In [1]:

from IPython import parallel

In [2]:

rc = parallel.Client()
dview = rc[:]
def ff(x):
    print(x)
    return x**2
sync = dview.map_sync(ff,[1,2,3,4])
print('sync res=%r' % sync)
async = dview.map_async(ff,[1,2,3,4])
print('async res=%r' % async)

sync res=[1, 4, 9, 16]
async res=<AsyncMapResult: ff>

Wait for the asyncresult to finish

In [3]:

async.get()

Out[3]:

[1, 4, 9, 16]

AsyncResult.stdout is a list of strings, where each string is a single engine's stdout

In [4]:

async.stdout

Out[4]:

['1\n2\n', '3\n4\n']

display_outputs() actually prints / displays the output of the engines. It does not return anything.

In [5]:

print('async output:')
async.display_outputs()

async output:
[stdout:0] 
1
2
[stdout:1] 
3
4

We can even make a simple table, showing stdout of each engine side-by-side

In [6]:

split_output = [ stdout.splitlines() for stdout in async.stdout ]
transposed = zip(*split_output)

header = '\t'.join("[%i]" %i for i in dview.targets)
print(header)
print('-' * 8 * len(dview))

for row in zip(*split_output):
    print('\t'.join(' %s ' % s for s in row))

[0]	[1]
----------------
 1 	 3 
 2 	 4