Fast, fault-tolerant PyTorch training on AI Runtime

Here's the thing: Databricks claims a 90% goodput for PyTorch training, which sounds impressive but requires scrutiny beyond the marketing metrics. Goodput, while a useful measure, can often be manipulated or misrepresented without context. What they're not saying is how this goodput translates into real-world performance across different datasets and architectures. Benchmarks can be misleading if they're not independently verified, and I would urge you to take these figures with a grain of salt until you see them in practice.

The fault tolerance through checkpointing every 5 minutes is a solid feature, especially for long-running training jobs. If you've ever dealt with interruptions during model training, you know how crucial this can be. But the real question is: does this fault tolerance impact your training speed or introduce overhead? With the ability to scale to 1000 GPUs, it’s clear that this setup caters to teams handling massive models, but scaling comes with its own challenges, particularly around data management and orchestration.

If you're already using Delta Lake, the integration here stands out. It streamlines data management and ensures you're not wrestling with data versioning during your training cycles. However, if you're not in that ecosystem, the additional complexity may not justify the switch. And with competitors like TensorFlow and Horovod already established in this space, you need to consider whether the benefits of switching are worth the potential vendor lock-in.

In short, if you’re looking for a new PyTorch training setup, evaluate Databricks AI Runtime against your current stack. It may be worth testing in a subset of your workflows, especially if you're scaling up, but don’t rush into a full-on adoption without considering the trade-offs involved.