We explore the close relationship between the tensor-based computations performed during modern machine learning, and relational database computations. We consider how to make a very small set of changes to a modern RDBMS to make it suitable for distributed learning computations. Changes include adding better support for recursion, and optimization and execution of very large compute plans. We also show that there are key advantages to using an RDBMS as a machine learning platform. In particular, DBMSbased learning allows for trivial scaling to large data sets and especially large models, where different computational units operate on different parts of a model that may be too large to fit into RAM.

中文翻译：

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RDBMS 上的声明式递归计算

我们探讨了在现代机器学习期间执行的基于张量的计算与关系数据库计算之间的密切关系。我们考虑如何对现代 RDBMS 进行少量更改，以使其适用于分布式学习计算。更改包括添加对递归的更好支持，以及超大型计算计划的优化和执行。我们还展示了使用 RDBMS 作为机器学习平台的关键优势。特别是，基于 DBMS 的学习可以轻松扩展到大型数据集，尤其是大型模型，其中不同的计算单元在模型的不同部分上运行，这些部分可能太大而无法放入 RAM。