  Parametric computer-aided design (CAD) is the dominant paradigm in mechanical
engineering for physical design. Distinguished by relational geometry,
parametric CAD models begin as two-dimensional sketches consisting of geometric
primitives (e.g., line segments, arcs) and explicit constraints between them
(e.g., coincidence, perpendicularity) that form the basis for three-dimensional
construction operations. Training machine learning models to reason about and
synthesize parametric CAD designs has the potential to reduce design time and
enable new design workflows. Additionally, parametric CAD designs can be viewed
as instances of constraint programming and they offer a well-scoped test bed
for exploring ideas in program synthesis and induction. To facilitate this
research, we introduce SketchGraphs, a collection of 15 million sketches
extracted from real-world CAD models coupled with an open-source data
processing pipeline. Each sketch is represented as a geometric constraint graph
where edges denote designer-imposed geometric relationships between primitives,
the nodes of the graph. We demonstrate and establish benchmarks for two use
cases of the dataset: generative modeling of sketches and conditional
generation of likely constraints given unconstrained geometry.
