.. _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 ` 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 `. 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]: In [5]: view.apply 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 ` 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/ in () ----> 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] [0] Out[79]: [] [1] Out[79]: [] [2] Out[79]: [] [3] Out[79]: [] ``%%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 --existing or, if you are local, you can connect with just: $> ipython --existing kernel-60125.json or even just: $> ipython --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 [78]: dview.block = True In [79]: dview.execute("1/0") [0:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) in () ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero [1:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) in () ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero [2:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) in () ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero [3:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) in () ----> 1 1/0 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', block=True) ....: except parallel.error.CompositeError, e: ....: e.raise_exception() ....: ....: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) in () ----> 1 1/0 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') [0:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) in () ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero [1:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) in () ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero [2:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) in () ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero [3:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) in () ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero In [82]: %debug > /.../site-packages/IPython/parallel/client/asyncresult.py(125)get() 124 else: --> 125 raise self._exception 126 else: # Here, self._exception is the CompositeError instance: ipdb> e = self._exception ipdb> e CompositeError(4) # we can tab-complete on e to see available methods: ipdb> e. e.args e.message e.traceback e.elist e.msg e.ename e.print_traceback e.engine_info e.raise_exception e.evalue e.render_traceback # We can then display the individual tracebacks, if we want: ipdb> e.print_traceback(1) [1:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) in () ----> 1 1/0 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() [0:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) in () ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero [1:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) in () ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero [2:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) in () ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero [3:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) in () ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero