Dumping the SVD-based model to the disk

Before we build our model, which will take a long time to train, we should create a mechanism for us to load and dump our model to the disk. If we have a way of saving the parameterization of the factored matrix, then we can reuse our model without having to train it every time we want to use it this is a very big deal since this model will take hours to train! Luckily, Python has a built-in tool for serializing and deserializing Python objects - the pickle module.

How to do it...

Update the Recommender class as follows:

In [26]: import pickle
...: class Recommender(object):
...: @classmethod
...: def load(klass, pickle_path):
...: """
...: Instantiates the class by deserializing the pickle.
...: Note that the object returned may not be an exact match
...: to the code in this class (if it was saved
...: before updates).
...: """
...: with open(pickle_path, 'rb') as pkl:
...: return pickle.load(pkl)
...:
...: def __init__(self, udata, description=None...