  Learning continually from non-stationary data streams is a long-standing goal
and a challenging problem in machine learning. Recently, we have witnessed a
renewed and fast-growing interest in continual learning, especially within the
deep learning community. However, algorithmic solutions are often difficult to
re-implement, evaluate and port across different settings, where even results
on standard benchmarks are hard to reproduce. In this work, we propose
Avalanche, an open-source end-to-end library for continual learning research
based on PyTorch. Avalanche is designed to provide a shared and collaborative
codebase for fast prototyping, training, and reproducible evaluation of
continual learning algorithms.
