Optimize AI model training reliability and performance using continuous checkpointing in Orbax and MaxText. Maximize I/O bandwidth and minimize resource waste with asynchronous, non-blocking checkpoint saves.

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Continuous checkpointing in Orbax and MaxText maximizes I/O bandwidth utilization while minimizing training job reliability risks from hardware failures or preemptions. Unlike fixed-interval checkpointing, it asynchronously saves checkpoints as soon as the previous save completes, reducing the mean time lost on failure. Enabling it in MaxText requires just a few config flags. Benchmarks on llama-3.1-70B CPT tasks show significantly smaller P50 checkpoint intervals with modest training step time overhead. Orbax also supports customizable save and preservation policies, including minimum interval cooldowns and periodic preservation. Multi-slice DCN bandwidth is not significantly impacted since checkpointing is asynchronous and confined to a single slice. Co-locating the storage bucket with the training cluster is strongly recommended for best performance.

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