DDP WORLD_SIZE-safe dataloader workers

[glenn-jocher]

Improved DDP reporting of total worker count and safe limiting of total worker count. Partially addresses #5628

🛠️ PR Summary

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🌟 Summary

Optimized data loading in distributed training environments for YOLOv5.

📊 Key Changes

• Adjusted the number of dataloader workers in train.py to consider the WORLD_SIZE variable.
• Modified the --workers command-line argument description to reflect its relationship with distributed training (DDP mode).
• Updated utils/datasets.py to factor in WORLD_SIZE when calculating the number of worker threads.

🎯 Purpose & Impact

• The changes ensure better utilization of system resources during distributed training by dynamically adjusting the number of dataloader workers based on the number of nodes (WORLD_SIZE).
• Users will likely experience more efficient data loading and potentially faster training times in multi-node environments.
• The update clarifies to users how worker counts will be affected when training in a distributed fashion.