Is multi-GPU scaling with PyTorch Lightning really as simple as changing a single flag?

All answers to this question.

For the most part, yes, it really is that simple. You just set devices=4 and strategy="ddp" in your Trainer initialization. However, you do need to be careful with how you handle things like logging or saving checkpoints. You want to make sure only the global rank zero process is writing to the disk to avoid race conditions. I spent a week debugging a corrupted checkpoint issue before I realized my custom saving logic wasn't rank-aware. Other than that, the library handles the heavy lifting of sharding the data across the available GPUs.

   Answered 2025-06-23 by Melissa Henderson

Have you noticed any significant performance drops when using DDP compared to DataParallel, especially regarding the communication overhead between the nodes?

   Answered 2025-06-24 by William Taylor

• William, DDP is actually faster than DataParallel because it doesn't have the single-process bottleneck. The communication overhead is usually negligible if you have a fast interconnect like NVLink. Lightning's implementation is very optimized.

     Commented 2025-06-25 by Christopher Martinez

The abstraction is great, but always double-check your batch size. It’s usually calculated per GPU.

   Answered 2025-06-26 by Thomas Anderson

• Good point, Thomas. I always forget that the effective batch size is the product of the batch size and the number of GPUs.

     Commented 2025-06-27 by Melissa Henderson