The presence of Advanced Driver Assistance Systems (ADAS) in modern vehicles has become a reality in recent years, enhancing the comfort and safety of drivers and road users. In the track to achieve full autonomous driving it is vital to include Driver Monitoring Systems (DMS) as part of the automation set of systems to assure possible hand-over/hand-back actions. The development of DMS usually involve the integration of different computer vision and deep learning components. In this work we present a modular approach for rapid prototyping of DMS by defining atomic processing units (i.e. Analyzers) and the interface (i.e. Measures) between these units. This approach allows the definition of a network of Analyzers which can be easily interconnected in pipelines to perform specific DMS tasks (drowsiness, distraction, identity recognition). A key advantage of our approach is that a single step can be re-used for multiple DMS functionalities without the need to double computational resources. In addition, it is possible to test and validate different methods that share the same interfaces and produce the same measures. Therefore, it is easy to switch between different algorithms in a pipeline. The distributed processing capabilities of the resulting DMS architectures obtained from the proposed framework allow the generation of parallel processes in specialized hardware (i.e. Multi-core CPU and GPU boards) with a positive impact on real-time performance. Our DMS framework is compatible with RTMaps automotive-level platform for real-time multi-sensor data processing and the interfaces are compliant with the ASAM OpenLABEL concept paper by using VCD description format.