.. _parallelmultiengine: =============================== IPython's multiengine interface =============================== The 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:`ipcluster` command:: $ ipcluster local -n 4 For more detailed information about starting the controller and engines, see our :ref:`introduction ` to using IPython for parallel computing. Creating a ``MultiEngineClient`` instance ========================================= The first step is to import the IPython :mod:`IPython.kernel.client` module and then create a :class:`MultiEngineClient` instance: .. sourcecode:: ipython In [1]: from IPython.kernel import client In [2]: mec = client.MultiEngineClient() This form assumes that the :file:`ipcontroller-mec.furl` is in the :file:`~./ipython/security` directory on the client's host. If not, the location of the FURL file must be given as an argument to the constructor: .. sourcecode:: ipython In [2]: mec = client.MultiEngineClient('/path/to/my/ipcontroller-mec.furl') To make sure there are engines connected to the controller, use can get a list of engine ids: .. sourcecode:: ipython In [3]: mec.get_ids() Out[3]: [0, 1, 2, 3] Here we see that there are four engines ready to do work for us. Quick and easy parallelism ========================== In many cases, you simply want to apply a Python function to a sequence of objects, but *in parallel*. The multiengine interface provides two simple ways of accomplishing this: a parallel version of :func:`map` and ``@parallel`` function decorator. 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, the multiengine interface in IPython already has a parallel version of :meth:`map` that works just like its serial counterpart: .. sourcecode:: ipython In [63]: serial_result = map(lambda x:x**10, range(32)) In [64]: parallel_result = mec.map(lambda x:x**10, range(32)) In [65]: serial_result==parallel_result Out[65]: True .. note:: The multiengine interface version of :meth:`map` does not do any load balancing. For a load balanced version, see the task interface. .. seealso:: The :meth:`map` method has a number of options that can be controlled by the :meth:`mapper` method. See its docstring for more information. Parallel function decorator --------------------------- Parallel functions are just like normal function, but they can be called on sequences and *in parallel*. The multiengine interface provides a decorator that turns any Python function into a parallel function: .. sourcecode:: ipython In [10]: @mec.parallel() ....: def f(x): ....: return 10.0*x**4 ....: In [11]: f(range(32)) # this is done in parallel Out[11]: [0.0,10.0,160.0,...] See the docstring for the :meth:`parallel` decorator for options. Running Python commands ======================= The most basic type of operation that can be performed on the engines is to execute Python code. Executing Python code can be done in blocking or non-blocking mode (blocking is default) using the :meth:`execute` method. Blocking execution ------------------ In blocking mode, the :class:`MultiEngineClient` object (called ``mec`` in these examples) submits the command to the controller, which places the command in the engines' queues for execution. The :meth:`execute` call then blocks until the engines are done executing the command: .. sourcecode:: ipython # The default is to run on all engines In [4]: mec.execute('a=5') Out[4]: [0] In [1]: a=5 [1] In [1]: a=5 [2] In [1]: a=5 [3] In [1]: a=5 In [5]: mec.execute('b=10') Out[5]: [0] In [2]: b=10 [1] In [2]: b=10 [2] In [2]: b=10 [3] In [2]: b=10 Python commands can be executed on specific engines by calling execute using the ``targets`` keyword argument: .. sourcecode:: ipython In [6]: mec.execute('c=a+b',targets=[0,2]) Out[6]: [0] In [3]: c=a+b [2] In [3]: c=a+b In [7]: mec.execute('c=a-b',targets=[1,3]) Out[7]: [1] In [3]: c=a-b [3] In [3]: c=a-b In [8]: mec.execute('print c') Out[8]: [0] In [4]: print c [0] Out[4]: 15 [1] In [4]: print c [1] Out[4]: -5 [2] In [4]: print c [2] Out[4]: 15 [3] In [4]: print c [3] Out[4]: -5 This example also shows one of the most important things about the IPython engines: they have a persistent user namespaces. The :meth:`execute` method returns a Python ``dict`` that contains useful information: .. sourcecode:: ipython In [9]: result_dict = mec.execute('d=10; print d') In [10]: for r in result_dict: ....: print r ....: ....: {'input': {'translated': 'd=10; print d', 'raw': 'd=10; print d'}, 'number': 5, 'id': 0, 'stdout': '10\n'} {'input': {'translated': 'd=10; print d', 'raw': 'd=10; print d'}, 'number': 5, 'id': 1, 