<jupyter_start><jupyter_text>IPython parallel schedulerIPython cluster consists of one or more engines running as *individual processes* on local or remote computers. This brings a number of advantages, such as scalability or safety. However, only *picklable* (serializable) actors can be used. For exaple, lamda functions are not picklable and thus FuncActor with lamba (or interactively defined) function cannot be used.**Important**: You must start ipython cluster first. Either use the Clusters tab in the main Jupyter screen or follow the [documentation](http://ipython.org/ipython-doc/stable/parallel/parallel_process.html).  A trivial workflow with numpy actors<jupyter_code>from numpy.random import random_sample
from numpy import dot
from numpy.linalg import norm, det
from wowp.actors import FuncActor
dims = 4, 4
A = random_sample(dims)
B = random_sample(dims)
dot_actor = FuncActor(dot, inports=('a', 'b'))
det_actor = FuncActor(det)
det_actor.inports['a'] += dot_actor.outports['out']
wf = det_actor.get_workflow()<jupyter_output><empty_output><jupyter_text>Try to run with default scheduler<jupyter_code>wf_res = wf(a=A, b=B)
assert wf_res['out'][0] == det(dot(A, B))<jupyter_output><empty_output><jupyter_text>Now with the IPython Cluster scheduler<jupyter_code>from wowp.schedulers import IPyClusterScheduler
ipyscheduler = IPyClusterScheduler()
wf_res = wf(scheduler=ipyscheduler, a=A, b=B)
assert wf_res['out'][0] == det(dot(A, B))<jupyter_output><empty_output>
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