Research on Distributed Training Method and Its Application in River Intelligent Detection

In recent years, deep learning has been widely applied in multiple fields, with computing power being one of its three major elements. Strong computing power support is an important guarantee for its comprehensive promotion and application. How to improve the efficiency of deep learning, especially in the training process, is the key to leveraging the advantages of deep learning algorithms. Traditionally, training efficiency has been improved by optimizing stand-alone model algorithms, enhancing hardware facilities for multiple machines and cards, compressing data to reduce transmission data volume, and other methods. However, optimizing stand-alone model algorithms does not significantly increase efficiency; The cost is too high by improving hardware facilities; Compressing data can only reduce the amount of transmitted data, and it also involves decompression at both ends, resulting in average efficiency improvement. This article proposes a distributed training method that optimizes training efficiency by constructing a fine-grained K8s cloud native GPU cluster, using Ring Allreduce hybrid parallel algorithm, and building a Horovod multi-source deep learning asynchronous training architecture. Compared with single machine training algorithms, this method utilizes GPU cluster technology to significantly improve training efficiency; Compared with optimizing hardware facilities, this method utilizes commonly used cluster machines to achieve efficiency improvement and reduce training costs; Meanwhile, by combining Kubeflow and Horovod technologies, the support for mainstream deep learning network architectures has been greatly simplified. Practical applications such as intelligent river detection have shown that the detection accuracy and training efficiency have been optimized, which effectively supports actual production.