面向分布式机器学习的网络模态创新

Network modal innovation for distributed machine learning

分布式机器学习作为人工智能的主流计算架构,目前仍然存在数据性能传输不高、模型训练速度慢等缺陷,传统的网络模态无法满足分布式机器学习场景的通信语义,继而无法解决这些缺陷以进一步提升模型训练性能。采用多模态网络技术,基于应用特点设计了面向分布式机器学习场景的新型网络模态及其运行逻辑,为多模态网络技术在垂直行业的应用提供了借鉴意义。

Distributed machine learning,as a popular computing architecture for artificial intelligence,still faces challenges of slow model training and poor data performance transmission.Traditional network modalities were un able to meet the communication needs of distributed machine learning scenarios,hindering the improvement of model training performance.New network modalities and operation logic for distributed machine learning scenarios using multimodal network technology were proposed.This approach was designed based on application characteristics and provides implications for the use of multimodal network technology in various industries.