A Theoretical Framework for Fault Tolerance in Distributed Deep Learning: From Containers to Clusters

Due to the large size and complex structure of the distributed deep learning systems, fault tolerance has become one of the most important issues in the area of system reliability and performance. This paper proposes a theoretical model to discuss and solve fault tolerance problem in various archi-tectural layers of distributed deep learning systems including container and cluster. First, a new fault taxonomy is proposed in which faults are classified into those occurring at the container level and at the cluster level so that specific recovery strategies can be applied to different types of faults. The framework incorporates state synchronization protocols that reduce the communication cost and ensure data convergence at the distributed nodes, and provides efficient fault recovery and system fault-tolerance mechanisms. On the efficiency of fault tolerance for various distributed deep learning workloads, the framework achieves reduction in recovery time and recovery time inter-val while ensuring the model convergence and high training completion reliability in large-scale environments. This work establishes the theoretical underpinnings and best practices for the development of fault-tolerant distributed deep learning systems, and makes a significant contribution to the knowledge base on fault tolerance in contemporary AI infrastructure.