An empirical study of 38,742 issue reports and 19 interviews produces a 20-theme, 75-sub-theme taxonomy of LLM-centric framework challenges and five recommendations.
Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives
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abstract
While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design, speedy computation on distributed setting, and compatibility between different tools. In this paper, we introduce Neural Network Libraries (https://nnabla.org), a deep learning framework designed from engineer's perspective, with emphasis on usability and compatibility as its core design principles. We elaborate on each of our design principles and its merits, and validate our attempts via experiments.
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2025 1verdicts
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Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis
An empirical study of 38,742 issue reports and 19 interviews produces a 20-theme, 75-sub-theme taxonomy of LLM-centric framework challenges and five recommendations.