  We present a new method for scaling automatic configuration of computer
networks. The key idea is to relax the computationally hard search problem of
finding a configuration that satisfies a given specification into an
approximate objective amenable to learning-based techniques. Based on this
idea, we train a neural algorithmic model which learns to generate
configurations likely to (fully or partially) satisfy a given specification
under existing routing protocols. By relaxing the rigid satisfaction
guarantees, our approach (i) enables greater flexibility: it is
protocol-agnostic, enables cross-protocol reasoning, and does not depend on
hardcoded rules; and (ii) finds configurations for much larger computer
networks than previously possible. Our learned synthesizer is up to 490x faster
than state-of-the-art SMT-based methods, while producing configurations which
on average satisfy more than 93% of the provided requirements.