'stdout': '10\n'} {'input': {'translated': 'd=10; print d', 'raw': 'd=10; print d'}, 'number': 5, 'id': 2, 'stdout': '10\n'} {'input': {'translated': 'd=10; print d', 'raw': 'd=10; print d'}, 'number': 5, 'id': 3, 'stdout': '10\n'} Non-blocking execution ---------------------- In non-blocking mode, :meth:`execute` submits the command to be executed and then returns a :class:`PendingResult` object immediately. The :class:`PendingResult` object gives you a way of getting a result at a later time through its :meth:`get_result` method or :attr:`r` attribute. This allows you to quickly submit long running commands without blocking your local Python/IPython session: .. sourcecode:: ipython # In blocking mode In [6]: mec.execute('import time') Out[6]: [0] In [1]: import time [1] In [1]: import time [2] In [1]: import time [3] In [1]: import time # In non-blocking mode In [7]: pr = mec.execute('time.sleep(10)',block=False) # Now block for the result In [8]: pr.get_result() Out[8]: [0] In [2]: time.sleep(10) [1] In [2]: time.sleep(10) [2] In [2]: time.sleep(10) [3] In [2]: time.sleep(10) # Again in non-blocking mode In [9]: pr = mec.execute('time.sleep(10)',block=False) # Poll to see if the result is ready In [10]: pr.get_result(block=False) # A shorthand for get_result(block=True) In [11]: pr.r Out[11]: [0] In [3]: time.sleep(10) [1] In [3]: time.sleep(10) [2] In [3]: time.sleep(10) [3] In [3]: time.sleep(10) Often, it is desirable to wait until a set of :class:`PendingResult` objects are done. For this, there is a the method :meth:`barrier`. This method takes a tuple of :class:`PendingResult` objects and blocks until all of the associated results are ready: .. sourcecode:: ipython In [72]: mec.block=False # A trivial list of PendingResults objects In [73]: pr_list = [mec.execute('time.sleep(3)') for i in range(10)] # Wait until all of them are done In [74]: mec.barrier(pr_list) # Then, their results are ready using get_result or the r attribute In [75]: pr_list[0].r Out[75]: [0] In [20]: time.sleep(3) [1] In [19]: time.sleep(3) [2] In [20]: time.sleep(3) [3] In [19]: time.sleep(3) The ``block`` and ``targets`` keyword arguments and attributes -------------------------------------------------------------- Most methods in the multiengine interface (like :meth:`execute`) 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:`MultiEngineClient` 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. * Keyword argument, if provided override the instance attributes. The following examples demonstrate how to use the instance attributes: .. sourcecode:: ipython In [16]: mec.targets = [0,2] In [17]: mec.block = False In [18]: pr = mec.execute('a=5') In [19]: pr.r Out[19]: [0] In [6]: a=5 [2] In [6]: a=5 # Note targets='all' means all engines In [20]: mec.targets = 'all' In [21]: mec.block = True In [22]: mec.execute('b=10; print b') Out[22]: [0] In [7]: b=10; print b [0] Out[7]: 10 [1] In [6]: b=10; print b [1] Out[6]: 10 [2] In [7]: b=10; print b [2] Out[7]: 10 [3] In [6]: b=10; print b [3] Out[6]: 10 The :attr:`block` and :attr:`targets` instance attributes 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 more pleasant to execute Python commands on the engines interactively. These are simply shortcuts to :meth:`execute` and :meth:`get_result`. The ``%px`` magic executes a single Python command on the engines specified by the :attr:`targets` attribute of the :class:`MultiEngineClient` instance (by default this is ``'all'``): .. sourcecode:: ipython # Make this MultiEngineClient active for parallel magic commands In [23]: mec.activate() In [24]: mec.block=True In [25]: import numpy In [26]: %px import numpy Executing command on Controller Out[26]: [0] In [8]: import numpy [1] In [7]: import numpy [2] In [8]: import numpy [3] In [7]: import numpy In [27]: %px a = numpy.random.rand(2,2) Executing command on Controller Out[27]: [0] In [9]: a = numpy.random.rand(2,2) [1] In [8]: a = numpy.random.rand(2,2) [2] In [9]: a = numpy.random.rand(2,2) [3] In [8]: a = numpy.random.rand(2,2) In [28]: %px print numpy.linalg.eigvals(a) Executing command on Controller Out[28]: [0] In [10]: print numpy.linalg.eigvals(a) [0] Out[10]: [ 1.28167017 0.14197338] [1] In [9]: print numpy.linalg.eigvals(a) [1] Out[9]: [-0.14093616 1.27877273] [2] In [10]: print numpy.linalg.eigvals(a) [2] Out[10]: [-0.37023573 1.06779409] [3] In [9]: print numpy.linalg.eigvals(a) [3] Out[9]: [ 0.83664764 -0.25602658] The ``%result`` magic gets and prints the stdin/stdout/stderr of the last command executed on each engine. It is simply a shortcut to the :meth:`get_result` method: .. sourcecode:: ipython In [29]: %result Out[29]: [0] In [10]: print numpy.linalg.eigvals(a) [0] Out[10]: [ 1.28167017 0.14197338] [1] In [9]: print numpy.linalg.eigvals(a) [1] Out[9]: [-0.14093616 1.27877273] [2] In [10]: print numpy.linalg.eigvals(a) [2] Out[10]: [-0.37023573 1.06779409] [3] In [9]: print numpy.linalg.eigvals(a) [3] Out[9]: [ 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]: mec.block=False In [31]: %autopx Auto Parallel Enabled Type %autopx to disable 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]: %autopx Auto Parallel Disabled In [35]: mec.block=True In [36]: px print "Average max eigenvalue is: ", sum(max_evals)/len(max_evals) Executing command on Controller Out[36]: [0] In [13]: print "Average max eigenvalue is: ", sum(max_evals)/len(max_evals) [0] Out[13]: Average max eigenvalue is: 10.1387247332 [1] In [12]: print "Average max eigenvalue is: ", sum(max_evals)/len(max_evals) [1] Out[12]: Average max eigenvalue is: 10.2076902286 [2] In [13]: print "Average max eigenvalue is: ", sum(max_evals)/len(max_evals) [2] Out[13]: Average max eigenvalue is: 10.1891484655 [3] In [12]: print "Average max eigenvalue is: ", sum(max_evals)/len(max_evals) [3] Out[12]: Average max eigenvalue is: 10.1158837784 Moving Python objects around ============================ In addition to 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]: mec.push(dict(a=1.03234,b=3453)) Out[38]: [None, None, None, None] In [39]: mec.pull('a') Out[39]: [1.03234, 1.03234, 1.03234, 1.03234] In [40]: mec.pull('b',targets=0) Out[40]: [3453] In [41]: mec.pull(('a','b')) Out[41]: [[1.03234, 3453], [1.03234, 3453], [1.03234, 3453], [1.03234, 3453]] In [42]: mec.zip_pull(('a','b')) Out[42]: [(1.03234, 1.03234, 1.03234, 1.03234), (3453, 3453, 3453, 3453)] In [43]: mec.push(dict(c='speed')) Out[43]: [None, None, None, None] In [44]: %px print c Executing command on Controller Out[44]: [0] In [14]: print c [0] Out[14]: speed [1] In [13]: print c [1] Out[13]: speed [2] In [14]: print c [2] Out[14]: speed [3] In [13]: print c [3] Out[13]: speed In non-blocking mode :meth:`push` and :meth:`pull` also return :class:`PendingResult` objects: .. sourcecode:: ipython In [47]: mec.block=False In [48]: pr = mec.pull('a') In [49]: pr.r Out[49]: [1.03234, 1.03234, 1.03234, 1.03234] Push and pull for functions --------------------------- Functions can also be pushed and pulled using :meth:`push_function` and :meth:`pull_function`: .. sourcecode:: ipython In [52]: mec.block=True In [53]: def f(x): ....: return 2.0*x**4 ....: In [54]: mec.push_function(dict(f=f)) Out[54]: [None, None, None, None] In [55]: mec.execute('y = f(4.0)') Out[55]: [0] In [15]: y = f(4.0) [1] In [14]: y = f(4.0) [2] In [15]: y = f(4.0) [3] In [14]: y = f(4.0) In [56]: px print y Executing command on Controller Out[56]: [0] In [16]: print y [0] Out[16]: 512.0 [1] In [15]: print y [1] Out[15]: 512.0 [2] In [16]: print y [2] Out[16]: 512.0 [3] In [15]: print y [3] Out[15]: 512.0 Dictionary interface -------------------- As a shorthand to :meth:`push` and :meth:`pull`, the :class:`MultiEngineClient` class implements some of the Python dictionary interface. This make the remote namespaces of the engines appear as a local dictionary. Underneath, this uses :meth:`push` and :meth:`pull`: .. sourcecode:: ipython In [50]: mec.block=True In [51]: mec['a']=['foo','bar'] In [52]: mec['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:`MultiEngineClient` 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]: mec.scatter('a',range(16)) Out[58]: [None, None, None, None] In [59]: px print a Executing command on Controller Out[59]: [0] In [17]: print a [0] Out[17]: [0, 1, 2, 3] [1] In [16]: print a [1] Out[16]: [4, 5, 6, 7] [2] In [17]: print a [2] Out[17]: [8, 9, 10, 11] [3] In [16]: print a [3] Out[16]: [12, 13, 14, 15] In [60]: mec.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]: mec.scatter('x',range(64)) Out[66]: [None, None, None, None] In [67]: px y = [i**10 for i in x] Executing command on Controller Out[67]: [0] In [19]: y = [i**10 for i in x] [1] In [18]: y = [i**10 for i in x] [2] In [19]: y = [i**10 for i in x] [3] In [18]: y = [i**10 for i in x] In [68]: y = mec.