document promoting an Engine to a regular Kernel

docs/source/parallel/parallel_multiengine.txt

@@ -1,944 +1,1002 b''
 .. _parallel_multiengine:
 
 ==========================
 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 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:`ipcluster` command::
 
     $ ipcluster start -n 4
     
 For more detailed information about starting the controller and engines, see
 our :ref:`introduction <parallel_overview>` to using IPython for parallel computing.
 
 Creating a ``DirectView`` instance
 ==================================
 
 The first step is to import the IPython :mod:`IPython.parallel`
 module and then create a :class:`.Client` instance:
 
 .. sourcecode:: ipython
 
     In [1]: from IPython.parallel import Client
     
     In [2]: rc = Client()
 
 This form assumes that the default connection information (stored in
 :file:`ipcontroller-client.json` found in :file:`IPYTHONDIR/profile_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('/path/to/ipcontroller-client.json')
     # or to connect with a specific profile you have set up:
     In [3]: rc = Client(profile='mpi')
     
 
 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:: ipython
 
     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]: parallel_result = dview.map_sync(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`.
 
 .. 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]: @dview.remote(block=True)
        ....: def getpid():
        ....:     import os
        ....:     return os.getpid()
        ....: 
 
     In [11]: getpid()
     Out[11]: [12345, 12346, 12347, 12348]
 
 The ``@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]: @dview.parallel(block=True)
        ....: def pmul(A,B):
        ....:     return A*B
     
     In [15]: C_local = A*A
     
     In [16]: C_remote = pmul(A,A)
     
     In [17]: (C_local == C_remote).all()
     Out[17]: True
 
 Calling a ``@parallel`` function *does not* correspond to map. It is used for splitting
 element-wise operations that operate on a sequence or array.  For ``map`` behavior,
 parallel functions do have a map method.
 
 ====================    ============================    =============================
 call                    pfunc(seq)                      pfunc.map(seq)
 ====================    ============================    =============================
 # of tasks              # of engines (1 per engine)     # of engines (1 per engine)
 # of remote calls       # of engines (1 per engine)     ``len(seq)``
 argument to remote      ``seq[i:j]`` (sub-sequence)     ``seq[i]`` (single element)
 ====================    ============================    =============================
 
 A quick example to illustrate the difference in arguments for the two modes:
 
 .. sourcecode:: ipython
 
     In [16]: @dview.parallel(block=True)
        ....: def echo(x):
        ....:     return str(x)
        ....: 
 
     In [17]: echo(range(5))
     Out[17]: ['[0, 1]', '[2]', '[3]', '[4]']
 
     In [18]: echo.map(range(5))
     Out[18]: ['0', '1', '2', '3', '4']
 
 
 .. seealso::
 
     See the :func:`~.remotefunction.parallel` and :func:`~.remotefunction.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:`.View.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:`View.apply`.
 
 We strive to provide the cleanest interface we can, so `apply` has the following
 signature:
 
 .. sourcecode:: python
 
     view.apply(f, *args, **kwargs)
 
 There are various ways to call functions with IPython, and these flags are set as
 attributes of the View.  The ``DirectView`` has just two of these flags:
 
 dv.block : bool
     whether to wait for the result, or return an :class:`AsyncResult` object
     immediately
 dv.track : bool
     whether to instruct pyzmq to track when zeromq is done sending the message.
     This is primarily useful for non-copying sends of numpy arrays that you plan to
     edit in-place.  You need to know when it becomes safe to edit the buffer
     without corrupting the message.
 dv.targets : int, list of ints
     which targets this view is associated with.
 
 
 Creating a view is simple: 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_sync
 
 For convenience, you can set block temporarily for a single call with the extra sync/async methods.
 
 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(lambda x: a+b+x, 27)
     Out[6]: [42, 42, 42, 42]
 
 You can also select blocking execution on a call-by-call basis with the :meth:`apply_sync`
 method:
 
 .. sourcecode:: ipython
 
     In [7]: dview.block=False
 
     In [8]: dview.apply_sync(lambda x: a+b+x, 27)
     Out[8]: [42, 42, 42, 42]
 
 Python commands can be executed as strings on specific engines by using a View's ``execute``
 method:
 
 .. sourcecode:: ipython
 
     In [6]: rc[::2].execute('c=a+b')
 
     In [7]: rc[1::2].execute('c=a-b')
 
     In [8]: dview['c'] # shorthand for dview.pull('c', block=True)
     Out[8]: [15, -5, 15, -5]
 
 
 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.
 
 .. seealso::
 
     Docs on the :ref:`AsyncResult <parallel_asyncresult>` object.
 
