论文标题
Codereef：一种用于便携式MLOP，可重复使用的自动化操作和可重现基准测试的开放平台
CodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarking
论文作者
论文摘要
我们提供CodeEF-一个开放的平台，可以共享启用跨平台MLOPS（MLSYSOPS）所需的所有组件，即以最有效的方式自动化ML模型在不同系统中的部署。我们还介绍了CodeEREF解决方案 - 一种将模型包装和共享模型的方法，该模型是非虚拟化，便携式，可自定义和可再现的存档文件。这样的ML软件包包括具有各个依赖关系的模型，Python API，CLI操作和便携式工作流程的JSON META描述，以自动构建，基准测试，测试和自定义跨不同平台，AI框架，库，编译器，编译器和数据集的模型。我们演示了几种CodeEREF解决方案，可以从最新的MLPERF推理基准从Raspberry Pi，Android Phone和Iot设备到数据中心的广泛平台上自动构建，运行和测量对象检测，Tensorflow和COCO数据集。我们的长期目标是帮助研究人员分享他们的新技术作为生产准备包以及研究论文，以参与协作和可重复的基准测试，比较使用在线CodeReEf仪表板上的不同ML/Software/Hartware stacks并选择Pareto Frentier上的最有效的基准。
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.
