AI in Multiple GPUs: Gradient Accumulation & Data Parallelism

The article introduces methods to implement gradient accumulation and data parallelism in PyTorch from scratch, enabling efficient training across multiple GPUs. These techniques allow for larger batch sizes and improved resource utilization by aggregating gradients over multiple iterations and distributing computations, respectively, thereby enhancing the scalability and performance of deep learning models.