<ul><li>To run PyTorch DDP scripts, at least one NVIDIA GPU is required, preferably two or more.</li><li>Provider recommendation for renting GPUs: RunPod, offering the RTX 3090 at $0.22 per hour.</li><li>Torch version 2.4.0 is recommended for PyTorch DDP.</li><li>Initializing torch.dist and wrapping the model in a DistributedDataParallel container are essential steps.</li><li>Using the nccl backend for Linux and understanding terms like rank and world size in DDP scripts are important.</li><li>Processes are started per GPU, and torch.cuda.set_device(dist.get_rank()) sets the GPU for each process.</li><li>The DistributedDataParallel container handles model synchronization during training.</li><li>DistributedSampler is used to evenly distribute training data across all ranks or GPUs.</li><li>Loss values are local to each rank, and all_gather_object() can be used to gather loss across all ranks.</li><li>Training on multiple GPUs with DDP is similar to increasing the batch size, and gradient accumulation allows training with larger batch sizes within hardware constraints.</li></ul>

Getting Started with PyTorch DDP

Discover more