Boost Training Goodput: How Continuous Checkpointing Optimizes Reliability in Orbax and MaxText

The continuous checkpointing feature in Orbax and MaxText aims to optimize the balance between reliability and performance during model training by generating checkpoints more dynamically rather than at fixed intervals. Traditional fixed checkpointing can lead to inefficiencies, either through infrequent checkpoints that risk data loss during failures or too frequent checkpoints that can bottleneck performance. Continuous checkpointing minimizes these risks by asynchronously saving checkpoints only after the previous save operation completes, thereby maximizing resource utilization without significant performance degradation. This approach is particularly beneficial for large-scale training tasks, as it reduces device-to-host blocking times and scales efficiently with the size of operations. The system also allows for customizable policies to further tailor checkpointing behavior to specific use cases, promoting efficient resource conservation and management. However, the effectiveness of continuous checkpointing is highly dependent on network bandwidth, emphasizing the importance of co-locating storage with the training cluster to avoid cross-metro network delays that could compromise reliability.