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.

翻译：我们引入了PyGSL(PyGSL),这是一个Python图书馆,该图书馆提供高效实施最新图表结构学习模型以及各种数据集来评估这些模型。 实施过程以GPU友好的方式写成, 允许一种规模更大得多的网络任务。 引入了一个通用界面, 用于算法解动方法, 整合最新最新最先进技术的实施, 并允许通过避免重建基本无滚动基础设施来快速开发新方法。 不同图形结构学习模型的实施用PyToch撰写, 使我们能够利用现有的丰富的软件生态系统, 如伐木、超参数搜索和GPUPU- 通信等。 这也便于将这些模型作为基于较大梯度的学习系统的组成部分纳入其中, 其中对图形结构的不同估计可能有用, 例如在暗图学习中。 多样化的数据集和性能衡量标准可以在这个快速增长的字段中对不同模型进行一致的比较。 完整的代码储存库可以在 https://github.com/maxwas/pyGSL 上找到。