  We introduce pyGSL, a Python library that provides efficient implementations
of state-of-the-art graph structure learning models along with diverse datasets
to evaluate them on. The implementations are written in GPU-friendly ways,
allowing one to scale to much larger network tasks. A common interface is
introduced for algorithm unrolling methods, unifying implementations of recent
state-of-the-art techniques and allowing new methods to be quickly developed by
avoiding the need to rebuild the underlying unrolling infrastructure.
Implementations of differentiable graph structure learning models are written
in PyTorch, allowing us to leverage the rich software ecosystem that exists
e.g., around logging, hyperparameter search, and GPU-communication. This also
makes it easy to incorporate these models as components in larger gradient
based learning systems where differentiable estimates of graph structure may be
useful, e.g. in latent graph learning. Diverse datasets and performance metrics
allow consistent comparisons across models in this fast growing field. The full
code repository can be found on https://github.com/maxwass/pyGSL.
