DLSpec: A Deep Learning Task Exchange Specification

Abstract

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.

References in corpus (3)

• MLPerf Training Benchmark
• CodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarking
• The Design and Implementation of a Scalable DL Benchmarking Platform

Cited by in corpus (1)

• MLHarness: A Scalable Benchmarking System for MLCommons