The learnware paradigm proposed by Zhou [2016] aims to enable users to reuse
numerous existing well-trained models instead of building machine learning
models from scratch, with the hope of solving new user tasks even beyond
models' original purposes. In this paradigm, developers worldwide can submit
their high-performing models spontaneously to the learnware dock system
(formerly known as learnware market) without revealing their training data.
Once the dock system accepts the model, it assigns a specification and
accommodates the model. This specification allows the model to be adequately
identified and assembled to reuse according to future users' needs, even if
they have no prior knowledge of the model. This paradigm greatly differs from
the current big model direction and it is expected that a learnware dock system
housing millions or more high-performing models could offer excellent
capabilities for both planned tasks where big models are applicable; and
unplanned, specialized, data-sensitive scenarios where big models are not
present or applicable.
This paper describes Beimingwu, the first open-source learnware dock system
providing foundational support for future research of learnware paradigm.The
system significantly streamlines the model development for new user tasks,
thanks to its integrated architecture and engine design, extensive engineering
implementations and optimizations, and the integration of various algorithms
for learnware identification and reuse. Notably, this is possible even for
users with limited data and minimal expertise in machine learning, without
compromising the raw data's security. Beimingwu supports the entire process of
learnware paradigm. The system lays the foundation for future research in
learnware-related algorithms and systems, and prepares the ground for hosting a
vast array of learnwares and establishing a learnware ecosystem.

该论文描述了第一个开源的学软入口系统 Beimingwu，为学软范式未来的研究提供了基础支持。