Fast, fault-tolerant PyTorch training on AI Runtime

TL;DR

Efficient PyTorch training at scale hinges on maximizing 'goodput', the productive use of GPU time. Implementing robust data pipelines and checkpointing mechanisms can significantly enhance recovery from failures, reducing idle GPU time and costs.

Key Takeaways

• Adopt distributed checkpointing to reduce idle GPU time during failures.
• Use asynchronous saving to minimize checkpointing delays.
• Optimize data loading with UCVolumeDataset for better GPU utilization.
• Track data pipeline position in checkpoints to avoid silent failures.