Monolith is a distributed recommendation model framework and asynchronous training engine designed to build and train large-scale deep learning architectures. It functions as a distributed model trainer that processes massive datasets across multiple compute nodes using asynchronous update mechanisms.
The system features a dedicated embedding table manager that creates unique, feature-isolated tables to prevent representation collisions. It also includes a real-time weight updater to capture immediate changes in user interest and data hotspots through continuous parameter synchronization.
The framework covers the orchestration of distributed compute nodes, parameter server administration, and the construction of deep learning model graphs for recommendation tasks. These capabilities support asynchronous gradient updates and the management of complex feature representations.
