CodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarking
Abstract
We present CodeReef - an open platform to share all the components necessary to enable cross-platform MLOps (MLSysOps), i.e. automating the deployment of ML models across diverse systems in the most efficient way. We also introduce the CodeReef solution - a way to package and share models as non-virtualized, portable, customizable and reproducible archive files. Such ML packages include JSON meta description of models with all dependencies, Python APIs, CLI actions and portable workflows necessary to automatically build, benchmark, test and customize models across diverse platforms, AI frameworks, libraries, compilers and datasets. We demonstrate several CodeReef solutions to automatically build, run and measure object detection based on SSD-Mobilenets, TensorFlow and COCO dataset from the latest MLPerf inference benchmark across a wide range of platforms from Raspberry Pi, Android phones and IoT devices to data centers. Our long-term goal is to help researchers share their new techniques as production-ready packages along with research papers to participate in collaborative and reproducible benchmarking, compare the different ML/software/hardware stacks and select the most efficient ones on a Pareto frontier using online CodeReef dashboards.
Cite
@article{arxiv.2001.07935,
title = {CodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarking},
author = {Grigori Fursin and Herve Guillou and Nicolas Essayan},
journal= {arXiv preprint arXiv:2001.07935},
year = {2020}
}
Comments
Presented at the 1st Workshop on MLOps Systems co-located with the 3rd Conference on Machine Learning and Systems (MLSys'20) in Austin, TX, USA: https://mlops-systems.github.io . A live interactive demo: https://CodeReef.ai/demo