IPython Documentation

Table Of Contents

Previous topic

Details of Parallel Computing with IPython

Next topic

Configuration and customization

This Page

Note

This documentation is for a development version of IPython. There may be significant differences from the latest stable release.

Transitioning from IPython.kernel to IPython.parallel

We have rewritten our parallel computing tools to use 0MQ and Tornado. The redesign has resulted in dramatically improved performance, as well as (we think), an improved interface for executing code remotely. This doc is to help users of IPython.kernel transition their codes to the new code.

Processes

The process model for the new parallel code is very similar to that of IPython.kernel. There is still a Controller, Engines, and Clients. However, the the Controller is now split into multiple processes, and can even be split across multiple machines. There does remain a single ipcontroller script for starting all of the controller processes.

Note

TODO: fill this out after config system is updated

See also

Detailed Parallel Process doc for configuring and launching IPython processes.

Creating a Client

Creating a client with default settings has not changed much, though the extended options have. One significant change is that there are no longer multiple Client classes to represent the various execution models. There is just one low-level Client object for connecting to the cluster, and View objects are created from that Client that provide the different interfaces for execution.

To create a new client, and set up the default direct and load-balanced objects:

# old
In [1]: from IPython.kernel import client as kclient

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

In [3]: tc = kclient.TaskClient()

# new
In [1]: from IPython.parallel import Client

In [2]: rc = Client()

In [3]: dview = rc[:]

In [4]: lbview = rc.load_balanced_view()

Apply

The main change to the API is the addition of the apply() to the View objects. This is a method that takes view.apply(f,*args,**kwargs), and calls f(*args, **kwargs) remotely on one or more engines, returning the result. This means that the natural unit of remote execution is no longer a string of Python code, but rather a Python function.

  • non-copying sends (track)
  • remote References

The flags for execution have also changed. Previously, there was only block denoting whether to wait for results. This remains, but due to the addition of fully non-copying sends of arrays and buffers, there is also a track flag, which instructs PyZMQ to produce a MessageTracker that will let you know when it is safe again to edit arrays in-place.

The result of a non-blocking call to apply is now an AsyncResult object.

MultiEngine to DirectView

The multiplexing interface previously provided by the MultiEngineClient is now provided by the DirectView. Once you have a Client connected, you can create a DirectView with index-access to the client (view = client[1:5]). The core methods for communicating with engines remain: execute, run, push, pull, scatter, gather. These methods all behave in much the same way as they did on a MultiEngineClient.

# old
In [2]: mec.execute('a=5', targets=[0,1,2])

# new
In [2]: view.execute('a=5', targets=[0,1,2])
# or
In [2]: rc[0,1,2].execute('a=5')

This extends to any method that communicates with the engines.

Requests of the Hub (queue status, etc.) are no-longer asynchronous, and do not take a block argument.

  • get_ids() is now the property ids, which is passively updated by the Hub (no need for network requests for an up-to-date list).

  • barrier() has been renamed to wait(), and now takes an optional timeout. flush() is removed, as it is redundant with wait()

  • zip_pull() has been removed

  • keys() has been removed, but is easily implemented as:

    dview.apply(lambda : globals().keys())
    
  • push_function() and push_serialized() are removed, as push() handles functions without issue.

See also

Our Direct Interface doc for a simple tutorial with the DirectView.

The other major difference is the use of apply(). When remote work is simply functions, the natural return value is the actual Python objects. It is no longer the recommended pattern to use stdout as your results, due to stream decoupling and the asynchronous nature of how the stdout streams are handled in the new system.

Task to LoadBalancedView

Load-Balancing has changed more than Multiplexing. This is because there is no longer a notion of a StringTask or a MapTask, there are simply Python functions to call. Tasks are now simpler, because they are no longer composites of push/execute/pull/clear calls, they are a single function that takes arguments, and returns objects.

The load-balanced interface is provided by the LoadBalancedView class, created by the client:

In [10]: lbview = rc.load_balanced_view()

# load-balancing can also be restricted to a subset of engines:
In [10]: lbview = rc.load_balanced_view([1,2,3])

A simple task would consist of sending some data, calling a function on that data, plus some data that was resident on the engine already, and then pulling back some results. This can all be done with a single function.

Let’s say you want to compute the dot product of two matrices, one of which resides on the engine, and another resides on the client. You might construct a task that looks like this:

In [10]: st = kclient.StringTask("""
            import numpy
            C=numpy.dot(A,B)
            """,
            push=dict(B=B),
            pull='C'
            )

In [11]: tid = tc.run(st)

In [12]: tr = tc.get_task_result(tid)

In [13]: C = tc['C']

In the new code, this is simpler:

In [10]: import numpy

In [11]: from IPython.parallel import Reference

In [12]: ar = lbview.apply(numpy.dot, Reference('A'), B)

In [13]: C = ar.get()

Note the use of Reference This is a convenient representation of an object that exists in the engine’s namespace, so you can pass remote objects as arguments to your task functions.

Also note that in the kernel model, after the task is run, ‘A’, ‘B’, and ‘C’ are all defined on the engine. In order to deal with this, there is also a clear_after flag for Tasks to prevent pollution of the namespace, and bloating of engine memory. This is not necessary with the new code, because only those objects explicitly pushed (or set via globals()) will be resident on the engine beyond the duration of the task.

See also

Dependencies also work very differently than in IPython.kernel. See our doc on Dependencies for details.

See also

Our Task Interface doc for a simple tutorial with the LoadBalancedView.

PendingResults to AsyncResults

With the departure from Twisted, we no longer have the Deferred class for representing unfinished results. For this, we have an AsyncResult object, based on the object of the same name in the built-in multiprocessing.pool module. Our version provides a superset of that interface.

However, unlike in IPython.kernel, we do not have PendingDeferred, PendingResult, or TaskResult objects. Simply this one object, the AsyncResult. Every asynchronous (block=False) call returns one.

The basic methods of an AsyncResult are:

AsyncResult.wait([timeout]): # wait for the result to arrive
AsyncResult.get([timeout]): # wait for the result to arrive, and then return it
AsyncResult.metadata: # dict of extra information about execution.

There are still some things that behave the same as IPython.kernel:

# old
In [5]: pr = mec.pull('a', targets=[0,1], block=False)
In [6]: pr.r
Out[6]: [5, 5]

# new
In [5]: ar = dview.pull('a', targets=[0,1], block=False)
In [6]: ar.r
Out[6]: [5, 5]

The .r or .result property simply calls get(), waiting for and returning the result.