  We introduce a framework for online learning from a single continuous video
stream -- the way people and animals learn, without mini-batches, data
augmentation or shuffling. This poses great challenges given the high
correlation between consecutive video frames and there is very little prior
work on it. Our framework allows us to do a first deep dive into the topic and
includes a collection of streams and tasks composed from two existing video
datasets, plus methodology for performance evaluation that considers both
adaptation and generalization. We employ pixel-to-pixel modelling as a
practical and flexible way to switch between pre-training and single-stream
evaluation as well as between arbitrary tasks, without ever requiring changes
to models and always using the same pixel loss. Equipped with this framework we
obtained large single-stream learning gains from pre-training with a novel
family of future prediction tasks, found that momentum hurts, and that the pace
of weight updates matters. The combination of these insights leads to matching
the performance of IID learning with batch size 1, when using the same
architecture and without costly replay buffers.