 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/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. You can
     also manually import modules into the engine(s) namespace(s) via 
     :meth:`view.execute('import numpy')`.
 
 Often, it is desirable to wait until a set of :class:`AsyncResult` objects
 are done. For this, there is a the method :meth:`wait`. 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]: dview.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]: dview.wait(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`` and ``targets`` keyword arguments and attributes
 --------------------------------------------------------------
 
 Most DirectView methods (excluding :meth:`apply`) accept ``block`` and
 ``targets`` as keyword arguments. As we have seen above, these keyword arguments control the
 blocking mode and which engines the command is applied to. The :class:`View` class also has
 :attr:`block` and :attr:`targets` attributes that control the default behavior when the keyword
 arguments are not provided. Thus the following logic is used for :attr:`block` and :attr:`targets`:
 
 * If no keyword argument is provided, the instance attributes are used.
 * The Keyword arguments, if provided overrides the instance attributes for
   the duration of a single call.
   
 The following examples demonstrate how to use the instance attributes:
 
 .. sourcecode:: ipython
 
     In [16]: dview.targets = [0,2]
     
     In [17]: dview.block = False
 
     In [18]: ar = dview.apply(lambda : 10)
 
     In [19]: ar.get()
     Out[19]: [10, 10]
 
     In [20]: dview.targets = v.client.ids # all engines (4)
     
     In [21]: dview.block = True
 
     In [22]: dview.apply(lambda : 42)
     Out[22]: [42, 42, 42, 42]
 
 The :attr:`block` and :attr:`targets` instance attributes of the
 :class:`.DirectView` also determine the behavior of the parallel magic commands.
 
 Parallel magic commands
 -----------------------
 
 We provide a few IPython magic commands (``%px``, ``%autopx`` and ``%result``)
 that make it a bit more pleasant to execute Python commands on the engines interactively.
 These are simply shortcuts to :meth:`.DirectView.execute`
 and :meth:`.AsyncResult.display_outputs` methods repsectively.
 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
 
     # import numpy here and everywhere
     In [25]: with dv.sync_imports():
        ....:    import numpy
     importing numpy on engine(s)
 
     In [27]: %px a = numpy.random.rand(2,2)
     Parallel execution on engines: [0, 1, 2, 3]
 
     In [28]: %px numpy.linalg.eigvals(a)
     Parallel execution on engines: [0, 1, 2, 3]
     [0] Out[68]: array([ 0.77120707, -0.19448286])
     [1] Out[68]: array([ 1.10815921,  0.05110369])
     [2] Out[68]: array([ 0.74625527, -0.37475081])
     [3] Out[68]: array([ 0.72931905,  0.07159743])
     
     In [29]: %px print 'hi'
     Parallel execution on engine(s): [0, 1, 2, 3]
     [stdout:0] hi
     [stdout:1] hi
     [stdout:2] hi
     [stdout:3] hi
 
 
 Since engines are IPython as well, you can even run magics remotely:
 
 .. sourcecode:: ipython
 
     In [28]: %px %pylab inline
     Parallel execution on engine(s): [0, 1, 2, 3]
     [stdout:0] 
     Welcome to pylab, a matplotlib-based Python environment...
     For more information, type 'help(pylab)'.
     [stdout:1] 
     Welcome to pylab, a matplotlib-based Python environment...
     For more information, type 'help(pylab)'.
     [stdout:2] 
     Welcome to pylab, a matplotlib-based Python environment...
     For more information, type 'help(pylab)'.
     [stdout:3] 
     Welcome to pylab, a matplotlib-based Python environment...
     For more information, type 'help(pylab)'.
 
 And once in pylab mode with the inline backend,
 you can make plots and they will be displayed in your frontend
 if it suports the inline figures (e.g. notebook or qtconsole):
 
 .. sourcecode:: ipython
 
     In [40]: %px plot(rand(100))
     Parallel execution on engine(s): [0, 1, 2, 3]
     <plot0>
     <plot1>
     <plot2>
     <plot3>
     [0] Out[79]: [<matplotlib.lines.Line2D at 0x10a6286d0>]
     [1] Out[79]: [<matplotlib.lines.Line2D at 0x10b9476d0>]
     [2] Out[79]: [<matplotlib.lines.Line2D at 0x110652750>]
     [3] Out[79]: [<matplotlib.lines.Line2D at 0x10c6566d0>]
 
 
 ``%%px`` Cell Magic
 *******************
 
 `%%px` can also be used as a Cell Magic, which accepts ``--[no]block`` flags,
 and a ``--group-outputs`` argument, which adjust how the outputs of multiple
 engines are presented.
 
