What is the best strategy to maintain data integrity across distributed GPU nodes during AI model training?

Implementing a distributed file system with replication is the most effective strategy to maintain data integrity across distributed GPU nodes during AI model training. This approach ensures that all nodes have synchronized access to the latest versions of the data, which is crucial for consistency in model training. By using a distributed file system, data can be replicated across multiple locations and nodes, which helps to prevent data loss and ensures that all nodes operate on identical datasets. This synchronization minimizes discrepancies that could arise from nodes processing stale or outdated data, ultimately enhancing the overall effectiveness and reliability of the training process.

When considering other strategies, utilizing redundant GPU nodes can help in distributing the processing load but does not inherently address data integrity. A single master node could simplify data management, but it can create a bottleneck and potential single points of failure. Lastly, assigning data verification tasks to DPUs (Data Processing Units) might enhance certain aspects of efficiency, but it does not ensure that all nodes are operating on the same, up-to-date data, which is critical for maintaining the integrity necessary for successful AI model training.