  Continual learning is the problem of learning from a nonstationary stream of
data, a fundamental issue for sustainable and efficient training of deep neural
networks over time. Unfortunately, deep learning libraries only provide
primitives for offline training, assuming that model's architecture and data
are fixed. Avalanche is an open source library maintained by the ContinualAI
non-profit organization that extends PyTorch by providing first-class support
for dynamic architectures, streams of datasets, and incremental training and
evaluation methods. Avalanche provides a large set of predefined benchmarks and
training algorithms and it is easy to extend and modular while supporting a
wide range of continual learning scenarios. Documentation is available at
\url{https://avalanche.continualai.org}.
