. For scatter/gather operations
between engines, MPI should be used:

.. sourcecode:: ipython

    In [58]: dview.scatter('a',range(16))
    Out[58]: [None,None,None,None]

    In [59]: dview['a']
    Out[59]: [ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15] ]

    In [60]: dview.gather('a')
    Out[60]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

Other things to look at
=======================

How to do parallel list comprehensions
--------------------------------------

In many cases list comprehensions are nicer than using the map function. While
we don't have fully parallel list comprehensions, it is simple to get the
basic effect using :meth:`scatter` and :meth:`gather`:

.. sourcecode:: ipython

    In [66]: dview.scatter('x',range(64))

    In [67]: px y = [i**10 for i in x]
    Parallel execution on engines: [0, 1, 2, 3]
    Out[67]: 

    In [68]: y = dview.gather('y')

    In [69]: print y
    [0, 1, 1024, 59049, 1048576, 9765625, 60466176, 282475249, 1073741824,...]

Parallel exceptions
-------------------

In the multiengine interface, parallel commands can raise Python exceptions,
just like serial commands. But, it is a little subtle, because a single
parallel command can actually raise multiple exceptions (one for each engine
the command was run on). To express this idea, we have a
:exc:`CompositeError` exception class that will be raised in most cases. The
:exc:`CompositeError` class is a special type of exception that wraps one or
more other types of exceptions. Here is how it works:

.. sourcecode:: ipython

    In [76]: dview.block=True

    In [77]: dview.execute('1/0')
    ---------------------------------------------------------------------------
    CompositeError                            Traceback (most recent call last)
    /Users/minrk/<ipython-input-10-5d56b303a66c> in <module>()
    ----> 1 dview.execute('1/0')

    ...
    
    /Users/minrk/dev/ip/mine/IPython/zmq/parallel/client.pyc in apply(self, f, args, kwargs, bound, block, targets, balanced, after, follow, timeout)
       1012                 raise ValueError(msg)
       1013         else:
    -> 1014             return self._apply_direct(f, args, kwargs, **options)
       1015 
       1016     def _apply_balanced(self, f, args, kwargs, bound=None, block=None, targets=None,

    /Users/minrk/dev/ip/mine/IPython/zmq/parallel/client.pyc in _apply_direct(self, f, args, kwargs, bound, block, targets)
       1100         if block:
       1101             try:
    -> 1102                 return ar.get()
       1103             except KeyboardInterrupt:
       1104                 return ar

    /Users/minrk/dev/ip/mine/IPython/zmq/parallel/asyncresult.pyc in get(self, timeout)
         78                 return self._result
         79             else:
    ---> 80                 raise self._exception
         81         else:
         82             raise error.TimeoutError("Result not ready.")

    CompositeError: one or more exceptions from call to method: _execute
    [0:apply]: ZeroDivisionError: integer division or modulo by zero
    [1:apply]: ZeroDivisionError: integer division or modulo by zero
    [2:apply]: ZeroDivisionError: integer division or modulo by zero
    [3:apply]: ZeroDivisionError: integer division or modulo by zero

Notice how the error message printed when :exc:`CompositeError` is raised has
information about the individual exceptions that were raised on each engine.
If you want, you can even raise one of these original exceptions:

.. sourcecode:: ipython

    In [80]: try:
       ....:     rc.execute('1/0')
       ....: except client.CompositeError, e:
       ....:     e.raise_exception()
       ....:     
       ....:     
    ---------------------------------------------------------------------------
    ZeroDivisionError                         Traceback (most recent call last)

    /ipython1-client-r3021/docs/examples/<ipython console> in <module>()

    /ipython1-client-r3021/ipython1/kernel/error.pyc in raise_exception(self, excid)
        156             raise IndexError("an exception with index %i does not exist"%excid)
        157         else:
    --> 158             raise et, ev, etb
        159 
        160 def collect_exceptions(rlist, method):

    ZeroDivisionError: integer division or modulo by zero

If you are working in IPython, you can simple type ``%debug`` after one of
these :exc:`CompositeError` exceptions is raised, and inspect the exception
instance:

.. sourcecode:: ipython

    In [81]: rc.execute('1/0')
    ---------------------------------------------------------------------------
    CompositeError                            Traceback (most recent call last)
    /Users/minrk/<ipython-input-5-b0c7a2b62c52> in <module>()
    ----> 1 rc.execute('1/0')

    /Users/minrk/<string> in execute(self, code, targets, block)

    /Users/minrk/dev/ip/mine/IPython/zmq/parallel/client.pyc in defaultblock(f, self, *args, **kwargs)
         88     self.block = block
         89     try:
    ---> 90         ret = f(self, *args, **kwargs)
         91     finally:
         92         self.block = saveblock

    /Users/minrk/dev/ip/mine/IPython/zmq/parallel/client.pyc in execute(self, code, targets, block)
        855                 default: self.block
        856         """
    --> 857         result = self.apply(_execute, (code,), targets=targets, block=block, bound=True, balanced=False)
        858         if not block:
        859             return result

    /Users/minrk/<string> in apply(self, f, args, kwargs, bound, block, targets, balanced, after, follow, timeout)

