  Traditional machine learning systems are deployed under the closed-world
setting, which requires the entire training data before the offline training
process. However, real-world applications often face the incoming new classes,
and a model should incorporate them continually. The learning paradigm is
called Class-Incremental Learning (CIL). We propose a Python toolbox that
implements several key algorithms for class-incremental learning to ease the
burden of researchers in the machine learning community. The toolbox contains
implementations of a number of founding works of CIL such as EWC and iCaRL, but
also provides current state-of-the-art algorithms that can be used for
conducting novel fundamental research. This toolbox, named PyCIL for Python
Class-Incremental Learning, is available at https://github.com/G-U-N/PyCIL
