IPython’s multiengine interface — IPython v0.10 documentation

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# 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 ipcluster command:

    $ ipcluster local -n 4

For more detailed information about starting the controller and engines, see our introduction to using IPython for parallel computing.

## Creating a `MultiEngineClient` instance

The first step is to import the IPython `IPython.kernel.client` module and then create a `MultiEngineClient` instance:

    In [1]: from IPython.kernel import client

    In [2]: mec = client.MultiEngineClient()

This form assumes that the `ipcontroller-mec.furl` is in the `~./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:

    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:

    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 `map()` and `@parallel` function decorator.

### Parallel map

Python’s builtin `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 `map()` that works just like its serial counterpart:

    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 `map()` does not do any load balancing. For a load balanced version, see the task interface.

See also

The `map()` method has a number of options that can be controlled by the `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:

    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 `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 `execute()` method.

### Blocking execution

In blocking mode, the `MultiEngineClient` object (called `mec` in these examples) submits the command to the controller, which places the command in the engines’ queues for execution. The `execute()` call then blocks until the engines are done executing the command:

    # The default is to run on all engines
    In [4]: mec.execute('a=5')
    Out[4]:
    <Results List>
    [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]:
    <Results List>
    [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:

    In [6]: mec.execute('c=a+b',targets=[0,2])
    Out[6]:
    <Results List>
    [0] In [3]: c=a+b
    [2] In [3]: c=a+b

    In [7]: mec.execute('c=a-b',targets=[1,3])
    Out[7]:
    <Results List>
    [1] In [3]: c=a-b
    [3] In [3]: c=a-b

    In [8]: mec.execute('print c')
    Out[8]:
    <Results List>
    [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 `execute()` method returns a Python `dict` that contains useful information:

    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, `execute()` submits the command to be executed and then returns a `PendingResult` object immediately. The `PendingResult` object gives you a way of getting a result at a later time through its `get_result()` method or `r` attribute. This allows you to quickly submit long running commands without blocking your local Python/IPython session:

    # In blocking mode
    In [6]: mec.execute('import time')
    Out[6]:
    <Results List>
    [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]:
    <Results List>
    [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]:
    <Results List>
    [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 `PendingResult` objects are done. For this, there is a the method `barrier()`. This method takes a tuple of `PendingResult` objects and blocks until all of the associated results are ready:

    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]:
    <Results List>
    [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 `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 `MultiEngineClient` class also has `block` and `targets` attributes that control the default behavior when the keyword arguments are not provided. Thus the following logic is used for `block` and `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:

    In [16]: mec.targets = [0,2]

    In [17]: mec.block = False

    In [18]: pr = mec.execute('a=5')

    In [19]: pr.r
    Out[19]:
    <Results List>
    [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]:
    <Results List>
    [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 `block` and `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 `execute()` and `get_result()`. The `%px` magic executes a single Python command on the engines specified by the `targets` attribute of the `MultiEngineClient` instance (by default this is `'all'`):

    # 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]:
    <Results List>
    [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]:
    <Results List>
    [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]:
    <Results List>
    [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 `get_result()` method:

    In [29]: %result
    Out[29]:
    <Results List>
    [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 `targets` attribute:

    In [30]: mec.block=False

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

    In [32]: max_evals = []
    <IPython.kernel.multiengineclient.PendingResult 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.kernel.multiengineclient.PendingResult object at 0x17af8f0>

    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]:
    <Results List>
    [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 `push()` (sending an object to the engines) and `pull()` (getting an object from the engines).

### Basic push and pull

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

    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]:
    <Results List>
    [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 `push()` and `pull()` also return `PendingResult` objects:

    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 `push_function()` and `pull_function()`:

    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]:
        <Results List>
        [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]:
        <Results List>
        [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 `push()` and `pull()`, the `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 `push()` and `pull()`:

    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 `MultiEngineClient` class, `scatter()` is from the interactive IPython session to the engines and `gather()` is from the engines back to the interactive IPython session. For scatter/gather operations between engines, MPI should be used:

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

    In [59]: px print a
    Executing command on Controller
    Out[59]:
    <Results List>
    [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 `scatter()` and `gather()`:

    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]:
    <Results List>
    [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 `CompositeError` exception class that will be raised in most cases. The `CompositeError` class is a special type of exception that wraps one or more other types of exceptions. Here is how it works:

    In [76]: mec.block=True

    In [77]: mec.execute('1/0')
    ---------------------------------------------------------------------------
    CompositeError                            Traceback (most recent call last)

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

    /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 `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:

    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/<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 `CompositeError` exceptions is raised, and inspect the exception instance:

    In [81]: mec.execute('1/0')
    ---------------------------------------------------------------------------
    CompositeError                            Traceback (most recent call last)

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

    /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

    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/<string> in <module>()

    ZeroDivisionError: integer division or modulo by zero

    [1:execute]:
    ---------------------------------------------------------------------------
    ZeroDivisionError                         Traceback (most recent call last)

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

    ZeroDivisionError: integer division or modulo by zero

    [2:execute]:
    ---------------------------------------------------------------------------
    ZeroDivisionError                         Traceback (most recent call last)

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

    ZeroDivisionError: integer division or modulo by zero

    [3:execute]:
    ---------------------------------------------------------------------------
    ZeroDivisionError                         Traceback (most recent call last)

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

    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:

    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/<ipython console> in <module>()

    /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

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