Using Parallel Magics¶
IPython has a few magics for working with your engines.
This assumes you have started an IPython cluster, either with the notebook interface,
or the ipcluster/controller/engine commands.
from IPython import parallel
rc = parallel.Client()
dv = rc[:]
rc.ids
Creating a Client registers the parallel magics %px, %%px, %pxresult, pxconfig, and %autopx.
These magics are initially associated with a DirectView always associated with all currently registered engines.
Now we can execute code remotely with %px:
%px a=5
%px print a
%px a
with dv.sync_imports():
import sys
%px print >> sys.stderr, "ERROR"
You don't have to wait for results. The %pxconfig magic lets you change the default blocking/targets for the %px magics:
%pxconfig --noblock
%px import time
%px time.sleep(5)
%px time.time()
But you will notice that this didn't output the result of the last command.
For this, we have %pxresult, which displays the output of the latest request:
%pxresult
Remember, an IPython engine is IPython, so you can do magics remotely as well!
%pxconfig --block
%px %matplotlib inline
%%px
import numpy as np
import matplotlib.pyplot as plt
%%px can also be used as a cell magic, for submitting whole blocks.
This one acceps --block and --noblock flags to specify
the blocking behavior, though the default is unchanged.
dv.scatter('id', dv.targets, flatten=True)
dv['stride'] = len(dv)
%%px --noblock
x = np.linspace(0,np.pi,1000)
for n in range(id,12, stride):
print n
plt.plot(x,np.sin(n*x))
plt.title("Plot %i" % id)
%pxresult
It also lets you choose some amount of the grouping of the outputs with --group-outputs:
The choices are:
engine- all of an engine's output is collected togethertype- where stdout of each engine is grouped, etc. (the default)order- same astype, but individual displaypub outputs are interleaved. That is, it will output the first plot from each engine, then the second from each, etc.
%%px --group-outputs=engine
x = np.linspace(0,np.pi,1000)
for n in range(id+1,12, stride):
print n
plt.figure()
plt.plot(x,np.sin(n*x))
plt.title("Plot %i" % n)
When you specify 'order', then individual display outputs (e.g. plots) will be interleaved.
%pxresult takes the same output-ordering arguments as %%px,
so you can view the previous result in a variety of different ways with a few sequential calls to %pxresult:
%pxresult --group-outputs=order
Single-engine views¶
When a DirectView has a single target, the output is a bit simpler (no prefixes on stdout/err, etc.):
def generate_output():
"""function for testing output
publishes two outputs of each type, and returns something
"""
import sys,os
from IPython.display import display, HTML, Math
print "stdout"
print >> sys.stderr, "stderr"
display(HTML("<b>HTML</b>"))
print "stdout2"
print >> sys.stderr, "stderr2"
display(Math(r"\alpha=\beta"))
return os.getpid()
dv['generate_output'] = generate_output
You can also have more than one set of parallel magics registered at a time.
The View.activate() method takes a suffix argument, which is added to 'px'.
e0 = rc[-1]
e0.block = True
e0.activate('0')
%px0 generate_output()
%px generate_output()
As mentioned above, we can redisplay those same results with various grouping:
%pxresult --group-outputs order
%pxresult --group-outputs engine
Parallel Exceptions¶
When you raise exceptions with the parallel exception, the CompositeError raised locally will display your remote traceback.
%%px
from numpy.random import random
A = random((100,100,'invalid shape'))
Remote Cell Magics¶
Remember, Engines are IPython too, so the cell that is run remotely by %%px can in turn use a cell magic.
%%px
%%timeit
from numpy.random import random
from numpy.linalg import norm
A = random((100,100))
norm(A, 2)
Local Execution¶
As of IPython 0.14, you can instruct %%px to also execute the cell locally.
This is useful for interactive definitions,
or if you want to load a data source everywhere,
not just on the engines.
%%px --local
import os
thispid = os.getpid()
print thispid