A method, apparatus, equipment and medium for anomaly diagnosis and treatment in a large model

Abstract

This application provides a method, apparatus, device, and medium for anomaly diagnosis and handling of large models, relating to the field of artificial intelligence technology. After a large model enters a designated processing stage of the current task, when a preset anomaly event is determined based on monitoring data, current anomaly description information is extracted from the monitoring data. Based on a pre-constructed causal graph, a target observation node corresponding to the current anomaly description information is determined. From the root cause nodes connected to the target observation node, a target root cause node is determined. The target root cause node can characterize the anomaly cause corresponding to the current anomaly description information, achieving automatic and accurate diagnosis of the anomaly cause. From a pre-constructed anomaly handling strategy library, a target handling strategy corresponding to the target root cause node is determined and executed, forming a complete anomaly self-healing closed loop. This reduces manual intervention, improves the efficiency of anomaly diagnosis and handling, increases the probability of successful large model task startup, and saves computational resources.

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