Deep Learning (DL) has improved scientific applications across various scientific domains, including high-energy physics, meteorology, agriculture, and material science. This project introduces DLToolkit, a performance profiling infrastructure tailored for domain scientists to analyze and optimize science-driven DL applications. This project also contributes to education and supports broader usage; the outcomes of this project will be integrated into the Computer Science (CS) curriculum, and both George Mason University and the University of California - Merced are minority-serving institutions, offering opportunities for delivering knowledge about cutting-edge techniques to underrepresented students. Together with industry and national laboratory partners, the project will also provide research training, symposia, and internship opportunities for students, aiming to foster a cohort of performance engineers. The overarching objective of this project is to improve scientific DL applications. The intellectual merits include three novel profiling capabilities: (a) synergistic tool-framework profiling to streamline extensive domain-specific knowledge from existing DL frameworks to DLToolkit, significantly lowering the barrier for domain scientists to use DLToolkit; (b) just-in-time (JIT)-aware profiling to ensure precise yet lightweight attribution of performance events to complex JIT-compiled DL operators; and (c) tensor-centric profiling to provide a holistic view of tensor