  Continual learning is a machine learning sub-field specialized in settings
with non-iid data. Hence, the training data distribution is not static and
drifts through time. Those drifts might cause interferences in the trained
model and knowledge learned on previous states of the data distribution might
be forgotten. Continual learning's challenge is to create algorithms able to
learn an ever-growing amount of knowledge while dealing with data distribution
drifts.
  One implementation difficulty in these field is to create data loaders that
simulate non-iid scenarios. Indeed, data loaders are a key component for
continual algorithms. They should be carefully designed and reproducible. Small
errors in data loaders have a critical impact on algorithm results, e.g. with
bad preprocessing, wrong order of data or bad test set. Continuum is a simple
and efficient framework with numerous data loaders that avoid researcher to
spend time on designing data loader and eliminate time-consuming errors. Using
our proposed framework, it is possible to directly focus on the model design by
using the multiple scenarios and evaluation metrics implemented. Furthermore
the framework is easily extendable to add novel settings for specific needs.
