Techniques for training large neural networks
OpenAI Blog
Training large neural networks requires coordinating multiple GPUs across a cluster to perform synchronized calculations. The practical engineering challenge involves managing communication and computation across distributed hardware without bottlenecks. Organizations now use specialized techniques like gradient accumulation, mixed precision training, and distributed data parallelism to make large-scale training feasible.
Why it matters
Large neural networks are at the core of many recent advances in AI, but training them is a difficult engineering and research challenge which requires orchestrating a cluster of GPUs to perform a single synchronized calculation.
