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.
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 start -n 4
For more detailed information about starting the controller and engines, see our introduction to using IPython for parallel computing.
The first step is to import the IPython IPython.parallel module and then create a Client instance:
In [1]: from IPython.parallel import Client
In [2]: rc = Client()
This form assumes that the default connection information (stored in ipcontroller-client.json found in 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:
# 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:
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 DirectView object, which can be constructed via list-access to the client:
In [4]: dview = rc[:] # use all engines
See also
For more information, see the in-depth explanation of Views.
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 map() method.
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, since IPython’s interface is all about functions anyway, you can just use the builtin map() with a RemoteFunction, or a DirectView’s map() method:
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 DirectView‘s version of map() does not do dynamic load balancing. For a load balanced version, use a LoadBalancedView.
See also
map() is implemented via ParallelFunction.
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:
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.
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:
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']
See also
See the parallel() and remote() decorators for options.
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 View.execute() method, and calling functions can be done via the View.apply() method.
The main method for doing remote execution (in fact, all methods that communicate with the engines are built on top of it), is View.apply().
We strive to provide the cleanest interface we can, so apply has the following signature:
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:
Creating a view is simple: index-access on a client creates a DirectView.
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.
In blocking mode, the DirectView object (called dview in these examples) submits the command to the controller, which places the command in the engines’ queues for execution. The apply() call then blocks until the engines are done executing the command:
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 apply_sync() method:
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:
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]
In non-blocking mode, apply() submits the command to be executed and then returns a AsyncResult object immediately. The AsyncResult object gives you a way of getting a result at a later time through its get() method.
See also
Docs on the AsyncResult object.
This allows you to quickly submit long running commands without blocking your local Python/IPython session:
# 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 view.execute('import numpy')().
Often, it is desirable to wait until a set of AsyncResult objects are done. For this, there is a the method wait(). This method takes a tuple of AsyncResult objects (or msg_ids or indices to the client’s History), and blocks until all of the associated results are ready:
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]
Most DirectView methods (excluding 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 View 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:
The following examples demonstrate how to use the instance attributes:
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 = rc.ids # all engines (4)
In [21]: dview.block = True
In [22]: dview.apply(lambda : 42)
Out[22]: [42, 42, 42, 42]
The block and targets instance attributes of the DirectView also determine the behavior of the parallel magic commands.
See also
See the documentation of the Parallel Magics.
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 push() (sending an object to the engines) and pull() (getting an object from the engines).
Here are some examples of how you use push() and pull():
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 push() and pull() also return AsyncResult objects:
In [48]: ar = dview.pull('a', block=False)
In [49]: ar.get()
Out[49]: [1.03234, 1.03234, 1.03234, 1.03234]
Since a Python namespace is just a dict, DirectView objects provide dictionary-style access by key and methods such as get() and update() for convenience. This make the remote namespaces of the engines appear as a local dictionary. Underneath, these methods call apply():
In [51]: dview['a']=['foo','bar']
In [52]: dview['a']
Out[52]: [ ['foo', 'bar'], ['foo', 'bar'], ['foo', 'bar'], ['foo', 'bar'] ]
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 Client 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, pyzmq, or some other direct interconnect should be used.
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]
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]: 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,...]
Sometimes you will want to import packages both in your interactive session and on your remote engines. This can be done with the ContextManager created by a DirectView’s sync_imports() method:
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.
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
Note
sync_imports() does not allow import foo as bar syntax, because the assignment represented by the as bar part is not available to the import hook.
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 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 [78]: dview.block = True
In [79]: dview.execute("1/0")
[0:execute]:
---------------------------------------------------------------------------
ZeroDivisionError Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero
[1:execute]:
---------------------------------------------------------------------------
ZeroDivisionError Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero
[2:execute]:
---------------------------------------------------------------------------
ZeroDivisionError Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero
[3:execute]:
---------------------------------------------------------------------------
ZeroDivisionError Traceback (most recent call last)
----> 1 1/0
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 [79]: from IPython.parallel import CompositeError
In [80]: try:
....: dview.execute('1/0', block=True)
....: except CompositeError, e:
....: e.raise_exception()
....:
....:
---------------------------------------------------------------------------
ZeroDivisionError Traceback (most recent call last)
----> 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 CompositeError exceptions is raised, and inspect the exception instance:
In [81]: dview.execute('1/0')
[0:execute]:
---------------------------------------------------------------------------
ZeroDivisionError Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero
[1:execute]:
---------------------------------------------------------------------------
ZeroDivisionError Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero
[2:execute]:
---------------------------------------------------------------------------
ZeroDivisionError Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero
[3:execute]:
---------------------------------------------------------------------------
ZeroDivisionError Traceback (most recent call last)
----> 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.<TAB>
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)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero
Since you might have 100 engines, you probably don’t want to see 100 tracebacks for a simple NameError because of a typo. For this reason, CompositeError truncates the list of exceptions it will print to CompositeError.tb_limit (default is five). You can change this limit to suit your needs with:
In [20]: from IPython.parallel import CompositeError
In [21]: CompositeError.tb_limit = 1
In [22]: %px x=z
[0:execute]:
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
----> 1 x=z
NameError: name 'z' is not defined
... 3 more exceptions ...
All of this same error handling magic even works in non-blocking mode:
In [83]: dview.block=False
In [84]: ar = dview.execute('1/0')
In [85]: ar.get()
[0:execute]:
---------------------------------------------------------------------------
ZeroDivisionError Traceback (most recent call last)
----> 1 1/0
ZeroDivisionError: integer division or modulo by zero
... 3 more exceptions ...