Merge pull request #6550 from drevicko/update-parallel-magic-doc

Update parallel magic doc

docs/source/parallel/magics.rst

@@ -1,387 +1,388 b''
 .. _parallel_magics:
 
 =======================
 Parallel Magic Commands
 =======================
 
 We provide a few IPython magic commands
 that make it a bit more pleasant to execute Python commands on the engines interactively.
 These are mainly shortcuts to :meth:`.DirectView.execute`
 and :meth:`.AsyncResult.display_outputs` methods respectively.
 
 These magics will automatically become available when you create a Client:
 
 .. sourcecode:: ipython
 
-    In [2]: rc = parallel.Client()
+    In [1]: from IPython.parallel import Client
+    In [2]: rc = Client()
 
 The initially active View will have attributes ``targets='all', block=True``,
 which is a blocking view of all engines, evaluated at request time
 (adding/removing engines will change where this view's tasks will run).
 
 The Magics
 ==========
 
 %px
 ---
 
 The %px magic executes a single Python command on the engines
 specified by the :attr:`targets` attribute of the :class:`DirectView` instance:
 
 .. sourcecode:: ipython
 
     # import numpy here and everywhere
     In [25]: with rc[:].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]
     Out [0:68]: array([ 0.77120707, -0.19448286])
     Out [1:68]: array([ 1.10815921,  0.05110369])
     Out [2:68]: array([ 0.74625527, -0.37475081])
     Out [3:68]: array([ 0.72931905,  0.07159743])
     
     In [29]: %px print 'hi'
     Parallel execution on engine(s): all
     [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): all
     [stdout:0] 
     Populating the interactive namespace from numpy and matplotlib
     [stdout:1] 
     Populating the interactive namespace from numpy and matplotlib
     [stdout:2] 
     Populating the interactive namespace from numpy and matplotlib
     [stdout:3] 
     Populating the interactive namespace from numpy and matplotlib
 
 And once in pylab mode with the inline backend,
 you can make plots and they will be displayed in your frontend
 if it supports the inline figures (e.g. notebook or qtconsole):
 
 .. sourcecode:: ipython
 
     In [40]: %px plot(rand(100))
     Parallel execution on engine(s): all
     <plot0>
     <plot1>
     <plot2>
     <plot3>
     Out[0:79]: [<matplotlib.lines.Line2D at 0x10a6286d0>]
     Out[1:79]: [<matplotlib.lines.Line2D at 0x10b9476d0>]
     Out[2:79]: [<matplotlib.lines.Line2D at 0x110652750>]
     Out[3:79]: [<matplotlib.lines.Line2D at 0x10c6566d0>]
 
 
 %%px Cell Magic
 ---------------
 
 %%px can be used as a Cell Magic, which accepts some arguments for controlling
 the execution.
 
 
 Targets and Blocking
 ********************
 
 %%px accepts ``--targets`` for controlling which engines on which to run,
 and ``--[no]block`` for specifying the blocking behavior of this cell,
 independent of the defaults for the View.
 
 .. sourcecode:: ipython
 
     In [6]: %%px --targets ::2
        ...: print "I am even"
        ...: 
     Parallel execution on engine(s): [0, 2]
     [stdout:0] I am even
     [stdout:2] I am even
 
     In [7]: %%px --targets 1
        ...: print "I am number 1"
        ...: 
     Parallel execution on engine(s): 1
     I am number 1
 
     In [8]: %%px
        ...: print "still 'all' by default"
        ...: 
     Parallel execution on engine(s): all
     [stdout:0] still 'all' by default
     [stdout:1] still 'all' by default
     [stdout:2] still 'all' by default
     [stdout:3] still 'all' by default
 
     In [9]: %%px --noblock
        ...: import time
        ...: time.sleep(1)
        ...: time.time()
        ...: 
     Async parallel execution on engine(s): all
     Out[9]: <AsyncResult: execute>
 
     In [10]: %pxresult
     Out[0:12]: 1339454561.069116
     Out[1:10]: 1339454561.076752
     Out[2:12]: 1339454561.072837
     Out[3:10]: 1339454561.066665
 
 
 .. seealso::
 
     :ref:`pxconfig` accepts these same arguments for changing the *default*
     values of targets/blocking for the active View.
 
 
 Output Display
 **************
 
 
 %%px also accepts a ``--group-outputs`` argument,
 which adjusts 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): all
     [stdout:0] [ 0.60640442  0.95919621]
     Out [0:73]: 0.9591962130899806
     [stdout:1] [ 0.38501813  1.29430871]
     Out [1:73]: 1.2943087091452372
     [stdout:2] [-0.85925141  0.9387692 ]
     Out [2:73]: 0.93876920456230284
     [stdout:3] [ 0.37998269  1.24218246]
     Out [3:73]: 1.2421824618493817
 
 
 %pxresult
 ---------
 
 If you are using %px in non-blocking mode, you won't get output.
 You can use %pxresult 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): all
     
     In [41]: %pxresult
     [stdout:0] hi
     [stdout:1] hi
     [stdout:2] hi
     [stdout:3] hi
 
 %pxresult simply calls :meth:`.AsyncResult.display_outputs` on the most recent request.
 It accepts the same output-grouping arguments as %%px, so you can use it to view
 a result in different ways.
 
 
 %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
 
 .. _pxconfig:
 
 %pxconfig
 ---------
 
 The default targets and blocking behavior for the magics are governed by the :attr:`block`
 and :attr:`targets` attribute of the active View.  If you have a handle for the view,
 you can set these attributes directly, but if you don't, you can change them with
 the %pxconfig magic:
 
 .. sourcecode:: ipython
 
     In [3]: %pxconfig --block
 
     In [5]: %px print 'hi'
     Parallel execution on engine(s): all
     [stdout:0] hi
     [stdout:1] hi
     [stdout:2] hi
     [stdout:3] hi
 
     In [6]: %pxconfig --targets ::2
 
     In [7]: %px print 'hi'
     Parallel execution on engine(s): [0, 2]
     [stdout:0] hi
     [stdout:2] hi
 
     In [8]: %pxconfig --noblock
 
     In [9]: %px print 'are you there?'
     Async parallel execution on engine(s): [0, 2]
     Out[9]: <AsyncResult: execute>
 
     In [10]: %pxresult
     [stdout:0] are you there?
     [stdout:2] are you there?
 
 
 Multiple Active Views
 =====================
 
 The parallel magics are associated with a particular :class:`~.DirectView` object.
 You can change the active view by calling the :meth:`~.DirectView.activate` method
 on any view.
 
 .. sourcecode:: ipython
 
     In [11]: even = rc[::2]
 
     In [12]: even.activate()
 
     In [13]: %px print 'hi'
     Async parallel execution on engine(s): [0, 2]
     Out[13]: <AsyncResult: execute>
 
     In [14]: even.block = True
 
     In [15]: %px print 'hi'
     Parallel execution on engine(s): [0, 2]
     [stdout:0] hi
     [stdout:2] hi
 
 When activating a View, you can also specify a *suffix*, so that a whole different
 set of magics are associated with that view, without replacing the existing ones.
 
 .. sourcecode:: ipython
 
     # restore the original DirecView to the base %px magics
     In [16]: rc.activate()
     Out[16]: <DirectView all>
 
     In [17]: even.activate('_even')
 
     In [18]: %px print 'hi all'
     Parallel execution on engine(s): all
     [stdout:0] hi all
     [stdout:1] hi all
     [stdout:2] hi all
     [stdout:3] hi all
 
     In [19]: %px_even print "We aren't odd!"
     Parallel execution on engine(s): [0, 2]
     [stdout:0] We aren't odd!
     [stdout:2] We aren't odd!
 
 This suffix is applied to the end of all magics, e.g. %autopx_even, %pxresult_even, etc.
 
 For convenience, the :class:`~.Client` has a :meth:`~.Client.activate` method as well,
 which creates a DirectView with block=True, activates it, and returns the new View.
 
 The initial magics registered when you create a client are the result of a call to
 :meth:`rc.activate` with default args.
 
 
 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): all
     [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 <app> --existing <file>
     or, if you are local, you can connect with just:
         $> ipython <app> --existing kernel-60125.json 
     or even just:
         $> ipython <app> --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.