 .. seealso::
 
     :meth:`.AsyncResult.display_outputs` for the grouping options.
 
 .. sourcecode:: ipython
 
     In [50]: %%px --block --group-outputs=engine
        ....: import numpy as np
        ....: A = np.random.random((2,2))
        ....: ev = numpy.linalg.eigvals(A)
        ....: print ev
        ....: ev.max()
        ....:
     Parallel execution on engine(s): [0, 1, 2, 3]
     [stdout:0] [ 0.60640442  0.95919621]
     [0] Out[73]: 0.9591962130899806
     [stdout:1] [ 0.38501813  1.29430871]
     [1] Out[73]: 1.2943087091452372
     [stdout:2] [-0.85925141  0.9387692 ]
     [2] Out[73]: 0.93876920456230284
     [stdout:3] [ 0.37998269  1.24218246]
     [3] Out[73]: 1.2421824618493817
 
 ``%result`` Magic
 *****************
 
 If you are using ``%px`` in non-blocking mode, you won't get output.
 You can use ``%result`` to display the outputs of the latest command,
 just as is done when ``%px`` is blocking:
 
 .. sourcecode:: ipython
 
     In [39]: dv.block = False
     
     In [40]: %px print 'hi'
     Async parallel execution on engine(s): [0, 1, 2, 3]
     
     In [41]: %result
     [stdout:0] hi
     [stdout:1] hi
     [stdout:2] hi
     [stdout:3] hi
 
 ``%result`` simply calls :meth:`.AsyncResult.display_outputs` on the most recent request.
 You can pass integers as indices if you want a result other than the latest,
 e.g. ``%result -2``, or ``%result 0`` for the first.
 
 
 ``%autopx``
 ***********
 
 The ``%autopx`` magic switches to a mode where everything you type is executed
 on the engines until you do ``%autopx`` again.
 
 .. sourcecode:: ipython
 
     In [30]: dv.block=True
 
     In [31]: %autopx
     %autopx enabled
 
     In [32]: max_evals = []
 
     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)
        ....:
 
     In [34]: print "Average max eigenvalue is: %f" % (sum(max_evals)/len(max_evals))
     [stdout:0] Average max eigenvalue is: 10.193101
     [stdout:1] Average max eigenvalue is: 10.064508
     [stdout:2] Average max eigenvalue is: 10.055724
     [stdout:3] Average max eigenvalue is: 10.086876
-    
+
     In [35]: %autopx
     Auto Parallel Disabled
 
 
+Engines as Kernels
+******************
+
+Engines are really the same object as the Kernels used elsewhere in IPython,
+with the minor exception that engines connect to a controller, while regular kernels
+bind their sockets, listening for connections from a QtConsole or other frontends.
+
+Sometimes for debugging or inspection purposes, you would like a QtConsole connected
+to an engine for more direct interaction.  You can do this by first instructing
+the Engine to *also* bind its kernel, to listen for connections:
+
+.. sourcecode:: ipython
+
+    In [50]: %px from IPython.parallel import bind_kernel; bind_kernel()
+
+Then, if your engines are local, you can start a qtconsole right on the engine(s):
+
+.. sourcecode:: ipython
+
+    In [51]: %px %qtconsole
+
+Careful with this one, because if your view is of 16 engines it will start 16 QtConsoles!
+
+Or you can view just the connection info, and work out the right way to connect to the engines,
+depending on where they live and where you are:
+
+.. sourcecode:: ipython
+
+    In [51]: %px %connect_info
+    Parallel execution on engine(s): [0, 1, 2, 3]
+    [stdout:0] 
+    {
+      "stdin_port": 60387, 
+      "ip": "127.0.0.1", 
+      "hb_port": 50835, 
+      "key": "eee2dd69-7dd3-4340-bf3e-7e2e22a62542", 
+      "shell_port": 55328, 
+      "iopub_port": 58264
+    }
+
+    Paste the above JSON into a file, and connect with:
+        $> ipython <app> --existing <file>
+    or, if you are local, you can connect with just:
+        $> ipython <app> --existing kernel-60125.json 
+    or even just:
+        $> ipython <app> --existing 
+    if this is the most recent IPython session you have started.
+    [stdout:1] 
+    {
+      "stdin_port": 61869,
+    ...
+
+.. note::
+
+    ``%qtconsole`` will call :func:`bind_kernel` on an engine if it hasn't been done already,
+    so you can often skip that first step.
+
+
 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]: dview.push(dict(a=1.03234,b=3453))
     Out[38]: [None,None,None,None]
 
