A Deep Dive into Distributed Checkpointing: Using Orbax with Torchax on TPUs

Training large deep learning models is an exercise in managing risks. Hardware glitches, network drops, spot instance preemption, and sudden cloud infrastructure hiccups can instantly wipe out days of expensive training progress. This is why checkpointing systems are critical. But as AI models grow, traditional ways of saving progress (checkpointing) can create massive data traffic jams, slowing everything down. If you are using Torchax or keeping an eye on Google’s upcoming native framework, TorchTPU to run your PyTorch projects you need a saving system built for massive scale...