DLSpec: A Deep Learning Task Exchange Specification
This addresses a foundational problem for the deep learning research community by enabling easier sharing and comparison of innovations, though it is incremental as it builds on existing specification needs.
The authors tackled the lack of standard specification for deep learning tasks, which hinders sharing and reproducibility, by proposing DLSpec, a model-, dataset-, software-, and hardware-agnostic specification that has been tested on hundreds of tasks.
Deep Learning (DL) innovations are being introduced at a rapid pace. However, the current lack of standard specification of DL tasks makes sharing, running, reproducing, and comparing these innovations difficult. To address this problem, we propose DLSpec, a model-, dataset-, software-, and hardware-agnostic DL specification that captures the different aspects of DL tasks. DLSpec has been tested by specifying and running hundreds of DL tasks.