Notebook dashboard. The number of threads to be used of course depends on the machine
and the processor you are running your code on. Figure 8-2 shows the IPython page for
starting a cluster.
Figure 8-2. Screenshot of IPython cluster page
IPython.parallel needs the information on which cluster to use for the parallel
execution of code. In this case, the cluster profile is stored in the “default” profile. In
addition, we need to generate a view on the cluster:
In [ 39 ]: from IPython.parallel import Client
c = Client(profile=“default”)
view = c.load_balanced_view()
The function implementing the parallel valuation of the options looks rather similar to the
sequential implementation:
In [ 40 ]: def par_value(n):
”’ Parallel option valuation.
Parameters
==========
n : int
