  Submodular functions are a special class of set functions which naturally
model the notion of representativeness, diversity, coverage etc. and have been
shown to be computationally very efficient. A lot of past work has applied
submodular optimization to find optimal subsets in various contexts. Some
examples include data summarization for efficient human consumption, finding
effective smaller subsets of training data to reduce the model development time
(training, hyper parameter tuning), finding effective subsets of unlabeled data
to reduce the labeling costs, etc. A recent work has also leveraged submodular
functions to propose submodular information measures which have been found to
be very useful in solving the problems of guided subset selection and guided
summarization. In this work, we present Submodlib which is an open-source,
easy-to-use, efficient and scalable Python library for submodular optimization
with a C++ optimization engine. Submodlib finds its application in
summarization, data subset selection, hyper parameter tuning, efficient
training and more. Through a rich API, it offers a great deal of flexibility in
the way it can be used. Source of Submodlib is available at
https://github.com/decile-team/submodlib.
