How can I optimize distributed training in PyTorch?

How can I optimize distributed training in PyTorch?

To optimize distributed training in PyTorch, you can use techniques like gradient accumulation, mixed precision training, and efficient data loading.

What is gradient accumulation and how does it help?

Gradient accumulation allows you to simulate a larger batch size by accumulating gradients over several smaller batches before updating the model.

Can you explain mixed precision training?

Mixed precision training uses both 16-bit and 32-bit floating-point types to speed up training and reduce memory usage without sacrificing model accuracy.

What are some best practices for data loading in distributed training?

Best practices include using torch.utils.data.DataLoader with multiple workers, prefetching data, and ensuring data is evenly distributed across nodes.