  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.
