电网调节领域基于机器学习过程的训练任务执行方法

Yingying Lao, Wangyu Dong, Ziyun Chen, Yanru Kong, Jialing Shen, Hao Li
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引用次数: 0

摘要

随着人工智能技术的快速发展,电力行业进入大数据时代,业务数据快速积累,传统的基于spring - boot的微服务架构对硬件资源提出越来越高的要求,已经不能满足我们对服务调用性能、数据一致性、弹性扩展和灵活部署的要求。针对上述问题,介绍了基于Kubernetes和Docker的分布式容器技术,提出了一种统一的基于json的机器学习过程描述语言结构,为机器学习训练过程提供了一些有用的配置模板,包括算法选择、超参数设置、损失函数、优化函数和执行计划。针对企业业务发展需求,设计构建了适应电网监管领域业务场景的机器学习模型训练任务调度系统,解决了样本数据无法重用和资源浪费的问题,实现了资源隔离和弹性扩展。通过构建可视化的机器学习任务流程,实现模型训练和评估,支持实时显示各算法节点的执行状态,平台实现了多租户资源隔离和弹性扩展的容器化机器学习模型训练环境。关键字
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A Machine Learning Process-based Training Task Execution Method in the Field of Power Grid Regulation
With the rapid development of artificial intelligence technology, the power industry has entered the era of big data, business data is rapidly accumulated, and the traditional Spring-Boot-based microservice architecture raises more and more requirements for hardware resources, which can no longer meet our requirements for service invocation performance, data consistency, elastic scaling and flexible deployment requirements. In response to the above problems, the distributed container technology which is based on Kubernetes and Docker is introduced, and a unified JSON-based machine learning process description language structure is proposed, some useful configuration templates are provided for machine learning training processes, including algorithm selection, hyper-parameter setting, loss function, optimization function and execution plan. In response to the needs of enterprise business development, a machine learning model training task scheduling system adapted to business scenarios in the field of power grid regulation is designed and constructed, which solves the problems of inability to reuse sample data and waste of resources and realizes resource isolation and elastic scaling. By building a visualized machine learning task process, implementing model training and evaluation, supporting real-time display of the execution status of each algorithm node, the platform implements a multi-tenant resource isolation and elastic scaling containerized machine learning model training environment. Keywords
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