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, the MultiEngine interface has 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]: mec.block=True In [77]: mec.execute('1/0') --------------------------------------------------------------------------- CompositeError Traceback (most recent call last) /ipython1-client-r3021/docs/examples/ in () /ipython1-client-r3021/ipython1/kernel/multiengineclient.pyc in execute(self, lines, targets, block) 432 targets, block = self._findTargetsAndBlock(targets, block) 433 result = blockingCallFromThread(self.smultiengine.execute, lines, --> 434 targets=targets, block=block) 435 if block: 436 result = ResultList(result) /ipython1-client-r3021/ipython1/kernel/twistedutil.pyc in blockingCallFromThread(f, *a, **kw) 72 result.raiseException() 73 except Exception, e: ---> 74 raise e 75 return result 76 CompositeError: one or more exceptions from call to method: execute [0:execute]: ZeroDivisionError: integer division or modulo by zero [1:execute]: ZeroDivisionError: integer division or modulo by zero [2:execute]: ZeroDivisionError: integer division or modulo by zero [3:execute]: 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: ....: mec.execute('1/0') ....: except client.CompositeError, e: ....: e.raise_exception() ....: ....: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) /ipython1-client-r3021/docs/examples/ in () /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]: mec.execute('1/0') --------------------------------------------------------------------------- CompositeError Traceback (most recent call last) /ipython1-client-r3021/docs/examples/ in () /ipython1-client-r3021/ipython1/kernel/multiengineclient.pyc in execute(self, lines, targets, block) 432 targets, block = self._findTargetsAndBlock(targets, block) 433 result = blockingCallFromThread(self.smultiengine.execute, lines, --> 434 targets=targets, block=block) 435 if block: 436 result = ResultList(result) /ipython1-client-r3021/ipython1/kernel/twistedutil.pyc in blockingCallFromThread(f, *a, **kw) 72 result.raiseException() 73 except Exception, e: ---> 74 raise e 75 return result 76 CompositeError: one or more exceptions from call to method: execute [0:execute]: ZeroDivisionError: integer division or modulo by zero [1:execute]: ZeroDivisionError: integer division or modulo by zero [2:execute]: ZeroDivisionError: integer division or modulo by zero [3:execute]: ZeroDivisionError: integer division or modulo by zero In [82]: %debug > /ipython1-client-r3021/ipython1/kernel/twistedutil.py(74)blockingCallFromThread() 73 except Exception, e: ---> 74 raise e 75 return result # 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:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) /ipython1-client-r3021/docs/examples/ in () ZeroDivisionError: integer division or modulo by zero [1:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) /ipython1-client-r3021/docs/examples/ in () ZeroDivisionError: integer division or modulo by zero [2:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) /ipython1-client-r3021/docs/examples/ in () ZeroDivisionError: integer division or modulo by zero [3:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) /ipython1-client-r3021/docs/examples/ in () ZeroDivisionError: integer division or modulo by zero .. note:: The above example appears to be broken right now because of a change in how we are using Twisted. All of this same error handling magic even works in non-blocking mode: .. sourcecode:: ipython In [83]: mec.block=False In [84]: pr = mec.execute('1/0') In [85]: pr.r --------------------------------------------------------------------------- CompositeError Traceback (most recent call last) /ipython1-client-r3021/docs/examples/ in () /ipython1-client-r3021/ipython1/kernel/multiengineclient.pyc in _get_r(self) 170 171 def _get_r(self): --> 172 return self.get_result(block=True) 173 174 r = property(_get_r) /ipython1-client-r3021/ipython1/kernel/multiengineclient.pyc in get_result(self, default, block) 131 return self.result 132 try: --> 133 result = self.client.get_pending_deferred(self.result_id, block) 134 except error.ResultNotCompleted: 135 return default /ipython1-client-r3021/ipython1/kernel/multiengineclient.pyc in get_pending_deferred(self, deferredID, block) 385 386 def get_pending_deferred(self, deferredID, block): --> 387 return blockingCallFromThread(self.smultiengine.get_pending_deferred, deferredID, block) 388 389 def barrier(self, pendingResults): /ipython1-client-r3021/ipython1/kernel/twistedutil.pyc in blockingCallFromThread(f, *a, **kw) 72 result.raiseException() 73 except Exception, e: ---> 74 raise e 75 return result 76 CompositeError: one or more exceptions from call to method: execute [0:execute]: ZeroDivisionError: integer division or modulo by zero [1:execute]: ZeroDivisionError: integer division or modulo by zero [2:execute]: ZeroDivisionError: integer division or modulo by zero [3:execute]: ZeroDivisionError: integer division or modulo by zero