     In [39]: dview.pull('a')
     Out[39]: [ 1.03234, 1.03234, 1.03234, 1.03234]
 
     In [40]: dview.pull('b', targets=0)
     Out[40]: 3453
 
     In [41]: dview.pull(('a','b'))
     Out[41]: [ [1.03234, 3453], [1.03234, 3453], [1.03234, 3453], [1.03234, 3453] ]
     
     In [42]: dview.push(dict(c='speed'))
     Out[42]: [None,None,None,None]
 
 In non-blocking mode :meth:`push` and :meth:`pull` also return
 :class:`AsyncResult` objects:
 
 .. sourcecode:: ipython
 
     In [48]: ar = dview.pull('a', block=False)
 
     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 [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, pyzmq, or some other direct interconnect 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]
 
     In [68]: y = dview.gather('y')
 
     In [69]: print y
     [0, 1, 1024, 59049, 1048576, 9765625, 60466176, 282475249, 1073741824,...]
 
 Remote imports
 --------------
 
 Sometimes you will want to import packages both in your interactive session
 and on your remote engines.  This can be done with the :class:`ContextManager`
 created by a DirectView's :meth:`sync_imports` method:
 
 .. sourcecode:: ipython
 
     In [69]: with dview.sync_imports():
        ....:     import numpy
     importing numpy on engine(s)
 
 Any imports made inside the block will also be performed on the view's engines.
 sync_imports also takes a `local` boolean flag that defaults to True, which specifies
 whether the local imports should also be performed.  However, support for `local=False`
 has not been implemented, so only packages that can be imported locally will work
 this way.
 
 You can also specify imports via the ``@require`` decorator.  This is a decorator
 designed for use in Dependencies, but can be used to handle remote imports as well.
 Modules or module names passed to ``@require`` will be imported before the decorated
 function is called.  If they cannot be imported, the decorated function will never
 execute and will fail with an UnmetDependencyError. Failures of single Engines will
 be collected and raise a CompositeError, as demonstrated in the next section.
 
 .. sourcecode:: ipython
 
     In [69]: from IPython.parallel import require
 
     In [70]: @require('re'):
        ....: def findall(pat, x):
        ....:     # re is guaranteed to be available
        ....:     return re.findall(pat, x)
           
     # you can also pass modules themselves, that you already have locally:
     In [71]: @require(time):
        ....: def wait(t):
        ....:     time.sleep(t)
        ....:     return t
 
 .. _parallel_exceptions:
 
 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)
     /home/user/<ipython-input-10-5d56b303a66c> in <module>()
     ----> 1 dview.execute('1/0')
 
     /path/to/site-packages/IPython/parallel/client/view.pyc in execute(self, code, targets, block)
         591                 default: self.block
         592         """
     --> 593         return self._really_apply(util._execute, args=(code,), block=block, targets=targets)
         594 
         595     def run(self, filename, targets=None, block=None):
 
     /home/user/<string> in _really_apply(self, f, args, kwargs, targets, block, track)
 
     /path/to/site-packages/IPython/parallel/client/view.pyc in sync_results(f, self, *args, **kwargs)
          55 def sync_results(f, self, *args, **kwargs):
          56     """sync relevant results from self.client to our results attribute."""
     ---> 57     ret = f(self, *args, **kwargs)
          58     delta = self.outstanding.difference(self.client.outstanding)
          59     completed = self.outstanding.intersection(delta)
 
     /home/user/<string> in _really_apply(self, f, args, kwargs, targets, block, track)
 
     /path/to/site-packages/IPython/parallel/client/view.pyc in save_ids(f, self, *args, **kwargs)
          44     n_previous = len(self.client.history)
          45     try:
     ---> 46         ret = f(self, *args, **kwargs)
          47     finally:
          48         nmsgs = len(self.client.history) - n_previous
 
     /path/to/site-packages/IPython/parallel/client/view.pyc in _really_apply(self, f, args, kwargs, targets, block, track)
         529         if block:
         530             try:
     --> 531                 return ar.get()
         532             except KeyboardInterrupt:
         533                 pass
 
     /path/to/site-packages/IPython/parallel/client/asyncresult.pyc in get(self, timeout)
         101                 return self._result
         102             else:
     --> 103                 raise self._exception
         104         else:
         105             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:
        ....:     dview.execute('1/0')
        ....: except parallel.error.CompositeError, e:
        ....:     e.raise_exception()
        ....:     
        ....:     
     ---------------------------------------------------------------------------
     RemoteError                               Traceback (most recent call last)
     /home/user/<ipython-input-17-8597e7e39858> in <module>()
           2     dview.execute('1/0')
           3 except CompositeError as e:
     ----> 4     e.raise_exception()
 
