Here’s how to save and load entity matching models.

Store and load pandas-based model"pandas_entity_matching_model.pkl")
from emm import PandasEntityMatching
p2 = PandasEntityMatching.load("pandas_entity_matching_model.pkl")

Apply the pandas model as usual:


Store and reopen a spark-based model in the same way, but in a directory"spark_entity_matching_model")
from emm import SparkEntityMatching
s2 = SparkEntityMatching.load("spark_entity_matching_model")

For both pandas and spark, by default we use the joblib library with compression to store and load all non-spark objects.

The load and dump functions used can be changed to different functions:

io =
io.writer = pickle.dump
io.reader = pickle.load

Note that reader and writer are global attributes, so they get picked up by all classes that use IOFunc, and only need to be set once.

For example, one will need to change these functions for writing and reading to s3.