    /Users/minrk/dev/ip/mine/IPython/zmq/parallel/client.pyc in defaultblock(f, self, *args, **kwargs)
         88     self.block = block
         89     try:
    ---> 90         ret = f(self, *args, **kwargs)
         91     finally:
         92         self.block = saveblock

    /Users/minrk/dev/ip/mine/IPython/zmq/parallel/client.pyc in apply(self, f, args, kwargs, bound, block, targets, balanced, after, follow, timeout)
       1012                 raise ValueError(msg)
       1013         else:
    -> 1014             return self._apply_direct(f, args, kwargs, **options)
       1015 
       1016     def _apply_balanced(self, f, args, kwargs, bound=None, block=None, targets=None,

    /Users/minrk/dev/ip/mine/IPython/zmq/parallel/client.pyc in _apply_direct(self, f, args, kwargs, bound, block, targets)
       1100         if block:
       1101             try:
    -> 1102                 return ar.get()
       1103             except KeyboardInterrupt:
       1104                 return ar

    /Users/minrk/dev/ip/mine/IPython/zmq/parallel/asyncresult.pyc in get(self, timeout)
         78                 return self._result
         79             else:
    ---> 80                 raise self._exception
         81         else:
         82             raise error.TimeoutError("Result not ready.")

    CompositeError: one or more exceptions from call to method: _execute
    [0:apply]: ZeroDivisionError: integer division or modulo by zero
    [1:apply]: ZeroDivisionError: integer division or modulo by zero
    [2:apply]: ZeroDivisionError: integer division or modulo by zero
    [3:apply]: ZeroDivisionError: integer division or modulo by zero
    
    In [82]: %debug
    > /Users/minrk/dev/ip/mine/IPython/zmq/parallel/asyncresult.py(80)get()
         79             else:
    ---> 80                 raise self._exception
         81         else:
    
    
    # With the debugger running, e is the exceptions instance.  We can tab complete
    # on it and see the extra methods that are available.
    ipdb> e.
    e.__class__         e.__getitem__       e.__new__           e.__setstate__      e.args
    e.__delattr__       e.__getslice__      e.__reduce__        e.__str__           e.elist
    e.__dict__          e.__hash__          e.__reduce_ex__     e.__weakref__       e.message
    e.__doc__           e.__init__          e.__repr__          e._get_engine_str   e.print_tracebacks
    e.__getattribute__  e.__module__        e.__setattr__       e._get_traceback    e.raise_exception
    ipdb> e.print_tracebacks()
    [0:apply]: 
    Traceback (most recent call last):
      File "/Users/minrk/dev/ip/mine/IPython/zmq/parallel/streamkernel.py", line 332, in apply_request
        exec code in working, working
      File "<string>", line 1, in <module>
      File "/Users/minrk/dev/ip/mine/IPython/zmq/parallel/client.py", line 69, in _execute
        exec code in globals()
      File "<string>", line 1, in <module>
    ZeroDivisionError: integer division or modulo by zero


    [1:apply]: 
    Traceback (most recent call last):
      File "/Users/minrk/dev/ip/mine/IPython/zmq/parallel/streamkernel.py", line 332, in apply_request
        exec code in working, working
      File "<string>", line 1, in <module>
      File "/Users/minrk/dev/ip/mine/IPython/zmq/parallel/client.py", line 69, in _execute
        exec code in globals()
      File "<string>", line 1, in <module>
    ZeroDivisionError: integer division or modulo by zero


    [2:apply]: 
    Traceback (most recent call last):
      File "/Users/minrk/dev/ip/mine/IPython/zmq/parallel/streamkernel.py", line 332, in apply_request
        exec code in working, working
      File "<string>", line 1, in <module>
      File "/Users/minrk/dev/ip/mine/IPython/zmq/parallel/client.py", line 69, in _execute
        exec code in globals()
      File "<string>", line 1, in <module>
    ZeroDivisionError: integer division or modulo by zero


    [3:apply]: 
    Traceback (most recent call last):
      File "/Users/minrk/dev/ip/mine/IPython/zmq/parallel/streamkernel.py", line 332, in apply_request
        exec code in working, working
      File "<string>", line 1, in <module>
      File "/Users/minrk/dev/ip/mine/IPython/zmq/parallel/client.py", line 69, in _execute
        exec code in globals()
      File "<string>", line 1, in <module>
    ZeroDivisionError: integer division or modulo by zero
    


All of this same error handling magic even works in non-blocking mode:

.. sourcecode:: ipython

    In [83]: rc.block=False

    In [84]: ar = rc.execute('1/0')

    In [85]: ar.get()
    ---------------------------------------------------------------------------
    CompositeError                            Traceback (most recent call last)
    /Users/minrk/<ipython-input-3-8531eb3d26fb> in <module>()
    ----> 1 ar.get()

    /Users/minrk/dev/ip/mine/IPython/zmq/parallel/asyncresult.pyc in get(self, timeout)
         78                 return self._result
         79             else:
    ---> 80                 raise self._exception
         81         else:
         82             raise error.TimeoutError("Result not ready.")

    CompositeError: one or more exceptions from call to method: _execute
    [0:apply]: ZeroDivisionError: integer division or modulo by zero
    [1:apply]: ZeroDivisionError: integer division or modulo by zero
    [2:apply]: ZeroDivisionError: integer division or modulo by zero
    [3:apply]: ZeroDivisionError: integer division or modulo by zero