<![CDATA[Recent advances in deep learning have driven remarkable achievements across various domains, including computer vision, natural language processing, and medical diagnostics. However, prevailing DL libraries often expose monolithic and tightly coupled codebases, making it difficult for researchers to inject custom mathematical formulations into core training routines. To address this limitation, we introduce a modular software library that empowers users in both academia and industry to extend and modify training functions with minimal friction. This paper focuses on the preparatory stages of library development in Java Programming, presenting a four-phase methodology comprising Preparation (ideation, research questions, literature review), Identification (term extraction, goal definition, environment setup), Design (architecture modeling, class and attribute specification, task scheduling), and Development (component exploration, functionality construction). Through these sequential activities, we have produced eleven detailed design documents, including vision statements, quality-attribute scenarios, architectural decision records, and API specifications, that collectively capture the rationale and technical blueprint of our library. By sharing our step-by-step process, we aim to provide a replicable framework for future researchers undertaking the architectural design of specialized Deep Learning libraries.]]>
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