     /path/to/site-packages/IPython/parallel/error.pyc in raise_exception(self, excid)
         266             raise IndexError("an exception with index %i does not exist"%excid)
         267         else:
     --> 268             raise RemoteError(en, ev, etb, ei)
         269 
         270 
 
     RemoteError: ZeroDivisionError(integer division or modulo by zero)
     Traceback (most recent call last):
       File "/path/to/site-packages/IPython/parallel/engine/streamkernel.py", line 330, in apply_request
         exec code in working,working
       File "<string>", line 1, in <module>
       File "/path/to/site-packages/IPython/parallel/util.py", line 354, in _execute
         exec code in globals()
       File "<string>", line 1, in <module>
     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]: dview.execute('1/0')
     ---------------------------------------------------------------------------
     CompositeError                            Traceback (most recent call last)
     /home/user/<ipython-input-10-5d56b303a66c> in <module>()
     ----> 1 dview.execute('1/0')
 
     /path/to/site-packages/IPython/parallel/client/view.pyc in execute(self, code, targets, block)
         591                 default: self.block
         592         """
     --> 593         return self._really_apply(util._execute, args=(code,), block=block, targets=targets)
         594 
         595     def run(self, filename, targets=None, block=None):
 
     /home/user/<string> in _really_apply(self, f, args, kwargs, targets, block, track)
 
     /path/to/site-packages/IPython/parallel/client/view.pyc in sync_results(f, self, *args, **kwargs)
          55 def sync_results(f, self, *args, **kwargs):
          56     """sync relevant results from self.client to our results attribute."""
     ---> 57     ret = f(self, *args, **kwargs)
          58     delta = self.outstanding.difference(self.client.outstanding)
          59     completed = self.outstanding.intersection(delta)
 
     /home/user/<string> in _really_apply(self, f, args, kwargs, targets, block, track)
 
     /path/to/site-packages/IPython/parallel/client/view.pyc in save_ids(f, self, *args, **kwargs)
          44     n_previous = len(self.client.history)
          45     try:
     ---> 46         ret = f(self, *args, **kwargs)
          47     finally:
          48         nmsgs = len(self.client.history) - n_previous
 
     /path/to/site-packages/IPython/parallel/client/view.pyc in _really_apply(self, f, args, kwargs, targets, block, track)
         529         if block:
         530             try:
     --> 531                 return ar.get()
         532             except KeyboardInterrupt:
         533                 pass
 
     /path/to/site-packages/IPython/parallel/client/asyncresult.pyc in get(self, timeout)
         101                 return self._result
         102             else:
     --> 103                 raise self._exception
         104         else:
         105             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
     > /path/to/site-packages/IPython/parallel/client/asyncresult.py(103)get()
         102             else:
     --> 103                 raise self._exception
         104         else:
     
     # With the debugger running, self._exception is the exceptions instance.  We can tab complete
     # on it and see the extra methods that are available.
     ipdb> self._exception.<tab>
     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> self._exception.print_tracebacks()
     [0:apply]: 
     Traceback (most recent call last):
       File "/path/to/site-packages/IPython/parallel/engine/streamkernel.py", line 330, in apply_request
         exec code in working,working
       File "<string>", line 1, in <module>
       File "/path/to/site-packages/IPython/parallel/util.py", line 354, 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 "/path/to/site-packages/IPython/parallel/engine/streamkernel.py", line 330, in apply_request
         exec code in working,working
       File "<string>", line 1, in <module>
       File "/path/to/site-packages/IPython/parallel/util.py", line 354, 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 "/path/to/site-packages/IPython/parallel/engine/streamkernel.py", line 330, in apply_request
         exec code in working,working
       File "<string>", line 1, in <module>
       File "/path/to/site-packages/IPython/parallel/util.py", line 354, 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 "/path/to/site-packages/IPython/parallel/engine/streamkernel.py", line 330, in apply_request
         exec code in working,working
       File "<string>", line 1, in <module>
       File "/path/to/site-packages/IPython/parallel/util.py", line 354, 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]: dview.block=False
 
     In [84]: ar = dview.execute('1/0')
 
     In [85]: ar.get()
     ---------------------------------------------------------------------------
     CompositeError                            Traceback (most recent call last)
     /home/user/<ipython-input-21-8531eb3d26fb> in <module>()
     ----> 1 ar.get()
 
     /path/to/site-packages/IPython/parallel/client/asyncresult.pyc in get(self, timeout)
         101                 return self._result
         102             else:
     --> 103                 raise self._exception
         104         else:
         105             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