  In many practical applications of machine learning data arrives sequentially
over time in large chunks. Practitioners have then to decide how to allocate
their computational budget in order to obtain the best performance at any point
in time. Online learning theory for convex optimization suggests that the best
strategy is to use data as soon as it arrives. However, this might not be the
best strategy when using deep non-linear networks, particularly when these
perform multiple passes over each chunk of data rendering the overall
distribution non i.i.d.. In this paper, we formalize this learning setting in
the simplest scenario in which each data chunk is drawn from the same
underlying distribution, and make a first attempt at empirically answering the
following questions: How long should the learner wait before training on the
newly arrived chunks? What architecture should the learner adopt? Should the
learner increase capacity over time as more data is observed? We probe this
learning setting using convolutional neural networks trained on classic
computer vision benchmarks as well as a large transformer model trained on a
large-scale language modeling task. Code is available at
\url{www.github.com/facebookresearch/ALMA}.
